SQL Tuning Guide
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Oracle® Database SQL Tuning Guide 12c Release 2 (12.2) E85762-03 March 2018 Oracle Database SQL Tuning Guide, 12c Release 2 (12.2) E85762-03 Copyright © 2013, 2018, Oracle and/or its affiliates. All rights reserved. Primary Author: Lance Ashdown Contributing Authors: Nigel Bayliss, Maria Colgan, Tom Kyte Contributors: Hermann Baer, Ali Cakmak, Sunil Chakkappen, Immanuel Chan, Deba Chatterjee, Chris Chiappa, Dinesh Das, Leonidas Galanis, William Endress, Marcus Fallen, Bruce Golbus, Katsumi Inoue, Shantanu Joshi, Adam Kociubes, Keith Laker, Allison Lee, Sue Lee, David McDermid, Colin McGregor, Ajit Mylavarapu, Ted Persky, Lei Sheng, Ekrem Soylemez, Hong Su, Murali Thiyagarajah, Randy Urbano, Sahil Vazirani, Bharath Venkatakrishnan, Hailing Yu, John Zimmerman This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. 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Contents Preface Audience xxv Documentation Accessibility xxv Related Documents xxvi Conventions xxvi Changes in This Release for Oracle Database SQL Tuning Guide Changes in Oracle Database 12c Release 2 (12.2.0.1) xxvii Changes in Oracle Database 12c Release 1 (12.1.0.2) xxx Changes in Oracle Database 12c Release 1 (12.1.0.1) xxxi Part I 1 SQL Performance Fundamentals Introduction to SQL Tuning 1.1 About SQL Tuning 1-1 1.2 Purpose of SQL Tuning 1-1 1.3 Prerequisites for SQL Tuning 1-2 1.4 Tasks and Tools for SQL Tuning 1-2 1.4.1 SQL Tuning Tasks 1-3 1.4.2 SQL Tuning Tools 1-5 1.4.2.1 Automated SQL Tuning Tools 1-5 1.4.2.2 Manual SQL Tuning Tools 1-8 1.4.3 2 User Interfaces to SQL Tuning Tools 1-11 SQL Performance Methodology 2.1 2.2 Guidelines for Designing Your Application 2-1 2.1.1 Guideline for Data Modeling 2-1 2.1.2 Guideline for Writing Efficient Applications 2-2 Guidelines for Deploying Your Application 2-3 iii Part II 3 2.2.1 Guideline for Deploying in a Test Environment 2-3 2.2.2 Guidelines for Application Rollout 2-4 Query Optimizer Fundamentals SQL Processing 3.1 About SQL Processing 3.1.1 3.2 3.3 4 SQL Parsing 3-1 3-3 3.1.1.1 Syntax Check 3-3 3.1.1.2 Semantic Check 3-4 3.1.1.3 Shared Pool Check 3-4 3.1.2 SQL Optimization 3-6 3.1.3 SQL Row Source Generation 3-6 3.1.4 SQL Execution 3-7 How Oracle Database Processes DML 3-9 3.2.1 How Row Sets Are Fetched 3.2.2 Read Consistency 3-10 3.2.3 Data Changes 3-10 How Oracle Database Processes DDL 3-9 3-10 Query Optimizer Concepts 4.1 4.2 Introduction to the Query Optimizer 4-1 4.1.1 Purpose of the Query Optimizer 4-2 4.1.2 Cost-Based Optimization 4-2 4.1.3 Execution Plans 4-3 4.1.3.1 Query Blocks 4-3 4.1.3.2 Query Subplans 4-4 4.1.3.3 Analogy for the Optimizer 4-4 About Optimizer Components 4-5 4.2.1 Query Transformer 4-6 4.2.2 Estimator 4-7 4.2.2.1 Selectivity 4-9 4.2.2.2 Cardinality 4-10 4.2.2.3 Cost 4-10 Plan Generator 4-11 4.2.3 4.3 About Automatic Tuning Optimizer 4-13 4.4 About Adaptive Query Optimization 4-13 4.4.1 Adaptive Query Plans 4.4.1.1 About Adaptive Query Plans 4-14 4-15 iv 4.4.1.2 Purpose of Adaptive Query Plans 4-16 4.4.1.3 How Adaptive Query Plans Work 4-16 4.4.1.4 When Adaptive Query Plans Are Enabled 4-24 4.4.2 5 Adaptive Statistics 4-24 4.4.2.1 Dynamic Statistics 4-24 4.4.2.2 Automatic Reoptimization 4-25 4.4.2.3 SQL Plan Directives 4-28 4.4.2.4 When Adaptive Statistics Are Enabled 4-28 4.5 About Approximate Query Processing 4-29 4.6 About SQL Plan Management 4-31 4.7 About the Expression Statistics Store (ESS) 4-32 Query Transformations 5.1 OR Expansion 5-2 5.2 View Merging 5-3 5.2.1 Query Blocks in View Merging 5-4 5.2.2 Simple View Merging 5-5 5.2.3 Complex View Merging 5-7 5.3 Predicate Pushing 5-9 5.4 Subquery Unnesting 5-10 5.5 Query Rewrite with Materialized Views 5-11 5.6 Star Transformation 5-12 5.6.1 About Star Schemas 5-12 5.6.2 Purpose of Star Transformations 5-13 5.6.3 How Star Transformation Works 5-13 5.6.4 Controls for Star Transformation 5-14 5.6.5 Star Transformation: Scenario 5-14 5.6.6 Temporary Table Transformation: Scenario 5-17 5.7 In-Memory Aggregation (VECTOR GROUP BY) 5-19 5.8 Cursor-Duration Temporary Tables 5-19 5.8.1 Purpose of Cursor-Duration Temporary Tables 5-19 5.8.2 How Cursor-Duration Temporary Tables Work 5-20 5.8.3 Cursor-Duration Temporary Tables: Example 5-20 5.9 Table Expansion 5-21 5.9.1 Purpose of Table Expansion 5-22 5.9.2 How Table Expansion Works 5-22 5.9.3 Table Expansion: Scenario 5-22 5.9.4 Table Expansion and Star Transformation: Scenario 5-25 5.10 Join Factorization 5.10.1 Purpose of Join Factorization 5-27 5-27 v Part III 6 How Join Factorization Works 5-27 5.10.3 Factorization and Join Orders: Scenario 5-28 5.10.4 Factorization of Outer Joins: Scenario 5-30 Query Execution Plans Generating and Displaying Execution Plans 6.1 Introduction to Execution Plans 6-1 6.2 About Plan Generation and Display 6-1 6.3 6.4 7 5.10.2 6.2.1 About the Plan Explanation 6-2 6.2.2 Why Execution Plans Change 6-2 6.2.2.1 Different Schemas 6-3 6.2.2.2 Different Costs 6-3 6.2.3 Guideline for Minimizing Throw-Away 6-4 6.2.4 Guidelines for Evaluating Execution Plans Using EXPLAIN PLAN 6-4 6.2.5 Guidelines for Evaluating Plans Using the V$SQL_PLAN Views 6-5 6.2.6 EXPLAIN PLAN Restrictions 6-5 6.2.7 Guidelines for Creating PLAN_TABLE 6-6 Generating Execution Plans 6-6 6.3.1 Executing EXPLAIN PLAN for a Single Statement 6-7 6.3.2 Executing EXPLAIN PLAN Using a Statement ID 6-8 6.3.3 Directing EXPLAIN PLAN Output to a Nondefault Table 6-8 Displaying PLAN_TABLE Output 6.4.1 Displaying an Execution Plan: Example 6.4.2 Customizing PLAN_TABLE Output 6-8 6-9 6-10 Reading Execution Plans 7.1 Reading Execution Plans: Basic 7-1 7.2 Reading Execution Plans: Advanced 7-2 7.2.1 Reading Adaptive Query Plans 7-2 7.2.2 Viewing Parallel Execution with EXPLAIN PLAN 7-6 7.2.2.1 About EXPLAIN PLAN and Parallel Queries 7-7 7.2.2.2 Viewing Parallel Queries with EXPLAIN PLAN: Example 7-8 7.2.3 Viewing Bitmap Indexes with EXPLAIN PLAN 7.2.4 Viewing Result Cache with EXPLAIN PLAN 7-10 7.2.5 Viewing Partitioned Objects with EXPLAIN PLAN 7-10 7.2.5.1 Displaying Range and Hash Partitioning with EXPLAIN PLAN: Examples 7-9 7-11 vi 7.2.5.2 Pruning Information with Composite Partitioned Objects: Examples 7-13 7.2.5.3 Examples of Partial Partition-Wise Joins 7-14 7.2.5.4 Example of Full Partition-Wise Join 7-16 7.2.5.5 Examples of INLIST ITERATOR and EXPLAIN PLAN 7-17 7.2.5.6 Example of Domain Indexes and EXPLAIN PLAN 7-18 7.2.6 7.3 Part IV 8 PLAN_TABLE Columns Execution Plan Reference 7-18 7-29 7.3.1 Execution Plan Views 7-30 7.3.2 PLAN_TABLE Columns 7-30 7.3.3 DBMS_XPLAN Program Units 7-39 SQL Operators: Access Paths and Joins Optimizer Access Paths 8.1 Introduction to Access Paths 8-1 8.2 Table Access Paths 8-2 8.2.1 8-3 8.2.1.1 Row Storage in Data Blocks and Segments: A Primer 8-3 8.2.1.2 Importance of Rowids for Row Access 8-4 8.2.1.3 Direct Path Reads 8-5 8.2.2 Full Table Scans 8-6 8.2.2.1 When the Optimizer Considers a Full Table Scan 8-6 8.2.2.2 How a Full Table Scan Works 8-7 8.2.2.3 Full Table Scan: Example 8-8 8.2.3 Table Access by Rowid 8-9 8.2.3.1 When the Optimizer Chooses Table Access by Rowid 8.2.3.2 How Table Access by Rowid Works 8-10 8.2.3.3 Table Access by Rowid: Example 8-10 8.2.4 Sample Table Scans 8-9 8-10 8.2.4.1 When the Optimizer Chooses a Sample Table Scan 8-11 8.2.4.2 Sample Table Scans: Example 8-11 8.2.5 8.3 About Heap-Organized Table Access In-Memory Table Scans 8-12 8.2.5.1 When the Optimizer Chooses an In-Memory Table Scan 8-12 8.2.5.2 In-Memory Query Controls 8-12 8.2.5.3 In-Memory Table Scans: Example 8-13 B-Tree Index Access Paths 8.3.1 About B-Tree Index Access 8-14 8-15 8.3.1.1 B-Tree Index Structure 8-15 8.3.1.2 How Index Storage Affects Index Scans 8-16 vii 8.3.1.3 Unique and Nonunique Indexes 8-17 8.3.1.4 B-Tree Indexes and Nulls 8-17 8.3.2 8-19 8.3.2.1 When the Optimizer Considers Index Unique Scans 8-19 8.3.2.2 How Index Unique Scans Work 8-20 8.3.2.3 Index Unique Scans: Example 8-21 8.3.3 Index Range Scans 8-22 8.3.3.1 When the Optimizer Considers Index Range Scans 8-22 8.3.3.2 How Index Range Scans Work 8-23 8.3.3.3 Index Range Scan: Example 8-24 8.3.3.4 Index Range Scan Descending: Example 8-25 8.3.4 Index Full Scans 8-26 8.3.4.1 When the Optimizer Considers Index Full Scans 8-26 8.3.4.2 How Index Full Scans Work 8-26 8.3.4.3 Index Full Scans: Example 8-27 8.3.5 Index Fast Full Scans 8-28 8.3.5.1 When the Optimizer Considers Index Fast Full Scans 8-28 8.3.5.2 How Index Fast Full Scans Work 8-28 8.3.5.3 Index Fast Full Scans: Example 8-29 8.3.6 Index Skip Scans 8-29 8.3.6.1 When the Optimizer Considers Index Skips Scans 8-29 8.3.6.2 How Index Skip Scans Work 8-30 8.3.6.3 Index Skip Scans: Example 8-30 8.3.7 8.4 Index Unique Scans Index Join Scans 8-31 8.3.7.1 When the Optimizer Considers Index Join Scans 8-32 8.3.7.2 How Index Join Scans Work 8-32 8.3.7.3 Index Join Scans: Example 8-32 Bitmap Index Access Paths 8.4.1 About Bitmap Index Access 8-33 8-34 8.4.1.1 Differences Between Bitmap and B-Tree Indexes 8-34 8.4.1.2 Purpose of Bitmap Indexes 8-35 8.4.1.3 Bitmaps and Rowids 8-36 8.4.1.4 Bitmap Join Indexes 8-37 8.4.1.5 Bitmap Storage 8-38 Bitmap Conversion to Rowid 8-39 8.4.2 8.4.2.1 When the Optimizer Chooses Bitmap Conversion to Rowid 8-39 8.4.2.2 How Bitmap Conversion to Rowid Works 8-39 8.4.2.3 Bitmap Conversion to Rowid: Example 8-39 8.4.3 Bitmap Index Single Value 8-40 8.4.3.1 When the Optimizer Considers Bitmap Index Single Value 8-40 8.4.3.2 How Bitmap Index Single Value Works 8-40 viii 8.4.3.3 8.4.4 8-41 When the Optimizer Considers Bitmap Index Range Scans 8-42 8.4.4.2 How Bitmap Index Range Scans Work 8-42 8.4.4.3 Bitmap Index Range Scans: Example 8-42 Bitmap Merge 8-43 8.4.5.1 When the Optimizer Considers Bitmap Merge 8-43 8.4.5.2 How Bitmap Merge Works 8-43 8.4.5.3 Bitmap Merge: Example 8-44 Table Cluster Access Paths 8.5.1 Cluster Scans 8-44 8-45 8.5.1.1 When the Optimizer Considers Cluster Scans 8-45 8.5.1.2 How a Cluster Scan Works 8-45 8.5.1.3 Cluster Scans: Example 8-46 8.5.2 9 Bitmap Index Range Scans 8-41 8.4.4.1 8.4.5 8.5 Bitmap Index Single Value: Example Hash Scans 8-47 8.5.2.1 When the Optimizer Considers a Hash Scan 8-47 8.5.2.2 How a Hash Scan Works 8-47 8.5.2.3 Hash Scans: Example 8-47 Joins 9.1 9.2 About Joins 9-1 9.1.1 Join Trees 9-2 9.1.2 How the Optimizer Executes Join Statements 9-3 9.1.3 How the Optimizer Chooses Execution Plans for Joins 9-4 Join Methods 9.2.1 Nested Loops Joins 9-5 9-6 9.2.1.1 When the Optimizer Considers Nested Loops Joins 9-7 9.2.1.2 How Nested Loops Joins Work 9-8 9.2.1.3 Nested Nested Loops 9-8 9.2.1.4 Current Implementation for Nested Loops Joins 9-11 9.2.1.5 Original Implementation for Nested Loops Joins 9-13 9.2.1.6 Nested Loops Controls 9-14 9.2.2 Hash Joins 9-16 9.2.2.1 When the Optimizer Considers Hash Joins 9-16 9.2.2.2 How Hash Joins Work 9-17 9.2.2.3 How Hash Joins Work When the Hash Table Does Not Fit in the PGA 9-19 Hash Join Controls 9-20 9.2.2.4 9.2.3 Sort Merge Joins 9-20 9.2.3.1 When the Optimizer Considers Sort Merge Joins 9-21 9.2.3.2 How Sort Merge Joins Work 9-21 ix 9.2.3.3 9.2.4 9.3 Cartesian Joins 9-25 9.2.4.2 How Cartesian Joins Work 9-26 9.2.4.3 Cartesian Join Controls 9-27 9-27 Inner Joins 9-28 9.3.1.1 Equijoins 9-28 9.3.1.2 Nonequijoins 9-28 9.3.1.3 Band Joins 9-29 9.3.2 Outer Joins 9-32 9.3.2.1 Nested Loops Outer Joins 9-33 9.3.2.2 Hash Join Outer Joins 9-34 9.3.2.3 Sort Merge Outer Joins 9-35 9.3.2.4 Full Outer Joins 9-36 9.3.2.5 Multiple Tables on the Left of an Outer Join 9-37 9.3.3 Semijoins 9-37 9.3.3.1 When the Optimizer Considers Semijoins 9-38 9.3.3.2 How Semijoins Work 9-38 9.3.4 Antijoins 9-40 9.3.4.1 When the Optimizer Considers Antijoins 9-40 9.3.4.2 How Antijoins Work 9-41 9.3.4.3 How Antijoins Handle Nulls 9-42 Join Optimizations 9.4.1 Bloom Filters 9-44 9-45 9.4.1.1 Purpose of Bloom Filters 9-45 9.4.1.2 How Bloom Filters Work 9-46 9.4.1.3 Bloom Filter Controls 9-46 9.4.1.4 Bloom Filter Metadata 9-47 9.4.1.5 Bloom Filters: Scenario 9-47 9.4.2 Partition-Wise Joins 9-48 9.4.2.1 Purpose of Partition-Wise Joins 9-49 9.4.2.2 How Partition-Wise Joins Work 9-49 9.4.3 Part V 9-25 When the Optimizer Considers Cartesian Joins Join Types 9.4 9-25 9.2.4.1 9.3.1 10 Sort Merge Join Controls In-Memory Join Groups 9-52 Optimizer Statistics Optimizer Statistics Concepts 10.1 Introduction to Optimizer Statistics 10-1 10.2 About Optimizer Statistics Types 10-3 x 10.2.1 Table Statistics 10-4 10.2.2 Column Statistics 10-4 10.2.3 Index Statistics 10-5 10.2.3.1 Types of Index Statistics 10-5 10.2.3.2 Index Clustering Factor 10-6 10.2.3.3 Effect of Index Clustering Factor on Cost: Example 10-9 10.2.4 Session-Specific Statistics for Global Temporary Tables 10.2.4.1 10.2.4.2 Shared and Session-Specific Statistics for Global Temporary Tables 10-11 Effect of DBMS_STATS on Transaction-Specific Temporary Tables 10-11 10.2.5 System Statistics 10-12 10.2.6 User-Defined Optimizer Statistics 10-12 10.3 How the Database Gathers Optimizer Statistics 10-13 10.3.1 DBMS_STATS Package 10-13 10.3.2 Supplemental Dynamic Statistics 10-14 10.3.3 Online Statistics Gathering for Bulk Loads 10-15 10.4 11 10-10 10.3.3.1 Purpose of Online Statistics Gathering for Bulk Loads 10-16 10.3.3.2 Global Statistics During Inserts into Empty Partitioned Tables 10-16 10.3.3.3 Index Statistics and Histograms During Bulk Loads 10-16 10.3.3.4 Restrictions for Online Statistics Gathering for Bulk Loads 10-17 10.3.3.5 Hints for Online Statistics Gathering for Bulk Loads 10-18 When the Database Gathers Optimizer Statistics 10-19 10.4.1 Sources for Optimizer Statistics 10-19 10.4.2 SQL Plan Directives 10-20 10.4.2.1 When the Database Creates SQL Plan Directives 10-20 10.4.2.2 How the Database Uses SQL Plan Directives 10-21 10.4.2.3 SQL Plan Directive Maintenance 10-22 10.4.2.4 How the Optimizer Uses SQL Plan Directives: Example 10-22 10.4.2.5 How the Optimizer Uses Extensions and SQL Plan Directives: Example 10-27 10.4.3 When the Database Samples Data 10-30 10.4.4 How the Database Samples Data 10-32 Histograms 11.1 Purpose of Histograms 11-2 11.2 When Oracle Database Creates Histograms 11-2 11.3 How Oracle Database Chooses the Histogram Type 11-4 11.4 Cardinality Algorithms When Using Histograms 11-4 11.4.1 Endpoint Numbers and Values 11-5 11.4.2 Popular and Nonpopular Values 11-5 xi 11.4.3 11.5 Frequency Histograms 11-6 Criteria For Frequency Histograms 11-7 11.5.2 Generating a Frequency Histogram 11-7 Top Frequency Histograms 11-10 11.6.1 Criteria For Top Frequency Histograms 11-10 11.6.2 Generating a Top Frequency Histogram 11-11 11.7 Height-Balanced Histograms (Legacy) 11-14 11.7.1 Criteria for Height-Balanced Histograms 11-14 11.7.2 Generating a Height-Balanced Histogram 11-15 11.8 Hybrid Histograms 11-18 11.8.1 How Endpoint Repeat Counts Work 11-18 11.8.2 Criteria for Hybrid Histograms 11-20 11.8.3 Generating a Hybrid Histogram 11-21 Configuring Options for Optimizer Statistics Gathering 12.1 About Optimizer Statistics Collection 12-1 12.1.1 Purpose of Optimizer Statistics Collection 12-2 12.1.2 User Interfaces for Optimizer Statistics Management 12-2 12.2 12.1.2.1 Graphical Interface for Optimizer Statistics Management 12-2 12.1.2.2 Command-Line Interface for Optimizer Statistics Management 12-3 Setting Optimizer Statistics Preferences 12.2.1 About Optimizer Statistics Preferences 12-3 12-4 12.2.1.1 Purpose of Optimizer Statistics Preferences 12-4 12.2.1.2 DBMS_STATS Procedures for Setting Statistics Preferences 12-5 12.2.1.3 Statistics Preference Overrides 12-6 12.2.1.4 Setting Statistics Preferences: Example 12-7 12.2.2 Setting Global Optimizer Statistics Preferences Using Cloud Control 12-9 12.2.3 Setting Object-Level Optimizer Statistics Preferences Using Cloud Control 12-9 12.2.4 12.3 Setting Optimizer Statistics Preferences from the Command Line Configuring Options for Dynamic Statistics 12-10 12-11 12.3.1 About Dynamic Statistics Levels 12-12 12.3.2 Setting Dynamic Statistics Levels Manually 12-13 12.3.3 Disabling Dynamic Statistics 12-16 12.4 13 11-6 11.5.1 11.6 12 Bucket Compression Managing SQL Plan Directives 12-16 Gathering Optimizer Statistics 13.1 Configuring Automatic Optimizer Statistics Collection 13.1.1 About Automatic Optimizer Statistics Collection 13-1 13-2 xii 13.1.2 13.1.3 13.2 Configuring Automatic Optimizer Statistics Collection Using Cloud Control 13-2 Configuring Automatic Optimizer Statistics Collection from the Command Line 13-4 Gathering Optimizer Statistics Manually 13-6 13.2.1 About Manual Statistics Collection with DBMS_STATS 13-7 13.2.2 Guidelines for Gathering Optimizer Statistics Manually 13-8 13.2.2.1 Guideline for Setting the Sample Size 13-9 13.2.2.2 Guideline for Gathering Statistics in Parallel 13-9 13.2.2.3 Guideline for Partitioned Objects 13-10 13.2.2.4 Guideline for Frequently Changing Objects 13-10 13.2.2.5 Guideline for External Tables 13-10 13.2.3 Determining When Optimizer Statistics Are Stale 13-11 13.2.4 Gathering Schema and Table Statistics 13-12 13.2.5 Gathering Statistics for Fixed Objects 13-13 13.2.6 Gathering Statistics for Volatile Tables Using Dynamic Statistics 13-14 13.2.7 Gathering Optimizer Statistics Concurrently 13-15 13.2.7.1 About Concurrent Statistics Gathering 13-15 13.2.7.2 Enabling Concurrent Statistics Gathering 13-18 13.2.7.3 Monitoring Statistics Gathering Operations 13-20 Gathering Incremental Statistics on Partitioned Objects 13-22 13.2.8 13.2.8.1 Purpose of Incremental Statistics 13-22 13.2.8.2 How DBMS_STATS Derives Global Statistics for Partitioned tables 13-23 13.2.8.3 Gathering Statistics for a Partitioned Table: Basic Steps 13-27 13.2.8.4 Maintaining Incremental Statistics for Partition Maintenance Operations 13-30 Maintaining Incremental Statistics for Tables with Stale or Locked Partition Statistics 13-32 13.2.8.5 13.3 Gathering System Statistics Manually 13-34 13.3.1 About Gathering System Statistics with DBMS_STATS 13-35 13.3.2 Guidelines for Gathering System Statistics 13-37 13.3.3 Gathering Workload Statistics 13-37 13.3.3.1 About Workload Statistics 13-38 13.3.3.2 Starting and Stopping System Statistics Gathering 13-39 13.3.3.3 Gathering System Statistics During a Specified Interval 13-40 13.3.4 Gathering Noworkload Statistics 13-41 13.3.5 Deleting System Statistics 13-43 13.4 Running Statistics Gathering Functions in Reporting Mode 13-43 xiii 14 Managing Extended Statistics 14.1 Managing Column Group Statistics 14.1.1 About Statistics on Column Groups 14.1.1.1 Why Column Group Statistics Are Needed: Example 14-3 14.1.1.2 Automatic and Manual Column Group Statistics 14-5 14.1.1.3 User Interface for Column Group Statistics 14-5 Detecting Useful Column Groups for a Specific Workload 14-6 14.1.3 Creating Column Groups Detected During Workload Monitoring 14-9 14.1.4 Creating and Gathering Statistics on Column Groups Manually 14-11 14.1.5 Displaying Column Group Information 14-12 14.1.6 Dropping a Column Group 14-13 Managing Expression Statistics 14.2.1 About Expression Statistics 14.2.1.1 When Expression Statistics Are Useful: Example 14-13 14-14 14-15 14.2.2 Creating Expression Statistics 14-15 14.2.3 Displaying Expression Statistics 14-16 14.2.4 Dropping Expression Statistics 14-17 Controlling the Use of Optimizer Statistics 15.1 Locking and Unlocking Optimizer Statistics 15-1 15.1.1 Locking Statistics 15-1 15.1.2 Unlocking Statistics 15-2 15.2 Publishing Pending Optimizer Statistics 15-3 15.2.1 About Pending Optimizer Statistics 15-3 15.2.2 User Interfaces for Publishing Optimizer Statistics 15-5 15.2.3 Managing Published and Pending Statistics 15-6 15.3 16 14-2 14.1.2 14.2 15 14-1 Creating Artificial Optimizer Statistics for Testing 15-9 15.3.1 About Artificial Optimizer Statistics 15-9 15.3.2 Setting Artificial Optimizer Statistics for a Table 15-10 15.3.3 Setting Optimizer Statistics: Example 15-11 Managing Historical Optimizer Statistics 16.1 Restoring Optimizer Statistics 16-1 16.1.1 About Restore Operations for Optimizer Statistics 16-1 16.1.2 Guidelines for Restoring Optimizer Statistics 16-2 16.1.3 Restrictions for Restoring Optimizer Statistics 16-2 16.1.4 Restoring Optimizer Statistics Using DBMS_STATS 16-3 16.2 Managing Optimizer Statistics Retention 16.2.1 Obtaining Optimizer Statistics History 16-4 16-5 xiv 16.2.2 Changing the Optimizer Statistics Retention Period 16-5 16.2.3 Purging Optimizer Statistics 16-6 16.3 17 18 Reporting on Past Statistics Gathering Operations Transporting Optimizer Statistics 17.1 About Transporting Optimizer Statistics 17-1 17.2 Transporting Optimizer Statistics to a Test Database: Tutorial 17-2 Analyzing Statistics Using Optimizer Statistics Advisor 18.1 About Optimizer Statistics Advisor 18.1.1 18-2 Problems with a Traditional Script-Based Approach 18-3 18.1.1.2 Advantages of Optimizer Statistics Advisor 18-4 Optimizer Statistics Advisor Concepts 18-4 18.1.2.1 Components of Optimizer Statistics Advisor 18-5 18.1.2.2 Operational Modes for Optimizer Statistics Advisor 18-9 18.1.3 18.2 Purpose of Optimizer Statistics Advisor 18-1 18.1.1.1 18.1.2 Command-Line Interface to Optimizer Statistics Advisor Basic Tasks for Optimizer Statistics Advisor 18-9 18-11 18.2.1 Creating an Optimizer Statistics Advisor Task 18-14 18.2.2 Listing Optimizer Statistics Advisor Tasks 18-15 18.2.3 Creating Filters for an Optimizer Advisor Task 18-16 18.2.3.1 About Filters for Optimizer Statistics Advisor 18-16 18.2.3.2 Creating an Object Filter for an Optimizer Advisor Task 18-17 18.2.3.3 Creating a Rule Filter for an Optimizer Advisor Task 18-19 18.2.3.4 Creating an Operation Filter for an Optimizer Advisor Task 18-22 18.2.4 Executing an Optimizer Statistics Advisor Task 18-25 18.2.5 Generating a Report for an Optimizer Statistics Advisor Task 18-26 18.2.6 Implementing Optimizer Statistics Advisor Recommendations 18-30 18.2.6.1 18.2.6.2 Part VI 19 16-7 Implementing Actions Recommended by Optimizer Statistics Advisor 18-30 Generating a Script Using Optimizer Statistics Advisor 18-32 Optimizer Controls Influencing the Optimizer 19.1 Techniques for Influencing the Optimizer 19-1 19.2 Influencing the Optimizer with Initialization Parameters 19-3 19.2.1 About Optimizer Initialization Parameters 19-4 xv 19.2.2 Enabling Optimizer Features 19-8 19.2.3 Choosing an Optimizer Goal 19-9 19.2.4 Controlling Adaptive Optimization 19.3 Influencing the Optimizer with Hints 19.3.1 19-11 19-12 19.3.1.1 Types of Hints 19-13 19.3.1.2 Scope of Hints 19-14 19.3.1.3 Guidelines for Hints 19-14 Guidelines for Join Order Hints 19-15 19.3.2 20 About Optimizer Hints 19-10 Improving Real-World Performance Through Cursor Sharing 20.1 Overview of Cursor Sharing 20.1.1 About Cursors 20-1 20.1.1.1 Private and Shared SQL Areas 20-2 20.1.1.2 Parent and Child Cursors 20-4 20.1.2 About Cursors and Parsing 20.1.3 About Literals and Bind Variables 20-7 20-11 20.1.3.1 Literals and Cursors 20-11 20.1.3.2 Bind Variables and Cursors 20-12 20.1.3.3 Bind Variable Peeking 20-13 20.1.4 20.2 20-1 About the Life Cycle of Shared Cursors 20-16 20.1.4.1 Cursor Marked Invalid 20-16 20.1.4.2 Cursors Marked Rolling Invalid 20-18 CURSOR_SHARING and Bind Variable Substitution 20-20 20.2.1 CURSOR_SHARING Initialization Parameter 20-20 20.2.2 Parsing Behavior When CURSOR_SHARING = FORCE 20-21 20.3 Adaptive Cursor Sharing 20-23 20.3.1 Purpose of Adaptive Cursor Sharing 20-23 20.3.2 How Adaptive Cursor Sharing Works: Example 20-24 20.3.3 Bind-Sensitive Cursors 20-26 20.3.4 Bind-Aware Cursors 20-29 20.3.5 Cursor Merging 20-32 20.3.6 Adaptive Cursor Sharing Views 20-33 20.4 Real-World Performance Guidelines for Cursor Sharing 20.4.1 20-33 Develop Applications with Bind Variables for Security and Performance 20-34 20.4.2 Do Not Use CURSOR_SHARING = FORCE as a Permanent Fix 20-35 20.4.3 Establish Coding Conventions to Increase Cursor Reuse 20-36 20.4.4 Minimize Session-Level Changes to the Optimizer Environment 20-38 xvi Part VII 21 Monitoring and Tracing SQL Monitoring Database Operations 21.1 About Monitoring Database Operations 21.1.1 Simple Database Operation Use Cases 21-3 21.1.1.2 Composite Database Operation Use Cases 21-3 Database Operation Monitoring Concepts 21-4 21.1.2.1 About the Architecture of Real-Time SQL Monitoring 21-4 21.1.2.2 When the Database Monitors Operations 21-6 21.1.2.3 Attributes of Database Operations 21-7 21.1.3 User Interfaces for Database Operations Monitoring 21-8 21.1.3.1 Monitored SQL Executions Page in Cloud Control 21-9 21.1.3.2 DBMS_SQL_MONITOR Package 21-9 21.1.3.3 Views for Monitoring and Reporting on Database Operations 21.1.4 22 21-2 21.1.1.1 21.1.2 21.2 Purpose of Monitoring Database Operations 21-1 21-10 Basic Tasks in Database Operations Monitoring 21-12 Enabling and Disabling Monitoring of Database Operations 21-12 21.2.1 Enabling Monitoring of Database Operations at the System Level 21-13 21.2.2 Enabling and Disabling Monitoring of Database Operations at the Statement Level 21-14 21.3 Creating a Database Operation 21-14 21.4 Monitoring SQL Executions Using Cloud Control 21-17 Gathering Diagnostic Data with SQL Test Case Builder 22.1 Purpose of SQL Test Case Builder 22-1 22.2 Concepts for SQL Test Case Builder 22-1 22.2.1 SQL Incidents 22-2 22.2.2 What SQL Test Case Builder Captures 22-2 22.2.3 Output of SQL Test Case Builder 22-3 22.3 User Interfaces for SQL Test Case Builder 22.3.1 22-4 22.3.1.1 Accessing the Incident Manager 22-5 22.3.1.2 Accessing the Support Workbench 22-6 22.3.2 22.4 Graphical Interface for SQL Test Case Builder 22-4 Command-Line Interface for SQL Test Case Builder Running SQL Test Case Builder 22-6 22-7 xvii 23 Performing Application Tracing 23.1 Overview of End-to-End Application Tracing 23-1 23.1.1 Purpose of End-to-End Application Tracing 23-2 23.1.2 End-to-End Application Tracing in a Multitenant Environment 23-3 23.1.3 Tools for End-to-End Application Tracing 23-3 23.2 23.1.3.1 Overview of the SQL Trace Facility 23-4 23.1.3.2 Overview of TKPROF 23-5 Enabling Statistics Gathering for End-to-End Tracing 23-5 23.2.1 Enabling Statistics Gathering for a Client ID 23-5 23.2.2 Enabling Statistics Gathering for Services, Modules, and Actions 23-6 23.3 Enabling End-to-End Application Tracing 23-7 23.3.1 Enabling Tracing for a Client Identifier 23-7 23.3.2 Enabling Tracing for a Service, Module, and Action 23-8 23.3.3 Enabling Tracing for a Session 23-9 23.3.4 Enabling Tracing for the Instance or Database 23.4 Generating Output Files Using SQL Trace and TKPROF 23-10 23-11 23.4.1 Step 1: Setting Initialization Parameters for Trace File Management 23-12 23.4.2 Step 2: Enabling the SQL Trace Facility 23-13 23.4.3 Step 3: Generating Output Files with TKPROF 23-14 23.4.4 Step 4: Storing SQL Trace Facility Statistics 23-15 23.5 23.4.4.1 Generating the TKPROF Output SQL Script 23-16 23.4.4.2 Editing the TKPROF Output SQL Script 23-16 23.4.4.3 Querying the Output Table 23-16 Guidelines for Interpreting TKPROF Output 23-18 23.5.1 Guideline for Interpreting the Resolution of Statistics 23-18 23.5.2 Guideline for Recursive SQL Statements 23-18 23.5.3 Guideline for Deciding Which Statements to Tune 23-19 23.5.4 Guidelines for Avoiding Traps in TKPROF Interpretation 23-20 23.6.1 23.5.4.1 Guideline for Avoiding the Argument Trap 23-20 23.5.4.2 Guideline for Avoiding the Read Consistency Trap 23-20 23.5.4.3 Guideline for Avoiding the Schema Trap 23-21 23.5.4.4 Guideline for Avoiding the Time Trap 23-22 Application Tracing Utilities 23.6.1.1 TRCSESS 23-22 23-22 23.6.1.1.1 Purpose 23-23 23.6.1.1.2 Guidelines 23-23 23.6.1.1.3 Syntax 23-23 23.6.1.1.4 Options 23-23 23.6.1.1.5 Examples 23-24 23.6.1.2 TKPROF 23-24 xviii 23.7.1 Purpose 23-25 23.6.1.2.2 Guidelines 23-25 23.6.1.2.3 Syntax 23-25 23.6.1.2.4 Options 23-26 23.6.1.2.5 Output 23-28 23.6.1.2.6 Examples 23-31 Views for Application Tracing Views Relevant for Trace Statistics 23-35 23.7.1.2 Views Related to Enabling Tracing 23-36 Automatic SQL Tuning Managing SQL Tuning Sets 24.1 About SQL Tuning Sets 24-1 24.1.1 Purpose of SQL Tuning Sets 24-2 24.1.2 Concepts for SQL Tuning Sets 24-2 24.1.3 User Interfaces for SQL Tuning Sets 24-4 24.1.3.1 Accessing the SQL Tuning Sets Page in Cloud Control 24-4 24.1.3.2 Command-Line Interface to SQL Tuning Sets 24-5 24.1.4 Basic Tasks for SQL Tuning Sets 24-5 24.2 Creating a SQL Tuning Set 24-6 24.3 Loading a SQL Tuning Set 24-7 24.4 Displaying the Contents of a SQL Tuning Set 24-8 24.5 Modifying a SQL Tuning Set 24-10 24.6 Transporting a SQL Tuning Set 24-12 24.6.1 24.7 About Transporting SQL Tuning Sets 24-12 24.6.1.1 Basic Steps for Transporting SQL Tuning Sets 24-12 24.6.1.2 Basic Steps for Transporting SQL Tuning Sets When the CON_DBID Values Differ 24-13 24.6.2 25 23-35 23.7.1.1 Part VIII 24 23.6.1.2.1 Transporting SQL Tuning Sets with DBMS_SQLTUNE Dropping a SQL Tuning Set 24-14 24-16 Analyzing SQL with SQL Tuning Advisor 25.1 About SQL Tuning Advisor 25-1 25.1.1 Purpose of SQL Tuning Advisor 25-1 25.1.2 SQL Tuning Advisor Architecture 25-3 25.1.2.1 Input to SQL Tuning Advisor 25-4 25.1.2.2 Output of SQL Tuning Advisor 25-5 25.1.2.3 Automatic Tuning Optimizer Analyses 25-5 xix 25.1.3 25.2 SQL Tuning Advisor Operation 25.1.3.1 Automatic and On-Demand SQL Tuning 25-16 25.1.3.2 Local and Remote SQL Tuning 25-17 Managing the Automatic SQL Tuning Task 25-18 25.2.1 About the Automatic SQL Tuning Task Purpose of Automatic SQL Tuning 25-19 25.2.1.2 Automatic SQL Tuning Concepts 25-20 25.2.1.3 Command-Line Interface to SQL Tuning Advisor 25-20 25.2.1.4 Basic Tasks for Automatic SQL Tuning 25-21 Enabling and Disabling the Automatic SQL Tuning Task 25.2.2.1 25.2.2.2 25.2.3 25.2.3.2 25.2.4 25-22 Enabling and Disabling the Automatic SQL Tuning Task from the Command Line 25-23 25-25 Configuring the Automatic SQL Tuning Task Using the Command Line 25-26 Viewing Automatic SQL Tuning Reports Using the Command Line Running SQL Tuning Advisor On Demand 25.3.1 25-24 Configuring the Automatic SQL Tuning Task Using Cloud Control Viewing Automatic SQL Tuning Reports 25.2.4.1 25-22 Enabling and Disabling the Automatic SQL Tuning Task Using Cloud Control Configuring the Automatic SQL Tuning Task 25.2.3.1 26 25-19 25.2.1.1 25.2.2 25.3 25-16 About On-Demand SQL Tuning 25-27 25-28 25-30 25-31 25.3.1.1 Purpose of On-Demand SQL Tuning 25-31 25.3.1.2 User Interfaces for On-Demand SQL Tuning 25-31 25.3.1.3 Basic Tasks in On-Demand SQL Tuning 25-33 25.3.2 Creating a SQL Tuning Task 25-34 25.3.3 Configuring a SQL Tuning Task 25-36 25.3.4 Executing a SQL Tuning Task 25-38 25.3.5 Monitoring a SQL Tuning Task 25-39 25.3.6 Displaying the Results of a SQL Tuning Task 25-40 Optimizing Access Paths with SQL Access Advisor 26.1 About SQL Access Advisor 26-1 26.1.1 Purpose of SQL Access Advisor 26-2 26.1.2 SQL Access Advisor Architecture 26-2 26.1.2.1 Input to SQL Access Advisor 26-4 26.1.2.2 Filter Options for SQL Access Advisor 26-4 26.1.2.3 SQL Access Advisor Recommendations 26-5 26.1.2.4 SQL Access Advisor Actions 26-6 26.1.2.5 SQL Access Advisor Repository 26-8 xx 26.1.3 User Interfaces for SQL Access Advisor 26.1.3.1 26.1.3.2 26.2 Command-Line Interface to SQL Tuning Sets Optimizing Access Paths with SQL Access Advisor: Basic Tasks 26-9 26-10 26-10 26.2.1 Creating a SQL Tuning Set as Input for SQL Access Advisor 26-12 26.2.2 Populating a SQL Tuning Set with a User-Defined Workload 26-13 26.2.3 Creating and Configuring a SQL Access Advisor Task 26-15 26.2.4 Executing a SQL Access Advisor Task 26-17 26.2.5 Viewing SQL Access Advisor Task Results 26-18 26.2.6 Generating and Executing a Task Script 26-22 26.3 Performing a SQL Access Advisor Quick Tune 26-23 26.4 Using SQL Access Advisor: Advanced Tasks 26-24 26.4.1 Evaluating Existing Access Structures 26-25 26.4.2 Updating SQL Access Advisor Task Attributes 26-25 26.4.3 Creating and Using SQL Access Advisor Task Templates 26-26 26.4.4 Terminating SQL Access Advisor Task Execution 26-28 26.4.4.1 Interrupting SQL Access Advisor Tasks 26-29 26.4.4.2 Canceling SQL Access Advisor Tasks 26-29 26.4.5 Deleting SQL Access Advisor Tasks 26-30 26.4.6 Marking SQL Access Advisor Recommendations 26-31 26.4.7 Modifying SQL Access Advisor Recommendations 26-32 26.5 SQL Access Advisor Examples 26-33 26.6 SQL Access Advisor Reference 26-33 26.6.1 Action Attributes in the DBA_ADVISOR_ACTIONS View 26-34 26.6.2 Categories for SQL Access Advisor Task Parameters 26-35 26.6.3 SQL Access Advisor Constants 26-36 Part IX 27 Accessing the SQL Access Advisor: Initial Options Page Using Cloud Control 26-8 SQL Controls: Profiles and Plan Baselines Managing SQL Profiles 27.1 About SQL Profiles 27-1 27.1.1 Purpose of SQL Profiles 27-2 27.1.2 Concepts for SQL Profiles 27-2 27.1.2.1 SQL Profile Recommendations 27-4 27.1.2.2 SQL Profiles and SQL Plan Baselines 27-6 27.1.3 User Interfaces for SQL Profiles 27-6 27.1.4 Basic Tasks for SQL Profiles 27-6 27.2 Implementing a SQL Profile 27.2.1 About SQL Profile Implementation 27-7 27-8 xxi 27.2.2 28 27-9 27.3 Listing SQL Profiles 27.4 Altering a SQL Profile 27-10 27.5 Dropping a SQL Profile 27-11 27.6 Transporting a SQL Profile 27-12 27-9 Overview of SQL Plan Management 28.1 Purpose of SQL Plan Management 28-2 28.1.1 Benefits of SQL Plan Management 28-2 28.1.2 Differences Between SQL Plan Baselines and SQL Profiles 28-3 28.2 Plan Capture 28.2.1 Automatic Initial Plan Capture 28-4 28-4 28.2.1.1 Eligibility for Automatic Initial Plan Capture 28-5 28.2.1.2 Plan Matching for Automatic Initial Plan Capture 28-6 28.2.2 Manual Plan Capture 28-6 28.3 Plan Selection 28-8 28.4 Plan Evolution 28-9 28.4.1 Purpose of Plan Evolution 28-10 28.4.2 PL/SQL Subprograms for Plan Evolution 28-10 28.5 29 Implementing a SQL Profile Storage Architecture for SQL Plan Management 28-11 28.5.1 SQL Management Base 28-11 28.5.2 SQL Statement Log 28-12 28.5.3 SQL Plan History 28-14 28.5.3.1 Enabled Plans 28-15 28.5.3.2 Accepted Plans 28-15 28.5.3.3 Fixed Plans 28-15 Managing SQL Plan Baselines 29.1 About Managing SQL Plan Baselines 29.1.1 29-2 29.1.1.1 Accessing the SQL Plan Baseline Page in Cloud Control 29-2 29.1.1.2 DBMS_SPM Package 29-3 29.1.2 29.2 User Interfaces for SQL Plan Management 29-1 Basic Tasks in SQL Plan Management Configuring SQL Plan Management 29.2.1 Configuring the Capture and Use of SQL Plan Baselines 29.2.1.1 29-4 29-5 29-5 Enabling Automatic Initial Plan Capture for SQL Plan Management 29-7 29.2.1.2 Configuring Filters for Automatic Plan Capture 29-8 29.2.1.3 Disabling All SQL Plan Baselines 29.2.2 Managing the SPM Evolve Advisor Task 29-10 29-10 xxii 29-11 29.2.2.2 Enabling and Disabling the SPM Evolve Advisor Task 29-11 29.2.2.3 Configuring the Automatic SPM Evolve Advisor Task 29-12 Displaying Plans in a SQL Plan Baseline 29-14 29.4 Loading SQL Plan Baselines 29-15 29.4.1 About Loading SQL Plan Baselines 29-16 29.4.2 Loading Plans from AWR 29-17 29.4.3 Loading Plans from the Shared SQL Area 29-19 29.4.4 Loading Plans from a SQL Tuning Set 29-21 29.4.5 Loading Plans from a Staging Table 29-23 Evolving SQL Plan Baselines Manually 29-26 29.5.1 About the DBMS_SPM Evolve Functions 29-26 29.5.2 Managing an Evolve Task 29-28 29.6 Dropping SQL Plan Baselines 29-35 29.7 Managing the SQL Management Base 29-37 29.7.1 About Managing the SMB 29-37 29.7.2 Changing the Disk Space Limit for the SMB 29-38 29.7.3 Changing the Plan Retention Policy in the SMB 29-39 Migrating Stored Outlines to SQL Plan Baselines 30.1 A About the SPM Evolve Advisor Task 29.3 29.5 30 29.2.2.1 About Stored Outline Migration 30-1 30.1.1 Purpose of Stored Outline Migration 30-2 30.1.2 How Stored Outline Migration Works 30-3 30.1.2.1 Stages of Stored Outline Migration 30-3 30.1.2.2 Outline Categories and Baseline Modules 30-4 30.1.3 User Interface for Stored Outline Migration 30-5 30.1.4 Basic Steps in Stored Outline Migration 30-7 30.2 Preparing for Stored Outline Migration 30-7 30.3 Migrating Outlines to Utilize SQL Plan Management Features 30-9 30.4 Migrating Outlines to Preserve Stored Outline Behavior 30-10 30.5 Performing Follow-Up Tasks After Stored Outline Migration 30-11 Guidelines for Indexes and Table Clusters A.1 Guidelines for Tuning Index Performance A-1 A.1.1 Guidelines for Tuning the Logical Structure A-2 A.1.2 Guidelines for Choosing Columns and Expressions to Index A-3 A.1.3 Guidelines for Choosing Composite Indexes A-4 A.1.3.1 Guidelines for Choosing Keys for Composite Indexes A-5 A.1.3.2 Guidelines for Ordering Keys for Composite Indexes A-5 xxiii A.1.4 Guidelines for Writing SQL Statements That Use Indexes A-6 A.1.5 Guidelines for Writing SQL Statements That Avoid Using Indexes A-6 A.1.6 Guidelines for Re-Creating Indexes A-6 A.1.7 Guidelines for Compacting Indexes A-7 A.1.8 Guidelines for Using Nonunique Indexes to Enforce Uniqueness A-7 A.1.9 Guidelines for Using Enabled Novalidated Constraints A-8 A.2 Guidelines for Using Function-Based Indexes for Performance A-9 A.3 Guidelines for Using Partitioned Indexes for Performance A-10 A.4 Guidelines for Using Index-Organized Tables for Performance A-10 A.5 Guidelines for Using Bitmap Indexes for Performance A-11 A.6 Guidelines for Using Bitmap Join Indexes for Performance A-12 A.7 Guidelines for Using Domain Indexes for Performance A-12 A.8 Guidelines for Using Table Clusters A-13 A.9 Guidelines for Using Hash Clusters for Performance A-14 Glossary Index xxiv Preface This manual explains how to tune Oracle SQL. This preface contains the following topics: • Audience • Documentation Accessibility • Related Documents • Conventions Audience This document is intended for database administrators and application developers who perform the following tasks: • Generating and interpreting SQL execution plans • Managing optimizer statistics • Influencing the optimizer through initialization parameters or SQL hints • Controlling cursor sharing for SQL statements • Monitoring SQL execution • Performing application tracing • Managing SQL tuning sets • Using SQL Tuning Advisor or SQL Access Advisor • Managing SQL profiles • Managing SQL baselines Documentation Accessibility For information about Oracle's commitment to accessibility, visit the Oracle Accessibility Program website at http://www.oracle.com/pls/topic/lookup? ctx=acc&id=docacc. Access to Oracle Support Oracle customers that have purchased support have access to electronic support through My Oracle Support. For information, visit http://www.oracle.com/pls/topic/ lookup?ctx=acc&id=info or visit http://www.oracle.com/pls/topic/lookup?ctx=acc&id=trs if you are hearing impaired. xxv Preface Related Documents This manual assumes that you are familiar with Oracle Database Concepts. The following books are frequently referenced: • Oracle Database Data Warehousing Guide • Oracle Database VLDB and Partitioning Guide • Oracle Database SQL Language Reference • Oracle Database Reference Many examples in this book use the sample schemas, which are installed by default when you select the Basic Installation option with an Oracle Database. See Oracle Database Sample Schemas for information on how these schemas were created and how you can use them. Conventions The following text conventions are used in this document: Convention Meaning boldface Boldface type indicates graphical user interface elements associated with an action, or terms defined in text or the glossary. italic Italic type indicates book titles, emphasis, or placeholder variables for which you supply particular values. monospace Monospace type indicates commands within a paragraph, URLs, code in examples, text that appears on the screen, or text that you enter. xxvi Changes in This Release for Oracle Database SQL Tuning Guide This preface describes the most important changes in Oracle Database SQL Tuning Guide. This preface contains the following topics: • Changes in Oracle Database 12c Release 2 (12.2.0.1) Oracle Database SQL Tuning Guide for Oracle Database 12c Release 2 (12.2.0.1) has the following changes. • Changes in Oracle Database 12c Release 1 (12.1.0.2) Oracle Database SQL Tuning Guide for Oracle Database 12c Release 1 (12.1.0.2) has the following changes. • Changes in Oracle Database 12c Release 1 (12.1.0.1) Oracle Database SQL Tuning Guide for Oracle Database 12c Release 1 (12.1) has the following changes. Changes in Oracle Database 12c Release 2 (12.2.0.1) Oracle Database SQL Tuning Guide for Oracle Database 12c Release 2 (12.2.0.1) has the following changes. • New Features The following features are new in this release: • Desupported Features The following features are desupported in Oracle Database 12c Release 2 (12.2.0.1). • Other Changes This topic describes additional changes in the release. New Features The following features are new in this release: • Advisor enhancements – Optimizer Statistics Advisor Optimizer Statistics Advisor is built-in diagnostic software that analyzes the quality of statistics and statistics-related tasks. The advisor task runs automatically in the maintenance window, but you can also run it on demand. You can then view the advisor report. If the advisor makes recommendations, then in some cases you can run system-generated scripts to implement them. See "Analyzing Statistics Using Optimizer Statistics Advisor". xxvii Changes in This Release for Oracle Database SQL Tuning Guide – Active Data Guard Support for SQL Tuning Advisor Using database links, you can tune a standby database workload on a primary database. See "Local and Remote SQL Tuning". • DBMS_STATS enhancements – DBMS_STATS preference for automatic column group statistics If the DBMS_STATS preference AUTO_STAT_EXTENSIONS is set to ON (by default it is OFF), then a SQL plan directive can automatically trigger the creation of column group statistics based on usage of predicates in the workload. See "Purpose of Optimizer Statistics Preferences". – DBMS_STATS support for external table scan rates and In-Memory column store (IM column store) statistics If the database uses an IM column store, then you can set the im_imcu_count parameter to the number of IMCUs in the table or partition, and im_block_count to the number of blocks. For an external table, scanrate specifies the rate at which data is scanned in MB/second. See "Guideline for External Tables". – DBMS_STATS statistics preference PREFERENCE_OVERRIDES_PARAMETER The PREFERENCE_OVERRIDES_PARAMETER statistics preference determines whether, when gathering optimizer statistics, to override the input value of a parameter with the statistics preference. In this way, you control when the database honors a parameter value passed to the statistics gathering procedures. See "Statistics Preference Overrides". – Access to current statistics does not require FLUSH_DATABASE_MONITORING_INFO You no longer need to ensure that view metadata is up-to-date by using DBMS_STATS.FLUSH_DATABASE_MONITORING_INFO to save monitoring information to disk. The statistics shown in DBA_TAB_STATISTICS and DBA_IND_STATISTICS come from the same source as DBA_TAB_MODIFICATIONS, which means these views show statistics obtained from disk and memory. See "Determining When Optimizer Statistics Are Stale". • Separate controls for adaptive plans and adaptive statistics The OPTIMIZER_ADAPTIVE_PLANS initialization parameter enables (default) or disables adaptive plans. The OPTIMIZER_ADAPTIVE_STATISTICS initialization parameter enables or disables (default) adaptive statistics. See "When Adaptive Query Plans Are Enabled" and "When Adaptive Statistics Are Enabled". • Join enhancements – Join groups A join group is a user-created object that lists two columns that can be meaningfully joined. In certain queries, join groups enable the database to eliminate the performance overhead of decompressing and hashing column values. Join groups require an IM column store. See "In-Memory Join Groups". xxviii Changes in This Release for Oracle Database SQL Tuning Guide – Band join enhancements A band join is a special type of nonequijoin in which key values in one data set must fall within the specified range (“band”) of the second data set. When the database detects a band join, the database evaluates the costs of band joins more efficiently, avoiding unnecessary scans of rows that fall outside the defined bands. In most cases, optimized performance is comparable to an equijoin. See "Band Joins". • Cursor management enhancements – Cursor-duration temporary tables To materialize the intermediate results of a query, Oracle Database may create a cursor-duration temporary table in memory during query compilation. For complex operations such as WITH clause queries and star transformations, this internal optimization, which enhances the materialization of intermediate results from repetitively used subqueries, improves performance and optimizes I/O. See "Cursor-Duration Temporary Tables". – Fine-grained cursor invalidation Starting in this release, you can specify deferred invalidation on DDL statements. When shared SQL areas are marked rolling invalid, the database assigns each one a randomly generated time period. A hard parse occurs only if the query executes after the time period has expired. In this way, the database can diffuse the performance overhead of hard parsing over time. See "About the Life Cycle of Shared Cursors". • OR expansion enhancement In previous releases, the optimizer used the CONCATENATION operator to perform the OR expansion. Now the optimizer uses the UNION-ALL operator instead. This enhancement provides several benefits, including enabling interaction among various transformations, and avoiding the sharing of query structures. See "OR Expansion". • • SQL plan management enhancements – You can now capture plans from AWR. See "Manual Plan Capture". – In previous releases, automatic capture applied to all repeatable queries. Starting in this release, you can create filters to capture only the plans for statements that you choose. See "Eligibility for Automatic Initial Plan Capture". Real-Time database operation monitoring enhancements A session can start or stop a database operation in a different session by specifying its session ID and serial number. See "Creating a Database Operation". • Expression tracking SQL statements commonly include expressions such as plus (+) or minus (-). More complicated examples include PL/SQL functions or SQL functions such as LTRIM and TO_NUMBER. The Expression Statistics Store (ESS) maintains usage information about expressions identified during compilation and captured during execution. xxix Changes in This Release for Oracle Database SQL Tuning Guide See "About the Expression Statistics Store (ESS)". • Enhancements for application tracing in a multitenant environment CDB administrators and PDB administrators can use new V$ views to access trace data that is relevant for a specific PDB. See "End-to-End Application Tracing in a Multitenant Environment". Desupported Features The following features are desupported in Oracle Database 12c Release 2 (12.2.0.1). • The OPTIMIZER_ADAPTIVE_FEATURES initialization parameter See Also: Oracle Database Upgrade Guide for a list of desupported features Other Changes This topic describes additional changes in the release. • New Real-World Performance content In this release, the book incorporates information provided by the Real-World Performance group, including the following: – "Improving Real-World Performance Through Cursor Sharing" explains how to use bind variables and new features such as adaptive cursor sharing Changes in Oracle Database 12c Release 1 (12.1.0.2) Oracle Database SQL Tuning Guide for Oracle Database 12c Release 1 (12.1.0.2) has the following changes. • New Features The following features are new in this release. New Features The following features are new in this release. • In-Memory aggregation This optimization minimizes the join and GROUP BY processing required for each row when joining a single large table to multiple small tables, as in a star schema. VECTOR GROUP BY aggregation uses the infrastructure related to parallel query (PQ) processing, and blends it with CPU-efficient algorithms to maximize the performance and effectiveness of the initial aggregation performed before redistributing fact data. See "In-Memory Aggregation (VECTOR GROUP BY)". • SQL Monitor support for adaptive query plans xxx Changes in This Release for Oracle Database SQL Tuning Guide SQL Monitor supports adaptive query plans in the following ways: – Indicates whether a query plan is adaptive, and show its current status: resolving or resolved. – Provides a list that enables you to select the current, full, or final query plans See "Adaptive Query Plans" to learn more about adaptive query plans, and "Monitoring SQL Executions Using Cloud Control" to learn more about SQL Monitor. Changes in Oracle Database 12c Release 1 (12.1.0.1) Oracle Database SQL Tuning Guide for Oracle Database 12c Release 1 (12.1) has the following changes. • New Features The following features are new in this release. • Deprecated Features The following features are deprecated in this release, and may be desupported in a future release. • Desupported Features Some features previously described in this document are desupported in Oracle Database 12c. • Other Changes The following are additional changes in the release. New Features The following features are new in this release. • Adaptive SQL Plan Management (SPM) The SPM Evolve Advisor is a task infrastructure that enables you to schedule an evolve task, rerun an evolve task, and generate persistent reports. The new automatic evolve task, SYS_AUTO_SPM_EVOLVE_TASK, runs in the default maintenance window. This task ranks all unaccepted plans and runs the evolve process for them. If the task finds a new plan that performs better than existing plan, the task automatically accepts the plan. You can also run evolution tasks manually using the DBMS_SPM package. See "Managing the SPM Evolve Advisor Task". • Adaptive query optimization Adaptive query optimization is a set of capabilities that enable the optimizer to make run-time adjustments to execution plans and discover additional information that can lead to better statistics. The set of capabilities include: – Adaptive query plans An adaptive query plan has built-in options that enable the final plan for a statement to differ from the default plan. During the first execution, before a specific subplan becomes active, the optimizer makes a final decision about which option to use. The optimizer bases its choice on observations made during the execution up to this point. The ability of the optimizer to adapt plans can improve query performance. xxxi Changes in This Release for Oracle Database SQL Tuning Guide See "Adaptive Query Plans". – Automatic reoptimization When using automatic reoptimization, the optimizer monitors the initial execution of a query. If the actual execution statistics vary significantly from the original plan statistics, then the optimizer records the execution statistics and uses them to choose a better plan the next time the statement executes. The database uses information obtained during automatic reoptimization to generate SQL plan directives automatically. See "Automatic Reoptimization". – SQL plan directives In releases earlier than Oracle Database 12c, the database stored compilation and execution statistics in the shared SQL area, which is nonpersistent. Starting in this release, the database can use a SQL plan directive, which is additional information and instructions that the optimizer can use to generate a more optimal plan. The database stores SQL plan directives persistently in the SYSAUX tablespace. When generating an execution plan, the optimizer can use SQL plan directives to obtain more information about the objects accessed in the plan. See "SQL Plan Directives". – Dynamic statistics enhancements In releases earlier than Oracle Database 12c, Oracle Database only used dynamic statistics (previously called dynamic sampling) when one or more of the tables in a query did not have optimizer statistics. Starting in this release, the optimizer automatically decides whether dynamic statistics are useful and which dynamic statistics level to use for all SQL statements. Dynamic statistics gathers are persistent and usable by other queries. See "Supplemental Dynamic Statistics". • New types of histograms This release introduces top frequency and hybrid histograms. If a column contains more than 254 distinct values, and if the top 254 most frequent values occupy more than 99% of the data, then the database creates a top frequency histogram using the top 254 most frequent values. By ignoring the nonpopular values, which are statistically insignificant, the database can produce a better quality histogram for highly popular values. A hybrid histogram is an enhanced height-based histogram that stores the exact frequency of each endpoint in the sample, and ensures that a value is never stored in multiple buckets. Also, regular frequency histograms have been enhanced. The optimizer computes frequency histograms during NDV computation based on a full scan of the data rather than a small sample (when AUTO_SAMPLING is used). The enhanced frequency histograms ensure that even highly infrequent values are properly represented with accurate bucket counts within a histogram. See "Histograms ". • Monitoring database operations Real-Time Database Operations Monitoring enables you to monitor long running database tasks such as batch jobs, scheduler jobs, and Extraction, Transformation, and Loading (ETL) jobs as a composite business operation. This feature tracks the progress of SQL and PL/SQL queries associated with the xxxii Changes in This Release for Oracle Database SQL Tuning Guide business operation being monitored. As a DBA or developer, you can define business operations for monitoring by explicitly specifying the start and end of the operation or implicitly with tags that identify the operation. See "Monitoring Database Operations ". • Concurrent statistics gathering You can concurrently gather optimizer statistics on multiple tables, table partitions, or table subpartitions. By fully utilizing multiprocessor environments, the database can reduce the overall time required to gather statistics. Oracle Scheduler and Advanced Queuing create and manage jobs to gather statistics concurrently. The scheduler decides how many jobs to execute concurrently, and how many to queue based on available system resources and the value of the JOB_QUEUE_PROCESSES initialization parameter. See "Gathering Optimizer Statistics Concurrently". • Reporting mode for DBMS_STATS statistics gathering functions You can run the DBMS_STATS functions in reporting mode. In this mode, the optimizer does not actually gather statistics, but reports objects that would be processed if you were to use a specified statistics gathering function. See "Running Statistics Gathering Functions in Reporting Mode". • Reports on past statistics gathering operations You can use DBMS_STATS functions to report on a specific statistics gathering operation or on operations that occurred during a specified time. See "Reporting on Past Statistics Gathering Operations". • Automatic column group creation With column group statistics, the database gathers optimizer statistics on a group of columns treated as a unit. Starting in Oracle Database 12c, the database automatically determines which column groups are required in a specified workload or SQL tuning set, and then creates the column groups. Thus, for any specified workload, you no longer need to know which columns from each table must be grouped. See "Detecting Useful Column Groups for a Specific Workload". • Session-private statistics for global temporary tables Starting in this release, global temporary tables have a different set of optimizer statistics for each session. Session-specific statistics improve performance and manageability of temporary tables because users no longer need to set statistics for a global temporary table in each session or rely on dynamic statistics. The possibility of errors in cardinality estimates for global temporary tables is lower, ensuring that the optimizer has the necessary information to determine an optimal execution plan. See "Session-Specific Statistics for Global Temporary Tables". • SQL Test Case Builder enhancements SQL Test Case Builder can capture and replay actions and events that enable you to diagnose incidents that depend on certain dynamic and volatile factors. This capability is especially useful for parallel query and automatic memory management. See Gathering Diagnostic Data with SQL Test Case Builder. xxxiii Changes in This Release for Oracle Database SQL Tuning Guide • Online statistics gathering for bulk loads A bulk load is a CREATE TABLE AS SELECT or INSERT INTO ... SELECT operation. In releases earlier than Oracle Database 12c, you needed to manually gather statistics after a bulk load to avoid the possibility of a suboptimal execution plan caused by stale statistics. Starting in this release, Oracle Database gathers optimizer statistics automatically, which improves both performance and manageability. See "Online Statistics Gathering for Bulk Loads". • Reuse of synopses after partition maintenance operations ALTER TABLE EXCHANGE is a common partition maintenance operation. During a partition exchange, the statistics of the partition and the table are also exchanged. A synopsis is a set of auxiliary statistics gathered on a partitioned table when the INCREMENTAL value is set to true. In releases earlier than Oracle Database 12c, you could not gather table-level synopses on a table. Thus, you could not gather tablelevel synopses on a table, exchange the table with a partition, and end up with synopses on the partition. You had to explicitly gather optimizer statistics in incremental mode to create the missing synopses. Starting in this release, you can gather table-level synopses on a table. When you exchange this table with a partition in an incremental mode table, the synopses are also exchanged. See "Maintaining Incremental Statistics for Partition Maintenance Operations". • Automatic updates of global statistics for tables with stale or locked partition statistics Incremental statistics can automatically calculate global statistics for a partitioned table even if the partition or subpartition statistics are stale and locked. See "Maintaining Incremental Statistics for Tables with Stale or Locked Partition Statistics". • Cube query performance enhancements These enhancements minimize CPU and memory consumption and reduce I/O for queries against cubes. See Table 7-7 to learn about the CUBE JOIN operation. Deprecated Features The following features are deprecated in this release, and may be desupported in a future release. • Stored outlines See "Managing SQL Plan Baselines" for information about alternatives. • The SIMILAR value for the CURSOR_SHARING initialization parameter This value is deprecated. Use FORCE instead. See "Do Not Use CURSOR_SHARING = FORCE as a Permanent Fix". Desupported Features Some features previously described in this document are desupported in Oracle Database 12c. xxxiv Changes in This Release for Oracle Database SQL Tuning Guide See Oracle Database Upgrade Guide for a list of desupported features. Other Changes The following are additional changes in the release. • New tuning books The Oracle Database 11g Oracle Database Performance Tuning Guide has been divided into two books for Oracle Database 12c: – Oracle Database Performance Tuning Guide, which contains only topics that pertain to tuning the database – Oracle Database SQL Tuning Guide, which contains only topics that pertain to tuning SQL xxxv Part I SQL Performance Fundamentals SQL tuning is improving SQL statement performance to meet specific, measurable, and achievable goals. This part contains the following chapters: • Introduction to SQL Tuning SQL tuning is the attempt to diagnose and repair SQL statements that fail to meet a performance standard. • SQL Performance Methodology This chapter describes the recommended methodology for SQL tuning. 1 Introduction to SQL Tuning SQL tuning is the attempt to diagnose and repair SQL statements that fail to meet a performance standard. This chapter contains the following topics: • About SQL Tuning SQL tuning is the iterative process of improving SQL statement performance to meet specific, measurable, and achievable goals. • Purpose of SQL Tuning A SQL statement becomes a problem when it fails to perform according to a predetermined and measurable standard. • Prerequisites for SQL Tuning SQL performance tuning requires a foundation of database knowledge. • Tasks and Tools for SQL Tuning After you have identified the goal for a tuning session, for example, reducing user response time from three minutes to less than a second, the problem becomes how to accomplish this goal. 1.1 About SQL Tuning SQL tuning is the iterative process of improving SQL statement performance to meet specific, measurable, and achievable goals. SQL tuning implies fixing problems in deployed applications. In contrast, application design sets the security and performance goals before deploying an application. See Also: • SQL Performance Methodology • "Guidelines for Designing Your Application" to learn how to design for SQL performance 1.2 Purpose of SQL Tuning A SQL statement becomes a problem when it fails to perform according to a predetermined and measurable standard. After you have identified the problem, a typical tuning session has one of the following goals: • Reduce user response time, which means decreasing the time between when a user issues a statement and receives a response 1-1 Chapter 1 Prerequisites for SQL Tuning • Improve throughput, which means using the least amount of resources necessary to process all rows accessed by a statement For a response time problem, consider an online book seller application that hangs for three minutes after a customer updates the shopping cart. Contrast with a threeminute parallel query in a data warehouse that consumes all of the database host CPU, preventing other queries from running. In each case, the user response time is three minutes, but the cause of the problem is different, and so is the tuning goal. 1.3 Prerequisites for SQL Tuning SQL performance tuning requires a foundation of database knowledge. If you are tuning SQL performance, then this manual assumes that you have the knowledge and skills shown in the following table. Table 1-1 Required Knowledge Required Knowledge Description To Learn More Database architecture Database architecture is not the domain of administrators alone. As a developer, you want to develop applications in the least amount of time against an Oracle database, which requires exploiting the database architecture and features. For example, not understanding Oracle Database concurrency controls and multiversioning read consistency may make an application corrupt the integrity of the data, run slowly, and decrease scalability. Oracle Database Concepts explains the basic relational data structures, transaction management, storage structures, and instance architecture of Oracle Database. SQL and PL/SQL Because of the existence of GUIbased tools, it is possible to create applications and administer a database without knowing SQL. However, it is impossible to tune applications or a database without knowing SQL. Oracle Database Concepts includes an introduction to Oracle SQL and PL/SQL. You must also have a working knowledge of Oracle Database SQL Language Reference, Oracle Database PL/SQL Packages and Types Reference, and Oracle Database PL/SQL Packages and Types Reference. SQL tuning tools The database generates performance statistics, and provides SQL tuning tools that interpret these statistics. Oracle Database 2 Day + Performance Tuning Guide provides an introduction to the principal SQL tuning tools. 1.4 Tasks and Tools for SQL Tuning After you have identified the goal for a tuning session, for example, reducing user response time from three minutes to less than a second, the problem becomes how to accomplish this goal. 1-2 Chapter 1 Tasks and Tools for SQL Tuning This section contains the following topics: • SQL Tuning Tasks The specifics of a tuning session depend on many factors, including whether you tune proactively or reactively. • SQL Tuning Tools SQL tuning tools are either automated or manual. • User Interfaces to SQL Tuning Tools Cloud Control is a system management tool that provides centralized management of a database environment. Cloud Control provides access to most tuning tools. 1.4.1 SQL Tuning Tasks The specifics of a tuning session depend on many factors, including whether you tune proactively or reactively. In proactive SQL tuning, you regularly use SQL Tuning Advisor to determine whether you can make SQL statements perform better. In reactive SQL tuning, you correct a SQL-related problem that a user has experienced. Whether you tune proactively or reactively, a typical SQL tuning session involves all or most of the following tasks: 1. Identifying high-load SQL statements Review past execution history to find the statements responsible for a large share of the application workload and system resources. 2. Gathering performance-related data The optimizer statistics are crucial to SQL tuning. If these statistics do not exist or are no longer accurate, then the optimizer cannot generate the best plan. Other data relevant to SQL performance include the structure of tables and views that the statement accessed, and definitions of any indexes available to the statement. 3. Determining the causes of the problem Typically, causes of SQL performance problems include: • Inefficiently designed SQL statements If a SQL statement is written so that it performs unnecessary work, then the optimizer cannot do much to improve its performance. Examples of inefficient design include • – Neglecting to add a join condition, which leads to a Cartesian join – Using hints to specify a large table as the driving table in a join – Specifying UNION instead of UNION ALL – Making a subquery execute for every row in an outer query Suboptimal execution plans The query optimizer (also called the optimizer) is internal software that determines which execution plan is most efficient. Sometimes the optimizer chooses a plan with a suboptimal access path, which is the means by which the database retrieves data from the database. For example, the plan for a 1-3 Chapter 1 Tasks and Tools for SQL Tuning query predicate with low selectivity may use a full table scan on a large table instead of an index. You can compare the execution plan of an optimally performing SQL statement to the plan of the statement when it performs suboptimally. This comparison, along with information such as changes in data volumes, can help identify causes of performance degradation. • Missing SQL access structures Absence of SQL access structures, such as indexes and materialized views, is a typical reason for suboptimal SQL performance. The optimal set of access structures can improve SQL performance by orders of magnitude. • Stale optimizer statistics Statistics gathered by DBMS_STATS can become stale when the statistics maintenance operations, either automatic or manual, cannot keep up with the changes to the table data caused by DML. Because stale statistics on a table do not accurately reflect the table data, the optimizer can make decisions based on faulty information and generate suboptimal execution plans. • Hardware problems Suboptimal performance might be connected with memory, I/O, and CPU problems. 4. Defining the scope of the problem The scope of the solution must match the scope of the problem. Consider a problem at the database level and a problem at the statement level. For example, the shared pool is too small, which causes cursors to age out quickly, which in turn causes many hard parses. Using an initialization parameter to increase the shared pool size fixes the problem at the database level and improves performance for all sessions. However, if a single SQL statement is not using a helpful index, then changing the optimizer initialization parameters for the entire database could harm overall performance. If a single SQL statement has a problem, then an appropriately scoped solution addresses just this problem with this statement. 5. Implementing corrective actions for suboptimally performing SQL statements These actions vary depending on circumstances. For example, you might rewrite a SQL statement to be more efficient, avoiding unnecessary hard parsing by rewriting the statement to use bind variables. You might also use equijoins, remove functions from WHERE clauses, and break a complex SQL statement into multiple simple statements. In some cases, you improve SQL performance not by rewriting the statement, but by restructuring schema objects. For example, you might index a new access path, or reorder columns in a concatenated index. You might also partition a table, introduce derived values, or even change the database design. 6. Preventing SQL performance regressions To ensure optimal SQL performance, verify that execution plans continue to provide optimal performance, and choose better plans if they come available. You can achieve these goals using optimizer statistics, SQL profiles, and SQL plan baselines. 1-4 Chapter 1 Tasks and Tools for SQL Tuning See Also: • "Shared Pool Check" • Oracle Database Concepts to learn more about the shared pool 1.4.2 SQL Tuning Tools SQL tuning tools are either automated or manual. In this context, a tool is automated if the database itself can provide diagnosis, advice, or corrective actions. A manual tool requires you to perform all of these operations. All tuning tools depend on the basic tools of the dynamic performance views, statistics, and metrics that the database instance collects. The database itself contains the data and metadata required to tune SQL statements. This section contains the following topics: • Automated SQL Tuning Tools Oracle Database provides several advisors relevant for SQL tuning. • Manual SQL Tuning Tools In some situations, you may want to run manual tools in addition to the automated tools. Alternatively, you may not have access to the automated tools. 1.4.2.1 Automated SQL Tuning Tools Oracle Database provides several advisors relevant for SQL tuning. Additionally, SQL plan management is a mechanism that can prevent performance regressions and also help you to improve SQL performance. All of the automated SQL tuning tools can use SQL tuning sets as input. A SQL tuning set (STS) is a database object that includes one or more SQL statements along with their execution statistics and execution context. This section contains the following topics: • Automatic Database Diagnostic Monitor (ADDM) ADDM is self-diagnostic software built into Oracle Database. • SQL Tuning Advisor SQL Tuning Advisor is internal diagnostic software that identifies problematic SQL statements and recommends how to improve statement performance. • SQL Access Advisor SQL Access Advisor is internal diagnostic software that recommends which materialized views, indexes, and materialized view logs to create, drop, or retain. • SQL Plan Management SQL plan management is a preventative mechanism that enables the optimizer to automatically manage execution plans, ensuring that the database uses only known or verified plans. 1-5 Chapter 1 Tasks and Tools for SQL Tuning • SQL Performance Analyzer SQL Performance Analyzer determines the effect of a change on a SQL workload by identifying performance divergence for each SQL statement. See Also: • "About SQL Tuning Sets" • Oracle Database 2 Day + Performance Tuning Guide to learn more about managing SQL tuning sets 1.4.2.1.1 Automatic Database Diagnostic Monitor (ADDM) ADDM is self-diagnostic software built into Oracle Database. ADDM can automatically locate the root causes of performance problems, provide recommendations for correction, and quantify the expected benefits. ADDM also identifies areas where no action is necessary. ADDM and other advisors use Automatic Workload Repository (AWR), which is an infrastructure that provides services to database components to collect, maintain, and use statistics. ADDM examines and analyzes statistics in AWR to determine possible performance problems, including high-load SQL. For example, you can configure ADDM to run nightly. In the morning, you can examine the latest ADDM report to see what might have caused a problem and if there is a recommended fix. The report might show that a particular SELECT statement consumed a huge amount of CPU, and recommend that you run SQL Tuning Advisor. See Also: • Oracle Database 2 Day + Performance Tuning Guide • Oracle Database Performance Tuning Guide 1.4.2.1.2 SQL Tuning Advisor SQL Tuning Advisor is internal diagnostic software that identifies problematic SQL statements and recommends how to improve statement performance. When run during database maintenance windows as an automated maintenance task, SQL Tuning Advisor is known as Automatic SQL Tuning Advisor. SQL Tuning Advisor takes one or more SQL statements as an input and invokes the Automatic Tuning Optimizer to perform SQL tuning on the statements. The advisor performs the following types of analysis: • Checks for missing or stale statistics • Builds SQL profiles A SQL profile is a set of auxiliary information specific to a SQL statement. A SQL profile contains corrections for suboptimal optimizer estimates discovered during 1-6 Chapter 1 Tasks and Tools for SQL Tuning Automatic SQL Tuning. This information can improve optimizer estimates for cardinality, which is the number of rows that is estimated to be or actually is returned by an operation in an execution plan, and selectivity. These improved estimates lead the optimizer to select better plans. • Explores whether a different access path can significantly improve performance • Identifies SQL statements that lend themselves to suboptimal plans The output is in the form of advice or recommendations, along with a rationale for each recommendation and its expected benefit. The recommendation relates to a collection of statistics on objects, creation of new indexes, restructuring of the SQL statement, or creation of a SQL profile. You can choose to accept the recommendations to complete the tuning of the SQL statements. See Also: • "Analyzing SQL with SQL Tuning Advisor" • Oracle Database 2 Day + Performance Tuning Guide 1.4.2.1.3 SQL Access Advisor SQL Access Advisor is internal diagnostic software that recommends which materialized views, indexes, and materialized view logs to create, drop, or retain. SQL Access Advisor takes an actual workload as input, or the advisor can derive a hypothetical workload from the schema. SQL Access Advisor considers the trade-offs between space usage and query performance, and recommends the most costeffective configuration of new and existing materialized views and indexes. The advisor also makes recommendations about partitioning. See Also: • "About SQL Access Advisor" • Oracle Database 2 Day + Performance Tuning Guide 1.4.2.1.4 SQL Plan Management SQL plan management is a preventative mechanism that enables the optimizer to automatically manage execution plans, ensuring that the database uses only known or verified plans. This mechanism can build a SQL plan baseline, which contains one or more accepted plans for each SQL statement. By using baselines, SQL plan management can prevent plan regressions from environmental changes, while permitting the optimizer to discover and use better plans. 1-7 Chapter 1 Tasks and Tools for SQL Tuning See Also: • "Overview of SQL Plan Management" • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SPM package 1.4.2.1.5 SQL Performance Analyzer SQL Performance Analyzer determines the effect of a change on a SQL workload by identifying performance divergence for each SQL statement. System changes such as upgrading a database or adding an index may cause changes to execution plans, affecting SQL performance. By using SQL Performance Analyzer, you can accurately forecast the effect of system changes on SQL performance. Using this information, you can tune the database when SQL performance regresses, or validate and measure the gain when SQL performance improves. See Also: Oracle Database Testing Guide 1.4.2.2 Manual SQL Tuning Tools In some situations, you may want to run manual tools in addition to the automated tools. Alternatively, you may not have access to the automated tools. This section contains the following topics: • Execution Plans Execution plans are the principal diagnostic tool in manual SQL tuning. For example, you can view plans to determine whether the optimizer selects the plan you expect, or identify the effect of creating an index on a table. • Real-Time SQL Monitoring and Real-Time Database Operations The Real-Time SQL Monitoring feature of Oracle Database enables you to monitor the performance of SQL statements while they are executing. By default, SQL monitoring starts automatically when a statement runs in parallel, or when it has consumed at least 5 seconds of CPU or I/O time in a single execution. • Application Tracing A SQL trace file provides performance information on individual SQL statements: parse counts, physical and logical reads, misses on the library cache, and so on. • Optimizer Hints A hint is an instruction passed to the optimizer through comments in a SQL statement. Hints enable you to make decisions normally made automatically by the optimizer. 1-8 Chapter 1 Tasks and Tools for SQL Tuning 1.4.2.2.1 Execution Plans Execution plans are the principal diagnostic tool in manual SQL tuning. For example, you can view plans to determine whether the optimizer selects the plan you expect, or identify the effect of creating an index on a table. You can display execution plans in multiple ways. The following tools are the most commonly used: • EXPLAIN PLAN This SQL statement enables you to view the execution plan that the optimizer would use to execute a SQL statement without actually executing the statement. See Oracle Database SQL Language Reference. • AUTOTRACE The AUTOTRACE command in SQL*Plus generates the execution plan and statistics about the performance of a query. This command provides statistics such as disk reads and memory reads. See SQL*Plus User's Guide and Reference. • V$SQL_PLAN and related views These views contain information about executed SQL statements, and their execution plans, that are still in the shared pool. See Oracle Database Reference. You can use the DBMS_XPLAN package methods to display the execution plan generated by the EXPLAIN PLAN command and query of V$SQL_PLAN. 1.4.2.2.2 Real-Time SQL Monitoring and Real-Time Database Operations The Real-Time SQL Monitoring feature of Oracle Database enables you to monitor the performance of SQL statements while they are executing. By default, SQL monitoring starts automatically when a statement runs in parallel, or when it has consumed at least 5 seconds of CPU or I/O time in a single execution. A database operation is a set of database tasks defined by end users or application code, for example, a batch job or Extraction, Transformation, and Loading (ETL) processing. You can define, monitor, and report on database operations. Real-Time Database Operations provides the ability to monitor composite operations automatically. The database automatically monitors parallel queries, DML, and DDL statements as soon as execution begins. Oracle Enterprise Manager Cloud Control (Cloud Control) provides easy-to-use SQL monitoring pages. Alternatively, you can monitor SQL-related statistics using the V$SQL_MONITOR and V$SQL_PLAN_MONITOR views. You can use these views with the following views to get more information about executions that you are monitoring: • V$ACTIVE_SESSION_HISTORY • V$SESSION • V$SESSION_LONGOPS • V$SQL • V$SQL_PLAN 1-9 Chapter 1 Tasks and Tools for SQL Tuning See Also: • "About Monitoring Database Operations" • Oracle Database Reference to learn about the V$ views 1.4.2.2.3 Application Tracing A SQL trace file provides performance information on individual SQL statements: parse counts, physical and logical reads, misses on the library cache, and so on. Trace files are sometimes useful for diagnosing SQL performance problems. You can enable and disable SQL tracing for a specific session using the DBMS_MONITOR or DBMS_SESSION packages. Oracle Database implements tracing by generating a trace file for each server process when you enable the tracing mechanism. Oracle Database provides the following command-line tools for analyzing trace files: • TKPROF This utility accepts as input a trace file produced by the SQL Trace facility, and then produces a formatted output file. • trcsess This utility consolidates trace output from multiple trace files based on criteria such as session ID, client ID, and service ID. After trcsess merges the trace information into a single output file, you can format the output file with TKPROF. trcsess is useful for consolidating the tracing of a particular session for performance or debugging purposes. End-to-End Application Tracing simplifies the process of diagnosing performance problems in multitier environments. In these environments, the middle tier routes a request from an end client to different database sessions, making it difficult to track a client across database sessions. End-to-End application tracing uses a client ID to uniquely trace a specific end-client through all tiers to the database. See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_MONITOR and DBMS_SESSION 1.4.2.2.4 Optimizer Hints A hint is an instruction passed to the optimizer through comments in a SQL statement. Hints enable you to make decisions normally made automatically by the optimizer. In a test or development environment, hints are useful for testing the performance of a specific access path. For example, you may know that a specific index is more selective for certain queries. In this case, you may use hints to instruct the optimizer to use a better execution plan, as in the following example: SELECT /*+ INDEX (employees emp_department_ix) */ employee_id, department_id 1-10 Chapter 1 Tasks and Tools for SQL Tuning FROM employees WHERE department_id > 50; See Also: • "Influencing the Optimizer with Hints" • Oracle Database SQL Language Reference to learn more about hints 1.4.3 User Interfaces to SQL Tuning Tools Cloud Control is a system management tool that provides centralized management of a database environment. Cloud Control provides access to most tuning tools. By combining a graphical console, Oracle Management Servers, Oracle Intelligent Agents, common services, and administrative tools, Cloud Control provides a comprehensive system management platform. You can also access all SQL tuning tools using a command-line interface. For example, the DBMS_ADVISOR package is the command-line interface for SQL Tuning Advisor. Oracle recommends Cloud Control as the best interface for database administration and tuning. In cases where the command-line interface better illustrates a particular concept or task, this manual uses command-line examples. However, in these cases the tuning tasks include a reference to the principal Cloud Control page associated with the task. 1-11 2 SQL Performance Methodology This chapter describes the recommended methodology for SQL tuning. Note: This book assumes that you have learned the Oracle Database performance methodology described in Oracle Database 2 Day + Performance Tuning Guide. This chapter contains the following topics: • Guidelines for Designing Your Application The key to obtaining good SQL performance is to design your application with performance in mind. • Guidelines for Deploying Your Application To achieve optimal performance, deploy your application with the same care that you put into designing it. 2.1 Guidelines for Designing Your Application The key to obtaining good SQL performance is to design your application with performance in mind. This section contains the following topics: • Guideline for Data Modeling Data modeling is important to successful application design. • Guideline for Writing Efficient Applications During the design and architecture phase of system development, ensure that the application developers understand SQL execution efficiency. 2.1.1 Guideline for Data Modeling Data modeling is important to successful application design. You must perform data modeling in a way that represents the business practices. Heated debates may occur about the correct data model. The important thing is to apply greatest modeling efforts to those entities affected by the most frequent business transactions. In the modeling phase, there is a great temptation to spend too much time modeling the non-core data elements, which results in increased development lead times. Use of modeling tools can then rapidly generate schema definitions and can be useful when a fast prototype is required. 2-1 Chapter 2 Guidelines for Designing Your Application 2.1.2 Guideline for Writing Efficient Applications During the design and architecture phase of system development, ensure that the application developers understand SQL execution efficiency. To achieve this goal, the development environment must support the following characteristics: • Good database connection management Connecting to the database is an expensive operation that is not scalable. Therefore, a best practice is to minimize the number of concurrent connections to the database. A simple system, where a user connects at application initialization, is ideal. However, in a web-based or multitiered application in which application servers multiplex database connections to users, this approach can be difficult. With these types of applications, design them to pool database connections, and not reestablish connections for each user request. • Good cursor usage and management Maintaining user connections is equally important to minimizing the parsing activity on the system. Parsing is the process of interpreting a SQL statement and creating an execution plan for it. This process has many phases, including syntax checking, security checking, execution plan generation, and loading shared structures into the shared pool. There are two types of parse operations: – Hard parsing A SQL statement is submitted for the first time, and no match is found in the shared pool. Hard parses are the most resource-intensive and unscalable, because they perform all the operations involved in a parse. – Soft parsing A SQL statement is submitted for the first time, and a match is found in the shared pool. The match can be the result of previous execution by another user. The SQL statement is shared, which is optimal for performance. However, soft parses are not ideal, because they still require syntax and security checking, which consume system resources. Because parsing should be minimized as much as possible, application developers should design their applications to parse SQL statements once and execute them many times. This is done through cursors. Experienced SQL programmers should be familiar with the concept of opening and re-executing cursors. • Effective use of bind variables Application developers must also ensure that SQL statements are shared within the shared pool. To achieve this goal, use bind variables to represent the parts of the query that change from execution to execution. If this is not done, then the SQL statement is likely to be parsed once and never re-used by other users. To ensure that SQL is shared, use bind variables and do not use string literals with SQL statements. For example: Statement with string literals: SELECT * FROM employees WHERE last_name LIKE 'KING'; 2-2 Chapter 2 Guidelines for Deploying Your Application Statement with bind variables: SELECT * FROM employees WHERE last_name LIKE :1; The following example shows the results of some tests on a simple OLTP application: Test #Users Supported No Parsing all statements 270 Soft Parsing all statements 150 Hard Parsing all statements 60 Re-Connecting for each Transaction 30 These tests were performed on a four-CPU computer. The differences increase as the number of CPUs on the system increase. 2.2 Guidelines for Deploying Your Application To achieve optimal performance, deploy your application with the same care that you put into designing it. This section contains the following topics: • Guideline for Deploying in a Test Environment The testing process mainly consists of functional and stability testing. At some point in the process, you must perform performance testing. • Guidelines for Application Rollout When new applications are rolled out, two strategies are commonly adopted: the Big Bang approach, in which all users migrate to the new system at once, and the trickle approach, in which users slowly migrate from existing systems to the new one. 2.2.1 Guideline for Deploying in a Test Environment The testing process mainly consists of functional and stability testing. At some point in the process, you must perform performance testing. The following list describes simple rules for performance testing an application. If correctly documented, then this list provides important information for the production application and the capacity planning process after the application has gone live. • Use the Automatic Database Diagnostic Monitor (ADDM) and SQL Tuning Advisor for design validation. • Test with realistic data volumes and distributions. All testing must be done with fully populated tables. The test database should contain data representative of the production system in terms of data volume and cardinality between tables. All the production indexes should be built and the schema statistics should be populated correctly. • Use the correct optimizer mode. Perform all testing with the optimizer mode that you plan to use in production. • Test a single user performance. 2-3 Chapter 2 Guidelines for Deploying Your Application Test a single user on an idle or lightly-used database for acceptable performance. If a single user cannot achieve acceptable performance under ideal conditions, then multiple users cannot achieve acceptable performance under real conditions. • Obtain and document plans for all SQL statements. Obtain an execution plan for each SQL statement. Use this process to verify that the optimizer is obtaining an optimal execution plan, and that the relative cost of the SQL statement is understood in terms of CPU time and physical I/Os. This process assists in identifying the heavy use transactions that require the most tuning and performance work in the future. • Attempt multiuser testing. This process is difficult to perform accurately, because user workload and profiles might not be fully quantified. However, transactions performing DML statements should be tested to ensure that there are no locking conflicts or serialization problems. • Test with the correct hardware configuration. Test with a configuration as close to the production system as possible. Using a realistic system is particularly important for network latencies, I/O subsystem bandwidth, and processor type and speed. Failing to use this approach may result in an incorrect analysis of potential performance problems. • Measure steady state performance. When benchmarking, it is important to measure the performance under steady state conditions. Each benchmark run should have a ramp-up phase, where users are connected to the application and gradually start performing work on the application. This process allows for frequently cached data to be initialized into the cache and single execution operations—such as parsing—to be completed before the steady state condition. Likewise, after a benchmark run, a ramp-down period is useful so that the system frees resources, and users cease work and disconnect. 2.2.2 Guidelines for Application Rollout When new applications are rolled out, two strategies are commonly adopted: the Big Bang approach, in which all users migrate to the new system at once, and the trickle approach, in which users slowly migrate from existing systems to the new one. Both approaches have merits and disadvantages. The Big Bang approach relies on reliable testing of the application at the required scale, but has the advantage of minimal data conversion and synchronization with the old system, because it is simply switched off. The Trickle approach allows debugging of scalability issues as the workload increases, but might mean that data must be migrated to and from legacy systems as the transition takes place. It is difficult to recommend one approach over the other, because each technique has associated risks that could lead to system outages as the transition takes place. Certainly, the Trickle approach allows profiling of real users as they are introduced to the new application, and allows the system to be reconfigured while only affecting the migrated users. This approach affects the work of the early adopters, but limits the load on support services. Thus, unscheduled outages only affect a small percentage of the user population. The decision on how to roll out a new application is specific to each business. Any adopted approach has its own unique pressures and stresses. The more testing and 2-4 Chapter 2 Guidelines for Deploying Your Application knowledge that you derive from the testing process, the more you realize what is best for the rollout. 2-5 Part II Query Optimizer Fundamentals To tune Oracle SQL, you must understand the query optimizer. The optimizer is builtin software that determines the most efficient method for a statement to access data. This part contains the following chapters: • SQL Processing This chapter explains how database processes DDL statements to create objects, DML to modify data, and queries to retrieve data. • Query Optimizer Concepts This chapter describes the most important concepts relating to the query optimizer, including its principal components. • Query Transformations The optimizer employs many query transformation techniques. This chapter describes some of the most important. 3 SQL Processing This chapter explains how database processes DDL statements to create objects, DML to modify data, and queries to retrieve data. This chapter contains the following topics: • About SQL Processing SQL processing is the parsing, optimization, row source generation, and execution of a SQL statement. • How Oracle Database Processes DML Most DML statements have a query component. In a query, execution of a cursor places the results of the query into a set of rows called the result set. • How Oracle Database Processes DDL Oracle Database processes DDL differently from DML. 3.1 About SQL Processing SQL processing is the parsing, optimization, row source generation, and execution of a SQL statement. The following figure depicts the general stages of SQL processing. Depending on the statement, the database may omit some of these stages. 3-1 Chapter 3 About SQL Processing Figure 3-1 Stages of SQL Processing SQL Statement Parsing Syntax Check Semantic Check Shared Pool Check Soft Parse Hard Parse Generation of multiple execution plans Optimization Generation of query plan Row Source Generation Execution This section contains the following topics: • SQL Parsing The first stage of SQL processing is parsing. • SQL Optimization During optimization, Oracle Database must perform a hard parse at least once for every unique DML statement and performs the optimization during this parse. • SQL Row Source Generation The row source generator is software that receives the optimal execution plan from the optimizer and produces an iterative execution plan that is usable by the rest of the database. • SQL Execution During execution, the SQL engine executes each row source in the tree produced by the row source generator. This step is the only mandatory step in DML processing. 3-2 Chapter 3 About SQL Processing 3.1.1 SQL Parsing The first stage of SQL processing is parsing. The parsing stage involves separating the pieces of a SQL statement into a data structure that other routines can process. The database parses a statement when instructed by the application, which means that only the application, and not the database itself, can reduce the number of parses. When an application issues a SQL statement, the application makes a parse call to the database to prepare the statement for execution. The parse call opens or creates a cursor, which is a handle for the session-specific private SQL area that holds a parsed SQL statement and other processing information. The cursor and private SQL area are in the program global area (PGA). During the parse call, the database performs checks that identify the errors that can be found before statement execution. Some errors cannot be caught by parsing. For example, the database can encounter deadlocks or errors in data conversion only during statement execution. This section contains the following topics: • Syntax Check Oracle Database must check each SQL statement for syntactic validity. • Semantic Check The semantics of a statement are its meaning. A semantic check determines whether a statement is meaningful, for example, whether the objects and columns in the statement exist. • Shared Pool Check During the parse, the database performs a shared pool check to determine whether it can skip resource-intensive steps of statement processing. See Also: Oracle Database Concepts to learn about deadlocks 3.1.1.1 Syntax Check Oracle Database must check each SQL statement for syntactic validity. A statement that breaks a rule for well-formed SQL syntax fails the check. For example, the following statement fails because the keyword FROM is misspelled as FORM: SQL> SELECT * FORM employees; SELECT * FORM employees * ERROR at line 1: ORA-00923: FROM keyword not found where expected 3-3 Chapter 3 About SQL Processing 3.1.1.2 Semantic Check The semantics of a statement are its meaning. A semantic check determines whether a statement is meaningful, for example, whether the objects and columns in the statement exist. A syntactically correct statement can fail a semantic check, as shown in the following example of a query of a nonexistent table: SQL> SELECT * FROM nonexistent_table; SELECT * FROM nonexistent_table * ERROR at line 1: ORA-00942: table or view does not exist 3.1.1.3 Shared Pool Check During the parse, the database performs a shared pool check to determine whether it can skip resource-intensive steps of statement processing. To this end, the database uses a hashing algorithm to generate a hash value for every SQL statement. The statement hash value is the SQL ID shown in V$SQL.SQL_ID. This hash value is deterministic within a version of Oracle Database, so the same statement in a single instance or in different instances has the same SQL ID. When a user submits a SQL statement, the database searches the shared SQL area to see if an existing parsed statement has the same hash value. The hash value of a SQL statement is distinct from the following values: • Memory address for the statement Oracle Database uses the SQL ID to perform a keyed read in a lookup table. In this way, the database obtains possible memory addresses of the statement. • Hash value of an execution plan for the statement A SQL statement can have multiple plans in the shared pool. Typically, each plan has a different hash value. If the same SQL ID has multiple plan hash values, then the database knows that multiple plans exist for this SQL ID. Parse operations fall into the following categories, depending on the type of statement submitted and the result of the hash check: • Hard parse If Oracle Database cannot reuse existing code, then it must build a new executable version of the application code. This operation is known as a hard parse, or a library cache miss. Note: The database always performs a hard parse of DDL. During the hard parse, the database accesses the library cache and data dictionary cache numerous times to check the data dictionary. When the database accesses these areas, it uses a serialization device called a latch on required 3-4 Chapter 3 About SQL Processing objects so that their definition does not change. Latch contention increases statement execution time and decreases concurrency. • Soft parse A soft parse is any parse that is not a hard parse. If the submitted statement is the same as a reusable SQL statement in the shared pool, then Oracle Database reuses the existing code. This reuse of code is also called a library cache hit. Soft parses can vary in how much work they perform. For example, configuring the session shared SQL area can sometimes reduce the amount of latching in the soft parses, making them "softer." In general, a soft parse is preferable to a hard parse because the database skips the optimization and row source generation steps, proceeding straight to execution. The following graphic is a simplified representation of a shared pool check of an UPDATE statement in a dedicated server architecture. Figure 3-2 Shared Pool Check User Global Area (SGA) System Shared Pool Library Cache Shared SQL Area 3667723989 3967354608 2190280494 Data Dictionary Cache Server Result Cache Private SQL Area Other Reserved Pool Comparison of hash values Update ... PGA Client Process Server Process SQL Work Areas Session Memory User 3967354608 Private SQL Area If a check determines that a statement in the shared pool has the same hash value, then the database performs semantic and environment checks to determine whether the statements have the same meaning. Identical syntax is not sufficient. For example, suppose two different users log in to the database and issue the following SQL statements: CREATE TABLE my_table ( some_col INTEGER ); SELECT * FROM my_table; The SELECT statements for the two users are syntactically identical, but two separate schema objects are named my_table. This semantic difference means that the second statement cannot reuse the code for the first statement. 3-5 Chapter 3 About SQL Processing Even if two statements are semantically identical, an environmental difference can force a hard parse. In this context, the optimizer environment is the totality of session settings that can affect execution plan generation, such as the work area size or optimizer settings (for example, the optimizer mode). Consider the following series of SQL statements executed by a single user: ALTER SESSION SET OPTIMIZER_MODE=ALL_ROWS; ALTER SYSTEM FLUSH SHARED_POOL; SELECT * FROM sh.sales; # optimizer environment 1 ALTER SESSION SET OPTIMIZER_MODE=FIRST_ROWS; # optimizer environment 2 SELECT * FROM sh.sales; ALTER SESSION SET SQL_TRACE=true; SELECT * FROM sh.sales; # optimizer environment 3 In the preceding example, the same SELECT statement is executed in three different optimizer environments. Consequently, the database creates three separate shared SQL areas for these statements and forces a hard parse of each statement. See Also: • Oracle Database Concepts to learn about private SQL areas and shared SQL areas • Oracle Database Performance Tuning Guide to learn how to configure the shared pool • Oracle Database Concepts to learn about latches 3.1.2 SQL Optimization During optimization, Oracle Database must perform a hard parse at least once for every unique DML statement and performs the optimization during this parse. The database does not optimize DDL. The only exception is when the DDL includes a DML component such as a subquery that requires optimization. Related Topics • Query Optimizer Concepts This chapter describes the most important concepts relating to the query optimizer, including its principal components. 3.1.3 SQL Row Source Generation The row source generator is software that receives the optimal execution plan from the optimizer and produces an iterative execution plan that is usable by the rest of the database. The iterative plan is a binary program that, when executed by the SQL engine, produces the result set. The plan takes the form of a combination of steps. Each step returns a row set. The next step either uses the rows in this set, or the last step returns the rows to the application issuing the SQL statement. 3-6 Chapter 3 About SQL Processing A row source is a row set returned by a step in the execution plan along with a control structure that can iteratively process the rows. The row source can be a table, view, or result of a join or grouping operation. The row source generator produces a row source tree, which is a collection of row sources. The row source tree shows the following information: • An ordering of the tables referenced by the statement • An access method for each table mentioned in the statement • A join method for tables affected by join operations in the statement • Data operations such as filter, sort, or aggregation Example 3-1 Execution Plan This example shows the execution plan of a SELECT statement when AUTOTRACE is enabled. The statement selects the last name, job title, and department name for all employees whose last names begin with the letter A. The execution plan for this statement is the output of the row source generator. SELECT FROM WHERE AND AND e.last_name, j.job_title, d.department_name hr.employees e, hr.departments d, hr.jobs j e.department_id = d.department_id e.job_id = j.job_id e.last_name LIKE 'A%'; Execution Plan ---------------------------------------------------------Plan hash value: 975837011 -------------------------------------------------------------------------------| Id| Operation | Name |Rows|Bytes|Cost(%CPU)|Time | -------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 3 | 189 | 7(15)| 00:00:01 | |*1 | HASH JOIN | | 3 | 189 | 7(15)| 00:00:01 | |*2 | HASH JOIN | | 3 | 141 | 5(20)| 00:00:01 | | 3 | TABLE ACCESS BY INDEX ROWID| EMPLOYEES | 3 | 60 | 2 (0)| 00:00:01 | |*4 | INDEX RANGE SCAN | EMP_NAME_IX | 3 | | 1 (0)| 00:00:01 | | 5 | TABLE ACCESS FULL | JOBS | 19 | 513 | 2 (0)| 00:00:01 | | 6 | TABLE ACCESS FULL | DEPARTMENTS | 27 | 432 | 2 (0)| 00:00:01 | -------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------1 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID") 2 - access("E"."JOB_ID"="J"."JOB_ID") 4 - access("E"."LAST_NAME" LIKE 'A%') filter("E"."LAST_NAME" LIKE 'A%') 3.1.4 SQL Execution During execution, the SQL engine executes each row source in the tree produced by the row source generator. This step is the only mandatory step in DML processing. Figure 3-3 is an execution tree, also called a parse tree, that shows the flow of row sources from one step to another in the plan in Example 3-1. In general, the order of the steps in execution is the reverse of the order in the plan, so you read the plan from the bottom up. 3-7 Chapter 3 About SQL Processing Each step in an execution plan has an ID number. The numbers in Figure 3-3 correspond to the Id column in the plan shown in Example 3-1. Initial spaces in the Operation column of the plan indicate hierarchical relationships. For example, if the name of an operation is preceded by two spaces, then this operation is a child of an operation preceded by one space. Operations preceded by one space are children of the SELECT statement itself. Figure 3-3 Row Source Tree 1 HASH JOIN 2 6 HASH JOIN TABLE ACCESS FULL departments 3 5 TABLE ACCESS BY INDEX ROWID employees TABLE ACCESS FULL jobs 4 INDEX RANGE SCAN emp_name_ix In Figure 3-3, each node of the tree acts as a row source, which means that each step of the execution plan in Example 3-1 either retrieves rows from the database or accepts rows from one or more row sources as input. The SQL engine executes each row source as follows: • Steps indicated by the black boxes physically retrieve data from an object in the database. These steps are the access paths, or techniques for retrieving data from the database. – Step 6 uses a full table scan to retrieve all rows from the departments table. – Step 5 uses a full table scan to retrieve all rows from the jobs table. 3-8 Chapter 3 How Oracle Database Processes DML • – Step 4 scans the emp_name_ix index in order, looking for each key that begins with the letter A and retrieving the corresponding rowid. For example, the rowid corresponding to Atkinson is AAAPzRAAFAAAABSAAe. – Step 3 retrieves from the employees table the rows whose rowids were returned by Step 4. For example, the database uses rowid AAAPzRAAFAAAABSAAe to retrieve the row for Atkinson. Steps indicated by the clear boxes operate on row sources. – Step 2 performs a hash join, accepting row sources from Steps 3 and 5, joining each row from the Step 5 row source to its corresponding row in Step 3, and returning the resulting rows to Step 1. For example, the row for employee Atkinson is associated with the job name Stock Clerk. – Step 1 performs another hash join, accepting row sources from Steps 2 and 6, joining each row from the Step 6 source to its corresponding row in Step 2, and returning the result to the client. For example, the row for employee Atkinson is associated with the department named Shipping. In some execution plans the steps are iterative and in others sequential. The hash join shown in Example 3-1 is sequential. The database completes the steps in their entirety based on the join order. The database starts with the index range scan of emp_name_ix. Using the rowids that it retrieves from the index, the database reads the matching rows in the employees table, and then scans the jobs table. After it retrieves the rows from the jobs table, the database performs the hash join. During execution, the database reads the data from disk into memory if the data is not in memory. The database also takes out any locks and latches necessary to ensure data integrity and logs any changes made during the SQL execution. The final stage of processing a SQL statement is closing the cursor. 3.2 How Oracle Database Processes DML Most DML statements have a query component. In a query, execution of a cursor places the results of the query into a set of rows called the result set. This section contains the following topics: • How Row Sets Are Fetched Result set rows can be fetched either a row at a time or in groups. • Read Consistency In general, a query retrieves data by using the Oracle Database read consistency mechanism, which guarantees that all data blocks read by a query are consistent to a single point in time. • Data Changes DML statements that must change data use read consistency to retrieve only the data that matched the search criteria when the modification began. 3.2.1 How Row Sets Are Fetched Result set rows can be fetched either a row at a time or in groups. 3-9 Chapter 3 How Oracle Database Processes DDL In the fetch stage, the database selects rows and, if requested by the query, orders the rows. Each successive fetch retrieves another row of the result until the last row has been fetched. In general, the database cannot determine for certain the number of rows to be retrieved by a query until the last row is fetched. Oracle Database retrieves the data in response to fetch calls, so that the more rows the database reads, the more work it performs. For some queries the database returns the first row as quickly as possible, whereas for others it creates the entire result set before returning the first row. 3.2.2 Read Consistency In general, a query retrieves data by using the Oracle Database read consistency mechanism, which guarantees that all data blocks read by a query are consistent to a single point in time. Read consistency uses undo data to show past versions of data. For an example, suppose a query must read 100 data blocks in a full table scan. The query processes the first 10 blocks while DML in a different session modifies block 75. When the first session reaches block 75, it realizes the change and uses undo data to retrieve the old, unmodified version of the data and construct a noncurrent version of block 75 in memory. See Also: Oracle Database Concepts to learn about multiversion read consistency 3.2.3 Data Changes DML statements that must change data use read consistency to retrieve only the data that matched the search criteria when the modification began. Afterward, these statements retrieve the data blocks as they exist in their current state and make the required modifications. The database must perform other actions related to the modification of the data such as generating redo and undo data. 3.3 How Oracle Database Processes DDL Oracle Database processes DDL differently from DML. For example, when you create a table, the database does not optimize the CREATE TABLE statement. Instead, Oracle Database parses the DDL statement and carries out the command. The database processes DDL differently because it is a means of defining an object in the data dictionary. Typically, Oracle Database must parse and execute many recursive SQL statements to execute a DDL statement. Suppose you create a table as follows: CREATE TABLE mytable (mycolumn INTEGER); Typically, the database would run dozens of recursive statements to execute the preceding statement. The recursive SQL would perform actions such as the following: 3-10 Chapter 3 How Oracle Database Processes DDL • Issue a COMMIT before executing the CREATE TABLE statement • Verify that user privileges are sufficient to create the table • Determine which tablespace the table should reside in • Ensure that the tablespace quota has not been exceeded • Ensure that no object in the schema has the same name • Insert rows that define the table into the data dictionary • Issue a COMMIT if the DDL statement succeeded or a ROLLBACK if it did not See Also: Oracle Database Development Guide to learn about processing DDL, transaction control, and other types of statements 3-11 4 Query Optimizer Concepts This chapter describes the most important concepts relating to the query optimizer, including its principal components. This chapter contains the following topics: • Introduction to the Query Optimizer The query optimizer (called simply the optimizer) is built-in database software that determines the most efficient method for a SQL statement to access requested data. • About Optimizer Components The optimizer contains three components: the transformer, estimator, and plan generator. • About Automatic Tuning Optimizer The optimizer performs different operations depending on how it is invoked. • About Adaptive Query Optimization In Oracle Database, adaptive query optimization enables the optimizer to make run-time adjustments to execution plans and discover additional information that can lead to better statistics. • About Approximate Query Processing Approximate query processing is a set of optimization techniques that speed analytic queries by calculating results within an acceptable range of error. • About SQL Plan Management SQL plan management enables the optimizer to automatically manage execution plans, ensuring that the database uses only known or verified plans. • About the Expression Statistics Store (ESS) The Expression Statistics Store (ESS) is a repository maintained by the optimizer to store statistics about expression evaluation. 4.1 Introduction to the Query Optimizer The query optimizer (called simply the optimizer) is built-in database software that determines the most efficient method for a SQL statement to access requested data. This section contains the following topics: • Purpose of the Query Optimizer The optimizer attempts to generate the most optimal execution plan for a SQL statement. • Cost-Based Optimization Query optimization is the process of choosing the most efficient means of executing a SQL statement. • Execution Plans An execution plan describes a recommended method of execution for a SQL statement. 4-1 Chapter 4 Introduction to the Query Optimizer 4.1.1 Purpose of the Query Optimizer The optimizer attempts to generate the most optimal execution plan for a SQL statement. The optimizer choose the plan with the lowest cost among all considered candidate plans. The optimizer uses available statistics to calculate cost. For a specific query in a given environment, the cost computation accounts for factors of query execution such as I/O, CPU, and communication. For example, a query might request information about employees who are managers. If the optimizer statistics indicate that 80% of employees are managers, then the optimizer may decide that a full table scan is most efficient. However, if statistics indicate that very few employees are managers, then reading an index followed by a table access by rowid may be more efficient than a full table scan. Because the database has many internal statistics and tools at its disposal, the optimizer is usually in a better position than the user to determine the optimal method of statement execution. For this reason, all SQL statements use the optimizer. 4.1.2 Cost-Based Optimization Query optimization is the process of choosing the most efficient means of executing a SQL statement. SQL is a nonprocedural language, so the optimizer is free to merge, reorganize, and process in any order. The database optimizes each SQL statement based on statistics collected about the accessed data. The optimizer determines the optimal plan for a SQL statement by examining multiple access methods, such as full table scan or index scans, different join methods such as nested loops and hash joins, different join orders, and possible transformations. For a given query and environment, the optimizer assigns a relative numerical cost to each step of a possible plan, and then factors these values together to generate an overall cost estimate for the plan. After calculating the costs of alternative plans, the optimizer chooses the plan with the lowest cost estimate. For this reason, the optimizer is sometimes called the cost-based optimizer (CBO) to contrast it with the legacy rule-based optimizer (RBO). Note: The optimizer may not make the same decisions from one version of Oracle Database to the next. In recent versions, the optimizer might make different decision because better information is available and more optimizer transformations are possible. Related Topics • Cost The optimizer cost model accounts for the machine resources that a query is predicted to use. 4-2 Chapter 4 Introduction to the Query Optimizer 4.1.3 Execution Plans An execution plan describes a recommended method of execution for a SQL statement. The plan shows the combination of the steps Oracle Database uses to execute a SQL statement. Each step either retrieves rows of data physically from the database or prepares them for the user issuing the statement. An execution plan displays the cost of the entire plan, indicated on line 0, and each separate operation. The cost is an internal unit that the execution plan only displays to allow for plan comparisons. Thus, you cannot tune or change the cost value. In the following graphic, the optimizer generates two possible execution plans for an input SQL statement, uses statistics to estimate their costs, compares their costs, and then chooses the plan with the lowest cost. Figure 4-1 Execution Plans GB NL Plan 1 NL GB Plan 2 HJ HJ Generates Multiple Plans and Compares Them Final Plan with Lowest Cost Parsed Representation of SQL Statement Input Optimizer Output GB HJ 1 0 1 1 0 0 1 0 0 Plan 2 HJ Statistics This section contains the following topics: • Query Blocks The input to the optimizer is a parsed representation of a SQL statement. • Query Subplans For each query block, the optimizer generates a query subplan. • Analogy for the Optimizer One analogy for the optimizer is an online trip advisor. 4.1.3.1 Query Blocks The input to the optimizer is a parsed representation of a SQL statement. 4-3 Chapter 4 Introduction to the Query Optimizer Each SELECT block in the original SQL statement is represented internally by a query block. A query block can be a top-level statement, subquery, or unmerged view. Example 4-1 Query Blocks The following SQL statement consists of two query blocks. The subquery in parentheses is the inner query block. The outer query block, which is the rest of the SQL statement, retrieves names of employees in the departments whose IDs were supplied by the subquery. The query form determines how query blocks are interrelated. SELECT FROM WHERE IN first_name, last_name hr.employees department_id (SELECT department_id FROM hr.departments WHERE location_id = 1800); See Also: • "View Merging" • Oracle Database Concepts for an overview of SQL processing 4.1.3.2 Query Subplans For each query block, the optimizer generates a query subplan. The database optimizes query blocks separately from the bottom up. Thus, the database optimizes the innermost query block first and generates a subplan for it, and then generates the outer query block representing the entire query. The number of possible plans for a query block is proportional to the number of objects in the FROM clause. This number rises exponentially with the number of objects. For example, the possible plans for a join of five tables are significantly higher than the possible plans for a join of two tables. 4.1.3.3 Analogy for the Optimizer One analogy for the optimizer is an online trip advisor. A cyclist wants to know the most efficient bicycle route from point A to point B. A query is like the directive "I need the most efficient route from point A to point B" or "I need the most efficient route from point A to point B by way of point C." The trip advisor uses an internal algorithm, which relies on factors such as speed and difficulty, to determine the most efficient route. The cyclist can influence the trip advisor's decision by using directives such as "I want to arrive as fast as possible" or "I want the easiest ride possible." In this analogy, an execution plan is a possible route generated by the trip advisor. Internally, the advisor may divide the overall route into several subroutes (subplans), and calculate the efficiency for each subroute separately. For example, the trip advisor may estimate one subroute at 15 minutes with medium difficulty, an alternative subroute at 22 minutes with minimal difficulty, and so on. 4-4 Chapter 4 About Optimizer Components The advisor picks the most efficient (lowest cost) overall route based on user-specified goals and the available statistics about roads and traffic conditions. The more accurate the statistics, the better the advice. For example, if the advisor is not frequently notified of traffic jams, road closures, and poor road conditions, then the recommended route may turn out to be inefficient (high cost). 4.2 About Optimizer Components The optimizer contains three components: the transformer, estimator, and plan generator. The following graphic illustrates the components. Figure 4-2 Optimizer Components Parsed Query (from Parser) Query Transformer Transformed query statistics Estimator Data Dictionary Query + estimates Plan Generator Query Plan (to Row Source Generator) A set of query blocks represents a parsed query, which is the input to the optimizer. The following table describes the optimizer operations. Table 4-1 Optimizer Operations Phase Operation Description To Learn More 1 Query Transformer The optimizer determines whether it is helpful "Query to change the form of the query so that the Transformer" optimizer can generate a better execution plan. 2 Estimator The optimizer estimates the cost of each plan "Estimator" based on statistics in the data dictionary. 4-5 Chapter 4 About Optimizer Components Table 4-1 (Cont.) Optimizer Operations Phase Operation Description To Learn More 3 Plan Generator The optimizer compares the costs of plans "Plan Generator" and chooses the lowest-cost plan, known as the execution plan, to pass to the row source generator. This section contains the following topics: • Query Transformer For some statements, the query transformer determines whether it is advantageous to rewrite the original SQL statement into a semantically equivalent SQL statement with a lower cost. • Estimator The estimator is the component of the optimizer that determines the overall cost of a given execution plan. • Plan Generator The plan generator explores various plans for a query block by trying out different access paths, join methods, and join orders. 4.2.1 Query Transformer For some statements, the query transformer determines whether it is advantageous to rewrite the original SQL statement into a semantically equivalent SQL statement with a lower cost. When a viable alternative exists, the database calculates the cost of the alternatives separately and chooses the lowest-cost alternative. The following graphic shows the query transformer rewriting an input query that uses OR into an output query that uses UNION ALL. 4-6 Chapter 4 About Optimizer Components Figure 4-3 SELECT FROM WHERE OR Query Transformer * sales promo_id=33 prod_id=136; Query Transformer SELECT FROM WHERE UNION SELECT FROM WHERE AND * sales prod_id=136 ALL * sales promo_id=33 LNNVL(prod_id=136); Related Topics • Query Transformations The optimizer employs many query transformation techniques. This chapter describes some of the most important. 4.2.2 Estimator The estimator is the component of the optimizer that determines the overall cost of a given execution plan. The estimator uses three different measures to determine cost: • Selectivity The percentage of rows in the row set that the query selects, with 0 meaning no rows and 1 meaning all rows. Selectivity is tied to a query predicate, such as WHERE last_name LIKE 'A%', or a combination of predicates. A predicate becomes more selective as the selectivity value approaches 0 and less selective (or more unselective) as the value approaches 1. Note: Selectivity is an internal calculation that is not visible in the execution plans. • Cardinality The cardinality is the number of rows returned by each operation in an execution plan. This input, which is crucial to obtaining an optimal plan, is common to all cost functions. The estimator can derive cardinality from the table statistics collected by 4-7 Chapter 4 About Optimizer Components DBMS_STATS, or derive it after accounting for effects from predicates (filter, join, and so on), DISTINCT or GROUP BY operations, and so on. The Rows column in an execution plan shows the estimated cardinality. • Cost This measure represents units of work or resource used. The query optimizer uses disk I/O, CPU usage, and memory usage as units of work. As shown in the following graphic, if statistics are available, then the estimator uses them to compute the measures. The statistics improve the degree of accuracy of the measures. Figure 4-4 Estimator Cardinality Selectivity GB Cost Plan Estimator HJ Total Cost HJ 1 0 1 0 0 0 0 0 1 1 0 1 1 0 1 Statistics For the query shown in Example 4-1, the estimator uses selectivity, estimated cardinality (a total return of 10 rows), and cost measures to produce its total cost estimate of 3: -------------------------------------------------------------------------------| Id| Operation |Name |Rows|Bytes|Cost %CPU|Time| -------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 10| 250| 3 (0)| 00:00:01| | 1 | NESTED LOOPS | | | | | | | 2 | NESTED LOOPS | | 10| 250| 3 (0)| 00:00:01| |*3 | TABLE ACCESS FULL |DEPARTMENTS | 1| 7| 2 (0)| 00:00:01| |*4 | INDEX RANGE SCAN |EMP_DEPARTMENT_IX| 10| | 0 (0)| 00:00:01| | 5 | TABLE ACCESS BY INDEX ROWID|EMPLOYEES | 10| 180| 1 (0)| 00:00:01| -------------------------------------------------------------------------------- This section contains the following topics: • Selectivity The selectivity represents a fraction of rows from a row set. • Cardinality The cardinality is the number of rows returned by each operation in an execution plan. • Cost The optimizer cost model accounts for the machine resources that a query is predicted to use. 4-8 Chapter 4 About Optimizer Components 4.2.2.1 Selectivity The selectivity represents a fraction of rows from a row set. The row set can be a base table, a view, or the result of a join. The selectivity is tied to a query predicate, such as last_name = 'Smith', or a combination of predicates, such as last_name = 'Smith' AND job_id = 'SH_CLERK'. Note: Selectivity is an internal calculation that is not visible in execution plans. A predicate filters a specific number of rows from a row set. Thus, the selectivity of a predicate indicates how many rows pass the predicate test. Selectivity ranges from 0.0 to 1.0. A selectivity of 0.0 means that no rows are selected from a row set, whereas a selectivity of 1.0 means that all rows are selected. A predicate becomes more selective as the value approaches 0.0 and less selective (or more unselective) as the value approaches 1.0. The optimizer estimates selectivity depending on whether statistics are available: • Statistics not available Depending on the value of the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter, the optimizer either uses dynamic statistics or an internal default value. The database uses different internal defaults depending on the predicate type. For example, the internal default for an equality predicate (last_name = 'Smith') is lower than for a range predicate (last_name > 'Smith') because an equality predicate is expected to return a smaller fraction of rows. • Statistics available When statistics are available, the estimator uses them to estimate selectivity. Assume there are 150 distinct employee last names. For an equality predicate last_name = 'Smith', selectivity is the reciprocal of the number n of distinct values of last_name, which in this example is .006 because the query selects rows that contain 1 out of 150 distinct values. If a histogram exists on the last_name column, then the estimator uses the histogram instead of the number of distinct values. The histogram captures the distribution of different values in a column, so it yields better selectivity estimates, especially for columns that have data skew. See Also: • "Histograms " • Oracle Database Reference to learn more about OPTIMIZER_DYNAMIC_SAMPLING 4-9 Chapter 4 About Optimizer Components 4.2.2.2 Cardinality The cardinality is the number of rows returned by each operation in an execution plan. For example, if the optimizer estimate for the number of rows returned by a full table scan is 100, then the cardinality estimate for this operation is 100. The cardinality estimate appears in the Rows column of the execution plan. The optimizer determines the cardinality for each operation based on a complex set of formulas that use both table and column level statistics, or dynamic statistics, as input. The optimizer uses one of the simplest formulas when a single equality predicate appears in a single-table query, with no histogram. In this case, the optimizer assumes a uniform distribution and calculates the cardinality for the query by dividing the total number of rows in the table by the number of distinct values in the column used in the WHERE clause predicate. For example, user hr queries the employees table as follows: SELECT first_name, last_name FROM employees WHERE salary='10200'; The employees table contains 107 rows. The current database statistics indicate that the number of distinct values in the salary column is 58. Therefore, the optimizer estimates the cardinality of the result set as 2, using the formula 107/58=1.84. Cardinality estimates must be as accurate as possible because they influence all aspects of the execution plan. Cardinality is important when the optimizer determines the cost of a join. For example, in a nested loops join of the employees and departments tables, the number of rows in employees determines how often the database must probe the departments table. Cardinality is also important for determining the cost of sorts. 4.2.2.3 Cost The optimizer cost model accounts for the machine resources that a query is predicted to use. The cost is an internal numeric measure that represents the estimated resource usage for a plan. The cost is specific to a query in an optimizer environment. To estimate cost, the optimizer considers factors such as the following: • System resources, which includes estimated I/O, CPU, and memory • Estimated number of rows returned (cardinality) • Size of the initial data sets • Distribution of the data • Access structures 4-10 Chapter 4 About Optimizer Components Note: The cost is an internal measure that the optimizer uses to compare different plans for the same query. You cannot tune or change cost. The execution time is a function of the cost, but cost does not equate directly to time. For example, if the plan for query A has a lower cost than the plan for query B, then the following outcomes are possible: • A executes faster than B. • A executes slower than B. • A executes in the same amount of time as B. Therefore, you cannot compare the costs of different queries with one another. Also, you cannot compare the costs of semantically equivalent queries that use different optimizer modes. 4.2.3 Plan Generator The plan generator explores various plans for a query block by trying out different access paths, join methods, and join orders. Many plans are possible because of the various combinations that the database can use to produce the same result. The optimizer picks the plan with the lowest cost. The following graphic shows the optimizer testing different plans for an input query. Figure 4-5 Plan Generator SELECT e.last_name, d.department_name FROM hr.employees e, hr.departments d WHERE e.department_id = d.department_id; Optimizer Transformer Join Method Join Order Hash, Nested Loop, Sort Merge departments 0 employees 1 employees 0 departments 1 Access Path Index Full Table Scan Lowest Cost Plan Hash Join departments 0, employees 1 4-11 Chapter 4 About Optimizer Components The following snippet from an optimizer trace file shows some computations that the optimizer performs: GENERAL PLANS *************************************** Considering cardinality-based initial join order. Permutations for Starting Table :0 Join order[1]: DEPARTMENTS[D]#0 EMPLOYEES[E]#1 *************** Now joining: EMPLOYEES[E]#1 *************** NL Join Outer table: Card: 27.00 Cost: 2.01 Resp: 2.01 Degree: 1 Bytes: 16 Access path analysis for EMPLOYEES . . . Best NL cost: 13.17 . . . SM Join SM cost: 6.08 resc: 6.08 resc_io: 4.00 resc_cpu: 2501688 resp: 6.08 resp_io: 4.00 resp_cpu: 2501688 . . . SM Join (with index on outer) Access Path: index (FullScan) . . . HA Join HA cost: 4.57 resc: 4.57 resc_io: 4.00 resc_cpu: 678154 resp: 4.57 resp_io: 4.00 resp_cpu: 678154 Best:: JoinMethod: Hash Cost: 4.57 Degree: 1 Resp: 4.57 Card: 106.00 Bytes: 27 . . . *********************** Join order[2]: EMPLOYEES[E]#1 DEPARTMENTS[D]#0 . . . *************** Now joining: DEPARTMENTS[D]#0 *************** . . . HA Join HA cost: 4.58 resc: 4.58 resc_io: 4.00 resc_cpu: 690054 resp: 4.58 resp_io: 4.00 resp_cpu: 690054 Join order aborted: cost > best plan cost *********************** The trace file shows the optimizer first trying the departments table as the outer table in the join. The optimizer calculates the cost for three different join methods: nested loops join (NL), sort merge (SM), and hash join (HA). The optimizer picks the hash join as the most efficient method: Best:: JoinMethod: Hash Cost: 4.57 Degree: 1 Resp: 4.57 Card: 106.00 Bytes: 27 The optimizer then tries a different join order, using employees as the outer table. This join order costs more than the previous join order, so it is abandoned. 4-12 Chapter 4 About Automatic Tuning Optimizer The optimizer uses an internal cutoff to reduce the number of plans it tries when finding the lowest-cost plan. The cutoff is based on the cost of the current best plan. If the current best cost is large, then the optimizer explores alternative plans to find a lower cost plan. If the current best cost is small, then the optimizer ends the search swiftly because further cost improvement is not significant. 4.3 About Automatic Tuning Optimizer The optimizer performs different operations depending on how it is invoked. The database provides the following types of optimization: • Normal optimization The optimizer compiles the SQL and generates an execution plan. The normal mode generates a reasonable plan for most SQL statements. Under normal mode, the optimizer operates with strict time constraints, usually a fraction of a second, during which it must find an optimal plan. • SQL Tuning Advisor optimization When SQL Tuning Advisor invokes the optimizer, the optimizer is known as Automatic Tuning Optimizer. In this case, the optimizer performs additional analysis to further improve the plan produced in normal mode. The optimizer output is not an execution plan, but a series of actions, along with their rationale and expected benefit for producing a significantly better plan. See Also: • "Analyzing SQL with SQL Tuning Advisor" • Oracle Database 2 Day + Performance Tuning Guide to learn more about SQL Tuning Advisor 4.4 About Adaptive Query Optimization In Oracle Database, adaptive query optimization enables the optimizer to make runtime adjustments to execution plans and discover additional information that can lead to better statistics. Adaptive optimization is helpful when existing statistics are not sufficient to generate an optimal plan. The following graphic shows the feature set for adaptive query optimization. 4-13 Chapter 4 About Adaptive Query Optimization Figure 4-6 Adaptive Query Optimization Adaptive Query Optimization Adaptive Plans Join Methods Parallel Distribution Methods Adaptive Statistics Bitmap Index Pruning Dynamic Statistics Automatic Reoptimization SQL Plan Directives This section contains the following topics: • Adaptive Query Plans An adaptive query plan enables the optimizer to make a plan decision for a statement during execution. • Adaptive Statistics The optimizer can use adaptive statistics when query predicates are too complex to rely on base table statistics alone. By default, adaptive statistics are disabled (OPTIMIZER_ADAPTIVE_STATISTICS is false). 4.4.1 Adaptive Query Plans An adaptive query plan enables the optimizer to make a plan decision for a statement during execution. Adaptive query plans enable the optimizer to fix some classes of problems at run time. Adaptive plans are enabled by default. This section contains the following topics: • About Adaptive Query Plans An adaptive query plan contains multiple predetermined subplans, and an optimizer statistics collector. Based on the statistics collected during execution, the dynamic plan coordinator chooses the best plan at run time. • Purpose of Adaptive Query Plans The ability of the optimizer to adapt a plan, based on statistics obtained during execution, can greatly improve query performance. • How Adaptive Query Plans Work For the first execution of a statement, the optimizer uses the default plan, and then stores an adaptive plan. The database uses the adaptive plan for subsequent executions unless specific conditions are met. • When Adaptive Query Plans Are Enabled Adaptive query plans are enabled by default. 4-14 Chapter 4 About Adaptive Query Optimization Related Topics • Introduction to Optimizer Statistics The optimizer cost model relies on statistics collected about the objects involved in a query, and the database and host where the query runs. • About SQL Tuning Advisor SQL Tuning Advisor is SQL diagnostic software in the Oracle Database Tuning Pack. • Overview of SQL Plan Management SQL plan management is a preventative mechanism that enables the optimizer to automatically manage execution plans, ensuring that the database uses only known or verified plans. 4.4.1.1 About Adaptive Query Plans An adaptive query plan contains multiple predetermined subplans, and an optimizer statistics collector. Based on the statistics collected during execution, the dynamic plan coordinator chooses the best plan at run time. Dynamic Plans To change plans at runtime, adaptive query plans use a dynamic plan, which is represented as a set of subplan groups. A subplan group is a set of subplans. A subplan is a portion of a plan that the optimizer can switch to as an alternative at run time. For example, a nested loops join could switch to a hash join during execution. The optimizer decides which subplan to use at run time. When notified of a new statistic value relevant to a subplan group, the coordinator dispatches it to the handler function for this subgroup. Figure 4-7 Dynamic Plan Coordinator Dynamic Plan Subplan Group Dynamic Plan Coordinator GB Subplan Group GB Subplan NL Subplan NL NL GB NL GB Subplan HJ Subplan HJ HJ HJ Optimizer Statistics Collector An optimizer statistics collector is a row source inserted into a plan at key points to collect run-time statistics relating to cardinality and histograms. These statistics help the optimizer make a final decision between multiple subplans. The collector also supports optional buffering up to an internal threshold. 4-15 Chapter 4 About Adaptive Query Optimization For parallel buffering statistics collectors, each parallel execution server collects the statistics, which the parallel query coordinator aggregates and then sends to the clients. In this context, a client is a consumer of the collected statistics, such as a dynamic plan. Each client specifies a callback function to be executed on each parallel server or on the query coordinator. 4.4.1.2 Purpose of Adaptive Query Plans The ability of the optimizer to adapt a plan, based on statistics obtained during execution, can greatly improve query performance. Adaptive query plans are useful because the optimizer occasionally picks a suboptimal default plan because of a cardinality misestimate. The ability of the optimizer to pick the best plan at run time based on actual execution statistics results in a more optimal final plan. After choosing the final plan, the optimizer uses it for subsequent executions, thus ensuring that the suboptimal plan is not reused. 4.4.1.3 How Adaptive Query Plans Work For the first execution of a statement, the optimizer uses the default plan, and then stores an adaptive plan. The database uses the adaptive plan for subsequent executions unless specific conditions are met. During the first execution of a statement, the database performs the following steps: 1. The database begins executing the statement using the default plan. 2. The statistics collector gathers information about the in-progress execution, and buffers some rows received by the subplan. For parallel buffering statistics collectors, each slave process collects the statistics, which the query coordinator aggregates before sending to the clients. 3. Based on the statistics gathered by the collector, the optimizer chooses a subplan. The dynamic plan coordinator decides which subplan to use at runtime for all such subplan groups. When notified of a new statistic value relevant to a subplan group, the coordinator dispatches it to the handler function for this subgroup. 4. The collector stops collecting statistics and buffering rows, permitting rows to pass through instead. 5. The database stores the adaptive plan in the child cursor, so that the next execution of the statement can use it. On subsequent executions of the child cursor, the optimizer continues to use the same adaptive plan unless one of the following conditions is true, in which case it picks a new plan for the current execution: • The current plan ages out of the shared pool. • A different optimizer feature (for example, adaptive cursor sharing or statistics feedback) invalidates the current plan. This section contains the following topics: • Adaptive Query Plans: Join Method Example This example shows how the optimizer can choose a different plan based on information collected at runtime. 4-16 Chapter 4 About Adaptive Query Optimization • Adaptive Query Plans: Parallel Distribution Methods Typically, parallel execution requires data redistribution to perform operations such as parallel sorts, aggregations, and joins. • Adaptive Query Plans: Bitmap Index Pruning Adaptive plans prune indexes that do not significantly reduce the number of matched rows. Related Topics • Reading Adaptive Query Plans The adaptive optimizer is a feature of the optimizer that enables it to adapt plans based on run-time statistics. All adaptive mechanisms can execute a final plan for a statement that differs from the default plan. • Controlling Adaptive Optimization In Oracle Database, adaptive query optimization is the process by which the optimizer adapts an execution plan based on statistics collected at run time. 4.4.1.3.1 Adaptive Query Plans: Join Method Example This example shows how the optimizer can choose a different plan based on information collected at runtime. The following query shows a join of the order_items and prod_info tables. SELECT FROM WHERE AND AND product_name order_items o, prod_info p o.unit_price = 15 quantity > 1 p.product_id = o.product_id An adaptive query plan for this statement shows two possible plans, one with a nested loops join and the other with a hash join: SELECT * FROM TABLE(DBMS_XPLAN.display_cursor(FORMAT => 'ADAPTIVE')); SQL_ID 7hj8dwwy6gm7p, child number 0 ------------------------------------SELECT product_name FROM order_items o, prod_info p WHERE o.unit_price = 15 AND quantity > 1 AND p.product_id = o.product_id Plan hash value: 1553478007 ----------------------------------------------------------------------------| Id | Operation | Name |Rows|Bytes|Cost (%CPU)|Time| ----------------------------------------------------------------------------| 0| SELECT STATEMENT | | | |7(100)| | | * 1| HASH JOIN | |4| 128 | 7 (0)|00:00:01| |- 2| NESTED LOOPS | |4| 128 | 7 (0)|00:00:01| |- 3| NESTED LOOPS | |4| 128 | 7 (0)|00:00:01| |- 4| STATISTICS COLLECTOR | | | | | | | * 5| TABLE ACCESS FULL | ORDER_ITEMS |4| 48 | 3 (0)|00:00:01| |-* 6| INDEX UNIQUE SCAN | PROD_INFO_PK |1| | 0 (0)| | |- 7| TABLE ACCESS BY INDEX ROWID| PROD_INFO |1| 20 | 1 (0)|00:00:01| | 8| TABLE ACCESS FULL | PROD_INFO |1| 20 | 1 (0)|00:00:01| ----------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------- 4-17 Chapter 4 About Adaptive Query Optimization 1 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") 5 - filter(("O"."UNIT_PRICE"=15 AND "QUANTITY">1)) 6 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") Note ----- this is an adaptive plan (rows marked '-' are inactive) A nested loops join is preferable if the database can avoid scanning a significant portion of prod_info because its rows are filtered by the join predicate. If few rows are filtered, however, then scanning the right table in a hash join is preferable. The following graphic shows the adaptive process. For the query in the preceding example, the adaptive portion of the default plan contains two subplans, each of which uses a different join method. The optimizer automatically determines when each join method is optimal, depending on the cardinality of the left side of the join. The statistics collector buffers enough rows coming from the order_items table to determine which join method to use. If the row count is below the threshold determined by the optimizer, then the optimizer chooses the nested loops join; otherwise, the optimizer chooses the hash join. In this case, the row count coming from the order_items table is above the threshold, so the optimizer chooses a hash join for the final plan, and disables buffering. 4-18 Chapter 4 About Adaptive Query Optimization Figure 4-8 Adaptive Join Methods Default plan is a nested loops join Nested Loops Hash Join Statistics Collector Table scan order_items Index scan prod_info_pk Table scan prod_info The optimizer buffers rows coming from the order_items table up to a point. If the row count is less than the threshold, then use a nested loops join. Otherwise, switch to a hash join. Threshold exceeded, so subplan switches The optimizer disables the statistics collector after making the decision, and lets the rows pass through. Final plan is a hash join Nested Loops Hash Join Statistics Collector Table scan order_items Index scan prod_info_pk Table scan prod_info The Note section of the execution plan indicates whether the plan is adaptive, and which rows in the plan are inactive. See Also: • "Controlling Adaptive Optimization" • "Reading Execution Plans: Advanced" for an extended example showing an adaptive query plan 4-19 Chapter 4 About Adaptive Query Optimization 4.4.1.3.2 Adaptive Query Plans: Parallel Distribution Methods Typically, parallel execution requires data redistribution to perform operations such as parallel sorts, aggregations, and joins. Oracle Database can use many different data distributions methods. The database chooses the method based on the number of rows to be distributed and the number of parallel server processes in the operation. For example, consider the following alternative cases: • Many parallel server processes distribute few rows. The database may choose the broadcast distribution method. In this case, each parallel server process receives each row in the result set. • Few parallel server processes distribute many rows. If a data skew is encountered during the data redistribution, then it could adversely affect the performance of the statement. The database is more likely to pick a hash distribution to ensure that each parallel server process receives an equal number of rows. The hybrid hash distribution technique is an adaptive parallel data distribution that does not decide the final data distribution method until execution time. The optimizer inserts statistic collectors in front of the parallel server processes on the producer side of the operation. If the number of rows is less than a threshold, defined as twice the degree of parallelism (DOP), then the data distribution method switches from hash to broadcast. Otherwise, the distribution method is a hash. Broadcast Distribution The following graphic depicts a hybrid hash join between the departments and employees tables, with a query coordinator directing 8 parallel server processes: P5-P8 are producers, whereas P1-P4 are consumers. Each producer has its own consumer. 4-20 Chapter 4 About Adaptive Query Optimization Figure 4-9 Adaptive Query with DOP of 4 Query Coordinator Statistics collector threshold is 2X the DOP The number of rows returned is below threshold, so optimizer chooses broadcast method. P1 P2 P3 P4 1 departments employees P5 2 P6 3 P7 4 P8 The database inserts a statistics collector in front of each producer process scanning the departments table. The query coordinator aggregates the collected statistics. The distribution method is based on the run-time statistics. In Figure 4-9, the number of rows is below the threshold (8), which is twice the DOP (4), so the optimizer chooses a broadcast technique for the departments table. Hybrid Hash Distribution Consider an example that returns a greater number of rows. In the following plan, the threshold is 8, or twice the specified DOP of 4. However, because the statistics collector (Step 10) discovers that the number of rows (27) is greater than the threshold (8), the optimizer chooses a hybrid hash distribution rather than a broadcast distribution. (The time column should show 00:00:01, but shows 0:01 so the plan can fit the page.) EXPLAIN PLAN FOR SELECT /*+ parallel(4) full(e) full(d) */ department_name, sum(salary) FROM employees e, departments d WHERE d.department_id=e.department_id GROUP BY department_name; Plan hash value: 2940813933 ----------------------------------------------------------------------------------------------|Id|Operation | Name |Rows|Bytes|Cost |Time| TQ |IN-OUT|PQ Distrib| ----------------------------------------------------------------------------------------------| 0|SELECT STATEMENT |DEPARTMENTS| 27|621 |6(34)|0:01| | | | | 1| PX COORDINATOR | | | | | | | | | | 2| PX SEND QC (RANDOM) | :TQ10003 | 27|621 |6(34)|0:01|Q1,03|P->S| QC (RAND) | 4-21 Chapter 4 About Adaptive Query Optimization | 3| HASH GROUP BY | | 27|621 |6(34)|0:01|Q1,03|PCWP| | | 4| PX RECEIVE | | 27|621 |6(34)|0:01|Q1,03|PCWP| | | 5| PX SEND HASH | :TQ10002 | 27|621 |6(34)|0:01|Q1,02|P->P| HASH | | 6| HASH GROUP BY | | 27|621 |6(34)|0:01|Q1,02|PCWP| | |*7| HASH JOIN | |106|2438|5(20)|0:01|Q1,02|PCWP| | | 8| PX RECEIVE | | 27|432 |2 (0)|0:01|Q1,02|PCWP| | | 9| PX SEND HYBRID HASH | :TQ10000 | 27|432 |2 (0)|0:01|Q1,00|P->P|HYBRID HASH| |10| STATISTICS COLLECTOR | | | | | |Q1,00|PCWC| | |11| PX BLOCK ITERATOR | | 27|432 |2 (0)|0:01|Q1,00|PCWC| | |12| TABLE ACCESS FULL |DEPARTMENTS| 27|432 |2 (0)|0:01|Q1,00|PCWP| | |13| PX RECEIVE | |107|749 |2 (0)|0:01|Q1,02|PCWP| | |14| PX SEND HYBRID HASH (SKEW)| :TQ10001 |107|749 |2 (0)|0:01|Q1,01|P->P|HYBRID HASH| |15| PX BLOCK ITERATOR | |107|749 |2 (0)|0:01|Q1,01|PCWC| | |16| TABLE ACCESS FULL | EMPLOYEES |107|749 |2 (0)|0:01|Q1,01|PCWP| | ----------------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------7 - access("D"."DEPARTMENT_ID"="E"."DEPARTMENT_ID") Note ----- Degree of Parallelism is 4 because of hint 32 rows selected. See Also: Oracle Database VLDB and Partitioning Guide to learn more about parallel data redistribution techniques 4.4.1.3.3 Adaptive Query Plans: Bitmap Index Pruning Adaptive plans prune indexes that do not significantly reduce the number of matched rows. When the optimizer generates a star transformation plan, it must choose the right combination of bitmap indexes to reduce the relevant set of rowids as efficiently as possible. If many indexes exist, some indexes might not reduce the rowid set substantially, but nevertheless introduce significant processing cost during query execution. Adaptive plans can solve this problem by not using indexes that degrade performance. Example 4-2 Bitmap Index Pruning In this example, you issue the following star query, which joins the cars fact table with multiple dimension tables (sample output included): SELECT /*+ star_transformation(r) */ l.color_name, k.make_name, h.filter_col, count(*) FROM cars r, colors l, makes k, models d, hcc_tab h WHERE r.make_id = k.make_id AND r.color_id = l.color_id AND r.model_id = d.model_id AND r.high_card_col = h.high_card_col AND d.model_name = 'RAV4' 4-22 Chapter 4 About Adaptive Query Optimization AND AND AND GROUP k.make_name = 'Toyota' l.color_name = 'Burgundy' h.filter_col = 100 BY l.color_name, k.make_name, h.filter_col; COLOR_NA MAKE_N FILTER_COL COUNT(*) -------- ------ ---------- ---------Burgundy Toyota 100 15000 The following sample execution plan shows that the query generated no rows for the bitmap node in Step 12 and Step 17. The adaptive optimizer determined that filtering rows by using the CAR_MODEL_IDX and CAR_MAKE_IDX indexes was inefficient. The query did not use the steps in the plan that begin with a dash (-). ----------------------------------------------------------| Id | Operation | Name | ----------------------------------------------------------| 0 | SELECT STATEMENT | | | 1 | SORT GROUP BY NOSORT | | | 2 | HASH JOIN | | | 3 | VIEW | VW_ST_5497B905 | | 4 | NESTED LOOPS | | | 5 | BITMAP CONVERSION TO ROWIDS | | | 6 | BITMAP AND | | | 7 | BITMAP MERGE | | | 8 | BITMAP KEY ITERATION | | | 9 | TABLE ACCESS FULL | COLORS | | 10 | BITMAP INDEX RANGE SCAN | CAR_COLOR_IDX | |- 11 | STATISTICS COLLECTOR | | |- 12 | BITMAP MERGE | | |- 13 | BITMAP KEY ITERATION | | |- 14 | TABLE ACCESS FULL | MODELS | |- 15 | BITMAP INDEX RANGE SCAN | CAR_MODEL_IDX | |- 16 | STATISTICS COLLECTOR | | |- 17 | BITMAP MERGE | | |- 18 | BITMAP KEY ITERATION | | |- 19 | TABLE ACCESS FULL | MAKES | |- 20 | BITMAP INDEX RANGE SCAN | CAR_MAKE_IDX | | 21 | TABLE ACCESS BY USER ROWID | CARS | | 22 | MERGE JOIN CARTESIAN | | | 23 | MERGE JOIN CARTESIAN | | | 24 | MERGE JOIN CARTESIAN | | | 25 | TABLE ACCESS FULL | MAKES | | 26 | BUFFER SORT | | | 27 | TABLE ACCESS FULL | MODELS | | 28 | BUFFER SORT | | | 29 | TABLE ACCESS FULL | COLORS | | 30 | BUFFER SORT | | | 31 | TABLE ACCESS FULL | HCC_TAB | ----------------------------------------------------------Note ----- dynamic statistics used: dynamic sampling (level=2) - star transformation used for this statement - this is an adaptive plan (rows marked '-' are inactive) 4-23 Chapter 4 About Adaptive Query Optimization 4.4.1.4 When Adaptive Query Plans Are Enabled Adaptive query plans are enabled by default. Adaptive plans are enabled when the following initialization parameters are set: • OPTIMIZER_ADAPTIVE_PLANS is TRUE (default) • OPTIMIZER_FEATURES_ENABLE is 12.1.0.1 or later • OPTIMIZER_ADAPTIVE_REPORTING_ONLY is FALSE (default) Adaptive plans control the following optimizations: • Nested loops and hash join selection • Star transformation bitmap pruning • Adaptive parallel distribution method See Also: • "Controlling Adaptive Optimization" • Oracle Database Reference to learn more about OPTIMIZER_ADAPTIVE_PLANS 4.4.2 Adaptive Statistics The optimizer can use adaptive statistics when query predicates are too complex to rely on base table statistics alone. By default, adaptive statistics are disabled (OPTIMIZER_ADAPTIVE_STATISTICS is false). The following topics describe types of adaptive statistics: • Dynamic Statistics Dynamic statistics are an optimization technique in which the database executes a recursive SQL statement to scan a small random sample of a table's blocks to estimate predicate cardinalities. • Automatic Reoptimization In automatic reoptimization, the optimizer changes a plan on subsequent executions after the initial execution. • SQL Plan Directives A SQL plan directive is additional information that the optimizer uses to generate a more optimal plan. • When Adaptive Statistics Are Enabled Adaptive statistics are disabled by default. 4.4.2.1 Dynamic Statistics Dynamic statistics are an optimization technique in which the database executes a recursive SQL statement to scan a small random sample of a table's blocks to estimate predicate cardinalities. 4-24 Chapter 4 About Adaptive Query Optimization During SQL compilation, the optimizer decides whether to use dynamic statistics by considering whether available statistics are sufficient to generate an optimal plan. If the available statistics are insufficient, then the optimizer uses dynamic statistics to augment the statistics. To improve the quality of optimizer decisions, the optimizer can use dynamic statistics for table scans, index access, joins, and GROUP BY operations. Related Topics • Supplemental Dynamic Statistics By default, when optimizer statistics are missing, stale, or insufficient, the database automatically gathers dynamic statistics during a parse. The database uses recursive SQL to scan a small random sample of table blocks. 4.4.2.2 Automatic Reoptimization In automatic reoptimization, the optimizer changes a plan on subsequent executions after the initial execution. Adaptive query plans are not feasible for all kinds of plan changes. For example, a query with an inefficient join order might perform suboptimally, but adaptive query plans do not support adapting the join order during execution. At the end of the first execution of a SQL statement, the optimizer uses the information gathered during execution to determine whether automatic reoptimization has a cost benefit. If execution information differs significantly from optimizer estimates, then the optimizer looks for a replacement plan on the next execution. The optimizer uses the information gathered during the previous execution to help determine an alternative plan. The optimizer can reoptimize a query several times, each time gathering additional data and further improving the plan. Automatic reoptimization takes the following forms: • Reoptimization: Statistics Feedback A form of reoptimization known as statistics feedback (formerly known as cardinality feedback) automatically improves plans for repeated queries that have cardinality misestimates. • Reoptimization: Performance Feedback Another form of reoptimization is performance feedback. This reoptimization helps improve the degree of parallelism automatically chosen for repeated SQL statements when PARALLEL_DEGREE_POLICY is set to ADAPTIVE. Related Topics • Controlling Adaptive Optimization In Oracle Database, adaptive query optimization is the process by which the optimizer adapts an execution plan based on statistics collected at run time. 4.4.2.2.1 Reoptimization: Statistics Feedback A form of reoptimization known as statistics feedback (formerly known as cardinality feedback) automatically improves plans for repeated queries that have cardinality misestimates. The optimizer can estimate cardinalities incorrectly for many reasons, such as missing statistics, inaccurate statistics, or complex predicates. The basic process of reoptimization using statistics feedback is as follows: 4-25 Chapter 4 About Adaptive Query Optimization 1. During the first execution of a SQL statement, the optimizer generates an execution plan. The optimizer may enable monitoring for statistics feedback for the shared SQL area in the following cases: 2. • Tables with no statistics • Multiple conjunctive or disjunctive filter predicates on a table • Predicates containing complex operators for which the optimizer cannot accurately compute selectivity estimates At the end of the first execution, the optimizer compares its initial cardinality estimates to the actual number of rows returned by each operation in the plan during execution. If estimates differ significantly from actual cardinalities, then the optimizer stores the correct estimates for subsequent use. The optimizer also creates a SQL plan directive so that other SQL statements can benefit from the information obtained during this initial execution. 3. If the query executes again, then the optimizer uses the corrected cardinality estimates instead of its usual estimates. The OPTIMIZER_ADAPTIVE_STATISTICS initialization parameter does not control all features of automatic reoptimization. Specifically, this parameter controls statistics feedback for join cardinality only in the context of automatic reoptimization. For example, setting OPTIMIZER_ADAPTIVE_STATISTICS to FALSE disables statistics feedback for join cardinality misestimates, but it does not disable statistics feedback for single-table cardinality misestimates. Example 4-3 Statistics Feedback This example shows how the database uses statistics feedback to adjust incorrect estimates. 1. The user oe runs the following query of the orders, order_items, and product_information tables: SELECT o.order_id, v.product_name FROM orders o, ( SELECT order_id, product_name FROM order_items o, product_information p WHERE p.product_id = o.product_id AND list_price < 50 AND min_price < 40 ) v WHERE o.order_id = v.order_id 2. Querying the plan in the cursor shows that the estimated rows (E-Rows) is far fewer than the actual rows (A-Rows). -------------------------------------------------------------------------------------------------| Id | Operation | Name |Starts|E-Rows|A-Rows|A-Time|Buffers|OMem|1Mem|O/1/M| -------------------------------------------------------------------------------------------------| 0| SELECT STATEMENT | | 1| | 269 |00:00:00.14|1338| | | | | 1| NESTED LOOPS | | 1| 1 | 269 |00:00:00.14|1338| | | | | 2| MERGE JOIN CARTESIAN| | 1| 4 |9135 |00:00:00.05| 33| | | | |*3| TABLE ACCESS FULL |PRODUCT_INFORMATION| 1| 1 | 87 |00:00:00.01| 32| | | | | 4| BUFFER SORT | | 87| 105 |9135 |00:00:00.02| 1|4096|4096|1/0/0| | 5| INDEX FULL SCAN |ORDER_PK | 1| 105 | 105 |00:00:00.01| 1| | | | |*6| INDEX UNIQUE SCAN |ORDER_ITEMS_UK |9135| 1 | 269 |00:00:00.04|1305| | | | -------------------------------------------------------------------------------------------------- 4-26 Chapter 4 About Adaptive Query Optimization Predicate Information (identified by operation id): --------------------------------------------------3 - filter(("MIN_PRICE"<40 AND "LIST_PRICE"<50)) 6 - access("O"."ORDER_ID"="ORDER_ID" AND "P"."PRODUCT_ID"="O"."PRODUCT_ID") 3. The user oe reruns the query in Step 1. 4. Querying the plan in the cursor shows that the optimizer used statistics feedback (shown in the Note) for the second execution, and also chose a different plan. -------------------------------------------------------------------------------------------------|Id | Operation | Name | Starts |E-Rows|A-Rows|A-Time|Buffers|Reads|OMem|1Mem|O/1/M| -------------------------------------------------------------------------------------------------| 0| SELECT STATEMENT | | 1| | 269 |00:00:00.05|60|1| | | | | 1| NESTED LOOPS | | 1|269| 269 |00:00:00.05|60|1| | | | |*2| HASH JOIN | | 1|313| 269 |00:00:00.05|39|1|1398K|1398K|1/0/0| |*3| TABLE ACCESS FULL |PRODUCT_INFORMATION| 1| 87| 87 |00:00:00.01|15|0| | | | | 4| INDEX FAST FULL SCAN|ORDER_ITEMS_UK | 1|665| 665 |00:00:00.01|24|1| | | | |*5| INDEX UNIQUE SCAN |ORDER_PK |269| 1| 269 |00:00:00.01|21|0| | | | -------------------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------2 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") 3 - filter(("MIN_PRICE"<40 AND "LIST_PRICE"<50)) 5 - access("O"."ORDER_ID"="ORDER_ID") Note ----- statistics feedback used for this statement In the preceding output, the estimated number of rows (269) in Step 1 matches the actual number of rows. 4.4.2.2.2 Reoptimization: Performance Feedback Another form of reoptimization is performance feedback. This reoptimization helps improve the degree of parallelism automatically chosen for repeated SQL statements when PARALLEL_DEGREE_POLICY is set to ADAPTIVE. The basic process of reoptimization using performance feedback is as follows: 1. During the first execution of a SQL statement, when PARALLEL_DEGREE_POLICY is set to ADAPTIVE, the optimizer determines whether to execute the statement in parallel, and if so, which degree of parallelism to use. The optimizer chooses the degree of parallelism based on the estimated performance of the statement. Additional performance monitoring is enabled for all statements. 2. At the end of the initial execution, the optimizer compares the following: • The degree of parallelism chosen by the optimizer • The degree of parallelism computed based on the performance statistics (for example, the CPU time) gathered during the actual execution of the statement 4-27 Chapter 4 About Adaptive Query Optimization If the two values vary significantly, then the database marks the statement for reparsing, and stores the initial execution statistics as feedback. This feedback helps better compute the degree of parallelism for subsequent executions. 3. If the query executes again, then the optimizer uses the performance statistics gathered during the initial execution to better determine a degree of parallelism for the statement. Note: Even if PARALLEL_DEGREE_POLICY is not set to ADAPTIVE, statistics feedback may influence the degree of parallelism chosen for a statement. 4.4.2.3 SQL Plan Directives A SQL plan directive is additional information that the optimizer uses to generate a more optimal plan. The directive is a “note to self” by the optimizer that it is misestimating cardinalities of certain types of predicates, and also a reminder to DBMS_STATS to gather statistics needed to correct the misestimates in the future. For example, during query optimization, when deciding whether the table is a candidate for dynamic statistics, the database queries the statistics repository for directives on a table. If the query joins two tables that have a data skew in their join columns, then a SQL plan directive can direct the optimizer to use dynamic statistics to obtain an accurate cardinality estimate. The optimizer collects SQL plan directives on query expressions rather than at the statement level so that it can apply directives to multiple SQL statements. The optimizer not only corrects itself, but also records information about the mistake, so that the database can continue to correct its estimates even after a query—and any similar query—is flushed from the shared pool. The database automatically creates directives, and stores them in the SYSAUX tablespace. You can alter, save to disk, and transport directives using the PL/SQL package DBMS_SPD. See Also: • "SQL Plan Directives" • "Managing SQL Plan Directives" • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SPD package 4.4.2.4 When Adaptive Statistics Are Enabled Adaptive statistics are disabled by default. Adaptive statistics are enabled when the following initialization parameters are set: 4-28 Chapter 4 About Approximate Query Processing • OPTIMIZER_ADAPTIVE_STATISTICS is TRUE (the default is FALSE) • OPTIMIZER_FEATURES_ENABLE is 12.1.0.1 or later Setting OPTIMIZER_ADAPTIVE_STATISTICS to TRUE enables the following features: • SQL plan directives • Statistics feedback for join cardinality • Performance feedback • Adaptive dynamic sampling Note: Setting OPTIMIZER_ADAPTIVE_STATISTICS to FALSE preserves statistics feedback for single-table cardinality misestimates. See Also: • • "Controlling Adaptive Optimization" Oracle Database Reference to learn more about OPTIMIZER_ADAPTIVE_STATISTICS 4.5 About Approximate Query Processing Approximate query processing is a set of optimization techniques that speed analytic queries by calculating results within an acceptable range of error. Business intelligence (BI) queries heavily rely on aggregate functions (SUM, RANK, MEDIAN, and so on) that require sorting. For example, an application generates reports showing how many distinct customers are logged on, or which products were most popular last week. It is not uncommon for BI applications to have the following requirements: • Queries must be able to process data sets that are orders of magnitude larger than in traditional data warehouses. For example, the daily volumes of web logs of a popular website can reach tens or hundreds of terabytes a day. • Queries must provide near real-time response. For example, a company requires quick detection and response to credit card fraud. • Explorative queries of large data sets must be fast. For example, a user might want to find out a list of departments whose sales have approximately reached a specific threshold. A user would form targeted queries on these departments to find more detailed information, such as the exact sales number, the locations of these departments, and so on. 4-29 Chapter 4 About Approximate Query Processing For large data sets, exact aggregation queries consume extensive memory, often spilling to temp space, and can be unacceptably slow. Applications are often more interested in a general pattern than exact results, so customers are willing to sacrifice exactitude for speed. For example, if the goal is to show a bar chart depicting the most popular products, then whether a product sold 1 million units or .999 million units is statistically insignificant. Oracle Database implements its solution through approximate query processing. Typically, the accuracy of the approximate aggregation is over 97% (with 95% confidence), but the processing time is orders of magnitude faster. The database uses less CPU, and avoids writing to temp files. You can implement approximate query processing without changing existing code by using the APPROX_FOR_* initialization parameters. You can set these parameters at the database or session level. The following table describes initialization parameters and SQL functions relevant to approximation techniques. Table 4-2 Approximate Query User Interface User Interface Description See Also APPROX_FOR_AGGREGATION initialization parameter Enables approximate query processing. Oracle Database Reference Setting this parameter to false disables all automatic conversion from exact aggregate to approximate aggregate, regardless of the settings of the APPROX_FOR_COUNT_DISTINCT and APPROX_FOR_PERCENTILE parameters. APPROX_FOR_COUNT_DISTINCT initialization parameter Converts COUNT(DISTINCT) to APPROX_COUNT_DISTINCT. Oracle Database Reference APPROX_FOR_PERCENTILE initialization parameter Converts eligible exact percentile functions to their APPROX_PERCENTILE_* counterparts. Oracle Database Reference APPROX_COUNT_DISTINCT function Returns the approximate number Oracle Database SQL of rows that contain distinct values Language Reference of an expression. APPROX_COUNT_DISTINCT_AGG function Aggregates the precomputed approximate count distinct synopses to a higher level. Oracle Database SQL Language Reference APPROX_COUNT_DISTINCT_DETAIL function Returns the synopses of the APPROX_COUNT_DISTINCT function as a BLOB. Oracle Database SQL Language Reference The database can persist the returned result to disk for further aggregation. 4-30 Chapter 4 About SQL Plan Management Table 4-2 (Cont.) Approximate Query User Interface User Interface Description See Also APPROX_PERCENTILE function Accepts a percentile value and a sort specification, and returns an approximate interpolated value that falls into that percentile value with respect to the sort specification. Oracle Database SQL Language Reference This function provides an alternative to the PERCENTILE_CONT function. APPROX_MEDIAN function Accepts a numeric or date-time Oracle Database SQL value, and returns an approximate Language Reference middle or approximate interpolated value that would be the middle value when the values are sorted. This function provides an alternative to the MEDIAN function. See Also: "NDV Algorithms: Adaptive Sampling and HyperLogLog" 4.6 About SQL Plan Management SQL plan management enables the optimizer to automatically manage execution plans, ensuring that the database uses only known or verified plans. SQL plan management can build a SQL plan baseline, which contains one or more accepted plans for each SQL statement. The optimizer can access and manage the plan history and SQL plan baselines of SQL statements. The main objectives are as follows: • Identify repeatable SQL statements • Maintain plan history, and possibly SQL plan baselines, for a set of SQL statements • Detect plans that are not in the plan history • Detect potentially better plans that are not in the SQL plan baseline The optimizer uses the normal cost-based search method. 4-31 Chapter 4 About the Expression Statistics Store (ESS) See Also: • "Managing SQL Plan Baselines" • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SPM package 4.7 About the Expression Statistics Store (ESS) The Expression Statistics Store (ESS) is a repository maintained by the optimizer to store statistics about expression evaluation. When an IM column store is enabled, the database leverages the ESS for its InMemory Expressions (IM expressions) feature. However, the ESS is independent of the IM column store. The ESS is a permanent component of the database and cannot be disabled. The database uses the ESS to determine whether an expression is “hot” (frequently accessed), and thus a candidate for an IM expression. During a hard parse of a query, the ESS looks for active expressions in the SELECT list, WHERE clause, GROUP BY clause, and so on. For each segment, the ESS maintains expression statistics such as the following: • Frequency of execution • Cost of evaluation • Timestamp evaluation The optimizer assigns each expression a weighted score based on cost and the number of times it was evaluated. The values are approximate rather than exact. More active expressions have higher scores. The ESS maintains an internal list of the most frequently accessed expressions. The ESS resides in the SGA and also persists on disk. The database saves the statistics to disk every 15 minutes, or immediately using the DBMS_STATS.FLUSH_DATABASE_MONITORING_INFO procedure. The statistics are visible in the DBA_EXPRESSION_STATISTICS view. See Also: • Oracle Database In-Memory Guide to learn more about the ESS • Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_STATS.FLUSH_DATABASE_MONITORING_INFO 4-32 5 Query Transformations The optimizer employs many query transformation techniques. This chapter describes some of the most important. This chapter contains the following topics: • OR Expansion In OR expansion, the optimizer transforms a query block containing top-level disjunctions into the form of a UNION ALL query that contains two or more branches. The optimizer achieves this goal by splitting the disjunction into its components, and then associating each component with a branch of a UNION ALL query. • View Merging In view merging, the optimizer merges the query block representing a view into the query block that contains it. • Predicate Pushing In predicate pushing, the optimizer "pushes" the relevant predicates from the containing query block into the view query block. • Subquery Unnesting In subquery unnesting, the optimizer transforms a nested query into an equivalent join statement, and then optimizes the join. • Query Rewrite with Materialized Views A materialized view is a query result that the database materializes and stores in a table. • Star Transformation Star transformation is an optimizer transformation that avoids full table scans of fact tables in a star schema. • In-Memory Aggregation (VECTOR GROUP BY) The key optimization of in-memory aggregation is to aggregate while scanning. • Cursor-Duration Temporary Tables To materialize the intermediate results of a query, Oracle Database may implicitly create a cursor-duration temporary table in memory during query compilation. • Table Expansion In table expansion, the optimizer generates a plan that uses indexes on the readmostly portion of a partitioned table, but not on the active portion of the table. • Join Factorization In the cost-based transformation known as join factorization, the optimizer can factorize common computations from branches of a UNION ALL query. Related Topics • Query Transformer For some statements, the query transformer determines whether it is advantageous to rewrite the original SQL statement into a semantically equivalent SQL statement with a lower cost. 5-1 Chapter 5 OR Expansion 5.1 OR Expansion In OR expansion, the optimizer transforms a query block containing top-level disjunctions into the form of a UNION ALL query that contains two or more branches. The optimizer achieves this goal by splitting the disjunction into its components, and then associating each component with a branch of a UNION ALL query. The optimizer can choose OR expansion for various reasons. For example, it may enable more efficient access paths or alternative join methods that avoid Cartesian products. As always, the optimizer performs the expansion only if the cost of the transformed statement is lower than the cost of the original statement. In previous releases, the optimizer used the CONCATENATION operator to perform the OR expansion. Starting in Oracle Database 12c Release 2 (12.2), the optimizer uses the UNION-ALL operator instead. The framework provides the following enhancements: Example 5-1 • Enables interaction among various transformations • Avoids sharing query structures • Enables the exploration of various search strategies • Provides the reuse of cost annotation • Supports the standard SQL syntax Transformed Query: UNION ALL Condition To prepare for this example, log in to the database as an administrator, execute the following statements to add a unique constraint to the hr.departments.department_name column, and then add 100,000 rows to the hr.employees table: ALTER TABLE hr.departments ADD CONSTRAINT department_name_uk UNIQUE (department_name); DELETE FROM hr.employees WHERE employee_id > 999; DECLARE v_counter NUMBER(7) := 1000; BEGIN FOR i IN 1..100000 LOOP INSERT INTO hr.employees VALUES (v_counter,null,'Doe','Doe' || v_counter || '@example.com',null,'07JUN-02','AC_ACCOUNT',null,null,null,50); v_counter := v_counter + 1; END LOOP; END; / COMMIT; EXEC DBMS_STATS.GATHER_TABLE_STATS ( ownname => 'hr', tabname => 'employees'); You then connect as the user hr, and execute the following query, which joins the employees and departments tables: SELECT FROM WHERE AND * employees e, departments d (e.email='SSTILES' OR d.department_name='Treasury') e.department_id = d.department_id; Without OR expansion, the optimizer treats e.email='SSTILES' OR d.department_name='Treasury' as a single unit. Consequently, the optimizer cannot use 5-2 Chapter 5 View Merging the index on either the e.email or d.department_name column, and so performs a full table scan of employees and departments. With OR expansion, the optimizer breaks the disjunctive predicate into two independent predicates, as shown in the following example: SELECT * FROM employees e, departments d WHERE e.email = 'SSTILES' AND e.department_id = d.department_id UNION ALL SELECT * FROM employees e, departments d WHERE d.department_name = 'Treasury' AND e.department_id = d.department_id; This transformation enables the e.email and d.department_name columns to serve as index keys. Performance improves because the database filters data using two unique indexes instead of two full table scans, as shown in the following execution plan: Plan hash value: 2512933241 ----------------------------------------------------------------------------------------------| Id| Operation | Name |Rows|Bytes|Cost(%CPU)|Time | ----------------------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | |122 (100)| | | 1 | VIEW |VW_ORE_19FF4E3E |9102|1679K|122 (5) |00:00:01| | 2 | UNION-ALL | | | | | | | 3 | NESTED LOOPS | | 1 | 78 | 4 (0) |00:00:01| | 4 | TABLE ACCESS BY INDEX ROWID | EMPLOYEES | 1 | 57 | 3 (0) |00:00:01| |*5 | INDEX UNIQUE SCAN | EMP_EMAIL_UK | 1 | | 2 (0) |00:00:01| | 6 | TABLE ACCESS BY INDEX ROWID | DEPARTMENTS | 1 | 21 | 1 (0) |00:00:01| |*7 | INDEX UNIQUE SCAN | DEPT_ID_PK | 1 | | 0 (0) | | | 8 | NESTED LOOPS | |9101| 693K|118 (5) |00:00:01| | 9 | TABLE ACCESS BY INDEX ROWID | DEPARTMENTS | 1 | 21 | 1 (0) |00:00:01| |*10| INDEX UNIQUE SCAN |DEPARTMENT_NAME_UK| 1 | | 0 (0) | | |*11| TABLE ACCESS BY INDEX ROWID BATCHED| EMPLOYEES |9101| 506K|117 (5) |00:00:01| |*12| INDEX RANGE SCAN |EMP_DEPARTMENT_IX |9101| | 35 (6) |00:00:01| ----------------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------5 7 10 11 12 - access("E"."EMAIL"='SSTILES') access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID") access("D"."DEPARTMENT_NAME"='Treasury') filter(LNNVL("E"."EMAIL"='SSTILES')) access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID") 35 rows selected. 5.2 View Merging In view merging, the optimizer merges the query block representing a view into the query block that contains it. View merging can improve plans by enabling the optimizer to consider additional join orders, access methods, and other transformations. For example, after a view has been merged and several tables reside in one query block, a table inside a view may permit the optimizer to use join elimination to remove a table outside the view. 5-3 Chapter 5 View Merging For certain simple views in which merging always leads to a better plan, the optimizer automatically merges the view without considering cost. Otherwise, the optimizer uses cost to make the determination. The optimizer may choose not to merge a view for many reasons, including cost or validity restrictions. If OPTIMIZER_SECURE_VIEW_MERGING is true (default), then Oracle Database performs checks to ensure that view merging and predicate pushing do not violate the security intentions of the view creator. To disable these additional security checks for a specific view, you can grant the MERGE VIEW privilege to a user for this view. To disable additional security checks for all views for a specific user, you can grant the MERGE ANY VIEW privilege to that user. Note: You can use hints to override view merging rejected because of cost or heuristics, but not validity. This section contains the following topics: • Query Blocks in View Merging The optimizer represents each nested subquery or unmerged view by a separate query block. • Simple View Merging In simple view merging, the optimizer merges select-project-join views. • Complex View Merging In view merging, the optimizer merges views containing GROUP BY and DISTINCT views. Like simple view merging, complex merging enables the optimizer to consider additional join orders and access paths. See Also: • Oracle Database SQL Language Reference for more information about the MERGE ANY VIEW and MERGE VIEW privileges • Oracle Database Reference for more information about the OPTIMIZER_SECURE_VIEW_MERGING initialization parameter 5.2.1 Query Blocks in View Merging The optimizer represents each nested subquery or unmerged view by a separate query block. The database optimizes query blocks separately from the bottom up. Thus, the database optimizes the innermost query block first, generates the part of the plan for it, and then generates the plan for the outer query block, representing the entire query. The parser expands each view referenced in a query into a separate query block. The block essentially represents the view definition, and thus the result of a view. One option for the optimizer is to analyze the view query block separately, generate a view 5-4 Chapter 5 View Merging subplan, and then process the rest of the query by using the view subplan to generate an overall execution plan. However, this technique may lead to a suboptimal execution plan because the view is optimized separately. View merging can sometimes improve performance. As shown in "Example 5-2", view merging merges the tables from the view into the outer query block, removing the inner query block. Thus, separate optimization of the view is not necessary. 5.2.2 Simple View Merging In simple view merging, the optimizer merges select-project-join views. For example, a query of the employees table contains a subquery that joins the departments and locations tables. Simple view merging frequently results in a more optimal plan because of the additional join orders and access paths available after the merge. A view may not be valid for simple view merging because: • The view contains constructs not included in select-project-join views, including: – GROUP BY – DISTINCT – Outer join – MODEL – CONNECT BY – Set operators – Aggregation • The view appears on the right side of a semijoin or antijoin. • The view contains subqueries in the SELECT list. • The outer query block contains PL/SQL functions. • The view participates in an outer join, and does not meet one of the several additional validity requirements that determine whether the view can be merged. Example 5-2 Simple View Merging The following query joins the hr.employees table with the dept_locs_v view, which returns the street address for each department. dept_locs_v is a join of the departments and locations tables. SELECT e.first_name, e.last_name, dept_locs_v.street_address, dept_locs_v.postal_code FROM employees e, ( SELECT d.department_id, d.department_name, l.street_address, l.postal_code FROM departments d, locations l WHERE d.location_id = l.location_id ) dept_locs_v WHERE dept_locs_v.department_id = e.department_id AND e.last_name = 'Smith'; The database can execute the preceding query by joining departments and locations to generate the rows of the view, and then joining this result to employees. Because the 5-5 Chapter 5 View Merging query contains the view dept_locs_v, and this view contains two tables, the optimizer must use one of the following join orders: • employees, dept_locs_v (departments, locations) • employees, dept_locs_v (locations, departments) • dept_locs_v (departments, locations), employees • dept_locs_v (locations, departments), employees Join methods are also constrained. The index-based nested loops join is not feasible for join orders that begin with employees because no index exists on the column from this view. Without view merging, the optimizer generates the following execution plan: ----------------------------------------------------------------| Id | Operation | Name | Cost (%CPU)| ----------------------------------------------------------------| 0 | SELECT STATEMENT | | 7 (15)| |* 1 | HASH JOIN | | 7 (15)| | 2 | TABLE ACCESS BY INDEX ROWID| EMPLOYEES | 2 (0)| |* 3 | INDEX RANGE SCAN | EMP_NAME_IX | 1 (0)| | 4 | VIEW | | 5 (20)| |* 5 | HASH JOIN | | 5 (20)| | 6 | TABLE ACCESS FULL | LOCATIONS | 2 (0)| | 7 | TABLE ACCESS FULL | DEPARTMENTS | 2 (0)| ----------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------1 - access("DEPT_LOCS_V"."DEPARTMENT_ID"="E"."DEPARTMENT_ID") 3 - access("E"."LAST_NAME"='Smith') 5 - access("D"."LOCATION_ID"="L"."LOCATION_ID") View merging merges the tables from the view into the outer query block, removing the inner query block. After view merging, the query is as follows: SELECT FROM WHERE AND AND e.first_name, e.last_name, l.street_address, l.postal_code employees e, departments d, locations l d.location_id = l.location_id d.department_id = e.department_id e.last_name = 'Smith'; Because all three tables appear in one query block, the optimizer can choose from the following six join orders: • employees, departments, locations • employees, locations, departments • departments, employees, locations • departments, locations, employees • locations, employees, departments • locations, departments, employees The joins to employees and departments can now be index-based. After view merging, the optimizer chooses the following more efficient plan, which uses nested loops: ------------------------------------------------------------------| Id | Operation | Name | Cost (%CPU)| ------------------------------------------------------------------- 5-6 Chapter 5 View Merging | 0 | SELECT STATEMENT | | 4 (0)| | 1 | NESTED LOOPS | | | | 2 | NESTED LOOPS | | 4 (0)| | 3 | NESTED LOOPS | | 3 (0)| | 4 | TABLE ACCESS BY INDEX ROWID| EMPLOYEES | 2 (0)| |* 5 | INDEX RANGE SCAN | EMP_NAME_IX | 1 (0)| | 6 | TABLE ACCESS BY INDEX ROWID| DEPARTMENTS | 1 (0)| |* 7 | INDEX UNIQUE SCAN | DEPT_ID_PK | 0 (0)| |* 8 | INDEX UNIQUE SCAN | LOC_ID_PK | 0 (0)| | 9 | TABLE ACCESS BY INDEX ROWID | LOCATIONS | 1 (0)| ------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------5 - access("E"."LAST_NAME"='Smith') 7 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID") 8 - access("D"."LOCATION_ID"="L"."LOCATION_ID") See Also: The Oracle Optimizer blog at https://blogs.oracle.com/optimizer/ to learn about outer join view merging, which is a special case of simple view merging 5.2.3 Complex View Merging In view merging, the optimizer merges views containing GROUP BY and DISTINCT views. Like simple view merging, complex merging enables the optimizer to consider additional join orders and access paths. The optimizer can delay evaluation of GROUP BY or DISTINCT operations until after it has evaluated the joins. Delaying these operations can improve or worsen performance depending on the data characteristics. If the joins use filters, then delaying the operation until after joins can reduce the data set on which the operation is to be performed. Evaluating the operation early can reduce the amount of data to be processed by subsequent joins, or the joins could increase the amount of data to be processed by the operation. The optimizer uses cost to evaluate view merging and merges the view only when it is the lower cost option. Aside from cost, the optimizer may be unable to perform complex view merging for the following reasons: • The outer query tables do not have a rowid or unique column. • The view appears in a CONNECT BY query block. • The view contains GROUPING SETS, ROLLUP, or PIVOT clauses. • The view or outer query block contains the MODEL clause. Example 5-3 Complex View Joins with GROUP BY The following view uses a GROUP BY clause: CREATE VIEW cust_prod_totals_v AS SELECT SUM(s.quantity_sold) total, s.cust_id, s.prod_id FROM sales s GROUP BY s.cust_id, s.prod_id; 5-7 Chapter 5 View Merging The following query finds all of the customers from the United States who have bought at least 100 fur-trimmed sweaters: SELECT FROM WHERE AND AND AND AND c.cust_id, c.cust_first_name, c.cust_last_name, c.cust_email customers c, products p, cust_prod_totals_v c.country_id = 52790 c.cust_id = cust_prod_totals_v.cust_id cust_prod_totals_v.total > 100 cust_prod_totals_v.prod_id = p.prod_id p.prod_name = 'T3 Faux Fur-Trimmed Sweater'; The cust_prod_totals_v view is eligible for complex view merging. After merging, the query is as follows: SELECT c.cust_id, cust_first_name, cust_last_name, cust_email FROM customers c, products p, sales s WHERE c.country_id = 52790 AND c.cust_id = s.cust_id AND s.prod_id = p.prod_id AND p.prod_name = 'T3 Faux Fur-Trimmed Sweater' GROUP BY s.cust_id, s.prod_id, p.rowid, c.rowid, c.cust_email, c.cust_last_name, c.cust_first_name, c.cust_id HAVING SUM(s.quantity_sold) > 100; The transformed query is cheaper than the untransformed query, so the optimizer chooses to merge the view. In the untransformed query, the GROUP BY operator applies to the entire sales table in the view. In the transformed query, the joins to products and customers filter out a large portion of the rows from the sales table, so the GROUP BY operation is lower cost. The join is more expensive because the sales table has not been reduced, but it is not much more expensive because the GROUP BY operation does not reduce the size of the row set very much in the original query. If any of the preceding characteristics were to change, merging the view might no longer be lower cost. The final plan, which does not include a view, is as follows: -------------------------------------------------------| Id | Operation | Name | Cost (%CPU)| -------------------------------------------------------| 0 | SELECT STATEMENT | | 2101 (18)| |* 1 | FILTER | | | | 2 | HASH GROUP BY | | 2101 (18)| |* 3 | HASH JOIN | | 2099 (18)| |* 4 | HASH JOIN | | 1801 (19)| |* 5 | TABLE ACCESS FULL| PRODUCTS | 96 (5)| | 6 | TABLE ACCESS FULL| SALES | 1620 (15)| |* 7 | TABLE ACCESS FULL | CUSTOMERS | 296 (11)| -------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------1 - filter(SUM("QUANTITY_SOLD")>100) 3 - access("C"."CUST_ID"="CUST_ID") 4 - access("PROD_ID"="P"."PROD_ID") 5 - filter("P"."PROD_NAME"='T3 Faux Fur-Trimmed Sweater') 7 - filter("C"."COUNTRY_ID"='US') Example 5-4 Complex View Joins with DISTINCT The following query of the cust_prod_v view uses a DISTINCT operator: SELECT c.cust_id, c.cust_first_name, c.cust_last_name, c.cust_email FROM customers c, products p, ( SELECT DISTINCT s.cust_id, s.prod_id 5-8 Chapter 5 Predicate Pushing WHERE AND AND AND FROM sales s) cust_prod_v c.country_id = 52790 c.cust_id = cust_prod_v.cust_id cust_prod_v.prod_id = p.prod_id p.prod_name = 'T3 Faux Fur-Trimmed Sweater'; After determining that view merging produces a lower-cost plan, the optimizer rewrites the query into this equivalent query: SELECT nwvw.cust_id, nwvw.cust_first_name, nwvw.cust_last_name, nwvw.cust_email FROM ( SELECT DISTINCT(c.rowid), p.rowid, s.prod_id, s.cust_id, c.cust_first_name, c.cust_last_name, c.cust_email FROM customers c, products p, sales s WHERE c.country_id = 52790 AND c.cust_id = s.cust_id AND s.prod_id = p.prod_id AND p.prod_name = 'T3 Faux Fur-Trimmed Sweater' ) nwvw; The plan for the preceding query is as follows: ------------------------------------------| Id | Operation | Name | ------------------------------------------| 0 | SELECT STATEMENT | | | 1 | VIEW | VM_NWVW_1 | | 2 | HASH UNIQUE | | |* 3 | HASH JOIN | | |* 4 | HASH JOIN | | |* 5 | TABLE ACCESS FULL| PRODUCTS | | 6 | TABLE ACCESS FULL| SALES | |* 7 | TABLE ACCESS FULL | CUSTOMERS | ------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------3 - access("C"."CUST_ID"="S"."CUST_ID") 4 - access("S"."PROD_ID"="P"."PROD_ID") 5 - filter("P"."PROD_NAME"='T3 Faux Fur-Trimmed Sweater') 7 - filter("C"."COUNTRY_ID"='US') The preceding plan contains a view named vm_nwvw_1, known as a projection view, even after view merging has occurred. Projection views appear in queries in which a DISTINCT view has been merged, or a GROUP BY view is merged into an outer query block that also contains GROUP BY, HAVING, or aggregates. In the latter case, the projection view contains the GROUP BY, HAVING, and aggregates from the original outer query block. In the preceding example of a projection view, when the optimizer merges the view, it moves the DISTINCT operator to the outer query block, and then adds several additional columns to maintain semantic equivalence with the original query. Afterward, the query can select only the desired columns in the SELECT list of the outer query block. The optimization retains all of the benefits of view merging: all tables are in one query block, the optimizer can permute them as needed in the final join order, and the DISTINCT operation has been delayed until after all of the joins complete. 5.3 Predicate Pushing In predicate pushing, the optimizer "pushes" the relevant predicates from the containing query block into the view query block. 5-9 Chapter 5 Subquery Unnesting For views that are not merged, this technique improves the subplan of the unmerged view. The database can use the pushed-in predicates to access indexes or to use as filters. For example, suppose you create a table hr.contract_workers as follows: DROP TABLE contract_workers; CREATE TABLE contract_workers AS (SELECT * FROM employees where 1=2); INSERT INTO contract_workers VALUES (306, 'Bill', 'Jones', 'BJONES', '555.555.2000', '07-JUN-02', 'AC_ACCOUNT', 8300, 0,205, 110); INSERT INTO contract_workers VALUES (406, 'Jill', 'Ashworth', 'JASHWORTH', '555.999.8181', '09-JUN-05', 'AC_ACCOUNT', 8300, 0,205, 50); INSERT INTO contract_workers VALUES (506, 'Marcie', 'Lunsford', 'MLUNSFORD', '555.888.2233', '22-JUL-01', 'AC_ACCOUNT', 8300, 0,205, 110); COMMIT; CREATE INDEX contract_workers_index ON contract_workers(department_id); You create a view that references employees and contract_workers. The view is defined with a query that uses the UNION set operator, as follows: CREATE VIEW all_employees_vw AS ( SELECT employee_id, last_name, job_id, commission_pct, department_id FROM employees ) UNION ( SELECT employee_id, last_name, job_id, commission_pct, department_id FROM contract_workers ); You then query the view as follows: SELECT last_name FROM all_employees_vw WHERE department_id = 50; Because the view is a UNION set query, the optimizer cannot merge the view's query into the accessing query block. Instead, the optimizer can transform the accessing statement by pushing its predicate, the WHERE clause condition department_id=50, into the view's UNION set query. The equivalent transformed query is as follows: SELECT last_name FROM ( SELECT employee_id, last_name, job_id, commission_pct, department_id FROM employees WHERE department_id=50 UNION SELECT employee_id, last_name, job_id, commission_pct, department_id FROM contract_workers WHERE department_id=50 ); The transformed query can now consider index access in each of the query blocks. 5.4 Subquery Unnesting In subquery unnesting, the optimizer transforms a nested query into an equivalent join statement, and then optimizes the join. This transformation enables the optimizer to consider the subquery tables during access path, join method, and join order selection. The optimizer can perform this transformation only if the resulting join statement is guaranteed to return the same rows as the original statement, and if subqueries do not contain aggregate functions such as AVG. 5-10 Chapter 5 Query Rewrite with Materialized Views For example, suppose you connect as user sh and execute the following query: SELECT * FROM sales WHERE cust_id IN ( SELECT cust_id FROM customers ); Because the customers.cust_id column is a primary key, the optimizer can transform the complex query into the following join statement that is guaranteed to return the same data: SELECT sales.* FROM sales, customers WHERE sales.cust_id = customers.cust_id; If the optimizer cannot transform a complex statement into a join statement, it selects execution plans for the parent statement and the subquery as though they were separate statements. The optimizer then executes the subquery and uses the rows returned to execute the parent query. To improve execution speed of the overall execution plan, the optimizer orders the subplans efficiently. 5.5 Query Rewrite with Materialized Views A materialized view is a query result that the database materializes and stores in a table. When the optimizer finds a user query compatible with the query associated with a materialized view, then the database can rewrite the query in terms of the materialized view. This technique improves query execution because the database has precomputed most of the query result. The optimizer looks for any materialized views that are compatible with the user query, and then selects one or more materialized views to rewrite the user query. The use of materialized views to rewrite a query is cost-based. Thus, the optimizer does not rewrite the query when the plan generated unless the materialized views has a lower cost than the plan generated with the materialized views. Consider the following materialized view, cal_month_sales_mv, which aggregates the dollar amount sold each month: CREATE MATERIALIZED VIEW cal_month_sales_mv ENABLE QUERY REWRITE AS SELECT t.calendar_month_desc, SUM(s.amount_sold) AS dollars FROM sales s, times t WHERE s.time_id = t.time_id GROUP BY t.calendar_month_desc; Assume that sales number is around one million in a typical month. The view has the precomputed aggregates for the dollar amount sold for each month. Consider the following query, which asks for the sum of the amount sold for each month: SELECT t.calendar_month_desc, SUM(s.amount_sold) FROM sales s, times t WHERE s.time_id = t.time_id GROUP BY t.calendar_month_desc; Without query rewrite, the database must access sales directly and compute the sum of the amount sold. This method involves reading many million rows from sales, which 5-11 Chapter 5 Star Transformation invariably increases query response time. The join also further slows query response because the database must compute the join on several million rows. With query rewrite, the optimizer transparently rewrites the query as follows: SELECT calendar_month, dollars FROM cal_month_sales_mv; See Also: Oracle Database Data Warehousing Guide to learn more about query rewrite 5.6 Star Transformation Star transformation is an optimizer transformation that avoids full table scans of fact tables in a star schema. This section contains the following topics: • About Star Schemas A star schema divides data into facts and dimensions. • Purpose of Star Transformations In joins of fact and dimension tables, a star transformation can avoid a full scan of a fact table. • How Star Transformation Works Star transformation adds subquery predicates, called bitmap semijoin predicates, corresponding to the constraint dimensions. • Controls for Star Transformation The STAR_TRANSFORMATION_ENABLED initialization parameter controls star transformations. • Star Transformation: Scenario This scenario demonstrates a star transformation of a star query. • Temporary Table Transformation: Scenario In the preceding scenario, the optimizer does not join back the table channels to the sales table because it is not referenced outside and the channel_id is unique. 5.6.1 About Star Schemas A star schema divides data into facts and dimensions. Facts are the measurements of an event such as a sale and are typically numbers. Dimensions are the categories that identify facts, such as date, location, and product. A fact table has a composite key made up of the primary keys of the dimension tables of the schema. Dimension tables act as lookup or reference tables that enable you to choose values that constrain your queries. Diagrams typically show a central fact table with lines joining it to the dimension tables, giving the appearance of a star. The following graphic shows sales as the fact table and products, times, customers, and channels as the dimension tables. 5-12 Chapter 5 Star Transformation Figure 5-1 Star Schema products times sales (amount_sold, quantity_sold) Fact Table customers channels Dimension Table Dimension Table A snowflake schema is a star schema in which the dimension tables reference other tables. A snowstorm schema is a combination of snowflake schemas. See Also: Oracle Database Data Warehousing Guide to learn more about star schemas 5.6.2 Purpose of Star Transformations In joins of fact and dimension tables, a star transformation can avoid a full scan of a fact table. The star transformation improves performance by fetching only relevant fact rows that join to the constraint dimension rows. In some cases, queries have restrictive filters on other columns of the dimension tables. The combination of filters can dramatically reduce the data set that the database processes from the fact table. 5.6.3 How Star Transformation Works Star transformation adds subquery predicates, called bitmap semijoin predicates, corresponding to the constraint dimensions. The optimizer performs the transformation when indexes exist on the fact join columns. By driving bitmap AND and OR operations of key values supplied by the subqueries, the database only needs to retrieve relevant rows from the fact table. If the predicates on the dimension tables filter out significant data, then the transformation can be more efficient than a full scan on the fact table. After the database has retrieved the relevant rows from the fact table, the database may need to join these rows back to the dimension tables using the original predicates. The database can eliminate the join back of the dimension table when the following conditions are met: • All the predicates on dimension tables are part of the semijoin subquery predicate. • The columns selected from the subquery are unique. • The dimension columns are not in the SELECT list, GROUP BY clause, and so on. 5-13 Chapter 5 Star Transformation 5.6.4 Controls for Star Transformation The STAR_TRANSFORMATION_ENABLED initialization parameter controls star transformations. This parameter takes the following values: • true The optimizer performs the star transformation by identifying the fact and constraint dimension tables automatically. The optimizer performs the star transformation only if the cost of the transformed plan is lower than the alternatives. Also, the optimizer attempts temporary table transformation automatically whenever materialization improves performance (see "Temporary Table Transformation: Scenario"). • false (default) The optimizer does not perform star transformations. • TEMP_DISABLE This value is identical to true except that the optimizer does not attempt temporary table transformation. See Also: Oracle Database Reference to learn about the STAR_TRANSFORMATION_ENABLED initialization parameter 5.6.5 Star Transformation: Scenario This scenario demonstrates a star transformation of a star query. Example 5-5 Star Query The following query finds the total Internet sales amount in all cities in California for quarters Q1 and Q2 of year 1999: SELECT c.cust_city, t.calendar_quarter_desc, SUM(s.amount_sold) sales_amount FROM sales s, times t, customers c, channels ch WHERE s.time_id = t.time_id AND s.cust_id = c.cust_id AND s.channel_id = ch.channel_id AND c.cust_state_province = 'CA' AND ch.channel_desc = 'Internet' AND t.calendar_quarter_desc IN ('1999-01','1999-02') GROUP BY c.cust_city, t.calendar_quarter_desc; Sample output is as follows: CUST_CITY CALENDA SALES_AMOUNT ------------------------------ ------- ------------ 5-14 Chapter 5 Star Transformation Montara Pala Cloverdale Cloverdale . . . 1999-02 1999-01 1999-01 1999-02 1618.01 3263.93 52.64 266.28 In this example, sales is the fact table, and the other tables are dimension tables. The sales table contains one row for every sale of a product, so it could conceivably contain billions of sales records. However, only a few products are sold to customers in California through the Internet for the specified quarters. Example 5-6 Star Transformation This example shows a star transformation of the query in Example 5-5. The transformation avoids a full table scan of sales. SELECT FROM WHERE AND AND AND AND c.cust_city, t.calendar_quarter_desc, SUM(s.amount_sold) sales_amount sales s, times t, customers c s.time_id = t.time_id s.cust_id = c.cust_id c.cust_state_province = 'CA' t.calendar_quarter_desc IN ('1999-01','1999-02') s.time_id IN ( SELECT time_id FROM times WHERE calendar_quarter_desc IN('1999-01','1999-02') ) AND s.cust_id IN ( SELECT cust_id FROM customers WHERE cust_state_province='CA' ) AND s.channel_id IN ( SELECT channel_id FROM channels WHERE channel_desc = 'Internet' ) GROUP BY c.cust_city, t.calendar_quarter_desc; Example 5-7 Partial Execution Plan for Star Transformation This example shows an edited version of the execution plan for the star transformation in Example 5-6. Line 26 shows that the sales table has an index access path instead of a full table scan. For each key value that results from the subqueries of channels (line 14), times (line 19), and customers (line 24), the database retrieves a bitmap from the indexes on the sales fact table (lines 15, 20, 25). Each bit in the bitmap corresponds to a row in the fact table. The bit is set when the key value from the subquery is same as the value in the row of the fact table. For example, in the bitmap 101000... (the ellipses indicates that the values for the remaining rows are 0), rows 1 and 3 of the fact table have matching key values from the subquery. The operations in lines 12, 17, and 22 iterate over the keys from the subqueries and retrieve the corresponding bitmaps. In Example 5-6, the customers subquery seeks the IDs of customers whose state or province is CA. Assume that the bitmap 101000... corresponds to the customer ID key value 103515 from the customers table subquery. Also assume that the customers subquery produces the key value 103516 with the bitmap 010000..., which means that only row 2 in sales has a matching key value from the subquery. The database merges (using the OR operator) the bitmaps for each subquery (lines 11, 16, 21). In our customers example, the database produces a single bitmap 111000... for the customers subquery after merging the two bitmaps: 5-15 Chapter 5 Star Transformation 101000... 010000... --------111000... # bitmap corresponding to key 103515 # bitmap corresponding to key 103516 # result of OR operation In line 10, the database applies the AND operator to the merged bitmaps. Assume that after the database has performed all OR operations, the resulting bitmap for channels is 100000... If the database performs an AND operation on this bitmap and the bitmap from customers subquery, then the result is as follows: 100000... 111000... --------100000... # channels bitmap after all OR operations performed # customers bitmap after all OR operations performed # bitmap result of AND operation for channels and customers In line 9, the database generates the corresponding rowids of the final bitmap. The database retrieves rows from the sales fact table using the rowids (line 26). In our example, the database generate only one rowid, which corresponds to the first row, and thus fetches only a single row instead of scanning the entire sales table. ------------------------------------------------------------------------------| Id | Operation | Name ------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 1 | HASH GROUP BY | |* 2 | HASH JOIN | |* 3 | TABLE ACCESS FULL | CUSTOMERS |* 4 | HASH JOIN | |* 5 | TABLE ACCESS FULL | TIMES | 6 | VIEW | VW_ST_B1772830 | 7 | NESTED LOOPS | | 8 | PARTITION RANGE SUBQUERY | | 9 | BITMAP CONVERSION TO ROWIDS| | 10 | BITMAP AND | | 11 | BITMAP MERGE | | 12 | BITMAP KEY ITERATION | | 13 | BUFFER SORT | |* 14 | TABLE ACCESS FULL | CHANNELS |* 15 | BITMAP INDEX RANGE SCAN| SALES_CHANNEL_BIX | 16 | BITMAP MERGE | | 17 | BITMAP KEY ITERATION | | 18 | BUFFER SORT | |* 19 | TABLE ACCESS FULL | TIMES |* 20 | BITMAP INDEX RANGE SCAN| SALES_TIME_BIX | 21 | BITMAP MERGE | | 22 | BITMAP KEY ITERATION | | 23 | BUFFER SORT | |* 24 | TABLE ACCESS FULL | CUSTOMERS |* 25 | BITMAP INDEX RANGE SCAN| SALES_CUST_BIX | 26 | TABLE ACCESS BY USER ROWID | SALES ------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------2 3 4 5 - access("ITEM_1"="C"."CUST_ID") filter("C"."CUST_STATE_PROVINCE"='CA') access("ITEM_2"="T"."TIME_ID") filter(("T"."CALENDAR_QUARTER_DESC"='1999-01' OR "T"."CALENDAR_QUARTER_DESC"='1999-02')) 14 - filter("CH"."CHANNEL_DESC"='Internet') 5-16 Chapter 5 Star Transformation 15 - access("S"."CHANNEL_ID"="CH"."CHANNEL_ID") 19 - filter(("T"."CALENDAR_QUARTER_DESC"='1999-01' OR "T"."CALENDAR_QUARTER_DESC"='1999-02')) 20 - access("S"."TIME_ID"="T"."TIME_ID") 24 - filter("C"."CUST_STATE_PROVINCE"='CA') 25 - access("S"."CUST_ID"="C"."CUST_ID") Note ----- star transformation used for this statement 5.6.6 Temporary Table Transformation: Scenario In the preceding scenario, the optimizer does not join back the table channels to the sales table because it is not referenced outside and the channel_id is unique. If the optimizer cannot eliminate the join back, however, then the database stores the subquery results in a temporary table to avoid rescanning the dimension table for bitmap key generation and join back. Also, if the query runs in parallel, then the database materializes the results so that each parallel execution server can select the results from the temporary table instead of executing the subquery again. Example 5-8 Star Transformation Using Temporary Table In this example, the database materializes the results of the subquery on customers into a temporary table: SELECT t1.c1 cust_city, t.calendar_quarter_desc calendar_quarter_desc, SUM(s.amount_sold) sales_amount FROM sales s, sh.times t, sys_temp_0fd9d6621_e7e24 t1 WHERE s.time_id=t.time_id AND s.cust_id=t1.c0 AND (t.calendar_quarter_desc='1999-q1' OR t.calendar_quarter_desc='1999-q2') AND s.cust_id IN ( SELECT t1.c0 FROM sys_temp_0fd9d6621_e7e24 t1 ) AND s.channel_id IN ( SELECT ch.channel_id FROM channels ch WHERE ch.channel_desc='internet' ) AND s.time_id IN ( SELECT t.time_id FROM times t WHERE t.calendar_quarter_desc='1999-q1' OR t.calendar_quarter_desc='1999-q2' ) GROUP BY t1.c1, t.calendar_quarter_desc The optimizer replaces customers with the temporary table sys_temp_0fd9d6621_e7e24, and replaces references to columns cust_id and cust_city with the corresponding columns of the temporary table. The database creates the temporary table with two columns: (c0 NUMBER, c1 VARCHAR2(30)). These columns correspond to cust_id and cust_city of the customers table. The database populates the temporary table by executing the following query at the beginning of the execution of the previous query: SELECT c.cust_id, c.cust_city FROM customers WHERE c.cust_state_province = 'CA' Example 5-9 Table Partial Execution Plan for Star Transformation Using Temporary The following example shows an edited version of the execution plan for the query in Example 5-8: 5-17 Chapter 5 Star Transformation ------------------------------------------------------------------------------| Id | Operation | Name ------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 1 | TEMP TABLE TRANSFORMATION | | 2 | LOAD AS SELECT | |* 3 | TABLE ACCESS FULL | CUSTOMERS | 4 | HASH GROUP BY | |* 5 | HASH JOIN | | 6 | TABLE ACCESS FULL | SYS_TEMP_0FD9D6613_C716F |* 7 | HASH JOIN | |* 8 | TABLE ACCESS FULL | TIMES | 9 | VIEW | VW_ST_A3F94988 | 10 | NESTED LOOPS | | 11 | PARTITION RANGE SUBQUERY | | 12 | BITMAP CONVERSION TO ROWIDS| | 13 | BITMAP AND | | 14 | BITMAP MERGE | | 15 | BITMAP KEY ITERATION | | 16 | BUFFER SORT | |* 17 | TABLE ACCESS FULL | CHANNELS |* 18 | BITMAP INDEX RANGE SCAN| SALES_CHANNEL_BIX | 19 | BITMAP MERGE | | 20 | BITMAP KEY ITERATION | | 21 | BUFFER SORT | |* 22 | TABLE ACCESS FULL | TIMES |* 23 | BITMAP INDEX RANGE SCAN| SALES_TIME_BIX | 24 | BITMAP MERGE | | 25 | BITMAP KEY ITERATION | | 26 | BUFFER SORT | | 27 | TABLE ACCESS FULL | SYS_TEMP_0FD9D6613_C716F |* 28 | BITMAP INDEX RANGE SCAN| SALES_CUST_BIX | 29 | TABLE ACCESS BY USER ROWID | SALES ------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------3 5 7 8 - 17 18 22 23 28 - filter("C"."CUST_STATE_PROVINCE"='CA') access("ITEM_1"="C0") access("ITEM_2"="T"."TIME_ID") filter(("T"."CALENDAR_QUARTER_DESC"='1999-01' OR "T"."CALENDAR_QUARTER_DESC"='1999-02')) filter("CH"."CHANNEL_DESC"='Internet') access("S"."CHANNEL_ID"="CH"."CHANNEL_ID") filter(("T"."CALENDAR_QUARTER_DESC"='1999-01' OR "T"."CALENDAR_QUARTER_DESC"='1999-02')) access("S"."TIME_ID"="T"."TIME_ID") access("S"."CUST_ID"="C0") Lines 1, 2, and 3 of the plan materialize the customers subquery into the temporary table. In line 6, the database scans the temporary table (instead of the subquery) to build the bitmap from the fact table. Line 27 scans the temporary table for joining back instead of scanning customers. The database does not need to apply the filter on customers on the temporary table because the filter is applied while materializing the temporary table. 5-18 Chapter 5 In-Memory Aggregation (VECTOR GROUP BY) 5.7 In-Memory Aggregation (VECTOR GROUP BY) The key optimization of in-memory aggregation is to aggregate while scanning. To optimize query blocks involving aggregation and joins from a single large table to multiple small tables, such as in a typical star query, the transformation uses KEY VECTOR and VECTOR GROUP BY operations. These operations use efficient in-memory arrays for joins and aggregation, and are especially effective when the underlying tables are in-memory columnar tables. See Also: Oracle Database In-Memory Guide to learn more about in-memory aggregation 5.8 Cursor-Duration Temporary Tables To materialize the intermediate results of a query, Oracle Database may implicitly create a cursor-duration temporary table in memory during query compilation. This section contains the following topics: • Purpose of Cursor-Duration Temporary Tables Complex queries sometimes process the same query block multiple times, which creates unnecessary performance overhead. • How Cursor-Duration Temporary Tables Work The definition of the cursor-definition temporary table resides in memory. The table definition is associated with the cursor, and is only visible to the session executing the cursor. • Cursor-Duration Temporary Tables: Example A WITH query that repeats the same subquery can sometimes benefit from a cursor-duration temporary table. 5.8.1 Purpose of Cursor-Duration Temporary Tables Complex queries sometimes process the same query block multiple times, which creates unnecessary performance overhead. To avoid this scenario, Oracle Database can automatically create temporary tables for the query results and store them in memory for the duration of the cursor. For complex operations such as WITH clause queries, star transformations, and grouping sets, this optimization enhances the materialization of intermediate results from repetitively used subqueries. In this way, cursor-duration temporary tables improve performance and optimize I/O. 5-19 Chapter 5 Cursor-Duration Temporary Tables 5.8.2 How Cursor-Duration Temporary Tables Work The definition of the cursor-definition temporary table resides in memory. The table definition is associated with the cursor, and is only visible to the session executing the cursor. When using cursor-duration temporary tables, the database performs the following steps: 1. Chooses a plan that uses a cursor-duration temporary table 2. Creates the temporary table using a unique name 3. Rewrites the query to refer to the temporary table 4. Loads data into memory until no memory remains, in which case it creates temporary segments on disk 5. Executes the query, returning data from the temporary table 6. Truncates the table, releasing memory and any on-disk temporary segments Note: The metadata for the cursor-duration temporary table stays in memory as long as the cursor is in memory. The metadata is not stored in the data dictionary, which means it is not visible through data dictionary views. You cannot drop the metadata explicitly. The preceding scenario depends on the availability of memory. For serial queries, the temporary tables use PGA memory. The implementation of cursor-duration temporary tables is similar to sorts. If no more memory is available, then the database writes data to temporary segments. For cursor-duration temporary tables, the differences are as follows: • The database releases memory and temporary segments at the end of the query rather than when the row source is no longer active. • Data in memory stays in memory, unlike in sorts where data can move between memory and temporary segments. When the database uses cursor-duration temporary tables, the keyword CURSOR DURATION MEMORY appears in the execution plan. 5.8.3 Cursor-Duration Temporary Tables: Example A WITH query that repeats the same subquery can sometimes benefit from a cursorduration temporary table. The following query uses a WITH clause to create three subquery blocks: WITH q1 AS (SELECT department_id, SUM(salary) sum_sal FROM hr.employees GROUP BY department_id), q2 AS (SELECT * FROM q1), 5-20 Chapter 5 Table Expansion q3 AS (SELECT department_id, sum_sal FROM q1) SELECT * FROM q1 UNION ALL SELECT * FROM q2 UNION ALL SELECT * FROM q3; The following sample plan shows the transformation: SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'BASIC +ROWS +COST')); PLAN_TABLE_OUTPUT -------------------------------------------------------------------------------------------| Id | Operation | Name |Rows |Cost (%CPU)| -------------------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | |6 (100)| | 1 | TEMP TABLE TRANSFORMATION | | | | | 2 | LOAD AS SELECT (CURSOR DURATION MEMORY) | SYS_TEMP_0FD9D6606_1AE004 | | | | 3 | HASH GROUP BY | | 11 | 3 (34)| | 4 | TABLE ACCESS FULL | EMPLOYEES | 107 | 2 (0) | | 5 | UNION-ALL | | | | | 6 | VIEW | | 11 | 2 (0) | | 7 | TABLE ACCESS FULL | SYS_TEMP_0FD9D6606_1AE004 | 11 | 2 (0) | | 8 | VIEW | | 11 | 2 (0) | | 9 | TABLE ACCESS FULL | SYS_TEMP_0FD9D6606_1AE004 | 11 | 2 (0) | | 10 | VIEW | | 11 | 2 (0) | | 11 | TABLE ACCESS FULL | SYS_TEMP_0FD9D6606_1AE004 | 11 | 2 (0) | -------------------------------------------------------------------------------------------- In the preceding plan, TEMP TABLE TRANSFORMATION in Step 1 indicates that the database used cursor-duration temporary tables to execute the query. The CURSOR DURATION MEMORY keyword in Step 2 indicates that the database used memory, if available, to store the results of SYS_TEMP_0FD9D6606_1AE004. If memory was unavailable, then the database wrote the temporary data to disk. 5.9 Table Expansion In table expansion, the optimizer generates a plan that uses indexes on the readmostly portion of a partitioned table, but not on the active portion of the table. This section contains the following topics: • Purpose of Table Expansion Index-based plans can improve performance, but index maintenance creates overhead. In many databases, DML affects only a small portion of the data. • How Table Expansion Works Table partitioning makes table expansion possible. • Table Expansion: Scenario The optimizer keeps track of which partitions must be accessed from each table, based on predicates that appear in the query. Partition pruning enables the optimizer to use table expansion to generate more optimal plans. • Table Expansion and Star Transformation: Scenario Star transformation enables specific types of queries to avoid accessing large portions of big fact tables. 5-21 Chapter 5 Table Expansion 5.9.1 Purpose of Table Expansion Index-based plans can improve performance, but index maintenance creates overhead. In many databases, DML affects only a small portion of the data. Table expansion uses index-based plans for high-update tables. You can create an index only on the read-mostly data, eliminating index overhead on the active data. In this way, table expansion improves performance while avoiding index maintenance. 5.9.2 How Table Expansion Works Table partitioning makes table expansion possible. If a local index exists on a partitioned table, then the optimizer can mark the index as unusable for specific partitions. In effect, some partitions are not indexed. In table expansion, the optimizer transforms the query into a UNION ALL statement, with some subqueries accessing indexed partitions and other subqueries accessing unindexed partitions. The optimizer can choose the most efficient access method available for a partition, regardless of whether it exists for all of the partitions accessed in the query. The optimizer does not always choose table expansion: • Table expansion is cost-based. While the database accesses each partition of the expanded table only once across all branches of the UNION ALL, any tables that the database joins to it are accessed in each branch. • Semantic issues may render expansion invalid. For example, a table appearing on the right side of an outer join is not valid for table expansion. You can control table expansion with the hint EXPAND_TABLE hint. The hint overrides the cost-based decision, but not the semantic checks. See Also: • "Influencing the Optimizer with Hints" • Oracle Database SQL Language Reference to learn more about SQL hints 5.9.3 Table Expansion: Scenario The optimizer keeps track of which partitions must be accessed from each table, based on predicates that appear in the query. Partition pruning enables the optimizer to use table expansion to generate more optimal plans. Assumptions This scenario assumes the following: 5-22 Chapter 5 Table Expansion • You want to run a star query against the sh.sales table, which is range-partitioned on the time_id column. • You want to disable indexes on specific partitions to see the benefits of table expansion. To use table expansion: 1. Log in to the database as the sh user. 2. Run the following query: SELECT FROM WHERE AND 3. * sales time_id >= TO_DATE('2000-01-01 00:00:00', 'SYYYY-MM-DD HH24:MI:SS') prod_id = 38; Explain the plan by querying DBMS_XPLAN: SET LINESIZE 150 SET PAGESIZE 0 SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(format => 'BASIC,PARTITION')); As shown in the Pstart and Pstop columns in the following plan, the optimizer determines from the filter that only 16 of the 28 partitions in the table must be accessed: Plan hash value: 3087065703 -------------------------------------------------------------------------|Id| Operation | Name |Pstart|Pstop| -------------------------------------------------------------------------| 0| SELECT STATEMENT | | | | | 1| PARTITION RANGE ITERATOR | | 13 | 28 | | 2| TABLE ACCESS BY LOCAL INDEX ROWID BATCHED| SALES | 13 | 28 | | 3| BITMAP CONVERSION TO ROWIDS | | | | |*4| BITMAP INDEX SINGLE VALUE |SALES_PROD_BIX| 13 | 28 | -------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------4 - access("PROD_ID"=38) After the optimizer has determined the partitions to be accessed, it considers any index that is usable on all of those partitions. In the preceding plan, the optimizer chose to use the sales_prod_bix bitmap index. 4. Disable the index on the SALES_1995 partition of the sales table: ALTER INDEX sales_prod_bix MODIFY PARTITION sales_1995 UNUSABLE; The preceding DDL disables the index on partition 1, which contains all sales from before 1996. Note: You can obtain the partition information by querying the USER_IND_PARTITIONS view. 5-23 Chapter 5 Table Expansion 5. Execute the query of sales again, and then query DBMS_XPLAN to obtain the plan. The output shows that the plan did not change: Plan hash value: 3087065703 --------------------------------------------------------------------------|Id| Operation | Name |Pstart|Pstop --------------------------------------------------------------------------| 0| SELECT STATEMENT | | | | | 1| PARTITION RANGE ITERATOR | | 13 | 28 | | 2| TABLE ACCESS BY LOCAL INDEX ROWID BATCHED| SALES | 13 | 28 | | 3| BITMAP CONVERSION TO ROWIDS | | | | |*4| BITMAP INDEX SINGLE VALUE | SALES_PROD_BIX| 13 | 28 | --------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------4 - access("PROD_ID"=38) The plan is the same because the disabled index partition is not relevant to the query. If all partitions that the query accesses are indexed, then the database can answer the query using the index. Because the query only accesses partitions 16 through 28, disabling the index on partition 1 does not affect the plan. 6. Disable the indexes for partition 28 (SALES_Q4_2003), which is a partition that the query needs to access: ALTER INDEX sales_prod_bix MODIFY PARTITION sales_q4_2003 UNUSABLE; ALTER INDEX sales_time_bix MODIFY PARTITION sales_q4_2003 UNUSABLE; By disabling the indexes on a partition that the query does need to access, the query can no longer use this index (without table expansion). 7. Query the plan using DBMS_XPLAN. As shown in the following plan, the optimizer does not use the index: Plan hash value: 3087065703 --------------------------------------------------------------------------| Id| Operation | Name |Pstart|Pstop --------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | | | 1 | PARTITION RANGE ITERATOR | | 13 | 28 | |*2 | TABLE ACCESS FULL | SALES | 13 | 28 | --------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------2 - access("PROD_ID"=38) In the preceding example, the query accesses 16 partitions. On 15 of these partitions, an index is available, but no index is available for the final partition. Because the optimizer has to choose one access path or the other, the optimizer cannot use the index on any of the partitions. 8. With table expansion, the optimizer rewrites the original query as follows: SELECT * FROM sales WHERE time_id >= TO_DATE('2000-01-01 00:00:00', 'SYYYY-MM-DD HH24:MI:SS') 5-24 Chapter 5 Table Expansion AND time_id AND prod_id UNION ALL SELECT * FROM sales WHERE time_id AND time_id AND prod_id < TO_DATE('2003-10-01 00:00:00', 'SYYYY-MM-DD HH24:MI:SS') = 38 >= TO_DATE('2003-10-01 00:00:00', 'SYYYY-MM-DD HH24:MI:SS') < TO_DATE('2004-01-01 00:00:00', 'SYYYY-MM-DD HH24:MI:SS') = 38; In the preceding query, the first query block in the UNION ALL accesses the partitions that are indexed, while the second query block accesses the partition that is not. The two subqueries enable the optimizer to choose to use the index in the first query block, if it is more optimal than using a table scan of all of the partitions that are accessed. 9. Query the plan using DBMS_XPLAN. The plan appears as follows: Plan hash value: 2120767686 --------------------------------------------------------------------------|Id| Operation | Name |Pstart|Pstop| --------------------------------------------------------------------------| 0| SELECT STATEMENT | | | | | 1| VIEW | VW_TE_2 | | | | 2| UNION-ALL | | | | | 3| PARTITION RANGE ITERATOR | | 13| 27| | 4| TABLE ACCESS BY LOCAL INDEX ROWID BATCHED| SALES | 13| 27| | 5| BITMAP CONVERSION TO ROWIDS | | | | |*6| BITMAP INDEX SINGLE VALUE | SALES_PROD_BIX| 13| 27| | 7| PARTITION RANGE SINGLE | | 28| 28| |*8| TABLE ACCESS FULL | SALES | 28| 28| --------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------6 - access("PROD_ID"=38) 8 - filter("PROD_ID"=38) As shown in the preceding plan, the optimizer uses a UNION ALL for two query blocks (Step 2). The optimizer chooses an index to access partitions 13 to 27 in the first query block (Step 6). Because no index is available for partition 28, the optimizer chooses a full table scan in the second query block (Step 8). 5.9.4 Table Expansion and Star Transformation: Scenario Star transformation enables specific types of queries to avoid accessing large portions of big fact tables. Star transformation requires defining several indexes, which in an actively updated table can have overhead. With table expansion, you can define indexes on only the inactive partitions so that the optimizer can consider star transformation on only the indexed portions of the table. Assumptions This scenario assumes the following: 5-25 Chapter 5 Table Expansion • You query the same schema used in "Star Transformation: Scenario". • The last partition of sales is actively being updated, as is often the case with timepartitioned tables. • You want the optimizer to take advantage of table expansion. To take advantage of table expansion in a star query: 1. Disable the indexes on the last partition as follows: ALTER INDEX sales_channel_bix MODIFY PARTITION sales_q4_2003 UNUSABLE; ALTER INDEX sales_cust_bix MODIFY PARTITION sales_q4_2003 UNUSABLE; 2. Execute the following star query: SELECT t.calendar_quarter_desc, SUM(s.amount_sold) sales_amount FROM sales s, times t, customers c, channels ch WHERE s.time_id = t.time_id AND s.cust_id = c.cust_id AND s.channel_id = ch.channel_id AND c.cust_state_province = 'CA' AND ch.channel_desc = 'Internet' AND t.calendar_quarter_desc IN ('1999-01','1999-02') GROUP BY t.calendar_quarter_desc; 3. Query the cursor using DBMS_XPLAN, which shows the following plan: --------------------------------------------------------------------------|Id| Operation | Name | Pstart| Pstop | --------------------------------------------------------------------------| 0| SELECT STATEMENT | | | | | 1| HASH GROUP BY | | | | | 2| VIEW |VW_TE_14 | | | | 3| UNION-ALL | | | | | 4| HASH JOIN | | | | | 5| TABLE ACCESS FULL |TIMES | | | | 6| VIEW |VW_ST_1319B6D8 | | | | 7| NESTED LOOPS | | | | | 8| PARTITION RANGE SUBQUERY | |KEY(SQ)|KEY(SQ)| | 9| BITMAP CONVERSION TO ROWIDS| | | | |10| BITMAP AND | | | | |11| BITMAP MERGE | | | | |12| BITMAP KEY ITERATION | | | | |13| BUFFER SORT | | | | |14| TABLE ACCESS FULL |CHANNELS | | | |15| BITMAP INDEX RANGE SCAN|SALES_CHANNEL_BIX|KEY(SQ)|KEY(SQ)| |16| BITMAP MERGE | | | | |17| BITMAP KEY ITERATION | | | | |18| BUFFER SORT | | | | |19| TABLE ACCESS FULL |TIMES | | | |20| BITMAP INDEX RANGE SCAN|SALES_TIME_BIX |KEY(SQ)|KEY(SQ)| |21| BITMAP MERGE | | | | |22| BITMAP KEY ITERATION | | | | |23| BUFFER SORT | | | | |24| TABLE ACCESS FULL |CUSTOMERS | | | |25| BITMAP INDEX RANGE SCAN|SALES_CUST_BIX |KEY(SQ)|KEY(SQ)| |26| TABLE ACCESS BY USER ROWID |SALES | ROWID | ROWID | |27| NESTED LOOPS | | | | |28| NESTED LOOPS | | | | |29| NESTED LOOPS | | | | |30| NESTED LOOPS | | | | |31| PARTITION RANGE SINGLE | | 28 | 28 | 5-26 Chapter 5 Join Factorization |32| TABLE ACCESS FULL |SALES | 28 | 28 | |33| TABLE ACCESS BY INDEX ROWID|CHANNELS | | | |34| INDEX UNIQUE SCAN |CHANNELS_PK | | | |35| TABLE ACCESS BY INDEX ROWID |CUSTOMERS | | | |36| INDEX UNIQUE SCAN |CUSTOMERS_PK | | | |37| INDEX UNIQUE SCAN |TIMES_PK | | | |38| TABLE ACCESS BY INDEX ROWID |TIMES | | | --------------------------------------------------------------------------- The preceding plan uses table expansion. The UNION ALL branch that is accessing every partition except the last partition uses star transformation. Because the indexes on partition 28 are disabled, the database accesses the final partition using a full table scan. Related Topics • Star Transformation Star transformation is an optimizer transformation that avoids full table scans of fact tables in a star schema. 5.10 Join Factorization In the cost-based transformation known as join factorization, the optimizer can factorize common computations from branches of a UNION ALL query. This section contains the following topics: • Purpose of Join Factorization UNION ALL queries are common in database applications, especially in data integration applications. • How Join Factorization Works Join factorization can factorize multiple tables and from more than two UNION ALL branches. • Factorization and Join Orders: Scenario Join factorization can create more possibilities for join orders • Factorization of Outer Joins: Scenario The database supports join factorization of outer joins, antijoins, and semijoins, but only for the right tables in such joins. 5.10.1 Purpose of Join Factorization UNION ALL queries are common in database applications, especially in data integration applications. Often, branches in a UNION ALL query refer to the same base tables. Without join factorization, the optimizer evaluates each branch of a UNION ALL query independently, which leads to repetitive processing, including data access and joins. Join factorization transformation can share common computations across the UNION ALL branches. Avoiding an extra scan of a large base table can lead to a huge performance improvement. 5.10.2 How Join Factorization Works Join factorization can factorize multiple tables and from more than two UNION ALL branches. 5-27 Chapter 5 Join Factorization Join factorization is best explained through examples. Example 5-10 UNION ALL Query The following query shows a query of four tables (t1, t2, t3, and t4) and two UNION ALL branches: SELECT t1.c1, t2.c2 FROM t1, t2, t3 WHERE t1.c1 = t2.c1 AND t1.c1 > 1 AND t2.c2 = 2 AND t2.c2 = t3.c2 UNION ALL SELECT t1.c1, t2.c2 t1, t2, t4 FROM WHERE t1.c1 = t2.c1 t1.c1 > 1 AND AND t2.c3 = t4.c3 In the preceding query, table t1 appears in both UNION ALL branches, as does the filter predicate t1.c1 > 1 and the join predicate t1.c1 = t2.c1. Without any transformation, the database must perform the scan and the filtering on table t1 twice, one time for each branch. Example 5-11 Factorized Query Example 5-10 SELECT t1.c1, VW_JF_1.item_2 FROM t1, (SELECT t2.c1 item_1, t2.c2 item_2 FROM t2, t3 WHERE t2.c2 = t3.c2 AND t2.c2 = 2 UNION ALL SELECT t2.c1 item_1, t2.c2 item_2 FROM t2, t4 WHERE t2.c3 = t4.c3) VW_JF_1 WHERE t1.c1 = VW_JF_1.item_1 AND t1.c1 > 1 In this case, because table t1 is factorized, the database performs the table scan and the filtering on t1 only one time. If t1 is large, then this factorization avoids the huge performance cost of scanning and filtering t1 twice. Note: If the branches in a UNION ALL query have clauses that use the DISTINCT function, then join factorization is not valid. 5.10.3 Factorization and Join Orders: Scenario Join factorization can create more possibilities for join orders Example 5-12 Query Involving Five Tables In the following query, view V is same as the query as in Example 5-10: 5-28 Chapter 5 Join Factorization SELECT * FROM t5, (SELECT t1.c1, t2.c2 FROM t1, t2, t3 WHERE t1.c1 = t2.c1 AND t1.c1 > 1 AND t2.c2 = 2 AND t2.c2 = t3.c2 UNION ALL SELECT t1.c1, t2.c2 FROM t1, t2, t4 WHERE t1.c1 = t2.c1 AND t1.c1 > 1 AND t2.c3 = t4.c3) V WHERE t5.c1 = V.c1 t1t2t3t5 Example 5-13 Factorization of t1 from View V If join factorization factorizes t1 from view V, as shown in the following query, then the database can join t1 with t5.: SELECT * t5, ( SELECT t1.c1, VW_JF_1.item_2 FROM FROM t1, (SELECT t2.c1 item_1, t2.c2 item_2 FROM t2, t3 WHERE t2.c2 = t3.c2 AND t2.c2 = 2 UNION ALL SELECT t2.c1 item_1, t2.c2 item_2 FROM t2, t4 WHERE t2.c3 = t4.c3) VW_JF_1 WHERE t1.c1 = VW_JF_1.item_1 AND t1.c1 > 1 ) WHERE t5.c1 = V.c1 The preceding query transformation opens up new join orders. However, join factorization imposes specific join orders. For example, in the preceding query, tables t2 and t3 appear in the first branch of the UNION ALL query in view VW_JF_1. The database must join t2 with t3 before it can join with t1, which is not defined within the VW_JF_1 view. The imposed join order may not necessarily be the best join order. For this reason, the optimizer performs join factorization using the cost-based transformation framework. The optimizer calculates the cost of the plans with and without join factorization, and then chooses the cheapest plan. Example 5-14 Factorization of t1 from View V with View Definition Removed The following query is the same query in Example 5-13, but with the view definition removed so that the factorization is easier to see: SELECT * FROM t5, (SELECT t1.c1, VW_JF_1.item_2 FROM t1, VW_JF_1 WHERE t1.c1 = VW_JF_1.item_1 AND t1.c1 > 1) WHERE t5.c1 = V.c1 5-29 Chapter 5 Join Factorization 5.10.4 Factorization of Outer Joins: Scenario The database supports join factorization of outer joins, antijoins, and semijoins, but only for the right tables in such joins. For example, join factorization can transform the following UNION ALL query by factorizing t2: SELECT t1.c2, t2.c2 FROM t1, t2 WHERE t1.c1 = t2.c1(+) AND t1.c1 = 1 UNION ALL SELECT t1.c2, t2.c2 FROM t1, t2 WHERE t1.c1 = t2.c1(+) AND t1.c1 = 2 The following example shows the transformation. Table t2 now no longer appears in the UNION ALL branches of the subquery. SELECT VW_JF_1.item_2, t2.c2 FROM t2, (SELECT t1.c1 item_1, t1.c2 item_2 FROM t1 WHERE t1.c1 = 1 UNION ALL SELECT t1.c1 item_1, t1.c2 item_2 FROM t1 WHERE t1.c1 = 2) VW_JF_1 WHERE VW_JF_1.item_1 = t2.c1(+) 5-30 Part III Query Execution Plans If a query has suboptimal performance, the execution plan is the key tool for understanding the problem and supplying a solution. This part contains the following chapters: • Generating and Displaying Execution Plans A thorough understanding of execution plans is essential to SQL tuning. • Reading Execution Plans Execution plans are represented as a tree of operations. 6 Generating and Displaying Execution Plans A thorough understanding of execution plans is essential to SQL tuning. This chapter contains the following topics: • Introduction to Execution Plans The combination of the steps that Oracle Database uses to execute a statement is an execution plan. • About Plan Generation and Display The EXPLAIN PLAN statement displays execution plans that the optimizer chooses for SELECT, UPDATE, INSERT, and DELETE statements. • Generating Execution Plans The EXPLAIN PLAN statement enables you to examine the execution plan that the optimizer chose for a SQL statement. • Displaying PLAN_TABLE Output You can use scripts or a package to display the plan output. 6.1 Introduction to Execution Plans The combination of the steps that Oracle Database uses to execute a statement is an execution plan. Each step either retrieves rows of data physically from the database or prepares them for the user issuing the statement. An execution plan includes an access path for each table that the statement accesses and an ordering of the tables (the join order) with the appropriate join method. Related Topics • Joins Oracle Database provides several optimizations for joining row sets. 6.2 About Plan Generation and Display The EXPLAIN PLAN statement displays execution plans that the optimizer chooses for SELECT, UPDATE, INSERT, and DELETE statements. This section contains the following topics: • About the Plan Explanation A statement execution plan is the sequence of operations that the database performs to run the statement. • Why Execution Plans Change Execution plans can and do change as the underlying optimizer inputs change. • Guideline for Minimizing Throw-Away Examining an explain plan enables you to look for rows that are thrown-away. 6-1 Chapter 6 About Plan Generation and Display • Guidelines for Evaluating Execution Plans Using EXPLAIN PLAN The execution plan operation alone cannot differentiate between well-tuned statements and those that perform suboptimally. • Guidelines for Evaluating Plans Using the V$SQL_PLAN Views As an alternative to running the EXPLAIN PLAN command and displaying the plan, you can display the plan by querying the V$SQL_PLAN view. • EXPLAIN PLAN Restrictions Oracle Database does not support EXPLAIN PLAN for statements performing implicit type conversion of date bind variables. • Guidelines for Creating PLAN_TABLE The PLAN_TABLE is automatically created as a public synonym to a global temporary table. 6.2.1 About the Plan Explanation A statement execution plan is the sequence of operations that the database performs to run the statement. The row source tree is the core of the execution plan. The tree shows the following information: • An ordering of the tables referenced by the statement • An access method for each table mentioned in the statement • A join method for tables affected by join operations in the statement • Data operations like filter, sort, or aggregation In addition to the row source tree, the plan table contains information about the following: • Optimization, such as the cost and cardinality of each operation • Partitioning, such as the set of accessed partitions • Parallel execution, such as the distribution method of join inputs You can use the EXPLAIN PLAN results to determine whether the optimizer chose a particular execution plan, such as a nested loops join. The results also help you to understand the optimizer decisions, such as why the optimizer chose a nested loops join instead of a hash join. See Also: • "SQL Row Source Generation" • Oracle Database SQL Language Reference to learn about the EXPLAIN PLAN statement 6.2.2 Why Execution Plans Change Execution plans can and do change as the underlying optimizer inputs change. 6-2 Chapter 6 About Plan Generation and Display EXPLAIN PLAN output shows how the database would run the SQL statement when the statement was explained. This plan can differ from the actual execution plan a SQL statement uses because of differences in the execution environment and explain plan environment. Note: To avoid possible SQL performance regression that may result from execution plan changes, consider using SQL plan management. This section contains the following topics: • Different Schemas Schemas can differ for various reasons. • Different Costs Even if the schemas are the same, the optimizer can choose different execution plans when the costs are different. See Also: • "Overview of SQL Plan Management" • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SPM package 6.2.2.1 Different Schemas Schemas can differ for various reasons. Principal reasons include the following: • The execution and explain plan occur on different databases. • The user explaining the statement is different from the user running the statement. Two users might be pointing to different objects in the same database, resulting in different execution plans. • Schema changes (often changes in indexes) between the two operations. 6.2.2.2 Different Costs Even if the schemas are the same, the optimizer can choose different execution plans when the costs are different. Some factors that affect the costs include the following: • Data volume and statistics • Bind variable types and values • Initialization parameters set globally or at session level 6-3 Chapter 6 About Plan Generation and Display 6.2.3 Guideline for Minimizing Throw-Away Examining an explain plan enables you to look for rows that are thrown-away. The database often throws away rows in the following situations: • Full scans • Unselective range scans • Late predicate filters • Wrong join order • Late filter operations In the plan shown in Example 6-1, the last step is a very unselective range scan that is executed 76,563 times, accesses 11,432,983 rows, throws away 99% of them, and retains 76,563 rows. Why access 11,432,983 rows to realize that only 76,563 rows are needed? Example 6-1 Looking for Thrown-Away Rows in an Explain Plan Rows Execution Plan -------- ---------------------------------------------------12 SORT AGGREGATE 2 SORT GROUP BY 76563 NESTED LOOPS 76575 NESTED LOOPS 19 TABLE ACCESS FULL CN_PAYRUNS_ALL 76570 TABLE ACCESS BY INDEX ROWID CN_POSTING_DETAILS_ALL 76570 INDEX RANGE SCAN (object id 178321) 76563 TABLE ACCESS BY INDEX ROWID CN_PAYMENT_WORKSHEETS_ALL 11432983 INDEX RANGE SCAN (object id 186024) 6.2.4 Guidelines for Evaluating Execution Plans Using EXPLAIN PLAN The execution plan operation alone cannot differentiate between well-tuned statements and those that perform suboptimally. For example, an EXPLAIN PLAN output that shows that a statement uses an index does not necessarily mean that the statement runs efficiently. Sometimes indexes are extremely inefficient. In this case, a good practice is to examine the following: • The columns of the index being used • Their selectivity (fraction of table being accessed) It is best to use EXPLAIN PLAN to determine an access plan, and then later prove that it is the optimal plan through testing. When evaluating a plan, examine the statement's actual resource consumption. This section contains the following topics: 6-4 Chapter 6 About Plan Generation and Display 6.2.5 Guidelines for Evaluating Plans Using the V$SQL_PLAN Views As an alternative to running the EXPLAIN PLAN command and displaying the plan, you can display the plan by querying the V$SQL_PLAN view. V$SQL_PLAN contains the execution plan for every statement stored in the shared SQL area. Its definition is similar to PLAN_TABLE. The advantage of V$SQL_PLAN over EXPLAIN PLAN is that you do not need to know the compilation environment that was used to execute a particular statement. For EXPLAIN PLAN, you would need to set up an identical environment to get the same plan when executing the statement. The V$SQL_PLAN_STATISTICS view provides the actual execution statistics for every operation in the plan, such as the number of output rows and elapsed time. All statistics, except the number of output rows, are cumulative. For example, the statistics for a join operation also includes the statistics for its two inputs. The statistics in V$SQL_PLAN_STATISTICS are available for cursors that have been compiled with the STATISTICS_LEVEL initialization parameter set to ALL. The V$SQL_PLAN_STATISTICS_ALL view enables side by side comparisons of the estimates that the optimizer provides for the number of rows and elapsed time. This view combines both V$SQL_PLAN and V$SQL_PLAN_STATISTICS information for every cursor. See Also: • • "PLAN_TABLE Columns" "Monitoring Database Operations " for information about the V$SQL_PLAN_MONITOR view • Oracle Database Reference for more information about V$SQL_PLAN views • Oracle Database Reference for information about the STATISTICS_LEVEL initialization parameter 6.2.6 EXPLAIN PLAN Restrictions Oracle Database does not support EXPLAIN PLAN for statements performing implicit type conversion of date bind variables. With bind variables in general, the EXPLAIN PLAN output might not represent the real execution plan. From the text of a SQL statement, TKPROF cannot determine the types of the bind variables. It assumes that the type is VARCHAR, and gives an error message otherwise. You can avoid this limitation by putting appropriate type conversions in the SQL statement. 6-5 Chapter 6 Generating Execution Plans See Also: • "Performing Application Tracing " • "Guideline for Avoiding the Argument Trap" • Oracle Database SQL Language Reference to learn more about SQL data types 6.2.7 Guidelines for Creating PLAN_TABLE The PLAN_TABLE is automatically created as a public synonym to a global temporary table. This temporary table holds the output of EXPLAIN PLAN statements for all users. PLAN_TABLE is the default sample output table into which the EXPLAIN PLAN statement inserts rows describing execution plans. While a PLAN_TABLE table is automatically set up for each user, you can use the SQL script catplan.sql to manually create the global temporary table and the PLAN_TABLE synonym. The name and location of this script depends on your operating system. On UNIX and Linux, the script is located in the $ORACLE_HOME/rdbms/admin directory. For example, start a SQL*Plus session, connect with SYSDBA privileges, and run the script as follows: @$ORACLE_HOME/rdbms/admin/catplan.sql Oracle recommends that you drop and rebuild your local PLAN_TABLE table after upgrading the version of the database because the columns might change. This can cause scripts to fail or cause TKPROF to fail, if you are specifying the table. If you do not want to use the name PLAN_TABLE, create a new synonym after running the catplan.sql script. For example: CREATE OR REPLACE PUBLIC SYNONYM my_plan_table for plan_table$ See Also: • "PLAN_TABLE Columns" for a description of the columns in the table • Oracle Database SQL Language Reference to learn about CREATE SYNONYM 6.3 Generating Execution Plans The EXPLAIN PLAN statement enables you to examine the execution plan that the optimizer chose for a SQL statement. When the statement is issued, the optimizer chooses an execution plan and then inserts data describing the plan into a database table. Issue the EXPLAIN PLAN statement and then query the output table. 6-6 Chapter 6 Generating Execution Plans This section contains the following topics: • Executing EXPLAIN PLAN for a Single Statement Explain the plan using database-supplied scripts. • Executing EXPLAIN PLAN Using a Statement ID With multiple statements, you can specify a statement identifier and use that to identify your specific execution plan. • Directing EXPLAIN PLAN Output to a Nondefault Table You can specify the INTO clause to specify a different table. 6.3.1 Executing EXPLAIN PLAN for a Single Statement Explain the plan using database-supplied scripts. The basics of using the EXPLAIN PLAN statement are as follows: • Use the SQL script catplan.sql to create a sample output table called PLAN_TABLE in your schema. • Include the EXPLAIN PLAN FOR clause before the SQL statement. • After issuing the EXPLAIN PLAN statement, use a script or package provided by Oracle Database to display the most recent plan table output. • The execution order in EXPLAIN PLAN output begins with the line that is the furthest indented to the right. The next step is the parent of that line. If two lines are indented equally, then the top line is normally executed first. Note: – The EXPLAIN PLAN output tables in this chapter were displayed with the utlxpls.sql script. – The steps in the EXPLAIN PLAN output in this chapter may be different on your database. The optimizer may choose different execution plans, depending on database configurations. To explain a SQL statement, use the EXPLAIN PLAN FOR clause immediately before the statement. For example: EXPLAIN PLAN FOR SELECT last_name FROM employees; The preceding plan explains the plan and stores the output in the PLAN_TABLE table. You can then select the execution plan from PLAN_TABLE. 6-7 Chapter 6 Displaying PLAN_TABLE Output See Also: • "Guidelines for Creating PLAN_TABLE" • "Displaying PLAN_TABLE Output" • Oracle Database SQL Language Reference for the syntax and semantics of EXPLAIN PLAN 6.3.2 Executing EXPLAIN PLAN Using a Statement ID With multiple statements, you can specify a statement identifier and use that to identify your specific execution plan. Before using SET STATEMENT ID, remove any existing rows for that statement ID. In the following example, st1 is specified as the statement identifier. Example 6-2 Using EXPLAIN PLAN with the STATEMENT ID Clause EXPLAIN PLAN SET STATEMENT_ID = 'st1' FOR SELECT last_name FROM employees; 6.3.3 Directing EXPLAIN PLAN Output to a Nondefault Table You can specify the INTO clause to specify a different table. The following statement directs output to my_plan_table: EXPLAIN PLAN INTO my_plan_table FOR SELECT last_name FROM employees; You can specify a statement ID when using the INTO clause, as in the following statement: EXPLAIN PLAN SET STATEMENT_ID = 'st1' INTO my_plan_table FOR SELECT last_name FROM employees; See Also: Oracle Database SQL Language Reference for a complete description of EXPLAIN PLAN syntax. 6.4 Displaying PLAN_TABLE Output You can use scripts or a package to display the plan output. After you have explained the plan, use the following SQL scripts or PL/SQL package provided by Oracle Database to display the most recent plan table output: 6-8 Chapter 6 Displaying PLAN_TABLE Output • utlxpls.sql This script displays the plan table output for serial processing. Example 6-4 is an example of the plan table output when using the utlxpls.sql script. • utlxplp.sql This script displays the plan table output including parallel execution columns. • DBMS_XPLAN.DISPLAY table function This function accepts options for displaying the plan table output. You can specify: – A plan table name if you are using a table different than PLAN_TABLE – A statement ID if you have set a statement ID with the EXPLAIN PLAN – A format option that determines the level of detail: BASIC, SERIAL, TYPICAL, and ALL Examples of using DBMS_XPLAN to display PLAN_TABLE output are: SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY()); SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY('MY_PLAN_TABLE', 'st1','TYPICAL')); This section contains the following topics: See Also: Oracle Database PL/SQL Packages and Types Reference for more information about the DBMS_XPLAN package • Displaying an Execution Plan: Example This example uses EXPLAIN PLAN to examine a SQL statement that selects the employee_id, job_title, salary, and department_name for the employees whose IDs are less than 103. • Customizing PLAN_TABLE Output If you have specified a statement identifier, then you can write your own script to query the PLAN_TABLE. 6.4.1 Displaying an Execution Plan: Example This example uses EXPLAIN PLAN to examine a SQL statement that selects the employee_id, job_title, salary, and department_name for the employees whose IDs are less than 103. Example 6-3 Using EXPLAIN PLAN EXPLAIN PLAN FOR SELECT e.employee_id, j.job_title, e.salary, d.department_name FROM employees e, jobs j, departments d WHERE e.employee_id < 103 AND e.job_id = j.job_id AND e.department_id = d.department_id; 6-9 Chapter 6 Displaying PLAN_TABLE Output Example 6-4 EXPLAIN PLAN Output The following output table shows the execution plan that the optimizer chose to execute the SQL statement in Example 6-3: ----------------------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| ----------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 3 | 189 | 10 (10)| | 1 | NESTED LOOPS | | 3 | 189 | 10 (10)| | 2 | NESTED LOOPS | | 3 | 141 | 7 (15)| |* 3 | TABLE ACCESS FULL | EMPLOYEES | 3 | 60 | 4 (25)| | 4 | TABLE ACCESS BY INDEX ROWID| JOBS | 19 | 513 | 2 (50)| |* 5 | INDEX UNIQUE SCAN | JOB_ID_PK | 1 | | | | 6 | TABLE ACCESS BY INDEX ROWID | DEPARTMENTS | 27 | 432 | 2 (50)| |* 7 | INDEX UNIQUE SCAN | DEPT_ID_PK | 1 | | | ----------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------3 - filter("E"."EMPLOYEE_ID"<103) 5 - access("E"."JOB_ID"="J"."JOB_ID") 7 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID" -----------------------------------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | -----------------------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 3 | 189 | 8 (13)| 00:00:01 | | 1 | NESTED LOOPS | | | | | | | 2 | NESTED LOOPS | | 3 | 189 | 8 (13)| 00:00:01 | | 3 | MERGE JOIN | | 3 | 141 | 5 (20)| 00:00:01 | | 4 | TABLE ACCESS BY INDEX ROWID | JOBS | 19 | 513 | 2 (0)| 00:00:01 | | 5 | INDEX FULL SCAN | JOB_ID_PK | 19 | | 1 (0)| 00:00:01 | |* 6 | SORT JOIN | | 3 | 60 | 3 (34)| 00:00:01 | | 7 | TABLE ACCESS BY INDEX ROWID| EMPLOYEES | 3 | 60 | 2 (0)| 00:00:01 | |* 8 | INDEX RANGE SCAN | EMP_EMP_ID_PK | 3 | | 1 (0)| 00:00:01 | |* 9 | INDEX UNIQUE SCAN | DEPT_ID_PK | 1 | | 0 (0)| 00:00:01 | | 10 | TABLE ACCESS BY INDEX ROWID | DEPARTMENTS | 1 | 16 | 1 (0)| 00:00:01 | -----------------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------6 - access("E"."JOB_ID"="J"."JOB_ID") filter("E"."JOB_ID"="J"."JOB_ID") 8 - access("E"."EMPLOYEE_ID"<103) 9 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID") 6.4.2 Customizing PLAN_TABLE Output If you have specified a statement identifier, then you can write your own script to query the PLAN_TABLE. For example: • Start with ID = 0 and given STATEMENT_ID. • Use the CONNECT BY clause to walk the tree from parent to child, the join keys being STATEMENT_ID = PRIOR STATMENT_ID and PARENT_ID = PRIOR ID. • Use the pseudo-column LEVEL (associated with CONNECT BY) to indent the children. 6-10 Chapter 6 Displaying PLAN_TABLE Output SELECT cardinality "Rows", lpad(' ',level-1)||operation||' '||options||' '||object_name "Plan" FROM PLAN_TABLE CONNECT BY prior id = parent_id AND prior statement_id = statement_id START WITH id = 0 AND statement_id = 'st1' ORDER BY id; Rows Plan ------- ---------------------------------------SELECT STATEMENT TABLE ACCESS FULL EMPLOYEES The NULL in the Rows column indicates that the optimizer does not have any statistics on the table. Analyzing the table shows the following: Rows ------16957 16957 Plan ---------------------------------------SELECT STATEMENT TABLE ACCESS FULL EMPLOYEES You can also select the COST. This is useful for comparing execution plans or for understanding why the optimizer chooses one execution plan over another. Note: These simplified examples are not valid for recursive SQL. 6-11 7 Reading Execution Plans Execution plans are represented as a tree of operations. This chapter contains the following topics: • Reading Execution Plans: Basic This section uses EXPLAIN PLAN examples to illustrate execution plans. • Reading Execution Plans: Advanced In some cases, execution plans can be complicated and challenging to read. • Execution Plan Reference This section describes V$ views and PLAN_COLUMN columns. 7.1 Reading Execution Plans: Basic This section uses EXPLAIN PLAN examples to illustrate execution plans. The following query displays the execution plans: SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY(NULL, 'statement_id','BASIC')); Examples of the output from this statement are shown in Example 7-4 and Example 7-1. Example 7-1 EXPLAIN PLAN for Statement ID ex_plan1 The following plan shows execution of a SELECT statement. The table employees is accessed using a full table scan. Every row in the table employees is accessed, and the WHERE clause criteria is evaluated for every row. EXPLAIN PLAN SET statement_id = 'ex_plan1' FOR SELECT phone_number FROM employees WHERE phone_number LIKE '650%'; --------------------------------------| Id | Operation | Name | --------------------------------------| 0 | SELECT STATEMENT | | | 1 | TABLE ACCESS FULL| EMPLOYEES | --------------------------------------- Example 7-2 EXPLAIN PLAN for Statement ID ex_plan2 This following plan shows the execution of a SELECT statement. In this example, the database range scans the EMP_NAME_IX index to evaluate the WHERE clause criteria. EXPLAIN PLAN SET statement_id = 'ex_plan2' FOR SELECT last_name FROM employees 7-1 Chapter 7 Reading Execution Plans: Advanced WHERE last_name LIKE 'Pe%'; SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY(NULL, 'ex_plan2','BASIC')); ---------------------------------------| Id | Operation | Name | ---------------------------------------| 0 | SELECT STATEMENT | | | 1 | INDEX RANGE SCAN| EMP_NAME_IX | ---------------------------------------- 7.2 Reading Execution Plans: Advanced In some cases, execution plans can be complicated and challenging to read. This section contains the following topics: • Reading Adaptive Query Plans The adaptive optimizer is a feature of the optimizer that enables it to adapt plans based on run-time statistics. All adaptive mechanisms can execute a final plan for a statement that differs from the default plan. • Viewing Parallel Execution with EXPLAIN PLAN Plans for parallel queries differ in important ways from plans for serial queries. • Viewing Bitmap Indexes with EXPLAIN PLAN Index row sources using bitmap indexes appear in the EXPLAIN PLAN output with the word BITMAP indicating the type of the index. • Viewing Result Cache with EXPLAIN PLAN When your query contains the result_cache hint, the ResultCache operator is inserted into the execution plan. • Viewing Partitioned Objects with EXPLAIN PLAN Use EXPLAIN PLAN to determine how Oracle Database accesses partitioned objects for specific queries. • PLAN_TABLE Columns The PLAN_TABLE used by the EXPLAIN PLAN statement contains the columns listed in this topic. 7.2.1 Reading Adaptive Query Plans The adaptive optimizer is a feature of the optimizer that enables it to adapt plans based on run-time statistics. All adaptive mechanisms can execute a final plan for a statement that differs from the default plan. An adaptive query plan chooses among subplans during the current statement execution. In contrast, automatic reoptimization changes a plan only on executions that occur after the current statement execution. You can determine whether the database used adaptive query optimization for a SQL statement based on the comments in the Notes section of plan. The comments indicate whether row sources are dynamic, or whether automatic reoptimization adapted a plan. 7-2 Chapter 7 Reading Execution Plans: Advanced Assumptions This tutorial assumes the following: • The STATISTICS_LEVEL initialization parameter is set to ALL. • The database uses the default settings for adaptive execution. • As user oe, you want to issue the following separate queries: SELECT o.order_id, v.product_name FROM orders o, ( SELECT order_id, product_name FROM order_items o, product_information p WHERE p.product_id = o.product_id AND list_price < 50 AND min_price < 40 ) v WHERE o.order_id = v.order_id SELECT FROM WHERE AND AND product_name order_items o, product_information p o.unit_price = 15 quantity > 1 p.product_id = o.product_id • Before executing each query, you want to query DBMS_XPLAN.DISPLAY_PLAN to see the default plan, that is, the plan that the optimizer chose before applying its adaptive mechanism. • After executing each query, you want to query DBMS_XPLAN.DISPLAY_CURSOR to see the final plan and adaptive query plan. • SYS has granted oe the following privileges: – GRANT SELECT ON V_$SESSION TO oe – GRANT SELECT ON V_$SQL TO oe – GRANT SELECT ON V_$SQL_PLAN TO oe – GRANT SELECT ON V_$SQL_PLAN_STATISTICS_ALL TO oe To see the results of adaptive optimization: 1. Start SQL*Plus, and then connect to the database as user oe. 2. Query orders. For example, use the following statement: SELECT o.order_id, v.product_name FROM orders o, ( SELECT order_id, product_name FROM order_items o, product_information p WHERE p.product_id = o.product_id AND list_price < 50 AND min_price < 40 ) v WHERE o.order_id = v.order_id; 3. View the plan in the cursor. For example, run the following commands: 7-3 Chapter 7 Reading Execution Plans: Advanced SET LINESIZE 165 SET PAGESIZE 0 SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'+ALLSTATS')); The following sample output has been reformatted to fit on the page. In this plan, the optimizer chooses a nested loops join. The original optimizer estimates are shown in the E-Rows column, whereas the actual statistics gathered during execution are shown in the A-Rows column. In the MERGE JOIN operation, the difference between the estimated and actual number of rows is significant. -------------------------------------------------------------------------------------------|Id| Operation | Name |Start|E-Rows|A-Rows|A-Time|Buff|OMem|1Mem|O/1/M| -------------------------------------------------------------------------------------------| 0| SELECT STATEMENT | | 1| | 269|00:00:00.09|1338| | | | | 1| NESTED LOOPS | | 1| 1| 269|00:00:00.09|1338| | | | | 2| MERGE JOIN CARTESIAN| | 1| 4|9135|00:00:00.03| 33| | | | |*3| TABLE ACCESS FULL |PRODUCT_INFORMAT| 1| 1| 87|00:00:00.01| 32| | | | | 4| BUFFER SORT | | 87|105|9135|00:00:00.01| 1|4096|4096|1/0/0| | 5| INDEX FULL SCAN | ORDER_PK | 1|105| 105|00:00:00.01| 1| | | | |*6| INDEX UNIQUE SCAN | ORDER_ITEMS_UK |9135| 1| 269|00:00:00.03|1305| | | | -------------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------3 - filter(("MIN_PRICE"<40 AND "LIST_PRICE"<50)) 6 - access("O"."ORDER_ID"="ORDER_ID" AND "P"."PRODUCT_ID"="O"."PRODUCT_ID") 4. Run the same query of orders that you ran in Step 2. 5. View the execution plan in the cursor by using the same SELECT statement that you ran in Step 3. The following example shows that the optimizer has chosen a different plan, using a hash join. The Note section shows that the optimizer used statistics feedback to adjust its cost estimates for the second execution of the query, thus illustrating automatic reoptimization. -------------------------------------------------------------------------------------------|Id| Operation |Name |Start|E-Rows|A-Rows|A-Time|Buff|Reads|OMem|1Mem|O/1/M| -------------------------------------------------------------------------------------------| 0| SELECT STATEMENT | | 1 | |269|00:00:00.02|60|1| | | | | 1| NESTED LOOPS | | 1 |269|269|00:00:00.02|60|1| | | | |*2| HASH JOIN | | 1 |313|269|00:00:00.02|39|1|1000K|1000K|1/0/0| |*3| TABLE ACCESS FULL |PRODUCT_INFORMA| 1 | 87| 87|00:00:00.01|15|0| | | | | 4| INDEX FAST FULL SCAN|ORDER_ITEMS_UK | 1 |665|665|00:00:00.01|24|1| | | | |*5| INDEX UNIQUE SCAN |ORDER_PK |269| 1|269|00:00:00.01|21|0| | | | -------------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------2 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") 3 - filter(("MIN_PRICE"<40 AND "LIST_PRICE"<50)) 5 - access("O"."ORDER_ID"="ORDER_ID") Note ----- statistics feedback used for this statement 6. Query V$SQL to verify the performance improvement. 7-4 Chapter 7 Reading Execution Plans: Advanced The following query shows the performance of the two statements (sample output included). SELECT CHILD_NUMBER, CPU_TIME, ELAPSED_TIME, BUFFER_GETS FROM V$SQL WHERE SQL_ID = 'gm2npz344xqn8'; CHILD_NUMBER CPU_TIME ELAPSED_TIME BUFFER_GETS ------------ ---------- ------------ ----------0 92006 131485 1831 1 12000 24156 60 The second statement executed, which is child number 1, used statistics feedback. CPU time, elapsed time, and buffer gets are all significantly lower. 7. Explain the plan for the query of order_items. For example, use the following statement: EXPLAIN PLAN FOR SELECT product_name FROM order_items o, product_information p WHERE o.unit_price = 15 AND quantity > 1 AND p.product_id = o.product_id 8. View the plan in the plan table. For example, run the following statement: SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY); Sample output appears below: ------------------------------------------------------------------------------|Id| Operation | Name |Rows|Bytes|Cost (%CPU)|Time| ------------------------------------------------------------------------------| 0| SELECT STATEMENT | |4|128|7 (0)|00:00:01| | 1| NESTED LOOPS | | | | | | | 2| NESTED LOOPS | |4|128|7 (0)|00:00:01| |*3| TABLE ACCESS FULL |ORDER_ITEMS |4|48 |3 (0)|00:00:01| |*4| INDEX UNIQUE SCAN |PRODUCT_INFORMATION_PK|1| |0 (0)|00:00:01| | 5| TABLE ACCESS BY INDEX ROWID|PRODUCT_INFORMATION |1|20 |1 (0)|00:00:01| ------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------3 - filter("O"."UNIT_PRICE"=15 AND "QUANTITY">1) 4 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") In this plan, the optimizer chooses a nested loops join. 9. Run the query that you previously explained. For example, use the following statement: SELECT FROM WHERE AND AND product_name order_items o, product_information p o.unit_price = 15 quantity > 1 p.product_id = o.product_id 10. View the plan in the cursor. 7-5 Chapter 7 Reading Execution Plans: Advanced For example, run the following commands: SET LINESIZE 165 SET PAGESIZE 0 SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY(FORMAT=>'+ADAPTIVE')); Sample output appears below. Based on statistics collected at run time (Step 4), the optimizer chose a hash join rather than the nested loops join. The dashes (-) indicate the steps in the nested loops plan that the optimizer considered but do not ultimately choose. The switch illustrates the adaptive query plan feature. ------------------------------------------------------------------------------|Id | Operation | Name |Rows|Bytes|Cost(%CPU)|Time | ------------------------------------------------------------------------------| 0| SELECT STATEMENT | |4|128|7(0)|00:00:01| | *1| HASH JOIN | |4|128|7(0)|00:00:01| |- 2| NESTED LOOPS | | | | | | |- 3| NESTED LOOPS | | |128|7(0)|00:00:01| |- 4| STATISTICS COLLECTOR | | | | | | | *5| TABLE ACCESS FULL | ORDER_ITEMS |4| 48|3(0)|00:00:01| |-*6| INDEX UNIQUE SCAN | PRODUCT_INFORMATI_PK|1| |0(0)|00:00:01| |- 7| TABLE ACCESS BY INDEX ROWID| PRODUCT_INFORMATION |1| 20|1(0)|00:00:01| | 8| TABLE ACCESS FULL | PRODUCT_INFORMATION |1| 20|1(0)|00:00:01| ------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------1 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") 5 - filter("O"."UNIT_PRICE"=15 AND "QUANTITY">1) 6 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") Note ----- this is an adaptive plan (rows marked '-' are inactive) See Also: • "Adaptive Query Plans" • "Table 7-8" • "Controlling Adaptive Optimization" • Oracle Database Reference to learn about the STATISTICS_LEVEL initialization parameter • Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_XPLAN 7.2.2 Viewing Parallel Execution with EXPLAIN PLAN Plans for parallel queries differ in important ways from plans for serial queries. This section contains the following topics: • About EXPLAIN PLAN and Parallel Queries Tuning a parallel query begins much like a non-parallel query tuning exercise by choosing the driving table. However, the rules governing the choice are different. 7-6 Chapter 7 Reading Execution Plans: Advanced • Viewing Parallel Queries with EXPLAIN PLAN: Example When using EXPLAIN PLAN with parallel queries, the database compiles and executes one parallel plan. This plan is derived from the serial plan by allocating row sources specific to the parallel support in the QC plan. 7.2.2.1 About EXPLAIN PLAN and Parallel Queries Tuning a parallel query begins much like a non-parallel query tuning exercise by choosing the driving table. However, the rules governing the choice are different. In the serial case, the best driving table produces the fewest numbers of rows after applying limiting conditions. The database joins a small number of rows to larger tables using non-unique indexes. For example, consider a table hierarchy consisting of customer, account, and transaction. Figure 7-1 A Table Hierarchy TRANSACTION ACCOUNT CUSTOMER In this example, customer is the smallest table, whereas transaction is the largest table. A typical OLTP query retrieves transaction information about a specific customer account. The query drives from the customer table. The goal is to minimize logical I/O, which typically minimizes other critical resources including physical I/O and CPU time. For parallel queries, the driving table is usually the largest table. It would not be efficient to use parallel query in this case because only a few rows from each table are accessed. However, what if it were necessary to identify all customers who had transactions of a certain type last month? It would be more efficient to drive from the transaction table because no limiting conditions exist on the customer table. The database would join rows from the transaction table to the account table, and then finally join the result set to the customer table. In this case, the used on the account and customer table are probably highly selective primary key or unique indexes rather than the non-unique indexes used in the first query. Because the transaction table is large and the column is not selective, it would be beneficial to use parallel query driving from the transaction table. Parallel operations include the following: • PARALLEL_TO_PARALLEL • PARALLEL_TO_SERIAL A PARALLEL_TO_SERIAL operation is always the step that occurs when the query coordinator consumes rows from a parallel operation. Another type of operation that does not occur in this query is a SERIAL operation. If these types of operations occur, then consider making them parallel operations to improve performance because they too are potential bottlenecks. 7-7 Chapter 7 Reading Execution Plans: Advanced • PARALLEL_FROM_SERIAL • PARALLEL_TO_PARALLEL If the workloads in each step are relatively equivalent, then the PARALLEL_TO_PARALLEL operations generally produce the best performance. • PARALLEL_COMBINED_WITH_CHILD • PARALLEL_COMBINED_WITH_PARENT A PARALLEL_COMBINED_WITH_PARENT operation occurs when the database performs the step simultaneously with the parent step. If a parallel step produces many rows, then the QC may not be able to consume the rows as fast as they are produced. Little can be done to improve this situation. See Also: The OTHER_TAG column in "PLAN_TABLE Columns" 7.2.2.2 Viewing Parallel Queries with EXPLAIN PLAN: Example When using EXPLAIN PLAN with parallel queries, the database compiles and executes one parallel plan. This plan is derived from the serial plan by allocating row sources specific to the parallel support in the QC plan. The table queue row sources (PX Send and PX Receive), the granule iterator, and buffer sorts, required by the two parallel execution server set PQ model, are directly inserted into the parallel plan. This plan is the same plan for all parallel execution servers when executed in parallel or for the QC when executed serially. Example 7-3 Parallel Query Explain Plan The following simple example illustrates an EXPLAIN PLAN for a parallel query: CREATE TABLE emp2 AS SELECT * FROM employees; ALTER TABLE emp2 PARALLEL 2; EXPLAIN PLAN FOR SELECT SUM(salary) FROM emp2 GROUP BY department_id; SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY()); -----------------------------------------------------------------------------------------------| Id | Operation | Name | Rows| Bytes |Cost %CPU| TQ |IN-OUT| PQ Distrib | -----------------------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 107 | 2782 | 3 (34) | | | | | 1 | PX COORDINATOR | | | | | | | | | 2 | PX SEND QC (RANDOM) | :TQ10001 | 107 | 2782 | 3 (34) | Q1,01 | P->S | QC (RAND) | | 3 | HASH GROUP BY | | 107 | 2782 | 3 (34) | Q1,01 | PCWP | | | 4 | PX RECEIVE | | 107 | 2782 | 3 (34) | Q1,01 | PCWP | | | 5 | PX SEND HASH | :TQ10000 | 107 | 2782 | 3 (34) | Q1,00 | P->P | HASH | | 6 | HASH GROUP BY | | 107 | 2782 | 3 (34) | Q1,00 | PCWP | | | 7 | PX BLOCK ITERATOR | | 107 | 2782 | 2 (0) | Q1,00 | PCWP | | 7-8 Chapter 7 Reading Execution Plans: Advanced | 8 | TABLE ACCESS FULL| EMP2 | 107 | 2782 | 2 (0) | Q1,00 | PCWP | | ------------------------------------------------------------------------------------------------ One set of parallel execution servers scans EMP2 in parallel, while the second set performs the aggregation for the GROUP BY operation. The PX BLOCK ITERATOR row source represents the splitting up of the table EMP2 into pieces to divide the scan workload between the parallel execution servers. The PX SEND and PX RECEIVE row sources represent the pipe that connects the two sets of parallel execution servers as rows flow up from the parallel scan, get repartitioned through the HASH table queue, and then read by and aggregated on the top set. The PX SEND QC row source represents the aggregated values being sent to the QC in random (RAND) order. The PX COORDINATOR row source represents the QC or Query Coordinator which controls and schedules the parallel plan appearing below it in the plan tree. 7.2.3 Viewing Bitmap Indexes with EXPLAIN PLAN Index row sources using bitmap indexes appear in the EXPLAIN PLAN output with the word BITMAP indicating the type of the index. Note: Queries using bitmap join index indicate the bitmap join index access path. The operation for bitmap join index is the same as bitmap index. Example 7-4 EXPLAIN PLAN with Bitmap Indexes In this example, the predicate c1=2 yields a bitmap from which a subtraction can take place. From this bitmap, the bits in the bitmap for c2=6 are subtracted. Also, the bits in the bitmap for c2 IS NULL are subtracted, explaining why there are two MINUS row sources in the plan. The NULL subtraction is necessary for semantic correctness unless the column has a NOT NULL constraint. The TO ROWIDS option generates the rowids necessary for the table access. EXPLAIN FROM WHERE AND OR PLAN FOR SELECT * t c1 = 2 c2 <> 6 c3 BETWEEN 10 AND 20; SELECT STATEMENT TABLE ACCESS T BY INDEX ROWID BITMAP CONVERSION TO ROWID BITMAP OR BITMAP MINUS BITMAP MINUS BITMAP INDEX C1_IND SINGLE VALUE BITMAP INDEX C2_IND SINGLE VALUE BITMAP INDEX C2_IND SINGLE VALUE BITMAP MERGE BITMAP INDEX C3_IND RANGE SCAN 7-9 Chapter 7 Reading Execution Plans: Advanced 7.2.4 Viewing Result Cache with EXPLAIN PLAN When your query contains the result_cache hint, the ResultCache operator is inserted into the execution plan. For example, consider the following query: SELECT /*+ result_cache */ deptno, avg(sal) FROM emp GROUP BY deptno; To view the EXPLAIN PLAN for this query, use the following command: EXPLAIN PLAN FOR SELECT /*+ result_cache */ deptno, avg(sal) FROM emp GROUP BY deptno; SELECT PLAN_TABLE_OUTPUT FROM TABLE (DBMS_XPLAN.DISPLAY()); The EXPLAIN PLAN output for this query should look similar to the following: -------------------------------------------------------------------------------|Id| Operation | Name |Rows|Bytes|Cost(%CPU)|Time | -------------------------------------------------------------------------------|0| SELECT STATEMENT | | 11 | 77 | 4 (25)| 00:00:01| |1| RESULT CACHE |b06ppfz9pxzstbttpbqyqnfbmy| | | | | |2| HASH GROUP BY | | 11 | 77 | 4 (25)| 00:00:01| |3| TABLE ACCESS FULL| EMP |107 | 749| 3 (0) | 00:00:01| -------------------------------------------------------------------------------- In this EXPLAIN PLAN, the ResultCache operator is identified by its CacheId, which is b06ppfz9pxzstbttpbqyqnfbmy. You can now run a query on the V$RESULT_CACHE_OBJECTS view by using this CacheId. 7.2.5 Viewing Partitioned Objects with EXPLAIN PLAN Use EXPLAIN PLAN to determine how Oracle Database accesses partitioned objects for specific queries. Partitions accessed after pruning are shown in the PARTITION START and PARTITION STOP columns. The row source name for the range partition is PARTITION RANGE. For hash partitions, the row source name is PARTITION HASH. A join is implemented using partial partition-wise join if the DISTRIBUTION column of the plan table of one of the joined tables contains PARTITION(KEY). Partial partition-wise join is possible if one of the joined tables is partitioned on its join column and the table is parallelized. A join is implemented using full partition-wise join if the partition row source appears before the join row source in the EXPLAIN PLAN output. Full partition-wise joins are possible only if both joined tables are equipartitioned on their respective join columns. Examples of execution plans for several types of partitioning follow. This section contains the following topics: 7-10 Chapter 7 Reading Execution Plans: Advanced • Displaying Range and Hash Partitioning with EXPLAIN PLAN: Examples This example illustrates pruning by using the emp_range table, which partitioned by range on hire_date. • Pruning Information with Composite Partitioned Objects: Examples To illustrate how Oracle Database displays pruning information for composite partitioned objects, consider the table emp_comp. It is range-partitioned on hiredate and subpartitioned by hash on deptno. • Examples of Partial Partition-Wise Joins In these examples, the PQ_DISTRIBUTE hint explicitly forces a partial partition-wise join because the query optimizer could have chosen a different plan based on cost in this query. • Example of Full Partition-Wise Join In this example, emp_comp and dept_hash are joined on their hash partitioning columns, enabling use of a full partition-wise join. • Examples of INLIST ITERATOR and EXPLAIN PLAN An INLIST ITERATOR operation appears in the EXPLAIN PLAN output if an index implements an IN-list predicate. • Example of Domain Indexes and EXPLAIN PLAN You can use EXPLAIN PLAN to derive user-defined CPU and I/O costs for domain indexes. 7.2.5.1 Displaying Range and Hash Partitioning with EXPLAIN PLAN: Examples This example illustrates pruning by using the emp_range table, which partitioned by range on hire_date. Assume that the tables employees and departments from the Oracle Database sample schema exist. CREATE TABLE emp_range PARTITION BY RANGE(hire_date) ( PARTITION emp_p1 VALUES LESS PARTITION emp_p2 VALUES LESS PARTITION emp_p3 VALUES LESS PARTITION emp_p4 VALUES LESS PARTITION emp_p5 VALUES LESS ) AS SELECT * FROM employees; THAN THAN THAN THAN THAN (TO_DATE('1-JAN-1992','DD-MON-YYYY')), (TO_DATE('1-JAN-1994','DD-MON-YYYY')), (TO_DATE('1-JAN-1996','DD-MON-YYYY')), (TO_DATE('1-JAN-1998','DD-MON-YYYY')), (TO_DATE('1-JAN-2001','DD-MON-YYYY')) For the first example, consider the following statement: EXPLAIN PLAN FOR SELECT * FROM emp_range; Oracle Database displays something similar to the following: -------------------------------------------------------------------|Id| Operation | Name |Rows| Bytes|Cost|Pstart|Pstop| -------------------------------------------------------------------| 0| SELECT STATEMENT | | 105| 13965 | 2 | | | | 1| PARTITION RANGE ALL| | 105| 13965 | 2 | 1 | 5 | | 2| TABLE ACCESS FULL | EMP_RANGE | 105| 13965 | 2 | 1 | 5 | -------------------------------------------------------------------- 7-11 Chapter 7 Reading Execution Plans: Advanced The database creates a partition row source on top of the table access row source. It iterates over the set of partitions to be accessed. In this example, the partition iterator covers all partitions (option ALL), because a predicate was not used for pruning. The PARTITION_START and PARTITION_STOP columns of the PLAN_TABLE show access to all partitions from 1 to 5. For the next example, consider the following statement: EXPLAIN PLAN FOR SELECT * FROM emp_range WHERE hire_date >= TO_DATE('1-JAN-1996','DD-MON-YYYY'); ----------------------------------------------------------------------| Id | Operation | Name |Rows|Bytes|Cost|Pstart|Pstop| ----------------------------------------------------------------------| 0 | SELECT STATEMENT | | 3 | 399 | 2 | | | | 1 | PARTITION RANGE ITERATOR| | 3 | 399 | 2 | 4 | 5 | | *2 | TABLE ACCESS FULL |EMP_RANGE| 3 | 399 | 2 | 4 | 5 | ----------------------------------------------------------------------- In the previous example, the partition row source iterates from partition 4 to 5 because the database prunes the other partitions using a predicate on hire_date. Finally, consider the following statement: EXPLAIN PLAN FOR SELECT * FROM emp_range WHERE hire_date < TO_DATE('1-JAN-1992','DD-MON-YYYY'); ----------------------------------------------------------------------| Id | Operation | Name |Rows|Bytes|Cost|Pstart|Pstop| ----------------------------------------------------------------------| 0 | SELECT STATEMENT | | 1 | 133 | 2 | | | | 1 | PARTITION RANGE SINGLE| | 1 | 133 | 2 | 1 | 1 | |* 2 | TABLE ACCESS FULL | EMP_RANGE | 1 | 133 | 2 | 1 | 1 | ----------------------------------------------------------------------- In the previous example, only partition 1 is accessed and known at compile time; thus, there is no need for a partition row source. Note: Oracle Database displays the same information for hash partitioned objects, except the partition row source name is PARTITION HASH instead of PARTITION RANGE. Also, with hash partitioning, pruning is only possible using equality or IN-list predicates. 7-12 Chapter 7 Reading Execution Plans: Advanced 7.2.5.2 Pruning Information with Composite Partitioned Objects: Examples To illustrate how Oracle Database displays pruning information for composite partitioned objects, consider the table emp_comp. It is range-partitioned on hiredate and subpartitioned by hash on deptno. CREATE TABLE emp_comp PARTITION BY RANGE(hire_date) SUBPARTITION BY HASH(department_id) SUBPARTITIONS 3 ( PARTITION emp_p1 VALUES LESS THAN (TO_DATE('1-JAN-1992','DD-MON-YYYY')), PARTITION emp_p2 VALUES LESS THAN (TO_DATE('1-JAN-1994','DD-MON-YYYY')), PARTITION emp_p3 VALUES LESS THAN (TO_DATE('1-JAN-1996','DD-MON-YYYY')), PARTITION emp_p4 VALUES LESS THAN (TO_DATE('1-JAN-1998','DD-MON-YYYY')), PARTITION emp_p5 VALUES LESS THAN (TO_DATE('1-JAN-2001','DD-MON-YYYY')) ) AS SELECT * FROM employees; For the first example, consider the following statement: EXPLAIN PLAN FOR SELECT * FROM emp_comp; ----------------------------------------------------------------------|Id| Operation | Name | Rows | Bytes |Cost|Pstart|Pstop| ----------------------------------------------------------------------| 0| SELECT STATEMENT | | 10120 | 1314K| 78 | | | | 1| PARTITION RANGE ALL| | 10120 | 1314K| 78 | 1 | 5 | | 2| PARTITION HASH ALL| | 10120 | 1314K| 78 | 1 | 3 | | 3| TABLE ACCESS FULL| EMP_COMP | 10120 | 1314K| 78 | 1 | 15 | ----------------------------------------------------------------------- This example shows the plan when Oracle Database accesses all subpartitions of all partitions of a composite object. The database uses two partition row sources for this purpose: a range partition row source to iterate over the partitions, and a hash partition row source to iterate over the subpartitions of each accessed partition. In the following example, the range partition row source iterates from partition 1 to 5, because the database performs no pruning. Within each partition, the hash partition row source iterates over subpartitions 1 to 3 of the current partition. As a result, the table access row source accesses subpartitions 1 to 15. In other words, the database accesses all subpartitions of the composite object. EXPLAIN PLAN FOR SELECT * FROM emp_comp WHERE hire_date = TO_DATE('15-FEB-1998', 'DD-MON-YYYY'); ----------------------------------------------------------------------| Id | Operation | Name |Rows|Bytes |Cost|Pstart|Pstop| ----------------------------------------------------------------------| 0 | SELECT STATEMENT | | 20 | 2660 | 17 | | | | 1 | PARTITION RANGE SINGLE| | 20 | 2660 | 17 | 5 | 5 | | 2 | PARTITION HASH ALL | | 20 | 2660 | 17 | 1 | 3 | |* 3 | TABLE ACCESS FULL | EMP_COMP | 20 | 2660 | 17 | 13 | 15 | ----------------------------------------------------------------------- In the previous example, only the last partition, partition 5, is accessed. This partition is known at compile time, so the database does not need to show it in the plan. The hash 7-13 Chapter 7 Reading Execution Plans: Advanced partition row source shows accessing of all subpartitions within that partition; that is, subpartitions 1 to 3, which translates into subpartitions 13 to 15 of the emp_comp table. Now consider the following statement: EXPLAIN PLAN FOR SELECT * FROM emp_comp WHERE department_id = 20; -----------------------------------------------------------------------| Id | Operation |Name |Rows | Bytes |Cost|Pstart|Pstop| -----------------------------------------------------------------------| 0 | SELECT STATEMENT | | 101 | 13433 | 78 | | | | 1 | PARTITION RANGE ALL | | 101 | 13433 | 78 | 1 | 5 | | 2 | PARTITION HASH SINGLE| | 101 | 13433 | 78 | 3 | 3 | |* 3 | TABLE ACCESS FULL | EMP_COMP | 101 | 13433 | 78 | | | ------------------------------------------------------------------------ In the previous example, the predicate deptno=20 enables pruning on the hash dimension within each partition. Therefore, Oracle Database only needs to access a single subpartition. The number of this subpartition is known at compile time, so the hash partition row source is not needed. Finally, consider the following statement: VARIABLE dno NUMBER; EXPLAIN PLAN FOR SELECT * FROM emp_comp WHERE department_id = :dno; ----------------------------------------------------------------------| Id| Operation | Name |Rows| Bytes |Cost|Pstart|Pstop| ----------------------------------------------------------------------| 0 | SELECT STATEMENT | | 101| 13433 | 78 | | | | 1 | PARTITION RANGE ALL | | 101| 13433 | 78 | 1 | 5 | | 2 | PARTITION HASH SINGLE| | 101| 13433 | 78 | KEY | KEY | |*3 | TABLE ACCESS FULL | EMP_COMP | 101| 13433 | 78 | | | ----------------------------------------------------------------------- The last two examples are the same, except that department_id = :dno replaces deptno=20. In this last case, the subpartition number is unknown at compile time, and a hash partition row source is allocated. The option is SINGLE for this row source because Oracle Database accesses only one subpartition within each partition. In Step 2, both PARTITION_START and PARTITION_STOP are set to KEY. This value means that Oracle Database determines the number of subpartitions at run time. 7.2.5.3 Examples of Partial Partition-Wise Joins In these examples, the PQ_DISTRIBUTE hint explicitly forces a partial partition-wise join because the query optimizer could have chosen a different plan based on cost in this query. Example 7-5 Partial Partition-Wise Join with Range Partition emp_range_diddepartment_iddept2dept2 CREATE TABLE dept2 AS SELECT * FROM departments; ALTER TABLE dept2 PARALLEL 2; 7-14 Chapter 7 Reading Execution Plans: Advanced CREATE TABLE emp_range_did PARTITION BY RANGE(department_id) (PARTITION emp_p1 VALUES LESS THAN (150), PARTITION emp_p5 VALUES LESS THAN (MAXVALUE) ) AS SELECT * FROM employees; ALTER TABLE emp_range_did PARALLEL 2; EXPLAIN PLAN FOR SELECT /*+ PQ_DISTRIBUTE(d NONE PARTITION) ORDERED */ e.last_name, d.department_name FROM emp_range_did e, dept2 d WHERE e.department_id = d.department_id; -----------------------------------------------------------------------------------------------|Id| Operation |Name |Row|Byte|Cost|Pstart|Pstop|TQ|IN-OUT|PQ Distrib| -----------------------------------------------------------------------------------------------| 0| SELECT STATEMENT | |284 |16188|6 | | | | | | | 1| PX COORDINATOR | | | | | | | | | | | 2| PX SEND QC (RANDOM) |:TQ10001 |284 |16188|6 | | | Q1,01 |P->S |QC (RAND) | |*3| HASH JOIN | |284 |16188|6 | | | Q1,01 |PCWP | | | 4| PX PARTITION RANGE ALL | |284 |7668 |2 | 1 | 2 | Q1,01 |PCWC | | | 5| TABLE ACCESS FULL |EMP_RANGE_DID|284 |7668 |2 | 1 | 2 | Q1,01 |PCWP | | | 6| BUFFER SORT | | | | | | | Q1,01 |PCWC | | | 7| PX RECEIVE | | 21 | 630 |2 | | | Q1,01 |PCWP | | | 8| PX SEND PARTITION (KEY)|:TQ10000 | 21 | 630 |2 | | | |S->P |PART (KEY)| | 9| TABLE ACCESS FULL |DEPT2 | 21 | 630 |2 | | | | | | ------------------------------------------------------------------------------------------------ The execution plan shows that the table dept2 is scanned serially and all rows with the same partitioning column value of emp_range_did (department_id) are sent through a PART (KEY), or partition key, table queue to the same parallel execution server doing the partial partition-wise join. Example 7-6 Partial Partition-Wise Join with Composite Partition In the following example, emp_comp is joined on the partitioning column and is parallelized, enabling use of a partial partition-wise join because dept2 is not partitioned. The database dynamically partitions dept2 before the join. ALTER TABLE emp_comp PARALLEL 2; EXPLAIN PLAN FOR SELECT /*+ PQ_DISTRIBUTE(d NONE PARTITION) ORDERED */ e.last_name, d.department_name FROM emp_comp e, dept2 d WHERE e.department_id = d.department_id; SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY()); -----------------------------------------------------------------------------------------------| Id| Operation | Name |Rows |Bytes |Cost|Pstart|Pstop|TQ |IN-OUT|PQ Distrib| -----------------------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 445 | 17800 | 5 | | | | | | | 1 | PX COORDINATOR | | | | | | | | | | | 2 | PX SEND QC (RANDOM) |:TQ10001| 445 | 17800 | 5 | | | Q1,01 | P->S | QC (RAND)| |*3 | HASH JOIN | | 445 | 17800 | 5 | | | Q1,01 | PCWP | | | 4 | PX PARTITION RANGE ALL | | 107 | 1070 | 3 | 1 | 5 | Q1,01 | PCWC | | | 5 | PX PARTITION HASH ALL | | 107 | 1070 | 3 | 1 | 3 | Q1,01 | PCWC | | | 6 | TABLE ACCESS FULL |EMP_COMP| 107 | 1070 | 3 | 1 | 15| Q1,01 | PCWP | | | 7 | PX RECEIVE | | 21 | 630 | 1 | | | Q1,01 | PCWP | | 7-15 Chapter 7 Reading Execution Plans: Advanced | 8 | PX SEND PARTITION (KEY)|:TQ10000| 21 | 630 | 1 | | | Q1,00 | P->P |PART (KEY)| | 9 | PX BLOCK ITERATOR | | 21 | 630 | 1 | | | Q1,00 | PCWC | | |10 | TABLE ACCESS FULL |DEPT2 | 21 | 630 | 1 | | | Q1,00 | PCWP | | ------------------------------------------------------------------------------------------------ The plan shows that the optimizer selects partial partition-wise join from one of two columns. The PX SEND node type is PARTITION (KEY) and the PQ Distrib column contains the text PART (KEY), or partition key. This implies that the table dept2 is repartitioned based on the join column department_id to be sent to the parallel execution servers executing the scan of EMP_COMP and the join. 7.2.5.4 Example of Full Partition-Wise Join In this example, emp_comp and dept_hash are joined on their hash partitioning columns, enabling use of a full partition-wise join. The PARTITION HASH row source appears on top of the join row source in the plan table output. CREATE TABLE dept_hash PARTITION BY HASH(department_id) PARTITIONS 3 PARALLEL 2 AS SELECT * FROM departments; EXPLAIN PLAN FOR SELECT /*+ PQ_DISTRIBUTE(e NONE NONE) ORDERED */ e.last_name, d.department_name FROM emp_comp e, dept_hash d WHERE e.department_id = d.department_id; ----------------------------------------------------------------------------------------------|Id| Operation | Name |Rows|Bytes|Cost|Pstart|Pstop|TQ |IN-OUT| PQ Distrib| ----------------------------------------------------------------------------------------------| 0| SELECT STATEMENT | | 106 | 2544 | 8 | | | | | | | 1| PX COORDINATOR | | | | | | | | | | | 2| PX SEND QC (RANDOM) | :TQ10000 | 106 | 2544 | 8 | | | Q1,00 | P->S |QC (RAND)| | 3| PX PARTITION HASH ALL | | 106 | 2544 | 8 | 1 | 3 | Q1,00 | PCWC | | |*4| HASH JOIN | | 106 | 2544 | 8 | | | Q1,00 | PCWP | | | 5| PX PARTITION RANGE ALL| | 107 | 1070 | 3 | 1 | 5 | Q1,00 | PCWC | | | 6| TABLE ACCESS FULL | EMP_COMP | 107 | 1070 | 3 | 1 | 15 | Q1,00 | PCWP | | | 7| TABLE ACCESS FULL | DEPT_HASH | 27 | 378 | 4 | 1 | 3 | Q1,00 | PCWP | | ----------------------------------------------------------------------------------------------- The PX PARTITION HASH row source appears on top of the join row source in the plan table output while the PX PARTITION RANGE row source appears over the scan of emp_comp. Each parallel execution server performs the join of an entire hash partition of emp_comp with an entire partition of dept_hash. 7-16 Chapter 7 Reading Execution Plans: Advanced 7.2.5.5 Examples of INLIST ITERATOR and EXPLAIN PLAN An INLIST ITERATOR operation appears in the EXPLAIN PLAN output if an index implements an IN-list predicate. Consider the following statement: SELECT * FROM emp WHERE empno IN (7876, 7900, 7902); The EXPLAIN PLAN output appears as follows: OPERATION ---------------SELECT STATEMENT INLIST ITERATOR TABLE ACCESS INDEX OPTIONS --------------- OBJECT_NAME -------------- BY ROWID RANGE SCAN EMP EMP_EMPNO The INLIST ITERATOR operation iterates over the next operation in the plan for each value in the IN-list predicate. The following sections describe the three possible types of IN-list columns for partitioned tables and indexes. This section contains the following topics: • When the IN-List Column is an Index Column: Example If the IN-list column empno is an index column but not a partition column, then the IN-list operator appears before the table operation but after the partition operation in the plan. • When the IN-List Column is an Index and a Partition Column: Example If empno is an indexed and a partition column, then the plan contains an INLIST ITERATOR operation before the partition operation. • When the IN-List Column is a Partition Column: Example If empno is a partition column and no indexes exist, then no INLIST ITERATOR operation is allocated. 7.2.5.5.1 When the IN-List Column is an Index Column: Example If the IN-list column empno is an index column but not a partition column, then the IN-list operator appears before the table operation but after the partition operation in the plan. OPERATION ---------------SELECT STATEMENT PARTITION RANGE INLIST ITERATOR TABLE ACCESS INDEX OPTIONS ------------ OBJECT_NAME PARTIT_START PARTITION_STOP ----------- ------------ -------------- ALL KEY(INLIST) KEY(INLIST) BY LOCAL INDEX ROWID EMP RANGE SCAN EMP_EMPNO KEY(INLIST) KEY(INLIST) KEY(INLIST) KEY(INLIST) The KEY(INLIST) designation for the partition start and stop keys specifies that an IN-list predicate appears on the index start and stop keys. 7-17 Chapter 7 Reading Execution Plans: Advanced 7.2.5.5.2 When the IN-List Column is an Index and a Partition Column: Example If empno is an indexed and a partition column, then the plan contains an INLIST ITERATOR operation before the partition operation. OPERATION ---------------SELECT STATEMENT INLIST ITERATOR PARTITION RANGE TABLE ACCESS INDEX OPTIONS ------------ OBJECT_NAME PARTITION_START PARTITION_STOP ----------- --------------- -------------- ITERATOR BY LOCAL INDEX ROWID EMP RANGE SCAN EMP_EMPNO KEY(INLIST) KEY(INLIST) KEY(INLIST) KEY(INLIST) KEY(INLIST) KEY(INLIST) 7.2.5.5.3 When the IN-List Column is a Partition Column: Example If empno is a partition column and no indexes exist, then no INLIST ITERATOR operation is allocated. OPERATION OPTIONS ---------------- -----------SELECT STATEMENT PARTITION RANGE INLIST TABLE ACCESS FULL OBJECT_NAME ----------- PARTITION_START --------------- PARTITION_STOP -------------- EMP KEY(INLIST) KEY(INLIST) KEY(INLIST) KEY(INLIST) If emp_empno is a bitmap index, then the plan is as follows: OPERATION ---------------SELECT STATEMENT INLIST ITERATOR TABLE ACCESS BITMAP CONVERSION BITMAP INDEX OPTIONS --------------- OBJECT_NAME -------------- BY INDEX ROWID TO ROWIDS SINGLE VALUE EMP EMP_EMPNO 7.2.5.6 Example of Domain Indexes and EXPLAIN PLAN You can use EXPLAIN PLAN to derive user-defined CPU and I/O costs for domain indexes. EXPLAIN PLAN displays domain index statistics in the OTHER column of PLAN_TABLE. For example, assume table emp has user-defined operator CONTAINS with a domain index emp_resume on the resume column, and the index type of emp_resume supports the operator CONTAINS. You explain the plan for the following query: SELECT * FROM emp WHERE CONTAINS(resume, 'Oracle') = 1 The database could display the following plan: OPERATION ----------------SELECT STATEMENT TABLE ACCESS DOMAIN INDEX OPTIONS OBJECT_NAME ----------- -----------BY ROWID EMP EMP_RESUME OTHER ---------------- CPU: 300, I/O: 4 7.2.6 PLAN_TABLE Columns The PLAN_TABLE used by the EXPLAIN PLAN statement contains the columns listed in this topic. 7-18 Chapter 7 Reading Execution Plans: Advanced Table 7-1 PLAN_TABLE Columns Column Type Description STATEMENT_ID VARCHAR2(30) Value of the optional STATEMENT_ID parameter specified in the EXPLAIN PLAN statement. PLAN_ID NUMBER Unique identifier of a plan in the database. TIMESTAMP DATE Date and time when the EXPLAIN PLAN statement was generated. REMARKS VARCHAR2(80) Any comment (of up to 80 bytes) you want to associate with each step of the explained plan. This column indicates whether the database used an outline or SQL profile for the query. If you need to add or change a remark on any row of the PLAN_TABLE, then use the UPDATE statement to modify the rows of the PLAN_TABLE. OPERATION VARCHAR2(30) Name of the internal operation performed in this step. In the first row generated for a statement, the column contains one of the following values: • DELETE STATEMENT • INSERT STATEMENT • SELECT STATEMENT • UPDATE STATEMENT See Table 7-3 for more information about values for this column. OPTIONS VARCHAR2(225) A variation on the operation described in the OPERATION column. See Table 7-3 for more information about values for this column. OBJECT_NODE VARCHAR2(128) Name of the database link used to reference the object (a table name or view name). For local queries using parallel execution, this column describes the order in which the database consumes output from operations. OBJECT_OWNER VARCHAR2(30) Name of the user who owns the schema containing the table or index. OBJECT_NAME VARCHAR2(30) Name of the table or index. OBJECT_ALIAS VARCHAR2(65) Unique alias of a table or view in a SQL statement. For indexes, it is the object alias of the underlying table. OBJECT_INSTANCE NUMERIC Number corresponding to the ordinal position of the object as it appears in the original statement. The numbering proceeds from left to right, outer to inner for the original statement text. View expansion results in unpredictable numbers. 7-19 Chapter 7 Reading Execution Plans: Advanced Table 7-1 (Cont.) PLAN_TABLE Columns Column Type Description OBJECT_TYPE VARCHAR2(30) Modifier that provides descriptive information about the object; for example, NON-UNIQUE for indexes. OPTIMIZER VARCHAR2(255) Current mode of the optimizer. SEARCH_COLUMNS NUMBERIC Not currently used. ID NUMERIC A number assigned to each step in the execution plan. PARENT_ID NUMERIC The ID of the next execution step that operates on the output of the ID step. DEPTH NUMERIC Depth of the operation in the row source tree that the plan represents. You can use the value to indent the rows in a plan table report. POSITION NUMERIC For the first row of output, this indicates the optimizer's estimated cost of executing the statement. For the other rows, it indicates the position relative to the other children of the same parent. COST NUMERIC Cost of the operation as estimated by the optimizer's query approach. Cost is not determined for table access operations. The value of this column does not have any particular unit of measurement; it is a weighted value used to compare costs of execution plans. The value of this column is a function of the CPU_COST and IO_COST columns. CARDINALITY NUMERIC Estimate by the query optimization approach of the number of rows that the operation accessed. BYTES NUMERIC Estimate by the query optimization approach of the number of bytes that the operation accessed. 7-20 Chapter 7 Reading Execution Plans: Advanced Table 7-1 (Cont.) PLAN_TABLE Columns Column Type Description OTHER_TAG VARCHAR2(255) Describes the contents of the OTHER column. Values are: • • • • • • • PARTITION_START VARCHAR2(255) SERIAL (blank): Serial execution. Currently, SQL is not loaded in the OTHER column for this case. SERIAL_FROM_REMOTE (S -> R): Serial execution at a remote site. PARALLEL_FROM_SERIAL (S -> P): Serial execution. Output of step is partitioned or broadcast to parallel execution servers. PARALLEL_TO_SERIAL (P -> S): Parallel execution. Output of step is returned to serial QC process. PARALLEL_TO_PARALLEL (P -> P): Parallel execution. Output of step is repartitioned to second set of parallel execution servers. PARALLEL_COMBINED_WITH_PARENT (PWP): Parallel execution; Output of step goes to next step in same parallel process. No interprocess communication to parent. PARALLEL_COMBINED_WITH_CHILD (PWC): Parallel execution. Input of step comes from prior step in same parallel process. No interprocess communication from child. Start partition of a range of accessed partitions. It can take one of the following values: n indicates that the start partition has been identified by the SQL compiler, and its partition number is given by n. KEY indicates that the start partition is identified at run time from partitioning key values. ROW REMOVE_LOCATION indicates that the database computes the start partition (same as the stop partition) at run time from the location of each retrieved record. The record location is obtained by a user or from a global index. INVALID indicates that the range of accessed partitions is empty. 7-21 Chapter 7 Reading Execution Plans: Advanced Table 7-1 (Cont.) PLAN_TABLE Columns Column Type Description PARTITION_STOP VARCHAR2(255) Stop partition of a range of accessed partitions. It can take one of the following values: n indicates that the stop partition has been identified by the SQL compiler, and its partition number is given by n. KEY indicates that the stop partition is identified at run time from partitioning key values. ROW REMOVE_LOCATION indicates that the database computes the stop partition (same as the start partition) at run time from the location of each retrieved record. The record location is obtained by a user or from a global index. INVALID indicates that the range of accessed partitions is empty. PARTITION_ID NUMERIC Step that has computed the pair of values of the PARTITION_START and PARTITION_STOP columns. OTHER LONG Other information that is specific to the execution step that a user might find useful. See the OTHER_TAG column. DISTRIBUTION VARCHAR2(30) Method used to distribute rows from producer query servers to consumer query servers. See Table 7-2 for more information about the possible values for this column. For more information about consumer and producer query servers, see Oracle Database VLDB and Partitioning Guide. CPU_COST NUMERIC CPU cost of the operation as estimated by the query optimizer's approach. The value of this column is proportional to the number of machine cycles required for the operation. For statements that use the rule-based approach, this column is null. IO_COST NUMERIC I/O cost of the operation as estimated by the query optimizer's approach. The value of this column is proportional to the number of data blocks read by the operation. For statements that use the rule-based approach, this column is null. TEMP_SPACE NUMERIC Temporary space, in bytes, that the operation uses as estimated by the query optimizer's approach. For statements that use the rule-based approach, or for operations that do not use any temporary space, this column is null. 7-22 Chapter 7 Reading Execution Plans: Advanced Table 7-1 (Cont.) PLAN_TABLE Columns Column Type Description ACCESS_PREDICATES VARCHAR2(4000) Predicates used to locate rows in an access structure. For example, start or stop predicates for an index range scan. FILTER_PREDICATES VARCHAR2(4000) Predicates used to filter rows before producing them. PROJECTION VARCHAR2(4000) Expressions produced by the operation. TIME NUMBER(20,2) Elapsed time in seconds of the operation as estimated by query optimization. For statements that use the rule-based approach, this column is null. The DBMS_XPLAN.DISPLAY_PLAN out, the time is in the HH:MM:SS format. QBLOCK_NAME VARCHAR2(30) Name of the query block, either systemgenerated or defined by the user with the QB_NAME hint. Table 7-2 describes the values that can appear in the DISTRIBUTION column: Table 7-2 Values of DISTRIBUTION Column of the PLAN_TABLE DISTRIBUTION Text Interpretation PARTITION (ROWID) Maps rows to query servers based on the partitioning of a table or index using the rowid of the row to UPDATE or DELETE. PARTITION (KEY) Maps rows to query servers based on the partitioning of a table or index using a set of columns. Used for partial partition-wise join, PARALLEL INSERT, CREATE TABLE AS SELECT of a partitioned table, and CREATE PARTITIONED GLOBAL INDEX. HASH Maps rows to query servers using a hash function on the join key. Used for PARALLEL JOIN or PARALLEL GROUP BY. RANGE Maps rows to query servers using ranges of the sort key. Used when the statement contains an ORDER BY clause. ROUND-ROBIN Randomly maps rows to query servers. BROADCAST Broadcasts the rows of the entire table to each query server. Used for a parallel join when one table is very small compared to the other. QC (ORDER) The QC consumes the input in order, from the first to the last query server. Used when the statement contains an ORDER BY clause. QC (RANDOM) The QC consumes the input randomly. Used when the statement does not have an ORDER BY clause. Table 7-3 lists each combination of OPERATION and OPTIONS produced by the EXPLAIN PLAN statement and its meaning within an execution plan. 7-23 Chapter 7 Reading Execution Plans: Advanced Table 7-3 OPERATION and OPTIONS Values Produced by EXPLAIN PLAN Operation Option Operation accepting multiple sets of rowids, returning the intersection of the sets, eliminating duplicates. Used for the single-column indexes access path. AND-EQUAL BITMAP Description CONVERSION TO ROWIDS converts bitmap representations to actual rowids that you can use to access the table. FROM ROWIDS converts the rowids to a bitmap representation. COUNT returns the number of rowids if the actual values are not needed. BITMAP INDEX SINGLE VALUE looks up the bitmap for a single key value in the index. RANGE SCAN retrieves bitmaps for a key value range. FULL SCAN performs a full scan of a bitmap index if there is no start or stop key. BITMAP MERGE Merges several bitmaps resulting from a range scan into one bitmap. BITMAP MINUS Subtracts bits of one bitmap from another. Row source is used for negated predicates. Use this option only if there are nonnegated predicates yielding a bitmap from which the subtraction can take place. An example appears in "Viewing Bitmap Indexes with EXPLAIN PLAN". BITMAP OR Computes the bitwise OR of two bitmaps. BITMAP AND Computes the bitwise AND of two bitmaps. BITMAP KEY ITERATION Takes each row from a table row source and finds the corresponding bitmap from a bitmap index. This set of bitmaps are then merged into one bitmap in a following BITMAP MERGE operation. CONNECT BY Retrieves rows in hierarchical order for a query containing a CONNECT BY clause. CONCATENATION Operation accepting multiple sets of rows returning the union-all of the sets. COUNT Operation counting the number of rows selected from a table. COUNT STOPKEY Count operation where the number of rows returned is limited by the ROWNUM expression in the WHERE clause. Uses inner joins for all cube access. CUBE SCAN CUBE SCAN PARTIAL OUTER Uses an outer join for at least one dimension, and inner joins for the other dimensions. CUBE SCAN OUTER Uses outer joins for all cube access. DOMAIN INDEX Retrieval of one or more rowids from a domain index. The options column contain information supplied by a user-defined domain index cost function, if any. FILTER Operation accepting a set of rows, eliminates some of them, and returns the rest. FIRST ROW Retrieval of only the first row selected by a query. 7-24 Chapter 7 Reading Execution Plans: Advanced Table 7-3 PLAN (Cont.) OPERATION and OPTIONS Values Produced by EXPLAIN Operation Option Description Operation retrieving and locking the rows selected by a query containing a FOR UPDATE clause. FOR UPDATE HASH GROUP BY Operation hashing a set of rows into groups for a query with a GROUP BY clause. HASH GROUP BY PIVOT Operation hashing a set of rows into groups for a query with a GROUP BY clause. The PIVOT option indicates a pivot-specific optimization for the HASH GROUP BY operator. Operation joining two sets of rows and returning the result. This join method is useful for joining large data sets of data (DSS, Batch). The join condition is an efficient way of accessing the second table. HASH JOIN (These are join operations.) Query optimizer uses the smaller of the two tables/data sources to build a hash table on the join key in memory. Then it scans the larger table, probing the hash table to find the joined rows. HASH JOIN ANTI Hash (left) antijoin HASH JOIN SEMI Hash (left) semijoin HASH JOIN RIGHT ANTI Hash right antijoin HASH JOIN RIGHT SEMI Hash right semijoin HASH JOIN OUTER Hash (left) outer join HASH JOIN RIGHT OUTER Hash right outer join INDEX UNIQUE SCAN Retrieval of a single rowid from an index. INDEX RANGE SCAN Retrieval of one or more rowids from an index. Indexed values are scanned in ascending order. INDEX RANGE SCAN DESCENDING Retrieval of one or more rowids from an index. Indexed values are scanned in descending order. INDEX FULL SCAN Retrieval of all rowids from an index when there is no start or stop key. Indexed values are scanned in ascending order. INDEX FULL SCAN DESCENDING Retrieval of all rowids from an index when there is no start or stop key. Indexed values are scanned in descending order. INDEX FAST FULL SCAN Retrieval of all rowids (and column values) using multiblock reads. No sorting order can be defined. Compares to a full table scan on only the indexed columns. Only available with the cost based optimizer. INDEX SKIP SCAN Retrieval of rowids from a concatenated index without using the leading column(s) in the index. Only available with the cost based optimizer. (These are access methods.) INLIST ITERATOR Iterates over the next operation in the plan for each value in the IN-list predicate. 7-25 Chapter 7 Reading Execution Plans: Advanced Table 7-3 PLAN (Cont.) OPERATION and OPTIONS Values Produced by EXPLAIN Operation Option Description INTERSECTION Operation accepting two sets of rows and returning the intersection of the sets, eliminating duplicates. MERGE JOIN Operation accepting two sets of rows, each sorted by a value, combining each row from one set with the matching rows from the other, and returning the result. (These are join operations.) MERGE JOIN OUTER Merge join operation to perform an outer join statement. MERGE JOIN ANTI Merge antijoin. MERGE JOIN SEMI Merge semijoin. MERGE JOIN CARTESIAN Can result from 1 or more of the tables not having any join conditions to any other tables in the statement. Can occur even with a join and it may not be flagged as CARTESIAN in the plan. Retrieval of rows in hierarchical order for a query containing a CONNECT BY clause. CONNECT BY FULL Retrieval of all rows from a materialized view. MAT_VIEW REWITE ACCESS SAMPLE Retrieval of sampled rows from a materialized view. MAT_VIEW REWITE ACCESS CLUSTER Retrieval of rows from a materialized view based on a value of an indexed cluster key. MAT_VIEW REWITE ACCESS HASH Retrieval of rows from materialized view based on hash cluster key value. MAT_VIEW REWITE ACCESS BY ROWID RANGE Retrieval of rows from a materialized view based on a rowid range. MAT_VIEW REWITE ACCESS SAMPLE BY ROWID Retrieval of sampled rows from a materialized view based on a rowid range. RANGE MAT_VIEW REWITE ACCESS BY USER ROWID If the materialized view rows are located using usersupplied rowids. MAT_VIEW REWITE ACCESS BY INDEX ROWID If the materialized view is nonpartitioned and rows are located using index(es). MAT_VIEW REWITE ACCESS BY GLOBAL INDEX If the materialized view is partitioned and rows are located using only global indexes. ROWID MAT_VIEW REWITE ACCESS (These are access methods.) 7-26 Chapter 7 Reading Execution Plans: Advanced Table 7-3 PLAN (Cont.) OPERATION and OPTIONS Values Produced by EXPLAIN Operation Option Description MAT_VIEW REWITE ACCESS BY LOCAL INDEX ROWID If the materialized view is partitioned and rows are located using one or more local indexes and possibly some global indexes. Partition Boundaries: The partition boundaries might have been computed by: A previous PARTITION step, in which case the PARTITION_START and PARTITION_STOP column values replicate the values present in the PARTITION step, and the PARTITION_ID contains the ID of the PARTITION step. Possible values for PARTITION_START and PARTITION_STOP are NUMBER(n), KEY, INVALID. The MAT_VIEW REWRITE ACCESS or INDEX step itself, in which case the PARTITION_ID contains the ID of the step. Possible values for PARTITION_START and PARTITION_STOP are NUMBER(n), KEY, ROW REMOVE_LOCATION (MAT_VIEW REWRITE ACCESS only), and INVALID. MINUS Operation accepting two sets of rows and returning rows appearing in the first set but not in the second, eliminating duplicates. NESTED LOOPS Operation accepting two sets of rows, an outer set and an inner set. Oracle Database compares each row of the outer set with each row of the inner set, returning rows that satisfy a condition. This join method is useful for joining small subsets of data (OLTP). The join condition is an efficient way of accessing the second table. (These are join operations.) NESTED LOOPS OUTER Nested loops operation to perform an outer join statement. Iterates over the next operation in the plan for each partition in the range given by the PARTITION_START and PARTITION_STOP columns. PARTITION describes partition boundaries applicable to a single partitioned object (table or index) or to a set of equipartitioned objects (a partitioned table and its local indexes). The partition boundaries are provided by the values of PARTITION_START and PARTITION_STOP of the PARTITION. Refer to Table 7-1 for valid values of partition start and stop. PARTITION PARTITION SINGLE Access one partition. PARTITION ITERATOR Access many partitions (a subset). PARTITION ALL Access all partitions. PARTITION INLIST Similar to iterator, but based on an IN-list predicate. PARTITION INVALID Indicates that the partition set to be accessed is empty. PX ITERATOR BLOCK, CHUNK Implements the division of an object into block or chunk ranges among a set of parallel execution servers. 7-27 Chapter 7 Reading Execution Plans: Advanced Table 7-3 PLAN (Cont.) OPERATION and OPTIONS Values Produced by EXPLAIN Operation Option Description PX COORDINATOR Implements the query coordinator that controls, schedules, and executes the parallel plan below it using parallel execution servers. It also represents a serialization point, as the end of the part of the plan executed in parallel and always has a PX SEND QC operation below it. PX PARTITION Same semantics as the regular PARTITION operation except that it appears in a parallel plan. PX RECEIVE Shows the consumer/receiver parallel execution node reading repartitioned data from a send/producer (QC or parallel execution server) executing on a PX SEND node. This information was formerly displayed into the DISTRIBUTION column. See Table 7-2. PX SEND QC (RANDOM), HASH, RANGE Implements the distribution method taking place between two parallel execution servers. Shows the boundary between two sets and how data is repartitioned on the send/producer side. This information was formerly displayed into the DISTRIBUTION column. See Table 7-2. REMOTE Retrieval of data from a remote database. SEQUENCE Operation involving accessing values of a sequence. SORT AGGREGATE Retrieval of a single row that is the result of applying a group function to a group of selected rows. SORT UNIQUE Operation sorting a set of rows to eliminate duplicates. SORT GROUP BY Operation sorting a set of rows into groups for a query with a GROUP BY clause. SORT GROUP BY PIVOT Operation sorting a set of rows into groups for a query with a GROUP BY clause. The PIVOT option indicates a pivot-specific optimization for the SORT GROUP BY operator. SORT JOIN Operation sorting a set of rows before a merge-join. SORT ORDER BY Operation sorting a set of rows for a query with an ORDER BY clause. TABLE ACCESS FULL Retrieval of all rows from a table. TABLE ACCESS SAMPLE Retrieval of sampled rows from a table. TABLE ACCESS CLUSTER Retrieval of rows from a table based on a value of an indexed cluster key. TABLE ACCESS HASH Retrieval of rows from table based on hash cluster key value. TABLE ACCESS BY ROWID RANGE Retrieval of rows from a table based on a rowid range. TABLE ACCESS SAMPLE BY ROWID Retrieval of sampled rows from a table based on a rowid range. RANGE (These are access methods.) 7-28 Chapter 7 Execution Plan Reference Table 7-3 PLAN (Cont.) OPERATION and OPTIONS Values Produced by EXPLAIN Operation Option Description TABLE ACCESS BY USER ROWID If the table rows are located using user-supplied rowids. TABLE ACCESS BY INDEX ROWID If the table is nonpartitioned and rows are located using indexes. TABLE ACCESS BY GLOBAL INDEX If the table is partitioned and rows are located using only global indexes. ROWID TABLE ACCESS BY LOCAL INDEX ROWID If the table is partitioned and rows are located using one or more local indexes and possibly some global indexes. Partition Boundaries: The partition boundaries might have been computed by: A previous PARTITION step, in which case the PARTITION_START and PARTITION_STOP column values replicate the values present in the PARTITION step, and the PARTITION_ID contains the ID of the PARTITION step. Possible values for PARTITION_START and PARTITION_STOP are NUMBER(n), KEY, INVALID. The TABLE ACCESS or INDEX step itself, in which case the PARTITION_ID contains the ID of the step. Possible values for PARTITION_START and PARTITION_STOP are NUMBER(n), KEY, ROW REMOVE_LOCATION (TABLE ACCESS only), and INVALID. TRANSPOSE Operation evaluating a PIVOT operation by transposing the results of GROUP BY to produce the final pivoted data. UNION Operation accepting two sets of rows and returns the union of the sets, eliminating duplicates. UNPIVOT Operation that rotates data from columns into rows. VIEW Operation performing a view's query and then returning the resulting rows to another operation. See Also: Oracle Database Reference for more information about PLAN_TABLE 7.3 Execution Plan Reference This section describes V$ views and PLAN_COLUMN columns. This section contains the following topics: • Execution Plan Views The following dynamic performance and data dictionary views provide information on execution plans. 7-29 Chapter 7 Execution Plan Reference • PLAN_TABLE Columns The PLAN_TABLE is used by the EXPLAIN PLAN statement. • DBMS_XPLAN Program Units The functions DISPLAY_PLAN and DISPLAY_CURSOR in DBMS_XPLAN are relevant for accessing adapted plans. 7.3.1 Execution Plan Views The following dynamic performance and data dictionary views provide information on execution plans. Table 7-4 Execution Plan Views View Description V$SQL_SHARED_CURSOR Explains why a particular child cursor is not shared with existing child cursors. Each column identifies a specific reason why the cursor cannot be shared. The USE_FEEDBACK_STATS column shows whether a child cursor fails to match because of reoptimization. V$SQL_PLAN Includes a superset of all rows appearing in all final plans. PLAN_LINE_ID is consecutively numbered, but for a single final plan, the IDs may not be consecutive. V$SQL_PLAN_STATISTICS_ALL Contains memory usage statistics for row sources that use SQL memory (sort or hash join). This view concatenates information in V$SQL_PLAN with execution statistics from V$SQL_PLAN_STATISTICS and V$SQL_WORKAREA. 7.3.2 PLAN_TABLE Columns The PLAN_TABLE is used by the EXPLAIN PLAN statement. PLAN_TABLE contains the columns listed in Table 7-5. Table 7-5 PLAN_TABLE Columns Column Type Description STATEMENT_ID VARCHAR2(30) Value of the optional STATEMENT_ID parameter specified in the EXPLAIN PLAN statement. PLAN_ID NUMBER Unique identifier of a plan in the database. TIMESTAMP DATE Date and time when the EXPLAIN PLAN statement was generated. REMARKS VARCHAR2(80) Any comment (of up to 80 bytes) you want to associate with each step of the explained plan. This column indicates whether the database used an outline or SQL profile for the query. If you need to add or change a remark on any row of the PLAN_TABLE, then use the UPDATE statement to modify the rows of the PLAN_TABLE. 7-30 Chapter 7 Execution Plan Reference Table 7-5 (Cont.) PLAN_TABLE Columns Column Type Description OPERATION VARCHAR2(30) Name of the internal operation performed in this step. In the first row generated for a statement, the column contains one of the following values: • DELETE STATEMENT • INSERT STATEMENT • SELECT STATEMENT • UPDATE STATEMENT See Table 7-6 for more information about values for this column. OPTIONS VARCHAR2(225) A variation on the operation that the OPERATION column describes. See Table 7-6 for more information about values for this column. OBJECT_NODE VARCHAR2(128) Name of the database link used to reference the object (a table name or view name). For local queries using parallel execution, this column describes the order in which the database consumes output from operations. OBJECT_OWNER VARCHAR2(30) Name of the user who owns the schema containing the table or index. OBJECT_NAME VARCHAR2(30) Name of the table or index. OBJECT_ALIAS VARCHAR2(65) Unique alias of a table or view in a SQL statement. For indexes, it is the object alias of the underlying table. OBJECT_INSTANCE NUMERIC Number corresponding to the ordinal position of the object as it appears in the original statement. The numbering proceeds from left to right, outer to inner for the original statement text. View expansion results in unpredictable numbers. OBJECT_TYPE VARCHAR2(30) Modifier that provides descriptive information about the object; for example, NONUNIQUE for indexes. OPTIMIZER VARCHAR2(255) Current mode of the optimizer. SEARCH_COLUMNS NUMBERIC Not currently used. ID NUMERIC A number assigned to each step in the execution plan. PARENT_ID NUMERIC The ID of the next execution step that operates on the output of the ID step. DEPTH NUMERIC Depth of the operation in the row source tree that the plan represents. You can use this value to indent the rows in a plan table report. POSITION NUMERIC For the first row of output, this indicates the optimizer's estimated cost of executing the statement. For the other rows, it indicates the position relative to the other children of the same parent. 7-31 Chapter 7 Execution Plan Reference Table 7-5 (Cont.) PLAN_TABLE Columns Column Type Description COST NUMERIC Cost of the operation as estimated by the optimizer's query approach. Cost is not determined for table access operations. The value of this column does not have any particular unit of measurement; it is a weighted value used to compare costs of execution plans. The value of this column is a function of the CPU_COST and IO_COST columns. CARDINALITY NUMERIC Estimate by the query optimization approach of the number of rows that the operation accessed. BYTES NUMERIC Estimate by the query optimization approach of the number of bytes that the operation accessed. OTHER_TAG VARCHAR2(255) Describes the contents of the OTHER column. Values are: • • • • • • • PARTITION_START VARCHAR2(255) SERIAL (blank): Serial execution. Currently, SQL is not loaded in the OTHER column for this case. SERIAL_FROM_REMOTE (S -> R): Serial execution at a remote site. PARALLEL_FROM_SERIAL (S -> P): Serial execution. Output of step is partitioned or broadcast to parallel execution servers. PARALLEL_TO_SERIAL (P -> S): Parallel execution. Output of step is returned to serial QC process. PARALLEL_TO_PARALLEL (P -> P): Parallel execution. Output of step is repartitioned to second set of parallel execution servers. PARALLEL_COMBINED_WITH_PARENT (PWP): Parallel execution; Output of step goes to next step in same parallel process. No interprocess communication to parent. PARALLEL_COMBINED_WITH_CHILD (PWC): Parallel execution. Input of step comes from prior step in same parallel process. No interprocess communication from child. Start partition of a range of accessed partitions. It can take one of the following values: n indicates that the start partition has been identified by the SQL compiler, and its partition number is given by n. KEY indicates that the start partition is identified at run time from partitioning key values. ROW REMOVE_LOCATION indicates that the database computes the start partition (same as the stop partition) at run time from the location of each retrieved record. The record location is obtained by a user or from a global index. INVALID indicates that the range of accessed partitions is empty. 7-32 Chapter 7 Execution Plan Reference Table 7-5 (Cont.) PLAN_TABLE Columns Column Type Description PARTITION_STOP VARCHAR2(255) Stop partition of a range of accessed partitions. It can take one of the following values: n indicates that the stop partition has been identified by the SQL compiler, and its partition number is given by n. KEY indicates that the stop partition is identified at run time from partitioning key values. ROW REMOVE_LOCATION indicates that the database computes the stop partition (same as the start partition) at run time from the location of each retrieved record. The record location is obtained by a user or from a global index. INVALID indicates that the range of accessed partitions is empty. PARTITION_ID NUMERIC Step that has computed the pair of values of the PARTITION_START and PARTITION_STOP columns. OTHER LONG Other information that is specific to the execution step that a user might find useful. See the OTHER_TAG column. DISTRIBUTION VARCHAR2(30) Method used to distribute rows from producer query servers to consumer query servers. See "Table 7-6" for more information about the possible values for this column. For more information about consumer and producer query servers, see Oracle Database VLDB and Partitioning Guide. CPU_COST NUMERIC CPU cost of the operation as estimated by the query optimizer's approach. The value of this column is proportional to the number of machine cycles required for the operation. For statements that use the rule-based approach, this column is null. IO_COST NUMERIC I/O cost of the operation as estimated by the query optimizer's approach. The value of this column is proportional to the number of data blocks read by the operation. For statements that use the rule-based approach, this column is null. TEMP_SPACE NUMERIC Temporary space, in bytes, used by the operation as estimated by the query optimizer's approach. For statements that use the rule-based approach, or for operations that do not use any temporary space, this column is null. ACCESS_PREDICATES VARCHAR2(4000) Predicates used to locate rows in an access structure. For example, start or stop predicates for an index range scan. FILTER_PREDICATES VARCHAR2(4000) Predicates used to filter rows before producing them. PROJECTION VARCHAR2(4000) Expressions produced by the operation. TIME NUMBER(20,2) Elapsed time in seconds of the operation as estimated by query optimization. For statements that use the rulebased approach, this column is null. QBLOCK_NAME VARCHAR2(30) Name of the query block, either system-generated or defined by the user with the QB_NAME hint. 7-33 Chapter 7 Execution Plan Reference Table 7-6 Values of DISTRIBUTION Column of the PLAN_TABLE DISTRIBUTION Text Interpretation PARTITION (ROWID) Maps rows to query servers based on the partitioning of a table or index using the rowid of the row to UPDATE/DELETE. PARTITION (KEY) Maps rows to query servers based on the partitioning of a table or index using a set of columns. Used for partial partition-wise join, PARALLEL INSERT, CREATE TABLE AS SELECT of a partitioned table, and CREATE PARTITIONED GLOBAL INDEX. HASH Maps rows to query servers using a hash function on the join key. Used for PARALLEL JOIN or PARALLEL GROUP BY. RANGE Maps rows to query servers using ranges of the sort key. Used when the statement contains an ORDER BY clause. ROUND-ROBIN Randomly maps rows to query servers. BROADCAST Broadcasts the rows of the entire table to each query server. Used for a parallel join when one table is very small compared to the other. QC (ORDER) The QC consumes the input in order, from the first to the last query server. Used when the statement contains an ORDER BY clause. QC (RANDOM) The QC consumes the input randomly. Used when the statement does not have an ORDER BY clause. Table 7-7 lists each combination of OPERATION and OPTIONS produced by the EXPLAIN PLAN statement and its meaning within an execution plan. Table 7-7 Operation OPERATION and OPTIONS Values Produced by EXPLAIN PLAN Option Operation accepting multiple sets of rowids, returning the intersection of the sets, eliminating duplicates. Used for the singlecolumn indexes access path. AND-EQUAL BITMAP Description CONVERSION TO ROWIDS converts bitmap representations to actual rowids that you can use to access the table. FROM ROWIDS converts the rowids to a bitmap representation. COUNT returns the number of rowids if the actual values are not needed. BITMAP INDEX SINGLE VALUE looks up the bitmap for a single key value in the index. RANGE SCAN retrieves bitmaps for a key value range. FULL SCAN performs a full scan of a bitmap index if there is no start or stop key. BITMAP MERGE Merges several bitmaps resulting from a range scan into one bitmap. BITMAP MINUS Subtracts bits of one bitmap from another. Row source is used for negated predicates. This option is usable only if there are nonnegated predicates yielding a bitmap from which the subtraction can take place. BITMAP OR Computes the bitwise OR of two bitmaps. BITMAP AND Computes the bitwise AND of two bitmaps. 7-34 Chapter 7 Execution Plan Reference Table 7-7 (Cont.) OPERATION and OPTIONS Values Produced by EXPLAIN PLAN Operation Option Description BITMAP KEY ITERATION Takes each row from a table row source and finds the corresponding bitmap from a bitmap index. This set of bitmaps are then merged into one bitmap in a following BITMAP MERGE operation. CONNECT BY Retrieves rows in hierarchical order for a query containing a CONNECT BY clause. CONCATENATION Operation accepting multiple sets of rows returning the union-all of the sets. COUNT Operation counting the number of rows selected from a table. COUNT STOPKEY Count operation where the number of rows returned is limited by the ROWNUM expression in the WHERE clause. Joins a table or view on the left and a cube on the right. CUBE JOIN See Oracle Database SQL Language Reference to learn about the NO_USE_CUBE and USE_CUBE hints. CUBE JOIN ANTI Uses an antijoin for a table or view on the left and a cube on the right. CUBE JOIN ANTI SNA Uses an antijoin (single-sided null aware) for a table or view on the left and a cube on the right. The join column on the right (cube side) is NOT NULL. CUBE JOIN OUTER Uses an outer join for a table or view on the left and a cube on the right. CUBE JOIN RIGHT SEMI Uses a right semijoin for a table or view on the left and a cube on the right. Uses inner joins for all cube access. CUBE SCAN CUBE SCAN PARTIAL OUTER Uses an outer join for at least one dimension, and inner joins for the other dimensions. CUBE SCAN OUTER Uses outer joins for all cube access. DOMAIN INDEX Retrieval of one or more rowids from a domain index. The options column contain information supplied by a user-defined domain index cost function, if any. FILTER Operation accepting a set of rows, eliminates some of them, and returns the rest. FIRST ROW Retrieval of only the first row selected by a query. FOR UPDATE Operation retrieving and locking the rows selected by a query containing a FOR UPDATE clause. HASH GROUP BY Operation hashing a set of rows into groups for a query with a GROUP BY clause. HASH GROUP BY PIVOT Operation hashing a set of rows into groups for a query with a GROUP BY clause. The PIVOT option indicates a pivot-specific optimization for the HASH GROUP BY operator. HASH JOIN (These are join operations.) Operation joining two sets of rows and returning the result. This join method is useful for joining large data sets of data (DSS, Batch). The join condition is an efficient way of accessing the second table. Query optimizer uses the smaller of the two tables/data sources to build a hash table on the join key in memory. Then it scans the larger table, probing the hash table to find the joined rows. 7-35 Chapter 7 Execution Plan Reference Table 7-7 (Cont.) OPERATION and OPTIONS Values Produced by EXPLAIN PLAN Operation Option Description HASH JOIN ANTI Hash (left) antijoin HASH JOIN SEMI Hash (left) semijoin HASH JOIN RIGHT ANTI Hash right antijoin HASH JOIN RIGHT SEMI Hash right semijoin HASH JOIN OUTER Hash (left) outer join HASH JOIN RIGHT OUTER Hash right outer join INDEX UNIQUE SCAN Retrieval of a single rowid from an index. INDEX RANGE SCAN Retrieval of one or more rowids from an index. Indexed values are scanned in ascending order. INDEX RANGE SCAN DESCENDING Retrieval of one or more rowids from an index. Indexed values are scanned in descending order. INDEX FULL SCAN Retrieval of all rowids from an index when there is no start or stop key. Indexed values are scanned in ascending order. INDEX FULL SCAN DESCENDING Retrieval of all rowids from an index when there is no start or stop key. Indexed values are scanned in descending order. INDEX FAST FULL SCAN Retrieval of all rowids (and column values) using multiblock reads. No sorting order can be defined. Compares to a full table scan on only the indexed columns. Only available with the cost based optimizer. INDEX SKIP SCAN Retrieval of rowids from a concatenated index without using the leading column(s) in the index. Only available with the cost based optimizer. (These are access methods.) INLIST ITERATOR Iterates over the next operation in the plan for each value in the INlist predicate. INTERSECTION Operation accepting two sets of rows and returning the intersection of the sets, eliminating duplicates. MERGE JOIN Operation accepting two sets of rows, each sorted by a value, combining each row from one set with the matching rows from the other, and returning the result. (These are join operations.) MERGE JOIN OUTER Merge join operation to perform an outer join statement. MERGE JOIN ANTI Merge antijoin. MERGE JOIN SEMI Merge semijoin. MERGE JOIN CARTESIAN Can result from 1 or more of the tables not having any join conditions to any other tables in the statement. Can occur even with a join and it may not be flagged as CARTESIAN in the plan. Retrieval of rows in hierarchical order for a query containing a CONNECT BY clause. CONNECT BY MAT_VIEW REWITE ACCESS FULL Retrieval of all rows from a materialized view. (These are access methods.) 7-36 Chapter 7 Execution Plan Reference Table 7-7 (Cont.) OPERATION and OPTIONS Values Produced by EXPLAIN PLAN Operation Option Description MAT_VIEW REWITE ACCESS SAMPLE Retrieval of sampled rows from a materialized view. MAT_VIEW REWITE ACCESS CLUSTER Retrieval of rows from a materialized view based on a value of an indexed cluster key. MAT_VIEW REWITE ACCESS HASH Retrieval of rows from materialized view based on hash cluster key value. MAT_VIEW REWITE ACCESS BY ROWID RANGE Retrieval of rows from a materialized view based on a rowid range. MAT_VIEW REWITE ACCESS SAMPLE BY ROWID RANGE Retrieval of sampled rows from a materialized view based on a rowid range. MAT_VIEW REWITE ACCESS BY USER ROWID If the materialized view rows are located using user-supplied rowids. MAT_VIEW REWITE ACCESS BY INDEX ROWID If the materialized view is nonpartitioned and rows are located using indexes. MAT_VIEW REWITE ACCESS BY GLOBAL INDEX ROWID If the materialized view is partitioned and rows are located using only global indexes. MAT_VIEW REWITE ACCESS BY LOCAL INDEX ROWID If the materialized view is partitioned and rows are located using one or more local indexes and possibly some global indexes. Partition Boundaries: The partition boundaries might have been computed by: A previous PARTITION step, in which case the PARTITION_START and PARTITION_STOP column values replicate the values present in the PARTITION step, and the PARTITION_ID contains the ID of the PARTITION step. Possible values for PARTITION_START and PARTITION_STOP are NUMBER(n), KEY, and INVALID. The MAT_VIEW REWRITE ACCESS or INDEX step itself, in which case the PARTITION_ID contains the ID of the step. Possible values for PARTITION_START and PARTITION_STOP are NUMBER(n), KEY, ROW REMOVE_LOCATION (MAT_VIEW REWRITE ACCESS only), and INVALID. MINUS Operation accepting two sets of rows and returning rows appearing in the first set but not in the second, eliminating duplicates. NESTED LOOPS Operation accepting two sets of rows, an outer set and an inner set. Oracle Database compares each row of the outer set with each row of the inner set, returning rows that satisfy a condition. This join method is useful for joining small subsets of data (OLTP). The join condition is an efficient way of accessing the second table. (These are join operations.) NESTED LOOPS OUTER Nested loops operation to perform an outer join statement. Iterates over the next operation in the plan for each partition in the range given by the PARTITION_START and PARTITION_STOP columns. PARTITION describes partition boundaries applicable to a single partitioned object (table or index) or to a set of equipartitioned objects (a partitioned table and its local indexes). The partition boundaries are provided by the values of PARTITION_START and PARTITION_STOP of the PARTITION. Refer to Table 7-4 for valid values of partition start and stop. PARTITION PARTITION SINGLE Access one partition. PARTITION ITERATOR Access many partitions (a subset). 7-37 Chapter 7 Execution Plan Reference Table 7-7 (Cont.) OPERATION and OPTIONS Values Produced by EXPLAIN PLAN Operation Option Description PARTITION ALL Access all partitions. PARTITION INLIST Similar to iterator, but based on an IN-list predicate. PARTITION INVALID Indicates that the partition set to be accessed is empty. PX ITERATOR BLOCK, CHUNK Implements the division of an object into block or chunk ranges among a set of parallel execution servers. PX COORDINATOR Implements the Query Coordinator which controls, schedules, and executes the parallel plan below it using parallel execution servers. It also represents a serialization point, as the end of the part of the plan executed in parallel and always has a PX SEND QC operation below it. PX PARTITION Same semantics as the regular PARTITION operation except that it appears in a parallel plan. PX RECEIVE Shows the consumer/receiver parallel execution node reading repartitioned data from a send/producer (QC or parallel execution server) executing on a PX SEND node. This information was formerly displayed into the DISTRIBUTION column. See Table 7-5. PX SEND QC (RANDOM), HASH, RANGE Implements the distribution method taking place between two sets of parallel execution servers. Shows the boundary between two sets and how data is repartitioned on the send/producer side (QC or side. This information was formerly displayed into the DISTRIBUTION column. See Table 7-5. REMOTE Retrieval of data from a remote database. SEQUENCE Operation involving accessing values of a sequence. SORT AGGREGATE Retrieval of a single row that is the result of applying a group function to a group of selected rows. SORT UNIQUE Operation sorting a set of rows to eliminate duplicates. SORT GROUP BY Operation sorting a set of rows into groups for a query with a GROUP BY clause. SORT GROUP BY PIVOT Operation sorting a set of rows into groups for a query with a GROUP BY clause. The PIVOT option indicates a pivot-specific optimization for the SORT GROUP BY operator. SORT JOIN Operation sorting a set of rows before a merge-join. SORT ORDER BY Operation sorting a set of rows for a query with an ORDER BY clause. TABLE ACCESS FULL Retrieval of all rows from a table. TABLE ACCESS SAMPLE Retrieval of sampled rows from a table. TABLE ACCESS CLUSTER Retrieval of rows from a table based on a value of an indexed cluster key. TABLE ACCESS HASH Retrieval of rows from table based on hash cluster key value. TABLE ACCESS BY ROWID RANGE Retrieval of rows from a table based on a rowid range. TABLE ACCESS SAMPLE BY ROWID RANGE Retrieval of sampled rows from a table based on a rowid range. TABLE ACCESS BY USER ROWID If the table rows are located using user-supplied rowids. (These are access methods.) 7-38 Chapter 7 Execution Plan Reference Table 7-7 (Cont.) OPERATION and OPTIONS Values Produced by EXPLAIN PLAN Operation Option Description TABLE ACCESS BY INDEX ROWID If the table is nonpartitioned and rows are located using index(es). TABLE ACCESS BY GLOBAL INDEX ROWID If the table is partitioned and rows are located using only global indexes. TABLE ACCESS BY LOCAL INDEX ROWID If the table is partitioned and rows are located using one or more local indexes and possibly some global indexes. Partition Boundaries: The partition boundaries might have been computed by: A previous PARTITION step, in which case the PARTITION_START and PARTITION_STOP column values replicate the values present in the PARTITION step, and the PARTITION_ID contains the ID of the PARTITION step. Possible values for PARTITION_START and PARTITION_STOP are NUMBER(n), KEY, and INVALID. The TABLE ACCESS or INDEX step itself, in which case the PARTITION_ID contains the ID of the step. Possible values for PARTITION_START and PARTITION_STOP are NUMBER(n), KEY, ROW REMOVE_LOCATION (TABLE ACCESS only), and INVALID. TRANSPOSE Operation evaluating a PIVOT operation by transposing the results of GROUP BY to produce the final pivoted data. UNION Operation accepting two sets of rows and returns the union of the sets, eliminating duplicates. UNPIVOT Operation that rotates data from columns into rows. VIEW Operation performing a view's query and then returning the resulting rows to another operation. See Also: Oracle Database Reference for more information about PLAN_TABLE 7.3.3 DBMS_XPLAN Program Units The functions DISPLAY_PLAN and DISPLAY_CURSOR in DBMS_XPLAN are relevant for accessing adapted plans. 7-39 Chapter 7 Execution Plan Reference Table 7-8 Queries DBMS_XPLAN Functions and Parameters Relevant for Adaptive Functions Notes DISPLAY_PLAN The FORMAT argument supports the modifier ADAPTIVE. When you specify ADAPTIVE, the output includes the default plan. For each dynamic subplan, the plan shows a list of the row sources from the original that may be replaced, and the row sources that would replace them. If the format argument specifies the outline display, then the function displays the hints for each option in the dynamic subplan. If the plan is not an adaptive query plan, then the function displays the default plan. When you do not specify ADAPTIVE, the plan is shown as-is, but with additional comments in the Note section that show any row sources that are dynamic. DISPLAY_CURSOR The FORMAT argument supports the modifier ADAPTIVE. When you specify ADAPTIVE, the output includes: • • • • The final plan. If the execution has not completed, then the output shows the current plan. This section also includes notes about run-time optimizations that affect the plan. Recommended plan. In reporting mode, the output includes the plan that would be chosen based on execution statistics. Dynamic plan. The output summarizes the portions of the plan that differ from the default plan chosen by the optimizer. Reoptimization. The output displays the plan that would be chosen on a subsequent execution because of reoptimization. See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_XPLAN 7-40 Part IV SQL Operators: Access Paths and Joins A row source is a set of rows returned by a step in the execution plan. A SQL operator acts on a row source. A unary operator acts on one input, as with access paths. A binary operator acts on two outputs, as with joins. This part contains the following chapters: • Optimizer Access Paths An access path is a technique used by a query to retrieve rows from a row source. • Joins Oracle Database provides several optimizations for joining row sets. 8 Optimizer Access Paths An access path is a technique used by a query to retrieve rows from a row source. This chapter contains the following topics: • Introduction to Access Paths A row source is a set of rows returned by a step in an execution plan. A row source can be a table, view, or result of a join or grouping operation. • Table Access Paths A table is the basic unit of data organization in an Oracle database. • B-Tree Index Access Paths An index is an optional structure, associated with a table or table cluster, that can sometimes speed data access. • Bitmap Index Access Paths Bitmap indexes combine the indexed data with a rowid range. • Table Cluster Access Paths A table cluster is a group of tables that share common columns and store related data in the same blocks. When tables are clustered, a single data block can contain rows from multiple tables. 8.1 Introduction to Access Paths A row source is a set of rows returned by a step in an execution plan. A row source can be a table, view, or result of a join or grouping operation. A unary operation such as an access path, which is a technique used by a query to retrieve rows from a row source, accepts a single row source as input. For example, a full table scan is the retrieval of rows of a single row source. In contrast, a join operation is binary and receives inputs from two row sources The database uses different access paths for different relational data structures. The following table summarizes common access paths for the major data structures. Table 8-1 Data Structures and Access Paths Access Path Heap-Organized Tables Full Table Scans x Table Access by Rowid x Sample Table Scans x B-Tree Indexes and IOTs Index Unique Scans x Index Range Scans x Index Full Scans x Index Fast Full Scans x Bitmap Indexes Table Clusters 8-1 Chapter 8 Table Access Paths Table 8-1 (Cont.) Data Structures and Access Paths Access Path Heap-Organized Tables B-Tree Indexes and IOTs Index Skip Scans x Index Join Scans x Bitmap Indexes Bitmap Index Single Value x Bitmap Index Range Scans x Bitmap Merge x Bitmap Index Range Scans x Table Clusters Cluster Scans x Hash Scans x The optimizer considers different possible execution plans, and then assigns each plan a cost. The optimizer chooses the plan with the lowest cost. In general, index access paths are more efficient for statements that retrieve a small subset of table rows, whereas full table scans are more efficient when accessing a large portion of a table. See Also: • "Joins" • "Cost-Based Optimization" • Oracle Database Concepts for an overview of these structures 8.2 Table Access Paths A table is the basic unit of data organization in an Oracle database. Relational tables are the most common table type. Relational tables have with the following organizational characteristics: • A heap-organized table does not store rows in any particular order. • An index-organized table orders rows according to the primary key values. • An external table is a read-only table whose metadata is stored in the database but whose data is stored outside the database. This section explains optimizer access paths for heap-organized tables, and contains the following topics: • About Heap-Organized Table Access By default, a table is organized as a heap, which means that the database places rows where they fit best rather than in a user-specified order. 8-2 Chapter 8 Table Access Paths • Full Table Scans A full table scan reads all rows from a table, and then filters out those rows that do not meet the selection criteria. • Table Access by Rowid A rowid is an internal representation of the storage location of data. • Sample Table Scans A sample table scan retrieves a random sample of data from a simple table or a complex SELECT statement, such as a statement involving joins and views. • In-Memory Table Scans An In-Memory scan retrieves rows from the In-Memory Column Store (IM column store). See Also: • Oracle Database Concepts for an overview of tables • Oracle Database Administrator’s Guide to learn how to manage tables 8.2.1 About Heap-Organized Table Access By default, a table is organized as a heap, which means that the database places rows where they fit best rather than in a user-specified order. As users add rows, the database places the rows in the first available free space in the data segment. Rows are not guaranteed to be retrieved in the order in which they were inserted. This section contains the following topics: • Row Storage in Data Blocks and Segments: A Primer The database stores rows in data blocks. In tables, the database can write a row anywhere in the bottom part of the block. Oracle Database uses the block overhead, which contains the row directory and table directory, to manage the block itself. • Importance of Rowids for Row Access Every row in a heap-organized table has a rowid unique to this table that corresponds to the physical address of a row piece. A rowid is a 10-byte physical address of a row. • Direct Path Reads In a direct path read, the database reads buffers from disk directly into the PGA, bypassing the SGA entirely. 8.2.1.1 Row Storage in Data Blocks and Segments: A Primer The database stores rows in data blocks. In tables, the database can write a row anywhere in the bottom part of the block. Oracle Database uses the block overhead, which contains the row directory and table directory, to manage the block itself. An extent is made up of logically contiguous data blocks. The blocks may not be physically contiguous on disk. A segment is a set of extents that contains all the data for a logical storage structure within a tablespace. For example, Oracle Database 8-3 Chapter 8 Table Access Paths allocates one or more extents to form the data segment for a table. The database also allocates one or more extents to form the index segment for a table. By default, the database uses automatic segment space management (ASSM) for permanent, locally managed tablespaces. When a session first inserts data into a table, the database formats a bitmap block. The bitmap tracks the blocks in the segment. The database uses the bitmap to find free blocks and then formats each block before writing to it. ASSM spread out inserts among blocks to avoid concurrency issues. The high water mark (HWM) is the point in a segment beyond which data blocks are unformatted and have never been used. Below the HWM, a block may be formatted and written to, formatted and empty, or unformatted. The low high water mark (low HWM) marks the point below which all blocks are known to be formatted because they either contain data or formerly contained data. During a full table scan, the database reads all blocks up to the low HWM, which are known to be formatted, and then reads the segment bitmap to determine which blocks between the HWM and low HWM are formatted and safe to read. The database knows not to read past the HWM because these blocks are unformatted. See Also: Oracle Database Concepts to learn about data block storage 8.2.1.2 Importance of Rowids for Row Access Every row in a heap-organized table has a rowid unique to this table that corresponds to the physical address of a row piece. A rowid is a 10-byte physical address of a row. The rowid points to a specific file, block, and row number. For example, in the rowid AAAPecAAFAAAABSAAA, the final AAA represents the row number. The row number is an index into a row directory entry. The row directory entry contains a pointer to the location of the row on the block. The database can sometimes move a row in the bottom part of the block. For example, if row movement is enabled, then the row can move because of partition key updates, Flashback Table operations, shrink table operations, and so on. If the database moves a row within a block, then the database updates the row directory entry to modify the pointer. The rowid stays constant. Oracle Database uses rowids internally for the construction of indexes. For example, each key in a B-tree index is associated with a rowid that points to the address of the associated row. Physical rowids provide the fastest possible access to a table row, enabling the database to retrieve a row in as little as a single I/O. See Also: Oracle Database Concepts to learn about rowids 8-4 Chapter 8 Table Access Paths 8.2.1.3 Direct Path Reads In a direct path read, the database reads buffers from disk directly into the PGA, bypassing the SGA entirely. The following figure shows the difference between scattered and sequential reads, which store buffers in the SGA, and direct path reads. Figure 8-1 Direct Path Reads Database Buffer Cache Database Buffer Cache SGA Buffer Cache SGA Buffer Cache Process PGA Sort Area Hash Area Session Memory Bitmap Merge Area Persistent Area Runtime Area Direct path read DB File Sequential Read DB File Scattered Read Direct Path Read Situations in which Oracle Database may perform direct path reads include: • Execution of a CREATE TABLE AS SELECT statement • Execution of an ALTER REBUILD or ALTER MOVE statement • Reads from a temporary tablespace • Parallel queries • Reads from a LOB segment See Also: Oracle Database Performance Tuning Guide to learn about wait events for direct path reads 8-5 Chapter 8 Table Access Paths 8.2.2 Full Table Scans A full table scan reads all rows from a table, and then filters out those rows that do not meet the selection criteria. This section contains the following topics: • When the Optimizer Considers a Full Table Scan In general, the optimizer chooses a full table scan when it cannot use a different access path, or another usable access path is higher cost. • How a Full Table Scan Works In a full table scan, the database sequentially reads every formatted block under the high water mark. The database reads each block only once. • Full Table Scan: Example This example scans the hr.employees table. 8.2.2.1 When the Optimizer Considers a Full Table Scan In general, the optimizer chooses a full table scan when it cannot use a different access path, or another usable access path is higher cost. The following table shows typical reasons for choosing a full table scan. Table 8-2 Typical Reasons for a Full Table Scan Reason Explanation To Learn More No index exists. If no index exists, then the optimizer uses a full table scan. Oracle Database Concepts The query predicate applies a function to the indexed column. Unless the index is a function-based "Guidelines for Using index, the database indexes the Function-Based Indexes values of the column, not the values for Performance" of the column with the function applied. A typical application-level mistake is to index a character column, such as char_col, and then query the column using syntax such as WHERE char_col=1. The database implicitly applies a TO_NUMBER function to the constant number 1, which prevents use of the index. A SELECT COUNT(*) query is issued, and an index exists, but the indexed column contains nulls. The optimizer cannot use the index to "B-Tree Indexes and count the number of table rows Nulls" because the index cannot contain null entries. The query predicate does For example, an index might exist on "Index Skip Scans" not use the leading edge of employees(first_name,last_name). a B-tree index. If a user issues a query with the predicate WHERE last_name='KING', then the optimizer may not choose an index because column first_name is not in the predicate. However, in this situation the optimizer may choose to use an index skip scan. 8-6 Chapter 8 Table Access Paths Table 8-2 (Cont.) Typical Reasons for a Full Table Scan Reason Explanation To Learn More The query is unselective. If the optimizer determines that the query requires most of the blocks in the table, then it uses a full table scan, even though indexes are available. Full table scans can use larger I/O calls. Making fewer large I/O calls is cheaper than making many smaller calls. "Selectivity" The table statistics are stale. For example, a table was small, but "Introduction to Optimizer now has grown large. If the table Statistics" statistics are stale and do not reflect the current size of the table, then the optimizer does not know that an index is now most efficient than a full table scan. The table is small. If a table contains fewer than n blocks Oracle Database under the high water mark, where n Reference equals the setting for the DB_FILE_MULTIBLOCK_READ_COUNT initialization parameter, then a full table scan may be cheaper than an index range scan. The scan may be less expensive regardless of the fraction of tables being accessed or indexes present. The table has a high degree of parallelism. A high degree of parallelism for a table skews the optimizer toward full table scans over range scans. Query the value in the ALL_TABLES.DEGREE column to determine the degree of parallelism. The query uses a full table scan hint. The hint FULL(table alias) instructs Oracle Database SQL the optimizer to use a full table scan. Language Reference Oracle Database Reference 8.2.2.2 How a Full Table Scan Works In a full table scan, the database sequentially reads every formatted block under the high water mark. The database reads each block only once. The following graphic depicts a scan of a table segment, showing how the scan skips unformatted blocks below the high water mark. 8-7 Chapter 8 Table Access Paths Figure 8-2 High Water Mark Low HWM Sequential Read HWM Used Never Used, Unformatted Because the blocks are adjacent, the database can speed up the scan by making I/O calls larger than a single block, known as a multiblock read. The size of a read call ranges from one block to the number of blocks specified by the DB_FILE_MULTIBLOCK_READ_COUNT initialization parameter. For example, setting this parameter to 4 instructs the database to read up to 4 blocks in a single call. The algorithms for caching blocks during full table scans are complex. For example, the database caches blocks differently depending on whether tables are small or large. See Also: • "Table 19-2" • Oracle Database Concepts for an overview of the default caching mode • Oracle Database Reference to learn about the DB_FILE_MULTIBLOCK_READ_COUNT initialization parameter 8.2.2.3 Full Table Scan: Example This example scans the hr.employees table. The following statement queries monthly salaries over $4000: SELECT salary FROM hr.employees WHERE salary > 4000; Example 8-1 Full Table Scan The following plan was retrieved using the DBMS_XPLAN.DISPLAY_CURSOR function. Because no index exists on the salary column, the optimizer cannot use an index range scan, and so uses a full table scan. 8-8 Chapter 8 Table Access Paths SQL_ID 54c20f3udfnws, child number 0 ------------------------------------select salary from hr.employees where salary > 4000 Plan hash value: 3476115102 ------------------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | | 3 (100)| | |* 1 | TABLE ACCESS FULL| EMPLOYEES | 98 | 6762 | 3 (0)| 00:00:01 | ------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------1 - filter("SALARY">4000) 8.2.3 Table Access by Rowid A rowid is an internal representation of the storage location of data. The rowid of a row specifies the data file and data block containing the row and the location of the row in that block. Locating a row by specifying its rowid is the fastest way to retrieve a single row because it specifies the exact location of the row in the database. Note: Rowids can change between versions. Accessing data based on position is not recommended because rows can move. This section contains the following topics: • When the Optimizer Chooses Table Access by Rowid In most cases, the database accesses a table by rowid after a scan of one or more indexes. • How Table Access by Rowid Works To access a table by rowid, the database performs multiple steps. • Table Access by Rowid: Example This example demonstrates rowid access of the hr.employees table. See Also: Oracle Database Development Guide to learn more about rowids 8.2.3.1 When the Optimizer Chooses Table Access by Rowid In most cases, the database accesses a table by rowid after a scan of one or more indexes. 8-9 Chapter 8 Table Access Paths However, table access by rowid need not follow every index scan. If the index contains all needed columns, then access by rowid might not occur. Related Topics • Index Fast Full Scans An index fast full scan reads the index blocks in unsorted order, as they exist on disk. This scan does not use the index to probe the table, but reads the index instead of the table, essentially using the index itself as a table. 8.2.3.2 How Table Access by Rowid Works To access a table by rowid, the database performs multiple steps. The database does the following: 1. Obtains the rowids of the selected rows, either from the statement WHERE clause or through an index scan of one or more indexes Table access may be needed for columns in the statement not present in the index. 2. Locates each selected row in the table based on its rowid 8.2.3.3 Table Access by Rowid: Example This example demonstrates rowid access of the hr.employees table. Assume that you run the following query: SELECT * FROM employees WHERE employee_id > 190; Step 2 of the following plan shows a range scan of the emp_emp_id_pk index on the hr.employees table. The database uses the rowids obtained from the index to find the corresponding rows from the employees table, and then retrieve them. The BATCHED access shown in Step 1 means that the database retrieves a few rowids from the index, and then attempts to access rows in block order to improve the clustering and reduce the number of times that the database must access a block. -------------------------------------------------------------------------------|Id| Operation | Name |Rows|Bytes|Cost(%CPU)|Time| -------------------------------------------------------------------------------| 0| SELECT STATEMENT | | | |2(100)| | | 1| TABLE ACCESS BY INDEX ROWID BATCHED|EMPLOYEES |16|1104|2 (0)|00:00:01| |*2| INDEX RANGE SCAN |EMP_EMP_ID_PK|16| |1 (0)|00:00:01| -------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------2 - access("EMPLOYEE_ID">190) 8.2.4 Sample Table Scans A sample table scan retrieves a random sample of data from a simple table or a complex SELECT statement, such as a statement involving joins and views. 8-10 Chapter 8 Table Access Paths This section contains the following topics: • When the Optimizer Chooses a Sample Table Scan The database uses a sample table scan when a statement FROM clause includes the SAMPLE keyword. • Sample Table Scans: Example This example uses a sample table scan to access 1% of the employees table, sampling by blocks instead of rows. 8.2.4.1 When the Optimizer Chooses a Sample Table Scan The database uses a sample table scan when a statement FROM clause includes the SAMPLE keyword. The SAMPLE clause has the following forms: • SAMPLE (sample_percent) The database reads a specified percentage of rows in the table to perform a sample table scan. • SAMPLE BLOCK (sample_percent) The database reads a specified percentage of table blocks to perform a sample table scan. The sample_percent specifies the percentage of the total row or block count to include in the sample. The value must be in the range .000001 up to, but not including, 100. This percentage indicates the probability of each row, or each cluster of rows in block sampling, being selected for the sample. It does not mean that the database retrieves exactly sample_percent of the rows. Note: Block sampling is possible only during full table scans or index fast full scans. If a more efficient execution path exists, then the database does not sample blocks. To guarantee block sampling for a specific table or index, use the FULL or INDEX_FFS hint. See Also: • "Influencing the Optimizer with Hints" • Oracle Database SQL Language Reference to learn about the SAMPLE clause 8.2.4.2 Sample Table Scans: Example This example uses a sample table scan to access 1% of the employees table, sampling by blocks instead of rows. 8-11 Chapter 8 Table Access Paths Example 8-2 Sample Table Scan SELECT * FROM hr.employees SAMPLE BLOCK (1); The EXPLAIN PLAN output for this statement might look as follows: ------------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| ------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 1 | 68 | 3 (34)| | 1 | TABLE ACCESS SAMPLE | EMPLOYEES | 1 | 68 | 3 (34)| ------------------------------------------------------------------------- 8.2.5 In-Memory Table Scans An In-Memory scan retrieves rows from the In-Memory Column Store (IM column store). The IM column store is an optional SGA area that stores copies of tables and partitions in a special columnar format optimized for rapid scans. This section contains the following topics: • When the Optimizer Chooses an In-Memory Table Scan The optimizer cost model is aware of the content of the IM column store. • In-Memory Query Controls You can control In-Memory queries using initialization parameters. • In-Memory Table Scans: Example This example shows an execution plan that includes the TABLE ACCESS INMEMORY operation. See Also: Oracle Database In-Memory Guide for an introduction to the IM column store 8.2.5.1 When the Optimizer Chooses an In-Memory Table Scan The optimizer cost model is aware of the content of the IM column store. When a user executes a query that references a table in the IM column store, the optimizer calculates the cost of all possible access methods—including the In-Memory table scan—and selects the access method with the lowest cost. 8.2.5.2 In-Memory Query Controls You can control In-Memory queries using initialization parameters. The following database initialization parameters affect the In-Memory features: • INMEMORY_QUERY This parameter enables or disables In-Memory queries for the database at the session or system level. This parameter is helpful when you want to test workloads with and without the use of the IM column store. 8-12 Chapter 8 Table Access Paths • OPTIMIZER_INMEMORY_AWARE This parameter enables (TRUE) or disables (FALSE) all of the In-Memory enhancements made to the optimizer cost model, table expansion, bloom filters, and so on. Setting the parameter to FALSE causes the optimizer to ignore the InMemory property of tables during the optimization of SQL statements. • OPTIMIZER_FEATURES_ENABLE When set to values lower than 12.1.0.2, this parameter has the same effect as setting OPTIMIZER_INMEMORY_AWARE to FALSE. To enable or disable In-Memory queries, you can specify the INMEMORY or NO_INMEMORY hints, which are the per-query equivalent of the INMEMORY_QUERY initialization parameter. If a SQL statement uses the INMEMORY hint, but the object it references is not already loaded in the IM column store, then the database does not wait for the object to be populated in the IM column store before executing the statement. However, initial access of the object triggers the object population in the IM column store. See Also: • Oracle Database Reference to learn more about the INMEMORY_QUERY, OPTIMIZER_INMEMORY_AWARE, and OPTIMIZER_FEATURES_ENABLE initialization parameters • Oracle Database SQL Language Reference to learn more about the INMEMORY hints 8.2.5.3 In-Memory Table Scans: Example This example shows an execution plan that includes the TABLE ACCESS INMEMORY operation. The following example shows a query of the oe.product_information table, which has been altered with the INMEMORY HIGH option. Example 8-3 In-Memory Table Scan SELECT * FROM oe.product_information WHERE list_price > 10 ORDER BY product_id The plan for this statement might look as follows, with the INMEMORY keyword in Step 2 indicating that some or all of the object was accessed from the IM column store: SQL> SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR); SQL_ID 2mb4h57x8pabw, child number 0 ------------------------------------select * from oe.product_information where list_price > 10 order byproduct_id Plan hash value: 2256295385 -------------------------------------------------------------------------------------------|Id| Operation | Name |Rows|Bytes |TempSpc|Cost(%CPU)|Time| -------------------------------------------------------------------------------------------| 0| SELECT STATEMENT | | | | |21 (100)| | 8-13 Chapter 8 B-Tree Index Access Paths | 1| SORT ORDER BY | | 285| 62415|82000|21 (5)|00:00:01| |*2| TABLE ACCESS INMEMORY FULL| PRODUCT_INFORMATION | 285| 62415| | 5 (0)|00:00:01| -------------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------2 - inmemory("LIST_PRICE">10) filter("LIST_PRICE">10) 8.3 B-Tree Index Access Paths An index is an optional structure, associated with a table or table cluster, that can sometimes speed data access. By creating an index on one or more columns of a table, you gain the ability in some cases to retrieve a small set of randomly distributed rows from the table. Indexes are one of many means of reducing disk I/O. This section contains the following topics: • About B-Tree Index Access B-trees, short for balanced trees, are the most common type of database index. • Index Unique Scans An index unique scan returns at most 1 rowid. • Index Range Scans An index range scan is an ordered scan of values. • Index Full Scans An index full scan reads the entire index in order. An index full scan can eliminate a separate sorting operation because the data in the index is ordered by index key. • Index Fast Full Scans An index fast full scan reads the index blocks in unsorted order, as they exist on disk. This scan does not use the index to probe the table, but reads the index instead of the table, essentially using the index itself as a table. • Index Skip Scans An index skip scan occurs when the initial column of a composite index is "skipped" or not specified in the query. • Index Join Scans An index join scan is a hash join of multiple indexes that together return all columns requested by a query. The database does not need to access the table because all data is retrieved from the indexes. See Also: • Oracle Database Concepts for an overview of indexes • Oracle Database Administrator’s Guide to learn how to manage indexes 8-14 Chapter 8 B-Tree Index Access Paths 8.3.1 About B-Tree Index Access B-trees, short for balanced trees, are the most common type of database index. A B-tree index is an ordered list of values divided into ranges. By associating a key with a row or range of rows, B-trees provide excellent retrieval performance for a wide range of queries, including exact match and range searches. This section contains the following topics: • B-Tree Index Structure A B-tree index has two types of blocks: branch blocks for searching and leaf blocks that store values. • How Index Storage Affects Index Scans Bitmap index blocks can appear anywhere in the index segment. • Unique and Nonunique Indexes In a nonunique index, the database stores the rowid by appending it to the key as an extra column. The entry adds a length byte to make the key unique. • B-Tree Indexes and Nulls B-tree indexes never store completely null keys, which is important for how the optimizer chooses access paths. A consequence of this rule is that single-column B-tree indexes never store nulls. 8.3.1.1 B-Tree Index Structure A B-tree index has two types of blocks: branch blocks for searching and leaf blocks that store values. The following graphic illustrates the logical structure of a B-tree index. Branch blocks store the minimum key prefix needed to make a branching decision between two keys. The leaf blocks contain every indexed data value and a corresponding rowid used to locate the actual row. Each index entry is sorted by (key, rowid). The leaf blocks are doubly linked. 8-15 Chapter 8 B-Tree Index Access Paths Figure 8-3 B-Tree Index Structure Branch Blocks 0..40 41..80 81..120 .... 200..250 0..10 11..19 20..25 .... 32..40 41..48 49..53 54..65 .... 78..80 ... 200..209 210..220 221..228 .... 246..250 Leaf Blocks 0,rowid 0,rowid .... 10,rowid 11,rowid 11,rowid 12,rowid .... 19,rowid ... 221,rowid 222,rowid 223,rowid .... 228,rowid ... 246,rowid 248,rowid 248,rowid .... 250,rowid 8.3.1.2 How Index Storage Affects Index Scans Bitmap index blocks can appear anywhere in the index segment. Figure 8-3 shows the leaf blocks as adjacent to each other. For example, the 1-10 block is next to and before the 11-19 block. This sequencing illustrates the linked lists that connect the index entries. However, index blocks need not be stored in order within an index segment. For example, the 246-250 block could appear anywhere in the segment, including directly before the 1-10 block. For this reason, ordered index scans must perform single-block I/O. The database must read an index block to determine which index block it must read next. The index block body stores the index entries in a heap, just like table rows. For example, if the value 10 is inserted first into a table, then the index entry with key 10 might be inserted at the bottom of the index block. If 0 is inserted next into the table, then the index entry for key 0 might be inserted on top of the entry for 10. Thus, the index entries in the block body are not stored in key order. However, within the index block, the row header stores records in key order. For example, the first record in the header points to the index entry with key 0, and so on sequentially up to the record that points to the index entry with key 10. Thus, index scans can read the row header to determine where to begin and end range scans, avoiding the necessity of reading every entry in the block. 8-16 Chapter 8 B-Tree Index Access Paths See Also: Oracle Database Concepts to learn about index blocks 8.3.1.3 Unique and Nonunique Indexes In a nonunique index, the database stores the rowid by appending it to the key as an extra column. The entry adds a length byte to make the key unique. For example, the first index key in the nonunique index shown in Figure 8-3 is the pair 0,rowid and not simply 0. The database sorts the data by index key values and then by rowid ascending. For example, the entries are sorted as follows: 0,AAAPvCAAFAAAAFaAAa 0,AAAPvCAAFAAAAFaAAg 0,AAAPvCAAFAAAAFaAAl 2,AAAPvCAAFAAAAFaAAm In a unique index, the index key does not include the rowid. The database sorts the data only by the index key values, such as 0, 1, 2, and so on. See Also: Oracle Database Concepts for an overview of unique and nonunique indexes 8.3.1.4 B-Tree Indexes and Nulls B-tree indexes never store completely null keys, which is important for how the optimizer chooses access paths. A consequence of this rule is that single-column Btree indexes never store nulls. An example helps illustrate. The hr.employees table has a primary key index on employee_id, and a unique index on department_id. The department_id column can contain nulls, making it a nullable column, but the employee_id column cannot. SQL> SELECT COUNT(*) FROM employees WHERE department_id IS NULL; COUNT(*) ---------1 SQL> SELECT COUNT(*) FROM employees WHERE employee_id IS NULL; COUNT(*) ---------0 The following example shows that the optimizer chooses a full table scan for a query of all department IDs in hr.employees. The optimizer cannot use the index on employees.department_id because the index is not guaranteed to include entries for every row in the table. 8-17 Chapter 8 B-Tree Index Access Paths SQL> EXPLAIN PLAN FOR SELECT department_id FROM employees; Explained. SQL> SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY()); PLAN_TABLE_OUTPUT -------------------------------------------------------------------------------Plan hash value: 3476115102 ------------------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 107 | 321 | 2 (0)| 00:00:01 | | 1 | TABLE ACCESS FULL| EMPLOYEES | 107 | 321 | 2 (0)| 00:00:01 | ------------------------------------------------------------------------------8 rows selected. The following example shows the optimizer can use the index on department_id for a query of a specific department ID because all non-null rows are indexed. SQL> EXPLAIN PLAN FOR SELECT department_id FROM employees WHERE department_id=10; Explained. SQL> SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY()); PLAN_TABLE_OUTPUT -------------------------------------------------------------------------------Plan hash value: 67425611 -------------------------------------------------------------------------------| Id| Operation | Name | Rows |Bytes| Cost (%CPU)| Time | -------------------------------------------------------------------------------PLAN_TABLE_OUTPUT -------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 1 | 3 | 1 (0)| 00:0 0:01| |*1 | INDEX RANGE SCAN| EMP_DEPARTMENT_IX | 1 | 3 | 1 (0)| 00:0 0:01| -------------------------------------------------------------------------------Predicate Information (identified by operation id): PLAN_TABLE_OUTPUT -------------------------------------------------------------------------------1 - access("DEPARTMENT_ID"=10) The following example shows that the optimizer chooses an index scan when the predicate excludes null values: SQL> EXPLAIN PLAN FOR SELECT department_id FROM employees WHERE department_id IS NOT NULL; Explained. SQL> SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY()); PLAN_TABLE_OUTPUT -------------------------------------------------------------------------------Plan hash value: 1590637672 8-18 Chapter 8 B-Tree Index Access Paths -------------------------------------------------------------------------------| Id| Operation | Name | Rows|Bytes| Cost (%CPU)| Time | -------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 106 | 318 | 1 (0)| 00:0 0:01 | |*1 | INDEX FULL SCAN | EMP_DEPARTMENT_IX | 106 | 318 | 1 (0)| 00:0 0:01 | -------------------------------------------------------------------------------Predicate Information (identified by operation id): PLAN_TABLE_OUTPUT -------------------------------------------------------------------------------1 - filter("DEPARTMENT_ID" IS NOT NULL) 8.3.2 Index Unique Scans An index unique scan returns at most 1 rowid. This section contains the following topics: • When the Optimizer Considers Index Unique Scans An index unique scan requires an equality predicate. • How Index Unique Scans Work The scan searches the index in order for the specified key. An index unique scan stops processing as soon as it finds the first record because no second record is possible. The database obtains the rowid from the index entry, and then retrieves the row specified by the rowid. • Index Unique Scans: Example This example uses a unique scan to retrieve a row from the products table. 8.3.2.1 When the Optimizer Considers Index Unique Scans An index unique scan requires an equality predicate. Specifically, the database performs a unique scan only when a query predicate references all columns in a unique index key using an equality operator, such as WHERE prod_id=10. A unique or primary key constraint is insufficient by itself to produce an index unique scan because a non-unique index on the column may already exist. Consider the following example, which creates the t_table table and then creates a non-unique index on numcol: SQL> CREATE TABLE t_table(numcol INT); SQL> CREATE INDEX t_table_idx ON t_table(numcol); SQL> SELECT UNIQUENESS FROM USER_INDEXES WHERE INDEX_NAME = 'T_TABLE_IDX'; UNIQUENES --------NONUNIQUE The following code creates a primary key constraint on a column with a non-unique index, resulting in an index range scan rather than an index unique scan: SQL> ALTER TABLE t_table ADD CONSTRAINT t_table_pk PRIMARY KEY(numcol); SQL> SET AUTOTRACE TRACEONLY EXPLAIN SQL> SELECT * FROM t_table WHERE numcol = 1; 8-19 Chapter 8 B-Tree Index Access Paths Execution Plan ---------------------------------------------------------Plan hash value: 868081059 -------------------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | -------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 1 | 13 | 1 (0)| 00:00:01 | |* 1 | INDEX RANGE SCAN| T_TABLE_IDX | 1 | 13 | 1 (0)| 00:00:01 | -------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------1 - access("NUMCOL"=1) You can use the INDEX(alias index_name) hint to specify the index to use, but not a specific type of index access path. See Also: • • Oracle Database Concepts for more details on index structures and for detailed information on how a B-tree is searched Oracle Database SQL Language Reference to learn more about the INDEX hint 8.3.2.2 How Index Unique Scans Work The scan searches the index in order for the specified key. An index unique scan stops processing as soon as it finds the first record because no second record is possible. The database obtains the rowid from the index entry, and then retrieves the row specified by the rowid. The following figure illustrates an index unique scan. The statement requests the record for product ID 19 in the prod_id column, which has a primary key index. 8-20 Chapter 8 B-Tree Index Access Paths Figure 8-4 Index Unique Scan Branch Blocks 0..40 41..80 81..120 .... 200..250 0..10 11..19 20..25 .... 32..40 41..48 49..53 54..65 .... 78..80 ... 200..209 210..220 221..228 .... 246..250 Leaf Blocks 0,rowid 1,rowid .... 10,rowid 11,rowid 12,rowid 13,rowid .... 19,rowid ... 221,rowid 222,rowid 223,rowid .... 228,rowid ... 246,rowid 247,rowid 248,rowid .... 250,rowid 8.3.2.3 Index Unique Scans: Example This example uses a unique scan to retrieve a row from the products table. The following statement queries the record for product 19 in the sh.products table: SELECT * FROM sh.products WHERE prod_id = 19; Because a primary key index exists on the products.prod_id column, and the WHERE clause references all of the columns using an equality operator, the optimizer chooses a unique scan: SQL_ID 3ptq5tsd5vb3d, child number 0 ------------------------------------select * from sh.products where prod_id = 19 Plan hash value: 4047888317 -------------------------------------------------------------------------------| Id| Operation | Name |Rows|Bytes|Cost (%CPU)|Time | -------------------------------------------------------------------------------| 0| SELECT STATEMENT | | | | 1 (100)| | | 1| TABLE ACCESS BY INDEX ROWID| PRODUCTS | 1 | 173 | 1 (0)| 00:00:01| |* 2| INDEX UNIQUE SCAN | PRODUCTS_PK | 1 | | 0 (0)| | 8-21 Chapter 8 B-Tree Index Access Paths -------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------2 - access("PROD_ID"=19) 8.3.3 Index Range Scans An index range scan is an ordered scan of values. The range in the scan can be bounded on both sides, or unbounded on one or both sides. The optimizer typically chooses a range scan for queries with high selectivity. By default, the database stores indexes in ascending order, and scans them in the same order. For example, a query with the predicate department_id >= 20 uses a range scan to return rows sorted by index keys 20, 30, 40, and so on. If multiple index entries have identical keys, then the database returns them in ascending order by rowid, so that 0,AAAPvCAAFAAAAFaAAa is followed by 0,AAAPvCAAFAAAAFaAAg, and so on. An index range scan descending is identical to an index range scan except that the database returns rows in descending order. Usually, the database uses a descending scan when ordering data in a descending order, or when seeking a value less than a specified value. This section contains the following topics: • When the Optimizer Considers Index Range Scans For an index range scan, multiple values must be possible for the index key. • How Index Range Scans Work During an index range scan, Oracle Database proceeds from root to branch. • Index Range Scan: Example This example retrieves a set of values from the employees table using an index range scan. • Index Range Scan Descending: Example This example uses an index to retrieve rows from the employees table in sorted order. 8.3.3.1 When the Optimizer Considers Index Range Scans For an index range scan, multiple values must be possible for the index key. Specifically, the optimizer considers index range scans in the following circumstances: • One or more leading columns of an index are specified in conditions. A condition specifies a combination of one or more expressions and logical (Boolean) operators and returns a value of TRUE, FALSE, or UNKNOWN. Examples of conditions include: – department_id = :id – department_id < :id – department_id > :id – AND combination of the preceding conditions for leading columns in the index, such as department_id > :low AND department_id < :hi. 8-22 Chapter 8 B-Tree Index Access Paths Note: For the optimizer to consider a range scan, wild-card searches of the form col1 LIKE '%ASD' must not be in a leading position. • 0, 1, or more values are possible for an index key. Tip: If you require sorted data, then use the ORDER BY clause, and do not rely on an index. If an index can satisfy an ORDER BY clause, then the optimizer uses this option and thereby avoids a sort. The optimizer considers an index range scan descending when an index can satisfy an ORDER BY DESCENDING clause. If the optimizer chooses a full table scan or another index, then a hint may be required to force this access path. The INDEX(tbl_alias ix_name) and INDEX_DESC(tbl_alias ix_name) hints instruct the optimizer to use a specific index. See Also: Oracle Database SQL Language Reference to learn more about the INDEX and INDEX_DESC hints 8.3.3.2 How Index Range Scans Work During an index range scan, Oracle Database proceeds from root to branch. In general, the scan algorithm is as follows: 1. Read the root block. 2. Read the branch block. 3. Alternate the following steps until all data is retrieved: a. Read a leaf block to obtain a rowid. b. Read a table block to retrieve a row. Note: In some cases, an index scan reads a set of index blocks, sorts the rowids, and then reads a set of table blocks. Thus, to scan the index, the database moves backward or forward through the leaf blocks. For example, a scan for IDs between 20 and 40 locates the first leaf block that 8-23 Chapter 8 B-Tree Index Access Paths has the lowest key value that is 20 or greater. The scan proceeds horizontally through the linked list of leaf nodes until it finds a value greater than 40, and then stops. The following figure illustrates an index range scan using ascending order. A statement requests the employees records with the value 20 in the department_id column, which has a nonunique index. In this example, 2 index entries for department 20 exist. Figure 8-5 Index Range Scan Branch Blocks 0..40 41..80 81..120 .... 200..250 0..10 11..2 41..48 49..53 54..65 .... 78..80 .... 32..40 ... 200..209 210..220 221..228 .... 246..250 Leaf Blocks 0,rowid 0,rowid .... 10,rowid 11,rowid 11,rowid 12,rowid .... 20,rowid 20, rowid ... 221,rowid 222,rowid 223,rowid .... 228,rowid ... 246,rowid 248,rowid 248,rowid .... 250,rowid 8.3.3.3 Index Range Scan: Example This example retrieves a set of values from the employees table using an index range scan. The following statement queries the records for employees in department 20 with salaries greater than 1000: SELECT FROM WHERE AND * employees department_id = 20 salary > 1000; The preceding query has low cardinality (returns few rows), so the query uses the index on the department_id column. The database scans the index, fetches the records from the employees table, and then applies the salary > 1000 filter to these fetched records to generate the result. 8-24 Chapter 8 B-Tree Index Access Paths SQL_ID brt5abvbxw9tq, child number 0 ------------------------------------SELECT * FROM employees WHERE department_id = 20 AND salary > 1000 Plan hash value: 2799965532 ------------------------------------------------------------------------------------------|Id | Operation | Name |Rows|Bytes|Cost(%CPU)| Time | ------------------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | | 2 (100)| | |*1 | TABLE ACCESS BY INDEX ROWID BATCHED| EMPLOYEES | 2 | 138 | 2 (0)|00:00:01| |*2 | INDEX RANGE SCAN | EMP_DEPARTMENT_IX| 2 | | 1 (0)|00:00:01| ------------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------1 - filter("SALARY">1000) 2 - access("DEPARTMENT_ID"=20) 8.3.3.4 Index Range Scan Descending: Example This example uses an index to retrieve rows from the employees table in sorted order. The following statement queries the records for employees in department 20 in descending order: SELECT * FROM employees WHERE department_id < 20 ORDER BY department_id DESC; This preceding query has low cardinality, so the query uses the index on the department_id column. SQL_ID 8182ndfj1ttj6, child number 0 ------------------------------------SELECT * FROM employees WHERE department_id < 20 ORDER BY department_id DESC Plan hash value: 1681890450 -------------------------------------------------------------------------------|Id| Operation | Name |Rows|Bytes|Cost(%CPU)|Time | -------------------------------------------------------------------------------| 0| SELECT STATEMENT | | | | 2 (100)| | | 1| TABLE ACCESS BY INDEX ROWID | EMPLOYEES | 2|138| 2 (0)|00:00:01| |*2| INDEX RANGE SCAN DESCENDING| EMP_DEPARTMENT_IX | 2| | 1 (0)|00:00:01| -------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------2 - access("DEPARTMENT_ID"<20) The database locates the first index leaf block that contains the highest key value that is 20 or less. The scan then proceeds horizontally to the left through the linked list of leaf nodes. The database obtains the rowid from each index entry, and then retrieves the row specified by the rowid. 8-25 Chapter 8 B-Tree Index Access Paths 8.3.4 Index Full Scans An index full scan reads the entire index in order. An index full scan can eliminate a separate sorting operation because the data in the index is ordered by index key. This section contains the following topics: • When the Optimizer Considers Index Full Scans The optimizer considers an index full scan in a variety of situations. • How Index Full Scans Work The database reads the root block, and then navigates down the left hand side of the index (or right if doing a descending full scan) until it reaches a leaf block. • Index Full Scans: Example This example uses an index full scan to satisfy a query with an ORDER BY clause. 8.3.4.1 When the Optimizer Considers Index Full Scans The optimizer considers an index full scan in a variety of situations. The situations include the following: • A predicate references a column in the index. This column need not be the leading column. • No predicate is specified, but all of the following conditions are met: • – All columns in the table and in the query are in the index. – At least one indexed column is not null. A query includes an ORDER BY on indexed non-nullable columns. 8.3.4.2 How Index Full Scans Work The database reads the root block, and then navigates down the left hand side of the index (or right if doing a descending full scan) until it reaches a leaf block. Then the database reaches a leaf block, the scan proceeds across the bottom of the index, one block at a time, in sorted order. The database uses single-block I/O rather than multiblock I/O. The following graphic illustrates an index full scan. A statement requests the departments records ordered by department_id. 8-26 Chapter 8 B-Tree Index Access Paths Figure 8-6 Index Full Scan Branch Blocks 0..40 41..80 81..120 .... 200..250 0..10 11..19 20..25 .... 32..40 41..48 49..53 54..65 .... 78..80 ... 200..209 210..220 221..228 .... 246..250 Leaf Blocks 0,rowid 1,rowid .... 10,rowid 11,rowid 12,rowid 13,rowid .... 19,rowid ... 221,rowid 222,rowid 223,rowid .... 228,rowid ... 246,rowid 247,rowid 248,rowid .... 250,rowid 8.3.4.3 Index Full Scans: Example This example uses an index full scan to satisfy a query with an ORDER BY clause. The following statement queries the ID and name for departments in order of department ID: SELECT department_id, department_name FROM departments ORDER BY department_id; The following plan shows that the optimizer chose an index full scan: SQL_ID 94t4a20h8what, child number 0 ------------------------------------select department_id, department_name from departments order by department_id Plan hash value: 4179022242 -------------------------------------------------------------------------------|Id | Operation | Name |Rows|Bytes|Cost(%CPU)|Time | -------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | | 2 (100)| | | 1 | TABLE ACCESS BY INDEX ROWID| DEPARTMENTS | 27 | 432 | 2 (0)| 00:00:01 | | 2 | INDEX FULL SCAN | DEPT_ID_PK | 27 | | 1 (0)| 00:00:01 | -------------------------------------------------------------------------------- 8-27 Chapter 8 B-Tree Index Access Paths The database locates the first index leaf block, and then proceeds horizontally to the right through the linked list of leaf nodes. For each index entry, the database obtains the rowid from the entry, and then retrieves the table row specified by the rowid. Because the index is sorted on department_id, the database avoids a separate operation to sort the retrieved rows. 8.3.5 Index Fast Full Scans An index fast full scan reads the index blocks in unsorted order, as they exist on disk. This scan does not use the index to probe the table, but reads the index instead of the table, essentially using the index itself as a table. This section contains the following topics: • When the Optimizer Considers Index Fast Full Scans The optimizer considers this scan when a query only accesses attributes in the index. • How Index Fast Full Scans Work The database uses multiblock I/O to read the root block and all of the leaf and branch blocks. The databases ignores the branch and root blocks and reads the index entries on the leaf blocks. • Index Fast Full Scans: Example This examples uses a fast full index scan as a result of an optimizer hint. 8.3.5.1 When the Optimizer Considers Index Fast Full Scans The optimizer considers this scan when a query only accesses attributes in the index. Note: Unlike a full scan, a fast full scan cannot eliminate a sort operation because it does not read the index in order. The INDEX_FFS(table_name index_name) hint forces a fast full index scan. See Also: Oracle Database SQL Language Reference to learn more about the INDEX hint 8.3.5.2 How Index Fast Full Scans Work The database uses multiblock I/O to read the root block and all of the leaf and branch blocks. The databases ignores the branch and root blocks and reads the index entries on the leaf blocks. 8-28 Chapter 8 B-Tree Index Access Paths 8.3.5.3 Index Fast Full Scans: Example This examples uses a fast full index scan as a result of an optimizer hint. The following statement queries the ID and name for departments in order of department ID: SELECT /*+ INDEX_FFS(departments dept_id_pk) */ COUNT(*) FROM departments; The following plan shows that the optimizer chose a fast full index scan: SQL_ID fu0k5nvx7sftm, child number 0 ------------------------------------select /*+ index_ffs(departments dept_id_pk) */ count(*) from departments Plan hash value: 3940160378 -------------------------------------------------------------------------| Id | Operation | Name | Rows |Cost (%CPU)| Time | -------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | 2 (100)| | | 1 | SORT AGGREGATE | | 1 | | | | 2 | INDEX FAST FULL SCAN| DEPT_ID_PK | 27 | 2 (0)| 00:00:01 | -------------------------------------------------------------------------- 8.3.6 Index Skip Scans An index skip scan occurs when the initial column of a composite index is "skipped" or not specified in the query. This section contains the following topics: • When the Optimizer Considers Index Skips Scans Often, skip scanning index blocks is faster than scanning table blocks, and faster than performing full index scans. • How Index Skip Scans Work An index skip scan logically splits a composite index into smaller subindexes. • Index Skip Scans: Example This example uses an index skip scan to satisfy a query of the customers table. See Also: Oracle Database Concepts 8.3.6.1 When the Optimizer Considers Index Skips Scans Often, skip scanning index blocks is faster than scanning table blocks, and faster than performing full index scans. The optimizer considers a skip scan when the following criteria are met: • The leading column of a composite index is not specified in the query predicate. 8-29 Chapter 8 B-Tree Index Access Paths For example, the query predicate does not reference the cust_gender column, and the composite index key is (cust_gender,cust_email). • Few distinct values exist in the leading column of the composite index, but many distinct values exist in the nonleading key of the index. For example, if the composite index key is (cust_gender,cust_email), then the cust_gender column has only two distinct values, but cust_email has thousands. 8.3.6.2 How Index Skip Scans Work An index skip scan logically splits a composite index into smaller subindexes. The number of distinct values in the leading columns of the index determines the number of logical subindexes. The lower the number, the fewer logical subindexes the optimizer must create, and the more efficient the scan becomes. The scan reads each logical index separately, and "skips" index blocks that do not meet the filter condition on the non-leading column. 8.3.6.3 Index Skip Scans: Example This example uses an index skip scan to satisfy a query of the customers table. The customers table contains a column cust_gender whose values are either M or F. You create a composite index on the columns (cust_gender, cust_email) as follows: CREATE INDEX cust_gender_email_ix ON sh.customers (cust_gender, cust_email); Conceptually, a portion of the index might look as follows, with the gender value of F or M as the leading edge of the index. F,Wolf@company.example.com,rowid F,Wolsey@company.example.com,rowid F,Wood@company.example.com,rowid F,Woodman@company.example.com,rowid F,Yang@company.example.com,rowid F,Zimmerman@company.example.com,rowid M,Abbassi@company.example.com,rowid M,Abbey@company.example.com,rowid You run the following query for a customer in the sh.customers table: SELECT * FROM sh.customers WHERE cust_email = 'Abbey@company.example.com'; The database can use a skip scan of the customers_gender_email index even though cust_gender is not specified in the WHERE clause. In the sample index, the leading column cust_gender has two possible values: F and M. The database logically splits the index into two. One subindex has the key F, with entries in the following form: F,Wolf@company.example.com,rowid F,Wolsey@company.example.com,rowid F,Wood@company.example.com,rowid F,Woodman@company.example.com,rowid F,Yang@company.example.com,rowid F,Zimmerman@company.example.com,rowid The second subindex has the key M, with entries in the following form: 8-30 Chapter 8 B-Tree Index Access Paths M,Abbassi@company.example.com,rowid M,Abbey@company.example.com,rowid When searching for the record for the customer whose email is Abbey@company.com, the database searches the subindex with the leading value F first, and then searches the subindex with the leading value M. Conceptually, the database processes the query as follows: ( SELECT * FROM sh.customers WHERE cust_gender = 'F' AND cust_email = 'Abbey@company.com' ) UNION ALL ( SELECT * FROM sh.customers WHERE cust_gender = 'M' AND cust_email = 'Abbey@company.com' ) The plan for the query is as follows: SQL_ID d7a6xurcnx2dj, child number 0 ------------------------------------SELECT * FROM sh.customers WHERE cust_email = 'Abbey@company.example.com' Plan hash value: 797907791 ----------------------------------------------------------------------------------------|Id| Operation | Name |Rows|Bytes|Cost(%CPU)|Time| ----------------------------------------------------------------------------------------| 0|SELECT STATEMENT | | | |10(100)| | | 1| TABLE ACCESS BY INDEX ROWID BATCHED| CUSTOMERS |33|6237| 10(0)|00:00:01| |*2| INDEX SKIP SCAN | CUST_GENDER_EMAIL_IX |33| | 4(0)|00:00:01| ----------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------2 - access("CUST_EMAIL"='Abbey@company.example.com') filter("CUST_EMAIL"='Abbey@company.example.com') See Also: Oracle Database Concepts to learn more about skip scans 8.3.7 Index Join Scans An index join scan is a hash join of multiple indexes that together return all columns requested by a query. The database does not need to access the table because all data is retrieved from the indexes. This section contains the following topics: • When the Optimizer Considers Index Join Scans In some cases, avoiding table access is the most cost efficient option. 8-31 Chapter 8 B-Tree Index Access Paths • How Index Join Scans Work An index join involves scanning multiple indexes, and then using a hash join on the rowids obtained from these scans to return the rows. • Index Join Scans: Example This example queries the last name and email for employees whose last name begins with A, specifying an index join. 8.3.7.1 When the Optimizer Considers Index Join Scans In some cases, avoiding table access is the most cost efficient option. The optimizer considers an index join in the following circumstances: • A hash join of multiple indexes retrieves all data requested by the query, without requiring table access. • The cost of retrieving rows from the table is higher than reading the indexes without retrieving rows from the table. An index join is often expensive. For example, when scanning two indexes and joining them, it is often less costly to choose the most selective index, and then probe the table. You can specify an index join with the INDEX_JOIN(table_name) hint. See Also: Oracle Database SQL Language Reference 8.3.7.2 How Index Join Scans Work An index join involves scanning multiple indexes, and then using a hash join on the rowids obtained from these scans to return the rows. In an index join scan, table access is always avoided. For example, the process for joining two indexes on a single table is as follows: 1. Scan the first index to retrieve rowids. 2. Scan the second index to retrieve rowids. 3. Perform a hash join by rowid to obtain the rows. 8.3.7.3 Index Join Scans: Example This example queries the last name and email for employees whose last name begins with A, specifying an index join. SELECT /*+ INDEX_JOIN(employees) */ last_name, email FROM employees WHERE last_name like 'A%'; Separate indexes exist on the (last_name,first_name) and email columns. Part of the emp_name_ix index might look as follows: Banda,Amit,AAAVgdAALAAAABSABD Bates,Elizabeth,AAAVgdAALAAAABSABI Bell,Sarah,AAAVgdAALAAAABSABc 8-32 Chapter 8 Bitmap Index Access Paths Bernstein,David,AAAVgdAALAAAABSAAz Bissot,Laura,AAAVgdAALAAAABSAAd Bloom,Harrison,AAAVgdAALAAAABSABF Bull,Alexis,AAAVgdAALAAAABSABV The first part of the emp_email_uk index might look as follows: ABANDA,AAAVgdAALAAAABSABD ABULL,AAAVgdAALAAAABSABV ACABRIO,AAAVgdAALAAAABSABX AERRAZUR,AAAVgdAALAAAABSAAv AFRIPP,AAAVgdAALAAAABSAAV AHUNOLD,AAAVgdAALAAAABSAAD AHUTTON,AAAVgdAALAAAABSABL The following example retrieves the plan using the DBMS_XPLAN.DISPLAY_CURSOR function. The database retrieves all rowids in the emp_email_uk index, and then retrieves rowids in emp_name_ix for last names that begin with A. The database uses a hash join to search both sets of rowids for matches. For example, rowid AAAVgdAALAAAABSABD occurs in both sets of rowids, so the database probes the employees table for the record corresponding to this rowid. Example 8-4 Index Join Scan SQL_ID d2djchyc9hmrz, child number 0 ------------------------------------SELECT /*+ INDEX_JOIN(employees) */ last_name, email FROM WHERE last_name like 'A%' employees Plan hash value: 3719800892 ------------------------------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ------------------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | | 3 (100)| | |* 1 | VIEW | index$_join$_001 | 3 | 48 | 3 (34)| 00:00:01 | |* 2 | HASH JOIN | | | | | | |* 3 | INDEX RANGE SCAN | EMP_NAME_IX | 3 | 48 | 1 (0)| 00:00:01 | | 4 | INDEX FAST FULL SCAN| EMP_EMAIL_UK | 3 | 48 | 1 (0)| 00:00:01 | ------------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------1 - filter("LAST_NAME" LIKE 'A%') 2 - access(ROWID=ROWID) 3 - access("LAST_NAME" LIKE 'A%') 8.4 Bitmap Index Access Paths Bitmap indexes combine the indexed data with a rowid range. This section explains how bitmap indexes, and describes some of the more common bitmap index access paths: • About Bitmap Index Access In a conventional B-tree index, one index entry points to a single row. In a bitmap index, the key is the combination of the indexed data and the rowid range. 8-33 Chapter 8 Bitmap Index Access Paths • Bitmap Conversion to Rowid A bitmap conversion translates between an entry in the bitmap and a row in a table. The conversion can go from entry to row (TO ROWID), or from row to entry (FROM ROWID). • Bitmap Index Single Value This type of access path uses a bitmap index to look up a single key value. • Bitmap Index Range Scans This type of access path uses a bitmap index to look up a range of values. • Bitmap Merge This access path merges multiple bitmaps, and returns a single bitmap as a result. 8.4.1 About Bitmap Index Access In a conventional B-tree index, one index entry points to a single row. In a bitmap index, the key is the combination of the indexed data and the rowid range. The database stores at least one bitmap for each index key. Each value in the bitmap, which is a series of 1 and 0 values, points to a row within a rowid range. Thus, in a bitmap index, one index entry points to a set of rows rather than a single row. This section contains the following topics: • Differences Between Bitmap and B-Tree Indexes A bitmap index uses a different key from a B-tree index, but is stored in a B-tree structure. • Purpose of Bitmap Indexes Bitmap indexes are typically suitable for infrequently modified data with a low or medium number of distinct values (NDV). • Bitmaps and Rowids For a particular value in a bitmap, the value is 1 if the row values match the bitmap condition, and 0 if it does not. Based on these values, the database uses an internal algorithm to map bitmaps onto rowids. • Bitmap Join Indexes A bitmap join index is a bitmap index for the join of two or more tables. • Bitmap Storage A bitmap index resides in a B-tree structure, using branch blocks and leaf blocks just as in a B-tree. 8.4.1.1 Differences Between Bitmap and B-Tree Indexes A bitmap index uses a different key from a B-tree index, but is stored in a B-tree structure. The following table shows the differences among types of index entries. 8-34 Chapter 8 Bitmap Index Access Paths Table 8-3 Index Entries for B-Trees and Bitmaps Index Entry Key Data Example Unique B-tree Indexed data only Rowid In an entry of the index on the employees.employee_id column, employee ID 101 is the key, and the rowid AAAPvCAAFAAAAFaAAa is the data: 101,AAAPvCAAFAAAAFaAAa Nonunique B-tree Indexed data combined with rowid None In an entry of the index on the employees.last_name column, the name and rowid combination Smith,AAAPvCAAFAAAAFaAAa is the key, and there is no data: Smith,AAAPvCAAFAAAAFaAAa Bitmap Indexed data combined with rowid range Bitmap In an entry of the index on the customers.cust_gender column, the M,low-rowid,high-rowid part is the key, and the series of 1 and 0 values is the data: M,low-rowid,high-rowid,1000101010101010 The database stores a bitmap index in a B-tree structure. The database can search the B-tree quickly on the first part of the key, which is the set of attributes on which the index is defined, and then obtain the corresponding rowid range and bitmap. See Also: • "Bitmap Storage" • Oracle Database Concepts for an overview of bitmap indexes • Oracle Database Data Warehousing Guide for more information about bitmap indexes 8.4.1.2 Purpose of Bitmap Indexes Bitmap indexes are typically suitable for infrequently modified data with a low or medium number of distinct values (NDV). In general, B-tree indexes are suitable for columns with high NDV and frequent DML activity. For example, the optimizer might choose a B-tree index for a query of a sales.amount column that returns few rows. In contrast, the customers.state and customers.county columns are candidates for bitmap indexes because they have few distinct values, are infrequently updated, and can benefit from efficient AND and OR operations. Bitmap indexes are a useful way to speed ad hoc queries in a data warehouse. They are fundamental to star transformations. Specifically, bitmap indexes are useful in queries that contain the following: • Multiple conditions in the WHERE clause 8-35 Chapter 8 Bitmap Index Access Paths Before the table itself is accessed, the database filters out rows that satisfy some, but not all, conditions. • AND, OR, and NOT operations on columns with low or medium NDV Combining bitmap indexes makes these operations more efficient. The database can merge bitmaps from bitmap indexes very quickly. For example, if bitmap indexes exist on the customers.state and customers.county columns, then these indexes can enormously improve the performance of the following query: SELECT FROM WHERE AND * customers state = 'CA' county = 'San Mateo' The database can convert 1 values in the merged bitmap into rowids efficiently. • The COUNT function The database can scan the bitmap index without needing to scan the table. • Predicates that select for null values Unlike B-tree indexes, bitmap indexes can contain nulls. Queries that count the number of nulls in a column can use the bitmap index without scanning the table. • Columns that do not experience heavy DML The reason is that one index key points to many rows. If a session modifies the indexed data, then the database cannot lock a single bit in the bitmap: rather, the database locks the entire index entry, which in practice locks the rows pointed to by the bitmap. For example, if the county of residence for a specific customer changes from San Mateo to Alameda, then the database must get exclusive access to the San Mateo index entry and Alameda index entry in the bitmap. Rows containing these two values cannot be modified until COMMIT. See Also: • "Star Transformation" • Oracle Database SQL Language Reference to learn about the COUNT function 8.4.1.3 Bitmaps and Rowids For a particular value in a bitmap, the value is 1 if the row values match the bitmap condition, and 0 if it does not. Based on these values, the database uses an internal algorithm to map bitmaps onto rowids. The bitmap entry contains the indexed value, the rowid range (start and end rowids), and a bitmap. Each 0 or 1 value in the bitmap is an offset into the rowid range, and maps to a potential row in the table, even if the row does not exist. Because the number of possible rows in a block is predetermined, the database can use the range endpoints to determine the rowid of an arbitrary row in the range. 8-36 Chapter 8 Bitmap Index Access Paths Note: The Hakan factor is an optimization used by the bitmap index algorithms to limit the number of rows that Oracle Database assumes can be stored in a single block. By artificially limiting the number of rows, the database reduces the size of the bitmaps. Table 8-4 shows part of a sample bitmap for the sh.customers.cust_marital_status column, which is nullable. The actual index has 12 distinct values. Only 3 are shown in the sample: null, married, and single. Table 8-4 Bitmap Index Entries Column Start Value for Rowid in cust_marital Range _status End Rowid in Range 1st 2nd 3rd 4th 5th 6th Row in Row in Row in Row in Row in Row in Range Range Range Range Range Range (null) AAA ... CCC ... 0 0 0 0 0 1 married AAA ... CCC ... 1 0 1 1 1 0 single AAA ... CCC ... 0 1 0 0 0 0 single DDD ... EEE ... 1 0 1 0 1 1 As shown in Table 8-4, bitmap indexes can include keys that consist entirely of null values, unlike B-tree indexes. In Table 8-4, the null has a value of 1 for the 6th row in the range, which means that the cust_marital_status value is null for the 6th row in the range. Indexing nulls can be useful for some SQL statements, such as queries with the aggregate function COUNT. See Also: Oracle Database Concepts to learn about rowid formats 8.4.1.4 Bitmap Join Indexes A bitmap join index is a bitmap index for the join of two or more tables. The optimizer can use a bitmap join index to reduce or eliminate the volume of data that must be joined during plan execution. Bitmap join indexes can be much more efficient in storage than materialized join views. The following example creates a bitmap index on the sh.sales and sh.customers tables: CREATE BITMAP INDEX cust_sales_bji ON sales(c.cust_city) FROM sales s, customers c WHERE c.cust_id = s.cust_id LOCAL; The FROM and WHERE clause in the preceding CREATE statement represent the join condition between the tables. The customers.cust_city column is the index key. 8-37 Chapter 8 Bitmap Index Access Paths Each key value in the index represents a possible city in the customers table. Conceptually, key values for the index might look as follows, with one bitmap associated with each key value: San Francisco San Mateo Smithville . . . 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 . . . 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 . . . 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 . . . Each bit in a bitmap corresponds to one row in the sales table. In the Smithville key, the value 1 means that the first row in the sales table corresponds to a product sold to a Smithville customer, whereas the value 0 means that the second row corresponds to a product not sold to a Smithville customer. Consider the following query of the number of separate sales to Smithville customers: SELECT FROM WHERE AND COUNT (*) sales s, customers c c.cust_id = s.cust_id c.cust_city = 'Smithville'; The following plan shows that the database reads the Smithville bitmap to derive the number of Smithville sales (Step 4), thereby avoiding a join of the customers and sales tables. SQL_ID 57s100mh142wy, child number 0 ------------------------------------SELECT COUNT (*) FROM sales s, customers c WHERE c.cust_id = s.cust_id AND c.cust_city = 'Smithville' Plan hash value: 3663491772 -----------------------------------------------------------------------------------|Id| Operation | Name |Rows|Bytes|Cost (%CPU)| Time|Pstart|Pstop| -----------------------------------------------------------------------------------| 0| SELECT STATEMENT | | | |29 (100)| | | | | 1| SORT AGGREGATE | | 1 | 5| | | | | | 2| PARTITION RANGE ALL | | 1708|8540|29 (0)|00:00:01|1|28| | 3| BITMAP CONVERSION COUNT | | 1708|8540|29 (0)|00:00:01| | | |*4| BITMAP INDEX SINGLE VALUE|CUST_SALES_BJI| | | | |1|28| -----------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------4 - access("S"."SYS_NC00008$"='Smithville') See Also: Oracle Database Concepts to learn about the CREATE INDEX statement 8.4.1.5 Bitmap Storage A bitmap index resides in a B-tree structure, using branch blocks and leaf blocks just as in a B-tree. 8-38 Chapter 8 Bitmap Index Access Paths For example, if the customers.cust_marital_status column has 12 distinct values, then one branch block might point to the keys married,rowid-range and single,rowid-range, another branch block might point to the widowed,rowid-range key, and so on. Alternatively, a single branch block could point to a leaf block containing all 12 distinct keys. Each indexed column value may have one or more bitmap pieces, each with its own rowid range occupying a contiguous set of rows in one or more extents. The database can use a bitmap piece to break up an index entry that is large relative to the size of a block. For example, the database could break a single index entry into three pieces, with the first two pieces in separate blocks in the same extent, and the last piece in a separate block in a different extent. To conserve space, Oracle Database can compression consecutive ranges of 0 values. 8.4.2 Bitmap Conversion to Rowid A bitmap conversion translates between an entry in the bitmap and a row in a table. The conversion can go from entry to row (TO ROWID), or from row to entry (FROM ROWID). This section contains the following topics: • When the Optimizer Chooses Bitmap Conversion to Rowid The optimizer uses a conversion whenever it retrieves a row from a table using a bitmap index entry. • How Bitmap Conversion to Rowid Works Conceptually, a bitmap can be represented as table. • Bitmap Conversion to Rowid: Example In this example, the optimizer chooses a bitmap conversion operation to satisfy a query using a range predicate. 8.4.2.1 When the Optimizer Chooses Bitmap Conversion to Rowid The optimizer uses a conversion whenever it retrieves a row from a table using a bitmap index entry. 8.4.2.2 How Bitmap Conversion to Rowid Works Conceptually, a bitmap can be represented as table. For example, Table 8-4 represents the bitmap as a table with customers row numbers as columns and cust_marital_status values as rows. Each field in Table 8-4 has the value 1 or 0, and represents a column value in a row. Conceptually, the bitmap conversion uses an internal algorithm that says, "Field F in the bitmap corresponds to the Nth row of the Mth block of the table," or "The Nth row of the Mth block in the table corresponds to field F in the bitmap." 8.4.2.3 Bitmap Conversion to Rowid: Example In this example, the optimizer chooses a bitmap conversion operation to satisfy a query using a range predicate. A query of the sh.customers table selects the names of all customers born before 1918: 8-39 Chapter 8 Bitmap Index Access Paths SELECT cust_last_name, cust_first_name FROM customers WHERE cust_year_of_birth < 1918; The following plan shows that the database uses a range scan to find all key values less than 1918 (Step 3), converts the 1 values in the bitmap to rowids (Step 2), and then uses the rowids to obtain the rows from the customers table (Step 1): --------------------------------------------------------------------------------------------|Id| Operation | Name |Rows|Bytes|Cost(%CPU)| Time | --------------------------------------------------------------------------------------------| 0| SELECT STATEMENT | | | |421 (100)| | | 1| TABLE ACCESS BY INDEX ROWID BATCHED| CUSTOMERS |3604|68476|421 (1)| 00:00:01 | | 2| BITMAP CONVERSION TO ROWIDS | | | | | | |*3| BITMAP INDEX RANGE SCAN | CUSTOMERS_YOB_BIX| | | | | --------------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------3 - access("CUST_YEAR_OF_BIRTH"<1918) filter("CUST_YEAR_OF_BIRTH"<1918) 8.4.3 Bitmap Index Single Value This type of access path uses a bitmap index to look up a single key value. This section contains the following topics: • When the Optimizer Considers Bitmap Index Single Value The optimizer considers this access path when the predicate contains an equality operator. • How Bitmap Index Single Value Works The query scans a single bitmap for positions containing a 1 value. The database converts the 1 values into rowids, and then uses the rowids to find the rows. • Bitmap Index Single Value: Example In this example, the optimizer chooses a bitmap index single value operation to satisfy a query that uses an equality predicate. 8.4.3.1 When the Optimizer Considers Bitmap Index Single Value The optimizer considers this access path when the predicate contains an equality operator. 8.4.3.2 How Bitmap Index Single Value Works The query scans a single bitmap for positions containing a 1 value. The database converts the 1 values into rowids, and then uses the rowids to find the rows. The database only needs to process a single bitmap. For example, the following table represents the bitmap index (in two bitmap pieces) for the value widowed in the sh.customers.cust_marital_status column. To satisfy a query of customers with the status widowed, the database can search for each 1 value in the widowed bitmap and find the rowid of the corresponding row. 8-40 Chapter 8 Bitmap Index Access Paths Table 8-5 Bitmap Index Entries Column Value Start Rowid in Range End Rowid in Range 1st 2nd Row in Row in Range Range 3rd 4th Row in Row in Range Range 5th 6th Row in Row in Range Range widowed AAA ... CCC ... 0 1 0 0 0 0 widowed DDD ... EEE ... 1 0 1 0 1 1 8.4.3.3 Bitmap Index Single Value: Example In this example, the optimizer chooses a bitmap index single value operation to satisfy a query that uses an equality predicate. A query of the sh.customers table selects all widowed customers: SELECT * FROM customers WHERE cust_marital_status = 'Widowed'; The following plan shows that the database reads the entry with the Widowed key in the customers bitmap index (Step 3), converts the 1 values in the bitmap to rowids (Step 2), and then uses the rowids to obtain the rows from the customers table (Step 1): SQL_ID ff5an2xsn086h, child number 0 ------------------------------------SELECT * FROM customers WHERE cust_marital_status = 'Widowed' Plan hash value: 2579015045 --------------------------------------------------------------------------------------------|Id| Operation | Name |Rows|Bytes|Cost (%CPU)| Time| --------------------------------------------------------------------------------------------| 0| SELECT STATEMENT | | | |412 (100)| | | 1| TABLE ACCESS BY INDEX ROWID BATCHED|CUSTOMERS |3461|638K|412 (2)|00:00:01| | 2| BITMAP CONVERSION TO ROWIDS | | | | | | |*3| BITMAP INDEX SINGLE VALUE |CUSTOMERS_MARITAL_BIX| | | | | --------------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------3 - access("CUST_MARITAL_STATUS"='Widowed') 8.4.4 Bitmap Index Range Scans This type of access path uses a bitmap index to look up a range of values. This section contains the following topics: • When the Optimizer Considers Bitmap Index Range Scans The optimizer considers this access path when the predicate selects a range of values. • How Bitmap Index Range Scans Work This scan works similarly to a B-tree range scan. • Bitmap Index Range Scans: Example This example uses a range scan to select customers born before a single year. 8-41 Chapter 8 Bitmap Index Access Paths 8.4.4.1 When the Optimizer Considers Bitmap Index Range Scans The optimizer considers this access path when the predicate selects a range of values. The range in the scan can be bounded on both sides, or unbounded on one or both sides. The optimizer typically chooses a range scan for selective queries. See Also: "Index Range Scans" 8.4.4.2 How Bitmap Index Range Scans Work This scan works similarly to a B-tree range scan. For example, the following table represents three values in the bitmap index for the sh.customers.cust_year_of_birth column. If a query requests all customers born before 1917, then the database can scan this index for values lower than 1917, and then obtain the rowids for rows that have a 1. Table 8-6 Bitmap Index Entries Column Value Start Rowid in Range End Rowid in Range 1st 2nd Row in Row in Range Range 3rd 4th Row in Row in Range Range 5th 6th Row in Row in Range Range 1913 AAA ... CCC ... 0 0 0 0 0 1 1917 AAA ... CCC ... 1 0 1 1 1 0 1918 AAA ... CCC ... 0 1 0 0 0 0 1918 DDD ... EEE ... 1 0 1 0 1 1 See Also: "Index Range Scans" 8.4.4.3 Bitmap Index Range Scans: Example This example uses a range scan to select customers born before a single year. A query of the sh.customers table selects the names of customers born before 1918: SELECT cust_last_name, cust_first_name FROM customers WHERE cust_year_of_birth < 1918 8-42 Chapter 8 Bitmap Index Access Paths The following plan shows that the database obtains all bitmaps for cust_year_of_birth keys lower than 1918 (Step 3), converts the bitmaps to rowids (Step 2), and then fetches the rows (Step 1): SQL_ID 672z2h9rawyjg, child number 0 ------------------------------------SELECT cust_last_name, cust_first_name FROM cust_year_of_birth < 1918 customers WHERE Plan hash value: 4198466611 --------------------------------------------------------------------------------------------|Id| Operation | Name |Rows|Bytes|Cost(%CPU)|Time | --------------------------------------------------------------------------------------------| 0| SELECT STATEMENT | | | |421 (100)| | | 1| TABLE ACCESS BY INDEX ROWID BATCHED| CUSTOMERS |3604|68476|421 (1)|00:00:01 | | 2| BITMAP CONVERSION TO ROWIDS | | | | | | BITMAP INDEX RANGE SCAN |*3| | CUSTOMERS_YOB_BIX | | | | | --------------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------3 - access("CUST_YEAR_OF_BIRTH"<1918) filter("CUST_YEAR_OF_BIRTH"<1918) 8.4.5 Bitmap Merge This access path merges multiple bitmaps, and returns a single bitmap as a result. A bitmap merge is indicated by the BITMAP MERGE operation in an execution plan. This section contains the following topics: • When the Optimizer Considers Bitmap Merge The optimizer typically uses a bitmap merge to combine bitmaps generated from a bitmap index range scan. • How Bitmap Merge Works A merge uses a Boolean OR operation between two bitmaps. The resulting bitmap selects all rows from the first bitmap, plus all rows from every subsequent bitmap. • Bitmap Merge: Example This example shows how the database merges bitmaps to optimize a query using a range predicate. 8.4.5.1 When the Optimizer Considers Bitmap Merge The optimizer typically uses a bitmap merge to combine bitmaps generated from a bitmap index range scan. 8.4.5.2 How Bitmap Merge Works A merge uses a Boolean OR operation between two bitmaps. The resulting bitmap selects all rows from the first bitmap, plus all rows from every subsequent bitmap. A query might select all customers born before 1918. The following example shows sample bitmaps for three customers.cust_year_of_birth keys: 1917, 1916, and 1915. If any position in any bitmap has a 1, then the merged bitmap has a 1 in the same position. Otherwise, the merged bitmap has a 0. 8-43 Chapter 8 Table Cluster Access Paths 1917 1 0 1 0 0 0 0 0 0 0 0 0 0 1 1916 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1915 0 0 0 0 0 0 0 0 1 0 0 0 0 0 -----------------------------------merged: 1 1 1 0 0 0 0 0 1 0 0 0 0 1 The 1 values in resulting bitmap correspond to rows that contain the values 1915, 1916, or 1917. 8.4.5.3 Bitmap Merge: Example This example shows how the database merges bitmaps to optimize a query using a range predicate. A query of the sh.customers table selects the names of female customers born before 1918: SELECT FROM WHERE AND cust_last_name, cust_first_name customers cust_gender = 'F' cust_year_of_birth < 1918 The following plan shows that the database obtains all bitmaps for cust_year_of_birth keys lower than 1918 (Step 6), and then merges these bitmaps using OR logic to create a single bitmap (Step 5). The database obtains a single bitmap for the cust_gender key of F (Step 4), and then performs an AND operation on these two bitmaps. The result is a single bitmap that contains 1 values for the requested rows (Step 3). SQL_ID 1xf59h179zdg2, child number 0 ------------------------------------select cust_last_name, cust_first_name from customers where cust_gender = 'F' and cust_year_of_birth < 1918 Plan hash value: 49820847 --------------------------------------------------------------------------------------------|Id| Operation | Name |Rows|Bytes|Cost(%CPU)| Time | --------------------------------------------------------------------------------------------| 0| SELECT STATEMENT | | | |288 (100)| | | 1| TABLE ACCESS BY INDEX ROWID BATCHED|CUSTOMERS |1802|37842|288 (1)|00:00:01| | 2| BITMAP CONVERSION TO ROWIDS | | | | | | | 3| BITMAP AND | | | | | | |*4| BITMAP INDEX SINGLE VALUE |CUSTOMERS_GENDER_BIX| | | | | | 5| BITMAP MERGE | | | | | | |*6| BITMAP INDEX RANGE SCAN |CUSTOMERS_YOB_BIX | | | | | --------------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------4 - access("CUST_GENDER"='F') 6 - access("CUST_YEAR_OF_BIRTH"<1918) filter("CUST_YEAR_OF_BIRTH"<1918) 8.5 Table Cluster Access Paths A table cluster is a group of tables that share common columns and store related data in the same blocks. When tables are clustered, a single data block can contain rows from multiple tables. This section contains the following topics: 8-44 Chapter 8 Table Cluster Access Paths • Cluster Scans An index cluster is a table cluster that uses an index to locate data. • Hash Scans A hash cluster is like an indexed cluster, except the index key is replaced with a hash function. No separate cluster index exists. See Also: Oracle Database Concepts for an overview of table clusters 8.5.1 Cluster Scans An index cluster is a table cluster that uses an index to locate data. The cluster index is a B-tree index on the cluster key. A cluster scan retrieves all rows that have the same cluster key value from a table stored in an indexed cluster. This section contains the following topics: • When the Optimizer Considers Cluster Scans The database considers a cluster scan when a query accesses a table in an indexed cluster. • How a Cluster Scan Works In an indexed cluster, the database stores all rows with the same cluster key value in the same data block. • Cluster Scans: Example This example clusters the employees and departments tables on the department_id column, and then queries the cluster for a single department. 8.5.1.1 When the Optimizer Considers Cluster Scans The database considers a cluster scan when a query accesses a table in an indexed cluster. 8.5.1.2 How a Cluster Scan Works In an indexed cluster, the database stores all rows with the same cluster key value in the same data block. For example, if the hr.employees2 and hr.departments2 tables are clustered in emp_dept_cluster, and if the cluster key is department_id, then the database stores all employees in department 10 in the same block, all employees in department 20 in the same block, and so on. The B-tree cluster index associates the cluster key value with the database block address (DBA) of the block containing the data. For example, the index entry for key 30 shows the address of the block that contains rows for employees in department 30: 30,AADAAAA9d 8-45 Chapter 8 Table Cluster Access Paths When a user requests rows in the cluster, the database scans the index to obtain the DBAs of the blocks containing the rows. Oracle Database then locates the rows based on these DBAs. 8.5.1.3 Cluster Scans: Example This example clusters the employees and departments tables on the department_id column, and then queries the cluster for a single department. As user hr, you create a table cluster, cluster index, and tables in the cluster as follows: CREATE CLUSTER employees_departments_cluster (department_id NUMBER(4)) SIZE 512; CREATE INDEX idx_emp_dept_cluster ON CLUSTER employees_departments_cluster; CREATE TABLE employees2 CLUSTER employees_departments_cluster (department_id) AS SELECT * FROM employees; CREATE TABLE departments2 CLUSTER employees_departments_cluster (department_id) AS SELECT * FROM departments; You query the employees in department 30 as follows: SELECT * FROM employees2 WHERE department_id = 30; To perform the scan, Oracle Database first obtains the rowid of the row describing department 30 by scanning the cluster index (Step 2). Oracle Database then locates the rows in employees2 using this rowid (Step 1). SQL_ID b7xk1jzuwdc6t, child number 0 ------------------------------------SELECT * FROM employees2 WHERE department_id = 30 Plan hash value: 49826199 -------------------------------------------------------------------------------|Id| Operation | Name |Rows|Bytes|Cost(%CPU)| Time | -------------------------------------------------------------------------------| 0| SELECT STATEMENT | | | | 2 (100)| | | 1| TABLE ACCESS CLUSTER| EMPLOYEES2 | 6 | 798 | 2 (0)| 00:00:01| |*2| INDEX UNIQUE SCAN |IDX_EMP_DEPT_CLUSTER| 1 | | 1 (0)| 00:00:01| -------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------2 - access("DEPARTMENT_ID"=30) See Also: Oracle Database Concepts to learn about indexed clusters 8-46 Chapter 8 Table Cluster Access Paths 8.5.2 Hash Scans A hash cluster is like an indexed cluster, except the index key is replaced with a hash function. No separate cluster index exists. In a hash cluster, the data is the index. The database uses a hash scan to locate rows in a hash cluster based on a hash value. This section contains the following topics: • When the Optimizer Considers a Hash Scan The database considers a hash scan when a query accesses a table in a hash cluster. • How a Hash Scan Works In a hash cluster, all rows with the same hash value are stored in the same data block. • Hash Scans: Example This example hashes the employees and departments tables on the department_id column, and then queries the cluster for a single department. 8.5.2.1 When the Optimizer Considers a Hash Scan The database considers a hash scan when a query accesses a table in a hash cluster. 8.5.2.2 How a Hash Scan Works In a hash cluster, all rows with the same hash value are stored in the same data block. To perform a hash scan of the cluster, Oracle Database first obtains the hash value by applying a hash function to a cluster key value specified by the statement. Oracle Database then scans the data blocks containing rows with this hash value. 8.5.2.3 Hash Scans: Example This example hashes the employees and departments tables on the department_id column, and then queries the cluster for a single department. You create a hash cluster and tables in the cluster as follows: CREATE CLUSTER employees_departments_cluster (department_id NUMBER(4)) SIZE 8192 HASHKEYS 100; CREATE TABLE employees2 CLUSTER employees_departments_cluster (department_id) AS SELECT * FROM employees; CREATE TABLE departments2 CLUSTER employees_departments_cluster (department_id) AS SELECT * FROM departments; You query the employees in department 30 as follows: SELECT * FROM employees2 WHERE department_id = 30 8-47 Chapter 8 Table Cluster Access Paths To perform a hash scan, Oracle Database first obtains the hash value by applying a hash function to the key value 30, and then uses this hash value to scan the data blocks and retrieve the rows (Step 1). SQL_ID 919x7hyyxr6p4, child number 0 ------------------------------------SELECT * FROM employees2 WHERE department_id = 30 Plan hash value: 2399378016 ---------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost | ---------------------------------------------------------------| 0 | SELECT STATEMENT | | | | 1 | |* 1 | TABLE ACCESS HASH| EMPLOYEES2 | 10 | 1330 | | ---------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------1 - access("DEPARTMENT_ID"=30) See Also: Oracle Database Concepts to learn about hash clusters 8-48 9 Joins Oracle Database provides several optimizations for joining row sets. This chapter contains the following topics: • About Joins A join combines the output from exactly two row sources, such as tables or views, and returns one row source. The returned row source is the data set. • Join Methods A join method is the mechanism for joining two row sources. • Join Types A join type is determined by the type of join condition. • Join Optimizations Join optimizations enable joins to be more efficient. 9.1 About Joins A join combines the output from exactly two row sources, such as tables or views, and returns one row source. The returned row source is the data set. A join is characterized by multiple tables in the WHERE (non-ANSI) or FROM ... JOIN (ANSI) clause of a SQL statement. Whenever multiple tables exist in the FROM clause, Oracle Database performs a join. A join condition compares two row sources using an expression. The join condition defines the relationship between the tables. If the statement does not specify a join condition, then the database performs a Cartesian join, matching every row in one table with every row in the other table. This section contains the following topics: • Join Trees Typically, a join tree is represented as an upside-down tree structure. • How the Optimizer Executes Join Statements The database joins pairs of row sources. When multiple tables exist in the FROM clause, the optimizer must determine which join operation is most efficient for each pair. • How the Optimizer Chooses Execution Plans for Joins When determining the join order and method, the optimizer goal is to reduce the number of rows early so it performs less work throughout the execution of the SQL statement. 9-1 Chapter 9 About Joins See Also: • "Cartesian Joins" • Oracle Database SQL Language Reference for a concise discussion of joins in Oracle SQL 9.1.1 Join Trees Typically, a join tree is represented as an upside-down tree structure. As shown in the following graphic, table1 is the left table, and table2 is the right table. The optimizer processes the join from left to right. For example, if this graphic depicted a nested loops join, then table1 is the outer loop, and table2 is the inner loop. Figure 9-1 Join Tree result set table1 table2 The input of a join can be the result set from a previous join. If the right child of every internal node of a join tree is a table, then the tree is a left deep join tree, as shown in the following example. Most join trees are left deep joins. Figure 9-2 Left Deep Join Tree result set table4 table3 table1 table2 9-2 Chapter 9 About Joins If the left child of every internal node of a join tree is a table, then the tree is called a right deep join tree, as shown in the following diagram. Figure 9-3 Right Deep Join Tree result set table1 table2 table3 table4 If the left or the right child of an internal node of a join tree can be a join node, then the tree is called a bushy join tree. In the following example, table4 is a right child of a join node, table1 is the left child of a join node, and table2 is the left child of a join node. Figure 9-4 Bushy Join Tree result set table4 table1 table2 table3 In yet another variation, both inputs of a join are the results of a previous join. 9.1.2 How the Optimizer Executes Join Statements The database joins pairs of row sources. When multiple tables exist in the FROM clause, the optimizer must determine which join operation is most efficient for each pair. The optimizer must make the interrelated decisions shown in the following table. 9-3 Chapter 9 About Joins Table 9-1 Join Operations Operation Explanation To Learn More Access paths As for simple statements, the optimizer must choose an access path to retrieve data from each table in the join statement. For example, the optimizer might choose between a full table scan or an index scan.. "Optimizer Access Paths" Join methods To join each pair of row sources, Oracle Database must decide how to do it. The "how" is the join method. The possible join methods are nested loop, sort merge, and hash joins. A Cartesian join requires one of the preceding join methods. Each join method has specific situations in which it is more suitable than the others. "Join Methods" Join types The join condition determines the join type. For example, an inner join retrieves only rows that match the join condition. An outer join retrieves rows that do not match the join condition. "Join Types" Join order To execute a statement that joins more than N/A two tables, Oracle Database joins two tables and then joins the resulting row source to the next table. This process continues until all tables are joined into the result. For example, the database joins two tables, and then joins the result to a third table, and then joins this result to a fourth table, and so on. 9.1.3 How the Optimizer Chooses Execution Plans for Joins When determining the join order and method, the optimizer goal is to reduce the number of rows early so it performs less work throughout the execution of the SQL statement. The optimizer generates a set of execution plans, according to possible join orders, join methods, and available access paths. The optimizer then estimates the cost of each plan and chooses the one with the lowest cost. When choosing an execution plan, the optimizer considers the following factors: • The optimizer first determines whether joining two or more tables results in a row source containing at most one row. The optimizer recognizes such situations based on UNIQUE and PRIMARY KEY constraints on the tables. If such a situation exists, then the optimizer places these tables first in the join order. The optimizer then optimizes the join of the remaining set of tables. • For join statements with outer join conditions, the table with the outer join operator typically comes after the other table in the condition in the join order. In general, the optimizer does not consider join orders that violate this guideline, although the optimizer overrides this ordering condition in certain circumstances. Similarly, when a subquery has been converted into an antijoin or semijoin, the 9-4 Chapter 9 Join Methods tables from the subquery must come after those tables in the outer query block to which they were connected or correlated. However, hash antijoins and semijoins are able to override this ordering condition in certain circumstances. The optimizer estimates cost of a query plan by computing the estimated I/Os and CPU. These I/Os have specific costs associated with them: one cost for a single block I/O, and another cost for multiblock I/Os. Also, different functions and expressions have CPU costs associated with them. The optimizer determines the total cost of a query plan using these metrics. These metrics may be influenced by many initialization parameter and session settings at compile time, such as the DB_FILE_MULTI_BLOCK_READ_COUNT setting, system statistics, and so on. For example, the optimizer estimates costs in the following ways: • The cost of a nested loops join depends on the cost of reading each selected row of the outer table and each of its matching rows of the inner table into memory. The optimizer estimates these costs using statistics in the data dictionary. • The cost of a sort merge join depends largely on the cost of reading all the sources into memory and sorting them. • The cost of a hash join largely depends on the cost of building a hash table on one of the input sides to the join and using the rows from the other side of the join to probe it. Example 9-1 Estimating Costs for Join Order and Method Conceptually, the optimizer constructs a matrix of join orders and methods and the cost associated with each. For example, the optimizer must determine how best to join the date_dim and lineorder tables in a query. The following table shows the possible variations of methods and orders, and the cost for each. In this example, a nested loops join in the order date_dim, lineorder has the lowest cost. Table 9-2 Sample Costs for Join of date_dim and lineorder Tables Join Method Cost of date_dim, lineorder Cost of lineorder, date_dim Nested Loops 39,480 6,187,540 Hash Join 187,528 194,909 Sort Merge 217,129 217,129 See Also: • "Introduction to Optimizer Statistics" • "Influencing the Optimizer " for more information about optimizer hints • Oracle Database Reference to learn about DB_FILE_MULTIBLOCK_READ_COUNT 9.2 Join Methods A join method is the mechanism for joining two row sources. 9-5 Chapter 9 Join Methods Depending on the statistics, the optimizer chooses the method with the lowest estimated cost. As shown in Figure 9-5, each join method has two children: the driving (also called outer) row source and the driven-to (also called inner) row source. Figure 9-5 Join Method Join Method (Nested Loops, Hash Join, or Sort Merge) Driving Row Source, Outer row Source Driven-To Row Source, Inner Row Source This section contains the following topics: • Nested Loops Joins Nested loops join an outer data set to an inner data set. • Hash Joins The database uses a hash join to join larger data sets. • Sort Merge Joins A sort merge join is a variation on a nested loops join. • Cartesian Joins The database uses a Cartesian join when one or more of the tables does not have any join conditions to any other tables in the statement. 9.2.1 Nested Loops Joins Nested loops join an outer data set to an inner data set. For each row in the outer data set that matches the single-table predicates, the database retrieves all rows in the inner data set that satisfy the join predicate. If an index is available, then the database can use it to access the inner data set by rowid. This section contains the following topics: • When the Optimizer Considers Nested Loops Joins Nested loops joins are useful when the database joins small subsets of data, the database joins large sets of data with the optimizer mode set to FIRST_ROWS, or the join condition is an efficient method of accessing the inner table. • How Nested Loops Joins Work Conceptually, nested loops are equivalent to two nested for loops. • Nested Nested Loops The outer loop of a nested loop can itself be a row source generated by a different nested loop. • Current Implementation for Nested Loops Joins Oracle Database 11g introduced a new implementation for nested loops that reduces overall latency for physical I/O. 9-6 Chapter 9 Join Methods • Original Implementation for Nested Loops Joins In the current release, both the new and original implementation of nested loops are possible. • Nested Loops Controls For some SQL statements, the data is small enough for the optimizer to prefer full table scans and hash joins. However, you can add the USE_NL hint to instruct the optimizer to join each specified table to another row source with a nested loops join, using the specified table as the inner table. 9.2.1.1 When the Optimizer Considers Nested Loops Joins Nested loops joins are useful when the database joins small subsets of data, the database joins large sets of data with the optimizer mode set to FIRST_ROWS, or the join condition is an efficient method of accessing the inner table. Note: The number of rows expected from the join is what drives the optimizer decision, not the size of the underlying tables. For example, a query might join two tables of a billion rows each, but because of the filters the optimizer expects data sets of 5 rows each. In general, nested loops joins work best on small tables with indexes on the join conditions. If a row source has only one row, as with an equality lookup on a primary key value (for example, WHERE employee_id=101), then the join is a simple lookup. The optimizer always tries to put the smallest row source first, making it the driving table. Various factors enter into the optimizer decision to use nested loops. For example, the database may read several rows from the outer row source in a batch. Based on the number of rows retrieved, the optimizer may choose either a nested loop or a hash join to the inner row source. For example, if a query joins departments to driving table employees, and if the predicate specifies a value in employees.last_name, then the database might read enough entries in the index on last_name to determine whether an internal threshold is passed. If the threshold is not passed, then the optimizer picks a nested loop join to departments, and if the threshold is passed, then the database performs a hash join, which means reading the rest of employees, hashing it into memory, and then joining to departments. If the access path for the inner loop is not dependent on the outer loop, then the result can be a Cartesian product: for every iteration of the outer loop, the inner loop produces the same set of rows. To avoid this problem, use other join methods to join two independent row sources. See Also: • "Table 19-2" • "Adaptive Query Plans" 9-7 Chapter 9 Join Methods 9.2.1.2 How Nested Loops Joins Work Conceptually, nested loops are equivalent to two nested for loops. For example, if a query joins employees and departments, then a nested loop in pseudocode might be: FOR erow IN (select * from employees where X=Y) LOOP FOR drow IN (select * from departments where erow is matched) LOOP output values from erow and drow END LOOP END LOOP The inner loop is executed for every row of the outer loop. The employees table is the "outer" data set because it is in the exterior for loop. The outer table is sometimes called a driving table. The departments table is the "inner" data set because it is in the interior for loop. A nested loops join involves the following basic steps: 1. The optimizer determines the driving row source and designates it as the outer loop. The outer loop produces a set of rows for driving the join condition. The row source can be a table accessed using an index scan, a full table scan, or any other operation that generates rows. The number of iterations of the inner loop depends on the number of rows retrieved in the outer loop. For example, if 10 rows are retrieved from the outer table, then the database must perform 10 lookups in the inner table. If 10,000,000 rows are retrieved from the outer table, then the database must perform 10,000,000 lookups in the inner table. 2. The optimizer designates the other row source as the inner loop. The outer loop appears before the inner loop in the execution plan, as follows: NESTED LOOPS outer_loop inner_loop 3. For every fetch request from the client, the basic process is as follows: a. Fetch a row from the outer row source b. Probe the inner row source to find rows that match the predicate criteria c. Repeat the preceding steps until all rows are obtained by the fetch request Sometimes the database sorts rowids to obtain a more efficient buffer access pattern. 9.2.1.3 Nested Nested Loops The outer loop of a nested loop can itself be a row source generated by a different nested loop. The database can nest two or more outer loops to join as many tables as needed. Each loop is a data access method. The following template shows how the database iterates through three nested loops: 9-8 Chapter 9 Join Methods SELECT STATEMENT NESTED LOOPS 3 NESTED LOOPS 2 NESTED LOOPS 1 OUTER LOOP 1.1 INNER LOOP 1.2 INNER LOOP 2.2 INNER LOOP 3.2 - Row source becomes OUTER LOOP 3.1 - Row source becomes OUTER LOOP 2.1 The database orders the loops as follows: 1. The database iterates through NESTED LOOPS 1: NESTED LOOPS 1 OUTER LOOP 1.1 INNER LOOP 1.2 The output of NESTED LOOP 1 is a row source. 2. The database iterates through NESTED LOOPS 2, using the row source generated by NESTED LOOPS 1 as its outer loop: NESTED LOOPS 2 OUTER LOOP 2.1 INNER LOOP 2.2 - Row source generated by NESTED LOOPS 1 The output of NESTED LOOPS 2 is another row source. 3. The database iterates through NESTED LOOPS 3, using the row source generated by NESTED LOOPS 2 as its outer loop: NESTED LOOPS 3 OUTER LOOP 3.1 INNER LOOP 3.2 Example 9-2 - Row source generated by NESTED LOOPS 2 Nested Nested Loops Join Suppose you join the employees and departments tables as follows: SELECT FROM WHERE AND /*+ ORDERED USE_NL(d) */ e.last_name, e.first_name, d.department_name employees e, departments d e.department_id=d.department_id e.last_name like 'A%'; The plan reveals that the optimizer chose two nested loops (Step 1 and Step 2) to access the data: SQL_ID ahuavfcv4tnz4, child number 0 ------------------------------------SELECT /*+ ORDERED USE_NL(d) */ e.last_name, d.department_name FROM employees e, departments d WHERE e.department_id=d.department_id AND e.last_name like 'A%' Plan hash value: 1667998133 ---------------------------------------------------------------------------------|Id| Operation |Name |Rows|Bytes|Cost(%CPU)|Time| ---------------------------------------------------------------------------------| 0| SELECT STATEMENT | | | |5 (100)| | | 1| NESTED LOOPS | | | | | | | 2| NESTED LOOPS | | 3|102|5 (0)|00:00:01| | 3| TABLE ACCESS BY INDEX ROWID BATCHED| EMPLOYEES | 3| 54|2 (0)|00:00:01| |*4| INDEX RANGE SCAN | EMP_NAME_IX | 3| |1 (0)|00:00:01| 9-9 Chapter 9 Join Methods |*5| INDEX UNIQUE SCAN | DEPT_ID_PK | 1| |0 (0)| | | 6| TABLE ACCESS BY INDEX ROWID | DEPARTMENTS | 1| 16|1 (0)|00:00:01| ---------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------4 - access("E"."LAST_NAME" LIKE 'A%') filter("E"."LAST_NAME" LIKE 'A%') 5 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID") In this example, the basic process is as follows: 1. The database begins iterating through the inner nested loop (Step 2) as follows: a. The database searches the emp_name_ix for the rowids for all last names that begins with A (Step 4). For example: Abel,employees_rowid Ande,employees_rowid Atkinson,employees_rowid Austin,employees_rowid b. Using the rowids from the previous step, the database retrieves a batch of rows from the employees table (Step 3). For example: Abel,Ellen,80 Abel,John,50 These rows become the outer row source for the innermost nested loop. The batch step is typically part of adaptive execution plans. To determine whether a nested loop is better than a hash join, the optimizer needs to determine many rows come back from the row source. If too many rows are returned, then the optimizer switches to a different join method. c. For each row in the outer row source, the database scans the dept_id_pk index to obtain the rowid in departments of the matching department ID (Step 5), and joins it to the employees rows. For example: Abel,Ellen,80,departments_rowid Ande,Sundar,80,departments_rowid Atkinson,Mozhe,50,departments_rowid Austin,David,60,departments_rowid These rows become the outer row source for the outer nested loop (Step 1). 2. The database iterates through the outer nested loop as follows: a. The database reads the first row in outer row source. For example: Abel,Ellen,80,departments_rowid b. The database uses the departments rowid to retrieve the corresponding row from departments (Step 6), and then joins the result to obtain the requested values (Step 1). For example: Abel,Ellen,80,Sales 9-10 Chapter 9 Join Methods c. The database reads the next row in the outer row source, uses the departments rowid to retrieve the corresponding row from departments (Step 6), and iterates through the loop until all rows are retrieved. The result set has the following form: Abel,Ellen,80,Sales Ande,Sundar,80,Sales Atkinson,Mozhe,50,Shipping Austin,David,60,IT 9.2.1.4 Current Implementation for Nested Loops Joins Oracle Database 11g introduced a new implementation for nested loops that reduces overall latency for physical I/O. When an index or a table block is not in the buffer cache and is needed to process the join, a physical I/O is required. The database can batch multiple physical I/O requests and process them using a vector I/O (array) instead of one at a time. The database sends an array of rowids to the operating system, which performs the read. As part of the new implementation, two NESTED LOOPS join row sources might appear in the execution plan where only one would have appeared in prior releases. In such cases, Oracle Database allocates one NESTED LOOPS join row source to join the values from the table on the outer side of the join with the index on the inner side. A second row source is allocated to join the result of the first join, which includes the rowids stored in the index, with the table on the inner side of the join. Consider the query in "Original Implementation for Nested Loops Joins". In the current implementation, the execution plan for this query might be as follows: ------------------------------------------------------------------------------------| Id | Operation | Name |Rows|Bytes|Cost%CPU| Time | ------------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 19 | 722 | 3 (0)|00:00:01| | 1 | NESTED LOOPS | | | | | | | 2 | NESTED LOOPS | | 19 | 722 | 3 (0)|00:00:01| |* 3 | TABLE ACCESS FULL | DEPARTMENTS | 2 | 32 | 2 (0)|00:00:01| |* 4 | INDEX RANGE SCAN | EMP_DEPARTMENT_IX | 10 | | 0 (0)|00:00:01| | 5 | TABLE ACCESS BY INDEX ROWID| EMPLOYEES | 10 | 220 | 1 (0)|00:00:01| ------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------3 - filter("D"."DEPARTMENT_NAME"='Marketing' OR "D"."DEPARTMENT_NAME"='Sales') 4 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID") In this case, rows from the hr.departments table form the outer row source (Step 3) of the inner nested loop (Step 2). The index emp_department_ix is the inner row source (Step 4) of the inner nested loop. The results of the inner nested loop form the outer row source (Row 2) of the outer nested loop (Row 1). The hr.employees table is the outer row source (Row 5) of the outer nested loop. For each fetch request, the basic process is as follows: 1. The database iterates through the inner nested loop (Step 2) to obtain the rows requested in the fetch: a. The database reads the first row of departments to obtain the department IDs for departments named Marketing or Sales (Step 3). For example: 9-11 Chapter 9 Join Methods Marketing,20 This row set is the outer loop. The database caches the data in the PGA. b. The database scans emp_department_ix, which is an index on the employees table, to find employees rowids that correspond to this department ID (Step 4), and then joins the result (Step 2). The result set has the following form: Marketing,20,employees_rowid Marketing,20,employees_rowid Marketing,20,employees_rowid c. The database reads the next row of departments, scans emp_department_ix to find employees rowids that correspond to this department ID, and then iterates through the loop until the client request is satisfied. In this example, the database only iterates through the outer loop twice because only two rows from departments satisfy the predicate filter. Conceptually, the result set has the following form: Marketing,20,employees_rowid Marketing,20,employees_rowid Marketing,20,employees_rowid . . . Sales,80,employees_rowid Sales,80,employees_rowid Sales,80,employees_rowid . . . These rows become the outer row source for the outer nested loop (Step 1). This row set is cached in the PGA. 2. The database organizes the rowids obtained in the previous step so that it can more efficiently access them in the cache. 3. The database begins iterating through the outer nested loop as follows: a. The database retrieves the first row from the row set obtained in the previous step, as in the following example: Marketing,20,employees_rowid b. Using the rowid, the database retrieves a row from employees to obtain the requested values (Step 1), as in the following example: Michael,Hartstein,13000,Marketing c. The database retrieves the next row from the row set, uses the rowid to probe employees for the matching row, and iterates through the loop until all rows are retrieved. The result set has the following form: Michael,Hartstein,13000,Marketing Pat,Fay,6000,Marketing John,Russell,14000,Sales Karen,Partners,13500,Sales Alberto,Errazuriz,12000,Sales . 9-12 Chapter 9 Join Methods . . In some cases, a second join row source is not allocated, and the execution plan looks the same as it did before Oracle Database 11g. The following list describes such cases: • All of the columns needed from the inner side of the join are present in the index, and there is no table access required. In this case, Oracle Database allocates only one join row source. • The order of the rows returned might be different from the order returned in releases earlier than Oracle Database 12c. Thus, when Oracle Database tries to preserve a specific ordering of the rows, for example to eliminate the need for an ORDER BY sort, Oracle Database might use the original implementation for nested loops joins. • The OPTIMIZER_FEATURES_ENABLE initialization parameter is set to a release before Oracle Database 11g. In this case, Oracle Database uses the original implementation for nested loops joins. 9.2.1.5 Original Implementation for Nested Loops Joins In the current release, both the new and original implementation of nested loops are possible. For an example of the original implementation, consider the following join of the hr.employees and hr.departments tables: SELECT FROM WHERE AND e.first_name, e.last_name, e.salary, d.department_name hr.employees e, hr.departments d d.department_name IN ('Marketing', 'Sales') e.department_id = d.department_id; In releases before Oracle Database 11g, the execution plan for this query might appear as follows: -----------------------------------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | -----------------------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 19 | 722 | 3 (0)| 00:00:01 | | 1 | TABLE ACCESS BY INDEX ROWID| EMPLOYEES | 10 | 220 | 1 (0)| 00:00:01 | | 2 | NESTED LOOPS | | 19 | 722 | 3 (0)| 00:00:01 | |* 3 | TABLE ACCESS FULL | DEPARTMENTS | 2 | 32 | 2 (0)| 00:00:01 | |* 4 | INDEX RANGE SCAN | EMP_DEPARTMENT_IX | 10 | | 0 (0)| 00:00:01 | -----------------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------3 - filter("D"."DEPARTMENT_NAME"='Marketing' OR "D"."DEPARTMENT_NAME"='Sales') 4 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID") For each fetch request, the basic process is as follows: 1. The database iterates through the loop to obtain the rows requested in the fetch: a. The database reads the first row of departments to obtain the department IDs for departments named Marketing or Sales (Step 3). For example: Marketing,20 9-13 Chapter 9 Join Methods This row set is the outer loop. The database caches the row in the PGA. b. The database scans emp_department_ix, which is an index on the employees.department_id column, to find employees rowids that correspond to this department ID (Step 4), and then joins the result (Step 2). Conceptually, the result set has the following form: Marketing,20,employees_rowid Marketing,20,employees_rowid Marketing,20,employees_rowid c. The database reads the next row of departments, scans emp_department_ix to find employees rowids that correspond to this department ID, and iterates through the loop until the client request is satisfied. In this example, the database only iterates through the outer loop twice because only two rows from departments satisfy the predicate filter. Conceptually, the result set has the following form: Marketing,20,employees_rowid Marketing,20,employees_rowid Marketing,20,employees_rowid . . . Sales,80,employees_rowid Sales,80,employees_rowid Sales,80,employees_rowid . . . 2. Depending on the circumstances, the database may organize the cached rowids obtained in the previous step so that it can more efficiently access them. 3. For each employees rowid in the result set generated by the nested loop, the database retrieves a row from employees to obtain the requested values (Step 1). Thus, the basic process is to read a rowid and retrieve the matching employees row, read the next rowid and retrieve the matching employees row, and so on. Conceptually, the result set has the following form: Michael,Hartstein,13000,Marketing Pat,Fay,6000,Marketing John,Russell,14000,Sales Karen,Partners,13500,Sales Alberto,Errazuriz,12000,Sales . . . 9.2.1.6 Nested Loops Controls For some SQL statements, the data is small enough for the optimizer to prefer full table scans and hash joins. However, you can add the USE_NL hint to instruct the optimizer to join each specified table to another row source with a nested loops join, using the specified table as the inner table. The related hint USE_NL_WITH_INDEX(table index) hint instructs the optimizer to join the specified table to another row source with a nested loops join using the specified table 9-14 Chapter 9 Join Methods as the inner table. The index is optional. If no index is specified, then the nested loops join uses an index with at least one join predicate as the index key. Example 9-3 Nested Loops Hint Assume that the optimizer chooses a hash join for the following query: SELECT e.last_name, d.department_name FROM employees e, departments d WHERE e.department_id=d.department_id; The plan looks as follows: -----------------------------------------------------------------------------|Id | Operation | Name | Rows | Bytes |Cost(%CPU)| Time | -----------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | | 5 (100)| | |*1 | HASH JOIN | | 106 | 2862 | 5 (20)| 00:00:01 | | 2 | TABLE ACCESS FULL| DEPARTMENTS | 27 | 432 | 2 (0)| 00:00:01 | | 3 | TABLE ACCESS FULL| EMPLOYEES | 107 | 1177 | 2 (0)| 00:00:01 | ------------------------------------------------------------------------------ To force a nested loops join using departments as the inner table, add the USE_NL hint as in the following query: SELECT /*+ ORDERED USE_NL(d) */ e.last_name, d.department_name FROM employees e, departments d WHERE e.department_id=d.department_id; The plan looks as follows: -------------------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes |Cost (%CPU)| Time | -------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | | 34 (100)| | | 1 | NESTED LOOPS | | 106 | 2862 | 34 (3)| 00:00:01 | | 2 | TABLE ACCESS FULL| EMPLOYEES | 107 | 1177 | 2 (0)| 00:00:01 | |* 3 | TABLE ACCESS FULL| DEPARTMENTS | 1 | 16 | 0 (0)| | -------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------3 - filter("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID") The database obtains the result set as follows: 1. In the nested loop, the database reads employees to obtain the last name and department ID for an employee (Step 2). For example: De Haan,90 2. For the row obtained in the previous step, the database scans departments to find the department name that matches the employees department ID (Step 3), and joins the result (Step 1). For example: De Haan,Executive 3. The database retrieves the next row in employees, retrieves the matching row from departments, and then repeats this process until all rows are retrieved. The result set has the following form: 9-15 Chapter 9 Join Methods De Haan,Executive Kochnar,Executive Baer,Public Relations King,Executive . . . See Also: • "Guidelines for Join Order Hints" to learn more about the USE_NL hint • Oracle Database SQL Language Reference to learn about the USE_NL hint 9.2.2 Hash Joins The database uses a hash join to join larger data sets. The optimizer uses the smaller of two data sets to build a hash table on the join key in memory, using a deterministic hash function to specify the location in the hash table in which to store each row. The database then scans the larger data set, probing the hash table to find the rows that meet the join condition. This section contains the following topics: • When the Optimizer Considers Hash Joins In general, the optimizer considers a hash join when a relatively large amount of data must be joined (or a large percentage of a small table must be joined), and the join is an equijoin. • How Hash Joins Work A hashing algorithm takes a set of inputs and applies a deterministic hash function to generate a hash value between 1 and n, where n is the size of the hash table. • How Hash Joins Work When the Hash Table Does Not Fit in the PGA The database must use a different technique when the hash table does not fit entirely in the PGA. In this case, the database uses a temporary space to hold portions (called partitions) of the hash table, and sometimes portions of the larger table that probes the hash table. • Hash Join Controls The USE_HASH hint instructs the optimizer to use a hash join when joining two tables together. 9.2.2.1 When the Optimizer Considers Hash Joins In general, the optimizer considers a hash join when a relatively large amount of data must be joined (or a large percentage of a small table must be joined), and the join is an equijoin. A hash join is most cost effective when the smaller data set fits in memory. In this case, the cost is limited to a single read pass over the two data sets. 9-16 Chapter 9 Join Methods Because the hash table is in the PGA, Oracle Database can access rows without latching them. This technique reduces logical I/O by avoiding the necessity of repeatedly latching and reading blocks in the database buffer cache. If the data sets do not fit in memory, then the database partitions the row sources, and the join proceeds partition by partition. This can use a lot of sort area memory, and I/O to the temporary tablespace. This method can still be the most cost effective, especially when the database uses parallel query servers. 9.2.2.2 How Hash Joins Work A hashing algorithm takes a set of inputs and applies a deterministic hash function to generate a hash value between 1 and n, where n is the size of the hash table. In a hash join, the input values are the join keys. The output values are indexes (slots) in an array, which is the hash table. This section contains the following topics: • Hash Tables To illustrate a hash table, assume that the database hashes hr.departments in a join of departments and employees. The join key column is department_id. • Hash Join: Basic Steps The optimizer uses the smaller data source to build a hash table on the join key in memory, and then scans the larger table to find the joined rows. 9.2.2.2.1 Hash Tables To illustrate a hash table, assume that the database hashes hr.departments in a join of departments and employees. The join key column is department_id. The first 5 rows of departments are as follows: SQL> select * from departments where rownum < 6; DEPARTMENT_ID ------------10 20 30 40 50 DEPARTMENT_NAME MANAGER_ID LOCATION_ID ------------------------------ ---------- ----------Administration 200 1700 Marketing 201 1800 Purchasing 114 1700 Human Resources 203 2400 Shipping 121 1500 The database applies the hash function to each department_id in the table, generating a hash value for each. For this illustration, the hash table has 5 slots (it could have more or less). Because n is 5, the possible hash values range from 1 to 5. The hash functions might generate the following values for the department IDs: f(10) f(20) f(30) f(40) f(50) = = = = = 4 1 4 2 5 Note that the hash function happens to generate the same hash value of 4 for departments 10 and 30. This is known as a hash collision. In this case, the database puts the records for departments 10 and 30 in the same slot, using a linked list. Conceptually, the hash table looks as follows: 9-17 Chapter 9 Join Methods 1 2 3 4 5 20,Marketing,201,1800 40,Human Resources,203,2400 10,Administration,200,1700 -> 30,Purchasing,114,1700 50,Shipping,121,1500 9.2.2.2.2 Hash Join: Basic Steps The optimizer uses the smaller data source to build a hash table on the join key in memory, and then scans the larger table to find the joined rows. The basic steps are as follows: 1. The database performs a full scan of the smaller data set, called the build table, and then applies a hash function to the join key in each row to build a hash table in the PGA. In pseudocode, the algorithm might look as follows: FOR small_table_row IN (SELECT * FROM small_table) LOOP slot_number := HASH(small_table_row.join_key); INSERT_HASH_TABLE(slot_number,small_table_row); END LOOP; 2. The database probes the second data set, called the probe table, using whichever access mechanism has the lowest cost. Typically, the database performs a full scan of both the smaller and larger data set. The algorithm in pseudocode might look as follows: FOR large_table_row IN (SELECT * FROM large_table) LOOP slot_number := HASH(large_table_row.join_key); small_table_row = LOOKUP_HASH_TABLE(slot_number,large_table_row.join_key); IF small_table_row FOUND THEN output small_table_row + large_table_row; END IF; END LOOP; For each row retrieved from the larger data set, the database does the following: a. Applies the same hash function to the join column or columns to calculate the number of the relevant slot in the hash table. For example, to probe the hash table for department ID 30, the database applies the hash function to 30, which generates the hash value 4. b. Probes the hash table to determine whether rows exists in the slot. If no rows exist, then the database processes the next row in the larger data set. If rows exist, then the database proceeds to the next step. c. Checks the join column or columns for a match. If a match occurs, then the database either reports the rows or passes them to the next step in the plan, and then processes the next row in the larger data set. If multiple rows exist in the hash table slot, the database walks through the linked list of rows, checking each one. For example, if department 30 hashes to slot 4, then the database checks each row until it finds 30. 9-18 Chapter 9 Join Methods Example 9-4 Hash Joins An application queries the oe.orders and oe.order_items tables, joining on the order_id column. SELECT o.customer_id, l.unit_price * l.quantity FROM orders o, order_items l WHERE l.order_id = o.order_id; The execution plan is as follows: -------------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| -------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 665 | 13300 | 8 (25)| |* 1 | HASH JOIN | | 665 | 13300 | 8 (25)| | 2 | TABLE ACCESS FULL | ORDERS | 105 | 840 | 4 (25)| | 3 | TABLE ACCESS FULL | ORDER_ITEMS | 665 | 7980 | 4 (25)| -------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------1 - access("L"."ORDER_ID"="O"."ORDER_ID") Because the orders table is small relative to the order_items table, which is 6 times larger, the database hashes orders. In a hash join, the data set for the build table always appears first in the list of operations (Step 2). In Step 3, the database performs a full scan of the larger order_items later, probing the hash table for each row. 9.2.2.3 How Hash Joins Work When the Hash Table Does Not Fit in the PGA The database must use a different technique when the hash table does not fit entirely in the PGA. In this case, the database uses a temporary space to hold portions (called partitions) of the hash table, and sometimes portions of the larger table that probes the hash table. The basic process is as follows: 1. The database performs a full scan of the smaller data set, and then builds an array of hash buckets in both the PGA and on disk. When the PGA hash area fills up, the database finds the largest partition within the hash table and writes it to temporary space on disk. The database stores any new row that belongs to this on-disk partition on disk, and all other rows in the PGA. Thus, part of the hash table is in memory and part of it on disk. 2. The database takes a first pass at reading the other data set. For each row, the database does the following: a. Applies the same hash function to the join column or columns to calculate the number of the relevant hash bucket. b. Probes the hash table to determine whether rows exist in the bucket in memory. If the hashed value points to a row in memory, then the database completes the join and returns the row. If the value points to a hash partition on disk, however, then the database stores this row in the temporary tablespace, using the same partitioning scheme used for the original data set. 3. The database reads each on-disk temporary partition one by one 9-19 Chapter 9 Join Methods 4. The database joins each partition row to the row in the corresponding on-disk temporary partition. 9.2.2.4 Hash Join Controls The USE_HASH hint instructs the optimizer to use a hash join when joining two tables together. See Also: • "Guidelines for Join Order Hints" • Oracle Database SQL Language Reference to learn about USE_HASH 9.2.3 Sort Merge Joins A sort merge join is a variation on a nested loops join. If the two data sets in the join are not already sorted, then the database sorts them. These are the SORT JOIN operations. For each row in the first data set, the database probes the second data set for matching rows and joins them, basing its start position on the match made in the previous iteration. This is the MERGE JOIN operation. Figure 9-6 Sort Merge Join MERGE JOIN SORT JOIN SORT JOIN First Row Source Second Row Source This section contains the following topics: • When the Optimizer Considers Sort Merge Joins A hash join requires one hash table and one probe of this table, whereas a sort merge join requires two sorts. • How Sort Merge Joins Work As in a nested loops join, a sort merge join reads two data sets, but sorts them when they are not already sorted. For each row in the first data set, the database finds a starting row in the second data set, and then reads the second data set until it finds a nonmatching row. 9-20 Chapter 9 Join Methods • Sort Merge Join Controls The USE_MERGE hint instructs the optimizer to use a sort merge join. 9.2.3.1 When the Optimizer Considers Sort Merge Joins A hash join requires one hash table and one probe of this table, whereas a sort merge join requires two sorts. The optimizer may choose a sort merge join over a hash join for joining large amounts of data when any of the following conditions is true: • The join condition between two tables is not an equijoin, that is, uses an inequality condition such as <, <=, >, or >=. In contrast to sort merges, hash joins require an equality condition. • Because of sorts required by other operations, the optimizer finds it cheaper to use a sort merge. If an index exists, then the database can avoid sorting the first data set. However, the database always sorts the second data set, regardless of indexes. A sort merge has the same advantage over a nested loops join as the hash join: the database accesses rows in the PGA rather than the SGA, reducing logical I/O by avoiding the necessity of repeatedly latching and reading blocks in the database buffer cache. In general, hash joins perform better than sort merge joins because sorting is expensive. However, sort merge joins offer the following advantages over a hash join: • After the initial sort, the merge phase is optimized, resulting in faster generation of output rows. • A sort merge can be more cost-effective than a hash join when the hash table does not fit completely in memory. A hash join with insufficient memory requires both the hash table and the other data set to be copied to disk. In this case, the database may have to read from disk multiple times. In a sort merge, if memory cannot hold the two data sets, then the database writes them both to disk, but reads each data set no more than once. 9.2.3.2 How Sort Merge Joins Work As in a nested loops join, a sort merge join reads two data sets, but sorts them when they are not already sorted. For each row in the first data set, the database finds a starting row in the second data set, and then reads the second data set until it finds a nonmatching row. In pseudocode, the high-level algorithm for sort merge might look as follows: READ data_set_1 SORT BY JOIN KEY TO temp_ds1 READ data_set_2 SORT BY JOIN KEY TO temp_ds2 READ ds1_row FROM temp_ds1 READ ds2_row FROM temp_ds2 WHILE NOT eof ON temp_ds1,temp_ds2 LOOP IF ( temp_ds1.key = temp_ds2.key ) OUTPUT JOIN ds1_row,ds2_row ELSIF ( temp_ds1.key <= temp_ds2.key ) READ ds1_row FROM temp_ds1 ELSIF ( temp_ds1.key => temp_ds2.key ) READ ds2_row FROM temp_ds2 END LOOP 9-21 Chapter 9 Join Methods For example, the following table shows sorted values in two data sets: temp_ds1 and temp_ds2. Table 9-3 Sorted Data Sets temp_ds1 temp_ds2 10 20 20 20 30 40 40 40 50 40 60 40 70 40 . 60 . 70 . 70 As shown in the following table, the database begins by reading 10 in temp_ds1, and then reads the first value in temp_ds2. Because 20 in temp_ds2 is higher than 10 in temp_ds1, the database stops reading temp_ds2. Table 9-4 Start at 10 in temp_ds1 temp_ds1 temp_ds2 Action 10 [start here] 20 [start here] [stop here] 20 in temp_ds2 is higher than 10 in temp_ds1. Stop. Start again with next row in temp_ds1. 20 20 30 40 40 40 50 40 60 40 70 40 . 60 . 70 . 70 The database proceeds to the next value in temp_ds1, which is 20. The database proceeds through temp_ds2 as shown in the following table. Table 9-5 Start at 20 in temp_ds1 temp_ds1 temp_ds2 Action 10 20 [start here] Match. Proceed to next value in temp_ds2. 20 [start here] 20 Match. Proceed to next value in temp_ds2. 30 40 [stop here] 40 in temp_ds2 is higher than 20 in temp_ds1. Stop. Start again with next row in temp_ds1. 40 40 50 40 60 40 9-22 Chapter 9 Join Methods Table 9-5 (Cont.) Start at 20 in temp_ds1 temp_ds1 temp_ds2 70 40 . 60 . 70 . 70 Action The database proceeds to the next row in temp_ds1, which is 30. The database starts at the number of its last match, which was 20, and then proceeds through temp_ds2 looking for a match, as shown in the following table. Table 9-6 Start at 30 in temp_ds1 temp_ds1 temp_ds2 Action 10 20 20 20 [start at last match] 20 in temp_ds2 is lower than 30 in temp_ds1. Proceed to next value in temp_ds2. 30 [start here] 40 [stop here] 40 40 50 40 60 40 70 40 . 60 . 70 . 70 40 in temp_ds2 is higher than 30 in temp_ds1. Stop. Start again with next row in temp_ds1. The database proceeds to the next row in temp_ds1, which is 40. As shown in the following table, the database starts at the number of its last match in temp_ds2, which was 20, and then proceeds through temp_ds2 looking for a match. Table 9-7 Start at 40 in temp_ds1 temp_ds1 temp_ds2 Action 10 20 20 20 [start at last match] 20 in temp_ds2 is lower than 40 in temp_ds1. Proceed to next value in temp_ds2. 30 40 Match. Proceed to next value in temp_ds2. 40 [start here] 40 Match. Proceed to next value in temp_ds2. 50 40 Match. Proceed to next value in temp_ds2. 60 40 Match. Proceed to next value in temp_ds2. 70 40 Match. Proceed to next value in temp_ds2. . 60 [stop here] 60 in temp_ds2 is higher than 40 in temp_ds1. Stop. Start again with next row in temp_ds1. . 70 . 70 9-23 Chapter 9 Join Methods The database continues in this way until it has matched the final 70 in temp_ds2. This scenario demonstrates that the database, as it reads through temp_ds1, does not need to read every row in temp_ds2. This is an advantage over a nested loops join. Example 9-5 Sort Merge Join Using Index The following query joins the employees and departments tables on the department_id column, ordering the rows on department_id as follows: SELECT e.employee_id, e.last_name, e.first_name, e.department_id, d.department_name FROM employees e, departments d WHERE e.department_id = d.department_id ORDER BY department_id; A query of DBMS_XPLAN.DISPLAY_CURSOR shows that the plan uses a sort merge join: -------------------------------------------------------------------------------|Id| Operation | Name |Rows|Bytes |Cost (%CPU)| Time | -------------------------------------------------------------------------------| 0| SELECT STATEMENT | | | | 5(100)| | | 1| MERGE JOIN | |106 | 4028 | 5 (20)| 00:00:01 | | 2| TABLE ACCESS BY INDEX ROWID| DEPARTMENTS | 27 | 432 | 2 (0)| 00:00:01 | | 3| INDEX FULL SCAN | DEPT_ID_PK | 27 | | 1 (0)| 00:00:01 | |*4| SORT JOIN | |107 | 2354 | 3 (34)| 00:00:01 | | 5| TABLE ACCESS FULL | EMPLOYEES |107 | 2354 | 2 (0)| 00:00:01 | -------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------4 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID") filter("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID") The two data sets are the departments table and the employees table. Because an index orders the departments table by department_id, the database can read this index and avoid a sort (Step 3). The database only needs to sort the employees table (Step 4), which is the most CPU-intensive operation. Example 9-6 Sort Merge Join Without an Index You join the employees and departments tables on the department_id column, ordering the rows on department_id as follows. In this example, you specify NO_INDEX and USE_MERGE to force the optimizer to choose a sort merge: SELECT /*+ USE_MERGE(d e) NO_INDEX(d) */ e.employee_id, e.last_name, e.first_name, e.department_id, d.department_name FROM employees e, departments d WHERE e.department_id = d.department_id ORDER BY department_id; A query of DBMS_XPLAN.DISPLAY_CURSOR shows that the plan uses a sort merge join: -------------------------------------------------------------------------------| Id| Operation | Name | Rows | Bytes | Cost (%CPU)| Time | -------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | | 6 (100)| | | 1 | MERGE JOIN | | 106 | 9540 | 6 (34)| 00:00:01| | 2 | SORT JOIN | | 27 | 567 | 3 (34)| 00:00:01| | 3 | TABLE ACCESS FULL| DEPARTMENTS | 27 | 567 | 2 (0)| 00:00:01| |*4 | SORT JOIN | | 107 | 7383 | 3 (34)| 00:00:01| 9-24 Chapter 9 Join Methods | 5 | TABLE ACCESS FULL| EMPLOYEES | 107 | 7383 | 2 (0)| 00:00:01| -------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------4 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID") filter("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID") Because the departments.department_id index is ignored, the optimizer performs a sort, which increases the combined cost of Step 2 and Step 3 by 67% (from 3 to 5). 9.2.3.3 Sort Merge Join Controls The USE_MERGE hint instructs the optimizer to use a sort merge join. In some situations it may make sense to override the optimizer with the USE_MERGE hint. For example, the optimizer can choose a full scan on a table and avoid a sort operation in a query. However, there is an increased cost because a large table is accessed through an index and single block reads, as opposed to faster access through a full table scan. See Also: Oracle Database SQL Language Reference to learn about the USE_MERGE hint 9.2.4 Cartesian Joins The database uses a Cartesian join when one or more of the tables does not have any join conditions to any other tables in the statement. The optimizer joins every row from one data source with every row from the other data source, creating the Cartesian product of the two sets. Therefore, the total number of rows resulting from the join is calculated using the following formula, where rs1 is the number of rows in first row set and rs2 is the number of rows in the second row set: rs1 X rs2 = total rows in result set This section contains the following topics: • When the Optimizer Considers Cartesian Joins The optimizer uses a Cartesian join for two row sources only in specific circumstances. • How Cartesian Joins Work A Cartesian join uses nested FOR loops. • Cartesian Join Controls The ORDERED hint instructs the optimizer to join tables in the order in which they appear in the FROM clause. By forcing a join between two row sources that have no direct connection, the optimizer must perform a Cartesian join. 9.2.4.1 When the Optimizer Considers Cartesian Joins The optimizer uses a Cartesian join for two row sources only in specific circumstances. 9-25 Chapter 9 Join Methods Typically, the situation is one of the following: • No join condition exists. In some cases, the optimizer could pick up a common filter condition between the two tables as a possible join condition. Note: If a Cartesian join appears in a query plan, it could be caused by an inadvertently omitted join condition. In general, if a query joins n tables, then n-1 join conditions are required to avoid a Cartesian join. • A Cartesian join is an efficient method. For example, the optimizer may decide to generate a Cartesian product of two very small tables that are both joined to the same large table. • The ORDERED hint specifies a table before its join table is specified. 9.2.4.2 How Cartesian Joins Work A Cartesian join uses nested FOR loops. At a high level, the algorithm for a Cartesian join looks as follows, where ds1 is typically the smaller data set, and ds2 is the larger data set: FOR ds1_row IN ds1 LOOP FOR ds2_row IN ds2 LOOP output ds1_row and ds2_row END LOOP END LOOP Example 9-7 Cartesian Join In this example, a user intends to perform an inner join of the employees and departments tables, but accidentally leaves off the join condition: SELECT e.last_name, d.department_name FROM employees e, departments d The execution plan is as follows: -------------------------------------------------------------------------------| Id| Operation | Name | Rows | Bytes |Cost (%CPU)| Time | -------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | |11 (100)| | | 1 | MERGE JOIN CARTESIAN | | 2889 | 57780 |11 (0)| 00:00:01 | | 2 | TABLE ACCESS FULL | DEPARTMENTS | 27 | 324 | 2 (0)| 00:00:01 | | 3 | BUFFER SORT | | 107 | 856 | 9 (0)| 00:00:01 | | 4 | INDEX FAST FULL SCAN| EMP_NAME_IX | 107 | 856 | 0 (0)| | -------------------------------------------------------------------------------- In Step 1 of the preceding plan, the CARTESIAN keyword indicates the presence of a Cartesian join. The number of rows (2889) is the product of 27 and 107. In Step 3, the BUFFER SORT operation indicates that the database is copying the data blocks obtained by the scan of emp_name_ix from the SGA to the PGA. This strategy 9-26 Chapter 9 Join Types avoids multiple scans of the same blocks in the database buffer cache, which would generate many logical reads and permit resource contention. 9.2.4.3 Cartesian Join Controls The ORDERED hint instructs the optimizer to join tables in the order in which they appear in the FROM clause. By forcing a join between two row sources that have no direct connection, the optimizer must perform a Cartesian join. Example 9-8 ORDERED Hint In the following example, the ORDERED hint instructs the optimizer to join employees and locations, but no join condition connects these two row sources: SELECT FROM WHERE AND /*+ORDERED*/ e.last_name, d.department_name, l.country_id, l.state_province employees e, locations l, departments d e.department_id = d.department_id d.location_id = l.location_id The following execution plan shows a Cartesian product (Step 3) between locations (Step 6) and employees (Step 4), which is then joined to the departments table (Step 2): -------------------------------------------------------------------------------| Id| Operation | Name | Rows | Bytes |Cost (%CPU)|Time | -------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | | 37 (100)| | |*1 | HASH JOIN | | 106 | 4664 | 37 (6)| 00:00:01 | | 2 | TABLE ACCESS FULL | DEPARTMENTS | 27 | 513 | 2 (0)| 00:00:01 | | 3 | MERGE JOIN CARTESIAN| | 2461 | 61525 | 34 (3)| 00:00:01 | | 4 | TABLE ACCESS FULL | EMPLOYEES | 107 | 1177 | 2 (0)| 00:00:01 | | 5 | BUFFER SORT | | 23 | 322 | 32 (4)| 00:00:01 | | 6 | TABLE ACCESS FULL | LOCATIONS | 23 | 322 | 0 (0)| | -------------------------------------------------------------------------------- See Also: Oracle Database SQL Language Reference to learn about the ORDERED hint 9.3 Join Types A join type is determined by the type of join condition. This section contains the following topics: • Inner Joins An inner join (sometimes called a simple join) is a join that returns only rows that satisfy the join condition. Inner joins are either equijoins or nonequijoins. • Outer Joins An outer join returns all rows that satisfy the join condition and also rows from one table for which no rows from the other table satisfy the condition. Thus, the result set of an outer join is the superset of an inner join. • Semijoins A semijoin is a join between two data sets that returns a row from the first set when a matching row exists in the subquery data set. 9-27 Chapter 9 Join Types • Antijoins An antijoin is a join between two data sets that returns a row from the first set when a matching row does not exist in the subquery data set. 9.3.1 Inner Joins An inner join (sometimes called a simple join) is a join that returns only rows that satisfy the join condition. Inner joins are either equijoins or nonequijoins. This section contains the following topics: • Equijoins An equijoin is an inner join whose join condition contains an equality operator. • Nonequijoins A nonequijoin is an inner join whose join condition contains an operator that is not an equality operator. • Band Joins A band join is a special type of nonequijoin in which key values in one data set must fall within the specified range (“band”) of the second data set. The same table can serve as both the first and second data sets. 9.3.1.1 Equijoins An equijoin is an inner join whose join condition contains an equality operator. The following example is an equijoin because the join condition contains only an equality operator: SELECT e.employee_id, e.last_name, d.department_name FROM employees e, departments d WHERE e.department_id=d.department_id; In the preceding query, the join condition is e.department_id=d.department_id. If a row in the employees table has a department ID that matches the value in a row in the departments table, then the database returns the joined result; otherwise, the database does not return a result. 9.3.1.2 Nonequijoins A nonequijoin is an inner join whose join condition contains an operator that is not an equality operator. The following query lists all employees whose hire date occurred when employee 176 (who is listed in job_history because he changed jobs in 2007) was working at the company: SELECT FROM WHERE AND e.employee_id, e.first_name, e.last_name, e.hire_date employees e, job_history h h.employee_id = 176 e.hire_date BETWEEN h.start_date AND h.end_date; In the preceding example, the condition joining employees and job_history does not contain an equality operator, so it is a nonequijoin. Nonequijoins are relatively rare. Note that a hash join requires at least a partial equijoin. The following SQL script contains an equality join condition (e1.empno = e2.empno) and a nonequality condition: 9-28 Chapter 9 Join Types SET AUTOTRACE TRACEONLY EXPLAIN SELECT * FROM scott.emp e1 JOIN scott.emp e2 ON ( e1.empno = e2.empno AND e1.hiredate BETWEEN e2.hiredate-1 AND e2.hiredate+1 ) The optimizer chooses a hash join for the preceding query, as shown in the following plan: Execution Plan ---------------------------------------------------------Plan hash value: 3638257876 --------------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | --------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 1 | 174 | 5 (20)| 00:00:01 | |* 1 | HASH JOIN | | 1 | 174 | 5 (20)| 00:00:01 | | 2 | TABLE ACCESS FULL| EMP | 14 | 1218 | 2 (0)| 00:00:01 | | 3 | TABLE ACCESS FULL| EMP | 14 | 1218 | 2 (0)| 00:00:01 | --------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------1 - access("E1"."EMPNO"="E2"."EMPNO") filter("E1"."HIREDATE">=INTERNAL_FUNCTION("E2"."HIREDATE")-1 AND "E1"."HIREDATE"<=INTERNAL_FUNCTION("E2"."HIREDATE")+1) 9.3.1.3 Band Joins A band join is a special type of nonequijoin in which key values in one data set must fall within the specified range (“band”) of the second data set. The same table can serve as both the first and second data sets. Starting in Oracle Database 12c Release 2 (12.2), the database evaluates band joins more efficiently. The optimization avoids the unnecessary scanning of rows that fall outside the defined bands. The optimizer uses a cost estimate to choose the join method (hash, nested loops, or sort merge) and the parallel data distribution method. In most cases, optimized performance is comparable to an equijoin. This following examples query employees whose salaries are between $100 less and $100 more than the salary of each employee. Thus, the band has a width of $200. The examples assume that it is permissible to compare the salary of every employee with itself. The following query includes partial sample output: SELECT e1.last_name || ' has salary between 100 less and 100 more than ' || e2.last_name AS "SALARY COMPARISON" FROM employees e1, employees e2 WHERE e1.salary BETWEEN e2.salary - 100 AND e2.salary + 100; SALARY COMPARISON ------------------------------------------------------------King has salary between 100 less and 100 more than King 9-29 Chapter 9 Join Types Kochhar has salary between 100 less and 100 more than Kochhar Kochhar has salary between 100 less and 100 more than De Haan De Haan has salary between 100 less and 100 more than Kochhar De Haan has salary between 100 less and 100 more than De Haan Russell has salary between 100 less and 100 more than Russell Partners has salary between 100 less and 100 more than Partners ... Example 9-9 Query Without Band Join Optimization Without the band join optimization, the database uses the following query plan: -----------------------------------------PLAN_TABLE_OUTPUT ----------------------------------------------------------------------------------| Id | Operation | Name | -----------------------------------------| 0 | SELECT STATEMENT | | | 1 | MERGE JOIN | | | 2 | SORT JOIN | | | 3 | TABLE ACCESS FULL | EMPLOYEES | |* 4 | FILTER | | |* 5 | SORT JOIN | | | 6 | TABLE ACCESS FULL| EMPLOYEES | -----------------------------------------Predicate Information (identified by operation id): --------------------------------------------------4 - filter("E1"."SAL"<="E2"."SAL"+100) 5 - access(INTERNAL_FUNCTION("E1"."SAL")>="E2"."SAL"-100) filter(INTERNAL_FUNCTION("E1"."SAL")>="E2"."SAL"-100) In this plan, Step 2 sorts the e1 row source, and Step 5 sorts the e2 row source. The sorted row sources are illustrated in the following table. Table 9-8 Sorted row Sources e1 Sorted (Step 2 of Plan) e2 Sorted (Step 5 of Plan) 24000 (King) 24000 (King) 17000 (Kochhar) 17000 (Kochhar) 17000 (De Haan) 17000 (De Haan) 14000 (Russell) 14000 (Russell) 13500 (Partners) 13500 (Partners) The join begins by iterating through the sorted input (e1), which is the left branch of the join, corresponding to Step 2 of the plan. The original query contains two predicates: • e1.sal >= e2.sal–100, which is the Step 5 filter • e1.sal >= e2.sal+100, which is the Step 4 filter For each iteration of the sorted row source e1, the database iterates through row source e2, checking every row against Step 5 filter e1.sal >= e2.sal–100. If the row passes the Step 5 filter, then the database sends it to the Step 4 filter, and then proceeds to test the next row in e2 against the Step 5 filter. However, if a row fails the 9-30 Chapter 9 Join Types Step 5 filter, then the scan of e2 stops, and the database proceeds through the next iteration of e1. The following table shows the first iteration of e1, which begins with 24000 (King) in data set e1. The database determines that the first row in e2, which is 24000 (King), passes the Step 5 filter. The database then sends the row to the Step 4 filter, e1.sal <= w2.sal+100, which also passes. The database sends this row to the MERGE row source. Next, the database checks 17000 (Kochhar) against the Step 5 filter, which also passes. However, the row fails the Step 4 filter, and is discarded. The database proceeds to test 17000 (De Haan) against the Step 5 filter. Table 9-9 First Iteration of e1: Separate SORT JOIN and FILTER Scan e2 Step 5 Filter (e1.sal >= e2.sal–100) Step 4 Filter (e1.sal <= e2.sal+100) 24000 (King) Pass because 24000 >= 23900. Send to Step 4 filter. Pass because 24000 <= 24100. Return row for merging. 17000 (Kochhar) Pass because 24000 >= 16900. Send to Step 4 filter. Fail because 24000 <=17100 is false. Discard row. Scan next row in e2. 17000 (De Haan) Pass because 24000 >= 16900. Send to Step 4 filter. Fail because 24000 <=17100 is false. Discard row. Scan next row in e2. 14000 (Russell) Pass because 24000 >= 13900. Send to Step 4 filter. Fail because 24000 <=14100 is false. Discard row. Scan next row in e2. 13500 (Partners) Fail because 24000 <=13600 is false. Discard row. Scan next row in e2. Pass because 24000 >= 13400. Send to Step 4 filter. As shown in the preceding table, every e2 row necessarily passes the Step 5 filter because the e2 salaries are sorted in descending order. Thus, the Step 5 filter always sends the row to the Step 4 filter. Because the e2 salaries are sorted in descending order, the Step 4 filter necessarily fails every row starting with 17000 (Kochhar). The inefficiency occurs because the database tests every subsequent row in e2 against the Step 5 filter, which necessarily passes, and then against the Step 4 filter, which necessarily fails. Example 9-10 Query With Band Join Optimization Starting in Oracle Database 12c Release 2 (12.2), the database optimizes the band join by using the following plan, which does not have a separate FILTER operation: -----------------------------------------PLAN_TABLE_OUTPUT -----------------------------------------| Id | Operation | Name | -----------------------------------------| 0 | SELECT STATEMENT | | | 1 | MERGE JOIN | | | 2 | SORT JOIN | | | 3 | TABLE ACCESS FULL | EMPLOYEES | |* 4 | SORT JOIN | | | 5 | TABLE ACCESS FULL | EMPLOYEES | -----------------------------------------Predicate Information (identified by operation id): --------------------------------------------------4 - access(INTERNAL_FUNCTION("E1"."SALARY")>="E2"."SALARY"-100) filter(("E1"."SALARY"<="E2"."SALARY"+100 AND INTERNAL_FUNCTION("E1"."SALARY")>="E2"."SALARY"-100)) 9-31 Chapter 9 Join Types The difference is that Step 4 uses Boolean AND logic for the two predicates to create a single filter. Instead of checking a row against one filter, and then sending it to a different row source for checking against a second filter, the database performs one check against one filter. If the check fails, then processing stops. In this example, the query begins the first iteration of e1, which begins with 24000 (King). The following figure represents the range. e2 values below 23900 and above 24100 fall outside the range. Figure 9-7 Band Join The following table shows that the database tests the first row of e2, which is 24000 (King), against the Step 4 filter. The row passes the test, so the database sends the row to be merged. The next row in e2 is 17000 (Kochhar). This row falls outside of the range (band) and thus does not satisfy the filter predicate, so the database stops testing e2 rows in this iteration. The database stops testing because the descending sort of e2 ensures that all subsequent rows in e2 fail the filter test. Thus, the database can proceed to the second iteration of e1. Table 9-10 First Iteration of e1: Single SORT JOIN Scan e2 Filter 4 (e1.sal >= e2.sal – 100) AND (e1.sal <= e2.sal + 100) 24000 (King) Passes test because it is true that (24000 >= 23900) AND (24000 <= 24100). Send row to MERGE. Test next row. 17000 (Kochhar) Fails test because it is false that (24000 >= 16900) AND (24000 <= 17100). Stop scanning e2. Begin next iteration of e1. 17000 (De Haan) n/a 14000 (Russell) n/a 13500 (Partners) n/a In this way, the band join optimization eliminates unnecessary processing. Instead of scanning every row in e2 as in the unoptimized case, the database scans only the minimum two rows. 9.3.2 Outer Joins An outer join returns all rows that satisfy the join condition and also rows from one table for which no rows from the other table satisfy the condition. Thus, the result set of an outer join is the superset of an inner join. In ANSI syntax, the OUTER JOIN clause specifies an outer join. In the FROM clause, the left table appears to the left of the OUTER JOIN keywords, and the right table appears to 9-32 Chapter 9 Join Types the right of these keywords. The left table is also called the outer table, and the right table is also called the inner table. For example, in the following statement the employees table is the left or outer table: SELECT employee_id, last_name, first_name FROM employees LEFT OUTER JOIN departments ON (employees.department_id=departments.departments_id); Outer joins require the outer-joined table to be the driving table. In the preceding example, employees is the driving table, and departments is the driven-to table. This section contains the following topics: • Nested Loops Outer Joins The database uses this operation to loop through an outer join between two tables. The outer join returns the outer (preserved) table rows, even when no corresponding rows are in the inner (optional) table. • Hash Join Outer Joins The optimizer uses hash joins for processing an outer join when either the data volume is large enough to make a hash join efficient, or it is impossible to drive from the outer table to the inner table. • Sort Merge Outer Joins When an outer join cannot drive from the outer (preserved) table to the inner (optional) table, it cannot use a hash join or nested loops joins. • Full Outer Joins A full outer join is a combination of the left and right outer joins. • Multiple Tables on the Left of an Outer Join In Oracle Database 12c, multiple tables may exist on the left of an outer-joined table. This enhancement enables Oracle Database to merge a view that contains multiple tables and appears on the left of outer join. 9.3.2.1 Nested Loops Outer Joins The database uses this operation to loop through an outer join between two tables. The outer join returns the outer (preserved) table rows, even when no corresponding rows are in the inner (optional) table. In a standard nested loop, the optimizer chooses the order of tables—which is the driving table and which the driven table—based on the cost. However, in a nested loop outer join, the join condition determines the order of tables. The database uses the outer, row-preserved table to drive to the inner table. The optimizer uses nested loops joins to process an outer join in the following circumstances: • It is possible to drive from the outer table to the inner table. • Data volume is low enough to make the nested loop method efficient. For an example of a nested loop outer join, you can add the USE_NL hint to Example 9-11 to instruct the optimizer to use a nested loop. For example: SELECT /*+ USE_NL(c o) */ cust_last_name, SUM(NVL2(o.customer_id,0,1)) "Count" FROM customers c, orders o WHERE c.credit_limit > 1000 9-33 Chapter 9 Join Types AND c.customer_id = o.customer_id(+) GROUP BY cust_last_name; 9.3.2.2 Hash Join Outer Joins The optimizer uses hash joins for processing an outer join when either the data volume is large enough to make a hash join efficient, or it is impossible to drive from the outer table to the inner table. The cost determines the order of tables. The outer table, including preserved rows, may be used to build the hash table, or it may be used to probe the hash table. Example 9-11 Hash Join Outer Joins This example shows a typical hash join outer join query, and its execution plan. In this example, all the customers with credit limits greater than 1000 are queried. An outer join is needed so that the query captures customers who have no orders. • The outer table is customers. • The inner table is orders. • The join preserves the customers rows, including those rows without a corresponding row in orders. You could use a NOT EXISTS subquery to return the rows. However, because you are querying all the rows in the table, the hash join performs better (unless the NOT EXISTS subquery is not nested). SELECT cust_last_name, SUM(NVL2(o.customer_id,0,1)) "Count" FROM customers c, orders o WHERE c.credit_limit > 1000 AND c.customer_id = o.customer_id(+) GROUP BY cust_last_name; -------------------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | -------------------------------------------------------------------------------PLAN_TABLE_OUTPUT -------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | | 7 (100)| | | 1 | HASH GROUP BY | | 168 | 3192 | 7 (29)| 00:00:01 | |* 2 | HASH JOIN OUTER | | 318 | 6042 | 6 (17)| 00:00:01 | |* 3 | TABLE ACCESS FULL| CUSTOMERS | 260 | 3900 | 3 (0)| 00:00:01 | |* 4 | TABLE ACCESS FULL| ORDERS | 105 | 420 | 2 (0)| 00:00:01 | -------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------2 - access("C"."CUSTOMER_ID"="O"."CUSTOMER_ID") PLAN_TABLE_OUTPUT ------------------------------------------------------------------------------3 - filter("C"."CREDIT_LIMIT">1000) 4 - filter("O"."CUSTOMER_ID">0) The query looks for customers which satisfy various conditions. An outer join returns NULL for the inner table columns along with the outer (preserved) table rows when it 9-34 Chapter 9 Join Types does not find any corresponding rows in the inner table. This operation finds all the customers rows that do not have any orders rows. In this case, the outer join condition is the following: customers.customer_id = orders.customer_id(+) The components of this condition represent the following: Example 9-12 Outer Join to a Multitable View In this example, the outer join is to a multitable view. The optimizer cannot drive into the view like in a normal join or push the predicates, so it builds the entire row set of the view. SELECT c.cust_last_name, sum(revenue) FROM customers c, v_orders o WHERE c.credit_limit > 2000 AND o.customer_id(+) = c.customer_id GROUP BY c.cust_last_name; ---------------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| ---------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 144 | 4608 | 16 (32)| | 1 | HASH GROUP BY | | 144 | 4608 | 16 (32)| |* 2 | HASH JOIN OUTER | | 663 | 21216 | 15 (27)| |* 3 | TABLE ACCESS FULL | CUSTOMERS | 195 | 2925 | 6 (17)| | 4 | VIEW | V_ORDERS | 665 | 11305 | | | 5 | HASH GROUP BY | | 665 | 15960 | 9 (34)| |* 6 | HASH JOIN | | 665 | 15960 | 8 (25)| |* 7 | TABLE ACCESS FULL| ORDERS | 105 | 840 | 4 (25)| | 8 | TABLE ACCESS FULL| ORDER_ITEMS | 665 | 10640 | 4 (25)| ---------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------2 - access("O"."CUSTOMER_ID"(+)="C"."CUSTOMER_ID") 3 - filter("C"."CREDIT_LIMIT">2000) 6 - access("O"."ORDER_ID"="L"."ORDER_ID") 7 - filter("O"."CUSTOMER_ID">0) The view definition is as follows: CREATE OR REPLACE view v_orders AS SELECT l.product_id, SUM(l.quantity*unit_price) revenue, o.order_id, o.customer_id FROM orders o, order_items l WHERE o.order_id = l.order_id GROUP BY l.product_id, o.order_id, o.customer_id; 9.3.2.3 Sort Merge Outer Joins When an outer join cannot drive from the outer (preserved) table to the inner (optional) table, it cannot use a hash join or nested loops joins. In this case, it uses the sort merge outer join. The optimizer uses sort merge for an outer join in the following cases: 9-35 Chapter 9 Join Types • A nested loops join is inefficient. A nested loops join can be inefficient because of data volumes. • The optimizer finds it is cheaper to use a sort merge over a hash join because of sorts required by other operations. 9.3.2.4 Full Outer Joins A full outer join is a combination of the left and right outer joins. In addition to the inner join, rows from both tables that have not been returned in the result of the inner join are preserved and extended with nulls. In other words, full outer joins join tables together, yet show rows with no corresponding rows in the joined tables. Example 9-13 Full Outer Join The following query retrieves all departments and all employees in each department, but also includes: • Any employees without departments • Any departments without employees SELECT d.department_id, e.employee_id FROM employees e FULL OUTER JOIN departments d ON e.department_id = d.department_id ORDER BY d.department_id; The statement produces the following output: DEPARTMENT_ID EMPLOYEE_ID ------------- ----------10 200 20 201 20 202 30 114 30 115 30 116 ... 270 280 178 207 125 rows selected. Example 9-14 Execution Plan for a Full Outer Join Starting with Oracle Database 11g, Oracle Database automatically uses a native execution method based on a hash join for executing full outer joins whenever possible. When the database uses the new method to execute a full outer join, the execution plan for the query contains HASH JOIN FULL OUTER. The query in Example 9-13 uses the following execution plan: -------------------------------------------------------------------------------| Id| Operation | Name |Rows |Bytes |Cost (%CPU)| Time | -------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 122 | 4758 | 6 (34)| 00:0 0:01 | | 1 | SORT ORDER BY | | 122 | 4758 | 6 (34)| 00:0 0:01 | | 2 | VIEW | VW_FOJ_0 | 122 | 4758 | 5 (20)| 00:0 0:01 | 9-36 Chapter 9 Join Types |*3 | HASH JOIN FULL OUTER | | 122 | 1342 | 5 (20)| 00:0 0:01 | | 4 | INDEX FAST FULL SCAN| DEPT_ID_PK | 27 | 108 | 2 (0)| 00:0 0:01 | | 5 | TABLE ACCESS FULL | EMPLOYEES | 107 | 749 | 2 (0)| 00:0 0:01 | -------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------3 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID") HASH JOIN FULL OUTER is included in the preceding plan (Step 3), indicating that the query uses the hash full outer join execution method. Typically, when the full outer join condition between two tables is an equijoin, the hash full outer join execution method is possible, and Oracle Database uses it automatically. To instruct the optimizer to consider using the hash full outer join execution method, apply the NATIVE_FULL_OUTER_JOIN hint. To instruct the optimizer not to consider using the hash full outer join execution method, apply the NO_NATIVE_FULL_OUTER_JOIN hint. The NO_NATIVE_FULL_OUTER_JOIN hint instructs the optimizer to exclude the native execution method when joining each specified table. Instead, the full outer join is executed as a union of left outer join and an antijoin. 9.3.2.5 Multiple Tables on the Left of an Outer Join In Oracle Database 12c, multiple tables may exist on the left of an outer-joined table. This enhancement enables Oracle Database to merge a view that contains multiple tables and appears on the left of outer join. In releases before Oracle Database 12c, a query such as the following was invalid, and would trigger an ORA-01417 error message: SELECT FROM WHERE AND AND t1.d, t3.c t1, t2, t3 t1.z = t2.z t1.x = t3.x (+) t2.y = t3.y (+); Starting in Oracle Database 12c, the preceding query is valid. 9.3.3 Semijoins A semijoin is a join between two data sets that returns a row from the first set when a matching row exists in the subquery data set. The database stops processing the second data set at the first match. Thus, optimization does not duplicate rows from the first data set when multiple rows in the second data set satisfy the subquery criteria. Note: Semijoins and antijoins are considered join types even though the SQL constructs that cause them are subqueries. They are internal algorithms that the optimizer uses to flatten subquery constructs so that they can be resolved in a join-like way. 9-37 Chapter 9 Join Types This section contains the following topics: • When the Optimizer Considers Semijoins A semijoin avoids returning a huge number of rows when a query only needs to determine whether a match exists. • How Semijoins Work The semijoin optimization is implemented differently depending on what type of join is used. 9.3.3.1 When the Optimizer Considers Semijoins A semijoin avoids returning a huge number of rows when a query only needs to determine whether a match exists. With large data sets, this optimization can result in significant time savings over a nested loops join that must loop through every record returned by the inner query for every row in the outer query. The optimizer can apply the semijoin optimization to nested loops joins, hash joins, and sort merge joins. The optimizer may choose a semijoin in the following circumstances: • The statement uses either an IN or EXISTS clause. • The statement contains a subquery in the IN or EXISTS clause. • The IN or EXISTS clause is not contained inside an OR branch. 9.3.3.2 How Semijoins Work The semijoin optimization is implemented differently depending on what type of join is used. The following pseudocode shows a semijoin for a nested loops join: FOR ds1_row IN ds1 LOOP match := false; FOR ds2_row IN ds2_subquery LOOP IF (ds1_row matches ds2_row) THEN match := true; EXIT -- stop processing second data set when a match is found END IF END LOOP IF (match = true) THEN RETURN ds1_row END IF END LOOP In the preceding pseudocode, ds1 is the first data set, and ds2_subquery is the subquery data set. The code obtains the first row from the first data set, and then loops through the subquery data set looking for a match. The code exits the inner loop as soon as it finds a match, and then begins processing the next row in the first data set. Example 9-15 Semijoin Using WHERE EXISTS The following query uses a WHERE EXISTS clause to list only the departments that contain employees: SELECT department_id, department_name FROM departments WHERE EXISTS (SELECT 1 9-38 Chapter 9 Join Types FROM employees WHERE employees.department_id = departments.department_id) The execution plan reveals a NESTED LOOPS SEMI operation in Step 1: -------------------------------------------------------------------------------| Id| Operation | Name |Rows|Bytes|Cost (%CPU)| Time | -------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | | 2 (100)| | | 1 | NESTED LOOPS SEMI | | 11 | 209 | 2 (0)| 00:00:01 | | 2 | TABLE ACCESS FULL| DEPARTMENTS | 27 | 432 | 2 (0)| 00:00:01 | |*3 | INDEX RANGE SCAN | EMP_DEPARTMENT_IX | 44 | 132 | 0 (0)| | -------------------------------------------------------------------------------- For each row in departments, which forms the outer loop, the database obtains the department ID, and then probes the employees.department_id index for matching entries. Conceptually, the index looks as follows: 10,rowid 10,rowid 10,rowid 10,rowid 30,rowid 30,rowid 30,rowid ... If the first entry in the departments table is department 30, then the database performs a range scan of the index until it finds the first 30 entry, at which point it stops reading the index and returns the matching row from departments. If the next row in the outer loop is department 20, then the database scans the index for a 20 entry, and not finding any matches, performs the next iteration of the outer loop. The database proceeds in this way until all matching rows are returned. Example 9-16 Semijoin Using IN The following query uses a IN clause to list only the departments that contain employees: SELECT department_id, department_name FROM departments WHERE department_id IN (SELECT department_id FROM employees); The execution plan reveals a NESTED LOOPS SEMI operation in Step 1: -------------------------------------------------------------------------------| Id| Operation | Name |Rows|Bytes|Cost (%CPU)| Time | -------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | | 2 (100)| | | 1 | NESTED LOOPS SEMI | | 11 | 209 | 2 (0)| 00:00:01 | | 2 | TABLE ACCESS FULL| DEPARTMENTS | 27 | 432 | 2 (0)| 00:00:01 | |*3 | INDEX RANGE SCAN | EMP_DEPARTMENT_IX | 44 | 132 | 0 (0)| | -------------------------------------------------------------------------------- The plan is identical to the plan in Example 9-15. 9-39 Chapter 9 Join Types 9.3.4 Antijoins An antijoin is a join between two data sets that returns a row from the first set when a matching row does not exist in the subquery data set. Like a semijoin, an antijoin stops processing the subquery data set when the first match is found. Unlike a semijoin, the antijoin only returns a row when no match is found. This section contains the following topics: • When the Optimizer Considers Antijoins An antijoin avoids unnecessary processing when a query only needs to return a row when a match does not exist. • How Antijoins Work The antijoin optimization is implemented differently depending on what type of join is used. • How Antijoins Handle Nulls For semijoins, IN and EXISTS are functionally equivalent. However, NOT IN and NOT EXISTS are not functionally equivalent because of nulls. 9.3.4.1 When the Optimizer Considers Antijoins An antijoin avoids unnecessary processing when a query only needs to return a row when a match does not exist. With large data sets, this optimization can result in significant time savings over a nested loops join. The latter join must loop through every record returned by the inner query for every row in the outer query. The optimizer can apply the antijoin optimization to nested loops joins, hash joins, and sort merge joins. The optimizer may choose an antijoin in the following circumstances: • The statement uses either the NOT IN or NOT EXISTS clause. • The statement has a subquery in the NOT IN or NOT EXISTS clause. • The NOT IN or NOT EXISTS clause is not contained inside an OR branch. • The statement performs an outer join and applies an IS NULL condition to a join column, as in the following example: SET AUTOTRACE TRACEONLY EXPLAIN SELECT emp.* FROM emp, dept WHERE emp.deptno = dept.deptno(+) AND dept.deptno IS NULL Execution Plan ---------------------------------------------------------Plan hash value: 1543991079 --------------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | --------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 14 | 1400 | 5 (20)| 00:00:01 | |* 1 | HASH JOIN ANTI | | 14 | 1400 | 5 (20)| 00:00:01 | | 2 | TABLE ACCESS FULL| EMP | 14 | 1218 | 2 (0)| 00:00:01 | 9-40 Chapter 9 Join Types | 3 | TABLE ACCESS FULL| DEPT | 4 | 52 | 2 (0)| 00:00:01 | --------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------1 - access("EMP"."DEPTNO"="DEPT"."DEPTNO") Note ----- dynamic statistics used: dynamic sampling (level=2) 9.3.4.2 How Antijoins Work The antijoin optimization is implemented differently depending on what type of join is used. The following pseudocode shows an antijoin for a nested loops join: FOR ds1_row IN ds1 LOOP match := true; FOR ds2_row IN ds2 LOOP IF (ds1_row matches ds2_row) THEN match := false; EXIT -- stop processing second data set when a match is found END IF END LOOP IF (match = true) THEN RETURN ds1_row END IF END LOOP In the preceding pseudocode, ds1 is the first data set, and ds2 is the second data set. The code obtains the first row from the first data set, and then loops through the second data set looking for a match. The code exits the inner loop as soon as it finds a match, and begins processing the next row in the first data set. Example 9-17 Semijoin Using WHERE EXISTS The following query uses a WHERE EXISTS clause to list only the departments that contain employees: SELECT department_id, department_name FROM departments WHERE EXISTS (SELECT 1 FROM employees WHERE employees.department_id = departments.department_id) The execution plan reveals a NESTED LOOPS SEMI operation in Step 1: -------------------------------------------------------------------------------| Id| Operation | Name |Rows|Bytes |Cost(%CPU)| Time | -------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | | 2 (100)| | | 1 | NESTED LOOPS SEMI | | 11 | 209 | 2 (0)| 00:00:01 | | 2 | TABLE ACCESS FULL| DEPARTMENTS | 27 | 432 | 2 (0)| 00:00:01 | |*3 | INDEX RANGE SCAN | EMP_DEPARTMENT_IX | 44 | 132 | 0 (0)| | -------------------------------------------------------------------------------- 9-41 Chapter 9 Join Types For each row in departments, which forms the outer loop, the database obtains the department ID, and then probes the employees.department_id index for matching entries. Conceptually, the index looks as follows: 10,rowid 10,rowid 10,rowid 10,rowid 30,rowid 30,rowid 30,rowid ... If the first record in the departments table is department 30, then the database performs a range scan of the index until it finds the first 30 entry, at which point it stops reading the index and returns the matching row from departments. If the next row in the outer loop is department 20, then the database scans the index for a 20 entry, and not finding any matches, performs the next iteration of the outer loop. The database proceeds in this way until all matching rows are returned. 9.3.4.3 How Antijoins Handle Nulls For semijoins, IN and EXISTS are functionally equivalent. However, NOT IN and NOT EXISTS are not functionally equivalent because of nulls. If a null value is returned to a NOT IN operator, then the statement returns no records. To see why, consider the following WHERE clause: WHERE department_id NOT IN (null, 10, 20) The database tests the preceding expression as follows: WHERE (department_id != null) AND (department_id != 10) AND (department_id != 20) For the entire expression to be true, each individual condition must be true. However, a null value cannot be compared to another value, so the department_id !=null condition cannot be true, and thus the whole expression is always false. The following techniques enable a statement to return records even when nulls are returned to the NOT IN operator: • Apply an NVL function to the columns returned by the subquery. • Add an IS NOT NULL predicate to the subquery. • Implement NOT NULL constraints. In contrast to NOT IN, the NOT EXISTS clause only considers predicates that return the existence of a match, and ignores any row that does not match or could not be determined because of nulls. If at least one row in the subquery matches the row from the outer query, then NOT EXISTS returns false. If no tuples match, then NOT EXISTS returns true. The presence of nulls in the subquery does not affect the search for matching records. In releases earlier than Oracle Database 11g, the optimizer could not use an antijoin optimization when nulls could be returned by a subquery. However, starting in Oracle Database 11g, the ANTI NA (and ANTI SNA) optimizations described in the following sections enable the optimizer to use an antijoin even when nulls are possible. 9-42 Chapter 9 Join Types Example 9-18 Antijoin Using NOT IN Suppose that a user issues the following query with a NOT IN clause to list the departments that contain no employees: SELECT department_id, department_name FROM departments WHERE department_id NOT IN (SELECT department_id FROM employees); The preceding query returns no rows even though several departments contain no employees. This result, which was not intended by the user, occurs because the employees.department_id column is nullable. The execution plan reveals a NESTED LOOPS ANTI SNA operation in Step 2: -------------------------------------------------------------------------------| Id| Operation | Name |Rows|Bytes|Cost (%CPU)| Time | -------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | | 4(100)| | |*1 | FILTER | | | | | | NESTED LOOPS ANTI SNA| | 2 | | 17 | 323 | 4 (50)| 00:00:01 | | 3 | TABLE ACCESS FULL | DEPARTMENTS | 27 | 432 | 2 (0)| 00:00:01 | |*4 | INDEX RANGE SCAN | EMP_DEPARTMENT_IX | 41 | 123 | 0 (0)| | |*5 | TABLE ACCESS FULL | EMPLOYEES | 1 | 3 | 2 (0)| 00:00:01 | -------------------------------------------------------------------------------PLAN_TABLE_OUTPUT -------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------1 - filter( IS NULL) 4 - access("DEPARTMENT_ID"="DEPARTMENT_ID") 5 - filter("DEPARTMENT_ID" IS NULL) The ANTI SNA stands for "single null-aware antijoin." ANTI NA stands for "null-aware antijoin." The null-aware operation enables the optimizer to use the semijoin optimization even on a nullable column. In releases earlier than Oracle Database 11g, the database could not perform antijoins on NOT IN queries when nulls were possible. Suppose that the user rewrites the query by applying an IS NOT NULL condition to the subquery: SELECT department_id, department_name FROM departments WHERE department_id NOT IN (SELECT department_id FROM employees WHERE department_id IS NOT NULL); The preceding query returns 16 rows, which is the expected result. Step 1 in the plan shows a standard NESTED LOOPS ANTI join instead of an ANTI NA or ANTI SNA join because the subquery cannot returns nulls: -------------------------------------------------------------------------------|Id| Operation | Name | Rows| Bytes | Cost (%CPU)| Time | -------------------------------------------------------------------------------| 0| SELECT STATEMENT | | | | 2 (100)| | | 1| NESTED LOOPS ANTI | | 17 | 323 | 2 (0)| 00:00:01 | 9-43 Chapter 9 Join Optimizations | 2| TABLE ACCESS FULL| DEPARTMENTS | 27 | 432 | 2 (0)| 00:00:01 | |*3| INDEX RANGE SCAN | EMP_DEPARTMENT_IX | 41 | 123 | 0 (0)| | -------------------------------------------------------------------------------PLAN_TABLE_OUTPUT -------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------3 - access("DEPARTMENT_ID"="DEPARTMENT_ID") filter("DEPARTMENT_ID" IS NOT NULL) Example 9-19 Antijoin Using NOT EXISTS Suppose that a user issues the following query with a NOT EXISTS clause to list the departments that contain no employees: SELECT department_id, department_name FROM departments d WHERE NOT EXISTS (SELECT null FROM employees e WHERE e.department_id = d.department_id) The preceding query avoids the null problem for NOT IN clauses. Thus, even though employees.department_id column is nullable, the statement returns the desired result. Step 1 of the execution plan reveals a NESTED LOOPS ANTI operation, not the ANTI NA variant, which was necessary for NOT IN when nulls were possible: -------------------------------------------------------------------------------| Id| Operation | Name | Rows | Bytes | Cost (%CPU)|Time| -------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | | 2 (100)| | | 1 | NESTED LOOPS ANTI | | 17 | 323 | 2 (0)|00:00:01| | 2 | TABLE ACCESS FULL| DEPARTMENTS | 27 | 432 | 2 (0)|00:00:01| |*3 | INDEX RANGE SCAN | EMP_DEPARTMENT_IX | 41 | 123 | 0 (0)| | -------------------------------------------------------------------------------PLAN_TABLE_OUTPUT -------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------3 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID") 9.4 Join Optimizations Join optimizations enable joins to be more efficient. This section describes common join optimizations: • Bloom Filters A Bloom filter, named after its creator Burton Bloom, is a low-memory data structure that tests membership in a set. • Partition-Wise Joins A partition-wise join is an optimization that divides a large join of two tables, one of which must be partitioned on the join key, into several smaller joins. 9-44 Chapter 9 Join Optimizations • In-Memory Join Groups A join group is a user-created object that lists two or more columns that can be meaningfully joined. 9.4.1 Bloom Filters A Bloom filter, named after its creator Burton Bloom, is a low-memory data structure that tests membership in a set. A Bloom filter correctly indicates when an element is not in a set, but can incorrectly indicate when an element is in a set. Thus, false negatives are impossible but false positives are possible. This section contains the following topics: • Purpose of Bloom Filters A Bloom filter tests one set of values to determine whether they are members another set. • How Bloom Filters Work A Bloom filter uses an array of bits to indicate inclusion in a set. • Bloom Filter Controls The optimizer automatically determines whether to use Bloom filters. • Bloom Filter Metadata V$ views contain metadata about Bloom filters. • Bloom Filters: Scenario In this example, a parallel query joins the sales fact table to the products and times dimension tables, and filters on fiscal week 18. 9.4.1.1 Purpose of Bloom Filters A Bloom filter tests one set of values to determine whether they are members another set. For example, one set is (10,20,30,40) and the second set is (10,30,60,70). A Bloom filter can determine that 60 and 70 are guaranteed to be excluded from the first set, and that 10 and 30 are probably members. Bloom filters are especially useful when the amount of memory needed to store the filter is small relative to the amount of data in the data set, and when most data is expected to fail the membership test. Oracle Database uses Bloom filters to various specific goals, including the following: • Reduce the amount of data transferred to slave processes in a parallel query, especially when the database discards most rows because they do not fulfill a join condition • Eliminate unneeded partitions when building a partition access list in a join, known as partition pruning • Test whether data exists in the server result cache, thereby avoiding a disk read • Filter members in Exadata cells, especially when joining a large fact table and small dimension tables in a star schema Bloom filters can occur in both parallel and serial processing. 9-45 Chapter 9 Join Optimizations 9.4.1.2 How Bloom Filters Work A Bloom filter uses an array of bits to indicate inclusion in a set. For example, 8 elements (an arbitrary number used for this example) in an array are initially set to 0: e1 e2 e3 e4 e5 e6 e7 e8 0 0 0 0 0 0 0 0 This array represents a set. To represent an input value i in this array, three separate hash functions (three is arbitrary) are applied to i, each generating a hash value between 1 and 8: f1(i) = h1 f2(i) = h2 f3(i) = h3 For example, to store the value 17 in this array, the hash functions set i to 17, and then return the following hash values: f1(17) = 5 f2(17) = 3 f3(17) = 5 In the preceding example, two of the hash functions happened to return the same value of 5, known as a hash collision. Because the distinct hash values are 5 and 3, the 5th and 3rd elements in the array are set to 1: e1 e2 e3 e4 e5 e6 e7 e8 0 0 1 0 1 0 0 0 Testing the membership of 17 in the set reverses the process. To test whether the set excludes the value 17, element 3 or element 5 must contain a 0. If a 0 is present in either element, then the set cannot contain 17. No false negatives are possible. To test whether the set includes 17, both element 3 and element 5 must contain 1 values. However, if the test indicates a 1 for both elements, then it is still possible for the set not to include 17. False positives are possible. For example, the following array might represent the value 22, which also has a 1 for both element 3 and element 5: e1 e2 e3 e4 e5 e6 e7 e8 1 0 1 0 1 0 0 0 9.4.1.3 Bloom Filter Controls The optimizer automatically determines whether to use Bloom filters. To override optimizer decisions, use the hints PX_JOIN_FILTER and NO_PX_JOIN_FILTER. See Also: Oracle Database SQL Language Reference to learn more about the bloom filter hints 9-46 Chapter 9 Join Optimizations 9.4.1.4 Bloom Filter Metadata V$ views contain metadata about Bloom filters. You can query the following views: • V$SQL_JOIN_FILTER This view shows the number of rows filtered out (FILTERED column) and tested (PROBED column) by an active Bloom filter. • V$PQ_TQSTAT This view displays the number of rows processed through each parallel execution server at each stage of the execution tree. You can use it to monitor how much Bloom filters have reduced data transfer among parallel processes. In an execution plan, a Bloom filter is indicated by keywords JOIN FILTER in the Operation column, and the prefix :BF in the Name column, as in the 9th step of the following plan snippet: ---------------------------------------------------------------------------| Id | Operation | Name | TQ |IN-OUT| PQ Distrib | ---------------------------------------------------------------------------... | 9 | JOIN FILTER CREATE | :BF0000 | Q1,03 | PCWP | | In the Predicate Information section of the plan, filters that contain functions beginning with the string SYS_OP_BLOOM_FILTER indicate use of a Bloom filter. 9.4.1.5 Bloom Filters: Scenario In this example, a parallel query joins the sales fact table to the products and times dimension tables, and filters on fiscal week 18. SELECT FROM WHERE AND AND /*+ parallel(s) */ p.prod_name, s.quantity_sold sh.sales s, sh.products p, sh.times t s.prod_id = p.prod_id s.time_id = t.time_id t.fiscal_week_number = 18; Querying DBMS_XPLAN.DISPLAY_CURSOR provides the following output: SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(format => 'BASIC,+PARALLEL,+PREDICATE')); EXPLAINED SQL STATEMENT: -----------------------SELECT /*+ parallel(s) */ p.prod_name, s.quantity_sold FROM sh.sales s, sh.products p, sh.times t WHERE s.prod_id = p.prod_id AND s.time_id = t.time_id AND t.fiscal_week_number = 18 Plan hash value: 1183628457 ---------------------------------------------------------------------------| Id | Operation | Name | TQ |IN-OUT| PQ Distrib | ---------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | | | | 1 | PX COORDINATOR | | | | | | 2 | PX SEND QC (RANDOM) | :TQ10003 | Q1,03 | P->S | QC (RAND) | 9-47 Chapter 9 Join Optimizations |* 3 | HASH JOIN BUFFERED | | Q1,03 | PCWP | | | 4 | PX RECEIVE | | Q1,03 | PCWP | | | 5 | PX SEND BROADCAST | :TQ10001 | Q1,01 | S->P | BROADCAST | | 6 | PX SELECTOR | | Q1,01 | SCWC | | | 7 | TABLE ACCESS FULL | PRODUCTS | Q1,01 | SCWP | | |* 8 | HASH JOIN | | Q1,03 | PCWP | | | 9 | JOIN FILTER CREATE | :BF0000 | Q1,03 | PCWP | | | 10 | BUFFER SORT | | Q1,03 | PCWC | | | 11 | PX RECEIVE | | Q1,03 | PCWP | | | 12 | PX SEND HYBRID HASH| :TQ10000 | | S->P | HYBRID HASH| |* 13 | TABLE ACCESS FULL | TIMES | | | | | 14 | PX RECEIVE | | Q1,03 | PCWP | | | 15 | PX SEND HYBRID HASH | :TQ10002 | Q1,02 | P->P | HYBRID HASH| | 16 | JOIN FILTER USE | :BF0000 | Q1,02 | PCWP | | | 17 | PX BLOCK ITERATOR | | Q1,02 | PCWC | | |* 18 | TABLE ACCESS FULL | SALES | Q1,02 | PCWP | | ---------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------3 8 13 18 - access("S"."PROD_ID"="P"."PROD_ID") access("S"."TIME_ID"="T"."TIME_ID") filter("T"."FISCAL_WEEK_NUMBER"=18) access(:Z>=:Z AND :Z<=:Z) filter(SYS_OP_BLOOM_FILTER(:BF0000,"S"."TIME_ID")) A single server process scans the times table (Step 13), and then uses a hybrid hash distribution method to send the rows to the parallel execution servers (Step 12). The processes in set Q1,03 create a bloom filter (Step 9). The processes in set Q1,02 scan sales in parallel (Step 18), and then use the Bloom filter to discard rows from sales (Step 16) before sending them on to set Q1,03 using hybrid hash distribution (Step 15). The processes in set Q1,03 hash join the times rows to the filtered sales rows (Step 8). The processes in set Q1,01 scan products (Step 7), and then send the rows to Q1,03 (Step 5). Finally, the processes in Q1,03 join the products rows to the rows generated by the previous hash join (Step 3). The following figure illustrates the basic process. Figure 9-8 Bloom Filter Q1, 03 Create Bloom filter :BF0000 Q1, 01 Q1, 02 9.4.2 Partition-Wise Joins A partition-wise join is an optimization that divides a large join of two tables, one of which must be partitioned on the join key, into several smaller joins. 9-48 Chapter 9 Join Optimizations Partition-wise joins are either of the following: • Full partition-wise join Both tables must be equipartitioned on their join keys, or use reference partitioning (that is, be related by referential constraints). The database divides a large join into smaller joins between two partitions from the two joined tables. • Partial partition-wise joins Only one table is partitioned on the join key. The other table may or may not be partitioned. This section contains the following topics: • Purpose of Partition-Wise Joins Partition-wise joins reduce query response time by minimizing the amount of data exchanged among parallel execution servers when joins execute in parallel. • How Partition-Wise Joins Work When the database serially joins two partitioned tables without using a partitionwise join, a single server process performs the join. See Also: Oracle Database VLDB and Partitioning Guide explains partition-wise joins in detail 9.4.2.1 Purpose of Partition-Wise Joins Partition-wise joins reduce query response time by minimizing the amount of data exchanged among parallel execution servers when joins execute in parallel. This technique significantly reduces response time and improves the use of CPU and memory. In Oracle Real Application Clusters (Oracle RAC) environments, partitionwise joins also avoid or at least limit the data traffic over the interconnect, which is the key to achieving good scalability for massive join operations. 9.4.2.2 How Partition-Wise Joins Work When the database serially joins two partitioned tables without using a partition-wise join, a single server process performs the join. In the following illustration, the join is not partition-wise because the server process joins every partition of table t1 to every partition of table t2. 9-49 Chapter 9 Join Optimizations Figure 9-9 Join That Is Not Partition-Wise Server Process t1 t2 This section contains the following topics: • How a Full Partition-Wise Join Works The database performs a full partition-wise join either serially or in parallel. • How a Partial Partition-Wise Join Works Partial partition-wise joins, unlike their full partition-wise counterpart, must execute in parallel. 9.4.2.2.1 How a Full Partition-Wise Join Works The database performs a full partition-wise join either serially or in parallel. The following graphic shows a full partition-wise join performed in parallel. In this case, the granule of parallelism is a partition. Each parallel execution server joins the partitions in pairs. For example, the first parallel execution server joins the first partition of t1 to the first partition of t2. The parallel execution coordinator then assembles the result. 9-50 Chapter 9 Join Optimizations Figure 9-10 Full Partition-Wise Join in Parallel PE Coordinator t1 t2 PE Server PE Server PE Server PE Server A full partition-wise join can also join partitions to subpartitions, which is useful when the tables use different partitioning methods. For example, customers is partitioned by hash, but sales is partitioned by range. If you subpartition sales by hash, then the database can perform a full partition-wise join between the hash partitions of the customers and the hash subpartitions of sales. In the execution plan, the presence of a partition operation before the join signals the presence of a full partition-wise join, as in the following snippet: | 8 | |* 9 | PX PARTITION HASH ALL| HASH JOIN | See Also: Oracle Database VLDB and Partitioning Guide explains full partition-wise joins in detail, and includes several examples 9.4.2.2.2 How a Partial Partition-Wise Join Works Partial partition-wise joins, unlike their full partition-wise counterpart, must execute in parallel. The following graphic shows a partial partition-wise join between t1, which is partitioned, and t2, which is not partitioned. 9-51 Chapter 9 Join Optimizations Figure 9-11 Partial Partition-Wise Join PE Coordinator t1 t2 PE Server PE Server t1 t2 PE Server PE Server PE Server PE Server PE Server PE Server Dynamically created partitions Because t2 is not partitioned, a set of parallel execution servers must generate partitions from t2 as needed. A different set of parallel execution servers then joins the t1 partitions to the dynamically generated partitions. The parallel execution coordinator assembles the result. In the execution plan, the operation PX SEND PARTITION (KEY) signals a partial partitionwise join, as in the following snippet: | 11 | PX SEND PARTITION (KEY) | See Also: Oracle Database VLDB and Partitioning Guide explains full partition-wise joins in detail, and includes several examples 9.4.3 In-Memory Join Groups A join group is a user-created object that lists two or more columns that can be meaningfully joined. In certain queries, join groups eliminate the performance overhead of decompressing and hashing column values. Join groups require an In-Memory Column Store (IM column store). 9-52 Chapter 9 Join Optimizations See Also: Oracle Database In-Memory Guide to learn how to optimize In-Memory queries with join groups 9-53 Part V Optimizer Statistics The accuracy of an execution plan depends on the quality of the optimizer statistics. This part contains the following chapters: • Optimizer Statistics Concepts Oracle Database optimizer statistics describe details about the database and its objects. • Histograms A histogram is a special type of column statistic that provides more detailed information about the data distribution in a table column. A histogram sorts values into "buckets," as you might sort coins into buckets. • Configuring Options for Optimizer Statistics Gathering This chapter explains what optimizer statistics collection is and how to set statistics preferences. • Gathering Optimizer Statistics This chapter explains how to use the DBMS_STATS.GATHER_*_STATS program units. • Managing Extended Statistics DBMS_STATS enables you to collect extended statistics, which are statistics that can improve cardinality estimates when multiple predicates exist on different columns of a table, or when predicates use expressions. • Controlling the Use of Optimizer Statistics Using DBMS_STATS, you can specify when and how the optimizer uses statistics. • Managing Historical Optimizer Statistics This chapter how to retain, report on, and restore non-current statistics. • Transporting Optimizer Statistics You can export and import optimizer statistics from the data dictionary to userdefined statistics tables. You can also copy statistics from one database to another database. • Analyzing Statistics Using Optimizer Statistics Advisor Optimizer Statistics Advisor analyzes how optimizer statistics are gathered, and then makes recommendations. 10 Optimizer Statistics Concepts Oracle Database optimizer statistics describe details about the database and its objects. This chapter includes the following topics: • Introduction to Optimizer Statistics The optimizer cost model relies on statistics collected about the objects involved in a query, and the database and host where the query runs. • About Optimizer Statistics Types The optimizer collects statistics on different types of database objects and characteristics of the database environment. • How the Database Gathers Optimizer Statistics Oracle Database provides several mechanisms to gather statistics. • When the Database Gathers Optimizer Statistics The database collects optimizer statistics at various times and from various sources. Related Topics • Query Optimizer Concepts This chapter describes the most important concepts relating to the query optimizer, including its principal components. • Histograms A histogram is a special type of column statistic that provides more detailed information about the data distribution in a table column. A histogram sorts values into "buckets," as you might sort coins into buckets. • Gathering Optimizer Statistics This chapter explains how to use the DBMS_STATS.GATHER_*_STATS program units. • Managing Historical Optimizer Statistics This chapter how to retain, report on, and restore non-current statistics. 10.1 Introduction to Optimizer Statistics The optimizer cost model relies on statistics collected about the objects involved in a query, and the database and host where the query runs. The optimizer uses statistics to get an estimate of the number of rows (and number of bytes) retrieved from a table, partition, or index. The optimizer estimates the cost for the access, determines the cost for possible plans, and then picks the execution plan with the lowest cost. Optimizer statistics include the following: • Table statistics – Number of rows 10-1 Chapter 10 Introduction to Optimizer Statistics • • • – Number of blocks – Average row length Column statistics – Number of distinct values (NDV) in a column – Number of nulls in a column – Data distribution (histogram) – Extended statistics Index statistics – Number of leaf blocks – Number of levels – Index clustering factor System statistics – I/O performance and utilization – CPU performance and utilization As shown in Figure 10-1, the database stores optimizer statistics for tables, columns, indexes, and the system in the data dictionary. You can access these statistics using data dictionary views. Note: The optimizer statistics are different from the performance statistics visible through V$ views. 10-2 Chapter 10 About Optimizer Statistics Types Figure 10-1 Optimizer Statistics Database Optimizer Data Dictionary Optimizer Statistics Index Table Column System PERSON Table PERSON_ID_IX GB ID Name 100 Kumar HJ Execution Plan HJ CPU and I/O 10.2 About Optimizer Statistics Types The optimizer collects statistics on different types of database objects and characteristics of the database environment. This section contains the following topics: • Table Statistics In Oracle Database, table statistics include information about rows and blocks. • Column Statistics Column statistics track information about column values and data distribution. • Index Statistics The index statistics include information about the number of index levels, the number of index blocks, and the relationship between the index and the data blocks. The optimizer uses these statistics to determine the cost of index scans. • Session-Specific Statistics for Global Temporary Tables A global temporary table is a special table that stores intermediate sessionprivate data for a specific duration. 10-3 Chapter 10 About Optimizer Statistics Types • System Statistics The system statistics describe hardware characteristics such as I/O and CPU performance and utilization. • User-Defined Optimizer Statistics The extensible optimizer enables authors of user-defined functions and indexes to create statistics collection, selectivity, and cost functions. The optimizer cost model is extended to integrate information supplied by the user to assess CPU and the I/O cost. 10.2.1 Table Statistics In Oracle Database, table statistics include information about rows and blocks. The optimizer uses these statistics to determine the cost of table scans and table joins. DBMS_STATS can gather statistics for both permanent and temporary tables. The database tracks all relevant statistics about permanent tables. DBMS_STATS.GATHER_TABLE_STATS commits before gathering statistics on permanent tables. For example, table statistics stored in DBA_TAB_STATISTICS track the following: • Number of rows and average row length The database uses the row count stored in DBA_TAB_STATISTICS when determining cardinality. • Number of data blocks The optimizer uses the number of data blocks with the DB_FILE_MULTIBLOCK_READ_COUNT initialization parameter to determine the base table access cost. Example 10-1 Table Statistics This example queries some table statistics for the sh.customers table. sys@PROD> SELECT NUM_ROWS, AVG_ROW_LEN, BLOCKS, LAST_ANALYZED 2 FROM DBA_TAB_STATISTICS 3 WHERE OWNER='SH' 4 AND TABLE_NAME='CUSTOMERS'; NUM_ROWS AVG_ROW_LEN BLOCKS LAST_ANAL ---------- ----------- ---------- --------55500 181 1486 14-JUN-10 See Also: • "About Optimizer Initialization Parameters" • "Gathering Schema and Table Statistics" • Oracle Database Reference for a description of the DBA_TAB_STATISTICS view and the DB_FILE_MULTIBLOCK_READ_COUNT initialization parameter 10.2.2 Column Statistics Column statistics track information about column values and data distribution. 10-4 Chapter 10 About Optimizer Statistics Types The optimizer uses column statistics to generate accurate cardinality estimates and make better decisions about index usage, join orders, join methods, and so on. For example, statistics in DBA_TAB_COL_STATISTICS track the following: • Number of distinct values • Number of nulls • High and low values • Histogram-related information The optimizer can use extended statistics, which are a special type of column statistics. These statistics are useful for informing the optimizer of logical relationships among columns. See Also: • "Histograms " • "About Statistics on Column Groups" • Oracle Database Reference for a description of the DBA_TAB_COL_STATISTICS view 10.2.3 Index Statistics The index statistics include information about the number of index levels, the number of index blocks, and the relationship between the index and the data blocks. The optimizer uses these statistics to determine the cost of index scans. This section contains the following topics: • Types of Index Statistics The DBA_IND_STATISTICS view tracks index statistics. • Index Clustering Factor For a B-tree index, the index clustering factor measures the physical grouping of rows in relation to an index value, such as last name. • Effect of Index Clustering Factor on Cost: Example This example illustrates how the index clustering factor can influence the cost of table access. 10.2.3.1 Types of Index Statistics The DBA_IND_STATISTICS view tracks index statistics. Statistics include the following: • Levels The BLEVEL column shows the number of blocks required to go from the root block to a leaf block. A B-tree index has two types of blocks: branch blocks for searching and leaf blocks that store values. See Oracle Database Concepts for a conceptual overview of B-tree indexes. • Distinct keys 10-5 Chapter 10 About Optimizer Statistics Types This columns tracks the number of distinct indexed values. If a unique constraint is defined, and if no NOT NULL constraint is defined, then this value equals the number of non-null values. • Average number of leaf blocks for each distinct indexed key • Average number of data blocks pointed to by each distinct indexed key See Also: Oracle Database Reference for a description of the DBA_IND_STATISTICS view Example 10-2 Index Statistics This example queries some index statistics for the cust_lname_ix and customers_pk indexes on the sh.customers table (sample output included): SELECT INDEX_NAME, BLEVEL, LEAF_BLOCKS AS "LEAFBLK", DISTINCT_KEYS AS "DIST_KEY", AVG_LEAF_BLOCKS_PER_KEY AS "LEAFBLK_PER_KEY", AVG_DATA_BLOCKS_PER_KEY AS "DATABLK_PER_KEY" FROM DBA_IND_STATISTICS WHERE OWNER = 'SH' AND INDEX_NAME IN ('CUST_LNAME_IX','CUSTOMERS_PK'); INDEX_NAME BLEVEL LEAFBLK DIST_KEY LEAFBLK_PER_KEY DATABLK_PER_KEY -------------- ------ ------- -------- --------------- --------------CUSTOMERS_PK 1 115 55500 1 1 CUST_LNAME_IX 1 141 908 1 10 10.2.3.2 Index Clustering Factor For a B-tree index, the index clustering factor measures the physical grouping of rows in relation to an index value, such as last name. The index clustering factor helps the optimizer decide whether an index scan or full table scan is more efficient for certain queries). A low clustering factor indicates an efficient index scan. A clustering factor that is close to the number of blocks in a table indicates that the rows are physically ordered in the table blocks by the index key. If the database performs a full table scan, then the database tends to retrieve the rows as they are stored on disk sorted by the index key. A clustering factor that is close to the number of rows indicates that the rows are scattered randomly across the database blocks in relation to the index key. If the database performs a full table scan, then the database would not retrieve rows in any sorted order by this index key. The clustering factor is a property of a specific index, not a table. If multiple indexes exist on a table, then the clustering factor for one index might be small while the factor for another index is large. An attempt to reorganize the table to improve the clustering factor for one index may degrade the clustering factor of the other index. Example 10-3 Index Clustering Factor This example shows how the optimizer uses the index clustering factor to determine whether using an index is more effective than a full table scan. 10-6 Chapter 10 About Optimizer Statistics Types 1. Start SQL*Plus and connect to a database as sh, and then query the number of rows and blocks in the sh.customers table (sample output included): SELECT table_name, num_rows, blocks FROM user_tables WHERE table_name='CUSTOMERS'; TABLE_NAME NUM_ROWS BLOCKS ------------------------------ ---------- ---------CUSTOMERS 55500 1486 2. Create an index on the customers.cust_last_name column. For example, execute the following statement: CREATE INDEX CUSTOMERS_LAST_NAME_IDX ON customers(cust_last_name); 3. Query the index clustering factor of the newly created index. The following query shows that the customers_last_name_idx index has a high clustering factor because the clustering factor is significantly more than the number of blocks in the table: SELECT FROM WHERE AND index_name, blevel, leaf_blocks, clustering_factor user_indexes table_name='CUSTOMERS' index_name= 'CUSTOMERS_LAST_NAME_IDX'; INDEX_NAME BLEVEL LEAF_BLOCKS CLUSTERING_FACTOR ------------------------------ ---------- ----------- ----------------CUSTOMERS_LAST_NAME_IDX 1 141 9859 4. Create a new copy of the customers table, with rows ordered by cust_last_name. For example, execute the following statements: DROP TABLE customers3 PURGE; CREATE TABLE customers3 AS SELECT * FROM customers ORDER BY cust_last_name; 5. Gather statistics on the customers3 table. For example, execute the GATHER_TABLE_STATS procedure as follows: EXEC DBMS_STATS.GATHER_TABLE_STATS(null,'CUSTOMERS3'); 6. Query the number of rows and blocks in the customers3 table . For example, enter the following query (sample output included): SELECT FROM WHERE TABLE_NAME, NUM_ROWS, BLOCKS USER_TABLES TABLE_NAME='CUSTOMERS3'; TABLE_NAME NUM_ROWS BLOCKS ------------------------------ ---------- ---------CUSTOMERS3 55500 1485 7. Create an index on the cust_last_name column of customers3. For example, execute the following statement: CREATE INDEX CUSTOMERS3_LAST_NAME_IDX ON customers3(cust_last_name); 8. Query the index clustering factor of the customers3_last_name_idx index. 10-7 Chapter 10 About Optimizer Statistics Types The following query shows that the customers3_last_name_idx index has a lower clustering factor: SELECT FROM WHERE AND INDEX_NAME, BLEVEL, LEAF_BLOCKS, CLUSTERING_FACTOR USER_INDEXES TABLE_NAME = 'CUSTOMERS3' INDEX_NAME = 'CUSTOMERS3_LAST_NAME_IDX'; INDEX_NAME BLEVEL LEAF_BLOCKS CLUSTERING_FACTOR ------------------------------ ---------- ----------- ----------------CUSTOMERS3_LAST_NAME_IDX 1 141 1455 The table customers3 has the same data as the original customers table, but the index on customers3 has a much lower clustering factor because the data in the table is ordered by the cust_last_name. The clustering factor is now about 10 times the number of blocks instead of 70 times. 9. Query the customers table. For example, execute the following query (sample output included): SELECT cust_first_name, cust_last_name FROM customers WHERE cust_last_name BETWEEN 'Puleo' AND 'Quinn'; CUST_FIRST_NAME -------------------Vida Harriett Madeleine Caresse CUST_LAST_NAME ---------------------------------------Puleo Quinlan Quinn Puleo 10. Display the cursor for the query. For example, execute the following query (partial sample output included): SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR()); ------------------------------------------------------------------------------| Id | Operation | Name | Rows |Bytes|Cost (%CPU)| Time | ------------------------------------------------------------------------------| 0| SELECT STATEMENT | | | | 405 (100)| | |* 1| TABLE ACCESS STORAGE FULL| CUSTOMERS | 2335|35025| 405 (1)|00:00:01| ------------------------------------------------------------------------------- The preceding plan shows that the optimizer did not use the index on the original customers tables. 11. Query the customers3 table. For example, execute the following query (sample output included): SELECT cust_first_name, cust_last_name FROM customers3 WHERE cust_last_name BETWEEN 'Puleo' AND 'Quinn'; CUST_FIRST_NAME -------------------Vida Harriett Madeleine Caresse CUST_LAST_NAME ---------------------------------------Puleo Quinlan Quinn Puleo 12. Display the cursor for the query. 10-8 Chapter 10 About Optimizer Statistics Types For example, execute the following query (partial sample output included): SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR()); --------------------------------------------------------------------------------------|Id| Operation | Name |Rows|Bytes|Cost(%CPU)| Time| --------------------------------------------------------------------------------------| 0| SELECT STATEMENT | | | |69(100)| | | 1| TABLE ACCESS BY INDEX ROWID|CUSTOMERS3 |2335|35025|69(0) |00:00:01| |*2| INDEX RANGE SCAN |CUSTOMERS3_LAST_NAME_IDX|2335| |7(0) |00:00:01| --------------------------------------------------------------------------------------- The result set is the same, but the optimizer chooses the index. The plan cost is much less than the cost of the plan used on the original customers table. 13. Query customers with a hint that forces the optimizer to use the index. For example, execute the following query (partial sample output included): SELECT /*+ index (Customers CUSTOMERS_LAST_NAME_IDX) */ cust_first_name, cust_last_name FROM customers WHERE cust_last_name BETWEEN 'Puleo' and 'Quinn'; CUST_FIRST_NAME -------------------Vida Caresse Harriett Madeleine CUST_LAST_NAME ---------------------------------------Puleo Puleo Quinlan Quinn 14. Display the cursor for the query. For example, execute the following query (partial sample output included): SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR()); ----------------------------------------------------------------------------------------| Id | Operation | Name |Rows|Bytes|Cost(%CPU)| Time | ----------------------------------------------------------------------------------------| 0| SELECT STATEMENT | | | | 422(100) | | | 1| TABLE ACCESS BY INDEX ROWID|CUSTOMERS |335 |35025| 422(0) |00:00:01| |*2| INDEX RANGE SCAN |CUSTOMERS_LAST_NAME_IDX|2335| | 7(0) |00:00:01| ----------------------------------------------------------------------------------------- The preceding plan shows that the cost of using the index on customers is higher than the cost of a full table scan. Thus, using an index does not necessarily improve performance. The index clustering factor is a measure of whether an index scan is more effective than a full table scan. 10.2.3.3 Effect of Index Clustering Factor on Cost: Example This example illustrates how the index clustering factor can influence the cost of table access. Consider the following scenario: • A table contains 9 rows that are stored in 3 data blocks. • The col1 column currently stores the values A, B, and C. • A nonunique index named col1_idx exists on col1 for this table. 10-9 Chapter 10 About Optimizer Statistics Types Example 10-4 Collocated Data Assume that the rows are stored in the data blocks as follows: Block 1 ------A A A Block 2 ------B B B Block 3 ------C C C In this example, the index clustering factor for col1_idx is low. The rows that have the same indexed column values for col1 are in the same data blocks in the table. Thus, the cost of using an index range scan to return all rows with value A is low because only one block in the table must be read. Example 10-5 Scattered Data Assume that the same rows are scattered across the data blocks as follows: Block 1 ------A B C Block 2 ------A C B Block 3 ------B A C In this example, the index clustering factor for col1_idx is higher. The database must read all three blocks in the table to retrieve all rows with the value A in col1. See Also: Oracle Database Reference for a description of the DBA_INDEXES view 10.2.4 Session-Specific Statistics for Global Temporary Tables A global temporary table is a special table that stores intermediate session-private data for a specific duration. The ON COMMIT clause of CREATE GLOBAL TEMPORARY TABLE indicates whether the table is transaction-specific (DELETE ROWS) or session-specific (PRESERVE ROWS). Thus, a temporary table holds intermediate result sets for the duration of either a transaction or a session. When you create a global temporary table, you create a definition that is visible to all sessions. No physical storage is allocated. When a session first puts data into the table, the database allocates storage space. The data in the temporary table is only visible to the current session. This section contains the following topics: • Shared and Session-Specific Statistics for Global Temporary Tables Starting in Oracle Database 12c, you can set the table-level preference GLOBAL_TEMP_TABLE_STATS to make statistics on a global temporary table shared (SHARED) or session-specific (SESSION). • Effect of DBMS_STATS on Transaction-Specific Temporary Tables DBMS_STATS commits changes to session-specific global temporary tables, but not to transaction-specific global temporary tables. 10-10 Chapter 10 About Optimizer Statistics Types 10.2.4.1 Shared and Session-Specific Statistics for Global Temporary Tables Starting in Oracle Database 12c, you can set the table-level preference GLOBAL_TEMP_TABLE_STATS to make statistics on a global temporary table shared (SHARED) or session-specific (SESSION). When set to session-specific, you can gather statistics for a global temporary table in one session, and then use the statistics for this session only. Meanwhile, users can continue to maintain a shared version of the statistics. During optimization, the optimizer first checks whether a global temporary table has session-specific statistics. If yes, the optimizer uses them. Otherwise, the optimizer uses shared statistics if they exist. Session-specific statistics have the following characteristics: • Dictionary views that track statistics show both the shared statistics and the session-specific statistics in the current session. The views are DBA_TAB_STATISTICS, DBA_IND_STATISTICS, DBA_TAB_HISTOGRAMS, and DBA_TAB_COL_STATISTICS (each view has a corresponding USER_ and ALL_ version). The SCOPE column shows whether statistics are session-specific or shared. • Other sessions do not share the cursor using the session-specific statistics. Different sessions can share the cursor using shared statistics, as in releases earlier than Oracle Database 12c. The same session can share the cursor using session-specific statistics. • Pending statistics are not supported for session-specific statistics. • When the GLOBAL_TEMP_TABLE_STATS preference is set to SESSION, by default GATHER_TABLE_STATS immediately invalidates previous cursors compiled in the same session. However, this procedure does not invalidate cursors compiled in other sessions. 10.2.4.2 Effect of DBMS_STATS on Transaction-Specific Temporary Tables DBMS_STATS commits changes to session-specific global temporary tables, but not to transaction-specific global temporary tables. Before Oracle Database 12c, running DBMS_STATS.GATHER_TABLE_STATS on a transactionspecific temporary table (ON COMMIT DELETE ROWS) would delete all rows, making the statistics show the table as empty. Starting in Oracle Database 12c, the following procedures do not commit for transaction-specific temporary tables, so that rows in these tables are not deleted: • GATHER_TABLE_STATS • DELETE_TABLE_STATS • DELETE_COLUMN_STATS • DELETE_INDEX_STATS • SET_TABLE_STATS • SET_COLUMN_STATS • SET_INDEX_STATS • GET_TABLE_STATS 10-11 Chapter 10 About Optimizer Statistics Types • GET_COLUMN_STATS • GET_INDEX_STATS The preceding program units observe the GLOBAL_TEMP_TABLE_STATS preference. For example, if the table preference is set to SESSION, then SET_TABLE_STATS sets the session statistics, and GATHER_TABLE_STATS preserves all rows in a transaction-specific temporary table. If the table preference is set to SHARED, then SET_TABLE_STATS sets the shared statistics, and GATHER_TABLE_STATS deletes all rows from a transaction-specific temporary table. See Also: • "Gathering Schema and Table Statistics" • Oracle Database Concepts to learn about global temporary tables • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GATHER_TABLE_STATS procedure 10.2.5 System Statistics The system statistics describe hardware characteristics such as I/O and CPU performance and utilization. System statistics enable the query optimizer to more accurately estimate I/O and CPU costs when choosing execution plans. The database does not invalidate previously parsed SQL statements when updating system statistics. The database parses all new SQL statements using new statistics. See Also: • "Gathering System Statistics Manually" • Oracle Database Reference 10.2.6 User-Defined Optimizer Statistics The extensible optimizer enables authors of user-defined functions and indexes to create statistics collection, selectivity, and cost functions. The optimizer cost model is extended to integrate information supplied by the user to assess CPU and the I/O cost. Statistics types act as interfaces for user-defined functions that influence the choice of execution plan. However, to use a statistics type, the optimizer requires a mechanism to bind the type to a database object such as a column, standalone function, object type, index, indextype, or package. The SQL statement ASSOCIATE STATISTICS allows this binding to occur. Functions for user-defined statistics are relevant for columns that use both standard SQL data types and object types, and for domain indexes. When you associate a statistics type with a column or domain index, the database calls the statistics collection method in the statistics type whenever DBMS_STATS gathers statistics. 10-12 Chapter 10 How the Database Gathers Optimizer Statistics See Also: • "Gathering Schema and Table Statistics" • Oracle Database Data Cartridge Developer's Guide to learn about the extensible optimizer and user-defined statistics 10.3 How the Database Gathers Optimizer Statistics Oracle Database provides several mechanisms to gather statistics. This section contains the following topics: • DBMS_STATS Package The DBMS_STATS PL/SQL package collects and manages optimizer statistics. • Supplemental Dynamic Statistics By default, when optimizer statistics are missing, stale, or insufficient, the database automatically gathers dynamic statistics during a parse. The database uses recursive SQL to scan a small random sample of table blocks. • Online Statistics Gathering for Bulk Loads Starting in Oracle Database 12c, the database can gather table statistics automatically during the following types of bulk loads: INSERT INTO ... SELECT into an empty table using a direct path insert, and CREATE TABLE AS SELECT . Related Topics • Configuring Automatic Optimizer Statistics Collection Oracle Database can gather optimizer statistics automatically. • Gathering Optimizer Statistics Manually As an alternative or supplement to automatic statistics gathering, you can use the DBMS_STATS package to gather statistics manually. • Locking and Unlocking Optimizer Statistics You can lock statistics to prevent them from changing. 10.3.1 DBMS_STATS Package The DBMS_STATS PL/SQL package collects and manages optimizer statistics. This package enables you to control what and how statistics are collected, including the degree of parallelism for statistics collection, sampling methods, granularity of statistics collection in partitioned tables, and so on. Note: Do not use the COMPUTE and ESTIMATE clauses of the ANALYZE statement to collect optimizer statistics. These clauses have been deprecated. Instead, use DBMS_STATS. 10-13 Chapter 10 How the Database Gathers Optimizer Statistics Statistics gathered with the DBMS_STATS package are required for the creation of accurate execution plans. For example, table statistics gathered by DBMS_STATS include the number of rows, number of blocks, and average row length. By default, Oracle Database uses automatic optimizer statistics collection. In this case, the database automatically runs DBMS_STATS to collect optimizer statistics for all schema objects for which statistics are missing or stale. The process eliminates many manual tasks associated with managing the optimizer, and significantly reduces the risks of generating suboptimal execution plans because of missing or stale statistics. You can also update and manage optimizer statistics by manually executing DBMS_STATS. See Also: • "Configuring Automatic Optimizer Statistics Collection" • "Gathering Optimizer Statistics Manually" • Oracle Database Administrator’s Guide to learn more about automated maintenance tasks • Oracle Database PL/SQL Packages and Types Reference to learn about DBMS_STATS 10.3.2 Supplemental Dynamic Statistics By default, when optimizer statistics are missing, stale, or insufficient, the database automatically gathers dynamic statistics during a parse. The database uses recursive SQL to scan a small random sample of table blocks. Note: Dynamic statistics augment statistics rather than providing an alternative to them. Dynamic statistics supplement optimizer statistics such as table and index block counts, table and join cardinalities (estimated number of rows), join column statistics, and GROUP BY statistics. This information helps the optimizer improve plans by making better estimates for predicate cardinality. Dynamic statistics are beneficial in the following situations: • An execution plan is suboptimal because of complex predicates. • The sampling time is a small fraction of total execution time for the query. • The query executes many times so that the sampling time is amortized. Related Topics • When the Database Samples Data Starting in Oracle Database 12c, the optimizer automatically decides whether dynamic statistics are useful and which sample size to use for all SQL statements. In earlier releases, dynamic statistics were called dynamic sampling. 10-14 Chapter 10 How the Database Gathers Optimizer Statistics • Guideline for Setting the Sample Size In the context of optimizer statistics, sampling is the gathering of statistics from a random subset of table rows. By enabling the database to avoid full table scans and sorts of entire tables, sampling minimizes the resources necessary to gather statistics. • Configuring Options for Dynamic Statistics Dynamic statistics are an optimization technique in which the database uses recursive SQL to scan a small random sample of the blocks in a table. 10.3.3 Online Statistics Gathering for Bulk Loads Starting in Oracle Database 12c, the database can gather table statistics automatically during the following types of bulk loads: INSERT INTO ... SELECT into an empty table using a direct path insert, and CREATE TABLE AS SELECT . Note: By default, a parallel insert uses a direct path insert. You can force a direct path insert by using the /*+APPEND*/ hint. This section contains the following topics: • Purpose of Online Statistics Gathering for Bulk Loads Data warehouses typically load large amounts of data into the database. For example, a sales data warehouse might load sales data nightly. • Global Statistics During Inserts into Empty Partitioned Tables When inserting rows into an empty partitioned table, the database gathers global statistics during the insert. • Index Statistics and Histograms During Bulk Loads While gathering online statistics, the database does not gather index statistics or create histograms. If these statistics are required, then Oracle recommends running DBMS_STATS.GATHER_TABLE_STATS with the options parameter set to GATHER AUTO after the bulk load. • Restrictions for Online Statistics Gathering for Bulk Loads In some situations, optimizer statistics gathering does not occur automatically for bulk loads. • Hints for Online Statistics Gathering for Bulk Loads By default, the database gathers statistics during bulk loads. You can disable the feature at the statement level by using the NO_GATHER_OPTIMIZER_STATISTICS hint, and enable the feature at the statement level by using the GATHER_OPTIMIZER_STATISTICS hint. See Also: Oracle Database Data Warehousing Guide to learn more about bulk loads 10-15 Chapter 10 How the Database Gathers Optimizer Statistics 10.3.3.1 Purpose of Online Statistics Gathering for Bulk Loads Data warehouses typically load large amounts of data into the database. For example, a sales data warehouse might load sales data nightly. In releases earlier than Oracle Database 12c, to avoid the possibility of a suboptimal plan caused by stale statistics, you needed to gather statistics manually after a bulk load. The ability to gather statistics automatically during bulk loads has the following benefits: • Improved performance Gathering statistics during the load avoids an additional table scan to gather table statistics. • Improved manageability No user intervention is required to gather statistics after a bulk load. 10.3.3.2 Global Statistics During Inserts into Empty Partitioned Tables When inserting rows into an empty partitioned table, the database gathers global statistics during the insert. For example, if sales is an empty partitioned table, and if you run INSERT INTO sales SELECT, then the database gathers global statistics for sales. However, the database does not gather partition-level statistics. Assume a different case in which you use extended syntax to insert rows into a particular partition or subpartition, which is empty. The database gathers statistics on the empty partition during the insert. However, the database does not gather global statistics. Assume that you run INSERT INTO sales PARTITION (sales_q4_2000) SELECT. If partition sales_q4_2000 is empty before the insert (other partitions need not be empty), then the database gathers statistics during the insert. Moreover, if the INCREMENTAL preference is enabled for sales, then the database also gathers a synopsis for sales_q4_2000. Statistics are immediately available after the INSERT statement. However, if you roll back the transaction, then the database automatically deletes statistics gathered during the bulk load. See Also: • "Considerations for Incremental Statistics Maintenance" • Oracle Database SQL Language Reference for INSERT syntax and semantics 10.3.3.3 Index Statistics and Histograms During Bulk Loads While gathering online statistics, the database does not gather index statistics or create histograms. If these statistics are required, then Oracle recommends running DBMS_STATS.GATHER_TABLE_STATS with the options parameter set to GATHER AUTO after the bulk load. 10-16 Chapter 10 How the Database Gathers Optimizer Statistics For example, the following command gathers statistics for the bulk-loaded sh_ctas table: EXEC DBMS_STATS.GATHER_TABLE_STATS( user, 'SH_CTAS', options => 'GATHER AUTO' ); The preceding example only gathers missing or stale statistics. The database does not gather table and basic column statistics collected during the bulk load. Note: You can set the table preference options to GATHER AUTO on the tables that you plan to bulk load. In this way, you need not explicitly set the options parameter when running GATHER_TABLE_STATS. See Also: • "Gathering Schema and Table Statistics" • Oracle Database Data Warehousing Guide to learn more about bulk loading 10.3.3.4 Restrictions for Online Statistics Gathering for Bulk Loads In some situations, optimizer statistics gathering does not occur automatically for bulk loads. Specifically, bulk loads do not gather statistics automatically when any of the following conditions applies to the target table, partition, or subpartition: • It is not empty, and you perform an INSERT INTO ... SELECT. In this case, an OPTIMIZER STATISTICS GATHERING row source appears in the plan, but this row source is only a pass-through. The database does not actually gather optimizer statistics. Note: The DBA_TAB_COL_STATISTICS.NOTES column is set to STATS_ON_LOAD by a bulk load into an empty table. However, subsequent bulk loads into the non-empty table do not reset the NOTES column. One technique for determining whether the database gathered statistics is to query the USER_TAB_MODIFICATIONS.INSERTS column. If the query returns a row indicating the number of rows loaded, then the most recent bulk load did not gather statistics automatically. • It is loaded using an INSERT INTO ... SELECT, and neither of the following conditions is true: all columns of the target table are specified, or a subset of the target columns are specified and the unspecified columns have default values. 10-17 Chapter 10 How the Database Gathers Optimizer Statistics Put differently, the database only gathers statistics automatically for bulk loads when either all columns of the target table are specified, or a subset of the target columns are specified and the unspecified columns have default values. For example, the sales table has only columns c1, c2, c3, and c4. The column c4 does not have a default value. You load sales_copy by executing INSERT /*+ APPEND */ INTO sales_copy SELECT c1, c2, c3 FROM sales. In this case, the database does not gather online statistics for sales_copy. The database would gather statistics if c4 had a default value or if it were included in the SELECT list. • It is in an Oracle-owned schema such as SYS. • It is one of the following types of tables: nested table, index-organized table (IOT), external table, or global temporary table defined as ON COMMIT DELETE ROWS. • It has a PUBLISH preference set to FALSE. • Its statistics are locked. • It is partitioned, INCREMENTAL is set to true, and partition-extended syntax is not used. For example, assume that you execute DBMS_STATS.SET_TABLE_PREFS(null, 'sales', incremental', 'true'). In this case, the database does not gather statistics for INSERT INTO sales SELECT, even when sales is empty. However, the database does gather statistics automatically for INSERT INTO sales PARTITION (sales_q4_2000) SELECT. • It is loaded using a multitable INSERT statement. See Also: • "Gathering Schema and Table Statistics" • Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_STATS.SET_TABLE_PREFS 10.3.3.5 Hints for Online Statistics Gathering for Bulk Loads By default, the database gathers statistics during bulk loads. You can disable the feature at the statement level by using the NO_GATHER_OPTIMIZER_STATISTICS hint, and enable the feature at the statement level by using the GATHER_OPTIMIZER_STATISTICS hint. For example, the following statement disables online statistics gathering for bulk loads: CREATE TABLE employees2 AS SELECT /*+NO_GATHER_OPTIMIZER_STATISTICS*/ * FROM employees See Also: Oracle Database SQL Language Reference to learn about the GATHER_OPTIMIZER_STATISTICS and NO_GATHER_OPTIMIZER_STATISTICS hints 10-18 Chapter 10 When the Database Gathers Optimizer Statistics 10.4 When the Database Gathers Optimizer Statistics The database collects optimizer statistics at various times and from various sources. This section contains the following topics: • Sources for Optimizer Statistics The optimizer uses several different sources for optimizer statistics. • SQL Plan Directives A SQL plan directive is additional information and instructions that the optimizer can use to generate a more optimal plan. • When the Database Samples Data Starting in Oracle Database 12c, the optimizer automatically decides whether dynamic statistics are useful and which sample size to use for all SQL statements. In earlier releases, dynamic statistics were called dynamic sampling. • How the Database Samples Data At the beginning of optimization, when deciding whether a table is a candidate for dynamic statistics, the optimizer checks for the existence of persistent SQL plan directives on the table. 10.4.1 Sources for Optimizer Statistics The optimizer uses several different sources for optimizer statistics. The sources are as follows: • DBMS_STATS execution, automatic or manual This PL/SQL package is the primary means of gathering optimizer statistics. • SQL compilation During SQL compilation, the database can augment the statistics previously gathered by DBMS_STATS. In this stage, the database runs additional queries to obtain more accurate information on how many rows in the tables satisfy the WHERE clause predicates in the SQL statement. • SQL execution During execution, the database can further augment previously gathered statistics. In this stage, Oracle Database collects the number of rows produced by every row source during the execution of a SQL statement. At the end of execution, the optimizer determines whether the estimated number of rows is inaccurate enough to warrant reparsing at the next statement execution. If the cursor is marked for reparsing, then the optimizer uses actual row counts from the previous execution instead of estimates. • SQL profiles A SQL profile is a collection of auxiliary statistics on a query. The profile stores these supplemental statistics in the data dictionary. The optimizer uses SQL profiles during optimization to determine the most optimal plan. The database stores optimizer statistics in the data dictionary and updates or replaces them as needed. You can query statistics in data dictionary views. 10-19 Chapter 10 When the Database Gathers Optimizer Statistics See Also: • "When the Database Samples Data" • "About SQL Profiles" • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GATHER_TABLE_STATS procedure 10.4.2 SQL Plan Directives A SQL plan directive is additional information and instructions that the optimizer can use to generate a more optimal plan. The directive is a “note to self” by the optimizer that it is misestimating cardinalities of certain types of predicates, and also a reminder to DBMS_STATS to gather statistics needed to correct the misestimates in the future. For example, when joining two tables that have a data skew in their join columns, a SQL plan directive can direct the optimizer to use dynamic statistics to obtain a more accurate join cardinality estimate. This section contains the following topics: • When the Database Creates SQL Plan Directives The database creates SQL plan directives automatically based on information learned during automatic reoptimization. If a cardinality misestimate occurs during SQL execution, then the database creates SQL plan directives. • How the Database Uses SQL Plan Directives When compiling a SQL statement, if the optimizer sees a directive, then it obeys the directive by gathering additional information. • SQL Plan Directive Maintenance The database automatically creates SQL plan directives. You cannot create them manually. • How the Optimizer Uses SQL Plan Directives: Example This example shows how the database automatically creates and uses SQL plan directives for SQL statements. • How the Optimizer Uses Extensions and SQL Plan Directives: Example The example shows how the database uses a SQL plan directive until the optimizer verifies that an extension exists and the statistics are applicable. 10.4.2.1 When the Database Creates SQL Plan Directives The database creates SQL plan directives automatically based on information learned during automatic reoptimization. If a cardinality misestimate occurs during SQL execution, then the database creates SQL plan directives. For each new directive, the DBA_SQL_PLAN_DIRECTIVES.STATE column shows the value USABLE. This value indicates that the database can use the directive to correct misestimates. The optimizer defines a SQL plan directive on a query expression, for example, filter predicates on two columns being used together. A directive is not tied to a specific SQL statement or SQL ID. For this reason, the optimizer can use directives for 10-20 Chapter 10 When the Database Gathers Optimizer Statistics statements that are not identical. For example, directives can help the optimizer with queries that use similar patterns, such as queries that are identical except for a select list item. The Notes section of the execution plan indicates the number of SQL plan directives used for a statement. Obtain more information about the directives by querying the DBA_SQL_PLAN_DIRECTIVES and DBA_SQL_PLAN_DIR_OBJECTS views. See Also: Oracle Database Reference to learn more about DBA_SQL_PLAN_DIRECTIVES 10.4.2.2 How the Database Uses SQL Plan Directives When compiling a SQL statement, if the optimizer sees a directive, then it obeys the directive by gathering additional information. The optimizer uses directives in the following ways: • Dynamic statistics The optimizer uses dynamic statistics whenever it does not have sufficient statistics corresponding to the directive. For example, the cardinality estimates for a query whose predicate contains a specific pair of columns may be significantly wrong. A SQL plan directive indicates that the whenever a query that contains these columns is parsed, the optimizer needs to use dynamic sampling to avoid a serious cardinality misestimate. Dynamic statistics have some performance overhead. Every time the optimizer hard parses a query to which a dynamic statistics directive applies, the database must perform the extra sampling. Starting in Oracle Database 12c Release 2 (12.2), the database writes statistics from adaptive dynamic sampling to the SQL plan directives store, making them available to other queries. • Column groups The optimizer examines the query corresponding to the directive. If there is a missing column group, and if the DBMS_STATS preference AUTO_STAT_EXTENSIONS is set to ON (the default is OFF) for the affected table, then the optimizer automatically creates this column group the next time DBMS_STATS gathers statistics on the table. Otherwise, the optimizer does not automatically create the column group. If a column group exists, then the next time this statement executes, the optimizer uses the column group statistics in place of the SQL plan directive when possible (equality predicates, GROUP BY, and so on). In subsequent executions, the optimizer may create additional SQL plan directives to address other problems in the plan, such as join or GROUP BY cardinality misestimates. Note: Currently, the optimizer monitors only column groups. The optimizer does not create an extension on expressions. 10-21 Chapter 10 When the Database Gathers Optimizer Statistics When the problem that occasioned a directive is solved, either because a better directive exists or because a histogram or extension exists, the DBA_SQL_PLAN_DIRECTIVES.STATE value changes from USABLE to SUPERSEDED. More information about the directive state is exposed in the DBA_SQL_PLAN_DIRECTIVES.NOTES column. See Also: • "Managing Extended Statistics" • "About Statistics on Column Groups" • Oracle Database PL/SQL Packages and Types Reference to learn more about the AUTO_STAT_EXTENSIONS preference for DBMS_STATS.SET_TABLE_STATS 10.4.2.3 SQL Plan Directive Maintenance The database automatically creates SQL plan directives. You cannot create them manually. The database initially creates directives in the shared pool. The database periodically writes the directives to the SYSAUX tablespace. The database automatically purges any SQL plan directive that is not used after the specified number of weeks (SPD_RETENTION_WEEKS), which is 53 by default. You can manage directives by using the DBMS_SPD package. For example, you can: • Enable and disable SQL plan directives (ALTER_SQL_PLAN_DIRECTIVE) • Change the retention period for SQL plan directives (SET_PREFS) • Export a directive to a staging table (PACK_STGTAB_DIRECTIVE) • Drop a directive (DROP_SQL_PLAN_DIRECTIVE) • Force the database to write directives to disk (FLUSH_SQL_PLAN_DIRECTIVE) See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SPD package 10.4.2.4 How the Optimizer Uses SQL Plan Directives: Example This example shows how the database automatically creates and uses SQL plan directives for SQL statements. Assumptions You plan to run queries against the sh schema, and you have privileges on this schema and on data dictionary and V$ views. 10-22 Chapter 10 When the Database Gathers Optimizer Statistics To see how the database uses a SQL plan directive: 1. Query the sh.customers table. SELECT FROM WHERE AND /*+gather_plan_statistics*/ * customers cust_state_province='CA' country_id='US'; The gather_plan_statistics hint shows the actual number of rows returned from each operation in the plan. Thus, you can compare the optimizer estimates with the actual number of rows returned. 2. Query the plan for the preceding query. The following example shows the execution plan (sample output included): SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST')); PLAN_TABLE_OUTPUT ------------------------------------SQL_ID b74nw722wjvy3, child number 0 ------------------------------------select /*+gather_plan_statistics*/ * from customers where CUST_STATE_PROVINCE='CA' and country_id='US' Plan hash value: 1683234692 -------------------------------------------------------------------------------------------| Id| Operation | Name | Starts |E-Rows| A-Rows | A-Time |Buffers | Reads | -------------------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 1 | | 29 |00:00:00.01 | 17 | 14 | |*1 | TABLE ACCESS FULL| CUSTOMERS | 1 | 8 | 29 |00:00:00.01 | 17 | 14 | -------------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------1 - filter(("CUST_STATE_PROVINCE"='CA' AND "COUNTRY_ID"='US')) The actual number of rows (A-Rows) returned by each operation in the plan varies greatly from the estimates (E-Rows). This statement is a candidate for automatic reoptimization. 3. Check whether the customers query can be reoptimized. The following statement queries the V$SQL.IS_REOPTIMIZABLE value (sample output included): SELECT SQL_ID, CHILD_NUMBER, SQL_TEXT, IS_REOPTIMIZABLE FROM V$SQL WHERE SQL_TEXT LIKE 'SELECT /*+gather_plan_statistics*/%'; SQL_ID CHILD_NUMBER SQL_TEXT I ------------- ------------ ----------- b74nw722wjvy3 0 select /*+g Y ather_plan_ statistics* / * from cu stomers whe re CUST_STA TE_PROVINCE ='CA' and c 10-23 Chapter 10 When the Database Gathers Optimizer Statistics ountry_id=' US' The IS_REOPTIMIZABLE column is marked Y, so the database will perform a hard parse of the customers query on the next execution. The optimizer uses the execution statistics from this initial execution to determine the plan. The database persists the information learned from reoptimization as a SQL plan directive. 4. Display the directives for the sh schema. The following example uses DBMS_SPD to write the SQL plan directives to disk, and then shows the directives for the sh schema only: EXEC DBMS_SPD.FLUSH_SQL_PLAN_DIRECTIVE; SELECT TO_CHAR(d.DIRECTIVE_ID) dir_id, o.OWNER AS "OWN", o.OBJECT_NAME AS "OBJECT", o.SUBOBJECT_NAME col_name, o.OBJECT_TYPE, d.TYPE, d.STATE, d.REASON FROM DBA_SQL_PLAN_DIRECTIVES d, DBA_SQL_PLAN_DIR_OBJECTS o WHERE d.DIRECTIVE_ID=o.DIRECTIVE_ID AND o.OWNER IN ('SH') ORDER BY 1,2,3,4,5; DIR_ID OWN OBJECT COL_NAME OBJECT TYPE STATE REASON ------------------- --- --------- ----------- ------ ---------------- ------ -----------1484026771529551585 SH CUSTOMERS COUNTRY_ID COLUMN DYNAMIC_SAMPLING USABLE SINGLE TABLE CARDINALITY MISESTIMATE 1484026771529551585 SH CUSTOMERS CUST_STATE_ COLUMN DYNAMIC_SAMPLING USABLE SINGLE TABLE PROVINCE CARDINALITY MISESTIMATE 1484026771529551585 SH CUSTOMERS TABLE DYNAMIC_SAMPLING USABLE SINGLE TABLE CARDINALITY MISESTIMATE Initially, the database stores SQL plan directives in memory, and then writes them to disk every 15 minutes. Thus, the preceding example calls DBMS_SPD.FLUSH_SQL_PLAN_DIRECTIVE to force the database to write the directives to the SYSAUX tablespace. Monitor directives using the views DBA_SQL_PLAN_DIRECTIVES and DBA_SQL_PLAN_DIR_OBJECTS. Three entries appear in the views, one for the customers table itself, and one for each of the correlated columns. Because the customers query has the IS_REOPTIMIZABLE value of Y, if you reexecute the statement, then the database will hard parse it again, and then generate a plan based on the previous execution statistics. 5. Query the customers table again. For example, enter the following statement: SELECT FROM WHERE AND 6. /*+gather_plan_statistics*/ * customers cust_state_province='CA' country_id='US'; Query the plan in the cursor. The following example shows the execution plan (sample output included): SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST')); PLAN_TABLE_OUTPUT ------------------------------------- 10-24 Chapter 10 When the Database Gathers Optimizer Statistics SQL_ID b74nw722wjvy3, child number 1 ------------------------------------select /*+gather_plan_statistics*/ * from customers where CUST_STATE_PROVINCE='CA' and country_id='US' Plan hash value: 1683234692 --------------------------------------------------------------------------|Id | Operation |Name |Start|E-Rows|A-Rows| A-Time |Buffers| --------------------------------------------------------------------------| 0| SELECT STATEMENT | | 1| | 29|00:00:00.01| 17| |* 1| TABLE ACCESS FULL|CUSTOMERS| 1| 29| 29|00:00:00.01| 17| --------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------1 - filter(("CUST_STATE_PROVINCE"='CA' AND "COUNTRY_ID"='US')) Note ----- cardinality feedback used for this statement The Note section indicates that the database used reoptimization for this statement. The estimated number of rows (E-Rows) is now correct. The SQL plan directive has not been used yet. 7. Query the cursors for the customers query. For example, run the following query (sample output included): SELECT SQL_ID, CHILD_NUMBER, SQL_TEXT, IS_REOPTIMIZABLE FROM V$SQL WHERE SQL_TEXT LIKE 'SELECT /*+gather_plan_statistics*/%'; SQL_ID CHILD_NUMBER SQL_TEXT I ------------- ------------ ----------- b74nw722wjvy3 0 select /*+g Y ather_plan_ statistics* / * from cu stomers whe re CUST_STA TE_PROVINCE ='CA' and c ountry_id=' US' b74nw722wjvy3 1 select /*+g N ather_plan_ statistics* / * from cu stomers whe re CUST_STA TE_PROVINCE ='CA' and c ountry_id=' US' A new plan exists for the customers query, and also a new child cursor. 8. Confirm that a SQL plan directive exists and is usable for other statements. 10-25 Chapter 10 When the Database Gathers Optimizer Statistics For example, run the following query, which is similar but not identical to the original customers query (the state is MA instead of CA): SELECT FROM WHERE AND 9. /*+gather_plan_statistics*/ CUST_EMAIL CUSTOMERS CUST_STATE_PROVINCE='MA' COUNTRY_ID='US'; Query the plan in the cursor. The following statement queries the cursor (sample output included).: SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST')); PLAN_TABLE_OUTPUT ------------------------------------SQL_ID 3tk6hj3nkcs2u, child number 0 ------------------------------------Select /*+gather_plan_statistics*/ cust_email From cust_state_province='MA' And country_id='US' customers Where Plan hash value: 1683234692 --------------------------------------------------------------------------|Id | Operation | Name |Starts|E-Rows|A-Rows| A-Time |Buffers| --------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 1 | | 2 |00:00:00.01| 16 | |*1 | TABLE ACCESS FULL| CUSTOMERS | 1 | 2 | 2 |00:00:00.01| 16 | --------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------1 - filter(("CUST_STATE_PROVINCE"='MA' AND "COUNTRY_ID"='US')) Note ----- dynamic sampling used for this statement (level=2) - 1 Sql Plan Directive used for this statement The Note section of the plan shows that the optimizer used the SQL directive for this statement, and also used dynamic statistics. See Also: • "Automatic Reoptimization" • "Managing SQL Plan Directives" • Oracle Database Reference to learn about DBA_SQL_PLAN_DIRECTIVES, V$SQL, and other database views • Oracle Database Reference to learn about DBMS_SPD 10-26 Chapter 10 When the Database Gathers Optimizer Statistics 10.4.2.5 How the Optimizer Uses Extensions and SQL Plan Directives: Example The example shows how the database uses a SQL plan directive until the optimizer verifies that an extension exists and the statistics are applicable. At this point, the directive changes its status to SUPERSEDED. Subsequent compilations use the statistics instead of the directive. Assumptions This example assumes you have already followed the steps in "How the Optimizer Uses SQL Plan Directives: Example". To see how the optimizer uses an extension and SQL plan directive: 1. Gather statistics for the sh.customers table. For example, execute the following PL/SQL program: BEGIN DBMS_STATS.GATHER_TABLE_STATS('SH','CUSTOMERS'); END; / 2. Check whether an extension exists on the customers table. For example, execute the following query (sample output included): SELECT FROM WHERE AND TABLE_NAME, EXTENSION_NAME, EXTENSION DBA_STAT_EXTENSIONS OWNER='SH' TABLE_NAME='CUSTOMERS'; TABLE_NAM EXTENSION_NAME EXTENSION --------- ------------------------------ ----------------------CUSTOMERS SYS_STU#S#WF25Z#QAHIHE#MOFFMM_ ("CUST_STATE_PROVINCE", "COUNTRY_ID") The preceding output indicates that a column group extension exists on the cust_state_province and country_id columns. 3. Query the state of the SQL plan directive. Example 10-6 queries the data dictionary for information about the directive. Although column group statistics exist, the directive has a state of USABLE because the database has not yet recompiled the statement. During the next compilation, the optimizer verifies that the statistics are applicable. If they are applicable, then the status of the directive changes to SUPERSEDED. Subsequent compilations use the statistics instead of the directive. 4. Query the sh.customers table. SELECT FROM WHERE AND 5. /*+gather_plan_statistics*/ * customers cust_state_province='CA' country_id='US'; Query the plan in the cursor. Example 10-7 shows the execution plan (sample output included). 10-27 Chapter 10 When the Database Gathers Optimizer Statistics The Note section shows that the optimizer used the directive and not the extended statistics. During the compilation, the database verified the extended statistics. 6. Query the state of the SQL plan directive. Example 10-8 queries the data dictionary for information about the directive. The state of the directive, which has changed to SUPERSEDED, indicates that the corresponding column or groups have an extension or histogram, or that another SQL plan directive exists that can be used for the directive. 7. Query the sh.customers table again, using a slightly different form of the statement. For example, run the following query: SELECT FROM WHERE AND /*+gather_plan_statistics*/ /* force reparse */ * customers cust_state_province='CA' country_id='US'; If the cursor is in the shared SQL area, then the database typically shares the cursor. To force a reparse, this step changes the SQL text slightly by adding a comment. 8. Query the plan in the cursor. Example 10-9 shows the execution plan (sample output included). The absence of a Note shows that the optimizer used the extended statistics instead of the SQL plan directive. If the directive is not used for 53 weeks, then the database automatically purges it. See Also: • • "Managing SQL Plan Directives" Oracle Database Reference to learn about DBA_SQL_PLAN_DIRECTIVES, V$SQL, and other database views • Example 10-6 Oracle Database Reference to learn about DBMS_SPD Display Directives for sh Schema EXEC DBMS_SPD.FLUSH_SQL_PLAN_DIRECTIVE; SELECT TO_CHAR(d.DIRECTIVE_ID) dir_id, o.OWNER, o.OBJECT_NAME, o.SUBOBJECT_NAME col_name, o.OBJECT_TYPE, d.TYPE, d.STATE, d.REASON FROM DBA_SQL_PLAN_DIRECTIVES d, DBA_SQL_PLAN_DIR_OBJECTS o WHERE d.DIRECTIVE_ID=o.DIRECTIVE_ID AND o.OWNER IN ('SH') ORDER BY 1,2,3,4,5; DIR_ID OWN OBJECT_NA COL_NAME OBJECT TYPE STATE REASON ------------------- --- --------- ---------- ------- ---------------- ------ -----------1484026771529551585 SH CUSTOMERS COUNTRY_ID COLUMN DYNAMIC_SAMPLING USABLE SINGLE TABLE CARDINALITY MISESTIMATE 1484026771529551585 SH CUSTOMERS CUST_STATE_ COLUMN DYNAMIC_SAMPLING USABLE SINGLE TABLE PROVINCE CARDINALITY MISESTIMATE 10-28 Chapter 10 When the Database Gathers Optimizer Statistics 1484026771529551585 SH CUSTOMERS Example 10-7 TABLE DYNAMIC_SAMPLING USABLE SINGLE TABLE CARDINALITY MISESTIMATE Execution Plan SQL> SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST')); PLAN_TABLE_OUTPUT ------------------------------------SQL_ID b74nw722wjvy3, child number 0 ------------------------------------select /*+gather_plan_statistics*/ * from customers where CUST_STATE_PROVINCE='CA' and country_id='US' Plan hash value: 1683234692 ----------------------------------------------------------------------------------------| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | ----------------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 1 | | 29 |00:00:00.01 | 16 | |* 1 | TABLE ACCESS FULL| CUSTOMERS | 1 | 29 | 29 |00:00:00.01 | 16 | ----------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------1 - filter(("CUST_STATE_PROVINCE"='CA' AND "COUNTRY_ID"='US')) Note ----- dynamic sampling used for this statement (level=2) - 1 Sql Plan Directive used for this statement Example 10-8 Display Directives for sh Schema EXEC DBMS_SPD.FLUSH_SQL_PLAN_DIRECTIVE; SELECT TO_CHAR(d.DIRECTIVE_ID) dir_id, o.OWNER, o.OBJECT_NAME, o.SUBOBJECT_NAME col_name, o.OBJECT_TYPE, d.TYPE, d.STATE, d.REASON FROM DBA_SQL_PLAN_DIRECTIVES d, DBA_SQL_PLAN_DIR_OBJECTS o WHERE d.DIRECTIVE_ID=o.DIRECTIVE_ID AND o.OWNER IN ('SH') ORDER BY 1,2,3,4,5; DIR_ID OWN OBJECT_NA COL_NAME OBJECT TYPE STATE ------------------- --- --------- ---------- ------ -------- --------1484026771529551585 SH CUSTOMERS COUNTRY_ID COLUMN DYNAMIC_ SUPERSEDED SAMPLING REASON -----------SINGLE TABLE CARDINALITY MISESTIMATE 1484026771529551585 SH CUSTOMERS CUST_STATE_ COLUMN DYNAMIC_ SUPERSEDED SINGLE TABLE PROVINCE SAMPLING CARDINALITY MISESTIMATE 1484026771529551585 SH CUSTOMERS TABLE DYNAMIC_ SUPERSEDED SINGLE TABLE SAMPLING CARDINALITY MISESTIMATE Example 10-9 Execution Plan SQL> SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST')); 10-29 Chapter 10 When the Database Gathers Optimizer Statistics PLAN_TABLE_OUTPUT ------------------------------------SQL_ID b74nw722wjvy3, child number 0 ------------------------------------select /*+gather_plan_statistics*/ * from customers where CUST_STATE_PROVINCE='CA' and country_id='US' Plan hash value: 1683234692 ----------------------------------------------------------------------------------------| Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | ----------------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 1 | | 29 |00:00:00.01 | 17 | |* 1 | TABLE ACCESS FULL| CUSTOMERS | 1 | 29 | 29 |00:00:00.01 | 17 | ----------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------1 - filter(("CUST_STATE_PROVINCE"='CA' AND "COUNTRY_ID"='US')) 19 rows selected. 10.4.3 When the Database Samples Data Starting in Oracle Database 12c, the optimizer automatically decides whether dynamic statistics are useful and which sample size to use for all SQL statements. In earlier releases, dynamic statistics were called dynamic sampling. The primary factor in the decision to use dynamic statistics is whether available statistics are sufficient to generate an optimal plan. If statistics are insufficient, then the optimizer uses dynamic statistics. Automatic dynamic statistics are enabled when the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter is not set to 0. By default, the dynamic statistics level is set to 2. In general, the optimizer uses default statistics rather than dynamic statistics to compute statistics needed during optimizations on tables, indexes, and columns. The optimizer decides whether to use dynamic statistics based on several factors, including the following: • The SQL statement uses parallel execution. • A SQL plan directive exists. The following diagram illustrates the process of gathering dynamic statistics. 10-30 Chapter 10 When the Database Gathers Optimizer Statistics Figure 10-2 Dynamic Statistics Optimizer GB Statistics missing? Statistics insufficient? SQL directive exists? Parallel execution? Execution Plan HJ No HJ Yes CLIENT SQL Determine sampling size SELECT ... WHERE ... Pass results to optimizer for use in plan generation SELECT ... FROM sales WHERE ... Recursive SQL Sales As shown in Figure 10-2, the optimizer automatically gathers dynamic statistics in the following cases: • Missing statistics When tables in a query have no statistics, the optimizer gathers basic statistics on these tables before optimization. Statistics can be missing because the application creates new objects without a follow-up call to DBMS_STATS to gather statistics, or because statistics were locked on an object before statistics were gathered. In this case, the statistics are not as high-quality or as complete as the statistics gathered using the DBMS_STATS package. This trade-off is made to limit the impact on the compile time of the statement. • Insufficient statistics Statistics can be insufficient whenever the optimizer estimates the selectivity of predicates (filter or join) or the GROUP BY clause without taking into account correlation between columns, skew in the column data distribution, statistics on expressions, and so on. Extended statistics help the optimizer obtain accurate quality cardinality estimates for complex predicate expressions. The optimizer can use dynamic statistics to compensate for the lack of extended statistics or when it cannot use extended statistics, for example, for non-equality predicates. 10-31 Chapter 10 When the Database Gathers Optimizer Statistics Note: The database does not use dynamic statistics for queries that contain the AS OF clause. See Also: • "Configuring Options for Dynamic Statistics" • "About Statistics on Column Groups" • Oracle Database Reference to learn about the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter 10.4.4 How the Database Samples Data At the beginning of optimization, when deciding whether a table is a candidate for dynamic statistics, the optimizer checks for the existence of persistent SQL plan directives on the table. For each directive, the optimizer registers a statistics expression that the optimizer computes when determining the cardinality of a predicate involving the table. In Figure 10-2, the database issues a recursive SQL statement to scan a small random sample of the table blocks. The database applies the relevant single-table predicates and joins to estimate predicate cardinalities. The database persists the results of dynamic statistics as sharable statistics. The database can share the results during the SQL compilation of one query with recompilations of the same query. The database can also reuse the results for queries that have the same patterns. See Also: • "Configuring Options for Dynamic Statistics" to learn how to set the dynamic statistics level • Oracle Database Reference for details about the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter 10-32 11 Histograms A histogram is a special type of column statistic that provides more detailed information about the data distribution in a table column. A histogram sorts values into "buckets," as you might sort coins into buckets. Based on the NDV and the distribution of the data, the database chooses the type of histogram to create. (In some cases, when creating a histogram, the database samples an internally predetermined number of rows.) The types of histograms are as follows: • Frequency histograms and top frequency histograms • Height-Balanced histograms (legacy) • Hybrid histograms This chapter contains the following topics: • Purpose of Histograms By default the optimizer assumes a uniform distribution of rows across the distinct values in a column. • When Oracle Database Creates Histograms If DBMS_STATS gathers statistics for a table, and if queries have referenced the columns in this table, then Oracle Database creates histograms automatically as needed according to the previous query workload. • How Oracle Database Chooses the Histogram Type Oracle Database uses several criteria to determine which histogram to create: frequency, top frequency, height-balanced, or hybrid. • Cardinality Algorithms When Using Histograms For histograms, the algorithm for cardinality depends on factors such as the endpoint numbers and values, and whether column values are popular or nonpopular. • Frequency Histograms In a frequency histogram, each distinct column value corresponds to a single bucket of the histogram. Because each value has its own dedicated bucket, some buckets may have many values, whereas others have few. • Top Frequency Histograms A top frequency histogram is a variation on a frequency histogram that ignores nonpopular values that are statistically insignificant. • Height-Balanced Histograms (Legacy) In a legacy height-balanced histogram, column values are divided into buckets so that each bucket contains approximately the same number of rows. • Hybrid Histograms A hybrid histogram combines characteristics of both height-based histograms and frequency histograms. This "best of both worlds" approach enables the optimizer to obtain better selectivity estimates in some situations. 11-1 Chapter 11 Purpose of Histograms 11.1 Purpose of Histograms By default the optimizer assumes a uniform distribution of rows across the distinct values in a column. For columns that contain data skew (a nonuniform distribution of data within the column), a histogram enables the optimizer to generate accurate cardinality estimates for filter and join predicates that involve these columns. For example, a California-based book store ships 95% of the books to California, 4% to Oregon, and 1% to Nevada. The book orders table has 300,000 rows. A table column stores the state to which orders are shipped. A user queries the number of books shipped to Oregon. Without a histogram, the optimizer assumes an even distribution of 300000/3 (the NDV is 3), estimating cardinality at 100,000 rows. With this estimate, the optimizer chooses a full table scan. With a histogram, the optimizer calculates that 4% of the books are shipped to Oregon, and chooses an index scan. Related Topics • Introduction to Access Paths A row source is a set of rows returned by a step in an execution plan. A row source can be a table, view, or result of a join or grouping operation. 11.2 When Oracle Database Creates Histograms If DBMS_STATS gathers statistics for a table, and if queries have referenced the columns in this table, then Oracle Database creates histograms automatically as needed according to the previous query workload. The basic process is as follows: 1. You run DBMS_STATS for a table with the METHOD_OPT parameter set to the default SIZE AUTO. 2. A user queries the table. 3. The database notes the predicates in the preceding query and updates the data dictionary table SYS.COL_USAGE$. 4. You run DBMS_STATS again, causing DBMS_STATS to query SYS.COL_USAGE$ to determine which columns require histograms based on the previous query workload. Consequences of the AUTO feature include the following: • As queries change over time, DBMS_STATS may change which statistics it gathers. For example, even if the data in a table does not change, queries and DBMS_STATS operations can cause the plans for queries that reference these tables to change. • If you gather statistics for a table and do not query the table, then the database does not create histograms for columns in this table. For the database to create the histograms automatically, you must run one or more queries to populate the column usage information in SYS.COL_USAGE$. Example 11-1 Automatic Histogram Creation Assume that sh.sh_ext is an external table that contains the same rows as the sh.sales table. You create new table sales2 and perform a bulk load using sh_ext as a 11-2 Chapter 11 When Oracle Database Creates Histograms source, which automatically creates statistics for sales2. You also create indexes as follows: SQL> CREATE TABLE sales2 AS SELECT * FROM sh_ext; SQL> CREATE INDEX sh_12c_idx1 ON sales2(prod_id); SQL> CREATE INDEX sh_12c_idx2 ON sales2(cust_id,time_id); You query the data dictionary to determine whether histograms exist for the sales2 columns. Because sales2 has not yet been queried, the database has not yet created histograms: SQL> SELECT COLUMN_NAME, NOTES, HISTOGRAM 2 FROM USER_TAB_COL_STATISTICS 3 WHERE TABLE_NAME = 'SALES2'; COLUMN_NAME ------------AMOUNT_SOLD QUANTITY_SOLD PROMO_ID CHANNEL_ID TIME_ID CUST_ID PROD_ID NOTES -------------STATS_ON_LOAD STATS_ON_LOAD STATS_ON_LOAD STATS_ON_LOAD STATS_ON_LOAD STATS_ON_LOAD STATS_ON_LOAD HISTOGRAM --------NONE NONE NONE NONE NONE NONE NONE You query sales2 for the number of rows for product 42, and then gather table statistics using the GATHER AUTO option: SQL> SELECT COUNT(*) FROM sales2 WHERE prod_id = 42; COUNT(*) ---------12116 SQL> EXEC DBMS_STATS.GATHER_TABLE_STATS(USER,'SALES2',OPTIONS=>'GATHER AUTO'); A query of the data dictionary now shows that the database created a histogram on the prod_id column based on the information gather during the preceding query: SQL> SELECT COLUMN_NAME, NOTES, HISTOGRAM 2 FROM USER_TAB_COL_STATISTICS 3 WHERE TABLE_NAME = 'SALES2'; COLUMN_NAME ------------AMOUNT_SOLD QUANTITY_SOLD PROMO_ID CHANNEL_ID TIME_ID CUST_ID PROD_ID NOTES -------------STATS_ON_LOAD STATS_ON_LOAD STATS_ON_LOAD STATS_ON_LOAD STATS_ON_LOAD STATS_ON_LOAD HISTOGRAM_ONLY HISTOGRAM --------NONE NONE NONE NONE NONE NONE FREQUENCY Related Topics • Online Statistics Gathering for Bulk Loads Starting in Oracle Database 12c, the database can gather table statistics automatically during the following types of bulk loads: INSERT INTO ... SELECT into an empty table using a direct path insert, and CREATE TABLE AS SELECT . 11-3 Chapter 11 How Oracle Database Chooses the Histogram Type 11.3 How Oracle Database Chooses the Histogram Type Oracle Database uses several criteria to determine which histogram to create: frequency, top frequency, height-balanced, or hybrid. The histogram formula uses the following variables: • NDV This represents the number of distinct values in a column. For example, if a column only contains the values 100, 200, and 300, then the NDV for this column is 3. • n This variable represents the number of histogram buckets. The default is 254. • p This variable represents an internal percentage threshold that is equal to (1–(1/n)) * 100. For example, if n = 254, then p is 99.6. An additional criterion is whether the estimate_percent parameter in the DBMS_STATS statistics gathering procedure is set to AUTO_SAMPLE_SIZE (default). The following diagram shows the decision tree for histogram creation. Figure 11-1 ? Decision Tree for Histogram Creation NDV>n Yes ESTIMATE_PERCENT= AUTO_SAMPLE_SIZE No Frequency Histogram Yes Percentage of rows for top n frequent values >= p No Height-Balanced Histogram Yes Top n Frequency Histogram No Hybrid Histogram NDV = Number of distinct values n = Number of histogram buckets (default is 254) p = (1-(1/n))*100 11.4 Cardinality Algorithms When Using Histograms For histograms, the algorithm for cardinality depends on factors such as the endpoint numbers and values, and whether column values are popular or nonpopular. This section contains the following topics: 11-4 Chapter 11 Cardinality Algorithms When Using Histograms • Endpoint Numbers and Values An endpoint number is a number that uniquely identifies a bucket. In frequency and hybrid histograms, the endpoint number is the cumulative frequency of all values included in the current and previous buckets. • Popular and Nonpopular Values The popularity of a value in a histogram affects the cardinality estimate algorithm. • Bucket Compression In some cases, to reduce the total number of buckets, the optimizer compresses multiple buckets into a single bucket. 11.4.1 Endpoint Numbers and Values An endpoint number is a number that uniquely identifies a bucket. In frequency and hybrid histograms, the endpoint number is the cumulative frequency of all values included in the current and previous buckets. For example, a bucket with endpoint number 100 means the total frequency of values in the current and all previous buckets is 100. In height-balanced histograms, the optimizer numbers buckets sequentially, starting at 0 or 1. In all cases, the endpoint number is the bucket number. An endpoint value is the highest value in the range of values in a bucket. For example, if a bucket contains only the values 52794 and 52795, then the endpoint value is 52795. 11.4.2 Popular and Nonpopular Values The popularity of a value in a histogram affects the cardinality estimate algorithm. Specifically, the cardinality estimate is affected as follows: • Popular values A popular value occurs as an endpoint value of multiple buckets. The optimizer determines whether a value is popular by first checking whether it is the endpoint value for a bucket. If so, then for frequency histograms, the optimizer subtracts the endpoint number of the previous bucket from the endpoint number of the current bucket. Hybrid histograms already store this information for each endpoint individually. If this value is greater than 1, then the value is popular. The optimizer calculates its cardinality estimate for popular values using the following formula: cardinality of popular value = (num of rows in table) * (num of endpoints spanned by this value / total num of endpoints) • Nonpopular values Any value that is not popular is a nonpopular value. The optimizer calculates the cardinality estimates for nonpopular values using the following formula: cardinality of nonpopular value = (num of rows in table) * density The optimizer calculates density using an internal algorithm based on factors such as the number of buckets and the NDV. Density is expressed as a decimal number between 0 and 1. Values close to 1 indicate that the optimizer expects many rows 11-5 Chapter 11 Frequency Histograms to be returned by a query referencing this column in its predicate list. Values close to 0 indicate that the optimizer expects few rows to be returned. See Also: Oracle Database Reference to learn about the DBA_TAB_COL_STATISTICS.DENSITY column 11.4.3 Bucket Compression In some cases, to reduce the total number of buckets, the optimizer compresses multiple buckets into a single bucket. For example, the following frequency histogram indicates that the first bucket number is 1 and the last bucket number is 23: ENDPOINT_NUMBER ENDPOINT_VALUE --------------- -------------1 52792 6 52793 8 52794 9 52795 10 52796 12 52797 14 52798 23 52799 Several buckets are "missing." Originally, buckets 2 through 6 each contained a single instance of value 52793. The optimizer compressed all of these buckets into the bucket with the highest endpoint number (bucket 6), which now contains 5 instances of value 52793. This value is popular because the difference between the endpoint number of the current bucket (6) and the previous bucket (1) is 5. Thus, before compression the value 52793 was the endpoint for 5 buckets. The following annotations show which buckets are compressed, and which values are popular: ENDPOINT_NUMBER ENDPOINT_VALUE --------------- -------------1 52792 -> nonpopular 6 52793 -> buckets 2-6 compressed into 6; popular 8 52794 -> buckets 7-8 compressed into 8; popular 9 52795 -> nonpopular 10 52796 -> nonpopular 12 52797 -> buckets 11-12 compressed into 12; popular 14 52798 -> buckets 13-14 compressed into 14; popular 23 52799 -> buckets 15-23 compressed into 23; popular 11.5 Frequency Histograms In a frequency histogram, each distinct column value corresponds to a single bucket of the histogram. Because each value has its own dedicated bucket, some buckets may have many values, whereas others have few. 11-6 Chapter 11 Frequency Histograms An analogy to a frequency histogram is sorting coins so that each individual coin initially gets its own bucket. For example, the first penny is in bucket 1, the second penny is in bucket 2, the first nickel is in bucket 3, and so on. You then consolidate all the pennies into a single penny bucket, all the nickels into a single nickel bucket, and so on with the remainder of the coins. This section contains the following topics: • Criteria For Frequency Histograms Frequency histograms depend on the number of requested histogram buckets. • Generating a Frequency Histogram This scenario shows how to generate a frequency histogram using the sample schemas. 11.5.1 Criteria For Frequency Histograms Frequency histograms depend on the number of requested histogram buckets. As shown in the logic diagram in "How Oracle Database Chooses the Histogram Type", the database creates a frequency histogram when the following criteria are met: • NDV is less than or equal to n, where n is the number of histogram buckets (default 254). For example, the sh.countries.country_subregion_id column has 8 distinct values, ranging sequentially from 52792 to 52799. If n is the default of 254, then the optimizer creates a frequency histogram because 8 <= 254. • The estimate_percent parameter in the DBMS_STATS statistics gathering procedure is set to either a user-specified value or to AUTO_SAMPLE_SIZE. Starting in Oracle Database 12c, if the sampling size is the default of AUTO_SAMPLE_SIZE, then the database creates frequency histograms from a full table scan. For all other sampling percentage specifications, the database derives frequency histograms from a sample. In releases earlier than Oracle Database 12c, the database gathered histograms based on a small sample, which meant that low-frequency values often did not appear in the sample. Using density in this case sometimes led the optimizer to overestimate selectivity. See Also: Oracle Database PL/SQL Packages and Types Reference to learn about AUTO_SAMPLE_SIZE 11.5.2 Generating a Frequency Histogram This scenario shows how to generate a frequency histogram using the sample schemas. Assumptions This scenario assumes that you want to generate a frequency histogram on the sh.countries.country_subregion_id column. This table has 23 rows. 11-7 Chapter 11 Frequency Histograms The following query shows that the country_subregion_id column contains 8 distinct values (sample output included) that are unevenly distributed: SELECT country_subregion_id, count(*) FROM sh.countries GROUP BY country_subregion_id ORDER BY 1; COUNTRY_SUBREGION_ID COUNT(*) -------------------- ---------52792 1 52793 5 52794 2 52795 1 52796 1 52797 2 52798 2 52799 9 To generate a frequency histogram: 1. Gather statistics for sh.countries and the country_subregion_id column, letting the number of buckets default to 254. For example, execute the following PL/SQL anonymous block: BEGIN DBMS_STATS.GATHER_TABLE_STATS ( ownname => 'SH' , tabname => 'COUNTRIES' , method_opt => 'FOR COLUMNS COUNTRY_SUBREGION_ID' ); END; 2. Query the histogram information for the country_subregion_id column. For example, use the following query (sample output included): SELECT FROM WHERE AND TABLE_NAME, COLUMN_NAME, NUM_DISTINCT, HISTOGRAM USER_TAB_COL_STATISTICS TABLE_NAME='COUNTRIES' COLUMN_NAME='COUNTRY_SUBREGION_ID'; TABLE_NAME COLUMN_NAME NUM_DISTINCT HISTOGRAM ---------- -------------------- ------------ --------------COUNTRIES COUNTRY_SUBREGION_ID 8 FREQUENCY The optimizer chooses a frequency histogram because n or fewer distinct values exist in the column, where n defaults to 254. 3. Query the endpoint number and endpoint value for the country_subregion_id column. For example, use the following query (sample output included): SELECT FROM WHERE AND ENDPOINT_NUMBER, ENDPOINT_VALUE USER_HISTOGRAMS TABLE_NAME='COUNTRIES' COLUMN_NAME='COUNTRY_SUBREGION_ID'; ENDPOINT_NUMBER ENDPOINT_VALUE --------------- -------------1 52792 11-8 Chapter 11 Frequency Histograms 6 8 9 10 12 14 23 52793 52794 52795 52796 52797 52798 52799 Figure 11-2 is a graphical illustration of the 8 buckets in the histogram. Each value is represented as a coin that is dropped into a bucket. Figure 11-2 Frequency Histogram 52793 52793 52792 52793 52793 52793 52794 52794 Endpoint Value Endpoint Value Endpoint Value 52792 52793 52794 Endpoint Number: 1 Endpoint Number: 6 Endpoint Number: 8 52795 52796 52797 52797 Endpoint Value Endpoint Value Endpoint Value 52795 52796 52797 Endpoint Number: 9 Endpoint Number: 10 Endpoint Number: 12 52799 52799 52799 52799 52799 52799 52798 52798 52799 52799 52799 Endpoint Value Endpoint Value 52798 52799 Endpoint Number: 14 Endpoint Number: 23 11-9 Chapter 11 Top Frequency Histograms As shown in Figure 11-2, each distinct value has its own bucket. Because this is a frequency histogram, the endpoint number is the cumulative frequency of endpoints. For 52793, the endpoint number 6 indicates that the value appears 5 times (6 - 1). For 52794, the endpoint number 8 indicates that the value appears 2 times (8 - 6). Every bucket whose endpoint is at least 2 greater than the previous endpoint contains a popular value. Thus, buckets 6, 8, 12, 14, and 23 contain popular values. The optimizer calculates their cardinality based on endpoint numbers. For example, the optimizer calculates the cardinality (C) of value 52799 using the following formula, where the number of rows in the table is 23: C = 23 * ( 9 / 23 ) Buckets 1, 9, and 10 contain nonpopular values. The optimizer estimates their cardinality based on density. See Also: • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GATHER_TABLE_STATS procedure • Oracle Database Reference to learn about the USER_TAB_COL_STATISTICS view • Oracle Database Reference to learn about the USER_HISTOGRAMS view 11.6 Top Frequency Histograms A top frequency histogram is a variation on a frequency histogram that ignores nonpopular values that are statistically insignificant. For example, if a pile of 1000 coins contains only a single penny, then you can ignore the penny when sorting the coins into buckets. A top frequency histogram can produce a better histogram for highly popular values. This section contains the following topics: • Criteria For Top Frequency Histograms If a small number of values occupies most of the rows, then creating a frequency histogram on this small set of values is useful even when the NDV is greater than the number of requested histogram buckets. To create a better quality histogram for popular values, the optimizer ignores the nonpopular values and creates a top frequency histogram. • Generating a Top Frequency Histogram This scenario shows how to generate a top frequency histogram using the sample schemas. 11.6.1 Criteria For Top Frequency Histograms If a small number of values occupies most of the rows, then creating a frequency histogram on this small set of values is useful even when the NDV is greater than the number of requested histogram buckets. To create a better quality histogram for 11-10 Chapter 11 Top Frequency Histograms popular values, the optimizer ignores the nonpopular values and creates a top frequency histogram. As shown in the logic diagram in "How Oracle Database Chooses the Histogram Type", the database creates a top frequency histogram when the following criteria are met: • NDV is greater than n, where n is the number of histogram buckets (default 254). • The percentage of rows occupied by the top n frequent values is equal to or greater than threshold p, where p is (1-(1/n))*100. • The estimate_percent parameter in the DBMS_STATS statistics gathering procedure is set to AUTO_SAMPLE_SIZE. See Also: Oracle Database PL/SQL Packages and Types Reference to learn about AUTO_SAMPLE_SIZE 11.6.2 Generating a Top Frequency Histogram This scenario shows how to generate a top frequency histogram using the sample schemas. Assumptions This scenario assumes that you want to generate a top frequency histogram on the sh.countries.country_subregion_id column. This table has 23 rows. The following query shows that the country_subregion_id column contains 8 distinct values (sample output included) that are unevenly distributed: SELECT country_subregion_id, count(*) FROM sh.countries GROUP BY country_subregion_id ORDER BY 1; COUNTRY_SUBREGION_ID COUNT(*) -------------------- ---------52792 1 52793 5 52794 2 52795 1 52796 1 52797 2 52798 2 52799 9 To generate a top frequency histogram: 1. Gather statistics for sh.countries and the country_subregion_id column, specifying fewer buckets than distinct values. For example, enter the following command to specify 7 buckets: 11-11 Chapter 11 Top Frequency Histograms BEGIN DBMS_STATS.GATHER_TABLE_STATS ( ownname => 'SH' , tabname => 'COUNTRIES' , method_opt => 'FOR COLUMNS COUNTRY_SUBREGION_ID SIZE 7' ); END; 2. Query the histogram information for the country_subregion_id column. For example, use the following query (sample output included): SELECT FROM WHERE AND TABLE_NAME, COLUMN_NAME, NUM_DISTINCT, HISTOGRAM USER_TAB_COL_STATISTICS TABLE_NAME='COUNTRIES' COLUMN_NAME='COUNTRY_SUBREGION_ID'; TABLE_NAME COLUMN_NAME NUM_DISTINCT HISTOGRAM ---------- -------------------- ------------ --------------COUNTRIES COUNTRY_SUBREGION_ID 7 TOP-FREQUENCY The sh.countries.country_subregion_id column contains 8 distinct values, but the histogram only contains 7 buckets, making n=7. In this case, the database can only create a top frequency or hybrid histogram. In the country_subregion_id column, the top 7 most frequent values occupy 95.6% of the rows, which exceeds the threshold of 85.7%, generating a top frequency histogram. 3. Query the endpoint number and endpoint value for the column. For example, use the following query (sample output included): SELECT FROM WHERE AND ENDPOINT_NUMBER, ENDPOINT_VALUE USER_HISTOGRAMS TABLE_NAME='COUNTRIES' COLUMN_NAME='COUNTRY_SUBREGION_ID'; ENDPOINT_NUMBER ENDPOINT_VALUE --------------- -------------1 52792 6 52793 8 52794 9 52796 11 52797 13 52798 22 52799 Figure 11-3 is a graphical illustration of the 7 buckets in the top frequency histogram. The values are represented in the diagram as coins. 11-12 Chapter 11 Top Frequency Histograms Figure 11-3 Top Frequency Histogram 52793 52793 52792 52793 52793 52793 52794 52794 Endpoint Value Endpoint Value Endpoint Value 52792 52793 52794 Endpoint Number: 1 Endpoint Number: 6 Endpoint Number: 8 52796 52797 52797 Endpoint Value Endpoint Value 52796 52797 Endpoint Number: 9 Endpoint Number: 11 52799 52799 52799 52799 52799 52799 52798 52798 52799 52799 52799 Endpoint Value Endpoint Value 52798 52799 Endpoint Number: 13 Endpoint Number: 22 As shown in Figure 11-3, each distinct value has its own bucket except for 52795, which is excluded from the histogram because it is nonpopular and statistically insignificant. As in a standard frequency histogram, the endpoint number represents the cumulative frequency of values. 11-13 Chapter 11 Height-Balanced Histograms (Legacy) See Also: • "Criteria For Frequency Histograms" • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GATHER_TABLE_STATS procedure • Oracle Database Reference to learn about the USER_TAB_COL_STATISTICS view • Oracle Database Reference to learn about the USER_HISTOGRAMS view 11.7 Height-Balanced Histograms (Legacy) In a legacy height-balanced histogram, column values are divided into buckets so that each bucket contains approximately the same number of rows. For example, if you have 99 coins to distribute among 4 buckets, each bucket contains about 25 coins. The histogram shows where the endpoints fall in the range of values. This section contains the following topics: • Criteria for Height-Balanced Histograms Before Oracle Database 12c, the database created a height-balanced histogram when the NDV was greater than n. This type of histogram was useful for range predicates, and equality predicates on values that appear as endpoints in at least two buckets. • Generating a Height-Balanced Histogram This scenario shows how to generate a height-balanced histogram using the sample schemas. 11.7.1 Criteria for Height-Balanced Histograms Before Oracle Database 12c, the database created a height-balanced histogram when the NDV was greater than n. This type of histogram was useful for range predicates, and equality predicates on values that appear as endpoints in at least two buckets. As shown in the logic diagram in "How Oracle Database Chooses the Histogram Type", the database creates a height-balanced histogram when the following criteria are met: • NDV is greater than n, where n is the number of histogram buckets (default 254). • The estimate_percent parameter in the DBMS_STATS statistics gathering procedure is not set to AUTO_SAMPLE_SIZE. It follows that if Oracle Database 12c creates new histograms, and if the sampling percentage is AUTO_SAMPLE_SIZE, then the histograms are either top frequency or hybrid, but not height-balanced. If you upgrade Oracle Database 11g to Oracle Database 12c, then any height-based histograms created before the upgrade remain in use. However, if you refresh statistics on the table on which the histogram was created, then the database replaces existing height-balanced histograms on this table. The type of replacement histogram depends on both the NDV and the following criteria: 11-14 Chapter 11 Height-Balanced Histograms (Legacy) • If the sampling percentage is AUTO_SAMPLE_SIZE, then the database creates either hybrid or frequency histograms. • If the sampling percentage is not AUTO_SAMPLE_SIZE, then the database creates either height-balanced or frequency histograms. 11.7.2 Generating a Height-Balanced Histogram This scenario shows how to generate a height-balanced histogram using the sample schemas. Assumptions This scenario assumes that you want to generate a height-balanced histogram on the sh.countries.country_subregion_id column. This table has 23 rows. The following query shows that the country_subregion_id column contains 8 distinct values (sample output included) that are unevenly distributed: SELECT country_subregion_id, count(*) FROM sh.countries GROUP BY country_subregion_id ORDER BY 1; COUNTRY_SUBREGION_ID COUNT(*) -------------------- ---------52792 1 52793 5 52794 2 52795 1 52796 1 52797 2 52798 2 52799 9 To generate a height-balanced histogram: 1. Gather statistics for sh.countries and the country_subregion_id column, specifying fewer buckets than distinct values. Note: To simulate Oracle Database 11g behavior, which is necessary to create a height-based histogram, set estimate_percent to a nondefault value. If you specify a nondefault percentage, then the database creates frequency or height-balanced histograms. For example, enter the following command: BEGIN DBMS_STATS.GATHER_TABLE_STATS ( ownname => 'SH' , tabname => 'COUNTRIES' , method_opt => 'FOR COLUMNS COUNTRY_SUBREGION_ID SIZE 7' , estimate_percent => 100 ); END; 11-15 Chapter 11 Height-Balanced Histograms (Legacy) 2. Query the histogram information for the country_subregion_id column. For example, use the following query (sample output included): SELECT FROM WHERE AND TABLE_NAME, COLUMN_NAME, NUM_DISTINCT, HISTOGRAM USER_TAB_COL_STATISTICS TABLE_NAME='COUNTRIES' COLUMN_NAME='COUNTRY_SUBREGION_ID'; TABLE_NAME COLUMN_NAME NUM_DISTINCT HISTOGRAM ---------- -------------------- ------------ --------------COUNTRIES COUNTRY_SUBREGION_ID 8 HEIGHT BALANCED The optimizer chooses a height-balanced histogram because the number of distinct values (8) is greater than the number of buckets (7), and the estimate_percent value is nondefault. 3. Query the number of rows occupied by each distinct value. For example, use the following query (sample output included): SELECT COUNT(country_subregion_id) AS NUM_OF_ROWS, country_subregion_id FROM countries GROUP BY country_subregion_id ORDER BY 2; NUM_OF_ROWS COUNTRY_SUBREGION_ID ----------- -------------------1 52792 5 52793 2 52794 1 52795 1 52796 2 52797 2 52798 9 52799 4. Query the endpoint number and endpoint value for the country_subregion_id column. For example, use the following query (sample output included): SELECT FROM WHERE AND ENDPOINT_NUMBER, ENDPOINT_VALUE USER_HISTOGRAMS TABLE_NAME='COUNTRIES' COLUMN_NAME='COUNTRY_SUBREGION_ID'; ENDPOINT_NUMBER ENDPOINT_VALUE --------------- -------------0 52792 2 52793 3 52795 4 52798 7 52799 Figure 11-4 is a graphical illustration of the height-balanced histogram. The values are represented in the diagram as coins. 11-16 Chapter 11 Height-Balanced Histograms (Legacy) Figure 11-4 Height-Balanced Histogram 52793 52793 52794 52792 52793 52793 52793 52794 52795 Endpoint Value Endpoint Value Endpoint Value 52792 52793 52795 Endpoint Number: 0 Endpoint Number: 2 Endpoint Number: 3 52799 52799 52799 52797 52797 52799 52799 52799 52798 52798 52799 52799 52799 Endpoint Value Endpoint Value 52798 52799 Endpoint Number: 4 Endpoint Number: 7 The bucket number is identical to the endpoint number. The optimizer records the value of the last row in each bucket as the endpoint value, and then checks to ensure that the minimum value is the endpoint value of the first bucket, and the maximum value is the endpoint value of the last bucket. In this example, the optimizer adds bucket 0 so that the minimum value 52792 is the endpoint of a bucket. The optimizer must evenly distribute 23 rows into the 7 specified histogram buckets, so each bucket contains approximately 3 rows. However, the optimizer compresses buckets with the same endpoint. So, instead of bucket 1 containing 2 instances of value 52793, and bucket 2 containing 3 instances of value 52793, the optimizer puts all 5 instances of value 52793 into bucket 2. Similarly, instead of having buckets 5, 6, and 7 contain 3 values each, with the endpoint of each bucket as 52799, the optimizer puts all 9 instances of value 52799 into bucket 7. In this example, buckets 3 and 4 contain nonpopular values because the difference between the current endpoint number and previous endpoint number is 1. The optimizer calculates cardinality for these values based on density. The remaining buckets contain popular values. The optimizer calculates cardinality for these values based on endpoint numbers. 11-17 Chapter 11 Hybrid Histograms See Also: • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GATHER_TABLE_STATS procedure • Oracle Database Reference to learn about the USER_TAB_COL_STATISTICS view • Oracle Database Reference to learn about the USER_HISTOGRAMS view 11.8 Hybrid Histograms A hybrid histogram combines characteristics of both height-based histograms and frequency histograms. This "best of both worlds" approach enables the optimizer to obtain better selectivity estimates in some situations. The height-based histogram sometimes produces inaccurate estimates for values that are almost popular. For example, a value that occurs as an endpoint value of only one bucket but almost occupies two buckets is not considered popular. To solve this problem, a hybrid histogram distributes values so that no value occupies more than one bucket, and then stores the endpoint repeat count value, which is the number of times the endpoint value is repeated, for each endpoint (bucket) in the histogram. By using the repeat count, the optimizer can obtain accurate estimates for almost popular values. This section contains the following topics: • How Endpoint Repeat Counts Work The analogy of coins distributed among buckets illustrate show endpoint repeat counts work. • Criteria for Hybrid Histograms The only differentiating criterion for hybrid histograms as compared to top frequency histograms is that the top n frequent values is less than internal threshold p. • Generating a Hybrid Histogram This scenario shows how to generate a hybrid histogram using the sample schemas. 11.8.1 How Endpoint Repeat Counts Work The analogy of coins distributed among buckets illustrate show endpoint repeat counts work. The following figure illustrates a coins column that sorts values from low to high. Figure 11-5 1 1 1 Coins 5 5 5 10 10 25 25 25 25 50 100 100 11-18 Chapter 11 Hybrid Histograms You gather statistics for this table, setting the method_opt argument of DBMS_STATS.GATHER_TABLE_STATS to FOR ALL COLUMNS SIZE 3. In this case, the optimizer initially groups the values in the coins column into three buckets, as shown in the following figure. Figure 11-6 1 Initial Distribution of Values 1 5 1 5 5 10 25 10 25 25 25 50 100 100 Endpoint Value Endpoint Value Endpoint Value 5 25 100 Bucket 1 Bucket 2 Bucket 3 If a bucket border splits a value so that some occurrences of the value are in one bucket and some in another, then the optimizer shifts the bucket border (and all other following bucket borders) forward to include all occurrences of the value. For example, the optimizer shifts value 5 so that it is now wholly in the first bucket, and the value 25 is now wholly in the second bucket. Figure 11-7 1 Redistribution of Values 1 5 1 5 5 10 10 50 25 25 100 100 25 25 Endpoint Value Endpoint Value Endpoint Value 5 25 100 Bucket 1 Bucket 2 Bucket 3 The endpoint repeat count measures the number of times that the corresponding bucket endpoint, which is the value at the right bucket border, repeats itself. For example, in the first bucket, the value 5 is repeated 3 times, so the endpoint repeat count is 3. 11-19 Chapter 11 Hybrid Histograms Figure 11-8 1 Endpoint Repeat Count 1 5 1 5 5 10 10 50 25 25 100 100 25 25 Endpoint Value Endpoint Value Endpoint Value 5 25 100 Repeat Count: 3 Repeat Count: 4 Repeat Count: 2 Bucket 1 Bucket 2 Bucket 3 Height-balanced histograms do not store as much information as hybrid histograms. By using repeat counts, the optimizer can determine exactly how many occurrences of an endpoint value exist. For example, the optimizer knows that the value 5 appears 3 times, the value 25 appears 4 times, and the value 100 appears 2 times. This frequency information helps the optimizer to generate better cardinality estimates. 11.8.2 Criteria for Hybrid Histograms The only differentiating criterion for hybrid histograms as compared to top frequency histograms is that the top n frequent values is less than internal threshold p. As shown in the logic diagram in "How Oracle Database Chooses the Histogram Type", the database creates a hybrid histogram when the following criteria are met: • NDV is greater than n, where n is the number of histogram buckets (default is 254). • The criteria for top frequency histograms do not apply. This is another way to stating that the percentage of rows occupied by the top n frequent values is less than threshold p, where p is (1-(1/n))*100. • The estimate_percent parameter in the DBMS_STATS statistics gathering procedure is set to AUTO_SAMPLE_SIZE. If users specify their own percentage, then the database creates frequency or height-balanced histograms. See Also: • "Criteria For Top Frequency Histograms." • "Height-Balanced Histograms (Legacy)" • Oracle Database PL/SQL Packages and Types Reference to learn more about the estimate_percent parameter 11-20 Chapter 11 Hybrid Histograms 11.8.3 Generating a Hybrid Histogram This scenario shows how to generate a hybrid histogram using the sample schemas. Assumptions This scenario assumes that you want to generate a hybrid histogram on the sh.products.prod_subcategory_id column. This table has 72 rows. The prod_subcategory_id column contains 22 distinct values. To generate a hybrid histogram: 1. Gather statistics for sh.products and the prod_subcategory_id column, specifying 10 buckets. For example, enter the following command: BEGIN DBMS_STATS.GATHER_TABLE_STATS ( ownname => 'SH' , tabname => 'PRODUCTS' , method_opt => 'FOR COLUMNS PROD_SUBCATEGORY_ID SIZE 10' ); END; 2. Query the number of rows occupied by each distinct value. For example, use the following query (sample output included): SELECT COUNT(prod_subcategory_id) AS NUM_OF_ROWS, prod_subcategory_id FROM products GROUP BY prod_subcategory_id ORDER BY 1 DESC; NUM_OF_ROWS PROD_SUBCATEGORY_ID ----------- ------------------8 2014 7 2055 6 2032 6 2054 5 2056 5 2031 5 2042 5 2051 4 2036 3 2043 2 2033 2 2034 2 2013 2 2012 2 2053 2 2035 1 2022 1 2041 1 2044 1 2011 1 2021 1 2052 22 rows selected. 11-21 Chapter 11 Hybrid Histograms The column contains 22 distinct values. Because the number of buckets (10) is less than 22, the optimizer cannot create a frequency histogram. The optimizer considers both hybrid and top frequency histograms. To qualify for a top frequency histogram, the percentage of rows occupied by the top 10 most frequent values must be equal to or greater than threshold p, where p is (1-(1/10))*100, or 90%. However, in this case the top 10 most frequent values occupy 54 rows out of 72, which is only 75% of the total. Therefore, the optimizer chooses a hybrid histogram because the criteria for a top frequency histogram do not apply. 3. Query the histogram information for the country_subregion_id column. For example, use the following query (sample output included): SELECT FROM WHERE AND TABLE_NAME, COLUMN_NAME, NUM_DISTINCT, HISTOGRAM USER_TAB_COL_STATISTICS TABLE_NAME='PRODUCTS' COLUMN_NAME='PROD_SUBCATEGORY_ID'; TABLE_NAME COLUMN_NAME NUM_DISTINCT HISTOGRAM ---------- ------------------- ------------ --------PRODUCTS PROD_SUBCATEGORY_ID 22 HYBRID 4. Query the endpoint number, endpoint value, and endpoint repeat count for the country_subregion_id column. For example, use the following query (sample output included): SELECT ENDPOINT_NUMBER, ENDPOINT_VALUE, ENDPOINT_REPEAT_COUNT FROM USER_HISTOGRAMS WHERE TABLE_NAME='PRODUCTS' AND COLUMN_NAME='PROD_SUBCATEGORY_ID' ORDER BY 1; ENDPOINT_NUMBER ENDPOINT_VALUE ENDPOINT_REPEAT_COUNT --------------- -------------- --------------------1 2011 1 13 2014 8 26 2032 6 36 2036 4 45 2043 3 51 2051 5 52 2052 1 54 2053 2 60 2054 6 72 2056 5 10 rows selected. In a height-based histogram, the optimizer would evenly distribute 72 rows into the 10 specified histogram buckets, so that each bucket contains approximately 7 rows. Because this is a hybrid histogram, the optimizer distributes the values so that no value occupies more than one bucket. For example, the optimizer does not put some instances of value 2036 into one bucket and some instances of this value into another bucket: all instances are in bucket 36. The endpoint repeat count shows the number of times the highest value in the bucket is repeated. By using the endpoint number and repeat count for these values, the optimizer can estimate cardinality. For example, bucket 36 contains instances of values 2033, 2034, 2035, and 2036. The endpoint value 2036 has an endpoint repeat count of 4, so the optimizer knows that 4 instances of this value 11-22 Chapter 11 Hybrid Histograms exist. For values such as 2033, which are not endpoints, the optimizer estimates cardinality using density. See Also: • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GATHER_TABLE_STATS procedure • Oracle Database Reference to learn about the USER_TAB_COL_STATISTICS view • Oracle Database Reference to learn about the USER_HISTOGRAMS view 11-23 12 Configuring Options for Optimizer Statistics Gathering This chapter explains what optimizer statistics collection is and how to set statistics preferences. This chapter contains the following topics: • About Optimizer Statistics Collection In Oracle Database, optimizer statistics collection is the gathering of optimizer statistics for database objects, including fixed objects. • Setting Optimizer Statistics Preferences This topic explains how to set optimizer statistics defaults using DBMS_STATS.SET_*_PREFS procedures. • Configuring Options for Dynamic Statistics Dynamic statistics are an optimization technique in which the database uses recursive SQL to scan a small random sample of the blocks in a table. • Managing SQL Plan Directives A SQL plan directive is additional information and instructions that the optimizer can use to generate a more optimal plan. 12.1 About Optimizer Statistics Collection In Oracle Database, optimizer statistics collection is the gathering of optimizer statistics for database objects, including fixed objects. The database can collect optimizer statistics automatically. You can also collect them manually using the DBMS_STATS package. This section contains the following topics: • Purpose of Optimizer Statistics Collection The contents of tables and associated indexes change frequently, which can lead the optimizer to choose suboptimal execution plan for queries. To avoid potential performance issues, statistics must be kept current. • User Interfaces for Optimizer Statistics Management You can manage optimizer statistics either through Oracle Enterprise Manager Cloud Control (Cloud Control) or using PL/SQL on the command line. Related Topics • Gathering Optimizer Statistics Manually As an alternative or supplement to automatic statistics gathering, you can use the DBMS_STATS package to gather statistics manually. 12-1 Chapter 12 About Optimizer Statistics Collection 12.1.1 Purpose of Optimizer Statistics Collection The contents of tables and associated indexes change frequently, which can lead the optimizer to choose suboptimal execution plan for queries. To avoid potential performance issues, statistics must be kept current. To minimize DBA involvement, Oracle Database automatically gathers optimizer statistics at various times. Some automatic options are configurable, such enabling AutoTask to run DBMS_STATS. 12.1.2 User Interfaces for Optimizer Statistics Management You can manage optimizer statistics either through Oracle Enterprise Manager Cloud Control (Cloud Control) or using PL/SQL on the command line. This section contains the following topics: • Graphical Interface for Optimizer Statistics Management The Manage Optimizer Statistics page in Cloud Control is a GUI that enables you to manage optimizer statistics. • Command-Line Interface for Optimizer Statistics Management The DBMS_STATS package performs most optimizer statistics tasks. 12.1.2.1 Graphical Interface for Optimizer Statistics Management The Manage Optimizer Statistics page in Cloud Control is a GUI that enables you to manage optimizer statistics. This section contains the following topics: • Accessing the Database Home Page in Cloud Control Oracle Enterprise Manager Cloud Control enables you to manage multiple databases within a single GUI-based framework. • Accessing the Optimizer Statistics Console You can perform most necessary tasks relating to optimizer statistics through pages linked to by the Optimizer Statistics Console page. 12.1.2.1.1 Accessing the Database Home Page in Cloud Control Oracle Enterprise Manager Cloud Control enables you to manage multiple databases within a single GUI-based framework. To access a database home page using Cloud Control: 1. Log in to Cloud Control with the appropriate credentials. 2. Under the Targets menu, select Databases. 3. In the list of database targets, select the target for the Oracle Database instance that you want to administer. 4. If prompted for database credentials, then enter the minimum credentials necessary for the tasks you intend to perform. 12-2 Chapter 12 Setting Optimizer Statistics Preferences See Also: Cloud Control online help 12.1.2.1.2 Accessing the Optimizer Statistics Console You can perform most necessary tasks relating to optimizer statistics through pages linked to by the Optimizer Statistics Console page. To manage optimizer statistics using Cloud Control: 1. In Cloud Control, access the Database Home page. 2. From the Performance menu, select SQL, then Optimizer Statistics. The Optimizer Statistics Console appears. See Also: Online Help for Oracle Enterprise Manager Cloud Control 12.1.2.2 Command-Line Interface for Optimizer Statistics Management The DBMS_STATS package performs most optimizer statistics tasks. To enable and disable automatic statistics gathering, use the DBMS_AUTO_TASK_ADMIN PL/SQL package. See Also: Oracle Database PL/SQL Packages and Types Reference to learn how to use DBMS_STATS and DBMS_AUTO_TASK_ADMIN 12.2 Setting Optimizer Statistics Preferences This topic explains how to set optimizer statistics defaults using DBMS_STATS.SET_*_PREFS procedures. This section contains the following topics: • About Optimizer Statistics Preferences The optimizer statistics preferences set the default values of the parameters used by automatic statistics collection and the DBMS_STATS statistics gathering procedures. • Setting Global Optimizer Statistics Preferences Using Cloud Control A global preference applies to any object in the database that does not have an existing table preference. You can set optimizer statistics preferences at the global level using Cloud Control. 12-3 Chapter 12 Setting Optimizer Statistics Preferences • Setting Object-Level Optimizer Statistics Preferences Using Cloud Control You can set optimizer statistics preferences at the database, schema, and table level using Cloud Control. • Setting Optimizer Statistics Preferences from the Command Line If you do not use Cloud Control to set optimizer statistics preferences, then you can invoke the DBMS_STATS procedures from the command line. 12.2.1 About Optimizer Statistics Preferences The optimizer statistics preferences set the default values of the parameters used by automatic statistics collection and the DBMS_STATS statistics gathering procedures. You can set optimizer statistics preferences at the table, schema, database (all tables), and global level. A global preference refers to tables with no preferences and any tables created in the future. The procedure names follow the form SET_*_PREFS. This section contains the following topics: • Purpose of Optimizer Statistics Preferences Preferences enable you to maintain optimizer statistics automatically when some objects require settings that differ from the default. • DBMS_STATS Procedures for Setting Statistics Preferences The DBMS_STATS.SET_*_PREFS procedures change the defaults of parameters used by the DBMS_STATS.GATHER_*_STATS procedures. To query the current preferences, use the DBMS_STATS.GET_PREFS function. • Statistics Preference Overrides The preference_overrides_parameter statistics preference determines whether, when gathering optimizer statistics, to override the input value of a parameter with the statistics preference. In this way, you control when the database honors a parameter value passed to the statistics gathering procedures. • Setting Statistics Preferences: Example This example illustrates the relationship between SET_TABLE_PREFS, SET_SCHEMA_STATS, and SET_DATABASE_PREFS. 12.2.1.1 Purpose of Optimizer Statistics Preferences Preferences enable you to maintain optimizer statistics automatically when some objects require settings that differ from the default. Preferences give you more granular control over how Oracle Database gathers statistics. You can set optimizer statistics preferences at the following levels: • Table • Schema • Database (all tables) • Global (tables with no preferences and any tables created in the future) The DBMS_STATS procedures for setting preference have names of the form SET_*_PREFS. 12-4 Chapter 12 Setting Optimizer Statistics Preferences 12.2.1.2 DBMS_STATS Procedures for Setting Statistics Preferences The DBMS_STATS.SET_*_PREFS procedures change the defaults of parameters used by the DBMS_STATS.GATHER_*_STATS procedures. To query the current preferences, use the DBMS_STATS.GET_PREFS function. When setting statistics preferences, the order of precedence is: 1. Table preference (set for a specific table, all tables in a schema, or all tables in the database) 2. Global preference 3. Default preference The following table summarizes the relevant DBMS_STATS procedures. Table 12-1 DBMS_STATS Procedures for Setting Optimizer Statistics Preferences Procedure Scope SET_TABLE_PREFS Specified table only. SET_SCHEMA_PREFS All existing tables in the specified schema. This procedure calls SET_TABLE_PREFS for each table in the specified schema. Calling SET_SCHEMA_PREFS does not affect any new tables created after it has been run. New tables use the GLOBAL_PREF values for all parameters. SET_DATABASE_PREFS All user-defined schemas in the database. You can include system-owned schemas such as SYS and SYSTEM by setting the ADD_SYS parameter to true. This procedure calls SET_TABLE_PREFS for each table in the specified schema. Calling SET_DATABASE_PREFS does not affect any new objects created after it has been run. New objects use the GLOBAL_PREF values for all parameters. SET_GLOBAL_PREFS Any table that does not have an existing table preference. All parameters default to the global setting unless a table preference is set or the parameter is explicitly set in the DBMS_STATS.GATHER_*_STATS statement. Changes made by SET_GLOBAL_PREFS affect any new objects created after it runs. New objects use the SET_GLOBAL_PREFS values for all parameters. With SET_GLOBAL_PREFS, you can set a default value for the parameter AUTOSTATS_TARGET. This additional parameter controls which objects the automatic statistic gathering job running in the nightly maintenance window affects. Possible values for AUTOSTATS_TARGET are ALL, ORACLE, and AUTO (default). You can only set the CONCURRENT preference at the global level. You cannot set the preference INCREMENTAL_LEVEL using SET_GLOBAL_PREFS. 12-5 Chapter 12 Setting Optimizer Statistics Preferences See Also: • "About Concurrent Statistics Gathering" • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS procedures for setting optimizer statistics 12.2.1.3 Statistics Preference Overrides The preference_overrides_parameter statistics preference determines whether, when gathering optimizer statistics, to override the input value of a parameter with the statistics preference. In this way, you control when the database honors a parameter value passed to the statistics gathering procedures. When preference_overrides_parameter is set to FALSE (default), the input values for statistics gathering procedures are honored. When set to TRUE, the input values are ignored. Set the preference_overrides_parameter preference using the SET_TABLE_PREFS, SET_SCHEMA_PREFS, or SET_GLOBAL_PREFS procedures in DBMS_STATS. Regardless of whether preference_overrides_parameter is set, the database uses the same order of precedence for setting statistics: 1. Table preference (set for a specific table, all tables in a schema, or all tables in the database) 2. Global preference 3. Default preference Example 12-1 Overriding Statistics Preferences at the Table Level In this example, legacy scripts set estimate_percent explicitly rather than using the recommended AUTO_SAMPLE_SIZE. Your goal is to prevent users from using these scripts to set preferences on the sh.costs table. Table 12-2 Overriding Statistics Preferences at the Table Level Action Description SQL> SELECT DBMS_STATS.GET_PREFS ('estimate_percent', 'sh','costs') AS "STAT_PREFS" FROM DUAL; No preference for estimate_percent is set for sh.costs or at the global level, so the preference defaults to AUTO_SAMPLE_SIZE. STAT_PREFS ---------DBMS_STATS.AUTO_SAMPLE_SIZE SQL> EXEC DBMS_STATS.SET_TABLE_PREFS ('sh', 'costs', 'preference_overrides_parameter', 'true'); PL/SQL procedure successfully completed. By default, Oracle Database accepts preferences that are passed to the GATHER_*_STATS procedures. To override these parameters, you use SET_TABLE_PREFS to set the preference_overrides_parameter preference to true for the costs table only. 12-6 Chapter 12 Setting Optimizer Statistics Preferences Table 12-2 (Cont.) Overriding Statistics Preferences at the Table Level Action Description SQL> EXEC DBMS_STATS.GATHER_TABLE_STATS ('sh', 'costs', estimate_percent=>100); PL/SQL procedure successfully completed. Example 12-2 You attempt to set estimate_percent to 100 when gathering statistics for sh.costs. However, because preference_overrides_parameter is true for this table, Oracle Database gathers statistics using AUTO_SAMPLE_SIZE, which is the default. Overriding Statistics Preferences at the Global Level In this example, you set estimate_percent to 5 at the global level, which mean this preference applies to every table in the database that does not have a table preference set. You then set an override on the sh.sales table, which does not have a table-level preference set, to prevent users from overriding the global setting in their scripts. Table 12-3 Overriding Statistics Preferences at the Global Level Action Description SQL> EXEC DBMS_STATS.SET_GLOBAL_PREFS ('estimate_percent', '5'); PL/SQL procedure successfully completed. SQL> EXEC DBMS_STATS.SET_TABLE_PREFS ('sh', 'sales', 'preference_overrides_parameter', 'true'); You use the SET_GLOBAL_PREFS procedure to set the estimate_percent preference to 5 for every table in the database that does not have a table preference set. Because sh.costs does not have a preference set, the global setting applies to this table. You use SET_TABLE_PREFS to set the preference_overrides_parameter preference to true for the sh.sales table only. PL/SQL procedure successfully completed. SQL> EXEC DBMS_STATS.GATHER_TABLE_STATS ('sh', 'costs', estimate_percent=>10); PL/SQL procedure successfully completed. You attempt to set estimate_percent to 10 when gathering statistics for sh.sales. However, because preference_overrides_parameter is true for this table, and because a global preference is defined, Oracle Database gathers statistics using the global setting of 5. See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS procedures for setting optimizer statistics 12.2.1.4 Setting Statistics Preferences: Example This example illustrates the relationship between SET_TABLE_PREFS, SET_SCHEMA_STATS, and SET_DATABASE_PREFS. 12-7 Chapter 12 Setting Optimizer Statistics Preferences Table 12-4 Changing Preferences for Statistics Gathering Procedures Action SQL> SELECT DBMS_STATS.GET_PREFS ('incremental', 'sh','costs') AS "STAT_PREFS" FROM DUAL; Description You query the INCREMENTAL preference for costs and determine that it is set to true. STAT_PREFS ---------TRUE SQL> EXEC DBMS_STATS.SET_TABLE_PREFS ('sh', 'costs', 'incremental', 'false'); You use SET_TABLE_PREFS to set the INCREMENTAL preference to false for the costs table only. PL/SQL procedure successfully completed. SQL> SELECT DBMS_STATS.GET_PREFS ('incremental', 'sh', 'costs') AS "STAT_PREFS" FROM DUAL; You query the INCREMENTAL preference for costs and confirm that it is set to false. STAT_PREFS ---------FALSE SQL> EXEC DBMS_STATS.SET_SCHEMA_PREFS ('sh', 'incremental', 'true'); You use SET_SCHEMA_PREFS to set the INCREMENTAL preference to true for every table in the sh schema, including costs. PL/SQL procedure successfully completed. SQL> SELECT DBMS_STATS.GET_PREFS ('incremental', 'sh', 'costs') AS "STAT_PREFS" FROM DUAL; You query the INCREMENTAL preference for costs and confirm that it is set to true. STAT_PREFS ---------TRUE SQL> EXEC DBMS_STATS.SET_DATABASE_PREFS ('incremental', 'false'); You use SET_DATABASE_PREFS to set the INCREMENTAL preference for all tables in all user-defined schemas to false. PL/SQL procedure successfully completed. SQL> SELECT DBMS_STATS.GET_PREFS ('incremental', 'sh', 'costs') AS "STAT_PREFS" FROM DUAL; You query the INCREMENTAL preference for costs and confirm that it is set to false. STAT_PREFS ---------FALSE 12-8 Chapter 12 Setting Optimizer Statistics Preferences 12.2.2 Setting Global Optimizer Statistics Preferences Using Cloud Control A global preference applies to any object in the database that does not have an existing table preference. You can set optimizer statistics preferences at the global level using Cloud Control. To set global optimizer statistics preferences using Cloud Control: 1. In Cloud Control, access the Database Home page. 2. From the Performance menu, select SQL, then Optimizer Statistics. The Optimizer Statistics Console appears. 3. Click Global Statistics Gathering Options. The Global Statistics Gathering Options page appears. 4. Make your desired changes, and click Apply. See Also: Online Help for Oracle Enterprise Manager Cloud Control 12.2.3 Setting Object-Level Optimizer Statistics Preferences Using Cloud Control You can set optimizer statistics preferences at the database, schema, and table level using Cloud Control. To set object-level optimizer statistics preferences using Cloud Control: 1. In Cloud Control, access the Database Home page. 2. From the Performance menu, select SQL, then Optimizer Statistics. The Optimizer Statistics Console appears. 3. Click Object Level Statistics Gathering Preferences. The Object Level Statistics Gathering Preferences page appears. 4. To modify table preferences for a table that has preferences set at the table level, do the following (otherwise, skip to the next step): a. Enter values in Schema and Table Name. Leave Table Name blank to see all tables in the schema. The page refreshes with the table names. b. Select the desired tables and click Edit Preferences. The General subpage of the Edit Preferences page appears. c. Change preferences as needed and click Apply. 12-9 Chapter 12 Setting Optimizer Statistics Preferences 5. To set preferences for a table that does not have preferences set at the table level, do the following (otherwise, skip to the next step): a. Click Add Table Preferences. The General subpage of the Add Table Preferences page appears. 6. b. In Table Name, enter the schema and table name. c. Change preferences as needed and click OK. To set preferences for a schema, do the following: a. Click Set Schema Tables Preferences. The General subpage of the Edit Schema Preferences page appears. b. In Schema, enter the schema name. c. Change preferences as needed and click OK. See Also: Online Help for Oracle Enterprise Manager Cloud Control 12.2.4 Setting Optimizer Statistics Preferences from the Command Line If you do not use Cloud Control to set optimizer statistics preferences, then you can invoke the DBMS_STATS procedures from the command line. Prerequisites This task has the following prerequisites: • To set the global or database preferences, you must have SYSDBA privileges, or both ANALYZE ANY DICTIONARY and ANALYZE ANY system privileges. • To set schema preferences, you must connect as owner, or have SYSDBA privileges, or have the ANALYZE ANY system privilege. • To set table preferences, you must connect as owner of the table or have the ANALYZE ANY system privilege. To set optimizer statistics preferences from the command line: 1. In SQL*Plus or SQL Developer, log in to the database as a user with the necessary privileges. 2. Optionally, call the DBMS_STATS.GET_PREFS procedure to see preferences set at the object level, or at the global level if a specific table is not set. For example, obtain the STALE_PERCENT parameter setting for the sh.sales table as follows: SELECT DBMS_STATS.GET_PREFS('STALE_PERCENT', 'SH', 'SALES') FROM DUAL; 3. Execute the appropriate procedure from Table 12-1, specifying the following parameters: 12-10 Chapter 12 Configuring Options for Dynamic Statistics • ownname - Set schema name (SET_TAB_PREFS and SET_SCHEMA_PREFS only) • tabname - Set table name (SET_TAB_PREFS only) • pname - Set parameter name • pvalue - Set parameter value • add_sys - Include system tables (optional, SET_DATABASE_PREFS only) The following example specifies that 13% of rows in sh.sales must change before the statistics on that table are considered stale: EXEC DBMS_STATS.SET_TABLE_PREFS('SH', 'SALES', 'STALE_PERCENT', '13'); 4. Optionally, query the *_TAB_STAT_PREFS view to confirm the change. For example, query DBA_TAB_STAT_PREFS as follows: COL OWNER FORMAT a5 COL TABLE_NAME FORMAT a15 COL PREFERENCE_NAME FORMAT a20 COL PREFERENCE_VALUE FORMAT a30 SELECT * FROM DBA_TAB_STAT_PREFS; Sample output appears as follows: OWNER ----OE SH TABLE_NAME --------------CUSTOMERS SALES PREFERENCE_NAME -------------------NO_INVALIDATE STALE_PERCENT PREFERENCE_VALUE -----------------------------DBMS_STATS.AUTO_INVALIDATE 13 See Also: Oracle Database PL/SQL Packages and Types Reference for descriptions of the parameter names and values for program units 12.3 Configuring Options for Dynamic Statistics Dynamic statistics are an optimization technique in which the database uses recursive SQL to scan a small random sample of the blocks in a table. The sample scan estimate predicate selectivities. Using these estimates, the database determines better default statistics for unanalyzed segments, and verifies its estimates. By default, when optimizer statistics are missing, stale, or insufficient, dynamic statistics automatically run recursive SQL during parsing to scan a small random sample of table blocks. This section contains the following topics: • About Dynamic Statistics Levels The dynamic statistics level controls both when the database gathers dynamic statistics, and the size of the sample that the optimizer uses to gather the statistics. • Setting Dynamic Statistics Levels Manually Determining a database-level setting that would be beneficial to all SQL statements can be difficult. 12-11 Chapter 12 Configuring Options for Dynamic Statistics • Disabling Dynamic Statistics In general, the best practice is not to incur the cost of dynamic statistics for queries whose compile times must be as fast as possible, for example, unrepeated OLTP queries. Related Topics • Supplemental Dynamic Statistics By default, when optimizer statistics are missing, stale, or insufficient, the database automatically gathers dynamic statistics during a parse. The database uses recursive SQL to scan a small random sample of table blocks. 12.3.1 About Dynamic Statistics Levels The dynamic statistics level controls both when the database gathers dynamic statistics, and the size of the sample that the optimizer uses to gather the statistics. Set the dynamic statistics level using either the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter or a statement hint. Note: Dynamic statistics were called dynamic sampling in releases earlier than Oracle Database 12c Release 1 (12.1). The following table describes the levels for dynamic statistics. Note the following: Table 12-5 • If OPTIMIZER_DYNAMIC_STATISTICS is TRUE, and if dynamic statistics are not disabled, then the database may choose to use dynamic statistics when a SQL statement uses parallel execution. • If OPTIMIZER_ADAPTIVE_STATISTICS is TRUE, then the optimizer uses dynamic statistics when relevant SQL plan directives exist. The database maintains the resulting statistics in the SQL plan directives store, making them available to other queries. Dynamic Statistics Levels Level When the Optimizer Uses Dynamic Statistics Sample Size (Blocks) 0 Do not use dynamic statistics. n/a 1 Use dynamic statistics for all tables that do not have statistics, but only if the following criteria are met: 32 • • • 2 At least one nonpartitioned table in the query does not have statistics. This table has no indexes. This table has more blocks than the number of blocks that would be used for dynamic statistics of this table. Use dynamic statistics if at least one table in the statement has no statistics. This is the default value. 64 12-12 Chapter 12 Configuring Options for Dynamic Statistics Table 12-5 (Cont.) Dynamic Statistics Levels Level When the Optimizer Uses Dynamic Statistics Sample Size (Blocks) 3 Use dynamic statistics if any of the following conditions is true: 64 • • 4 At least one table in the statement has no statistics. The statement has one or more expressions used in the WHERE clause predicates, for example, WHERE SUBSTR(CUSTLASTNAME, 1,3). Use dynamic statistics if any of the following conditions is true: • • • 64 At least one table in the statement has no statistics. The statement has one or more expressions used in the WHERE clause predicates, for example, WHERE SUBSTR(CUSTLASTNAME, 1,3). The statement uses complex predicates (an OR or AND operator between multiple predicates on the same table). 5 The criteria are identical to level 4, but the database uses a different sample size. 128 6 The criteria are identical to level 4, but the database uses a different sample size. 256 7 The criteria are identical to level 4, but the database uses a different sample size. 512 8 The criteria are identical to level 4, but the database uses a different sample size. 1024 9 The criteria are identical to level 4, but the database uses a different sample size. 4086 10 The criteria are identical to level 4, but the database uses a different sample size. All blocks 11 The database uses adaptive dynamic sampling automatically when Automatically determined the optimizer deems it necessary. See Also: • • "When the Database Samples Data" Oracle Database Reference to learn about the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter 12.3.2 Setting Dynamic Statistics Levels Manually Determining a database-level setting that would be beneficial to all SQL statements can be difficult. When setting the level for dynamic statistics, Oracle recommends setting the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter at the session level. Assumptions This tutorial assumes the following: 12-13 Chapter 12 Configuring Options for Dynamic Statistics • You want correct selectivity estimates for the following query, which has WHERE clause predicates on two correlated columns: SELECT FROM WHERE AND * sh.customers cust_city='Los Angeles' cust_state_province='CA'; • The preceding query uses serial processing. • The sh.customers table contains 932 rows that meet the conditions in the query. • You have gathered statistics on the sh.customers table. • You created an index on the cust_city and cust_state_province columns. • The OPTIMIZER_DYNAMIC_SAMPLING initialization parameter is set to the default level of 2. To set the dynamic statistics level manually: 1. In SQL*Plus or SQL Developer, log in to the database as a user with the necessary privileges. 2. Explain the execution plan as follows: EXPLAIN PLAN FOR SELECT * FROM sh.customers WHERE cust_city='Los Angeles' AND cust_state_province='CA'; 3. Query the plan as follows: SET LINESIZE 130 SET PAGESIZE 0 SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY); The output appears below (the example has been reformatted to fit on the page): ------------------------------------------------------------------------------|Id| Operation | Name |Rows|Bytes|Cost | Time | ------------------------------------------------------------------------------| 0| SELECT STATEMENT | | 53| 9593|53(0)|00:00:01| | 1| TABLE ACCESS BY INDEX ROWID|CUSTOMERS | 53| 9593|53(0)|00:00:01| |*2| INDEX RANGE SCAN |CUST_CITY_STATE_IND| 53| 9593| 3(0)|00:00:01| ------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------2 - access("CUST_CITY"='Los Angeles' AND "CUST_STATE_PROVINCE"='CA') The columns in the WHERE clause have a real-world correlation, but the optimizer is not aware that Los Angeles is in California and assumes both predicates reduce the number of rows returned. Thus, the table contains 932 rows that meet the conditions, but the optimizer estimates 53, as shown in bold. If the database had used dynamic statistics for this plan, then the Note section of the plan output would have indicated this fact. The optimizer did not use dynamic statistics because the statement executed serially, standard statistics exist, and the parameter OPTIMIZER_DYNAMIC_SAMPLING is set to the default of 2. 12-14 Chapter 12 Configuring Options for Dynamic Statistics 4. Set the dynamic statistics level to 4 in the session using the following statement: ALTER SESSION SET OPTIMIZER_DYNAMIC_SAMPLING=4; 5. Explain the plan again: EXPLAIN PLAN FOR SELECT * FROM sh.customers WHERE cust_city='Los Angeles' AND cust_state_province='CA'; The new plan shows a more accurate estimate of the number of rows, as shown by the value 932 in bold: PLAN_TABLE_OUTPUT --------------------------------------------------------------------------Plan hash value: 2008213504 --------------------------------------------------------------------------| Id | Operation | Name |Rows | Bytes |Cost (%CPU)|Time | --------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 932 | 271K| 406 (1)| 00:00:05 | |* 1 | TABLE ACCESS FULL| CUSTOMERS | 932 | 271K| 406 (1)| 00:00:05 | --------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------1 - filter("CUST_CITY"='Los Angeles' AND "CUST_STATE_PROVINCE"='CA') Note ----- dynamic statistics used for this statement (level=4) The note at the bottom of the plan indicates that the sampling level is 4. The additional dynamic statistics made the optimizer aware of the real-world relationship between the cust_city and cust_state_province columns, thereby enabling it to produce a more accurate estimate for the number of rows: 932 rather than 53. See Also: • • Oracle Database SQL Language Reference to learn about setting sampling levels with the DYNAMIC_SAMPLING hint Oracle Database Reference to learn about the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter 12-15 Chapter 12 Managing SQL Plan Directives 12.3.3 Disabling Dynamic Statistics In general, the best practice is not to incur the cost of dynamic statistics for queries whose compile times must be as fast as possible, for example, unrepeated OLTP queries. To disable dynamic statistics at the session level: 1. Connect SQL*Plus to the database with the appropriate privileges. 2. Set the dynamic statistics level to 0. For example, run the following statement: ALTER SESSION SET OPTIMIZER_DYNAMIC_SAMPLING=0; See Also: Oracle Database Reference to learn about the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter 12.4 Managing SQL Plan Directives A SQL plan directive is additional information and instructions that the optimizer can use to generate a more optimal plan. A directive informs the database that the optimizer is misestimate cardinalities of certain types of predicates, and alerts DBMS_STATS to gather additional statistics in the future. Thus, directives have an effect on statistics gathering. The database automatically creates and manages SQL plan directives in the SGA, and then periodically writes them to the data dictionary. If the directives are not used within 53 weeks, then the database automatically purges them. You can use DBMS_SPD procedures and functions to alter, save, drop, and transport directives manually. The following table lists some of the more commonly used procedures and functions. Table 12-6 DBMS_SPD Procedures Procedure Description FLUSH_SQL_PLAN_DIRECTIVE Forces the database to write directives from memory to persistent storage in the SYSAUX tablespace. 12-16 Chapter 12 Managing SQL Plan Directives Table 12-6 (Cont.) DBMS_SPD Procedures Procedure Description DROP_SQL_PLAN_DIRECTIVE Drops a SQL plan directive. If a directive that triggers dynamic sampling is creating unacceptable performance overhead, then you may want to remove it manually. If a SQL plan directive is dropped manually or automatically, then the database can re-create it. To prevent its re-creation, you can use DBMS_SPM.ALTER_SQL_PLAN_DIRECTIVE to do the following: • • Disable the directive by setting ENABLED to NO Prevent the directive from being dropped by setting AUTO_DROP to NO To disable SQL plan directives, set OPTIMIZER_ADAPTIVE_STATISTICS to FALSE. Prerequisites You must have the Administer SQL Management Object privilege to execute the DBMS_SPD APIs. Assumptions This tutorial assumes that you want to do the following: • Write all directives for the sh schema to persistent storage. • Delete all directives for the sh schema. To write and then delete all sh schema plan directives: 1. In SQL*Plus or SQL Developer, log in to the database as a user with the necessary privileges. 2. Force the database to write the SQL plan directives to disk. For example, execute the following DBMS_SPD program: BEGIN DBMS_SPD.FLUSH_SQL_PLAN_DIRECTIVE; END; / 3. Query the data dictionary for information about existing directives in the sh schema. Example 12-3 queries the data dictionary for information about the directive. 4. Delete the existing SQL plan directive for the sh schema. The following PL/SQL program unit deletes the SQL plan directive with the ID 1484026771529551585: BEGIN DBMS_SPD.DROP_SQL_PLAN_DIRECTIVE ( directive_id => 1484026771529551585 ); END; / 12-17 Chapter 12 Managing SQL Plan Directives Example 12-3 Display Directives for sh Schema This example shows SQL plan directives, and the results of SQL plan directive dynamic sampling queries. SELECT TO_CHAR(d.DIRECTIVE_ID) dir_id, o.OWNER, o.OBJECT_NAME, o.SUBOBJECT_NAME col_name, o.OBJECT_TYPE object, d.TYPE, d.STATE, d.REASON FROM DBA_SQL_PLAN_DIRECTIVES d, DBA_SQL_PLAN_DIR_OBJECTS o WHERE d.DIRECTIVE_ID=o.DIRECTIVE_ID AND o.OWNER IN ('SH') ORDER BY 1,2,3,4,5; DIR_ID OWN OBJECT_NA COL_NAME OBJECT TYPE STATE ------------------- --- --------- ---------- ------- -------- ---------1484026771529551585 SH CUSTOMERS COUNTRY_ID COLUMN DYNAMIC_ SUPERSEDED SAMPLING 1484026771529551585 SH CUSTOMERS CUST_STATE_ COLUMN DYNAMIC_ SUPERSEDED PROVINCE SAMPLING 1484026771529551585 SH CUSTOMERS 9781501826140511330 SH dyg4msnst5 9872337207064898539 SH TIMES 9781501826140511330 SH 2nk1v0fdx0 TABLE DYNAMIC_ SUPERSEDED SAMPLING SQL STA DYNAMIC_ TEMENT SAMPLING _RESULT TABLE DYNAMIC_ SAMPLING _RESULT SQL STA DYNAMIC_ TEMENT SAMPLING _RESULT USABLE USABLE USABLE REASON -----------SINGLE TABLE CARDINALITY MISESTIMATE SINGLE TABLE CARDINALITY MISESTIMATE SINGLE TABLE CARDINALITY MISESTIMATE VERIFY CARDINALITY ESTIMATE VERIFY CARDINALITY ESTIMATE VERIFY CARDINALITY ESTIMATE See Also: • "SQL Plan Directives" • Oracle Database PL/SQL Packages and Types Reference for complete syntax and semantics for the DBMS_SPD package. • Oracle Database Reference to learn about DBA_SQL_PLAN_DIRECTIVES 12-18 13 Gathering Optimizer Statistics This chapter explains how to use the DBMS_STATS.GATHER_*_STATS program units. This chapter contains the following topics: • Configuring Automatic Optimizer Statistics Collection Oracle Database can gather optimizer statistics automatically. • Gathering Optimizer Statistics Manually As an alternative or supplement to automatic statistics gathering, you can use the DBMS_STATS package to gather statistics manually. • Gathering System Statistics Manually System statistics describe hardware characteristics, such as I/O and CPU performance and utilization, to the optimizer. • Running Statistics Gathering Functions in Reporting Mode You can run the DBMS_STATS statistics gathering procedures in reporting mode. See Also: • "Optimizer Statistics Concepts" • "Query Optimizer Concepts " • Oracle Database PL/SQL Packages and Types Reference to learn about DBMS_STATS.GATHER_TABLE_STATS 13.1 Configuring Automatic Optimizer Statistics Collection Oracle Database can gather optimizer statistics automatically. This section contains the following topics: • About Automatic Optimizer Statistics Collection The automated maintenance tasks infrastructure (known as AutoTask) schedules tasks to run automatically in Oracle Scheduler windows known as maintenance windows. • Configuring Automatic Optimizer Statistics Collection Using Cloud Control You can enable and disable all automatic maintenance tasks, including automatic optimizer statistics collection, using Cloud Control. • Configuring Automatic Optimizer Statistics Collection from the Command Line If you do not use Cloud Control to configure automatic optimizer statistics collection, then you must use the command line. 13-1 Chapter 13 Configuring Automatic Optimizer Statistics Collection 13.1.1 About Automatic Optimizer Statistics Collection The automated maintenance tasks infrastructure (known as AutoTask) schedules tasks to run automatically in Oracle Scheduler windows known as maintenance windows. By default, one window is scheduled for each day of the week. Automatic optimizer statistics collection runs as part of AutoTask. By default, the collection runs in all predefined maintenance windows. Note: Data visibility and privilege requirements may differ when using automatic optimizer statistics collection with pluggable databases. To collect the optimizer statistics, the database calls an internal procedure that operates similarly to the GATHER_DATABASE_STATS procedure with the GATHER AUTO option. Automatic statistics collection honors all preferences set in the database. The principal difference between manual and automatic collection is that the latter prioritizes database objects that need statistics. Before the maintenance window closes, automatic collection assesses all objects and prioritizes objects that have no statistics or very old statistics. Note: When gathering statistics manually, you can reproduce the object prioritization of automatic collection by using the DBMS_AUTO_TASK_IMMEDIATE package. This package runs the same statistics gathering job that is executed during the automatic nightly statistics gathering job. See Also: Oracle Database Administrator’s Guide for a table that summarizes how manageability features work in a container database (CDB) 13.1.2 Configuring Automatic Optimizer Statistics Collection Using Cloud Control You can enable and disable all automatic maintenance tasks, including automatic optimizer statistics collection, using Cloud Control. The default window timing works well for most situations. However, you may have operations such as bulk loads that occur during the window. In such cases, to avoid potential conflicts that result from operations occurring at the same time as automatic statistics collection, Oracle recommends that you change the window accordingly. 13-2 Chapter 13 Configuring Automatic Optimizer Statistics Collection Prerequisites Access the Database Home page, as described in "Accessing the Database Home Page in Cloud Control." To control automatic optimizer statistics collection using Cloud Control: 1. From the Administration menu, select Oracle Scheduler, then Automated Maintenance Tasks. The Automated Maintenance Tasks page appears. This page shows the predefined tasks. To retrieve information about each task, click the corresponding link for the task. 2. Click Configure. The Automated Maintenance Tasks Configuration page appears. By default, automatic optimizer statistics collection executes in all predefined maintenance windows in MAINTENANCE_WINDOW_GROUP. 3. Perform the following steps: a. In the Task Settings section for Optimizer Statistics Gathering, select either Enabled or Disabled to enable or disable an automated task. Note: Oracle strongly recommends that you not disable automatic statistics gathering because it is critical for the optimizer to generate optimal plans for queries against dictionary and user objects. If you disable automatic collection, ensure that you have a good manual statistics collection strategy for dictionary and user schemas. b. To disable statistics gathering for specific days in the week, check the appropriate box next to the window name. c. To change the characteristics of a window group, click Edit Window Group. d. To change the times for a window, click the name of the window (for example, MONDAY_WINDOW), and then in the Schedule section, click Edit. The Edit Window page appears. 13-3 Chapter 13 Configuring Automatic Optimizer Statistics Collection In this page, you can change the parameters such as duration and start time for window execution. e. Click Apply. See Also: Online Help for Oracle Enterprise Manager Cloud Control 13.1.3 Configuring Automatic Optimizer Statistics Collection from the Command Line If you do not use Cloud Control to configure automatic optimizer statistics collection, then you must use the command line. You have the following options: • Run the ENABLE or DISABLE procedure in the DBMS_AUTO_TASK_ADMIN PL/SQL package. This package is the recommended command-line technique. For both the ENABLE and DISABLE procedures, you can specify a particular maintenance window with the window_name parameter. • Set the STATISTICS_LEVEL initialization level to BASIC to disable collection of all advisories and statistics, including Automatic SQL Tuning Advisor. 13-4 Chapter 13 Configuring Automatic Optimizer Statistics Collection Note: Because monitoring and many automatic features are disabled, Oracle strongly recommends that you do not set STATISTICS_LEVEL to BASIC. To control automatic statistics collection using DBMS_AUTO_TASK_ADMIN: 1. In SQL*Plus or SQL Developer, log in to the database as a user with administrative privileges. 2. Do one of the following: • To enable the automated task, execute the following PL/SQL block: BEGIN DBMS_AUTO_TASK_ADMIN.ENABLE ( client_name => 'auto optimizer stats collection' , operation => NULL , window_name => NULL ); END; / • To disable the automated task, execute the following PL/SQL block: BEGIN DBMS_AUTO_TASK_ADMIN.DISABLE ( client_name => 'auto optimizer stats collection' , operation => NULL , window_name => NULL ); END; / 3. Query the data dictionary to confirm the change. For example, query DBA_AUTOTASK_CLIENT as follows: COL CLIENT_NAME FORMAT a31 SELECT CLIENT_NAME, STATUS FROM DBA_AUTOTASK_CLIENT WHERE CLIENT_NAME = 'auto optimizer stats collection'; Sample output appears as follows: CLIENT_NAME STATUS ------------------------------- -------auto optimizer stats collection ENABLED To change the window attributes for automatic statistics collection: 1. Connect SQL*Plus to the database with administrator privileges. 2. Change the attributes of the maintenance window as needed. For example, to change the Monday maintenance window so that it starts at 5 a.m., execute the following PL/SQL program: BEGIN DBMS_SCHEDULER.SET_ATTRIBUTE ( 'MONDAY_WINDOW' 13-5 Chapter 13 Gathering Optimizer Statistics Manually , 'repeat_interval' , 'freq=daily;byday=MON;byhour=05;byminute=0;bysecond=0' ); END; / See Also: • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_AUTO_TASK_ADMIN package • Oracle Database Reference to learn about the STATISTICS_LEVEL initialization parameter 13.2 Gathering Optimizer Statistics Manually As an alternative or supplement to automatic statistics gathering, you can use the DBMS_STATS package to gather statistics manually. This section contains the following topics: • About Manual Statistics Collection with DBMS_STATS Use the DBMS_STATS package to manipulate optimizer statistics. You can gather statistics on objects and columns at various levels of granularity: object, schema, and database. You can also gather statistics for the physical system. • Guidelines for Gathering Optimizer Statistics Manually In most cases, automatic statistics collection is sufficient for database objects modified at a moderate speed. • Determining When Optimizer Statistics Are Stale Stale statistics on a table do not accurately reflect its data. To help you determine when a database object needs new statistics, the database provides a table monitoring facility. • Gathering Schema and Table Statistics Use GATHER_TABLE_STATS to collect table statistics, and GATHER_SCHEMA_STATS to collect statistics for all objects in a schema. • Gathering Statistics for Fixed Objects Fixed objects are dynamic performance tables and their indexes. These objects record current database activity. • Gathering Statistics for Volatile Tables Using Dynamic Statistics Statistics for volatile tables, which are tables modified significantly during the day, go stale quickly. For example, a table may be deleted or truncated, and then rebuilt. • Gathering Optimizer Statistics Concurrently Oracle Database can gather statistics on multiple tables or partitions concurrently. • Gathering Incremental Statistics on Partitioned Objects Incremental statistics scan only changed partitions. When gathering statistics on large partitioned table by deriving global statistics from partition-level statistics, incremental statistics maintenance improves performance. 13-6 Chapter 13 Gathering Optimizer Statistics Manually See Also: • "Configuring Automatic Optimizer Statistics Collection" • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS package 13.2.1 About Manual Statistics Collection with DBMS_STATS Use the DBMS_STATS package to manipulate optimizer statistics. You can gather statistics on objects and columns at various levels of granularity: object, schema, and database. You can also gather statistics for the physical system. The following table summarizes the DBMS_STATS procedures for gathering optimizer statistics. This package does not gather statistics for table clusters. However, you can gather statistics on individual tables in a table cluster. Table 13-1 DBMS_STATS Procedures for Gathering Optimizer Statistics Procedure Purpose GATHER_INDEX_STATS Collects index statistics GATHER_TABLE_STATS Collects table, column, and index statistics GATHER_SCHEMA_STATS Collects statistics for all objects in a schema GATHER_DICTIONARY_STATS Collects statistics for all system schemas, including SYS and SYSTEM, and other optional schemas, such as CTXSYS and DRSYS GATHER_DATABASE_STATS Collects statistics for all objects in a database When the OPTIONS parameter is set to GATHER STALE or GATHER AUTO, the GATHER_SCHEMA_STATS and GATHER_DATABASE_STATS procedures gather statistics for any table that has stale statistics and any table that is missing statistics. If a monitored table has been modified more than 10%, then the database considers these statistics stale and gathers them again. Note: As explained in "Configuring Automatic Optimizer Statistics Collection", you can configure a nightly job to gather statistics automatically. See Also: • "Gathering System Statistics Manually" • Oracle Database PL/SQL Packages and Types Reference to learn more about the DBMS_STATS package 13-7 Chapter 13 Gathering Optimizer Statistics Manually 13.2.2 Guidelines for Gathering Optimizer Statistics Manually In most cases, automatic statistics collection is sufficient for database objects modified at a moderate speed. Automatic collection may sometimes be inadequate or unavailable, as shown in the following table. Table 13-2 Reasons for Gathering Statistics Manually Issue To Learn More You perform certain types of bulk load and cannot wait for the maintenance window to collect statistics because queries must be executed immediately. "Online Statistics Gathering for Bulk Loads" During a nonrepresentative workload, "Gathering Statistics for Fixed Objects" automatic statistics collection gathers statistics for fixed tables. Automatic statistics collection does not gather system statistics. "Gathering System Statistics Manually" Volatile tables are being deleted or truncated, and then rebuilt during the day. "Gathering Statistics for Volatile Tables Using Dynamic Statistics" This section offers guidelines for typical situations in which you may choose to gather statistically manually: • Guideline for Setting the Sample Size In the context of optimizer statistics, sampling is the gathering of statistics from a random subset of table rows. By enabling the database to avoid full table scans and sorts of entire tables, sampling minimizes the resources necessary to gather statistics. • Guideline for Gathering Statistics in Parallel By default, the database gathers statistics with the parallelism degree specified at the table or index level. • Guideline for Partitioned Objects For partitioned tables and indexes, DBMS_STATS can gather separate statistics for each partition and global statistics for the entire table or index. • Guideline for Frequently Changing Objects When tables are frequently modified, gather statistics often enough so that they do not go stale, but not so often that collection overhead degrades performance. • Guideline for External Tables Because the database does not permit data manipulation against external tables, the database never marks statistics on external tables as stale. If new statistics are required for an external table, for example, because the underlying data files change, then regather the statistics. 13-8 Chapter 13 Gathering Optimizer Statistics Manually 13.2.2.1 Guideline for Setting the Sample Size In the context of optimizer statistics, sampling is the gathering of statistics from a random subset of table rows. By enabling the database to avoid full table scans and sorts of entire tables, sampling minimizes the resources necessary to gather statistics. The database gathers the most accurate statistics when it processes all rows in the table, which is a 100% sample. However, larger sample sizes increase the time of statistics gathering operations. The challenge is determining a sample size that provides accurate statistics in a reasonable time. DBMS_STATS uses sampling when a user specifies the parameter ESTIMATE_PERCENT, which controls the percentage of the rows in the table to sample. To maximize performance gains while achieving necessary statistical accuracy, Oracle recommends that the ESTIMATE_PERCENT parameter use the default setting of DBMS_STATS.AUTO_SAMPLE_SIZE. In this case, Oracle Database chooses the sample size automatically. This setting enables the use of the following: • A hash-based algorithm that is much faster than sampling This algorithm reads all rows and produces statistics that are nearly as accurate as statistics from a 100% sample. The statistics computed using this technique are deterministic. • Incremental statistics • Concurrent statistics • New histogram types The DBA_TABLES.SAMPLE_SIZE column indicates the actual sample size used to gather statistics. See Also: • "Hybrid Histograms" • Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_STATS.AUTO_SAMPLE_SIZE 13.2.2.2 Guideline for Gathering Statistics in Parallel By default, the database gathers statistics with the parallelism degree specified at the table or index level. You can override this setting with the degree argument to the DBMS_STATS gathering procedures. Oracle recommends setting degree to DBMS_STATS.AUTO_DEGREE. This setting enables the database to choose an appropriate degree of parallelism based on the object size and the settings for the parallelism-related initialization parameters. The database can gather most statistics serially or in parallel. However, the database does not gather some index statistics in parallel, including cluster indexes, domain indexes, and bitmap join indexes. The database can use sampling when gathering parallel statistics. 13-9 Chapter 13 Gathering Optimizer Statistics Manually Note: Do not confuse gathering statistics in parallel with gathering statistics concurrently. See Also: • "About Concurrent Statistics Gathering" • Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_STATS.AUTO_DEGREE 13.2.2.3 Guideline for Partitioned Objects For partitioned tables and indexes, DBMS_STATS can gather separate statistics for each partition and global statistics for the entire table or index. Similarly, for composite partitioning, DBMS_STATS can gather separate statistics for subpartitions, partitions, and the entire table or index. To determine the type of partitioning statistics to be gathered, specify the granularity argument to the DBMS_STATS procedures. Oracle recommends setting granularity to the default value of AUTO to gather subpartition, partition, or global statistics, depending on partition type. The ALL setting gathers statistics for all types. See Also: "Gathering Incremental Statistics on Partitioned Objects" 13.2.2.4 Guideline for Frequently Changing Objects When tables are frequently modified, gather statistics often enough so that they do not go stale, but not so often that collection overhead degrades performance. You may only need to gather new statistics every week or month. The best practice is to use a script or job scheduler to regularly run the DBMS_STATS.GATHER_SCHEMA_STATS and DBMS_STATS.GATHER_DATABASE_STATS procedures. 13.2.2.5 Guideline for External Tables Because the database does not permit data manipulation against external tables, the database never marks statistics on external tables as stale. If new statistics are required for an external table, for example, because the underlying data files change, then regather the statistics. For external tables, use the same DBMS_STATS procedures that you use for internal tables. Note that the scanrate parameter of DBMS_STATS.SET_TABLE_STATS and DBMS_STATS.GET_TABLE_STATS specifies the rate (in MB/s) at which Oracle Database 13-10 Chapter 13 Gathering Optimizer Statistics Manually scans data in tables, and is relevant only for external tables. The SCAN_RATE column appears in the DBA_TAB_STATISTICS and DBA_TAB_PENDING_STATS data dictionary views. See Also: • • "Creating Artificial Optimizer Statistics for Testing" Oracle Database PL/SQL Packages and Types Reference to learn about SET_TABLE_STATS and GET_TABLE_STATS • Oracle Database Reference to learn about the DBA_TAB_STATISTICS view 13.2.3 Determining When Optimizer Statistics Are Stale Stale statistics on a table do not accurately reflect its data. To help you determine when a database object needs new statistics, the database provides a table monitoring facility. Monitoring tracks the approximate number of DML operations on a table and whether the table has been truncated since the most recent statistics collection. To check whether statistics are stale, query the STALE_STATS column in DBA_TAB_STATISTICS and DBA_IND_STATISTICS. This column is based on data in the DBA_TAB_MODIFICATIONS view and the STALE_PERCENT preference for DBMS_STATS. Note: Starting in Oracle Database 12c Release 2 (12.2), you no longer need to use DBMS_STATS.FLUSH_DATABASE_MONITORING_INFO to ensure that view metadata is current. The statistics shown in the DBA_TAB_STATISTICS, DBA_IND_STATISTICS, and DBA_TAB_MODIFICATIONS views are obtained from both disk and memory. The STALE_STATS column has the following possible values: • YES The statistics are stale. • NO The statistics are not stale. • null The statistics are not collected. Executing GATHER_SCHEMA_STATS or GATHER_DATABASE_STATS with the GATHER AUTO option collects statistics only for objects with no statistics or stale statistics. To determine stale statistics: 1. Start SQL*Plus, and then log in to the database as a user with the necessary privileges. 2. Query the data dictionary for stale statistics. 13-11 Chapter 13 Gathering Optimizer Statistics Manually The following example queries stale statistics for the sh.sales table (partial output included): COL PARTITION_NAME FORMAT a15 SELECT PARTITION_NAME, STALE_STATS FROM DBA_TAB_STATISTICS WHERE TABLE_NAME = 'SALES' AND OWNER = 'SH' ORDER BY PARTITION_NAME; PARTITION_NAME --------------SALES_1995 SALES_1996 SALES_H1_1997 SALES_H2_1997 SALES_Q1_1998 SALES_Q1_1999 . . . STA --NO NO NO NO NO NO See Also: Oracle Database Reference to learn about the DBA_TAB_MODIFICATIONS view 13.2.4 Gathering Schema and Table Statistics Use GATHER_TABLE_STATS to collect table statistics, and GATHER_SCHEMA_STATS to collect statistics for all objects in a schema. To gather schema statistics using DBMS_STATS: 1. Start SQL*Plus, and connect to the database with the appropriate privileges for the procedure that you intend to run. 2. Run the GATHER_TABLE_STATS or GATHER_SCHEMA_STATS procedure, specifying the desired parameters. Typical parameters include: • Owner - ownname • Object name - tabname, indname, partname • Degree of parallelism - degree Example 13-1 Gathering Statistics for a Table This example uses the DBMS_STATS package to gather statistics on the sh.customers table with a parallelism setting of 2. BEGIN DBMS_STATS.GATHER_TABLE_STATS ( ownname => 'sh' , tabname => 'customers' , degree => 2 ); 13-12 Chapter 13 Gathering Optimizer Statistics Manually END; / See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the GATHER_TABLE_STATS procedure 13.2.5 Gathering Statistics for Fixed Objects Fixed objects are dynamic performance tables and their indexes. These objects record current database activity. Unlike other database tables, the database does not automatically use dynamic statistics for SQL statement referencing X$ tables when optimizer statistics are missing. Instead, the optimizer uses predefined default values. These defaults may not be representative and could potentially lead to a suboptimal execution plan. Thus, it is important to keep fixed object statistics current. Oracle Database automatically gathers fixed object statistics as part of automated statistics gathering if they have not been previously collected. You can also manually collect statistics on fixed objects by calling DBMS_STATS.GATHER_FIXED_OBJECTS_STATS. Oracle recommends that you gather statistics when the database has representative activity. Prerequisites You must have the SYSDBA or ANALYZE ANY DICTIONARY system privilege to execute this procedure. To gather schema statistics using GATHER_FIXED_OBJECTS_STATS: 1. In SQL*Plus or SQL Developer, log in to the database as a user with the necessary privileges. 2. Run the DBMS_STATS.GATHER_FIXED_OBJECTS_STATS procedure, specifying the desired parameters. Typical parameters include: • Table identifier describing where to save the current statistics - stattab • Identifier to associate with these statistics within stattab (optional) - statid • Schema containing stattab (if different from current schema) - statown Example 13-2 Gathering Statistics for a Table This example uses the DBMS_STATS package to gather fixed object statistics. BEGIN DBMS_STATS.GATHER_FIXED_OBJECTS_STATS; END; / 13-13 Chapter 13 Gathering Optimizer Statistics Manually See Also: • "Configuring Automatic Optimizer Statistics Collection" • Oracle Database PL/SQL Packages and Types Reference to learn about the GATHER_TABLE_STATS procedure 13.2.6 Gathering Statistics for Volatile Tables Using Dynamic Statistics Statistics for volatile tables, which are tables modified significantly during the day, go stale quickly. For example, a table may be deleted or truncated, and then rebuilt. When you set the statistics of a volatile object to null, Oracle Database dynamically gathers the necessary statistics during optimization using dynamic statistics. The OPTIMIZER_DYNAMIC_SAMPLING initialization parameter controls this feature. Assumptions This tutorial assumes the following: • The oe.orders table is extremely volatile. • You want to delete and then lock the statistics on the orders table to prevent the database from gathering statistics on the table. In this way, the database can dynamically gather necessary statistics as part of query optimization. • The oe user has the necessary privileges to query DBMS_XPLAN.DISPLAY_CURSOR. To delete and the lock optimizer statistics: 1. Connect to the database as user oe, and then delete the statistics for the oe table. For example, execute the following procedure: BEGIN DBMS_STATS.DELETE_TABLE_STATS('OE','ORDERS'); END; / 2. Lock the statistics for the oe table. For example, execute the following procedure: BEGIN DBMS_STATS.LOCK_TABLE_STATS('OE','ORDERS'); END; / 3. You query the orders table. For example, use the following statement: SELECT COUNT(order_id) FROM orders; 4. You query the plan in the cursor. You run the following commands (partial output included): SET LINESIZE 150 SET PAGESIZE 0 13-14 Chapter 13 Gathering Optimizer Statistics Manually SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR); SQL_ID aut9632fr3358, child number 0 ------------------------------------SELECT COUNT(order_id) FROM orders Plan hash value: 425895392 --------------------------------------------------------------------| Id | Operation | Name | Rows | Cost (%CPU)| Time | --------------------------------------------------------------------| 0 | SELECT STATEMENT | | | 2 (100)| | | 1 | SORT AGGREGATE | | 1 | | | | 2 | TABLE ACCESS FULL| ORDERS | 105 | 2 (0)| 00:00:01 | --------------------------------------------------------------------Note ----- dynamic statistics used for this statement (level=2) The Note in the preceding execution plan shows that the database used dynamic statistics for the SELECT statement. See Also: • "Configuring Options for Dynamic Statistics" • "Locking and Unlocking Optimizer Statistics" to learn how to gather representative statistics and then lock them, which is an alternative technique for preventing statistics for volatile tables from going stale 13.2.7 Gathering Optimizer Statistics Concurrently Oracle Database can gather statistics on multiple tables or partitions concurrently. This section contains the following topics: • About Concurrent Statistics Gathering By default, each partition of a partition table is gathered sequentially. • Enabling Concurrent Statistics Gathering To enable concurrent statistics gathering, use the DBMS_STATS.SET_GLOBAL_PREFS procedure to set the CONCURRENT preference. • Monitoring Statistics Gathering Operations You can monitor statistics gathering jobs using data dictionary views. 13.2.7.1 About Concurrent Statistics Gathering By default, each partition of a partition table is gathered sequentially. When concurrent statistics gathering mode is enabled, the database can simultaneously gather optimizer statistics for multiple tables in a schema, or multiple partitions or subpartitions in a table. Concurrency can reduce the overall time required to gather statistics by enabling the database to fully use multiple processors. 13-15 Chapter 13 Gathering Optimizer Statistics Manually Note: Concurrent statistics gathering mode does not rely on parallel query processing, but is usable with it. This section contains the following topics: • How DBMS_STATS Gathers Statistics Concurrently Oracle Database employs multiple tools and technologies to create and manage multiple statistics gathering jobs concurrently. • Concurrent Statistics Gathering and Resource Management The DBMS_STATS package does not explicitly manage resources used by concurrent statistics gathering jobs that are part of a user-initiated statistics gathering call. 13.2.7.1.1 How DBMS_STATS Gathers Statistics Concurrently Oracle Database employs multiple tools and technologies to create and manage multiple statistics gathering jobs concurrently. The database uses the following: • Oracle Scheduler • Oracle Database Advanced Queuing (AQ) • Oracle Database Resource Manager (the Resource Manager) Enable concurrent statistics gathering by setting the CONCURRENT preference with DBMS_STATS.SET_GLOBAL_PREF. The database runs as many concurrent jobs as possible. The Job Scheduler decides how many jobs to execute concurrently and how many to queue. As running jobs complete, the scheduler dequeues and runs more jobs until the database has gathered statistics on all tables, partitions, and subpartitions. The maximum number of jobs is bounded by the JOB_QUEUE_PROCESSES initialization parameter and available system resources. In most cases, the DBMS_STATS procedures create a separate job for each table partition or subpartition. However, if the partition or subpartition is empty or very small, then the database may automatically batch the object with other small objects into a single job to reduce the overhead of job maintenance. The following figure illustrates the creation of jobs at different levels, where Table 3 is a partitioned table, and the other tables are nonpartitioned. Job 3 acts as a coordinator job for Table 3, and creates a job for each partition in that table, and a separate job for the global statistics of Table 3. This example assumes that incremental statistics gathering is disabled; if enabled, then the database derives global statistics from partition-level statistics after jobs for partitions complete. 13-16 Chapter 13 Gathering Optimizer Statistics Manually Figure 13-1 Concurrent Statistics Gathering Jobs Gather Database/Schema/Dictionary Statistics Level 1 Job 1 Job 2 Job 3 Job 4 Table 1 Global Statistics Table 2 Global Statistics Table 3 Coordinator Job Table 4 Global Statistics Level 2 Job 3.1 Job 3.2 Job 3.3 Table 3 Partition 1 Table 3 Partition 2 Table 3 Global Statistics See Also: • "Enabling Concurrent Statistics Gathering" • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS package • Oracle Database Reference to learn about the JOB_QUEUE_PROCESSES initialization parameter 13.2.7.1.2 Concurrent Statistics Gathering and Resource Management The DBMS_STATS package does not explicitly manage resources used by concurrent statistics gathering jobs that are part of a user-initiated statistics gathering call. Thus, the database may use system resources fully during concurrent statistics gathering. To address this situation, use the Resource Manager to cap resources consumed by concurrent statistics gathering jobs. The Resource Manager must be enabled to gather statistics concurrently. The system-supplied consumer group ORA$AUTOTASK registers all statistics gathering jobs. You can create a resource plan with proper resource allocations for ORA$AUTOTASK to prevent concurrent statistics gathering from consuming all available resources. If you lack your own resource plan, and if choose not to create one, then consider activating the Resource Manager with the system-supplied DEFAULT_PLAN. 13-17 Chapter 13 Gathering Optimizer Statistics Manually Note: The ORA$AUTOTASK consumer group is shared with the maintenance tasks that automatically run during the maintenance windows. Thus, when concurrency is activated for automatic statistics gathering, the database automatically manages resources, with no extra steps required. See Also: Oracle Database Administrator’s Guide to learn about the Resource Manager 13.2.7.2 Enabling Concurrent Statistics Gathering To enable concurrent statistics gathering, use the DBMS_STATS.SET_GLOBAL_PREFS procedure to set the CONCURRENT preference. Possible values are as follows: • MANUAL Concurrency is enabled only for manual statistics gathering. • AUTOMATIC Concurrency is enabled only for automatic statistics gathering. • ALL Concurrency is enabled for both manual and automatic statistics gathering. • OFF Concurrency is disabled for both manual and automatic statistics gathering. This is the default value. This tutorial in this section explains how to enable concurrent statistics gathering. Prerequisites This tutorial has the following prerequisites: • In addition to the standard privileges for gathering statistics, you must have the following privileges: – CREATE JOB – MANAGE SCHEDULER – MANAGE ANY QUEUE • The SYSAUX tablespace must be online because the scheduler stores its internal tables and views in this tablespace. • The JOB_QUEUE_PROCESSES initialization parameter must be set to at least 4. • The Resource Manager must be enabled. 13-18 Chapter 13 Gathering Optimizer Statistics Manually By default, the Resource Manager is disabled. If you do not have a resource plan, then consider enabling the Resource Manager with the system-supplied DEFAULT_PLAN. Assumptions This tutorial assumes that you want to do the following: • Enable concurrent statistics gathering • Gather statistics for the sh schema • Monitor the gathering of the sh statistics To enable concurrent statistics gathering: 1. Connect SQL*Plus to the database with the appropriate privileges, and then enable the Resource Manager. The following example uses the default plan for the Resource Manager: ALTER SYSTEM SET RESOURCE_MANAGER_PLAN = 'DEFAULT_PLAN'; 2. Set the JOB_QUEUE_PROCESSES initialization parameter to at least twice the number of CPU cores. In Oracle Real Application Clusters, the JOB_QUEUE_PROCESSES setting applies to each node. Assume that the system has 4 CPU cores. The following example sets the parameter to 8 (twice the number of cores): ALTER SYSTEM SET JOB_QUEUE_PROCESSES=8; 3. Confirm that the parameter change took effect. For example, enter the following command in SQL*Plus (sample output included): SHOW PARAMETER PROCESSES; NAME -------------------------------_high_priority_processes aq_tm_processes db_writer_processes gcs_server_processes global_txn_processes job_queue_processes log_archive_max_processes processes 4. TYPE ----------string integer integer integer integer integer integer integer VALUE ----VKTM 1 1 0 1 8 4 100 Enable concurrent statistics. For example, execute the following PL/SQL anonymous block: BEGIN DBMS_STATS.SET_GLOBAL_PREFS('CONCURRENT','ALL'); END; / 5. Confirm that the statistics were enabled. For example, execute the following query (sample output included): SELECT DBMS_STATS.GET_PREFS('CONCURRENT') FROM DUAL; 13-19 Chapter 13 Gathering Optimizer Statistics Manually DBMS_STATS.GET_PREFS('CONCURRENT') ---------------------------------ALL 6. Gather the statistics for the SH schema. For example, execute the following procedure: EXEC DBMS_STATS.GATHER_SCHEMA_STATS('SH'); 7. In a separate session, monitor the job progress by querying DBA_OPTSTAT_OPERATION_TASKS. For example, execute the following query (sample output included): SET LINESIZE 1000 COLUMN COLUMN COLUMN COLUMN TARGET FORMAT a8 TARGET_TYPE FORMAT a25 JOB_NAME FORMAT a14 START_TIME FORMAT a40 SELECT TARGET, TARGET_TYPE, JOB_NAME, TO_CHAR(START_TIME, 'dd-mon-yyyy hh24:mi:ss') FROM DBA_OPTSTAT_OPERATION_TASKS WHERE STATUS = 'IN PROGRESS' AND OPID = (SELECT MAX(ID) FROM DBA_OPTSTAT_OPERATIONS WHERE OPERATION = 'gather_schema_stats'); TARGET --------SH.SALES SH.SALES 8. TARGET_TYPE ------------------------TABLE (GLOBAL STATS ONLY) TABLE (COORDINATOR JOB) JOB_NAME -------------ST$T292_1_B29 ST$SD290_1_B10 TO_CHAR(START_TIME,' -------------------30-nov-2012 14:22:47 30-nov-2012 14:22:08 In the original session, disable concurrent statistics gathering. For example, execute the following query: EXEC DBMS_STATS.SET_GLOBAL_PREFS('CONCURRENT','OFF'); See Also: • "Monitoring Statistics Gathering Operations" • Oracle Database Administrator’s Guide • Oracle Database PL/SQL Packages and Types Reference to learn how to use the DBMS_STATS.SET_GLOBAL_PREFS procedure 13.2.7.3 Monitoring Statistics Gathering Operations You can monitor statistics gathering jobs using data dictionary views. The following views are relevant: • DBA_OPTSTAT_OPERATION_TASKS This view contains the history of tasks that are performed or currently in progress as part of statistics gathering operations (recorded in DBA_OPTSTAT_OPERATIONS). 13-20 Chapter 13 Gathering Optimizer Statistics Manually Each task represents a target object to be processed in the corresponding parent operation. • DBA_OPTSTAT_OPERATIONS This view contains a history of statistics operations performed or currently in progress at the table, schema, and database level using the DBMS_STATS package. The TARGET column in the preceding views shows the target object for that statistics gathering job in the following form: OWNER.TABLE_NAME.PARTITION_OR_SUBPARTITION_NAME All statistics gathering job names start with the string ST$. To display currently running statistics tasks and jobs: • To list statistics gathering currently running tasks from all user sessions, use the following SQL statement (sample output included): SELECT OPID, TARGET, JOB_NAnME, (SYSTIMESTAMP - START_TIME) AS elapsed_time FROM DBA_OPTSTAT_OPERATION_TASKS WHERE STATUS = 'IN PROGRESS'; OPID ---981 981 TARGET ------------------------SH.SALES.SALES_Q4_1998 SH.SALES JOB_NAME ------------ST$T82_1_B29 ST$SD80_1_B10 ELAPSED_TIME -------------------------+000000000 00:00:00.596321 +000000000 00:00:27.972033 To display completed statistics tasks and jobs: • To list only completed tasks and jobs from a particular operation, first identify the operation ID from the DBA_OPTSTAT_OPERATIONS view based on the statistics gathering operation name, target, and start time. After you identify the operation ID, you can query the DBA_OPTSTAT_OPERATION_TASKS view to find the corresponding tasks in that operation For example, to list operations with the ID 981, use the following commands in SQL*Plus (sample output included): VARIABLE id NUMBER EXEC :id := 981 SELECT FROM WHERE AND TARGET, JOB_NAME, (END_TIME - START_TIME) AS ELAPSED_TIME DBA_OPTSTAT_OPERATION_TASKS STATUS <> 'IN PROGRESS' OPID = :id; TARGET ------------------------SH.SALES_TRANSACTIONS_EXT SH.CAL_MONTH_SALES_MV SH.CHANNELS JOB_NAME ELAPSED_TIME ------------- -------------------------+000000000 00:00:45.479233 ST$SD88_1_B10 +000000000 00:00:45.382764 ST$SD88_1_B10 +000000000 00:00:45.307397 To display statistics gathering tasks and jobs that have failed: • Use the following SQL statement (partial sample output included): SET LONG 10000 SELECT TARGET, JOB_NAME, 13-21 Chapter 13 Gathering Optimizer Statistics Manually (END_TIME - START_TIME) AS ELAPSED_TIME, NOTES FROM DBA_OPTSTAT_OPERATION_TASKS WHERE STATUS = 'FAILED'; TARGET JOB_NAME ELAPSED_TIME NOTES ------------------ -------- -------------------------- ---------------------SYS.OPATCH_XML_INV +000000007 02:36:31.130314ORA-20011: Approximate NDV failed: ORA-29913: error in . . . See Also: Oracle Database Reference to learn about the DBA_SCHEDULER_JOBS view 13.2.8 Gathering Incremental Statistics on Partitioned Objects Incremental statistics scan only changed partitions. When gathering statistics on large partitioned table by deriving global statistics from partition-level statistics, incremental statistics maintenance improves performance. This section contains the following topics: • Purpose of Incremental Statistics In a typical case, an application loads data into a new partition of a rangepartitioned table. As applications add new partitions and load data, the database must gather statistics on the new partition and keep global statistics up to date. • How DBMS_STATS Derives Global Statistics for Partitioned tables When incremental statistics maintenance is enabled, DBMS_STATS gathers statistics and creates synopses for changed partitions only. The database also automatically merges partition-level synopses into a global synopsis, and derives global statistics from the partition-level statistics and global synopses. • Gathering Statistics for a Partitioned Table: Basic Steps This section explains how to gather optimizer statistics for a partitioned table. • Maintaining Incremental Statistics for Partition Maintenance Operations A partition maintenance operation is a partition-related operation such as adding, exchanging, merging, or splitting table partitions. • Maintaining Incremental Statistics for Tables with Stale or Locked Partition Statistics Starting in Oracle Database 12c, incremental statistics can automatically calculate global statistics for a partitioned table even if the partition or subpartition statistics are stale and locked. 13.2.8.1 Purpose of Incremental Statistics In a typical case, an application loads data into a new partition of a range-partitioned table. As applications add new partitions and load data, the database must gather statistics on the new partition and keep global statistics up to date. 13-22 Chapter 13 Gathering Optimizer Statistics Manually Typically, data warehouse applications access large partitioned tables. Often these tables are partitioned on date columns, with only the recent partitions subject to frequent DML changes. Without incremental statistics, statistics collection typically uses a two-pass approach: 1. The database scans the table to gather the global statistics. The full scan of the table for global statistics collection can be very expensive, depending on the size of the table. As the table adds partitions, the longer the execution time for GATHER_TABLE_STATS because of the full table scan required for the global statistics. The database must perform the scan of the entire table even if only a small subset of partitions change. 2. The database scans the changed partitions to gather their partition-level statistics. Incremental maintenance provides a huge performance benefit for data warehouse applications because of the following: • The database must scan the table only once to gather partition statistics and to derive the global statistics by aggregating partition-level statistics. Thus, the database avoids the two full scans that are required when not using incremental statistics: one scan for the partition-level statistics, and one scan for the globallevel statistics. • In subsequent statistics gathering, the database only needs to scan the stale partitions and update their statistics (including synopses). The database can derive global statistics from the fresh partition statistics, which saves a full table scan. When using incremental statistics, the database must still gather statistics on any partition that will change the global or table-level statistics. Incremental statistics maintenance yields the same statistics as gathering table statistics from scratch, but performs better. 13.2.8.2 How DBMS_STATS Derives Global Statistics for Partitioned tables When incremental statistics maintenance is enabled, DBMS_STATS gathers statistics and creates synopses for changed partitions only. The database also automatically merges partition-level synopses into a global synopsis, and derives global statistics from the partition-level statistics and global synopses. The database avoids a full table scan when computing global statistics by deriving some global statistics from the partition-level statistics. For example, the number of rows at the global level is the sum of number of rows of partitions. Even global histograms can be derived from partition histograms. However, the database cannot derive all statistics from partition-level statistics, including the NDV of a column. The following example shows the NDV for two partitions in a table: Table 13-3 NDV for Two Partitions Object Column Values NDV Partition 1 1,3,3,4,5 4 Partition 2 2,3,4,5,6 5 13-23 Chapter 13 Gathering Optimizer Statistics Manually Calculating the NDV in the table by adding the NDV of the individual partitions produces an NDV of 9, which is incorrect. Thus, a more accurate technique is required: synopses. This section contains the following topics: • Partition-Level Synopses A synopsis is special type of statistic that tracks the number of distinct values (NDV) for each column in a partition. You can consider a synopsis as an internal management structure that samples distinct values. • NDV Algorithms: Adaptive Sampling and HyperLogLog Starting in Oracle Database 12c Release 2 (12.2), the HyperLogLog algorithm can improve NDV (number of distinct values) calculation performance, and also reduce the storage space required for synopses. • Aggregation of Global Statistics Using Synopses: Example In this example, the database gathers statistics for the initial six partitions of the sales table, and then creates synopses for each partition (S1, S2, and so on). The database creates global statistics by aggregating the partition-level statistics and synopses. 13.2.8.2.1 Partition-Level Synopses A synopsis is special type of statistic that tracks the number of distinct values (NDV) for each column in a partition. You can consider a synopsis as an internal management structure that samples distinct values. The database can accurately derive the global-level NDV for each column by merging partition-level synopses. In the example shown in Table 13-3, the database can use synopses to calculate the NDV for the column as 6. Each partition maintains a synopsis in incremental mode. When a new partition is added to the table you only need to gather statistics for the new partition. The database automatically updates the global statistics by aggregating the new partition synopsis with the synopses for existing partitions. Subsequent statistics gathering operations are faster than when synopses are not used. The database stores synopses in data dictionary tables WRI$_OPTSTAT_SYNOPSIS_HEAD$ and WRI$_OPTSTAT_SYNOPSIS$ in the SYSAUX tablespace. The DBA_PART_COL_STATISTICS dictionary view contains information of the column statistics in partitions. If the NOTES column contains the keyword INCREMENTAL, then this column has synopses. See Also: Oracle Database Reference to learn more about DBA_PART_COL_STATISTICS 13.2.8.2.2 NDV Algorithms: Adaptive Sampling and HyperLogLog Starting in Oracle Database 12c Release 2 (12.2), the HyperLogLog algorithm can improve NDV (number of distinct values) calculation performance, and also reduce the storage space required for synopses. The legacy algorithm for calculating NDV uses adaptive sampling. A synopsis is a sample of the distinct values. When calculating the NDV, the database initially stores 13-24 Chapter 13 Gathering Optimizer Statistics Manually every distinct value in a hash table. Each distinct value occupies a distinct hash bucket, so a column with 5000 distinct values has 5000 hash buckets. The database then halves the number of hash buckets, and then continues to halve the result until a small number of buckets remain. The algorithm is “adaptive” because the sampling rate changes based on the number of hash table splits. To calculate the NDV for the column, the database uses the following formula, where B is the number of hash buckets remaining after all the splits have been performed, and S is the number of splits: NDV = B * 2^S Adaptive sampling produces accurate NDV statistics, but has the following consequences: • Synopses occupy significant disk space, especially when tables have many columns and partitions, and the NDV in each column is high. For example, a 60-column table might have 300,000 partitions, with an average per-column NDV of 5,000. In this example, each partition has 300,000 entries (60 x 5000). In total, the synopses tables have 90 billion entries (300,000 squared), which occupies at least 720 GB of storage space. • Bulk processing of synopses can negatively affect performance. Before the database regathers statistics on the stale partitions, it must delete the associated synopses. Bulk deletion can be slow because it generates significant amounts of undo and redo data. In contrast to dynamic sampling, the HyperLogLog algorithm uses a randomization technique. Although the algorithm is complex, the foundational insight is that in a stream of random values, n distinct values will be spaced on average 1/n apart. Therefore, if you know the smallest value in the stream, then you can roughly estimate the number of distinct values. For example, if the values range from 0 to 1, and if the smallest value you observe is .2, then the numbers will on average be evenly spaced . 2 apart, so the NDV estimate is 5. The HyperLogLog algorithm expands on and corrects the original estimate. The database applies a hash function to every column value, resulting in a set of hash values with the same cardinality as the column. For the base estimate, the NDV equals 2n, where n is the maximum number of trailing zeroes observed in the binary representation of the hash values. The database refines its NDV estimate by using part of the output to split values into different hash buckets. The advantages of the HyperLogLog algorithm over adaptive sampling are: • The accuracy of the new algorithm is similar to the original algorithm. • The memory required is significantly lower, which typically leads to huge reductions in synopsis size. Synopses can become large when many partitions exist, and they have many columns with high NDV. Synopses that use the HyperLogLog algorithm are more compact. Creating and deleting synopses affects batch run times. Any operational procedures that manage partitions reduce run time. The DBMS_STATS preference APPROXIMATE_NDV_ALGORITHM determines which algorithm the database uses for NDV calculation. 13-25 Chapter 13 Gathering Optimizer Statistics Manually See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the APPROXIMATE_NDV_ALGORITHM preference 13.2.8.2.3 Aggregation of Global Statistics Using Synopses: Example In this example, the database gathers statistics for the initial six partitions of the sales table, and then creates synopses for each partition (S1, S2, and so on). The database creates global statistics by aggregating the partition-level statistics and synopses. Figure 13-2 Aggregating Statistics Sales Table 1 The database gathers partition-level statistics, and creates synopses May 18 2012 S1 May 19 2012 S2 May 20 2012 S3 May 21 2012 S4 May 22 2012 S5 May 23 2012 S6 2 The database generates 1 global statistics by aggregating partition-level statistics and synopses Global Statistics Sysaux Tablespace The following graphic shows a new partition, containing data for May 24, being added to the sales table. The database gathers statistics for the newly added partition, retrieves synopses for the other partitions, and then aggregates the synopses to create global statistics. 13-26 Chapter 13 Gathering Optimizer Statistics Manually Figure 13-3 Aggregating Statistics after Adding a Partition Sales Table May 18 2012 S1 May 19 2012 S2 May 20 2012 S3 May 21 2012 S4 May 22 2012 S5 May 23 2012 S6 May 24 2012 S7 3 1 The table adds a new partition. 6 1 The database generates global statistics by aggregating partition-level synopses. Global Statistics 1 The database retrieves 5 statistics and synopses for other partitions. 4 1 The database gathers statistics and synopses for the new partition. Sysaux Tablespace 13.2.8.3 Gathering Statistics for a Partitioned Table: Basic Steps This section explains how to gather optimizer statistics for a partitioned table. This section contains the following topics: • Considerations for Incremental Statistics Maintenance Enabling incremental statistics maintenance has several consequences. • Enabling Incremental Statistics Using SET_TABLE_PREFS To enable incremental statistics maintenance for a partitioned table, use DBMS_STATS.SET_TABLE_PREFS to set the INCREMENTAL value to true. When INCREMENTAL is set to false, which is the default, the database uses a full table scan to maintain global statistics. • About the APPROXIMATE_NDV_ALGORITHM Settings The DBMS_STATS.APPROXIMATE_NDV_ALGORITHM preference specifies the synopsis generation algorithm, either HyperLogLog or adaptive sampling. The INCREMENTAL_STALENESS preference controls when the database reformats synopses that use the adaptive sampling format. • Configuring Synopsis Generation: Examples These examples show different approaches, both conservative and aggressive, to switching synopses to the new HyperLogLog format. 13.2.8.3.1 Considerations for Incremental Statistics Maintenance Enabling incremental statistics maintenance has several consequences. Specifically, note the following: 13-27 Chapter 13 Gathering Optimizer Statistics Manually • If a table uses composite partitioning, then the database only gathers statistics for modified subpartitions. The database does not gather statistics at the subpartition level for unmodified subpartitions. In this way, the database reduces work by skipping unmodified partitions. • If a table uses incremental statistics, and if this table has a locally partitioned index, then the database gathers index statistics at the global level and for modified (not unmodified) index partitions. The database does not generate global index statistics from the partition-level index statistics. Rather, the database gathers global index statistics by performing a full index scan. • The SYSAUX tablespace consumes additional space to maintain global statistics for partitioned tables. See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_STATS 13.2.8.3.2 Enabling Incremental Statistics Using SET_TABLE_PREFS To enable incremental statistics maintenance for a partitioned table, use DBMS_STATS.SET_TABLE_PREFS to set the INCREMENTAL value to true. When INCREMENTAL is set to false, which is the default, the database uses a full table scan to maintain global statistics. For the database to update global statistics incrementally by scanning only the partitions that have changed, the following conditions must be met: • The PUBLISH value for the partitioned table is true. • The INCREMENTAL value for the partitioned table is true. • The statistics gathering procedure must specify AUTO_SAMPLE_SIZE for ESTIMATE_PERCENT and AUTO for GRANULARITY. Example 13-3 Enabling Incremental Statistics Assume that the PUBLISH value for the partitioned table sh.sales is true. The following program enables incremental statistics for this table: EXEC DBMS_STATS.SET_TABLE_PREFS('sh', 'sales', 'INCREMENTAL', 'TRUE'); 13.2.8.3.3 About the APPROXIMATE_NDV_ALGORITHM Settings The DBMS_STATS.APPROXIMATE_NDV_ALGORITHM preference specifies the synopsis generation algorithm, either HyperLogLog or adaptive sampling. The INCREMENTAL_STALENESS preference controls when the database reformats synopses that use the adaptive sampling format. The APPROXIMATE_NDV_ALGORITHM preference has the following possible values: • REPEAT OR HYPERLOGLOG This is the default. If INCREMENTAL is enabled on the table, then the database preserves the format of any existing synopses that use the adaptive sampling algorithm. However, the database creates any new synopses in HyperLogLog 13-28 Chapter 13 Gathering Optimizer Statistics Manually format. This approach is attractive when existing performance is acceptable, and you do not want to incur the performance cost of reformatting legacy content. • ADAPTIVE SAMPLING The database uses the adaptive sampling algorithm for all synopses. This is the most conservative option. • HYPERLOGLOG The database uses the HyperLogLog algorithm for all new and stale synopses. The INCREMENTAL_STALENESS preference controls when a synopsis is considered stale. When the APPROXIMATE_NDV_ALGORITHM preference is set to HYPERLOGLOG, then the following INCREMENTAL_STALENESS settings apply: • ALLOW_MIXED_FORMAT This is the default. If this value is specified, and if the following conditions are met, then the database does not consider existing adaptive sampling synopses as stale: – The synopses are fresh. – You gather statistics manually. Thus, synopses in both the legacy and HyperLogLog formats can co-exist. However, over time the automatic statistics gathering job regathers statistics on synopses that use the old format, and replaces them with synopses in HyperLogLog format. In this way, the automatic statistics gather job gradually phases out the old format. Manual statistics gathering jobs do not reformat synopses that use the adaptive sampling format. • Null Any partitions with the synopses in the legacy format are considered stale, which immediately triggers the database to regather statistics for stale synopses. The advantage is that the performance cost occurs only once. The disadvantage is that regathering all statistics on large tables can be resource-intensive. 13.2.8.3.4 Configuring Synopsis Generation: Examples These examples show different approaches, both conservative and aggressive, to switching synopses to the new HyperLogLog format. Example 13-4 Taking a Conservative Approach to Reformatting Synopses In this example, you allow synopses in mixed formats to coexist for the sh.sales table. Mixed formats yield less accurate statistics. However, you do not need to regather statistics for all partitions of the table. To ensure that all new and stale synopses use the HyperLogLog algorithm, set the APPROXIMATE_NDV_ALGORITHM preference to HYPERLOGLOG. To ensure that the automatic statistics gathering job reformats stale synopses gradually over time, set the INCREMENTAL_STALENESS preference to ALLOW_MIXED_FORMAT. BEGIN DBMS_STATS.SET_TABLE_PREFS ( ownname => 'sh' , tabname => 'sales' , pname => 'approximate_ndv_algorithm' , pvalue => 'hyperloglog' ); 13-29 Chapter 13 Gathering Optimizer Statistics Manually DBMS_STATS.SET_TABLE_PREFS ( ownname => 'sh' , tabname => 'sales' , pname => 'incremental_staleness' , pvalue => 'allow_mixed_format' ); END; Example 13-5 Taking an Aggressive Approach to Reformatting Synopses In this example, you force all synopses to use the HyperLogLog algorithm for the sh.sales table. In this case, the database must regather statistics for all partitions of the table. To ensure that all new and stale synopses use the HyperLogLog algorithm, set the APPROXIMATE_NDV_ALGORITHM preference to HYPERLOGLOG. To force the database to immediately regather statistics for all partitions in the table and store them in the new format, set the INCREMENTAL_STALENESS preference to null. BEGIN DBMS_STATS.SET_TABLE_PREFS ( ownname => 'sh' , tabname => 'sales' , pname => 'approximate_ndv_algorithm' , pvalue => 'hyperloglog' ); DBMS_STATS.SET_TABLE_PREFS ( ownname => 'sh' , tabname => 'sales' , pname => 'incremental_staleness' , pvalue => 'null' ); END; 13.2.8.4 Maintaining Incremental Statistics for Partition Maintenance Operations A partition maintenance operation is a partition-related operation such as adding, exchanging, merging, or splitting table partitions. Oracle Database provides the following support for incremental statistics maintenance: • If a partition maintenance operation triggers statistics gathering, then the database can reuse synopses that would previously have been dropped with the old segments. • DBMS_STATS can create a synopsis on a nonpartitioned table. The synopsis enables the database to maintain incremental statistics as part of a partition exchange operation without having to explicitly gather statistics on the partition after the exchange. When the DBMS_STATS preference INCREMENTAL is set to true on a table, the INCREMENTAL_LEVEL preference controls which synopses are collected and when. This preference takes the following values: • TABLE DBMS_STATS gathers table-level synopses on this table. You can only set INCREMENTAL_LEVEL to TABLE at the table level, not at the schema, database, or global level. • PARTITION (default) 13-30 Chapter 13 Gathering Optimizer Statistics Manually DBMS_STATS only gathers synopsis at the partition level of partitioned tables. When performing a partition exchange, to have synopses after the exchange for the partition being exchanged, set INCREMENTAL to true and INCREMENTAL_LEVEL to TABLE on the table to be exchanged with the partition. Assumptions This tutorial assumes the following: • You want to load empty partition p_sales_01_2010 in a sales table. • You create a staging table t_sales_01_2010, and then populate the table. • You want the database to maintain incremental statistics as part of the partition exchange operation without having to explicitly gather statistics on the partition after the exchange. To maintain incremental statistics as part of a partition exchange operation: 1. Set incremental statistics preferences for staging table t_sales_01_2010. For example, run the following statement: BEGIN DBMS_STATS.SET_TABLE_PREFS ( ownname => 'sh' , tabname => 't_sales_01_2010' , pname => 'INCREMENTAL' , pvalue => 'true' ); DBMS_STATS.SET_TABLE_PREFS ( ownname => 'sh' , tabname => 't_sales_01_2010' , pname => 'INCREMENTAL_LEVEL' , pvalue => 'table' ); END; 2. Gather statistics on staging table t_sales_01_2010. For example, run the following PL/SQL code: BEGIN DBMS_STATS.GATHER_TABLE_STATS ( ownname => 'SH' , tabname => 'T_SALES_01_2010' ); END; / DBMS_STATS gathers table-level synopses on t_sales_01_2010. 3. Ensure that the INCREMENTAL preference is true on the sh.sales table. For example, run the following PL/SQL code: BEGIN DBMS_STATS.SET_TABLE_PREFS ( ownname => 'sh' , tabname => 'sales' , pname => 'INCREMENTAL' , pvalue => 'true' ); 13-31 Chapter 13 Gathering Optimizer Statistics Manually END; / 4. If you have never gathered statistics on sh.sales before with INCREMENTAL set to true, then gather statistics on the partition to be exchanged. For example, run the following PL/SQL code: BEGIN DBMS_STATS.GATHER_TABLE_STATS ( ownname => 'sh' , tabname => 'sales' , pname => 'p_sales_01_2010' , pvalue => granularity=>'partition' ); END; / 5. Perform the partition exchange. For example, use the following SQL statement: ALTER TABLE sales EXCHANGE PARTITION p_sales_01_2010 WITH TABLE t_sales_01_2010; After the exchange, the partitioned table has both statistics and a synopsis for partition p_sales_01_2010. In releases before Oracle Database 12c, the preceding statement swapped the segment data and statistics of p_sales_01_2010 with t_sales_01_2010. The database did not maintain synopses for nonpartitioned tables such as t_sales_01_2010. To gather global statistics on the partitioned table, you needed to rescan the p_sales_01_2010 partition to obtain its synopses. See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_STATS.SET_TABLE_PREFS 13.2.8.5 Maintaining Incremental Statistics for Tables with Stale or Locked Partition Statistics Starting in Oracle Database 12c, incremental statistics can automatically calculate global statistics for a partitioned table even if the partition or subpartition statistics are stale and locked. When incremental statistics are enabled in releases before Oracle Database 12c, if any DML occurs on a partition, then the optimizer considers statistics on this partition to be stale. Thus, DBMS_STATS must gather the statistics again to accurately aggregate the global statistics. Furthermore, if DML occurs on a partition whose statistics are locked, then DBMS_STATS cannot regather the statistics on the partition, so a full table scan is the only means of gathering global statistics. Regathering statistics creates performance overhead. In Oracle Database 12c, the statistics preference INCREMENTAL_STALENESS controls how the database determines whether the statistics on a partition or subpartition are stale. This preference takes the following values: 13-32 Chapter 13 Gathering Optimizer Statistics Manually • USE_STALE_PERCENT A partition or subpartition is not considered stale if DML changes are less than the STALE_PERCENT preference specified for the table. The default value of STALE_PERCENT is 10, which means that if DML causes more than 10% of row changes, then the table is considered stale. • USE_LOCKED_STATS Locked partition or subpartition statistics are not considered stale, regardless of DML changes. • NULL (default) A partition or subpartition is considered stale if it has any DML changes. This behavior is identical to the Oracle Database 11g behavior. When the default value is used, statistics gathered in incremental mode are guaranteed to be the same as statistics gathered in nonincremental mode. When a nondefault value is used, statistics gathered in incremental mode might be less accurate than those gathered in nonincremental mode. You can specify USE_STALE_PERCENT and USE_LOCKED_STATS together. For example, you can write the following anonymous block: BEGIN DBMS_STATS.SET_TABLE_PREFS ( ownname => null , table_name => 't' , pname => 'incremental_staleness' , pvalue => 'use_stale_percent,use_locked_stats' ); END; Assumptions This tutorial assumes the following: • The STALE_PERCENT for a partitioned table is set to 10. • The INCREMENTAL value is set to true. • The table has had statistics gathered in INCREMENTAL mode before. • You want to discover how statistics gathering changes depending on the setting for INCREMENTAL_STALENESS, whether the statistics are locked, and the percentage of DML changes. To test for tables with stale or locked partition statistics: 1. Set INCREMENTAL_STALENESS to NULL. Afterward, 5% of the rows in one partition change because of DML activity. 2. Use DBMS_STATS to gather statistics on the table. DBMS_STATS regathers statistics for the partition that had the 5% DML activity, and incrementally maintains the global statistics. 3. Set INCREMENTAL_STALENESS to USE_STALE_PERCENT. Afterward, 5% of the rows in one partition change because of DML activity. 4. Use DBMS_STATS to gather statistics on the table. 13-33 Chapter 13 Gathering System Statistics Manually DBMS_STATS does not regather statistics for the partition that had DML activity (because the changes are under the staleness threshold of 10%), and incrementally maintains the global statistics. 5. Lock the partition statistics. Afterward, 20% of the rows in one partition change because of DML activity. 6. Use DBMS_STATS to gather statistics on the table. DBMS_STATS does not regather statistics for the partition because the statistics are locked. The database gathers the global statistics with a full table scan. Afterward, 5% of the rows in one partition change because of DML activity. 7. Use DBMS_STATS to gather statistics on the table. When you gather statistics on this table, DBMS_STATS does not regather statistics for the partition because they are not considered stale. The database maintains global statistics incrementally using the existing statistics for this partition. 8. Set INCREMENTAL_STALENESS to USE_LOCKED_STATS and USE_STALE_PERCENT. Afterward, 20% of the rows in one partition change because of DML activity. 9. Use DBMS_STATS to gather statistics on the table. Because USE_LOCKED_STATS is set, DBMS_STATS ignores the fact that the statistics are stale and uses the locked statistics. The database maintains global statistics incrementally using the existing statistics for this partition. See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_STATS.SET_TABLE_PREFS 13.3 Gathering System Statistics Manually System statistics describe hardware characteristics, such as I/O and CPU performance and utilization, to the optimizer. System statistics enable the optimizer to choose a more efficient execution plan. Oracle recommends that you gather system statistics when a physical change occurs in the environment, for example, the server has faster CPUs, more memory, or different storage. This section contains the following topics: • About Gathering System Statistics with DBMS_STATS To gather system statistics, use DBMS_STATS.GATHER_SYSTEM_STATS. • Guidelines for Gathering System Statistics The database automatically gathers essential parts of system statistics at startup. • Gathering Workload Statistics Oracle recommends that you use DBMS_STATS.GATHER_SYSTEM_STATS to capture statistics when the database has the most typical workload. 13-34 Chapter 13 Gathering System Statistics Manually • Gathering Noworkload Statistics Noworkload statistics capture characteristics of the I/O system. • Deleting System Statistics Use the DBMS_STATS.DELETE_SYSTEM_STATS function to delete system statistics. 13.3.1 About Gathering System Statistics with DBMS_STATS To gather system statistics, use DBMS_STATS.GATHER_SYSTEM_STATS. When the database gathers system statistics, it analyzes activity in a specified time period (workload statistics) or simulates a workload (noworkload statistics). The input arguments to DBMS_STATS.GATHER_SYSTEM_STATS are: • NOWORKLOAD The optimizer gathers statistics based on system characteristics only, without regard to the workload. • INTERVAL After the specified number of minutes has passed, the optimizer updates system statistics either in the data dictionary, or in an alternative table (specified by stattab). Statistics are based on system activity during the specified interval. • START and STOP START initiates gathering statistics. STOP calculates statistics for the elapsed period (since START) and refreshes the data dictionary or an alternative table (specified by stattab). The optimizer ignores INTERVAL. • EXADATA The system statistics consider the unique capabilities provided by using Exadata, such as large I/O size and high I/O throughput. The optimizer sets the multiblock read count and I/O throughput statistics along with CPU speed. The following table lists the optimizer system statistics gathered by DBMS_STATS and the options for gathering or manually setting specific system statistics. Table 13-4 Optimizer System Statistics in the DBMS_STAT Package Parameter Name Description Initialization Options for Gathering or Setting Statistics cpuspeedNW Represents noworkload CPU speed. CPU speed is the average number of CPU cycles in each second. At system startup Set gathering_mode = NOWORKLOAD or set statistics manually. Unit Millions/sec. 13-35 Chapter 13 Gathering System Statistics Manually Table 13-4 (Cont.) Optimizer System Statistics in the DBMS_STAT Package Parameter Name Description Initialization Options for Gathering or Setting Statistics Unit ioseektim Represents the At system startup time it takes to 10 (default) position the disk head to read data. I/O seek time equals seek time + latency time + operating system overhead time. Set gathering_mode = NOWORKLOAD or set statistics manually. ms iotfrspeed Represents the rate at which an Oracle database can read data in the single read request. At system startup Set gathering_mode 4096 (default) = NOWORKLOAD or set statistics manually. Bytes/ms cpuspeed Represents workload CPU speed. CPU speed is the average number of CPU cycles in each second. None Set gathering_mode = NOWORKLOAD, INTERVAL, or START|STOP, or set statistics manually. Millions/sec. maxthr Maximum I/O throughput is the maximum throughput that the I/O subsystem can deliver. None Set gathering_mode = NOWORKLOAD, INTERVAL, or START|STOP, or set statistics manually. Bytes/sec. slavethr Slave I/O throughput is the average parallel execution server I/O throughput. None Set gathering_mode = INTERVAL or START|STOP, or set statistics manually. Bytes/sec. sreadtim Single-block read None time is the average time to read a single block randomly. Set gathering_mode = INTERVAL or START|STOP, or set statistics manually. ms mreadtim Multiblock read is None the average time to read a multiblock sequentially. Set gathering_mode = INTERVAL or START|STOP, or set statistics manually. ms 13-36 Chapter 13 Gathering System Statistics Manually Table 13-4 (Cont.) Optimizer System Statistics in the DBMS_STAT Package Parameter Name Description Initialization Options for Gathering or Setting Statistics Unit mbrc Multiblock count is the average multiblock read count sequentially. None Set gathering_mode = INTERVAL or START|STOP, or set statistics manually. blocks See Also: Oracle Database PL/SQL Packages and Types Reference for detailed information on the procedures in the DBMS_STATS package for implementing system statistics 13.3.2 Guidelines for Gathering System Statistics The database automatically gathers essential parts of system statistics at startup. CPU and I/O characteristics tend to remain constant over time. Typically, these characteristics only change when some aspect of the configuration is upgraded. For this reason, Oracle recommends that you gather system statistics only when a physical change occurs in your environment, for example, the server gets faster CPUs, more memory, or different disk storage. Note the following guidelines: • Oracle Database initializes noworkload statistics to default values at the first instance startup. Oracle recommends that you gather noworkload statistics after you create new tablespaces on storage that is not used by any other tablespace. • The best practice is to capture statistics in the interval of time when the system has the most common workload. Gathering workload statistics does not generate additional overhead. 13.3.3 Gathering Workload Statistics Oracle recommends that you use DBMS_STATS.GATHER_SYSTEM_STATS to capture statistics when the database has the most typical workload. For example, database applications can process OLTP transactions during the day and generate OLAP reports at night. This section contains the following topics: • About Workload Statistics Workload statistics analyze activity in a specified time period. 13-37 Chapter 13 Gathering System Statistics Manually • Starting and Stopping System Statistics Gathering This tutorial explains how to set the workload interval with the START and STOP parameters of GATHER_SYSTEM_STATS. • Gathering System Statistics During a Specified Interval This tutorial explains how to set the workload interval with the INTERVAL parameter of GATHER_SYSTEM_STATS. 13.3.3.1 About Workload Statistics Workload statistics analyze activity in a specified time period. Workload statistics include the following statistics listed in Table 13-4: • Single block (sreadtim) and multiblock (mreadtim) read times • Multiblock count (mbrc) • CPU speed (cpuspeed) • Maximum system throughput (maxthr) • Average parallel execution throughput (slavethr) The database computes sreadtim, mreadtim, and mbrc by comparing the number of physical sequential and random reads between two points in time from the beginning to the end of a workload. The database implements these values through counters that change when the buffer cache completes synchronous read requests. Because the counters are in the buffer cache, they include not only I/O delays, but also waits related to latch contention and task switching. Thus, workload statistics depend on system activity during the workload window. If system is I/O bound (both latch contention and I/O throughput), then the statistics promote a less I/O-intensive plan after the database uses the statistics. As shown in Figure 13-4, if you gather workload statistics, then the optimizer uses the mbrc value gathered for workload statistics to estimate the cost of a full table scan. Figure 13-4 Workload Statistics Counters Optimizer Database Buffer Cache Counters for Workload Statistics mreadtim mbrc sreadtim cpuspeed maxthr slavethr Estimate costs of full table scans May not be available if no full table scans occur May use if mbrc and mreadtim are not available DB_FILE_MULTIBLOCK_READ_COUNT When gathering workload statistics, the database may not gather the mbrc and mreadtim values if no table scans occur during serial workloads, as is typical of OLTP systems. However, full table scans occur frequently on DSS systems. These scans may run parallel and bypass the buffer cache. In such cases, the database still gathers the sreadtim because index lookups use the buffer cache. 13-38 Chapter 13 Gathering System Statistics Manually If the database cannot gather or validate gathered mbrc or mreadtim values, but has gathered sreadtim and cpuspeed, then the database uses only sreadtim and cpuspeed for costing. In this case, the optimizer uses the value of the initialization parameter DB_FILE_MULTIBLOCK_READ_COUNT to cost a full table scan. However, if DB_FILE_MULTIBLOCK_READ_COUNT is 0 or is not set, then the optimizer uses a value of 8 for calculating cost. Use the DBMS_STATS.GATHER_SYSTEM_STATS procedure to gather workload statistics. The GATHER_SYSTEM_STATS procedure refreshes the data dictionary or a staging table with statistics for the elapsed period. To set the duration of the collection, use either of the following techniques: • Specify START the beginning of the workload window, and then STOP at the end of the workload window. • Specify INTERVAL and the number of minutes before statistics gathering automatically stops. If needed, you can use GATHER_SYSTEM_STATS (gathering_mode=>'STOP') to end gathering earlier than scheduled. See Also: Oracle Database Reference to learn about the DB_FILE_MULTIBLOCK_READ_COUNT initialization parameter 13.3.3.2 Starting and Stopping System Statistics Gathering This tutorial explains how to set the workload interval with the START and STOP parameters of GATHER_SYSTEM_STATS. Assumptions This tutorial assumes the following: • The hour between 10 a.m. and 11 a.m. is representative of the daily workload. • You intend to collect system statistics directly in the data dictionary. To gather workload statistics using START and STOP: 1. Start SQL*Plus and connect to the database with administrator privileges. 2. Start statistics collection. For example, at 10 a.m., execute the following procedure to start collection: EXECUTE DBMS_STATS.GATHER_SYSTEM_STATS( gathering_mode => 'START' ); 3. Generate the workload. 4. End statistics collection. For example, at 11 a.m., execute the following procedure to end collection: EXECUTE DBMS_STATS.GATHER_SYSTEM_STATS( gathering_mode => 'STOP' ); The optimizer can now use the workload statistics to generate execution plans that are effective during the normal daily workload. 5. Optionally, query the system statistics. 13-39 Chapter 13 Gathering System Statistics Manually For example, run the following query: COL PNAME FORMAT a15 SELECT PNAME, PVAL1 FROM SYS.AUX_STATS$ WHERE SNAME = 'SYSSTATS_MAIN'; See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GATHER_SYSTEM_STATS procedure 13.3.3.3 Gathering System Statistics During a Specified Interval This tutorial explains how to set the workload interval with the INTERVAL parameter of GATHER_SYSTEM_STATS. Assumptions This tutorial assumes the following: • The database application processes OLTP transactions during the day and runs OLAP reports at night. To gather representative statistics, you collect them during the day for two hours and then at night for two hours. • You want to store statistics in a table named workload_stats. • You intend to switch between the statistics gathered. To gather workload statistics using INTERVAL: 1. Start SQL*Plus and connect to the production database as administrator dba1. 2. Create a table to hold the production statistics. For example, execute the following PL/SQL program to create user statistics table workload_stats: BEGIN DBMS_STATS.CREATE_STAT_TABLE ( ownname => 'dba1' , stattab => 'workload_stats' ); END; / 3. Ensure that JOB_QUEUE_PROCESSES is not 0 so that DBMS_JOB jobs and Oracle Scheduler jobs run. ALTER SYSTEM SET JOB_QUEUE_PROCESSES = 1; 4. Gather statistics during the day. For example, gather statistics for two hours with the following program: BEGIN DBMS_STATS.GATHER_SYSTEM_STATS ( interval => 120 , stattab => 'workload_stats' , statid => 'OLTP' 13-40 Chapter 13 Gathering System Statistics Manually ); END; / 5. Gather statistics during the evening. For example, gather statistics for two hours with the following program: BEGIN DBMS_STATS.GATHER_SYSTEM_STATS ( interval => 120 , stattab => 'workload_stats' , statid => 'OLAP' ); END; / 6. In the day or evening, import the appropriate statistics into the data dictionary. For example, during the day you can import the OLTP statistics from the staging table into the dictionary with the following program: BEGIN DBMS_STATS.IMPORT_SYSTEM_STATS ( stattab => 'workload_stats' , statid => 'OLTP' ); END; / For example, during the night you can import the OLAP statistics from the staging table into the dictionary with the following program: BEGIN DBMS_STATS.IMPORT_SYSTEM_STATS ( stattab => 'workload_stats' , statid => 'OLAP' ); END; / See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GATHER_SYSTEM_STATS procedure 13.3.4 Gathering Noworkload Statistics Noworkload statistics capture characteristics of the I/O system. By default, Oracle Database uses noworkload statistics and the CPU cost model. The values of noworkload statistics are initialized to defaults at the first instance startup. You can also use the DBMS_STATS.GATHER_SYSTEM_STATS procedure to gather noworkload statistics manually. Noworkload statistics include the following system statistics listed in Table 13-4: • I/O transfer speed (iotfrspeed) 13-41 Chapter 13 Gathering System Statistics Manually • I/O seek time (ioseektim) • CPU speed (cpuspeednw) The major difference between workload statistics and noworkload statistics is in the gathering method. Noworkload statistics gather data by submitting random reads against all data files, whereas workload statistics uses counters updated when database activity occurs. If you gather workload statistics, then Oracle Database uses them instead of noworkload statistics. To gather noworkload statistics, run DBMS_STATS.GATHER_SYSTEM_STATS with no arguments or with the gathering mode set to noworkload. There is an overhead on the I/O system during the gathering process of noworkload statistics. The gathering process may take from a few seconds to several minutes, depending on I/O performance and database size. When you gather noworkload statistics, the database analyzes the information and verifies it for consistency. In some cases, the values of noworkload statistics may retain their default values. You can either gather the statistics again, or use SET_SYSTEM_STATS to set the values manually to the I/O system specifications. Assumptions This tutorial assumes that you want to gather noworkload statistics manually. To gather noworkload statistics manually: 1. Start SQL*Plus and connect to the database with administrator privileges. 2. Gather the noworkload statistics. For example, run the following statement: BEGIN DBMS_STATS.GATHER_SYSTEM_STATS ( gathering_mode => 'NOWORKLOAD' ); END; 3. Optionally, query the system statistics. For example, run the following query: COL PNAME FORMAT a15 SELECT PNAME, PVAL1 FROM SYS.AUX_STATS$ WHERE SNAME = 'SYSSTATS_MAIN'; See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GATHER_SYSTEM_STATS procedure 13-42 Chapter 13 Running Statistics Gathering Functions in Reporting Mode 13.3.5 Deleting System Statistics Use the DBMS_STATS.DELETE_SYSTEM_STATS function to delete system statistics. This procedure deletes workload statistics collected using the INTERVAL or START and STOP options, and then resets the default to noworkload statistics. However, if the stattab parameter specifies a table for storing statistics, then the subprogram deletes all system statistics with the associated statid from the statistics table. Assumptions This tutorial assumes the following: • You gathered statistics for a specific intensive workload, but no longer want the optimizer to use these statistics. • You stored workload statistics in the default location, not in a user-specified table. To delete system statistics: 1. In SQL*Plus, log in to the database as a user with administrative privileges. 2. Delete the system statistics. For example, run the following statement: EXEC DBMS_STATS.DELETE_SYSTEM_STATS; See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.DELETE_SYSTEM_STATS procedure 13.4 Running Statistics Gathering Functions in Reporting Mode You can run the DBMS_STATS statistics gathering procedures in reporting mode. When you use the REPORT_* procedures, the optimizer does not actually gather statistics. Rather, the package reports objects that would be processed if you were to use a specified statistics gathering function. The following table lists the DBMS_STATS.REPORT_GATHER_*_STATS functions. For all functions, the input parameters are the same as for the corresponding GATHER_*_STATS procedure, with the following additional parameters: detail_level and format. Supported formats are XML, HTML, and TEXT. 13-43 Chapter 13 Running Statistics Gathering Functions in Reporting Mode Table 13-5 DBMS_STATS Reporting Mode Functions Function Description REPORT_GATHER_TABLE_STATS Runs GATHER_TABLE_STATS in reporting mode. The procedure does not collect statistics, but reports all objects that would be affected by invoking GATHER_TABLE_STATS. REPORT_GATHER_SCHEMA_STATS Runs GATHER_SCHEMA_STATS in reporting mode. The procedure does not actually collect statistics, but reports all objects that would be affected by invoking GATHER_SCHEMA_STATS. REPORT_GATHER_DICTIONARY_STATS Runs GATHER_DICTIONARY_STATS in reporting mode. The procedure does not actually collect statistics, but reports all objects that would be affected by invoking GATHER_DICTIONARY_STATS. REPORT_GATHER_DATABASE_STATS Runs GATHER_DATABASE_STATS in reporting mode. The procedure does not actually collect statistics, but reports all objects that would be affected by invoking GATHER_DATABASE_STATS. REPORT_GATHER_FIXED_OBJ_STATS Runs GATHER_FIXED_OBJ_STATS in reporting mode. The procedure does not actually collect statistics, but reports all objects that would be affected by invoking GATHER_FIXED_OBJ_STATS. REPORT_GATHER_AUTO_STATS Runs the automatic statistics gather job in reporting mode. The procedure does not actually collect statistics, but reports all objects that would be affected by running the job. Assumptions This tutorial assumes that you want to generate an HTML report of the objects that would be affected by running GATHER_SCHEMA_STATS on the oe schema. To report on objects affected by running GATHER_SCHEMA_STATS: 1. Start SQL*Plus and connect to the database with administrator privileges. 2. Run the DBMS_STATS.REPORT_GATHER_SCHEMA_STATS function. For example, run the following commands in SQL*Plus: SET LINES 200 PAGES 0 SET LONG 100000 COLUMN REPORT FORMAT A200 VARIABLE my_report CLOB; BEGIN :my_report :=DBMS_STATS.REPORT_GATHER_SCHEMA_STATS( ownname => 'OE' , detail_level => 'TYPICAL' , format => 'HTML' ); END; / The following graphic shows a partial example report: 13-44 Chapter 13 Running Statistics Gathering Functions in Reporting Mode See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_STATS 13-45 14 Managing Extended Statistics DBMS_STATS enables you to collect extended statistics, which are statistics that can improve cardinality estimates when multiple predicates exist on different columns of a table, or when predicates use expressions. An extension is either a column group or an expression. Column group statistics can improve cardinality estimates when multiple columns from the same table occur together in a SQL statement. Expression statistics improves optimizer estimates when predicates use expressions, for example, built-in or user-defined functions. Note: You cannot create extended statistics on virtual columns. This chapter contains the following topics: • Managing Column Group Statistics A column group is a set of columns that is treated as a unit. • Managing Expression Statistics The type of extended statistics known as expression statistics improve optimizer estimates when a WHERE clause has predicates that use expressions. See Also: Oracle Database SQL Language Reference for a list of restrictions on virtual columns 14.1 Managing Column Group Statistics A column group is a set of columns that is treated as a unit. Essentially, a column group is a virtual column. By gathering statistics on a column group, the optimizer can more accurately determine the cardinality estimate when a query groups these columns together. The following sections provide an overview of column group statistics, and explain how to manage them manually: • About Statistics on Column Groups Individual column statistics are useful for determining the selectivity of a single predicate in a WHERE clause. 14-1 Chapter 14 Managing Column Group Statistics • Detecting Useful Column Groups for a Specific Workload You can use DBMS_STATS.SEED_COL_USAGE and REPORT_COL_USAGE to determine which column groups are required for a table based on a specified workload. • Creating Column Groups Detected During Workload Monitoring You can use the DBMS_STATS.CREATE_EXTENDED_STATS function to create column groups that were detected previously by executing DBMS_STATS.SEED_COL_USAGE. • Creating and Gathering Statistics on Column Groups Manually In some cases, you may know the column group that you want to create. • Displaying Column Group Information To obtain the name of a column group, use the DBMS_STATS.SHOW_EXTENDED_STATS_NAME function or a database view. • Dropping a Column Group Use the DBMS_STATS.DROP_EXTENDED_STATS function to delete a column group from a table. See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS package 14.1.1 About Statistics on Column Groups Individual column statistics are useful for determining the selectivity of a single predicate in a WHERE clause. When the WHERE clause includes multiple predicates on different columns from the same table, individual column statistics do not show the relationship between the columns. This is the problem solved by a column group. The optimizer calculates the selectivity of the predicates independently, and then combines them. However, if a correlation between the individual columns exists, then the optimizer cannot take it into account when determining a cardinality estimate, which it creates by multiplying the selectivity of each table predicate by the number of rows. The following graphic contrasts two ways of gathering statistics on the cust_state_province and country_id columns of the sh.customers table. The diagram shows DBMS_STATS collecting statistics on each column individually and on the group. The column group has a system-generated name. 14-2 Chapter 14 Managing Column Group Statistics Figure 14-1 CUST_ID Column Group Statistics CUST_STATE_PROVINCE 101095 103105 CA Sao Paulo COUNTRY_ID ... 52790 52775 SYS_STU#S#WF25Z#QAHIHE#MOFFMM_ Statistics for Column Group Statistics for CUST_STATE_PROVINCE Statistics for COUNTRY_ID DBMS_STATS Note: The optimizer uses column group statistics for equality predicates, inlist predicates, and for estimating the GROUP BY cardinality. This section contains the following topics: • Why Column Group Statistics Are Needed: Example This example demonstrates how column group statistics enable the optimizer to give a more accurate cardinality estimate. • Automatic and Manual Column Group Statistics Oracle Database can create column group statistics either automatically or manually. • User Interface for Column Group Statistics Several DBMS_STATS program units have preferences that are relevant for column groups. 14.1.1.1 Why Column Group Statistics Are Needed: Example This example demonstrates how column group statistics enable the optimizer to give a more accurate cardinality estimate. The following query of the DBA_TAB_COL_STATISTICS table shows information about statistics that have been gathered on the columns cust_state_province and country_id from the sh.customers table: COL COLUMN_NAME FORMAT a20 COL NDV FORMAT 999 SELECT COLUMN_NAME, NUM_DISTINCT AS "NDV", HISTOGRAM FROM DBA_TAB_COL_STATISTICS WHERE OWNER = 'SH' 14-3 Chapter 14 Managing Column Group Statistics AND AND TABLE_NAME = 'CUSTOMERS' COLUMN_NAME IN ('CUST_STATE_PROVINCE', 'COUNTRY_ID'); Sample output is as follows: COLUMN_NAME NDV HISTOGRAM -------------------- ---------- --------------CUST_STATE_PROVINCE 145 FREQUENCY COUNTRY_ID 19 FREQUENCY As shown in the following query, 3341 customers reside in California: SELECT COUNT(*) FROM sh.customers WHERE cust_state_province = 'CA'; COUNT(*) ---------3341 Consider an explain plan for a query of customers in the state CA and in the country with ID 52790 (USA): EXPLAIN PLAN FOR SELECT * FROM sh.customers WHERE cust_state_province = 'CA' AND country_id=52790; Explained. sys@PROD> SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY); PLAN_TABLE_OUTPUT -------------------------------------------------------------------------------Plan hash value: 1683234692 ------------------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 128 | 24192 | 442 (7)| 00:00:06 | |* 1 | TABLE ACCESS FULL| CUSTOMERS | 128 | 24192 | 442 (7)| 00:00:06 | ------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------PLAN_TABLE_OUTPUT -------------------------------------------------------------------------------1 - filter("CUST_STATE_PROVINCE"='CA' AND "COUNTRY_ID"=52790) 13 rows selected. Based on the single-column statistics for the country_id and cust_state_province columns, the optimizer estimates that the query of California customers in the USA will return 128 rows. In fact, 3341 customers reside in California, but the optimizer does not know that the state of California is in the country of the USA, and so greatly underestimates cardinality by assuming that both predicates reduce the number of returned rows. 14-4 Chapter 14 Managing Column Group Statistics You can make the optimizer aware of the real-world relationship between values in country_id and cust_state_province by gathering column group statistics. These statistics enable the optimizer to give a more accurate cardinality estimate. See Also: • "Detecting Useful Column Groups for a Specific Workload" • "Creating Column Groups Detected During Workload Monitoring" • "Creating and Gathering Statistics on Column Groups Manually" 14.1.1.2 Automatic and Manual Column Group Statistics Oracle Database can create column group statistics either automatically or manually. The optimizer can use SQL plan directives to generate a more optimal plan. If the DBMS_STATS preference AUTO_STAT_EXTENSIONS is set to ON (by default it is OFF), then a SQL plan directive can automatically trigger the creation of column group statistics based on usage of predicates in the workload. You can set AUTO_STAT_EXTENSIONS with the SET_TABLE_PREFS, SET_GLOBAL_PREFS, or SET_SCHEMA_PREFS procedures. When you want to manage column group statistics manually, then use DBMS_STATS as follows: • Detect column groups • Create previously detected column groups • Create column groups manually and gather column group statistics See Also: • "Detecting Useful Column Groups for a Specific Workload" • "Creating Column Groups Detected During Workload Monitoring" • "Creating and Gathering Statistics on Column Groups Manually" • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS procedures for setting optimizer statistics 14.1.1.3 User Interface for Column Group Statistics Several DBMS_STATS program units have preferences that are relevant for column groups. 14-5 Chapter 14 Managing Column Group Statistics Table 14-1 DBMS_STATS APIs Relevant for Column Groups Program Unit or Preference Description SEED_COL_USAGE Procedure Iterates over the SQL statements in the specified workload, compiles them, and then seeds column usage information for the columns that appear in these statements. To determine the appropriate column groups, the database must observe a representative workload. You do not need to run the queries themselves during the monitoring period. Instead, you can run EXPLAIN PLAN for some longer-running queries in your workload to ensure that the database is recording column group information for these queries. REPORT_COL_USAGE Function Generates a report that lists the columns that were seen in filter predicates, join predicates, and GROUP BY clauses in the workload. You can use this function to review column usage information recorded for a specific table. CREATE_EXTENDED_STATS Function Creates extensions, which are either column groups or expressions. The database gathers statistics for the extension when either a user-generated or automatic statistics gathering job gathers statistics for the table. AUTO_STAT_EXTENSIONS Preference Controls the automatic creation of extensions, including column groups, when optimizer statistics are gathered. Set this preference using SET_TABLE_PREFS, SET_SCHEMA_PREFS, or SET_GLOBAL_PREFS. When AUTO_STAT_EXTENSIONS is set to OFF (default), the database does not create column group statistics automatically. To create extensions, you must execute the CREATE_EXTENDED_STATS function or specify extended statistics explicitly in the METHOD_OPT parameter in the DBMS_STATS API. When set to ON, a SQL plan directive can trigger the creation of column group statistics automatically based on usage of columns in the predicates in the workload. See Also: • "Setting Artificial Optimizer Statistics for a Table" • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS package 14.1.2 Detecting Useful Column Groups for a Specific Workload You can use DBMS_STATS.SEED_COL_USAGE and REPORT_COL_USAGE to determine which column groups are required for a table based on a specified workload. This technique is useful when you do not know which extended statistics to create. This technique does not work for expression statistics. 14-6 Chapter 14 Managing Column Group Statistics Assumptions This tutorial assumes the following: • Cardinality estimates have been incorrect for queries of the sh.customers_test table (created from the customers table) that use predicates referencing the columns country_id and cust_state_province. • You want the database to monitor your workload for 5 minutes (300 seconds). • You want the database to determine which column groups are needed automatically. To detect column groups: 1. Start SQL*Plus or SQL Developer, and log in to the database as user sh. 2. Create the customers_test table and gather statistics for it: DROP TABLE customers_test; CREATE TABLE customers_test AS SELECT * FROM customer; EXEC DBMS_STATS.GATHER_TABLE_STATS(user, 'customers_test'); 3. Enable workload monitoring. In a different SQL*Plus session, connect as SYS and run the following PL/SQL program to enable monitoring for 300 seconds: BEGIN DBMS_STATS.SEED_COL_USAGE(null,null,300); END; / 4. As user sh, run explain plans for two queries in the workload. The following examples show the explain plans for two queries on the customers_test table: EXPLAIN PLAN FOR SELECT * FROM customers_test WHERE cust_city = 'Los Angeles' AND cust_state_province = 'CA' AND country_id = 52790; SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY('plan_table', null,'basic rows')); EXPLAIN PLAN FOR SELECT country_id, cust_state_province, count(cust_city) FROM customers_test GROUP BY country_id, cust_state_province; SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY('plan_table', null,'basic rows')); Sample output appears below: PLAN_TABLE_OUTPUT --------------------------------------------------------------------------Plan hash value: 4115398853 ---------------------------------------------------- 14-7 Chapter 14 Managing Column Group Statistics | Id | Operation | Name | Rows | ---------------------------------------------------| 0 | SELECT STATEMENT | | 1 | | 1 | TABLE ACCESS FULL| CUSTOMERS_TEST | 1 | ---------------------------------------------------8 rows selected. PLAN_TABLE_OUTPUT --------------------------------------------------------------------------Plan hash value: 3050654408 ----------------------------------------------------| Id | Operation | Name | Rows | ----------------------------------------------------| 0 | SELECT STATEMENT | | 1949 | | 1 | HASH GROUP BY | | 1949 | | 2 | TABLE ACCESS FULL| CUSTOMERS_TEST | 55500 | ----------------------------------------------------9 rows selected. The first plan shows a cardinality of 1 row for a query that returns 932 rows. The second plan shows a cardinality of 1949 rows for a query that returns 145 rows. 5. Optionally, review the column usage information recorded for the table. Call the DBMS_STATS.REPORT_COL_USAGE function to generate a report: SET LONG 100000 SET LINES 120 SET PAGES 0 SELECT DBMS_STATS.REPORT_COL_USAGE(user, 'customers_test') FROM DUAL; The report appears below: LEGEND: ....... EQ : Used in single table EQuality predicate RANGE : Used in single table RANGE predicate LIKE : Used in single table LIKE predicate NULL : Used in single table is (not) NULL predicate EQ_JOIN : Used in EQuality JOIN predicate NONEQ_JOIN : Used in NON EQuality JOIN predicate FILTER : Used in single table FILTER predicate JOIN : Used in JOIN predicate GROUP_BY : Used in GROUP BY expression ........................................................................... ########################################################################### COLUMN USAGE REPORT FOR SH.CUSTOMERS_TEST ......................................... 1. 2. 3. 4. COUNTRY_ID CUST_CITY CUST_STATE_PROVINCE (CUST_CITY, CUST_STATE_PROVINCE, COUNTRY_ID) : EQ : EQ : EQ : FILTER 14-8 Chapter 14 Managing Column Group Statistics 5. (CUST_STATE_PROVINCE, COUNTRY_ID) : GROUP_BY ########################################################################### In the preceding report, the first three columns were used in equality predicates in the first monitored query: ... WHERE cust_city = 'Los Angeles' AND cust_state_province = 'CA' AND country_id = 52790; All three columns appeared in the same WHERE clause, so the report shows them as a group filter. In the second query, two columns appeared in the GROUP BY clause, so the report labels them as GROUP_BY. The sets of columns in the FILTER and GROUP_BY report are candidates for column groups. See Also: • "Managing SQL Tuning Sets" • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS package 14.1.3 Creating Column Groups Detected During Workload Monitoring You can use the DBMS_STATS.CREATE_EXTENDED_STATS function to create column groups that were detected previously by executing DBMS_STATS.SEED_COL_USAGE. Assumptions This tutorial assumes that you have performed the steps in "Detecting Useful Column Groups for a Specific Workload". To create column groups: 1. Create column groups for the customers_test table based on the usage information captured during the monitoring window. For example, run the following query: SELECT DBMS_STATS.CREATE_EXTENDED_STATS(user, 'customers_test') FROM DUAL; Sample output appears below: ########################################################################### EXTENSIONS FOR SH.CUSTOMERS_TEST ................................ 1. (CUST_CITY, CUST_STATE_PROVINCE, COUNTRY_ID) :SYS_STUMZ$C3AIHLPBROI#SKA58H_N created 2. (CUST_STATE_PROVINCE, COUNTRY_ID):SYS_STU#S#WF25Z#QAHIHE#MOFFMM_ created ########################################################################### The database created two column groups for customers_test: one column group for the filter predicate and one group for the GROUP BY operation. 2. Regather table statistics. 14-9 Chapter 14 Managing Column Group Statistics Run GATHER_TABLE_STATS to regather the statistics for customers_test: EXEC DBMS_STATS.GATHER_TABLE_STATS(user,'customers_test'); 3. As user sh, run explain plans for two queries in the workload. Check the USER_TAB_COL_STATISTICS view to determine which additional statistics were created by the database: SELECT COLUMN_NAME, NUM_DISTINCT, HISTOGRAM FROM USER_TAB_COL_STATISTICS WHERE TABLE_NAME = 'CUSTOMERS_TEST' ORDER BY 1; Partial sample output appears below: CUST_CITY ... SYS_STU#S#WF25Z#QAHIHE#MOFFMM_ SYS_STUMZ$C3AIHLPBROI#SKA58H_N 620 HEIGHT BALANCED 145 NONE 620 HEIGHT BALANCED This example shows the two column group names returned from the DBMS_STATS.CREATE_EXTENDED_STATS function. The column group created on CUST_CITY, CUST_STATE_PROVINCE, and COUNTRY_ID has a height-balanced histogram. 4. Explain the plans again. The following examples show the explain plans for two queries on the customers_test table: EXPLAIN PLAN FOR SELECT * FROM customers_test WHERE cust_city = 'Los Angeles' AND cust_state_province = 'CA' AND country_id = 52790; SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY('plan_table', null,'basic rows')); EXPLAIN PLAN FOR SELECT country_id, cust_state_province, count(cust_city) FROM customers_test GROUP BY country_id, cust_state_province; SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY('plan_table', null,'basic rows')); The new plans show more accurate cardinality estimates: ---------------------------------------------------| Id | Operation | Name | Rows | ---------------------------------------------------| 0 | SELECT STATEMENT | | 1093 | | 1 | TABLE ACCESS FULL| CUSTOMERS_TEST | 1093 | ---------------------------------------------------8 rows selected. Plan hash value: 3050654408 ----------------------------------------------------| Id | Operation | Name | Rows | 14-10 Chapter 14 Managing Column Group Statistics ----------------------------------------------------| 0 | SELECT STATEMENT | | 145 | | 1 | HASH GROUP BY | | 145 | | 2 | TABLE ACCESS FULL| CUSTOMERS_TEST | 55500 | ----------------------------------------------------9 rows selected. See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS package 14.1.4 Creating and Gathering Statistics on Column Groups Manually In some cases, you may know the column group that you want to create. The METHOD_OPT argument of the DBMS_STATS.GATHER_TABLE_STATS function can create and gather statistics on a column group automatically. You can create a new column group by specifying the group of columns using FOR COLUMNS. Assumptions This tutorial assumes the following: • You want to create a column group for the cust_state_province and country_id columns in the customers table in sh schema. • You want to gather statistics (including histograms) on the entire table and the new column group. To create a column group and gather statistics for this group: 1. In SQL*Plus, log in to the database as the sh user. 2. Create the column group and gather statistics. For example, execute the following PL/SQL program: BEGIN DBMS_STATS.GATHER_TABLE_STATS( 'sh','customers', METHOD_OPT => 'FOR ALL COLUMNS SIZE SKEWONLY ' || 'FOR COLUMNS SIZE SKEWONLY (cust_state_province,country_id)' ); END; / See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GATHER_TABLE_STATS procedure 14-11 Chapter 14 Managing Column Group Statistics 14.1.5 Displaying Column Group Information To obtain the name of a column group, use the DBMS_STATS.SHOW_EXTENDED_STATS_NAME function or a database view. You can also use views to obtain information such as the number of distinct values, and whether the column group has a histogram. Assumptions This tutorial assumes the following: • You created a column group for the cust_state_province and country_id columns in the customers table in sh schema. • You want to determine the column group name, the number of distinct values, and whether a histogram has been created for a column group. To monitor a column group: 1. Start SQL*Plus and connect to the database as the sh user. 2. To determine the column group name, do one of the following. • Execute the SHOW_EXTENDED_STATS_NAME function. For example, run the following PL/SQL program: SELECT SYS.DBMS_STATS.SHOW_EXTENDED_STATS_NAME( 'sh','customers', '(cust_state_province,country_id)' ) col_group_name FROM DUAL; The output is similar to the following: COL_GROUP_NAME ---------------SYS_STU#S#WF25Z#QAHIHE#MOFFMM_ • Query the USER_STAT_EXTENSIONS view. For example, run the following query: SELECT EXTENSION_NAME, EXTENSION FROM USER_STAT_EXTENSIONS WHERE TABLE_NAME='CUSTOMERS'; EXTENSION_NAME EXTENSION ----------------------------------------------------------------------SYS_STU#S#WF25Z#QAHIHE#MOFFMM_ ("CUST_STATE_PROVINCE","COUNTRY_ID") 3. Query the number of distinct values and find whether a histogram has been created for a column group. For example, run the following query: SELECT FROM WHERE AND AND e.EXTENSION col_group, t.NUM_DISTINCT, t.HISTOGRAM USER_STAT_EXTENSIONS e, USER_TAB_COL_STATISTICS t e.EXTENSION_NAME=t.COLUMN_NAME e.TABLE_NAME=t.TABLE_NAME t.TABLE_NAME='CUSTOMERS'; COL_GROUP NUM_DISTINCT HISTOGRAM 14-12 Chapter 14 Managing Expression Statistics ------------------------------------------------------------------("COUNTRY_ID","CUST_STATE_PROVINCE") 145 FREQUENCY See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.SHOW_EXTENDED_STATS_NAME function 14.1.6 Dropping a Column Group Use the DBMS_STATS.DROP_EXTENDED_STATS function to delete a column group from a table. Assumptions This tutorial assumes the following: • You created a column group for the cust_state_province and country_id columns in the customers table in sh schema. • You want to drop the column group. To drop a column group: 1. Start SQL*Plus and connect to the database as the sh user. 2. Drop the column group. For example, the following PL/SQL program deletes a column group from the customers table: BEGIN DBMS_STATS.DROP_EXTENDED_STATS( 'sh', 'customers', '(cust_state_province, country_id)' ); END; / See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.DROP_EXTENDED_STATS function 14.2 Managing Expression Statistics The type of extended statistics known as expression statistics improve optimizer estimates when a WHERE clause has predicates that use expressions. This section contains the following topics: • About Expression Statistics For an expression in the form (function(col)=constant) applied to a WHERE clause column, the optimizer does not know how this function affects predicate cardinality 14-13 Chapter 14 Managing Expression Statistics unless a function-based index exists. However, you can gather expression statistics on the expression(function(col) itself. • Creating Expression Statistics You can use DBMS_STATS to create statistics for a user-specified expression. • Displaying Expression Statistics To obtain information about expression statistics, use the database view DBA_STAT_EXTENSIONS and the DBMS_STATS.SHOW_EXTENDED_STATS_NAME function. • Dropping Expression Statistics To delete a column group from a table, use the DBMS_STATS.DROP_EXTENDED_STATS function. 14.2.1 About Expression Statistics For an expression in the form (function(col)=constant) applied to a WHERE clause column, the optimizer does not know how this function affects predicate cardinality unless a function-based index exists. However, you can gather expression statistics on the expression(function(col) itself. The following graphic shows the optimizer using statistics to generate a plan for a query that uses a function. The top shows the optimizer checking statistics for the column. The bottom shows the optimizer checking statistics corresponding to the expression used in the query. The expression statistics yield more accurate estimates. Figure 14-2 Expression Statistics SELECT * FROM sh.customers WHERE LOWER (cust_state_province) = ‘ca’ Optimizer Use Expression Statistics Yes Do expression statistics exist? No Use Default Column Statistics LOWER(cust_state_province) Expression Statistics cust_state_province Column Statistics Optimal Estimate Suboptimal Estimate As shown in Figure 14-2, when expression statistics are not available, the optimizer can produce suboptimal plans. This section contains the following topics: 14-14 Chapter 14 Managing Expression Statistics • When Expression Statistics Are Useful: Example See Also: Oracle Database SQL Language Reference to learn about SQL functions 14.2.1.1 When Expression Statistics Are Useful: Example The following query of the sh.customers table shows that 3341 customers are in the state of California: sys@PROD> SELECT COUNT(*) FROM sh.customers WHERE cust_state_province='CA'; COUNT(*) ---------3341 Consider the plan for the same query with the LOWER() function applied: sys@PROD> EXPLAIN PLAN FOR 2 SELECT * FROM sh.customers WHERE LOWER(cust_state_province)='ca'; Explained. sys@PROD> select * from table(dbms_xplan.display); PLAN_TABLE_OUTPUT ------------------------------------------------------------------------------Plan hash value: 2008213504 ------------------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 555 | 108K| 406 (1)| 00:00:05 | |* 1 | TABLE ACCESS FULL| CUSTOMERS | 555 | 108K| 406 (1)| 00:00:05 | ------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------1 - filter(LOWER("CUST_STATE_PROVINCE")='ca') Because no expression statistics exist for LOWER(cust_state_province)='ca', the optimizer estimate is significantly off. You can use DBMS_STATS procedures to correct these estimates. 14.2.2 Creating Expression Statistics You can use DBMS_STATS to create statistics for a user-specified expression. You can use either of the following program units: • GATHER_TABLE_STATS procedure • CREATE_EXTENDED_STATISTICS function followed by the GATHER_TABLE_STATS procedure 14-15 Chapter 14 Managing Expression Statistics Assumptions This tutorial assumes the following: • Selectivity estimates are inaccurate for queries of sh.customers that use the UPPER(cust_state_province) function. • You want to gather statistics on the UPPER(cust_state_province) expression. To create expression statistics: 1. Start SQL*Plus and connect to the database as the sh user. 2. Gather table statistics. For example, run the following command, specifying the function in the method_opt argument: BEGIN DBMS_STATS.GATHER_TABLE_STATS( 'sh' , 'customers' , method_opt => 'FOR ALL COLUMNS SIZE SKEWONLY ' || 'FOR COLUMNS (LOWER(cust_state_province)) SIZE SKEWONLY' ); END; See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GATHER_TABLE_STATS procedure 14.2.3 Displaying Expression Statistics To obtain information about expression statistics, use the database view DBA_STAT_EXTENSIONS and the DBMS_STATS.SHOW_EXTENDED_STATS_NAME function. You can also use views to obtain information such as the number of distinct values, and whether the column group has a histogram. Assumptions This tutorial assumes the following: • You created extended statistics for the LOWER(cust_state_province) expression. • You want to determine the column group name, the number of distinct values, and whether a histogram has been created for a column group. To monitor expression statistics: 1. Start SQL*Plus and connect to the database as the sh user. 2. Query the name and definition of the statistics extension. For example, run the following query: 14-16 Chapter 14 Managing Expression Statistics COL EXTENSION_NAME FORMAT a30 COL EXTENSION FORMAT a35 SELECT EXTENSION_NAME, EXTENSION FROM USER_STAT_EXTENSIONS WHERE TABLE_NAME='CUSTOMERS'; Sample output appears as follows: EXTENSION_NAME EXTENSION ------------------------------ -----------------------------SYS_STUBPHJSBRKOIK9O2YV3W8HOUE (LOWER("CUST_STATE_PROVINCE")) 3. Query the number of distinct values and find whether a histogram has been created for the expression. For example, run the following query: SELECT FROM WHERE AND AND e.EXTENSION expression, t.NUM_DISTINCT, t.HISTOGRAM USER_STAT_EXTENSIONS e, USER_TAB_COL_STATISTICS t e.EXTENSION_NAME=t.COLUMN_NAME e.TABLE_NAME=t.TABLE_NAME t.TABLE_NAME='CUSTOMERS'; EXPRESSION NUM_DISTINCT HISTOGRAM ------------------------------------------------------------------(LOWER("CUST_STATE_PROVINCE")) 145 FREQUENCY See Also: • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.SHOW_EXTENDED_STATS_NAME procedure • Oracle Database Reference to learn about the DBA_STAT_EXTENSIONS view 14.2.4 Dropping Expression Statistics To delete a column group from a table, use the DBMS_STATS.DROP_EXTENDED_STATS function. Assumptions This tutorial assumes the following: • You created extended statistics for the LOWER(cust_state_province) expression. • You want to drop the expression statistics. To drop expression statistics: 1. Start SQL*Plus and connect to the database as the sh user. 2. Drop the column group. For example, the following PL/SQL program deletes a column group from the customers table: BEGIN DBMS_STATS.DROP_EXTENDED_STATS( 14-17 Chapter 14 Managing Expression Statistics 'sh' , 'customers' , '(LOWER(cust_state_province))' ); END; / See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.DROP_EXTENDED_STATS procedure 14-18 15 Controlling the Use of Optimizer Statistics Using DBMS_STATS, you can specify when and how the optimizer uses statistics. This section contains the following topics: • Locking and Unlocking Optimizer Statistics You can lock statistics to prevent them from changing. • Publishing Pending Optimizer Statistics By default, the database automatically publishes statistics when the statistics collection ends. • Creating Artificial Optimizer Statistics for Testing To provide the optimizer with user-created statistics for testing purposes, you can use the DBMS_STATS.SET_*_STATS procedures. These procedures provide the optimizer with artificial values for the specified statistics. 15.1 Locking and Unlocking Optimizer Statistics You can lock statistics to prevent them from changing. After statistics are locked, you cannot make modifications to the statistics until the statistics have been unlocked. Locking procedures are useful in a static environment when you want to guarantee that the statistics and resulting plan never change. For example, you may want to prevent new statistics from being gathered on a table or schema by the DBMS_STATS_JOB process, such as highly volatile tables. When you lock statistics on a table, all dependent statistics are locked. The locked statistics include table statistics, column statistics, histograms, and dependent index statistics. To overwrite statistics even when they are locked, you can set the value of the FORCE argument in various DBMS_STATS procedures, for example, DELETE_*_STATS and RESTORE_*_STATS, to true. This section contains the following topics: • Locking Statistics The DBMS_STATS package provides two procedures for locking statistics: LOCK_SCHEMA_STATS and LOCK_TABLE_STATS. • Unlocking Statistics The DBMS_STATS package provides two procedures for unlocking statistics: UNLOCK_SCHEMA_STATS and UNLOCK_TABLE_STATS. 15.1.1 Locking Statistics The DBMS_STATS package provides two procedures for locking statistics: LOCK_SCHEMA_STATS and LOCK_TABLE_STATS. Assumptions This tutorial assumes the following: 15-1 Chapter 15 Locking and Unlocking Optimizer Statistics • You gathered statistics on the oe.orders table and on the hr schema. • You want to prevent the oe.orders table statistics and hr schema statistics from changing. To lock statistics: 1. Start SQL*Plus and connect to the database as the oe user. 2. Lock the statistics on oe.orders. For example, execute the following PL/SQL program: BEGIN DBMS_STATS.LOCK_TABLE_STATS('OE','ORDERS'); END; / 3. Connect to the database as the hr user. 4. Lock the statistics in the hr schema. For example, execute the following PL/SQL program: BEGIN DBMS_STATS.LOCK_SCHEMA_STATS('HR'); END; / See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.LOCK_TABLE_STATS procedure 15.1.2 Unlocking Statistics The DBMS_STATS package provides two procedures for unlocking statistics: UNLOCK_SCHEMA_STATS and UNLOCK_TABLE_STATS. Assumptions This tutorial assumes the following: • You locked statistics on the oe.orders table and on the hr schema. • You want to unlock these statistics. To unlock statistics: 1. Start SQL*Plus and connect to the database as the oe user. 2. Unlock the statistics on oe.orders. For example, execute the following PL/SQL program: BEGIN DBMS_STATS.UNLOCK_TABLE_STATS('OE','ORDERS'); END; / 3. Connect to the database as the hr user. 15-2 Chapter 15 Publishing Pending Optimizer Statistics 4. Unlock the statistics in the hr schema. For example, execute the following PL/SQL program: BEGIN DBMS_STATS.UNLOCK_SCHEMA_STATS('HR'); END; / See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.UNLOCK_TABLE_STATS procedure 15.2 Publishing Pending Optimizer Statistics By default, the database automatically publishes statistics when the statistics collection ends. Alternatively, you can use pending statistics to save the statistics and not publish them immediately after the collection. This technique is useful for testing queries in a session with pending statistics. When the test results are satisfactory, you can publish the statistics to make them available for the entire database. This section contains the following topics: • About Pending Optimizer Statistics The database stores pending statistics in the data dictionary just as for published statistics. • User Interfaces for Publishing Optimizer Statistics You can use the DBMS_STATS package to perform operations relating to publishing statistics. • Managing Published and Pending Statistics This section explains how to use DBMS_STATS program units to change the publishing behavior of optimizer statistics, and also to export and delete these statistics. 15.2.1 About Pending Optimizer Statistics The database stores pending statistics in the data dictionary just as for published statistics. By default, the optimizer uses published statistics. You can change the default behavior by setting the OPTIMIZER_USE_PENDING_STATISTICS initialization parameter to true (the default is false). The top part of the following graphic shows the optimizer gathering statistics for the sh.customers table and storing them in the data dictionary with pending status. The bottom part of the diagram shows the optimizer using only published statistics to process a query of sh.customers. 15-3 Chapter 15 Publishing Pending Optimizer Statistics Figure 15-1 Published and Pending Statistics Data Dictionary GATHER_TABLE_STATS Optimizer Statistics 1 0 0 1 1 1 0 1 0 0 0 1 Optimizer 0 0 1 0 0 0 1 1 0 0 1 0 Published Statistics Pending Statistics Publishing preferences set to false Customers Table Data Dictionary Optimizer Statistics SELECT ... FROM customers Optimizer 1 0 0 1 1 1 0 1 0 0 0 1 0 0 1 0 0 0 1 1 0 0 1 0 Published Statistics Pending Statistics OPTIMIZER_USE_PENDING_STATISTICS=false In some cases, the optimizer can use a combination of published and pending statistics. For example, the database stores both published and pending statistics for the customers table. For the orders table, the database stores only published statistics. If OPTIMIZER_USE_PENDING_STATS = true, then the optimizer uses pending statistics for customers and published statistics for orders. If OPTIMIZER_USE_PENDING_STATS = false, then the optimizer uses published statistics for customers and orders. See Also: Oracle Database Reference to learn about the OPTIMIZER_USE_PENDING_STATISTICS initialization parameter 15-4 Chapter 15 Publishing Pending Optimizer Statistics 15.2.2 User Interfaces for Publishing Optimizer Statistics You can use the DBMS_STATS package to perform operations relating to publishing statistics. The following table lists the relevant program units. Table 15-1 Statistics DBMS_STATS Program Units Relevant for Publishing Optimizer Program Unit Description GET_PREFS Check whether the statistics are automatically published as soon as DBMS_STATS gathers them. For the parameter PUBLISH, true indicates that the statistics must be published when the database gathers them, whereas false indicates that the database must keep the statistics pending. SET_TABLE_PREFS Set the PUBLISH setting to true or false at the table level. SET_SCHEMA_PREFS Set the PUBLISH setting to true or false at the schema level. PUBLISH_PENDING_STATS Publish valid pending statistics for all objects or only specified objects. DELETE_PENDING_STATS Delete pending statistics. EXPORT_PENDING_STATS Export pending statistics. The initialization parameter OPTIMIZER_USE_PENDING_STATISTICS determines whether the database uses pending statistics when they are available. The default value is false, which means that the optimizer uses only published statistics. Set to true to specify that the optimizer uses any existing pending statistics instead. The best practice is to set this parameter at the session level rather than at the database level. You can use access information about published statistics from data dictionary views. Table 15-2 lists relevant views. Table 15-2 Views Relevant for Publishing Optimizer Statistics View Description USER_TAB_STATISTICS Displays optimizer statistics for the tables accessible to the current user. USER_TAB_COL_STATISTICS Displays column statistics and histogram information extracted from ALL_TAB_COLUMNS. USER_PART_COL_STATISTICS Displays column statistics and histogram information for the table partitions owned by the current user. USER_SUBPART_COL_STATISTICS Describes column statistics and histogram information for subpartitions of partitioned objects owned by the current user. USER_IND_STATISTICS Displays optimizer statistics for the indexes accessible to the current user. 15-5 Chapter 15 Publishing Pending Optimizer Statistics Table 15-2 (Cont.) Views Relevant for Publishing Optimizer Statistics View Description USER_TAB_PENDING_STATS Describes pending statistics for tables, partitions, and subpartitions accessible to the current user. USER_COL_PENDING_STATS Describes the pending statistics of the columns accessible to the current user. USER_IND_PENDING_STATS Describes the pending statistics for tables, partitions, and subpartitions accessible to the current user collected using the DBMS_STATS package. See Also: • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS package • Oracle Database Reference to learn about USER_TAB_PENDING_STATS and related views 15.2.3 Managing Published and Pending Statistics This section explains how to use DBMS_STATS program units to change the publishing behavior of optimizer statistics, and also to export and delete these statistics. Assumptions This tutorial assumes the following: • You want to change the preferences for the sh.customers and sh.sales tables so that newly collected statistics have pending status. • You want the current session to use pending statistics. • You want to gather and publish pending statistics on the sh.customers table. • You gather the pending statistics on the sh.sales table, but decide to delete them without publishing them. • You want to change the preferences for the sh.customers and sh.sales tables so that newly collected statistics are published. To manage published and pending statistics: 1. Start SQL*Plus and connect to the database as user sh. 2. Query the global optimizer statistics publishing setting. Run the following query (sample output included): sh@PROD> SELECT DBMS_STATS.GET_PREFS('PUBLISH') PUBLISH FROM DUAL; PUBLISH ------TRUE 15-6 Chapter 15 Publishing Pending Optimizer Statistics The value true indicates that the database publishes statistics as it gathers them. Every table uses this value unless a specific table preference has been set. When using GET_PREFS, you can also specify a schema and table name. The function returns a table preference if it is set. Otherwise, the function returns the global preference. 3. Query the pending statistics. For example, run the following query (sample output included): sh@PROD> SELECT * FROM USER_TAB_PENDING_STATS; no rows selected This example shows that the database currently stores no pending statistics for the sh schema. 4. Change the publishing preferences for the sh.customers table. For example, execute the following procedure so that statistics are marked as pending: BEGIN DBMS_STATS.SET_TABLE_PREFS('sh', 'customers', 'publish', 'false'); END; / Subsequently, when you gather statistics on the customers table, the database does not automatically publish statistics when the gather job completes. Instead, the database stores the newly gathered statistics in the USER_TAB_PENDING_STATS table. 5. Gather statistics for sh.customers. For example, run the following program: BEGIN DBMS_STATS.GATHER_TABLE_STATS('sh','customers'); END; / 6. Query the pending statistics. For example, run the following query (sample output included): sh@PROD> SELECT TABLE_NAME, NUM_ROWS FROM USER_TAB_PENDING_STATS; TABLE_NAME NUM_ROWS ------------------------------ ---------CUSTOMERS 55500 This example shows that the database now stores pending statistics for the sh.customers table. 7. Instruct the optimizer to use the pending statistics in this session. Set the initialization parameter OPTIMIZER_USE_PENDING_STATISTICS to true as shown: ALTER SESSION SET OPTIMIZER_USE_PENDING_STATISTICS = true; 8. Run a workload. 15-7 Chapter 15 Publishing Pending Optimizer Statistics The following example changes the email addresses of all customers named Bruce Chalmers: UPDATE SET WHERE AND COMMIT; sh.customers cust_email='ChalmersB@company.com' cust_first_name = 'Bruce' cust_last_name = 'Chalmers'; The optimizer uses the pending statistics instead of the published statistics when compiling all SQL statements in this session. 9. Publish the pending statistics for sh.customers. For example, execute the following program: BEGIN DBMS_STATS.PUBLISH_PENDING_STATS('SH','CUSTOMERS'); END; / 10. Change the publishing preferences for the sh.sales table. For example, execute the following program: BEGIN DBMS_STATS.SET_TABLE_PREFS('sh', 'sales', 'publish', 'false'); END; / Subsequently, when you gather statistics on the sh.sales table, the database does not automatically publish statistics when the gather job completes. Instead, the database stores the statistics in the USER_TAB_PENDING_STATS table. 11. Gather statistics for sh.sales. For example, run the following program: BEGIN DBMS_STATS.GATHER_TABLE_STATS('sh','sales'); END; / 12. Delete the pending statistics for sh.sales. Assume you change your mind and now want to delete pending statistics for sh.sales. Run the following program: BEGIN DBMS_STATS.DELETE_PENDING_STATS('sh','sales'); END; / 13. Change the publishing preferences for the sh.customers and sh.sales tables back to their default setting. For example, execute the following program: BEGIN DBMS_STATS.SET_TABLE_PREFS('sh', 'customers', 'publish', null); DBMS_STATS.SET_TABLE_PREFS('sh', 'sales', 'publish', null); END; / 15-8 Chapter 15 Creating Artificial Optimizer Statistics for Testing 15.3 Creating Artificial Optimizer Statistics for Testing To provide the optimizer with user-created statistics for testing purposes, you can use the DBMS_STATS.SET_*_STATS procedures. These procedures provide the optimizer with artificial values for the specified statistics. This section contains the following topics: • About Artificial Optimizer Statistics For testing purposes, you can manually create artificial statistics for a table, index, or the system using the DBMS_STATS.SET_*_STATS procedures. These procedures insert the artificial statistics into the data dictionary directly (when stattab is null) or into a user-created table. • Setting Artificial Optimizer Statistics for a Table This topic explains how to set artificial statistics for a table using DBMS_STATS.SET_TABLE_STATS. The basic steps are the same for SET_INDEX_STATS and SET_SYSTEM_STATS. • Setting Optimizer Statistics: Example This example shows how to gather optimizer statistics for a table, set artificial statistics, and then compare the plans that the optimizer chooses based on the differing statistics. 15.3.1 About Artificial Optimizer Statistics For testing purposes, you can manually create artificial statistics for a table, index, or the system using the DBMS_STATS.SET_*_STATS procedures. These procedures insert the artificial statistics into the data dictionary directly (when stattab is null) or into a usercreated table. Caution: The DBMS_STATS.SET_*_STATS procedures are intended for development testing only. Do not use them in a production database. If you set statistics in the data dictionary, then Oracle Database considers the set statistics as the “real” statistics, which means that statistics gathering jobs may not re-gather artificial statistics when they do not meet the criteria for staleness. The most typical use cases for the DBMS_STATS.SET_*_STATS procedures are: • Showing how execution plans change as the numbers of rows or blocks in a table change For example, SET_TABLE_STATS can set number of rows and blocks in a small or empty table to a large number. When you execute a query using the altered statistics, the optimizer may change the execution plan. For example, the increased row count may lead the optimizer to choose an index scan rather than a full table scan. By experimenting with different values, you can see how the optimizer will change its execution plan over time. • Creating realistic statistics for temporary tables 15-9 Chapter 15 Creating Artificial Optimizer Statistics for Testing You may want to see what the optimizer does when a large temporary table is referenced in multiple SQL statements. You can create a regular table, load representative data, and then use GET_TABLE_STATS to retrieve the statistics. After you create the temporary table, you can “deceive” the optimizer into using these statistics by invoking SET_TABLE_STATS. Optionally, you can specify a unique ID for statistics in a user-created table. The SET_*_STATS procedures have corresponding GET_*_STATS procedures. Table 15-3 DBMS_STATS Procedures for Setting Optimizer Statistics DBMS_STATS Procedure Description SET_TABLE_STATS Sets table or partition statistics using parameters such as numrows, numblks, avgrlen, and so on. If the database uses the In-Memory Column store, you can set im_imcu_count to the number of IMCUs in the table or partition, and im_block_count to the number of blocks in the table or partition. For an external table, scanrate specifies the rate at which data is scanned in MB/second. The optimizer uses the cached data to estimate the number of cached blocks for index or statistics table access. The total cost is the I/O cost of reading data blocks from disk, the CPU cost of reading cached blocks from the buffer cache, and the CPU cost of processing the data. SET_COLUMN_STATS Sets column statistics using parameters such as distcnt, density, nullcnt, and so on. In the version of this procedure that deals with user-defined statistics, use stattypname to specify the type of statistics to store in the data dictionary. SET_SYSTEM_STATS Sets system statistics using parameters such as iotfrspeed, sreadtim, cpuspeed, and so on. SET_INDEX_STATS Sets index statistics using parameters such as numrows, numlblks, avglblk, clstfct, indlevel, and so on. In the version of this procedure that deals with user-defined statistics, use stattypname to specify the type of statistics to store in the data dictionary. See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_STATS.SET_TABLE_STATS and the other procedures for setting optimizer statistics 15.3.2 Setting Artificial Optimizer Statistics for a Table This topic explains how to set artificial statistics for a table using DBMS_STATS.SET_TABLE_STATS. The basic steps are the same for SET_INDEX_STATS and SET_SYSTEM_STATS. Note the following task prerequisites: 15-10 Chapter 15 Creating Artificial Optimizer Statistics for Testing • For an object not owned by SYS, you must be the owner of the object, or have the ANALYZE ANY privilege. • For an object owned by SYS, you must have the ANALYZE ANY DICTIONARY privilege or the SYSDBA privilege. • When invoking GET_*_STATS for a table, column, or index, the referenced object must exist. This task assumes the following: • You have the required privileges to use DBMS_STATS.SET_TABLE_STATS for the specified table. • You intend to store the statistics in the data dictionary. 1. In SQL*Plus, log in to the database as a user with the required privileges. 2. Run the DBMS_STATS.SET_TABLE_STATS procedure, specifying the appropriate parameters for the statistics. Typical parameters include the following: • ownname (not null) This parameter specifies the name of the schema containing the table. • tabname (not null) This parameter specifies the name of the table whose statistics you intend to set. • partname This parameter specifies the name of a partition of the table. • numrows This parameter specifies the number of rows in the table. • numblks This parameter specifies the number of blocks in the table. 3. Query the table. 4. Optionally, to determine how the statistics affected the optimizer, query the execution plan. 5. Optionally, to perform further testing, return to Step 2 and reset the optimizer statistics. 15.3.3 Setting Optimizer Statistics: Example This example shows how to gather optimizer statistics for a table, set artificial statistics, and then compare the plans that the optimizer chooses based on the differing statistics. This example assumes: • You are logged in to the database as a user with DBA privileges. • You want to test when the optimizer chooses an index scan. 1. Create a table called contractors, and index the salary column. 15-11 Chapter 15 Creating Artificial Optimizer Statistics for Testing CREATE TABLE contractors ( con_id NUMBER, last_name VARCHAR2(50), salary NUMBER, CONSTRAINT cond_id_pk PRIMARY KEY(con_id) ); CREATE INDEX salary_ix ON contractors(salary); 2. Insert a single row into this table. INSERT INTO contractors VALUES (8, 'JONES',1000); COMMIT; 3. Gather statistics for the table. EXECUTE DBMS_STATS.GATHER_TABLE_STATS( user, tabname => 'CONTRACTORS' ); 4. Query the number of rows for the table and index (sample output included): SQL> SELECT NUM_ROWS FROM USER_TABLES WHERE TABLE_NAME = 'CONTRACTORS'; NUM_ROWS ---------1 SQL> SELECT NUM_ROWS FROM USER_INDEXES WHERE INDEX_NAME = 'SALARY_IX'; NUM_ROWS ---------1 5. Query contractors whose salary is 1000, using the dynamic_sampling hint to disable dynamic sampling: SELECT /*+ dynamic_sampling(contractors 0) */ * FROM contractors WHERE salary = 1000; 6. Query the execution plan chosen by the optimizer (sample output included): SQL> SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR); SQL_ID cy0wzytc16g9n, child number 0 ------------------------------------SELECT /*+ dynamic_sampling(contractors 0) */ * FROM contractors WHERE salary = 1000 Plan hash value: 5038823 ---------------------------------------------------------------------| Id | Operation | Name |Rows|Bytes|Cost (%CPU)| Time| ---------------------------------------------------------------------| 0 | SELECT STATEMENT | | | | 2 (100)| | |* 1 | TABLE ACCESS FULL| CONTRACTORS | 1 | 12 | 2 (0)| 00:00:01 | ---------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------1 - filter("SALARY"=1000) 19 rows selected. 15-12 Chapter 15 Creating Artificial Optimizer Statistics for Testing Because only 1 row exists in the table, the optimizer chooses a full table scan over an index range scan. 7. Use SET_TABLE_STATS and SET_INDEX_STATS to simulate statistics for a table with 2000 rows stored in 10 data blocks: BEGIN DBMS_STATS.SET_TABLE_STATS( ownname => user , tabname => 'CONTRACTORS' , numrows => 2000 , numblks => 10 ); END; / BEGIN DBMS_STATS.SET_INDEX_STATS( ownname => user , indname => 'SALARY_IX' , numrows => 2000 ); END; / 8. Query the number of rows for the table and index (sample output included): SQL> SELECT NUM_ROWS FROM USER_TABLES WHERE TABLE_NAME = 'CONTRACTORS'; NUM_ROWS ---------2000 SQL> SELECT NUM_ROWS FROM USER_INDEXES WHERE INDEX_NAME = 'SALARY_IX'; NUM_ROWS ---------2000 Now the optimizer believes that the table contains 2000 rows in 10 blocks, even though only 1 row actually exists in one block. 9. Flush the shared pool to eliminate possibility of plan reuse, and then execute the same query of contractors: ALTER SYSTEM FLUSH SHARED_POOL; SELECT /*+ dynamic_sampling(contractors 0) */ * FROM contractors WHERE salary = 1000; 10. Query the execution plan chosen by the optimizer based on the artificial statistics (sample output included): SQL> SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR); SQL_ID cy0wzytc16g9n, child number 0 ------------------------------------SELECT /*+ dynamic_sampling(contractors 0) */ * FROM contractors WHERE salary = 1000 Plan hash value: 996794789 --------------------------------------------------------------------------------|Id| Operation | Name |Rows|Bytes|Cost(%CPU)|Time| 15-13 Chapter 15 Creating Artificial Optimizer Statistics for Testing --------------------------------------------------------------------------------| 0| SELECT STATEMENT | | | |3(100)| | | 1| TABLE ACCESS BY INDEX ROWID BATCHED|CONTRACTORS|2000|24000|3 (34)|00:00:01| |*2| INDEX RANGE SCAN |SALARY_IX |2000| |1 (0)|00:00:01| --------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------2 - access("SALARY"=1000) 20 rows selected. Based on the artificially generated statistics for the number of rows and block distribution, the optimizer considers an index range scan more cost-effective. 15-14 16 Managing Historical Optimizer Statistics This chapter how to retain, report on, and restore non-current statistics. This chapter contains the following topics: • Restoring Optimizer Statistics You can use DBMS_STATS to restore old versions of statistics that are stored in the data dictionary. • Managing Optimizer Statistics Retention By default, the database retains optimizer statistics for 31 days, after which time the statistics are scheduled for purging. • Reporting on Past Statistics Gathering Operations You can use DBMS_STATS functions to report on a specific statistics gathering operation or on operations that occurred during a specified time. 16.1 Restoring Optimizer Statistics You can use DBMS_STATS to restore old versions of statistics that are stored in the data dictionary. This topic contains the following topics: • About Restore Operations for Optimizer Statistics Whenever statistics in the data dictionary are modified, the database automatically saves old versions of statistics. If newly collected statistics lead to suboptimal execution plans, then you may want to revert to the previous statistics. • Guidelines for Restoring Optimizer Statistics Restoring statistics is similar to importing and exporting statistics. • Restrictions for Restoring Optimizer Statistics When restoring previous versions of statistics, various limitations apply. • Restoring Optimizer Statistics Using DBMS_STATS You can restore statistics using the DBMS_STATS.RESTORE_*_STATS procedures. 16.1.1 About Restore Operations for Optimizer Statistics Whenever statistics in the data dictionary are modified, the database automatically saves old versions of statistics. If newly collected statistics lead to suboptimal execution plans, then you may want to revert to the previous statistics. Restoring optimizer statistics can aid in troubleshooting suboptimal plans. The following graphic illustrates a timeline for restoring statistics. In the graphic, statistics collection occurs on August 10 and August 20. On August 24, the DBA determines that the current statistics may be causing the optimizer to generate suboptimal plans. On August 25, the administrator restores the statistics collected on August 10. 16-1 Chapter 16 Restoring Optimizer Statistics Figure 16-1 Restoring Optimizer Statistics 8/10 8/20 8/24 8/25 AAAAA BBBBB BBBBB AAAAA Statistics Gathered Statistics Gathered Recent Statistics May Be Causing Suboptimal Plans 8/10 Statistics Restored 16.1.2 Guidelines for Restoring Optimizer Statistics Restoring statistics is similar to importing and exporting statistics. In general, restore statistics instead of exporting them in the following situations: • You want to recover older versions of the statistics. For example, you want to restore the optimizer behavior to an earlier date. • You want the database to manage the retention and purging of statistics histories. Export statistics rather than restoring them in the following situations: • You want to experiment with multiple sets of statistics and change the values back and forth. • You want to move the statistics from one database to another database. For example, moving statistics from a production system to a test system. • You want to preserve a known set of statistics for a longer period than the desired retention date for restoring statistics. See Also: Oracle Database PL/SQL Packages and Types Reference for an overview of the procedures for restoring and importing statistics 16.1.3 Restrictions for Restoring Optimizer Statistics When restoring previous versions of statistics, various limitations apply. Limitations include the following: • DBMS_STATS.RESTORE_*_STATS procedures cannot restore user-defined statistics. • Old versions of statistics are not stored when the ANALYZE command has been used for collecting statistics. • When you drop a table, workload information used by the auto-histogram gathering feature and saved statistics history used by the RESTORE_*_STATS procedures is lost. Without this data, these features do not function properly. To remove all rows from a table, and to restore these statistics with DBMS_STATS, use TRUNCATE instead of dropping and re-creating the same table. 16-2 Chapter 16 Restoring Optimizer Statistics 16.1.4 Restoring Optimizer Statistics Using DBMS_STATS You can restore statistics using the DBMS_STATS.RESTORE_*_STATS procedures. The procedures listed in the following table accept a timestamp as an argument and restore statistics as of the specified time (as_of_timestamp). Table 16-1 DBMS_STATS Restore Procedures Procedure Description RESTORE_DICTIONARY_STATS Restores statistics of all dictionary tables (tables of SYS, SYSTEM, and RDBMS component schemas) as of a specified timestamp. RESTORE_FIXED_OBJECTS_STATS Restores statistics of all fixed tables as of a specified timestamp. RESTORE_SCHEMA_STATS Restores statistics of all tables of a schema as of a specified timestamp. RESTORE_SYSTEM_STATS Restores system statistics as of a specified timestamp. RESTORE_TABLE_STATS Restores statistics of a table as of a specified timestamp. The procedure also restores statistics of associated indexes and columns. If the table statistics were locked at the specified timestamp, then the procedure locks the statistics. Dictionary views display the time of statistics modifications. You can use the following views to determine the time stamp to be use for the restore operation: • The DBA_OPTSTAT_OPERATIONS view contain history of statistics operations performed at schema and database level using DBMS_STATS. • The DBA_TAB_STATS_HISTORY views contains a history of table statistics modifications. Assumptions This tutorial assumes the following: • After the most recent statistics collection for the oe.orders table, the optimizer began choosing suboptimal plans for queries of this table. • You want to restore the statistics from before the most recent statistics collection to see if the plans improve. To restore optimizer statistics: 1. Start SQL*Plus and connect to the database with administrator privileges. 2. Query the statistics history for oe.orders. For example, run the following query: COL TABLE_NAME FORMAT a10 SELECT TABLE_NAME, TO_CHAR(STATS_UPDATE_TIME,'YYYY-MM-DD:HH24:MI:SS') AS STATS_MOD_TIME FROM DBA_TAB_STATS_HISTORY WHERE TABLE_NAME='ORDERS' 16-3 Chapter 16 Managing Optimizer Statistics Retention AND OWNER='OE' ORDER BY STATS_UPDATE_TIME DESC; Sample output is as follows: TABLE_NAME ---------ORDERS ORDERS 3. STATS_MOD_TIME ------------------2012-08-20:11:36:38 2012-08-10:11:06:20 Restore the optimizer statistics to the previous modification time. For example, restore the oe.orders table statistics to August 10, 2012: BEGIN DBMS_STATS.RESTORE_TABLE_STATS( 'OE','ORDERS', TO_TIMESTAMP('2012-08-10:11:06:20','YYYY-MM-DD:HH24:MI:SS') ); END; / You can specify any date between 8/10 and 8/20 because DBMS_STATS restores statistics as of the specified time. See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about the DBMS_STATS.RESTORE_TABLE_STATS procedure 16.2 Managing Optimizer Statistics Retention By default, the database retains optimizer statistics for 31 days, after which time the statistics are scheduled for purging. You can use the DBMS_STATS package to determine the retention period, change the period, and manually purge old statistics. This section contains the following topics: • Obtaining Optimizer Statistics History You can use DBMS_STATS procedures to obtain historical information for optimizer statistics. • Changing the Optimizer Statistics Retention Period You can configure the retention period using the DBMS_STATS.ALTER_STATS_HISTORY_RETENTION procedure. The default is 31 days. • Purging Optimizer Statistics Automatic purging is enabled when the STATISTICS_LEVEL initialization parameter is set to TYPICAL or ALL. 16-4 Chapter 16 Managing Optimizer Statistics Retention 16.2.1 Obtaining Optimizer Statistics History You can use DBMS_STATS procedures to obtain historical information for optimizer statistics. Historical information is useful when you want to determine how long the database retains optimizer statistics, and how far back these statistics can be restored. You can use the following procedure to obtain information about the optimizer statistics history: • GET_STATS_HISTORY_RETENTION This function can retrieve the current statistics history retention value. • GET_STATS_HISTORY_AVAILABILITY This function retrieves the oldest time stamp when statistics history is available. Users cannot restore statistics to a time stamp older than the oldest time stamp. To obtain optimizer statistics history information: 1. Start SQL*Plus and connect to the database with the necessary privileges. 2. Execute the following PL/SQL program: DECLARE v_stats_retn NUMBER; v_stats_date DATE; BEGIN v_stats_retn := DBMS_STATS.GET_STATS_HISTORY_RETENTION; DBMS_OUTPUT.PUT_LINE('The retention setting is ' || v_stats_retn || '.'); v_stats_date := DBMS_STATS.GET_STATS_HISTORY_AVAILABILITY; DBMS_OUTPUT.PUT_LINE('Earliest restore date is ' || v_stats_date || '.'); END; / See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GET_STATS_HISTORY_RETENTION procedure 16.2.2 Changing the Optimizer Statistics Retention Period You can configure the retention period using the DBMS_STATS.ALTER_STATS_HISTORY_RETENTION procedure. The default is 31 days. Prerequisites To run this procedure, you must have either the SYSDBA privilege, or both the ANALYZE ANY DICTIONARY and ANALYZE ANY system privileges. Assumptions This tutorial assumes the following: • The current retention period for optimizer statistics is 31 days. 16-5 Chapter 16 Managing Optimizer Statistics Retention • You run queries annually as part of an annual report. To keep the statistics history for more than 365 days so that you have access to last year's plan (in case a suboptimal plan occurs now), you set the retention period to 366 days. • You want to create a PL/SQL procedure set_opt_stats_retention that you can use to change the optimizer statistics retention period. To change the optimizer statistics retention period: 1. Start SQL*Plus and connect to the database with the necessary privileges. 2. Create a procedure that changes the retention period. For example, create the following procedure: CREATE OR REPLACE PROCEDURE set_opt_stats_retention ( p_stats_retn IN NUMBER ) IS v_stats_retn NUMBER; BEGIN v_stats_retn := DBMS_STATS.GET_STATS_HISTORY_RETENTION; DBMS_OUTPUT.PUT_LINE('Old retention setting is ' ||v_stats_retn|| '.'); DBMS_STATS.ALTER_STATS_HISTORY_RETENTION(p_stats_retn); v_stats_retn := DBMS_STATS.GET_STATS_HISTORY_RETENTION; DBMS_OUTPUT.PUT_LINE('New retention setting is ' ||v_stats_retn|| '.'); END; / 3. Change the retention period to 366 days. For example, execute the procedure that you created in the previous step (sample output included): SQL> EXECUTE set_opt_stats_retention(366) The old retention setting is 31. The new retention setting is 366. PL/SQL procedure successfully completed. See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.ALTER_STATS_HISTORY_RETENTION procedure 16.2.3 Purging Optimizer Statistics Automatic purging is enabled when the STATISTICS_LEVEL initialization parameter is set to TYPICAL or ALL. The database purges all history older than the older of (current time - the ALTER_STATS_HISTORY_RETENTION setting) and (time of the most recent statistics gathering - 1). You can purge old statistics manually using the PURGE_STATS procedure. If you do not specify an argument, then this procedure uses the automatic purging policy. If you specify the before_timestamp parameter, then the database purges statistics saved before the specified timestamp. 16-6 Chapter 16 Reporting on Past Statistics Gathering Operations Prerequisites To run this procedure, you must have either the SYSDBA privilege, or both the ANALYZE ANY DICTIONARY and ANALYZE ANY system privileges. Assumptions This tutorial assumes that you want to purge statistics more than one week old. To purge optimizer statistics: 1. In SQL*Plus, log in to the database with the necessary privileges. 2. Execute the DBMS_STATS.PURGE_STATS procedure. For example, execute the procedure as follows: EXEC DBMS_STATS.PURGE_STATS( SYSDATE-7 ); See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.PURGE_STATS procedure 16.3 Reporting on Past Statistics Gathering Operations You can use DBMS_STATS functions to report on a specific statistics gathering operation or on operations that occurred during a specified time. Table 16-2 lists the functions. Table 16-2 DBMS_STATS Reporting Functions Function Description REPORT_STATS_OPERATIONS Generates a report of all statistics operations that occurred between two points in time. You can narrow the scope of the report to include only automatic statistics gathering runs. You can also provide a set of pluggable database (PDB) IDs so that the database reports only statistics operations from the specified PDBs. REPORT_SINGLE_STATS_OPERATION Generates a report of the specified operation. Optionally, you can specify a particular PDB ID in a container database (CDB). Assumptions This tutorial assumes that you want to generate HTML reports of the following: • All statistics gathering operations within the last day • The most recent statistics gathering operation 16-7 Chapter 16 Reporting on Past Statistics Gathering Operations To report on all operations in the past day: 1. Start SQL*Plus and connect to the database with administrator privileges. 2. Run the DBMS_STATS.REPORT_STATS_OPERATIONS function. For example, run the following commands: SET LINES 200 PAGES 0 SET LONG 100000 COLUMN REPORT FORMAT A200 VARIABLE my_report CLOB; BEGIN :my_report := DBMS_STATS.REPORT_STATS_OPERATIONS ( since => SYSDATE-1 , until => SYSDATE , detail_level => 'TYPICAL' , format => 'HTML' ); END; / The following graphic shows a sample report: 3. Run the DBMS_STATS.REPORT_SINGLE_STATS_OPERATION function for an individual operation. For example, run the following program to generate a report of operation 848: BEGIN :my_report :=DBMS_STATS.REPORT_SINGLE_STATS_OPERATION ( OPID => 848 , FORMAT => 'HTML' ); END; The following graphic shows a sample report: 16-8 Chapter 16 Reporting on Past Statistics Gathering Operations See Also: • "Graphical Interface for Optimizer Statistics Management" to learn about the Cloud Control GUI for statistics management • Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_STATS 16-9 17 Transporting Optimizer Statistics You can export and import optimizer statistics from the data dictionary to user-defined statistics tables. You can also copy statistics from one database to another database. This chapter contains the following topics: • About Transporting Optimizer Statistics When you transport optimizer statistics between databases, you must use DBMS_STATS to copy the statistics to and from a staging table, and tools to make the table contents accessible to the destination database. • Transporting Optimizer Statistics to a Test Database: Tutorial You can transport schema statistics from a production database to a test database using Oracle Data Pump. 17.1 About Transporting Optimizer Statistics When you transport optimizer statistics between databases, you must use DBMS_STATS to copy the statistics to and from a staging table, and tools to make the table contents accessible to the destination database. Importing and exporting are especially useful for testing an application using production statistics. You use DBMS_STATS.EXPORT_SCHEMA_STATS to export schema statistics from a production database to a test database so that developers can tune execution plans in a realistic environment before deploying applications. The following figure illustrates the process using Oracle Data Pump and ftp. Figure 17-1 Transporting Optimizer Statistics Production Database Test Database Data Dictionary EXPORT_SCHEMA_STATS Data Dictionary IMPORT_SCHEMA_STATS Staging Table Staging Table Data Pump Export Data Pump Import .dmp file Transport ftp, nfs .dmp file 17-1 Chapter 17 Transporting Optimizer Statistics to a Test Database: Tutorial As shown in Figure 17-1, the basic steps are as follows: 1. In the production database, copy the statistics from the data dictionary to a staging table using DBMS_STATS.EXPORT_SCHEMA_STATS. 2. Export the statistics from the staging table to a .dmp file using Oracle Data Pump. 3. Transfer the .dmp file from the production host to the test host using a transfer tool such as ftp. 4. In the test database, import the statistics from the .dmp file to a staging table using Oracle Data Pump. 5. Copy the statistics from the staging table to the data dictionary using DBMS_STATS.IMPORT_SCHEMA_STATS. 17.2 Transporting Optimizer Statistics to a Test Database: Tutorial You can transport schema statistics from a production database to a test database using Oracle Data Pump. Prerequisites and Restrictions When preparing to export optimizer statistics, note the following: • Before exporting statistics, you must create a table to hold the statistics. The procedure DBMS_STATS.CREATE_STAT_TABLE creates the statistics table. • The optimizer does not use statistics stored in a user-owned table. The only statistics used by the optimizer are the statistics stored in the data dictionary. To make the optimizer use statistics in user-defined tables, import these statistics into the data dictionary using the DBMS_STATS import procedure. • The Data Pump Export and Import utilities export and import optimizer statistics from the database along with the table. When a column has system-generated names, Original Export (exp) does not export statistics with the data, but this restriction does not apply to Data Pump Export. Note: Exporting and importing statistics using DBMS_STATS is a distinct operation from using Data Pump Export and Import. Assumptions This tutorial assumes the following: • You want to generate representative sh schema statistics on a production database and use DBMS_STATS to import them into a test database. • Administrative user dba1 exists on both production and test databases. • You intend to create table opt_stats to store the schema statistics. • You intend to use Oracle Data Pump to export and import table opt_stats. 17-2 Chapter 17 Transporting Optimizer Statistics to a Test Database: Tutorial To generate schema statistics and import them into a separate database: 1. On the production host, start SQL*Plus and connect to the production database as administrator dba1. 2. Create a table to hold the production statistics. For example, execute the following PL/SQL program to create user statistics table opt_stats: BEGIN DBMS_STATS.CREATE_STAT_TABLE ( ownname => 'dba1' , stattab => 'opt_stats' ); END; / 3. Gather schema statistics. For example, manually gather schema statistics as follows: -- generate representative workload EXEC DBMS_STATS.GATHER_SCHEMA_STATS('SH'); 4. Use DBMS_STATS to export the statistics. For example, retrieve schema statistics and store them in the opt_stats table created previously: BEGIN DBMS_STATS.EXPORT_SCHEMA_STATS ( ownname => 'dba1' , stattab => 'opt_stats' ); END; / 5. Use Oracle Data Pump to export the contents of the statistics table. For example, run the expdp command at the operating schema prompt: expdp dba1 DIRECTORY=dpump_dir1 DUMPFILE=stat.dmp TABLES=opt_stats 6. Transfer the dump file to the test database host. 7. Log in to the test host, and then use Oracle Data Pump to import the contents of the statistics table. For example, run the impdp command at the operating schema prompt: impdp dba1 DIRECTORY=dpump_dir1 DUMPFILE=stat.dmp TABLES=opt_stats 8. On the test host, start SQL*Plus and connect to the test database as administrator dba1. 9. Use DBMS_STATS to import statistics from the user statistics table and store them in the data dictionary. The following PL/SQL program imports schema statistics from table opt_stats into the data dictionary: BEGIN DBMS_STATS.IMPORT_SCHEMA_STATS( ownname => 'dba1' , stattab => 'opt_stats' 17-3 Chapter 17 Transporting Optimizer Statistics to a Test Database: Tutorial ); END; / See Also: • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.CREATE_STAT_TABLE function • Oracle Database PL/SQL Packages and Types Reference for an overview of the statistics transfer functions • Oracle Database Utilities to learn about Oracle Data Pump 17-4 18 Analyzing Statistics Using Optimizer Statistics Advisor Optimizer Statistics Advisor analyzes how optimizer statistics are gathered, and then makes recommendations. This chapter contains the following topics: • About Optimizer Statistics Advisor Optimizer Statistics Advisor is built-in diagnostic software that analyzes the quality of statistics and statistics-related tasks. • Basic Tasks for Optimizer Statistics Advisor This section explains the basic workflow for using Optimizer Statistics Advisor. All procedures and functions are in the DBMS_STATS package. 18.1 About Optimizer Statistics Advisor Optimizer Statistics Advisor is built-in diagnostic software that analyzes the quality of statistics and statistics-related tasks. The advisor task runs automatically in the maintenance window, but you can also run it on demand. You can then view the advisor report. If the advisor makes recommendations, then in some cases you can run system-generated scripts to implement them. The following figure provides a conceptual overview of Optimizer Statistics Advisor. 18-1 Chapter 18 About Optimizer Statistics Advisor Figure 18-1 Optimizer Statistics Advisor Optimizer Automatic Tuning Optimizer Filter Options DBA Optimizer Statistics Advisor Findings Recommendations Task Actions Rules Data Dictionary and V$ Views DBA_OPSTAT_OPERATIONS This section contains the following topics: • Purpose of Optimizer Statistics Advisor Optimizer Statistics Advisor inspects how optimizer statistics are gathered. • Optimizer Statistics Advisor Concepts Optimizer Statistics Advisor uses the same advisor framework as Automatic Database Diagnostic Monitor (ADDM), SQL Performance Analyzer, and other advisors. • Command-Line Interface to Optimizer Statistics Advisor Perform Optimizer Statistics Advisor tasks using the DBMS_STATS PL/SQL package. 18.1.1 Purpose of Optimizer Statistics Advisor Optimizer Statistics Advisor inspects how optimizer statistics are gathered. The advisor automatically diagnoses problems in the existing practices for gathering statistics. The advisor does not gather a new or alternative set of optimizer statistics. The output of the advisor is a report of findings and recommendations, which helps you follow best practices for gathering statistics. 18-2 Chapter 18 About Optimizer Statistics Advisor Optimizer statistics play a significant part in determining the execution plan for queries. Therefore, it is critical for the optimizer to gather and maintain accurate and up-to-date statistics. The optimizer provides the DBMS_STATS package, which evolves from release to release, for this purpose. Typically, users develop their own strategies for gathering statistics based on specific workloads, and then use homegrown scripts to implement these strategies. This section contains the following topics: • Problems with a Traditional Script-Based Approach The advantage of the scripted approach is that the scripts are typically tested and reviewed. However, the owner of suboptimal legacy scripts may not change them for fear of causing plan changes. • Advantages of Optimizer Statistics Advisor An advisor-based approach offers better scalability and maintainability than the traditional approach. 18.1.1.1 Problems with a Traditional Script-Based Approach The advantage of the scripted approach is that the scripts are typically tested and reviewed. However, the owner of suboptimal legacy scripts may not change them for fear of causing plan changes. The traditional approach has the following problems: • Legacy scripts may not keep pace with new best practices, which can change from release to release. Frequently, successive releases add enhancements to histograms, sampling, workload monitoring, concurrency, and other optimizer-related features. For example, in Oracle Database 12c, Oracle recommends setting AUTO_SAMPLE_SIZE instead of a percentage. However, legacy scripts typically specify a sampling percentage, which may lead to suboptimal execution plans. • Resources are wasted on unnecessary statistics gathering. A script may gather statistics multiple times each day on the same table. • Automatic statistics gathering jobs do not guarantee accurate and up-to-date statistics. For example, sometimes the automatic statistics gathering job is not running because an initialization parameter combination disables it, or the job is terminated. Moreover, sometimes the automatic job maintenance window is insufficient because of resource constraints, or because too many objects require statistics collection. Jobs that stop running before gathering all statistics cause either no statistics or stale statistics for some objects, which can in turn cause suboptimal plans. • Statistics can sometimes be missing, stale, or incorrect. For example, statistics may be inconsistent between a table and its index, or between tables with a primary key-foreign key relationship. Alternatively, a statistics gathering job may have been disabled by accident, or you may be unaware that a script has failed. • Lack of knowledge of the problem can be time-consuming and resource-intensive. For example, a service request might seek a resolution to a problem, unaware that the problem is caused by suboptimal statistics. The diagnosis might require a 18-3 Chapter 18 About Optimizer Statistics Advisor great deal of time emailing scripts of the problematic queries, enabling traces, and investigating traces. • Recommended fixes may not be feasible. Performance engineers may recommend changing the application code that maintains statistics. In some organizations, this requirement may be difficult or impossible to satisfy. 18.1.1.2 Advantages of Optimizer Statistics Advisor An advisor-based approach offers better scalability and maintainability than the traditional approach. If best practices change in a new release, then Optimizer Statistics Advisor encodes these practices in its rules. In this way, the advisor always provides the most up-todate recommendations. The advisor analyzes how you are currently gathering statistics (using manual scripts, explicitly setting parameters, and so on), the effectiveness of existing statistics gathering jobs, and the quality of the gathered statistics. Optimizer Statistics Advisor does not gather a new or alternative set of optimizer statistics, and so does not affect the workload. Rather, Optimizer Statistics Advisor analyzes information stored in the data dictionary, and then stores the findings and recommendations in the database. Optimizer Statistics Advisor provides the following advantages over the traditional approach: • Provides easy-to-understand reports The advisor applies rules to generate findings, recommendations, and actions. • Supplies scripts to implement necessary fixes without requiring changes to application code When you implement a recommended action, benefit accrues to every execution of the improved statements. For example, if you set a global preference so that the sample size is AUTO_SAMPLE_SIZE rather than a suboptimal percentage, then every plan based on the improved statistics can benefit from this change. • Runs a predefined task named AUTO_STATS_ADVISOR_TASK once every day in the maintenance window For the automated job to run, the STATISTICS_LEVEL initialization parameter must be set to TYPICAL or ALL. • Supplies an API in the DBMS_STATS package that enables you to create and run tasks manually, store findings and recommendations in data dictionary views, generate reports for the tasks, and implement corrections when necessary • Integrates with existing tools The advisor integrates with SQL Tuning Advisor and AWR, which summarize the Optimizer Statistics Advisor results. 18.1.2 Optimizer Statistics Advisor Concepts Optimizer Statistics Advisor uses the same advisor framework as Automatic Database Diagnostic Monitor (ADDM), SQL Performance Analyzer, and other advisors. This section contains the following topics: 18-4 Chapter 18 About Optimizer Statistics Advisor • Components of Optimizer Statistics Advisor The Optimizer Statistics Optimizer framework stores its metadata in data dictionary and dynamic performance views. • Operational Modes for Optimizer Statistics Advisor Optimizer Statistics Advisor supports both an automated and manual mode. 18.1.2.1 Components of Optimizer Statistics Advisor The Optimizer Statistics Optimizer framework stores its metadata in data dictionary and dynamic performance views. The following Venn diagram shows the relationships among rules, findings, recommendations, and actions for Optimizer Statistics Advisor. For example, all findings are derived from rules, but not all rules generate findings. Figure 18-2 Optimizer Statistics Advisor Components Rules Findings Recommendations Actions This section contains the following topics: • Rules for Optimizer Statistics Advisor An Optimizer Statistics Advisor rule is an Oracle-supplied standard by which Optimizer Statistics Advisor performs its checks. • Findings for Optimizer Statistics Advisor A finding results when Optimizer Statistics Advisor examines the evidence stored in the database and concludes that the rules were not followed. • Recommendations for Optimizer Statistics Advisor Based on each finding, Optimizer Statistics Advisor makes recommendations on how to achieve better statistics. • Actions for Optimizer Statistics Advisor An Optimizer Statistics Advisor action is a SQL or PL/SQL script that implements recommendations. When feasible, recommendations have corresponding actions. The advisor stores actions in DBA_ADVISOR_ACTIONS. 18.1.2.1.1 Rules for Optimizer Statistics Advisor An Optimizer Statistics Advisor rule is an Oracle-supplied standard by which Optimizer Statistics Advisor performs its checks. 18-5 Chapter 18 About Optimizer Statistics Advisor The rules embody Oracle best practices based on the current feature set. If the best practices change from release to release, then the Optimizer Statistics Advisor rules also change. The advisor organizes rules into the following classes: • System This class checks the preferences for statistics collection, status of the automated statistics gathering job, use of SQL plan directives, and so on. Rules in this class have the value SYSTEM in V$STATS_ADVISOR_RULES.RULE_TYPE. • Operation This class checks whether statistics collection uses the defaults, test statistics are created using the SET_*_STATS procedures, and so on. Rules in this class have the value OPERATION in V$STATS_ADVISOR_RULES.RULE_TYPE. • Object This class checks for the quality of the statistics, staleness of statistics, unnecessary collection of statistics, and so on. Rules in this class have the value OBJECT in V$STATS_ADVISOR_RULES.RULE_TYPE. The rules check for the following problems: • How to gather statistics For example, one rule might specify the recommended setting for an initialization parameter. Another rule might specify that statistics should be gathered at the schema level. • When to gather statistics For example, the advisor may recommend that the maintenance window for the automatic statistics gathering job should be enabled, or that the window should be extended. • How to improve the efficiency of statistics gathering For example, a rule might specify that default parameters should be used in DBMS_STATS, or that statistics should not be set manually. In V$STATS_ADVISOR_RULES, each rule has a unique string ID that is usable in the DBMS_STATS procedures and reports. You can use a rule filter to specify rules that Optimizer Statistics Advisor should check. However, you cannot write new rules. Example 18-1 Listing Rules in V$STATS_ADVISOR_RULES The following query, with sample output, lists a subset of the rules in V$STATS_ADVISOR_RULES. The rules may change from release to release. SET SET COL COL COL LINESIZE 208 PAGESIZE 100 ID FORMAT 99 NAME FORMAT a33 DESCRIPTION FORMAT a62 SELECT RULE_ID AS ID, NAME, RULE_TYPE, DESCRIPTION FROM V$STATS_ADVISOR_RULES WHERE RULE_ID BETWEEN 1 AND 12 ORDER BY RULE_ID; ID NAME RULE_TYPE DESCRIPTION 18-6 Chapter 18 About Optimizer Statistics Advisor -1 2 3 4 5 6 7 8 9 10 11 12 ------------------------------UseAutoJob CompleteAutoJob MaintainStatsHistory UseConcurrent UseDefaultPreference TurnOnSQLPlanDirective AvoidSetProcedures UseDefaultParams UseGatherSchemaStats AvoidInefficientStatsOprSeq AvoidUnnecessaryStatsCollection AvoidStaleStats --------SYSTEM SYSTEM SYSTEM SYSTEM SYSTEM SYSTEM OPERATION OPERATION OPERATION OPERATION OBJECT OBJECT ------------------------------------------------------Use Auto Job for Statistics Collection Auto Statistics Gather Job should complete successfully Maintain Statistics History Use Concurrent preference for Statistics Collection Use Default Preference for Stats Collection SQL Plan Directives should not be disabled Avoid Set Statistics Procedures Use Default Parameters in Statistics Collection Proc. Use gather_schema_stats procedure Avoid inefficient statistics operation sequences Avoid unnecessary statistics collection Avoid objects with stale or no statistics 12 rows selected. See Also: Oracle Database Reference to learn more about V$STATS_ADVISOR_RULES 18.1.2.1.2 Findings for Optimizer Statistics Advisor A finding results when Optimizer Statistics Advisor examines the evidence stored in the database and concludes that the rules were not followed. To generate findings, Optimizer Statistics Advisor executes a task, which is invoked either automatically or manually. This task analyzes the statistics history stored in the data dictionary, the statistics operation log, and the current statistics footprint that exists in SYSAUX. For example, the advisor queries DBA_TAB_STATISTICS and DBA_IND_STATISTICS to determine whether statistics are stale, or whether a discrepancy exists between the numbers of rows. Typically, Optimizer Statistics Advisor generates a finding when a specific rule is not followed or is violated, although some findings—such as object staleness—provide only information. For example, a finding may show that DBMS_STATS.GATHER_TABLE_STATS has used ESTIMATE_PERCENT=>0.01, which violates the ESTIMATE_PERCENT=>AUTO_SAMPLE_SIZE rule. A finding corresponds to exactly one rule. However, a rule can generate many findings. See Also: • Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_STATS • Oracle Database Reference to learn more about ALL_TAB_STATISTICS 18.1.2.1.3 Recommendations for Optimizer Statistics Advisor Based on each finding, Optimizer Statistics Advisor makes recommendations on how to achieve better statistics. 18-7 Chapter 18 About Optimizer Statistics Advisor For example, the advisor might discover a violation to the rule of not using sampling when gathering statistics, and recommend specifying AUTO_SAMPLE_SIZE instead. The advisor stores the recommendations in DBA_ADVISOR_RECOMMENDATIONS. Multiple recommendations may exist for a single finding. In this case, you must investigate to determine which recommendation to follow. Each recommendation includes one or more rationales that explain why Optimizer Statistics Advisor makes its recommendation. In some cases, findings may not generate recommendations. See Also: • "Guideline for Setting the Sample Size" to learn the guideline for the sample size • Oracle Database Reference to learn about DBA_ADVISOR_RECOMMENDATIONS 18.1.2.1.4 Actions for Optimizer Statistics Advisor An Optimizer Statistics Advisor action is a SQL or PL/SQL script that implements recommendations. When feasible, recommendations have corresponding actions. The advisor stores actions in DBA_ADVISOR_ACTIONS. For example, Optimizer Statistics Advisor executes a task that performs the following steps: 1. Checks rules The advisor checks conformity to the rule that stale statistics should be avoided. 2. Generates finding The advisor discovers that a number of objects have no statistics. 3. Generates recommendation The advisor recommends gathering statistics on the objects with no statistics. 4. Generates action The advisor generates a PL/SQL script that executes DBMS_STATS.GATHER_DATABASE_STATS, supplying a list of objects that need to have statistics gathered. See Also: • • "Statistics Preference Overrides" to learn how to override statistics gathering preferences "Guideline for Setting the Sample Size" to learn more about AUTO_SAMPLE_SIZE • Oracle Database Reference to learn about DBA_ADVISOR_ACTIONS 18-8 Chapter 18 About Optimizer Statistics Advisor 18.1.2.2 Operational Modes for Optimizer Statistics Advisor Optimizer Statistics Advisor supports both an automated and manual mode. • Automated The predefined task AUTO_STATS_ADVISOR_TASK runs automatically in the maintenance window once per day. The task runs as part of the automatic optimizer statistics collection client. The automated task generates findings and recommendations, but does not implement actions automatically. As for any other task, you can configure the automated task, and generate reports. If the report recommends actions, then you can implement the actions manually. • Manual You can create your own task using the DBMS_STATS.CREATE_ADVISOR_TASK function, and then run it at any time using the EXECUTE_ADVISOR_TASK procedure. Unlike the automated task, the manual task can implement actions automatically. Alternatively, you can configure the task to generate a PL/SQL script, which you can then run manually. See Also: • "Configuring Automatic Optimizer Statistics Collection" • Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_STATS.CREATE_ADVISOR_TASK 18.1.3 Command-Line Interface to Optimizer Statistics Advisor Perform Optimizer Statistics Advisor tasks using the DBMS_STATS PL/SQL package. Table 18-1 DBMS_STATS APIs for Task Creation and Deletion PL/SQL Procedure or Function Description CREATE_ADVISOR_TASK Creates an advisor task for Optimizer Statistics Advisor. If the task name is already specified, then the advisor uses the specified task name; otherwise, the advisor automatically generates a new task name. DROP_ADVISOR_TASK Deletes an Optimizer Statistics Advisor task and all its result data. Table 18-2 DBMS_STATS APIs for Task Execution PL/SQL Procedure or Function Description EXECUTE_ADVISOR_TASK Executes a previously created Optimizer Statistics Advisor task. 18-9 Chapter 18 About Optimizer Statistics Advisor Table 18-2 (Cont.) DBMS_STATS APIs for Task Execution PL/SQL Procedure or Function Description INTERRUPT_ADVISOR_TASK Interrupts a currently executing Optimizer Statistics Advisor task. The task ends its operations as it would in a normal exit, enabling you to access intermediate results. You can resume the task later. CANCEL_ADVISOR_TASK Cancels an Optimizer Statistics Advisor task execution, and removes all intermediate results of the current execution. RESET_ADVISOR_TASK Resets an Optimizer Statistics Advisor task execution to its initial state. Call this procedure on a task that is not currently executing. RESUME_ADVISOR_TASK Resumes the Optimizer Statistics Advisor task execution that was most recently interrupted. Table 18-3 DBMS_STATS APIs for Advisor Reports PL/SQL Procedure or Function Description REPORT_STATS_ADVISOR_TASK Reports the results of an Optimizer Statistics Advisor task. GET_ADVISOR_RECS Generates a recommendation report on the given item. Table 18-4 DBMS_STATS APIs for Task and Filter Configuration PL/SQL Procedure or Function Description CONFIGURE_ADVISOR_TASK Configures the Optimizer Statistics Advisor lists for the execution, reporting, script generation, and implementation of an advisor task. GET_ADVISOR_OPR_FILTER Creates an operation filter for a statistics operation. CONFIGURE_ADVISOR_RULE_FILTER Configures the rule filter for an Optimizer Statistics Advisor task. CONFIGURE_ADVISOR_OPR_FILTER Configures the operation filter for an Optimizer Statistics Advisor task. CONFIGURE_ADVISOR_OBJ_FILTER Configures the object filter for an Optimizer Statistics Advisor task. SET_ADVISOR_TASK_PARAMETER Updates the value of an Optimizer Statistics Advisor task parameter. Valid parameters are TIME_LIMIT and OP_START_TIME. Table 18-5 DBMS_STATS APIs for Implementation of Recommended Actions PL/SQL Procedure or Function Description SCRIPT_ADVISOR_TASK Gets the script that implements the recommended actions for the problems found by the advisor. You can check this script, and then choose which actions to execute. 18-10 Chapter 18 Basic Tasks for Optimizer Statistics Advisor Table 18-5 Actions (Cont.) DBMS_STATS APIs for Implementation of Recommended PL/SQL Procedure or Function Description IMPLEMENT_ADVISOR_TASK Implements the actions recommended by the advisor based on results from a specified Optimizer Statistics Advisor execution. See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS package 18.2 Basic Tasks for Optimizer Statistics Advisor This section explains the basic workflow for using Optimizer Statistics Advisor. All procedures and functions are in the DBMS_STATS package. The following figure shows the automatic and manual paths in the workflow. If AUTO_STATS_ADVISOR_TASK runs automatically in the maintenance window, then your workflow begins by querying the report. In the manual workflow, you must use PL/SQL to create and execute the tasks. 18-11 Chapter 18 Basic Tasks for Optimizer Statistics Advisor Figure 18-3 Basic Tasks for Optimizer Statistics Advisor Manual Mode Automatic Mode Create an advisor task CREATE_ADVISOR_TASK SELECT . . . FROM DBA_ADVISOR_EXECUTIONS Optionally, list tasks Optionally, alter the scope of the advisor checks CONFIGURE_ADVISOR_*_FILTER Execute the advisor task EXECUTE_ADVISOR_TASK Generate a report of findings and recommendations REPORT_ADVISOR_TASK IMPLEMENT_ADVISOR_TASK SCRIPT_ADVISOR_TASK Generate modifiable PL/SQL script Edit PL/SQL script Implement all advisor recommendations Run PL/SQL script Typically, you perform Optimizer Statistics Advisor steps in the sequence shown in the following table. Table 18-6 Optimizer Statistics Advisor Workflow Step Description To Learn More 1 Create an Optimizer Advisor task using DBMS_STATS.CREATE_ADVISOR_TASK (manual workflow only). "Creating an Optimizer Statistics Advisor Task" 2 Optionally, list executions of advisor tasks by querying DBA_ADVISOR_EXECUTIONS. "Listing Optimizer Statistics Advisor Tasks" 3 Optionally, configure a filter for the task using the DBMS_STATS.CONFIGURE_ADVISOR_*_FIL TER procedures. "Creating Filters for an Optimizer Advisor Task" 18-12 Chapter 18 Basic Tasks for Optimizer Statistics Advisor Table 18-6 (Cont.) Optimizer Statistics Advisor Workflow Step Description To Learn More 4 Execute the advisor task using DBMS_STATS.EXECUTE_ADVISOR_TASK (manual workflow only). "Executing an Optimizer Statistics Advisor Task" 5 Generate an advisor report. "Generating a Report for an Optimizer Statistics Advisor Task" 6 Implement the recommendations in either of following ways: "Implementing Actions Recommended by Optimizer Statistics Advisor" and "Generating a Script Using Optimizer Statistics Advisor" • • Implement all recommendations automatically using DBMS_STATS.IMPLEMENT_ADVISOR_TA SK. Generate a PL/SQL script that implements recommendations using DBMS_STATS.SCRIPT_ADVISOR_TASK, edit this script, and then run it manually. Example 18-2 Basic Script for Optimizer Statistics Advisor in Manual Workflow This script illustrates a basic Optimizer Statistics Advisor session. It creates a task, executes it, generates a report, and then implements the recommendations. DECLARE v_tname VARCHAR2(128) := 'my_task'; v_ename VARCHAR2(128) := NULL; v_report CLOB := null; v_script CLOB := null; v_implementation_result CLOB; BEGIN -- create a task v_tname := DBMS_STATS.CREATE_ADVISOR_TASK(v_tname); -- execute the task v_ename := DBMS_STATS.EXECUTE_ADVISOR_TASK(v_tname); -- view the task report v_report := DBMS_STATS.REPORT_ADVISOR_TASK(v_tname); DBMS_OUTPUT.PUT_LINE(v_report); -- implement all recommendations v_implementation_result := DBMS_STATS.IMPLEMENT_ADVISOR_TASK(v_tname); END; This section contains the following topics: • Creating an Optimizer Statistics Advisor Task The DBMS_STATS.CREATE_ADVISOR_TASK function creates a task for Optimizer Statistics Advisor. If you do not specify a task name, then Optimizer Statistics Advisor generates one automatically. 18-13 Chapter 18 Basic Tasks for Optimizer Statistics Advisor • Listing Optimizer Statistics Advisor Tasks The DBA_ADVISOR_EXECUTIONS view lists executions of Optimizer Statistics Advisor tasks. • Creating Filters for an Optimizer Advisor Task Filters enable you to include or exclude objects, rules, and operations from Optimizer Statistics Advisor tasks. • Executing an Optimizer Statistics Advisor Task The DBMS_STATS.EXECUTE_ADVISOR_TASK function executes a task for Optimizer Statistics Advisor. If you do not specify an execution name, then Optimizer Statistics Advisor generates one automatically. • Generating a Report for an Optimizer Statistics Advisor Task The DBMS_STATS.REPORT_ADVISOR_TASK function generates a report for an Optimizer Statistics Advisor task. • Implementing Optimizer Statistics Advisor Recommendations You can either implement all recommendations automatically using DBMS_STATS.IMPLEMENT_ADVISOR_TASK, or generate an editable script using DBMS_STATS.SCRIPT_ADVISOR_TASK. See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS package 18.2.1 Creating an Optimizer Statistics Advisor Task The DBMS_STATS.CREATE_ADVISOR_TASK function creates a task for Optimizer Statistics Advisor. If you do not specify a task name, then Optimizer Statistics Advisor generates one automatically. Prerequisites To execute this subprogram, you must have the ADVISOR privilege. Note: This subprogram executes using invoker's rights. To create an Optimizer Statistics Advisor task: 1. In SQL*Plus, log in to the database as a user with the necessary privileges. 2. Execute the DBMS_STATS.CREATE_ADVISOR_TASK function in the following form, where tname is the name of the task and ret is the variable that contains the returned output: EXECUTE ret := DBMS_STATS.CREATE_ADVISOR_TASK('tname'); For example, to create the task opt_adv_task1, use the following code: 18-14 Chapter 18 Basic Tasks for Optimizer Statistics Advisor DECLARE v_tname VARCHAR2(32767); v_ret VARCHAR2(32767); BEGIN v_tname := 'opt_adv_task1'; v_ret := DBMS_STATS.CREATE_ADVISOR_TASK(v_tname); END; / 3. Optionally, query USER_ADVISOR_TASKS: SELECT TASK_NAME, ADVISOR_NAME, CREATED, STATUS FROM USER_ADVISOR_TASKS; Sample output appears below: TASK_NAME ADVISOR_NAME CREATED STATUS --------------- -------------------- --------- ----------OPT_ADV_TASK1 Statistics Advisor 05-SEP-16 INITIAL See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about CREATE_ADVISOR_TASK 18.2.2 Listing Optimizer Statistics Advisor Tasks The DBA_ADVISOR_EXECUTIONS view lists executions of Optimizer Statistics Advisor tasks. To list Optimizer Statistics Advisor tasks: 1. In SQL*Plus, log in to the database as a user with administrator privileges. 2. Query DBA_ADVISOR_EXECUTIONS as follows: COL EXECUTION_NAME FORMAT a14 SELECT EXECUTION_NAME, EXECUTION_END, STATUS FROM DBA_ADVISOR_EXECUTIONS WHERE TASK_NAME = 'AUTO_STATS_ADVISOR_TASK' ORDER BY 2; The following sample output shows 8 executions: EXECUTION_NAME -------------EXEC_1 EXEC_17 EXEC_42 EXEC_67 EXEC_92 EXEC_117 EXEC_142 EXEC_167 EXECUTION --------27-AUG-16 28-AUG-16 29-AUG-16 30-AUG-16 01-SEP-16 02-SEP-16 03-SEP-16 04-SEP-16 STATUS ----------COMPLETED COMPLETED COMPLETED COMPLETED COMPLETED COMPLETED COMPLETED COMPLETED 8 rows selected. 18-15 Chapter 18 Basic Tasks for Optimizer Statistics Advisor See Also: Oracle Database Reference to learn more about DBA_ADVISOR_EXECUTIONS 18.2.3 Creating Filters for an Optimizer Advisor Task Filters enable you to include or exclude objects, rules, and operations from Optimizer Statistics Advisor tasks. This section contains the following topics: • About Filters for Optimizer Statistics Advisor A filter is the use of DBMS_STATS to restrict an Optimizer Statistics Advisor task to a user-specified set of rules, schemas, or operations. • Creating an Object Filter for an Optimizer Advisor Task The DBMS_STATS.CONFIGURE_ADVISOR_OBJ_FILTER function creates a rule filter for a specified Optimizer Statistics Advisor task. The function returns a CLOB that contains the updated values of the filter. • Creating a Rule Filter for an Optimizer Advisor Task The DBMS_STATS.CONFIGURE_ADVISOR_RULE_FILTER function creates a rule filter for a specified Optimizer Statistics Advisor task. The function returns a CLOB that contains the updated values of the filter. • Creating an Operation Filter for an Optimizer Advisor Task The DBMS_STATS.CONFIGURE_ADVISOR_OPR_FILTER function creates an operation filter for a specified Optimizer Statistics Advisor task. The function returns a CLOB that contains the updated values of the filter. 18.2.3.1 About Filters for Optimizer Statistics Advisor A filter is the use of DBMS_STATS to restrict an Optimizer Statistics Advisor task to a userspecified set of rules, schemas, or operations. Filters are useful for including or excluding a specific set of results. For example, you can configure an advisor task to include only recommendations for the sh schema. Also, you can exclude all violations of the rule for stale statistics. The primary advantage of filters is the ability to ignore recommendations that you are not interested in, and reduce the overhead of the advisor task. The simplest way to create filters is to use the following DBMS_STATS procedures either individually or in combination: • CONFIGURE_ADVISOR_OBJ_FILTER Use this procedure to include or exclude the specified database schemas or objects. The object filter takes in an owner name and an object name, with wildcards (%) supported. • CONFIGURE_ADVISOR_RULE_FILTER Use this procedure to include or exclude the specified rules. Obtain the names of rules by querying V$STATS_ADVISOR_RULES. • CONFIGURE_ADVISOR_OPR_FILTER 18-16 Chapter 18 Basic Tasks for Optimizer Statistics Advisor Use this procedure to include or exclude the specified DBMS_STATS operations. Obtain the IDs and names for operations by querying DBA_OPTSTAT_OPERATIONS. For the preceding functions, you can specify the type of operation to which the filter applies: EXECUTE, REPORT, SCRIPT, and IMPLEMENT. You can also combine types, as in EXECUTE + REPORT. Null indicates that the filter applies to all types of advisor operations. See Also: • Oracle Database Reference to learn more about V$STATS_ADVISOR_RULES • Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_STATS 18.2.3.2 Creating an Object Filter for an Optimizer Advisor Task The DBMS_STATS.CONFIGURE_ADVISOR_OBJ_FILTER function creates a rule filter for a specified Optimizer Statistics Advisor task. The function returns a CLOB that contains the updated values of the filter. You can use either of the following basic strategies: • Include findings for all objects (by default, all objects are considered), and then exclude findings for specified objects. • Exclude findings for all objects, and then include findings only for specified objects. Prerequisites To use the DBMS_STATS.CONFIGURE_ADVISOR_OBJ_FILTER function, you must meet the following prerequisites: • To execute this subprogram, you must have the ADVISOR privilege. • You must be the owner of the task. Note: This subprogram executes using invoker's rights. To create an object filter: 1. In SQL*Plus or SQL Developer, log in to the database as a user with the necessary privileges. 2. Either exclude or include objects for a specified task using the DBMS_STATS.CONFIGURE_ADVISOR_OBJ_FILTER function. Invoke the function in the following form, where the placeholders are defined as follows: • report is the CLOB variable that contains the returned XML. • tname is the name of the task. 18-17 Chapter 18 Basic Tasks for Optimizer Statistics Advisor • opr_type is the type of operation to perform. • rule is the name of the rule. • owner is the schema for the objects. • table is the name of the table. • action is the name of the action: ENABLE, DISABLE, DELETE, or SHOW. BEGIN report := DBMS_STATS.CONFIGURE_ADVISOR_OBJ_FILTER( task_name => 'tname' , stats_adv_opr_type => 'opr_type' , rule_name => 'rule' , ownname => 'owner' , tabname => 'table' , action => 'action' ); END; Example 18-3 Including Only Objects in a Single Schema In this example, for the task named opt_adv_task1, your goal is to disable recommendations for all objects except those in the sh schema. User account sh has been granted ADVISOR and READ ANY TABLE privileges. You perform the following steps: 1. Log in to the database as sh. 2. Drop any existing task named opt_adv_task1. DECLARE v_tname VARCHAR2(32767); BEGIN v_tname := 'opt_adv_task1'; DBMS_STATS.DROP_ADVISOR_TASK(v_tname); END; / 3. Create a procedure named sh_obj_filter that restricts a specified task to objects in the sh schema. CREATE OR REPLACE PROCEDURE sh_obj_filter(p_tname IN VARCHAR2) IS v_retc CLOB; BEGIN -- Filter out all objects that are not in the sh schema v_retc := DBMS_STATS.CONFIGURE_ADVISOR_OBJ_FILTER( task_name => p_tname , stats_adv_opr_type => 'EXECUTE' , rule_name => NULL , ownname => NULL , tabname => NULL , action => 'DISABLE' ); v_retc := DBMS_STATS.CONFIGURE_ADVISOR_OBJ_FILTER( task_name => p_tname , stats_adv_opr_type => 'EXECUTE' , rule_name => NULL , ownname => 'SH' , tabname => NULL , action => 'ENABLE' ); END; / SHOW ERRORS 18-18 Chapter 18 Basic Tasks for Optimizer Statistics Advisor 4. Create a task named opt_adv_task1, and then execute the sh_obj_filter procedure for this task. DECLARE v_tname VARCHAR2(32767); v_ret VARCHAR2(32767); BEGIN v_tname := 'opt_adv_task1'; v_ret := DBMS_STATS.CREATE_ADVISOR_TASK(v_tname); sh_obj_filter(v_tname); END; / 5. Execute the task opt_adv_task1. DECLARE v_tname v_ret begin v_tname v_ret END; / VARCHAR2(32767); VARCHAR2(32767); := 'opt_adv_task1'; := DBMS_STATS.EXECUTE_ADVISOR_TASK(v_tname); See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_STATS.CONFIGURE_ADVISOR_OBJ_FILTER 18.2.3.3 Creating a Rule Filter for an Optimizer Advisor Task The DBMS_STATS.CONFIGURE_ADVISOR_RULE_FILTER function creates a rule filter for a specified Optimizer Statistics Advisor task. The function returns a CLOB that contains the updated values of the filter. You can use either of the following basic strategies: • Enable all rules (by default, all rules are enabled), and then disable specified rules. • Disable all rules, and then enable only specified rules. Prerequisites To use the DBMS_STATS.CONFIGURE_ADVISOR_RULE_FILTER function, you must meet the following prerequisites: • To execute this subprogram, you must have the ADVISOR privilege. • You must be the owner of the task. Note: This subprogram executes using invoker's rights. 18-19 Chapter 18 Basic Tasks for Optimizer Statistics Advisor To create a rule filter: 1. In SQL*Plus or SQL Developer, log in to the database as a user with the necessary privileges. 2. Obtain the names of the advisor rules by querying V$STATS_ADVISOR_RULES. For example, query the view as follows (partial sample output included): SET SET COL COL COL LINESIZE 200 PAGESIZE 100 ID FORMAT 99 NAME FORMAT a27 DESCRIPTION FORMAT a54 SELECT RULE_ID AS ID, NAME, RULE_TYPE, DESCRIPTION FROM V$STATS_ADVISOR_RULES ORDER BY RULE_ID; ID NAME -- --------------------------1 UseAutoJob 2 CompleteAutoJob 3 MaintainStatsHistory 4 UseConcurrent ... 3. RULE_TYPE --------SYSTEM SYSTEM SYSTEM SYSTEM DESCRIPTION ------------------------------------------------------Use Auto Job for Statistics Collection Auto Statistics Gather Job should complete successfully Maintain Statistics History Use Concurrent preference for Statistics Collection Either exclude or include rules for a specified task using the DBMS_STATS.CONFIGURE_ADVISOR_RULE_FILTER function. Invoke the function in the following form, where the placeholders are defined as follows: • tname is the name of the task. • report is the CLOB variable that contains the returned XML. • opr_type is the type of operation to perform. • rule is the name of the rule. • action is the name of the action: ENABLE, DISABLE, DELETE, or SHOW. BEGIN report := DBMS_STATS.DBMS_STATS.CONFIGURE_ADVISOR_RULE_FILTER( task_name => 'tname' , stats_adv_opr_type => 'opr_type' , rule_name => 'rule' , action => 'action' ); END; Example 18-4 Excluding the Rule for Stale Statistics In this example, you know that statistics are stale because the automated statistics job did not run. You want to generate a report for the task named opt_adv_task1, but do not want to clutter it with recommendations about stale statistics. 1. You query V$STATS_ADVISOR_RULES for rules that deal with stale statistics (sample output included): COL NAME FORMAT a15 SELECT RULE_ID AS ID, NAME, RULE_TYPE, DESCRIPTION FROM V$STATS_ADVISOR_RULES WHERE DESCRIPTION LIKE '%tale%' 18-20 Chapter 18 Basic Tasks for Optimizer Statistics Advisor ORDER BY RULE_ID; ID NAME RULE_TYPE DESCRIPTION --- --------------- --------- ----------------------------------------12 AvoidStaleStats OBJECT Avoid objects with stale or no statistics 2. You configure a filter using CONFIGURE_ADVISOR_RULE_FILTER, specifying that task execution should exclude the rule AvoidStaleStats, but honor all other rules: VARIABLE b_ret CLOB BEGIN :b_ret := DBMS_STATS.CONFIGURE_ADVISOR_RULE_FILTER( task_name => 'opt_adv_task1' , stats_adv_opr_type => 'EXECUTE' , rule_name => 'AvoidStaleStats' , action => 'DISABLE' ); END; / Example 18-5 Including Only the Rule for Avoiding Stale Statistics This example is the inverse of the preceding example. You want to generate a report for the task named opt_adv_task1, but want to see only recommendations about stale statistics. 1. Query V$STATS_ADVISOR_RULES for rules that deal with stale statistics (sample output included): COL NAME FORMAT a15 SELECT RULE_ID AS ID, NAME, RULE_TYPE, DESCRIPTION FROM V$STATS_ADVISOR_RULES WHERE DESCRIPTION LIKE '%tale%' ORDER BY RULE_ID; ID NAME RULE_TYPE DESCRIPTION --- --------------- --------- ----------------------------------------12 AvoidStaleStats OBJECT Avoid objects with stale or no statistics 2. Configure a filter using CONFIGURE_ADVISOR_RULE_FILTER, specifying that task execution should exclude all rules: VARIABLE b_ret CLOB BEGIN :b_ret := DBMS_STATS.CONFIGURE_ADVISOR_RULE_FILTER( task_name => 'opt_adv_task1' , stats_adv_opr_type => 'EXECUTE' , rule_name => null , action => 'DISABLE' ); END; / 3. Configure a filter that enables only the AvoidStaleStats rule: BEGIN :b_ret := DBMS_STATS.CONFIGURE_ADVISOR_RULE_FILTER( task_name => 'opt_adv_task1' , stats_adv_opr_type => 'EXECUTE' , rule_name => 'AvoidStaleStats' , action => 'ENABLE' ); END; / 18-21 Chapter 18 Basic Tasks for Optimizer Statistics Advisor See Also: • Oracle Database Reference to learn more about V$STATS_ADVISOR_RULES • Oracle Database PL/SQL Packages and Types Reference to learn more about CONFIGURE_ADVISOR_RULE_FILTER 18.2.3.4 Creating an Operation Filter for an Optimizer Advisor Task The DBMS_STATS.CONFIGURE_ADVISOR_OPR_FILTER function creates an operation filter for a specified Optimizer Statistics Advisor task. The function returns a CLOB that contains the updated values of the filter. You can use either of the following basic strategies: • Disable all operations, and then enable only specified operations. • Enable all operations (by default, all operations are enabled), and then disable specified operations. The DBA_OPTSTAT_OPERATIONS view contains the IDs of statistics-related operations. Prerequisites To use DBMS_STATS.CONFIGURE_ADVISOR_OPR_FILTER function, you must meet the following prerequisites: • To execute this subprogram, you must have the ADVISOR privilege. Note: This subprogram executes using invoker's rights. • You must be the owner of the task. • To query the DBA_OPTSTAT_OPERATIONS view, you must have the SELECT ANY TABLE privilege. To create an operation filter: 1. In SQL*Plus or SQL Developer, log in to the database as a user with the necessary privileges. 2. Query the types of operations. For example, list all distinct operations in DBA_OPTSTAT_OPERATIONS (sample output included): SQL> SELECT DISTINCT(OPERATION) FROM DBA_OPTSTAT_OPERATIONS ORDER BY OPERATION; OPERATION ----------------------gather_dictionary_stats gather_index_stats gather_schema_stats gather_table_stats 18-22 Chapter 18 Basic Tasks for Optimizer Statistics Advisor purge_stats set_system_stats 3. Obtain the IDs of the operations to be filtered by querying DBA_OPTSTAT_OPERATIONS. For example, to obtain IDs for all statistics gathering operations for tables and indexes in the SYS and sh schemas, use the following query: SELECT ID FROM DBA_OPTSTAT_OPERATIONS WHERE ( OPERATION = 'gather_table_stats' OR OPERATION = 'gather_index_stats') AND ( TARGET LIKE 'SH.%' OR TARGET LIKE 'SYS.%'); 4. Exclude or include rules for a specified task using the DBMS_STATS.CONFIGURE_ADVISOR_OPR_FILTER function, specifying the IDs obtained in the previous step. Invoke the function in the following form, where the placeholders are defined as follows: • report is the CLOB variable that contains the returned XML. • tname is the name of the task. • opr_type is the type of operation to perform. This value cannot be null. • rule is the name of the rule. • opr_id is the ID (from DBA_OPTSTAT_OPERATIONS.ID) of the operation to perform. This value cannot be null. • action is the name of the action: ENABLE, DISABLE, DELETE, or SHOW. BEGIN report := DBMS_STATS.CONFIGURE_ADVISOR_OPR_FILTER( task_name => 'tname' , stats_adv_opr_type => 'opr_type' , rule_name => 'rule' , operation_id => 'op_id' , action => 'action' ); END; Example 18-6 Excluding Operations for Gathering Table Statistics In this example, your goal is to exclude operations that gather table statistics in the hr schema. User account stats has been granted the DBA role, ADVISOR privilege, and SELECT ON DBA_OPTSTAT_OPERATIONS privilege. You perform the following steps: 1. Log in to the database as stats. 2. Drop any existing task named opt_adv_task1. DECLARE v_tname VARCHAR2(32767); BEGIN v_tname := 'opt_adv_task1'; DBMS_STATS.DROP_ADVISOR_TASK(v_tname); END; / 3. Create a procedure named opr_filter that configures a task to advise on all operations except those that gather statistics for tables in the hr schema. 18-23 Chapter 18 Basic Tasks for Optimizer Statistics Advisor CREATE OR REPLACE PROCEDURE opr_filter(p_tname IN VARCHAR2) IS v_retc CLOB; BEGIN -- For all rules, prevent the advisor from operating -- on the operations selected in the following query FOR rec IN (SELECT ID FROM DBA_OPTSTAT_OPERATIONS WHERE OPERATION = 'gather_table_stats' AND TARGET LIKE 'HR.%') LOOP v_retc := DBMS_STATS.CONFIGURE_ADVISOR_OPR_FILTER( task_name => p_tname , stats_adv_opr_type => NULL , rule_name => NULL , operation_id => rec.id , action => 'DISABLE'); END LOOP; END; / SHOW ERRORS 4. Create a task named opt_adv_task1, and then execute the opr_filter procedure for this task. DECLARE v_tname VARCHAR2(32767); v_ret VARCHAR2(32767); BEGIN v_tname := 'opt_adv_task1'; v_ret := DBMS_STATS.CREATE_ADVISOR_TASK(v_tname); opr_filter(v_tname); END; / 5. Execute the task opt_adv_task1. DECLARE v_tname v_ret begin v_tname v_ret END; / 6. VARCHAR2(32767); VARCHAR2(32767); := 'opt_adv_task1'; := DBMS_STATS.EXECUTE_ADVISOR_TASK(v_tname); Print the report. SPOOL /tmp/rep.txt SET LONG 1000000 COLUMN report FORMAT A200 SET LINESIZE 250 SET PAGESIZE 1000 SELECT DBMS_STATS.REPORT_ADVISOR_TASK( task_name => 'opt_adv_task1' , execution_name => NULL , type => 'TEXT' , section => 'ALL' ) AS report FROM DUAL; SPOOL OFF 18-24 Chapter 18 Basic Tasks for Optimizer Statistics Advisor See Also: • Oracle Database Reference to learn more about DBA_OPTSTAT_OPERATIONS • Oracle Database PL/SQL Packages and Types Reference to learn more about CONFIGURE_ADVISOR_OPR_FILTER 18.2.4 Executing an Optimizer Statistics Advisor Task The DBMS_STATS.EXECUTE_ADVISOR_TASK function executes a task for Optimizer Statistics Advisor. If you do not specify an execution name, then Optimizer Statistics Advisor generates one automatically. The results of performing this task depend on the privileges of the executing user: • SYSTEM level Only users with both the ANALYZE ANY and ANALYZE ANY DICTIONARY privileges can perform this task on system-level rules. • Operation level The results depend on the following privileges: • – Users with both the ANALYZE ANY and ANALYZE ANY DICTIONARY privileges can perform this task for all statistics operations. – Users with the ANALYZE ANY privilege but not the ANALYZE ANY DICTIONARY privilege can perform this task for statistics operations related to any schema except SYS. – Users with the ANALYZE ANY DICTIONARY privilege but not the ANALYZE ANY privilege can perform this task for statistics operations related to their own schema and the SYS schema. – Users with neither the ANALYZE ANY nor the ANALYZE ANY DICTIONARY privilege can only perform this operation for statistics operations relating to their own schema. Object level Users can perform this task for any object for which they have statistics collection privileges. Prerequisites This task has the following prerequisites: • To execute this subprogram, you must have the ADVISOR privilege. • You must be the owner of the task. • If you specify an execution name, then this name must not conflict with an existing execution. 18-25 Chapter 18 Basic Tasks for Optimizer Statistics Advisor Note: This subprogram executes using invoker's rights. To execute an Optimizer Statistics Advisor task: 1. In SQL*Plus or SQL Developer, log in to the database as a user with the necessary privileges. 2. Execute the DBMS_STATS.EXECUTE_ADVISOR_TASK function in the following form, where tname is the name of the task, execname is the optional name of the execution, and ret is the variable that contains the returned output: EXECUTE ret := DBMS_STATS.EXECUTE_ADVISOR_TASK('tname','execname'); For example, to execute the task opt_adv_task1, use the following code: DECLARE v_tname VARCHAR2(32767); v_ret VARCHAR2(32767); BEGIN v_tname := 'opt_adv_task1'; v_ret := DBMS_STATS.EXECUTE_ADVISOR_TASK(v_tname); END; / 3. Optionally, obtain details about the execution by querying USER_ADVISOR_EXECUTIONS: SELECT TASK_NAME, EXECUTION_NAME, EXECUTION_END, EXECUTION_TYPE AS TYPE, STATUS FROM USER_ADVISOR_EXECUTIONS; Sample output appears below: TASK_NAME EXECUTION_NAME EXECUTION TYPE STATUS --------------- -------------------- --------- ---------- ----------OPT_ADV_TASK1 EXEC_136 23-NOV-15 STATISTICS COMPLETED See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about EXECUTE_ADVISOR_TASK 18.2.5 Generating a Report for an Optimizer Statistics Advisor Task The DBMS_STATS.REPORT_ADVISOR_TASK function generates a report for an Optimizer Statistics Advisor task. The report contains the following sections: • General information This section describes the task name, execution name, creation date, and modification date. • Summary 18-26 Chapter 18 Basic Tasks for Optimizer Statistics Advisor This section summarizes the findings and rules violated by the findings. • Findings Each finding section lists the relevant rule and findings. If the advisor has a recommendation, then the recommendation is described. In some cases, a recommendation also has a rationale. The name of the automated Optimizer Statistics Advisor task is AUTO_STATS_ADVISOR_TASK. If you follow the automated workflow, then you only need to query the automatically generated report. Prerequisites To generate a report with the DBMS_STATS.REPORT_ADVISOR_TASK function, you must meet the following prerequisites: • To execute this subprogram, you must have the ADVISOR privilege. • You must be the owner of the task. Note: This subprogram executes using invoker's rights. The results of performing this task depend on the privileges of the executing user: • SYSTEM level Only users with both the ANALYZE ANY and ANALYZE ANY DICTIONARY privileges can perform this task on system-level rules. • Operation level The results depend on the following privileges: • – Users with both the ANALYZE ANY and ANALYZE ANY DICTIONARY privileges can perform this task for all statistics operations. – Users with the ANALYZE ANY privilege but not the ANALYZE ANY DICTIONARY privilege can perform this task for statistics operations related to any schema except SYS. – Users with the ANALYZE ANY DICTIONARY privilege but not the ANALYZE ANY privilege can perform this task for statistics operations related to their own schema and the SYS schema. – Users with neither the ANALYZE ANY nor the ANALYZE ANY DICTIONARY privilege can only perform this operation for statistics operations relating to their own schema. Object level Users can perform this task for any object for which they have statistics collection privileges. To generate an Optimizer Statistics Advisor report: 1. In SQL*Plus, log in to the database as a user with ADVISOR privileges. 2. Query the DBMS_STATS.REPORT_ADVISOR_TASK function output. 18-27 Chapter 18 Basic Tasks for Optimizer Statistics Advisor Use the following query, where the placeholders have the following definitions: • tname is the name of the task. • exec is the name of the execution. • type is the type of output: TEXT, HTML, or XML. • sect is the section of the report: SUMMARY, FINDINGS, ERRORS, and ALL. • lvl is the format of the report: BASIC, TYPICAL, ALL, or SHOW_HIDDEN. SET SET SET SET LINESIZE 3000 LONG 500000 PAGESIZE 0 LONGCHUNKSIZE 100000 SELECT DBMS_STATS.REPORT_ADVISOR_TASK('tname', 'exec', 'type', 'sect', 'lvl') AS REPORT FROM DUAL; For example, to print a report for AUTO_STATS_ADVISOR_TASK, use the following query: SELECT DBMS_STATS.REPORT_ADVISOR_TASK('AUTO_STATS_ADVISOR_TASK',NULL,'TEXT','ALL','ALL') AS REPORT FROM DUAL; The following sample report shows four findings: GENERAL INFORMATION ------------------------------------------------------------------------------Task Name : AUTO_STATS_ADVISOR_TASK Execution Name : EXEC_136 Created : 09-05-16 02:52:34 Last Modified : 09-05-16 12:31:24 ------------------------------------------------------------------------------SUMMARY ------------------------------------------------------------------------------For execution EXEC_136 of task AUTO_STATS_ADVISOR_TASK, the Statistics Advisor has 4 findings. The findings are related to the following rules: AVOIDSETPROCEDURES, USEDEFAULTPARAMS, USEGATHERSCHEMASTATS, NOTUSEINCREMENTAL. Please refer to the finding section for detailed information. ------------------------------------------------------------------------------FINDINGS ------------------------------------------------------------------------------Rule Name: AvoidSetProcedures Rule Description: Avoid Set Statistics Procedures Finding: There are 5 SET_[COLUMN|INDEX|TABLE|SYSTEM]_STATS procedures being used for statistics gathering. Recommendation: Do not use SET_[COLUMN|INDEX|TABLE|SYSTEM]_STATS procedures. Gather statistics instead of setting them. Rationale: SET_[COLUMN|INDEX|TABLE|SYSTEM]_STATS will cause bad plans due to wrong or inconsistent statistics. ---------------------------------------------------Rule Name: UseDefaultParams Rule Description: Use Default Parameters in Statistics Collection Procedures Finding: There are 367 statistics operations using nondefault parameters. Recommendation: Use default parameters for statistics operations. Example: -- Gathering statistics for 'SH' schema using all default parameter values: 18-28 Chapter 18 Basic Tasks for Optimizer Statistics Advisor BEGIN dbms_stats.gather_schema_stats('SH'); END; Rationale: Using default parameter values for statistics gathering operations is more efficient. ---------------------------------------------------Rule Name: UseGatherSchemaStats Rule Description: Use gather_schema_stats procedure Finding: There are 318 uses of GATHER_TABLE_STATS. Recommendation: Use GATHER_SCHEMA_STATS instead of GATHER_TABLE_STATS. Example: -- Gather statistics for 'SH' schema: BEGIN dbms_stats.gather_schema_stats('SH'); END; Rationale: GATHER_SCHEMA_STATS has more options available, including checking for staleness and gathering statistics concurrently. Also it is more maintainable for new tables added to the schema. If you only want to gather statistics for certain tables in the schema, specify them in the obj_filter_list parameter of GATHER_SCHEMA_STATS. ---------------------------------------------------Rule Name: NotUseIncremental Rule Description: Statistics should not be maintained incrementally when it is not Finding: Incremental option has been turned on for 10 tables, which will not benefit from using the incremental option. Schema: SH Objects: CAL_MONTH_SALES_MV CAL_MONTH_SALES_MV CHANNELS COUNTRIES CUSTOMERS DIMENSION_EXCEPTIONS FWEEK_PSCAT_SALES_MV FWEEK_PSCAT_SALES_MV PRODUCTS PROMOTIONS SUPPLEMENTARY_DEMOGRAPHICS TIMES Recommendation: Do not use the incremental option for statistics gathering on these objects. Example: -Turn off the incremental option for 'SH.SALES': dbms_stats.set_table_prefs('SH', 'SALES', 'INCREMENTAL', 'FALSE'); Rationale: The overhead of using the incremental option on these tables outweighs the benefit of using the incremental option. See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about REPORT_ADVISOR_TASK 18-29 Chapter 18 Basic Tasks for Optimizer Statistics Advisor 18.2.6 Implementing Optimizer Statistics Advisor Recommendations You can either implement all recommendations automatically using DBMS_STATS.IMPLEMENT_ADVISOR_TASK, or generate an editable script using DBMS_STATS.SCRIPT_ADVISOR_TASK. This section contains the following topics: • Implementing Actions Recommended by Optimizer Statistics Advisor The DBMS_STATS.IMPLEMENT_ADVISOR_TASK function implements the recommendations for a specified Optimizer Statistics Advisor task. If you do not specify an execution name, then Optimizer Statistics Advisor uses the most recent execution. • Generating a Script Using Optimizer Statistics Advisor The DBMS_STATS.SCRIPT_ADVISOR_TASK function generates an editable script with recommendations for a specified Optimizer Statistics Advisor task. 18.2.6.1 Implementing Actions Recommended by Optimizer Statistics Advisor The DBMS_STATS.IMPLEMENT_ADVISOR_TASK function implements the recommendations for a specified Optimizer Statistics Advisor task. If you do not specify an execution name, then Optimizer Statistics Advisor uses the most recent execution. The simplest means of implementing recommendations is using DBMS_STATS.IMPLEMENT_ADVISOR_TASK. In this case, no generation of a script is necessary. You can specify that the advisor ignore the existing filters (level=>'ALL') or use the default, which honors the existing filters (level=>'TYPICAL'). Prerequisites To use DBMS_STATS.IMPLEMENT_ADVISOR_TASK, you must meet the following prerequisites: • To execute this subprogram, you must have the ADVISOR privilege. • You must be the owner of the task. Note: This subprogram executes using invoker's rights. The results of performing this task depend on the privileges of the executing user: • SYSTEM level Only users with both the ANALYZE ANY and ANALYZE ANY DICTIONARY privileges can perform this task on system-level rules. • Operation level The results depend on the following privileges: – Users with both the ANALYZE ANY and ANALYZE ANY DICTIONARY privileges can perform this task for all statistics operations. 18-30 Chapter 18 Basic Tasks for Optimizer Statistics Advisor • – Users with the ANALYZE ANY privilege but not the ANALYZE ANY DICTIONARY privilege can perform this task for statistics operations related to any schema except SYS. – Users with the ANALYZE ANY DICTIONARY privilege but not the ANALYZE ANY privilege can perform this task for statistics operations related to their own schema and the SYS schema. – Users with neither the ANALYZE ANY nor the ANALYZE ANY DICTIONARY privilege can only perform this operation for statistics operations relating to their own schema. Object level Users can perform this task for any object for which they have statistics collection privileges. To implement advisor actions: 1. In SQL*Plus, log in to the database as a user with the necessary privileges. 2. Execute the DBMS_STATS.IMPLEMENT_ADVISOR_TASK function in the following form, where the placeholders have the following definitions: • tname is the name of the task. • result is the CLOB variable that contains a list of the recommendations that have been successfully implemented. • filter_lvl is the level of implementation: TYPICAL (existing filters honored) or ALL (filters ignored). EXECUTE result := DBMS_STATS.IMPLEMENT_ADVISOR_TASK('tname', level => filter_lvl); For example, to implement all recommendations for the task opt_adv_task1, use the following code: VARIABLE b_ret CLOB DECLARE v_tname VARCHAR2(32767); BEGIN v_tname := 'opt_adv_task1'; :b_ret := DBMS_STATS.IMPLEMENT_ADVISOR_TASK(v_tname); END; / 3. Optionally, print the XML output to confirm the implemented actions. For example, to print the XML returned in the previous step, use the following code (sample output included): SET LONG 10000 SELECT XMLType(:b_ret) AS imp_results FROM DUAL; IMP_RESULTS ----------------------------------- See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_STATS.IMPLEMENT_ADVISOR_TASK 18.2.6.2 Generating a Script Using Optimizer Statistics Advisor The DBMS_STATS.SCRIPT_ADVISOR_TASK function generates an editable script with recommendations for a specified Optimizer Statistics Advisor task. Unlike IMPLEMENT_ADVISOR_TASK, the SCRIPT_ADVISOR_TASK generates a script that you can edit before execution. The output script contains both comments and executable code. As with IMPLEMENT_ADVISOR_TASK, you can specify that the advisor ignore the existing filters (level=>'ALL') or use the default, which honors the existing filters (level=>'TYPICAL'). You can specify that the function returns the script as a CLOB and file, or only a CLOB. Prerequisites To use the DBMS_STATS.SCRIPT_ADVISOR_TASK function, you must meet the following prerequisites: • To execute this subprogram, you must have the ADVISOR privilege. • You must be the owner of the task. Note: This subprogram executes using invoker's rights. The results of performing this task depend on the privileges of the executing user: • SYSTEM level Only users with both the ANALYZE ANY and ANALYZE ANY DICTIONARY privileges can perform this task on system-level rules. 18-32 Chapter 18 Basic Tasks for Optimizer Statistics Advisor • Operation level The results depend on the following privileges: • – Users with both the ANALYZE ANY and ANALYZE ANY DICTIONARY privileges can perform this task for all statistics operations. – Users with the ANALYZE ANY privilege but not the ANALYZE ANY DICTIONARY privilege can perform this task for statistics operations related to any schema except SYS. – Users with the ANALYZE ANY DICTIONARY privilege but not the ANALYZE ANY privilege can perform this task for statistics operations related to their own schema and the SYS schema. – Users with neither the ANALYZE ANY nor the ANALYZE ANY DICTIONARY privilege can only perform this operation for statistics operations relating to their own schema. Object level Users can perform this task for any object for which they have statistics collection privileges. To generate an advisor script: 1. In SQL*Plus, log in to the database as a user with ADVISOR privileges. 2. Execute the DBMS_STATS.SCRIPT_ADVISOR_TASK function in the following form, where the placeholders have the following definitions: • tname is the name of the task. • exec is the name of the execution (default is null). • dir is the name of the directory (default is null). • result is the CLOB variable that contains a list of the recommendations that have been successfully implemented. • filter_lvl is the level of implementation: TYPICAL (existing filters honored) or ALL (filters ignored). EXEC result := DBMS_STATS.SCRIPT_ADVISOR_TASK('tname', execution_name => 'exec', dir_name => 'dir', level => 'filter_lvl'); For example, to generate a script that contains recommendations for the task opt_adv_task1, use the following code: VARIABLE b_script CLOB DECLARE v_tname VARCHAR2(32767); BEGIN v_tname := 'opt_adv_task1'; :b_script := DBMS_STATS.SCRIPT_ADVISOR_TASK(v_tname); END; / 18-33 Chapter 18 Basic Tasks for Optimizer Statistics Advisor Note: If you do not specify an execution name, then Optimizer Statistics Advisor uses the most recent execution. 3. Print the script. For example, to print the script returned in the previous step, use the following code (sample output included): DECLARE v_len NUMBER(10); v_offset NUMBER(10) :=1; v_amount NUMBER(10) :=10000; BEGIN v_len := DBMS_LOB.getlength(:b_script); WHILE (v_offset < v_len) LOOP DBMS_OUTPUT.PUT_LINE(DBMS_LOB.SUBSTR(:b_script,v_amount,v_offset)); v_offset := v_offset + v_amount; END LOOP; END; / The following example shows a sample script: -- Script generated for the recommendations from execution EXEC_23 -- in the statistics advisor task OPT_ADV_TASK1 -- Script version 12.2 ------------------------------ No scripts will be provided for for more details. No scripts will be provided for for more details. No scripts will be provided for report for more details. No scripts will be provided for the report for more details. No scripts will be provided for report for more details. No scripts will be provided for for more details. No scripts will be provided for for more details. No scripts will be provided for for more details. No scripts will be provided for for more details. No scripts will be provided for for more details. No scripts will be provided for report for more details. No scripts will be provided for the report for more details. No scripts will be provided for report for more details. No scripts will be provided for for more details. No scripts will be provided for the rule AVOIDSETPROCEDURES. Please check the report the rule USEGATHERSCHEMASTATS. Please check the report the rule AVOIDINEFFICIENTSTATSOPRSEQ. Please check the the rule AVOIDUNNECESSARYSTATSCOLLECTION. Please check the rule GATHERSTATSAFTERBULKDML. Please check the the rule AVOIDDROPRECREATE. Please check the report the rule AVOIDOUTOFRANGE. Please check the report the rule AVOIDANALYZETABLE. Please check the report the rule AVOIDSETPROCEDURES. Please check the report the rule USEGATHERSCHEMASTATS. Please check the report the rule AVOIDINEFFICIENTSTATSOPRSEQ. Please check the the rule AVOIDUNNECESSARYSTATSCOLLECTION. Please check the rule GATHERSTATSAFTERBULKDML. Please check the the rule AVOIDDROPRECREATE. Please check the report the rule AVOIDOUTOFRANGE. Please check the report 18-34 Chapter 18 Basic Tasks for Optimizer Statistics Advisor -- for more details. -- No scripts will be provided for the rule AVOIDANALYZETABLE. Please check the report -- for more details. -- Scripts for rule USEDEFAULTPARAMS -- Rule Description: Use Default Parameters in Statistics Collection Procedures -- Use the default preference value for parameters begin dbms_stats.set_global_prefs('PREFERENCE_OVERRIDES_PARAMETER', 'TRUE'); end; / --------- Scripts for rule USEDEFAULTOBJECTPREFERENCE Rule Description: Use Default Object Preference for statistics collection Setting object-level preferences to default values setting CASCADE to default value for object level preference setting ESTIMATE_PERCENT to default value for object level preference setting METHOD_OPT to default value for object level preference setting GRANULARITY to default value for object level preference setting NO_INVALIDATE to default value for object levelpreference -- Scripts for rule USEINCREMENTAL -- Rule Description: Statistics should be maintained incrementally when it is beneficial. -- Turn on the incremental option for those objects for which using incremental is helpful. -- Scripts for rule UNLOCKNONVOLATILETABLE -- Rule Description: Statistics for objects with non-volatile should not be locked -- Unlock statistics for objects that are not volatile. -- Scripts for rule LOCKVOLATILETABLE -- Rule Description: Statistics for objects with volatile data should be locked -- Lock statistics for volatile objects. -- Scripts for rule NOTUSEINCREMENTAL -- Rule Description: Statistics should not be maintained incrementally when it is not beneficial -- Turn off incremental option for those objects for which using incremental is not helpful. begin / begin / begin / begin / begin / begin / begin / begin / begin / begin / dbms_stats.set_table_prefs('SH', 'CAL_MONTH_SALES_MV', 'INCREMENTAL', 'FALSE'); end; dbms_stats.set_table_prefs('SH', 'CHANNELS', 'INCREMENTAL', 'FALSE'); end; dbms_stats.set_table_prefs('SH', 'COUNTRIES', 'INCREMENTAL', 'FALSE'); end; dbms_stats.set_table_prefs('SH', 'CUSTOMERS', 'INCREMENTAL', 'FALSE'); end; dbms_stats.set_table_prefs('SH', 'DIMENSION_EXCEPTIONS', 'INCREMENTAL', 'FALSE'); end; dbms_stats.set_table_prefs('SH', 'FWEEK_PSCAT_SALES_MV', 'INCREMENTAL', 'FALSE'); end; dbms_stats.set_table_prefs('SH', 'PRODUCTS', 'INCREMENTAL', 'FALSE'); end; dbms_stats.set_table_prefs('SH', 'PROMOTIONS', 'INCREMENTAL', 'FALSE'); end; dbms_stats.set_table_prefs('SH', 'SUPPLEMENTARY_DEMOGRAPHICS', 'INCREMENTAL', 'FALSE'); end; dbms_stats.set_table_prefs('SH', 'TIMES', 'INCREMENTAL', 'FALSE'); end; -- Scripts for rule USEAUTODEGREE -- Rule Description: Use Auto Degree for statistics collection -- Turn on auto degree for those objects for which using auto degree is helpful. 18-35 Chapter 18 Basic Tasks for Optimizer Statistics Advisor -- Scripts for rule AVOIDSTALESTATS -- Rule Description: Avoid objects with stale or no statistics -- Gather statistics for those objcts that are missing or have no statistics. -- Scripts for rule MAINTAINSTATSCONSISTENCY -- Rule Description: Statistics of dependent objects should be consistent -- Gather statistics for those objcts that are missing or have no statistics. See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_STATS.SCRIPT_ADVISOR_TASK 18-36 Part VI Optimizer Controls You can use hints and initialization parameter to influence optimizer decisions and behavior. This part contains the following chapters: • Influencing the Optimizer Optimizer defaults are adequate for most operations, but not all. • Improving Real-World Performance Through Cursor Sharing Cursor sharing can improve database application performance by orders of magnitude. 19 Influencing the Optimizer Optimizer defaults are adequate for most operations, but not all. In some cases you may have information unknown to the optimizer, or need to tune the optimizer for a specific type of statement or workload. In such cases, influencing the optimizer may provide better performance. This chapter contains the following topics: • Techniques for Influencing the Optimizer You can influence the optimizer using several techniques, including SQL profiles, SQL Plan Management, initialization parameters, and hints. • Influencing the Optimizer with Initialization Parameters This chapter explains which initialization parameters affect optimization, and how to set them. • Influencing the Optimizer with Hints Optimizer hints are special comments in a SQL statement that pass instructions to the optimizer. 19.1 Techniques for Influencing the Optimizer You can influence the optimizer using several techniques, including SQL profiles, SQL Plan Management, initialization parameters, and hints. The following figure shows the principal techniques for influencing the optimizer. 19-1 Chapter 19 Techniques for Influencing the Optimizer Figure 19-1 Techniques for Influencing the Optimizer User DBMS_STATS Initialization Parameters SQL Plan Management SQL Profiles Hints Optimizer The overlapping squares in the preceding diagram show that SQL plan management uses both initialization parameters and hints. SQL profiles also technically include hints. Note: A stored outline is a legacy technique that serve a similar purpose to SQL plan baselines. You can use the following techniques to influence the optimizer: Table 19-1 Optimizer Techniques Technique Description To Learn More Initialization parameters Parameters influence many types of optimizer behavior at the database instance and session level. "Influencing the Optimizer with Initialization Parameters" Hints A hint is a commented instruction in a SQL "Influencing the Optimizer with statement. Hints control a wide range of Hints" behavior. DBMS_STATS This package updates and manages "Gathering Optimizer Statistics" optimizer statistics. The more accurate the statistics, the better the optimizer estimates. This chapter does not cover DBMS_STATS. 19-2 Chapter 19 Influencing the Optimizer with Initialization Parameters Table 19-1 (Cont.) Optimizer Techniques Technique Description To Learn More SQL profiles A SQL profile is a database object that contains auxiliary statistics specific to a SQL statement. Conceptually, a SQL profile is to a SQL statement what a set of object-level statistics is to a table or index. A SQL profile can correct suboptimal optimizer estimates discovered during SQL tuning. "Managing SQL Profiles" SQL plan management and stored outlines SQL plan management is a preventative "Managing SQL Plan mechanism that enables the optimizer to Baselines" automatically manage execution plans, ensuring that the database uses only known or verified plans. This chapter does not cover SQL plan management. In some cases, multiple techniques optimize the same behavior. For example, you can set optimizer goals using both initialization parameters and hints. See Also: "Migrating Stored Outlines to SQL Plan Baselines" to learn how to migrate stored outlines to SQL plan baselines 19.2 Influencing the Optimizer with Initialization Parameters This chapter explains which initialization parameters affect optimization, and how to set them. This section contains the following topics: • About Optimizer Initialization Parameters Oracle Database provides initialization parameters to influence various aspects of optimizer behavior, including cursor sharing, adaptive optimization, and the optimizer mode. • Enabling Optimizer Features The OPTIMIZER_FEATURES_ENABLE initialization parameter (or hint) controls a set of optimizer-related features, depending on the database release. • Choosing an Optimizer Goal The optimizer goal is the prioritization of resource usage by the optimizer. • Controlling Adaptive Optimization In Oracle Database, adaptive query optimization is the process by which the optimizer adapts an execution plan based on statistics collected at run time. 19-3 Chapter 19 Influencing the Optimizer with Initialization Parameters 19.2.1 About Optimizer Initialization Parameters Oracle Database provides initialization parameters to influence various aspects of optimizer behavior, including cursor sharing, adaptive optimization, and the optimizer mode. The following table lists some of the most important optimizer parameters. Table 19-2 Initialization Parameters That Control Optimizer Behavior Initialization Parameter Description APPROX_FOR_AGGREGATION Uses approximate query processing for all aggregation and analytic queries. Approximate processing is useful when you want to obtain faster query results and avoid writes to a temporary tablespaces. This optimizer uses a nondeterministic algorithm to make its estimations, which means that different queries can obtain different results. You can set this parameter to TRUE at the system or session level. This parameter changes the optimizer environment. It does not force the optimizer to change the SQL text for an affected query, but it does force the optimizer to reparse the query and create a new child cursor. APPROX_FOR_COUNT_DISTINCT Replaces queries that contain COUNT (DISTINCT expr) queries with APPROX_COUNT_DISTINCT. Approximate counts are useful when a column has a higher number of distinct values, and you want to obtain faster query results and avoid writes to a temporary tablespaces. Only use approximation when your application can tolerate a nonzero error rate. This parameter changes the optimizer environment. It does not force the optimizer to change the SQL text for an affected query, but it does force the optimizer to reparse the query and create a new child cursor. APPROX_FOR_PERCENTILE Converts exact percentile functions to their approximate percentile function counterparts. Approximate percentile function queries are faster than their exact percentile function query counterparts, so they can be useful in situations where a tolerable amount of error is acceptable in order to obtain faster query results. Set to PERCENTILE_CONT to convert PERCENTILE_CONT functions to APPROX_PERCENTILE, and PERCENTILE_DISC to convert PERCENTILE_DISC functions to APPROX_PERCENTILE (or ALL to convert both). This parameter changes the optimizer environment. It does not force the optimizer to change the SQL text for an affected query, but it does force the optimizer to reparse the query and create a new child cursor. 19-4 Chapter 19 Influencing the Optimizer with Initialization Parameters Table 19-2 (Cont.) Initialization Parameters That Control Optimizer Behavior Initialization Parameter Description CURSOR_INVALIDATION Provides the default cursor invalidation level for DDL statements. IMMEDIATE sets the same cursor invalidation behavior for DDL as in releases before Oracle Database 12c Release 2 (12.2). This is the default. DEFERRED allows an application to take advantage of the reduced cursor invalidation for DDL without making any application changes. Deferred invalidation reduces the number of cursor invalidations and spreads the recompilation workload over time. For this reason, a cursor may run with a suboptimal plan until it is recompiled, and may incur small execution-time overhead. You can set this parameter at the SYSTEM or SESSION level. See "About the Life Cycle of Shared Cursors". CURSOR_SHARING Converts literal values in SQL statements to bind variables. Converting the values improves cursor sharing and can affect the execution plans of SQL statements. The optimizer generates the execution plan based on the presence of the bind variables and not the actual literal values. Set to FORCE to enable the creation of a new cursor when sharing an existing cursor, or when the cursor plan is not optimal. Set to EXACT to allow only statements with identical text to share the same cursor. DB_FILE_MULTIBLOCK_READ_COUNT Specifies the number of blocks that are read in a single I/O during a full table scan or index fast full scan. The optimizer uses the value of this parameter to calculate the cost of full table scans and index fast full scans. Larger values result in a lower cost for full table scans, which may result in the optimizer choosing a full table scan over an index scan. The default value of this parameter corresponds to the maximum I/O size that the database can perform efficiently. This value is platform-dependent and is 1 MB for most platforms. Because the parameter is expressed in blocks, it is set to a value equal to the maximum I/O size that can be performed efficiently divided by the standard block size. If the number of sessions is extremely large, then the multiblock read count value decreases to avoid the buffer cache getting flooded with too many table scan buffers. OPTIMIZER_ADAPTIVE_PLANS Controls adaptive plans. An adaptive plan has alternative choices. The optimizer decides on a plan at run time based on statistics collected as the query executes. By default, this parameter is true, which means adaptive plans are enabled. Setting to this parameter to false disables the following features: • Nested loops and hash join selection • Star transformation bitmap pruning • Adaptive parallel distribution method See "About Adaptive Query Plans". 19-5 Chapter 19 Influencing the Optimizer with Initialization Parameters Table 19-2 (Cont.) Initialization Parameters That Control Optimizer Behavior Initialization Parameter Description OPTIMIZER_ADAPTIVE_REPORTING_ONLY Controls the reporting mode for automatic reoptimization and adaptive plans (see "Adaptive Query Plans"). By default, reporting mode is off (false), which means that adaptive optimizations are enabled. If set to true, then adaptive optimizations run in reporting-only mode. In this case, the database gathers information required for an adaptive optimization, but takes no action to change the plan. For example, an adaptive plan always choose the default plan, but the database collects information about which plan the database would use if the parameter were set to false. You can view the report by using DBMS_XPLAN.DISPLAY_CURSOR. OPTIMIZER_ADAPTIVE_STATISTICS Controls adaptive statistics. The optimizer can use adaptive statistics when query predicates are too complex to rely on base table statistics alone. By default, OPTIMIZER_ADAPTIVE_STATISTICS is false, which means that the following features are disabled: • SQL plan directives • Statistics feedback • Performance feedback • Adaptive dynamic sampling See "Adaptive Statistics". OPTIMIZER_MODE Sets the optimizer mode at database instance startup. Possible values are ALL_ROWS, FIRST_ROWS_n, and FIRST_ROWS. OPTIMIZER_INDEX_CACHING Controls the cost analysis of an index probe with a nested loop. The range of values 0 to 100 indicates percentage of index blocks in the buffer cache, which modifies optimizer assumptions about index caching for nested loops and IN-list iterators. A value of 100 infers that 100% of the index blocks are likely to be found in the buffer cache, so the optimizer adjusts the cost of an index probe or nested loop accordingly. Use caution when setting this parameter because execution plans can change in favor of index caching. OPTIMIZER_INDEX_COST_ADJ Adjusts the cost of index probes. The range of values is 1 to 10000. The default value is 100, which means that the optimizer evaluates indexes as an access path based on the normal cost model. A value of 10 means that the cost of an index access path is one-tenth the normal cost of an index access path. OPTIMIZER_INMEMORY_AWARE This parameter enables (TRUE) or disables (FALSE) all Oracle Database In-Memory (Database In-Memory) optimizer features, including the cost model for the IM column store, table expansion, Bloom filters, and so on. Setting the parameter to FALSE causes the optimizer to ignore the INMEMORY property of tables during the optimization of SQL statements. OPTIMIZER_USE_INVISIBLE_INDEXES Enables or disables the use of invisible indexes. 19-6 Chapter 19 Influencing the Optimizer with Initialization Parameters Table 19-2 (Cont.) Initialization Parameters That Control Optimizer Behavior Initialization Parameter Description RESULT_CACHE_MODE Controls whether the database uses the SQL query result cache for all queries, or only for the queries that are annotated with the result cache hint. When set to MANUAL (the default), you must use the RESULT_CACHE hint to specify that a specific result is to be stored in the cache. When set to FORCE, the database stores all results in the cache. When setting this parameter, consider how the result cache handles PL/SQL functions. The database invalidates query results in the result cache using the same mechanism that tracks data dependencies for PL/SQL functions, but otherwise permits caching of queries that contain PL/SQL functions. Because PL/SQL function result cache invalidation does not track all kinds of dependencies (such as on sequences, SYSDATE, SYS_CONTEXT, and package variables), indiscriminate use of the query result cache on queries calling such functions can result in changes to results, that is, incorrect results. Thus, consider correctness and performance when choosing to enable the result cache, especially when setting RESULT_CACHE_MODE to FORCE. RESULT_CACHE_MAX_SIZE Changes the memory allocated to the result cache. If you set this parameter to 0, then the result cache is disabled. The value of this parameter is rounded to the largest multiple of 32 KB that is not greater than the specified value. If the rounded value is 0, then the feature is disabled. RESULT_CACHE_MAX_RESULT Specifies the maximum amount of cache memory that any single result can use. The default value is 5%, but you can specify any percentage value between 1 and 100. RESULT_CACHE_REMOTE_EXPIRATION Specifies the number of minutes for which a result that depends on remote database objects remains valid. The default is 0, which implies that the database should not cache results using remote objects. Setting this parameter to a nonzero value can produce stale answers, such as if a remote database modifies a table that is referenced in a result. STAR_TRANSFORMATION_ENABLED Enables the optimizer to cost a star transformation for star queries (if true). The star transformation combines the bitmap indexes on the various fact table columns. 19-7 Chapter 19 Influencing the Optimizer with Initialization Parameters See Also: • Oracle Database Reference for complete information about the preceding initialization parameters • Oracle Database Performance Tuning Guide to learn how to tune the query result cache • Oracle Database Data Warehousing Guide to learn more about star transformations • Oracle Database In-Memory Guide to learn more about Database InMemory features 19.2.2 Enabling Optimizer Features The OPTIMIZER_FEATURES_ENABLE initialization parameter (or hint) controls a set of optimizer-related features, depending on the database release. The parameter accepts one of a list of valid string values corresponding to the release numbers, such as 11.2.0.2 or 12.2.0.1. You can use this parameter to preserve the old behavior of the optimizer after a database upgrade. For example, if you upgrade Oracle Database 12c Release 1 (12.1.0.2) to Oracle Database 12c Release 2 (12.2.0.1), then the default value of the OPTIMIZER_FEATURES_ENABLE parameter changes from 12.1.0.2 to 12.2.0.1. For backward compatibility, you may not want the execution plans to change because of new optimizer features in a new release. In such cases, you can set OPTIMIZER_FEATURES_ENABLE to an earlier version. If you upgrade to a new release, and if you want to enable the features in the new release, then you do not need to explicitly set the OPTIMIZER_FEATURES_ENABLE initialization parameter. Caution: Oracle does not recommend explicitly setting the OPTIMIZER_FEATURES_ENABLE initialization parameter to an earlier release. To avoid SQL performance regression that may result from execution plan changes, consider using SQL plan management instead. Assumptions This tutorial assumes the following: • You recently upgraded the database from Oracle Database 12c Release 1 (12 1.0.2) to Oracle Database 12c Release 2 (12.2.0.1). • You want to preserve the optimizer behavior from the earlier release. To enable query optimizer features for a specific release: 1. Log in to the database with the appropriate privileges, and then query the current optimizer features settings. 19-8 Chapter 19 Influencing the Optimizer with Initialization Parameters For example, run the following SQL*Plus command: SQL> SHOW PARAMETER optimizer_features_enable NAME TYPE VALUE ------------------------------------ ----------- -------optimizer_features_enable string 12.2.0.1 2. Set the optimizer features setting at the instance or session level. For example, run the following SQL statement to set the optimizer version to 12.1.0.2: SQL> ALTER SYSTEM SET OPTIMIZER_FEATURES_ENABLE='12.1.0.2'; The preceding statement restores the optimizer functionality that existed in Oracle Database 12c Release 1 (12.1.0.2). See Also: • "Managing SQL Plan Baselines" • Oracle Database Reference to learn about optimizer features enabled when you set OPTIMIZER_FEATURES_ENABLE to different release values 19.2.3 Choosing an Optimizer Goal The optimizer goal is the prioritization of resource usage by the optimizer. Using the OPTIMIZER_MODE initialization parameter, you can set the following optimizer goals: • Best throughput (default) When you set the OPTIMIZER_MODE value to ALL_ROWS, the database uses the least amount of resources necessary to process all rows that the statement accessed. For batch applications such as Oracle Reports, optimize for best throughput. Usually, throughput is more important in batch applications because the user is only concerned with the time necessary for the application to complete. Response time is less important because the user does not examine the results of individual statements while the application is running. • Best response time When you set the OPTIMIZER_MODE value to FIRST_ROWS_n, the database optimizes with a goal of best response time to return the first n rows, where n equals 1, 10, 100, or 1000. For interactive applications in Oracle Forms or SQL*Plus, optimize for response time. Usually, response time is important because the interactive user is waiting to see the first row or rows that the statement accessed. Assumptions This tutorial assumes the following: 19-9 Chapter 19 Influencing the Optimizer with Initialization Parameters • The primary application is interactive, so you want to set the optimizer goal for the database instance to minimize response time. • For the current session only, you want to run a report and optimize for throughput. To enable query optimizer features for a specific release: 1. Connect SQL*Plus to the database with the appropriate privileges, and then query the current optimizer mode. For example, run the following SQL*Plus command: dba1@PROD> SHOW PARAMETER OPTIMIZER_MODE NAME TYPE VALUE ------------------------------------ ----------- -------optimizer_mode string ALL_ROWS 2. At the instance level, optimize for response time. For example, run the following SQL statement to configure the system to retrieve the first 10 rows as quickly as possible: SQL> ALTER SYSTEM SET OPTIMIZER_MODE='FIRST_ROWS_10'; 3. At the session level only, optimize for throughput before running a report. For example, run the following SQL statement to configure only this session to optimize for throughput: SQL> ALTER SESSION SET OPTIMIZER_MODE='ALL_ROWS'; See Also: Oracle Database Reference to learn about the OPTIMIZER_MODE initialization parameter 19.2.4 Controlling Adaptive Optimization In Oracle Database, adaptive query optimization is the process by which the optimizer adapts an execution plan based on statistics collected at run time. Adaptive plans are enabled when the following initialization parameters are set: • OPTIMIZER_ADAPTIVE_PLANS is TRUE (default) • OPTIMIZER_FEATURES_ENABLE is 12.1.0.1 or later • OPTIMIZER_ADAPTIVE_REPORTING_ONLY is FALSE (default) If OPTIMIZER_ADAPTIVE_REPORTING_ONLY is set to true, then adaptive optimization runs in reporting-only mode. In this case, the database gathers information required for adaptive optimization, but does not change the plans. An adaptive plan always chooses the default plan, but the database collects information about the execution as if the parameter were set to false. Adaptive statistics are enabled when the following initialization parameters are set: • OPTIMIZER_ADAPTIVE_STATISTICS is TRUE (the default is FALSE) 19-10 Chapter 19 Influencing the Optimizer with Hints • OPTIMIZER_FEATURES_ENABLE is 12.1.0.1 or later Assumptions This tutorial assumes the following: • The OPTIMIZER_FEATURES_ENABLE initialization parameter is set to 12.1.0.1 or later. • The OPTIMIZER_ADAPTIVE_REPORTING_ONLY initialization parameter is set to false (default). • You want to disable adaptive plans for testing purposes so that the database generates only reports. To disable adaptive plans: 1. Connect SQL*Plus to the database as SYSTEM, and then query the current settings. For example, run the following SQL*Plus command: SHOW PARAMETER OPTIMIZER_ADAPTIVE_REPORTING_ONLY NAME TYPE VALUE ------------------------------------ ----------- ----optimizer_adaptive_reporting_only boolean FALSE 2. At the session level, set the OPTIMIZER_ADAPTIVE_REPORTING_ONLY initialization parameter to true. For example, in SQL*Plus run the following SQL statement: ALTER SESSION SET OPTIMIZER_ADAPTIVE_REPORTING_ONLY=true; 3. Run a query. 4. Run DBMS_XPLAN.DISPLAY_CURSOR with the +REPORT parameter. When the +REPORT parameter is set, the report shows the plan the optimizer would have picked if automatic reoptimization had been enabled. See Also: • • "About Adaptive Query Optimization" Oracle Database Reference to learn about the OPTIMIZER_ADAPTIVE_REPORTING_ONLY initialization parameter • Oracle Database PL/SQL Packages and Types Reference to learn about the +REPORT parameter of the DBMS_XPLAN.DISPLAY_CURSOR function 19.3 Influencing the Optimizer with Hints Optimizer hints are special comments in a SQL statement that pass instructions to the optimizer. The optimizer uses hints to choose an execution plan for the statement unless prevented by some condition. 19-11 Chapter 19 Influencing the Optimizer with Hints Note: Oracle Database SQL Language Reference contains a complete reference for all SQL hints This section contains the following topics: • About Optimizer Hints Use hints to influence the optimizer mode, query transformation, access path, join order, and join methods. • Guidelines for Join Order Hints The join order can have a significant effect on the performance of a SQL statement. In some cases, you can specify join order hints in a SQL statement so that it does not access rows that have no effect on the result. 19.3.1 About Optimizer Hints Use hints to influence the optimizer mode, query transformation, access path, join order, and join methods. For example, The following figure shows how you can use a hint to tell the optimizer to use a specific index for a specific statement. Figure 19-2 Optimizer Hint SELECT /*+ INDEX (employees emp_dep_ix)*/ ... Optimizer Generate Plan Id 0 1 *2 Operation Name SELECT STATEMENT TABLE ACCESS BY INDEX ROWID INDEX UNIQUE SCAN EMPLOYEES EMP_DEP_IX The advantage of hints is that they enable you to make decisions normally made by the optimizer. In a test environment, hints are useful for testing the performance of a specific access path. For example, you may know that an index is more selective for certain queries, as in Figure 19-2. In this case, the hint may cause the optimizer to generate a better plan. The disadvantage of hints is the extra code that you must manage, check, and control. Hints were introduced in Oracle7, when users had little recourse if the optimizer generated suboptimal plans. Because changes in the database and host environment 19-12 Chapter 19 Influencing the Optimizer with Hints can make hints obsolete or have negative consequences, a good practice is to test using hints, but use other techniques to manage execution plans. Oracle provides several tools, including SQL Tuning Advisor, SQL plan management, and SQL Performance Analyzer, to address performance problems not solved by the optimizer. Oracle strongly recommends that you use these tools instead of hints because they provide fresh solutions as the data and database environment change. This section contains the following topics: • Types of Hints You can use hints for tables, query blocks, and statements. • Scope of Hints When you specify a hint, it optimizes only the statement block in which it appears, overriding any instance-level or session-level parameters. • Guidelines for Hints You must enclose hints within a SQL comment. See Also: Oracle Database SQL Language Reference for the most common hints by functional category. 19.3.1.1 Types of Hints You can use hints for tables, query blocks, and statements. Hints fall into the following types: • Single-table Single-table hints are specified on one table or view. INDEX and USE_NL are examples of single-table hints. The following statement uses a single-table hint: SELECT /*+ INDEX (employees emp_department_ix)*/ employee_id, department_id FROM employees WHERE department_id > 50; • Multi-table Multi-table hints are like single-table hints except that the hint can specify multiple tables or views. LEADING is an example of a multi-table hint. The following statement uses a multi-table hint: SELECT FROM WHERE AND /*+ LEADING(e j) */ * employees e, departments d, job_history j e.department_id = d.department_id e.hire_date = j.start_date; Note: USE_NL(table1 table2) is not considered a multi-table hint because it is a shortcut for USE_NL(table1) and USE_NL(table2). 19-13 Chapter 19 Influencing the Optimizer with Hints • Query block Query block hints operate on single query blocks. STAR_TRANSFORMATION and UNNEST are examples of query block hints. The following statement uses a query block hint: SELECT /*+ STAR_TRANSFORMATION */ s.time_id, s.prod_id, s.channel_id FROM sales s, times t, products p, channels c WHERE s.time_id = t.time_id AND s.prod_id = p.prod_id AND s.channel_id = c.channel_id AND c.channel_desc = 'Tele Sales'; • Statement Statement hints apply to the entire SQL statement. ALL_ROWS is an example of a statement hint. The following statement uses a statement hint: SELECT /*+ ALL_ROWS */ * FROM sales; 19.3.1.2 Scope of Hints When you specify a hint, it optimizes only the statement block in which it appears, overriding any instance-level or session-level parameters. A statement block is one of the following: • A simple MERGE, SELECT, INSERT, UPDATE, or DELETE statement • A parent statement or a subquery of a complex statement • A part of a query using set operators (UNION, MINUS, INTERSECT) Example 19-1 Query Using a Set Operator The following query consists of two component queries and the UNION operator: SELECT /*+ FIRST_ROWS(10) */ prod_id, time_id FROM 2010_sales UNION ALL SELECT /*+ ALL_ROWS */ prod_id, time_id FROM current_year_sales; The preceding statement has two blocks, one for each component query. Hints in the first component query apply only to its optimization, not to the optimization of the second component query. For example, in the first week of 2015 you query current year and last year sales. You apply FIRST_ROWS(10) to the query of last year's (2014) sales and the ALL_ROWS hint to the query of this year's (2015) sales. See Also: Oracle Database SQL Language Reference for an overview of hints 19.3.1.3 Guidelines for Hints You must enclose hints within a SQL comment. The hint comment must immediately follow the first keyword of a SQL statement block. You can use either style of comment: a slash-star (/*) or pair of dashes (--). The plussign (+) hint delimiter must come immediately after the comment delimiter, as in the following fragment: SELECT /*+ hint_text */ ... 19-14 Chapter 19 Influencing the Optimizer with Hints The database ignores incorrectly specified hints. The database also ignores combinations of conflicting hints, even if these hints are correctly specified. If one hint is incorrectly specified, but a hint in the same comment is correctly specified, then the database considers the correct hint. Caution: The database does not issue error messages for hints that it ignores. A statement block can have only one comment containing hints, but it can contain many space-separated hints. For example, a complex query may include multiple table joins. If you specify only the INDEX hint for a specified table, then the optimizer must determine the remaining access paths and corresponding join methods. The optimizer may not use the INDEX hint because the join methods and access paths prevent it. Example 19-2 uses multiple hints to specify the exact join order. Example 19-2 Multiple Hints SELECT /*+ LEADING(e2 e1) USE_NL(e1) INDEX(e1 emp_emp_id_pk) USE_MERGE(j) FULL(j) */ e1.first_name, e1.last_name, j.job_id, sum(e2.salary) total_sal FROM employees e1, employees e2, job_history j WHERE e1.employee_id = e2.manager_id AND e1.employee_id = j.employee_id AND e1.hire_date = j.start_date GROUP BY e1.first_name, e1.last_name, j.job_id ORDER BY total_sal; See Also: Oracle Database SQL Language Reference to learn about the syntax rules for comments and hints 19.3.2 Guidelines for Join Order Hints The join order can have a significant effect on the performance of a SQL statement. In some cases, you can specify join order hints in a SQL statement so that it does not access rows that have no effect on the result. The driving table in a join is the table to which other tables are joined. In general, the driving table contains the filter condition that eliminates the highest percentage of rows in the table. Consider the following guidelines: • Avoid a full table scan when an index retrieves the requested rows more efficiently. • Avoid using an index that fetches many rows from the driving table when you can use a different index that fetches a small number of rows. • Choose the join order so that you join fewer rows to tables later in the join order. 19-15 Chapter 19 Influencing the Optimizer with Hints The following example shows how to tune join order effectively: SELECT * FROM taba a, tabb b, tabc c WHERE a.acol BETWEEN 100 AND 200 AND b.bcol BETWEEN 10000 AND 20000 AND c.ccol BETWEEN 10000 AND 20000 AND a.key1 = b.key1 AND a.key2 = c.key2; 1. Choose the driving table and the driving index (if any). Each of the first three conditions in the previous example is a filter condition that applies to a single table. The last two conditions are join conditions. Filter conditions dominate the choice of driving table and index. In general, the driving table contains the filter condition that eliminates the highest percentage of rows. Thus, because the range of 100 to 200 is narrow compared with the range of acol, but the ranges of 10000 and 20000 are relatively large, taba is the driving table, all else being equal. With nested loops joins, the joins occur through the join indexes, which are the indexes on the primary or foreign keys used to connect that table to an earlier table in the join tree. Rarely do you use the indexes on the non-join conditions, except for the driving table. Thus, after taba is chosen as the driving table, use the indexes on b.key1 and c.key2 to drive into tabb and tabc, respectively. 2. Choose the best join order, driving to the best unused filters earliest. You can reduce the work of the following join by first joining to the table with the best still-unused filter. Thus, if bcol BETWEEN is more restrictive (rejects a higher percentage of the rows) than ccol BETWEEN, then the last join becomes easier (with fewer rows) if tabb is joined before tabc. 3. You can use the ORDERED or STAR hint to force the join order. See Also: Oracle Database Reference to learn about OPTIMIZER_MODE 19-16 20 Improving Real-World Performance Through Cursor Sharing Cursor sharing can improve database application performance by orders of magnitude. This chapter contains the following topics: • Overview of Cursor Sharing Oracle Database can share cursors, which are pointers to private SQL areas in the shared pool. • CURSOR_SHARING and Bind Variable Substitution This topic explains what the CURSOR_SHARING initialization parameter is, and how setting it to different values affects how Oracle Database uses bind variables. • Adaptive Cursor Sharing The adaptive cursor sharing feature enables a single statement that contains bind variables to use multiple execution plans. • Real-World Performance Guidelines for Cursor Sharing The Real-World Performance team has created guidelines for how to optimize cursor sharing in Oracle database applications. 20.1 Overview of Cursor Sharing Oracle Database can share cursors, which are pointers to private SQL areas in the shared pool. This section contains the following topics: • About Cursors A private SQL area holds information about a parsed SQL statement and other session-specific information for processing. • About Cursors and Parsing If an application issues a statement, and if Oracle Database cannot reuse a cursor, then it must build a new executable version of the application code. This operation is known as a hard parse. • About Literals and Bind Variables Bind variables are essential to cursor sharing in Oracle database applications. • About the Life Cycle of Shared Cursors The database allocates a new shared SQL area when the optimizer parses a new SQL statement that is not DDL. The amount of memory required depends on the statement complexity. 20.1.1 About Cursors A private SQL area holds information about a parsed SQL statement and other session-specific information for processing. 20-1 Chapter 20 Overview of Cursor Sharing When a server process executes SQL or PL/SQL code, the process uses the private SQL area to store bind variable values, query execution state information, and query execution work areas. The private SQL areas for each execution of a statement are not shared and may contain different values and data. A cursor is a name or handle to a specific private SQL area. The cursor contains session-specific state information such as bind variable values and result sets. As shown in the following graphic, you can think of a cursor as a pointer on the client side and as a state on the server side. Because cursors are closely associated with private SQL areas, the terms are sometimes used interchangeably. Figure 20-1 Cursor PGA SQL Work Areas Session Memory Server Process Private SQL Area Cursor Data Area Pointer Client Process This section contains the following topics: • Private and Shared SQL Areas A cursor in the private SQL area points to a shared SQL area in the library cache. • Parent and Child Cursors Every parsed SQL statement has a parent cursor and one or more child cursors. 20.1.1.1 Private and Shared SQL Areas A cursor in the private SQL area points to a shared SQL area in the library cache. Unlike the private SQL area, which contains session state information, the shared SQL area contains the parse tree and execution plan for the statement. For example, an execution of SELECT * FROM employees has a plan and parse tree stored in one shared SQL area. An execution of SELECT * FROM departments, which differs both syntactically and semantically, has a plan and parse tree stored in a separate shared SQL area. Multiple private SQL areas in the same or different sessions can reference a single shared SQL area, a phenomenon known as cursor sharing. For example, an execution of SELECT * FROM employees in one session and an execution of the SELECT * FROM employees (accessing the same table) in a different session can use the same parse tree and plan. A shared SQL area that is accessed by multiple statements is known as a shared cursor. 20-2 Chapter 20 Overview of Cursor Sharing Figure 20-2 Cursor Sharing Instance System Global Area (SGA) Shared Pool Library Cache Shared SQL Area SELECT * FROM employees Data Dictionary Cache Server Result Cache Private SQL Area (Shared Server Only) Other Reserved Pool PGA Server Process SQL Work Areas Session Memory Private SQL Area PGA Server Process SELECT * FROM employees Client Process SQL Work Areas Session Memory Private SQL Area SELECT * FROM employees Client Process Oracle Database automatically determines whether the SQL statement or PL/SQL block being issued is textually identical to another statement currently in the library cache, using the following steps: 1. The text of the statement is hashed. 2. The database looks for a matching hash value for an existing SQL statement in the shared pool. The following options are possible: • No matching hash value exists. In this case, the SQL statement does not currently exist in the shared pool, so the database performs a hard parse. This ends the shared pool check. • A matching hash value exists. In this case, the database proceeds to the next step, which is a text match. 3. The database compares the text of the matched statement to the text of the hashed statement to determine whether they are identical. The following options are possible: • The textual match fails. In this case, the text match process stops, resulting in a hard parse. 20-3 Chapter 20 Overview of Cursor Sharing • The textual match succeeds. In this case, the database proceeds to the next step: determining whether the SQL can share an existing parent cursor. For a textual match to occur, the text of the SQL statements or PL/SQL blocks must be character-for-character identical, including spaces, case, and comments. For example, the following statements cannot use the same shared SQL area: SELECT * FROM employees; SELECT * FROM Employees; SELECT * FROM employees; Usually, SQL statements that differ only in literals cannot use the same shared SQL area. For example, the following statements do not resolve to the same SQL area: SELECT count(1) FROM employees WHERE manager_id = 121; SELECT count(1) FROM employees WHERE manager_id = 247; The only exception to this rule is when the parameter CURSOR_SHARING has been set to FORCE, in which case similar statements can share SQL areas. See Also: • "Parent and Child Cursors" • "Do Not Use CURSOR_SHARING = FORCE as a Permanent Fix" to learn about the costs involved in using CURSOR_SHARING • Oracle Database Reference to learn more about the CURSOR_SHARING initialization parameter 20.1.1.2 Parent and Child Cursors Every parsed SQL statement has a parent cursor and one or more child cursors. The parent cursor stores the text of the SQL statement. If the text of two statements is identical, then the statements share the same parent cursor. If the text is different, however, then the database creates a separate parent cursor. Example 20-1 Parent Cursors In this example, the first two statements are syntactically different (the letter “c” is lowercase in the first statement and uppercase in the second statement), but semantically identical. Because of the syntactic difference, these statements have different parent cursors. The third statement is syntactically identical to the first statement (lowercase “c”), but semantically different because it refers to a customers table in a different schema. Because of the syntactic identity, the third statement can share a parent cursor with the first statement. SQL> CONNECT oe@inst1 Enter password: ******* Connected. SQL> SELECT COUNT(*) FROM customers; 20-4 Chapter 20 Overview of Cursor Sharing COUNT(*) ---------319 SQL> SELECT COUNT(*) FROM Customers; COUNT(*) ---------319 SQL> CONNECT sh@inst1 Enter password: ******* Connected. SQL> SELECT COUNT(*) FROM customers; COUNT(*) ---------155500 The following query of V$SQL indicates the two parents. The statement with the SQL ID of 8h916vv2yw400, which is the lowercase “c” version of the statement, has one parent cursor and two child cursors: child 0 and child 1. The statement with the SQL ID of 5rn2uxjtpz0wd, which is the uppercase “c” version of the statement, has a different parent cursor and only one child cursor: child 0. SQL> CONNECT SYSTEM@inst1 Enter password: ******* Connected. SQL> COL SQL_TEXT FORMAT a30 SQL> COL CHILD# FORMAT 99999 SQL> COL EXEC FORMAT 9999 SQL> COL SCHEMA FORMAT a6 SQL> SELECT SQL_ID, PARSING_SCHEMA_NAME AS SCHEMA, SQL_TEXT, 2 CHILD_NUMBER AS CHILD#, EXECUTIONS AS EXEC FROM V$SQL 3 WHERE SQL_TEXT LIKE '%ustom%' AND SQL_TEXT NOT LIKE '%SQL_TEXT%' ORDER BY SQL_ID; SQL_ID ------------5rn2uxjtpz0wd 8h916vv2yw400 8h916vv2yw400 SCHEMA -----OE OE SH SQL_TEXT CHILD# EXEC ------------------------------ ------ ----SELECT COUNT(*) FROM Customers 0 1 SELECT COUNT(*) FROM customers 0 1 SELECT COUNT(*) FROM customers 1 1 This section contains the following topics: • Parent Cursors and V$SQLAREA The V$SQLAREA view contains one row for every parent cursor. • Child Cursors and V$SQL Every parent cursor has one or more child cursors. • Cursor Mismatches and V$SQL_SHARED_CURSOR If a parent cursor has multiple children, then the V$SQL_SHARED_CURSOR view provides information about why the cursor was not shared. For several types of incompatibility, the TRANSLATION_MISMATCH column indicates a mismatch with the value Y or N. 20-5 Chapter 20 Overview of Cursor Sharing 20.1.1.2.1 Parent Cursors and V$SQLAREA The V$SQLAREA view contains one row for every parent cursor. In the following example, a query of V$SQLAREA shows two parent cursors, each identified with a different SQL_ID. The VERSION_COUNT indicates the number of child cursors. COL SQL_TEXT FORMAT a30 SELECT SQL_TEXT, SQL_ID, VERSION_COUNT, HASH_VALUE FROM V$SQLAREA WHERE SQL_TEXT LIKE '%mployee%' AND SQL_TEXT NOT LIKE '%SQL_TEXT%'; SQL_TEXT -----------------------------SELECT * FROM Employees SELECT * FROM employees SQL_ID VERSION_COUNT HASH_VALUE ------------- ------------- ---------5bzhzpaa0wy9m 1 2483976499 4959aapufrm1k 2 1961610290 In the preceding output, the VERSION_COUNT of 2 for SELECT * FROM employees indicates multiple child cursors, which were necessary because the statement was executed against two different objects. In contrast, the statement SELECT * FROM Employees (note the capital "E") was executed once, and so has one parent cursor, and one child cursor (VERSION_COUNT of 1). 20.1.1.2.2 Child Cursors and V$SQL Every parent cursor has one or more child cursors. A child cursor contains the execution plan, bind variables, metadata about objects referenced in the query, optimizer environment, and other information. In contrast to the parent cursor, the child cursor does not store the text of the SQL statement. If a statement is able to reuse a parent cursor, then the database checks whether the statement can reuse an existing child cursor. The database performs several checks, including the following: • The database compares objects referenced in the issued statement to the objects referenced by the statement in the pool to ensure that they are all identical. References to schema objects in the SQL statements or PL/SQL blocks must resolve to the same object in the same schema. For example, if two users issue the following SQL statement, and if each user has its own employees table, then the following statement is not identical because the statement references different employees tables for each user: SELECT * FROM employees; • The database determines whether the optimizer mode is identical. For example, SQL statements must be optimized using the same optimizer goal (see "Choosing an Optimizer Goal"). Example 20-2 Multiple Child Cursors V$SQL describes the statements that currently reside in the library cache. It contains one row for every child cursor, as shown in the following example: SELECT SQL_TEXT, SQL_ID, USERNAME AS USR, CHILD_NUMBER AS CHILD#, HASH_VALUE, PLAN_HASH_VALUE AS PLAN_HASHV 20-6 Chapter 20 Overview of Cursor Sharing FROM WHERE AND AND V$SQL s, DBA_USERS d SQL_TEXT LIKE '%mployee%' SQL_TEXT NOT LIKE '%SQL_TEXT%' d.USER_ID = s.PARSING_USER_ID; SQL_TEXT ----------------------SELECT * FROM Employees SELECT * FROM employees SELECT * FROM employees SQL_ID ------------5bzhzpaa0wy9m 4959aapufrm1k 4959aapufrm1k USR CHILD# HASH_VALUE PLAN_HASHV --- ------ ---------- ---------HR 0 2483976499 1445457117 HR 0 1961610290 1445457117 SH 1 1961610290 1445457117 In the preceding results, the CHILD# of the bottom two statements is different (0 and 1), even though the SQL_ID is the same. This means that the statements have the same parent cursor, but different child cursors. In contrast, the statement with the SQL_ID of 5bzhzpaa0wy9m has one parent and one child (CHILD# of 0). All three SQL statements use the same execution plan, as indicated by identical values in the PLAN_HASH_VALUE column. 20.1.1.2.3 Cursor Mismatches and V$SQL_SHARED_CURSOR If a parent cursor has multiple children, then the V$SQL_SHARED_CURSOR view provides information about why the cursor was not shared. For several types of incompatibility, the TRANSLATION_MISMATCH column indicates a mismatch with the value Y or N. Example 20-3 Translation Mismatch In this example, the TRANSLATION_MISMATCH column shows that the two statements (SELECT * FROM employees) referenced different objects, resulting in a TRANSLATION_MISMATCH value of Y for the last statement. Because sharing was not possible, each statement had a separate child cursor, as indicated by CHILD_NUMBER of 0 and 1. SELECT S.SQL_TEXT, S.CHILD_NUMBER, s.CHILD_ADDRESS, C.TRANSLATION_MISMATCH FROM V$SQL S, V$SQL_SHARED_CURSOR C WHERE SQL_TEXT LIKE '%employee%' AND SQL_TEXT NOT LIKE '%SQL_TEXT%' AND S.CHILD_ADDRESS = C.CHILD_ADDRESS; SQL_TEXT CHILD_NUMBER CHILD_ADDRESS T ------------------------------ ------------ ---------------- SELECT * FROM employees 0 0000000081EE8690 N SELECT * FROM employees 1 0000000081F22508 Y 20.1.2 About Cursors and Parsing If an application issues a statement, and if Oracle Database cannot reuse a cursor, then it must build a new executable version of the application code. This operation is known as a hard parse. A soft parse is any parse that is not a hard parse, and occurs when the database can reuse existing code. Some soft parses are less resource-intensive than others. For example, if a parent cursor for the statement already exists, then Oracle Database can perform various optimizations, and then store the child cursor in the shared SQL area. If a parent cursor does not exist, however, then Oracle Database must also store the parent cursor in the shared SQL area, which creates additional memory overhead. 20-7 Chapter 20 Overview of Cursor Sharing Effectively, a hard parse recompiles a statement before running it. Hard parsing a SQL statement before every execution is analogous to recompiling a C program before every execution. A hard parse performs operations such as the following: • Checking the syntax of the SQL statement • Checking the semantics of the SQL statement • Checking the access rights of the user issuing the statement • Creating an execution plan • Accessing the library cache and data dictionary cache numerous times to check the data dictionary An especially resource-intensive aspect of hard parsing is accessing the library cache and data dictionary cache numerous times to check the data dictionary. When the database accesses these areas, it uses a serialization device called a latch on required objects so that their definition does not change during the check. Latch contention increases statement execution time and decreases concurrency. For all of the preceding reasons, the CPU and memory overhead of hard parses can create serious performance problems. The problems are especially evident in web applications that accept user input from a form, and then generate SQL statements dynamically. The Real-World Performance group strongly recommends reducing hard parsing as much as possible. Video: Video Example 20-4 Finding Parse Information Using V$SQL You can use various techniques to monitor hard and soft parsing. This example queries the session statistics to determine whether repeated executions of a DBA_JOBS query increase the hard parse count. The first execution of the statement increases the hard parse count to 49, but the second execution does not change the hard parse count, which means that Oracle Database reused application code. SQL> ALTER SYSTEM FLUSH SHARED_POOL; System altered. SQL> COL NAME FORMAT a18 SQL> 2 3 4 SELECT FROM WHERE AND s.NAME, m.VALUE V$STATNAME s, V$MYSTAT m s.STATISTIC# = m.STATISTIC# s.NAME LIKE '%(hard%'; NAME VALUE ------------------ ---------parse count (hard) 48 SQL> SELECT COUNT(*) FROM DBA_JOBS; COUNT(*) ---------- 20-8 Chapter 20 Overview of Cursor Sharing 0 SQL> 2 3 4 SELECT FROM WHERE AND s.NAME, m.VALUE V$STATNAME s, V$MYSTAT m s.STATISTIC# = m.STATISTIC# s.NAME LIKE '%(hard%'; NAME VALUE ------------------ ---------parse count (hard) 49 SQL> SELECT COUNT(*) FROM DBA_JOBS; COUNT(*) ---------0 SQL> 2 3 4 SELECT FROM WHERE AND s.NAME, m.VALUE V$STATNAME s, V$MYSTAT m s.STATISTIC# = m.STATISTIC# s.NAME LIKE '%(hard%'; NAME VALUE ------------------ ---------parse count (hard) 49 Example 20-5 Finding Parse Information Using Trace Files This example uses SQL Trace and the TKPROF utility to find parse information. You log in to the database with administrator privileges, and then query the directory location of the trace files (sample output included): SET LINESIZE 120 COLUMN value FORMAT A80 SELECT value FROM v$diag_info WHERE name = 'Default Trace File'; VALUE -------------------------------------------------------------------------------/disk1/oracle/log/diag/rdbms/orcl/orcl/trace/orcl_ora_23054.trc You enable tracing, use the TRACEFILE_IDENTIFIER initialization parameter to give the trace file a meaningful name, and then query hr.employees: EXEC DBMS_MONITOR.SESSION_TRACE_ENABLE(waits=>TRUE, binds=>TRUE); ALTER SESSION SET TRACEFILE_IDENTIFIER = "emp_stmt"; SELECT * FROM hr.employees; EXIT; Search the default trace file directory for the trace file that you generated: % ls *emp_stmt.trc orcl_ora_17950_emp_stmt.trc Use TKPROF to format the trace file, and then open the formatted file: % tkprof orcl_ora_17950_emp_stmt.trc emp.out; vi emp.out The formatted trace file contains the parse information for the query of hr.employees. 20-9 Chapter 20 Overview of Cursor Sharing SQL ID: brmjpfs7dcnub Plan Hash: 1445457117 SELECT * FROM hr.employees call count cpu elapsed disk query current rows ------- ------ -------- ---------- ---------- ---------- ---------- ---------Parse 1 0.07 0.08 0 0 0 0 Execute 1 0.00 0.00 0 0 0 0 Fetch 9 0.00 0.00 3 12 0 107 ------- ------ -------- ---------- ---------- ---------- ---------- ---------total 11 0.07 0.08 3 12 0 107 Misses in library cache during parse: 1 Optimizer mode: ALL_ROWS Parsing user id: SYSTEM Number of plan statistics captured: 1 Rows (1st) Rows (avg) Rows (max) Row Source Operation ---------- ---------- ---------- --------------------------------------------------107 107 107 TABLE ACCESS FULL EMPLOYEES (cr=12 pr=3 pw=0 time=497 us starts=1 cost=2 size=7383 card=107) A library cache miss indicates a hard parse. Performing the same steps for a second execution of the same statement produces the following trace output, which shows no library cache misses: SQL ID: brmjpfs7dcnub Plan Hash: 1445457117 SELECT * FROM hr.employees call count cpu elapsed disk query current rows ------- ------ -------- ---------- ---------- ---------- ---------- ---------Parse 1 0.00 0.00 0 0 0 0 Execute 1 0.00 0.00 0 0 0 0 Fetch 9 0.00 0.00 3 12 0 107 ------- ------ -------- ---------- ---------- ---------- ---------- ---------total 11 0.00 0.00 3 12 0 107 Misses in library cache during parse: 0 Optimizer mode: ALL_ROWS Parsing user id: SYSTEM Number of plan statistics captured: 1 Rows (1st) Rows (avg) Rows (max) Row Source Operation ---------- ---------- ---------- --------------------------------------------------107 107 107 TABLE ACCESS FULL EMPLOYEES (cr=12 pr=3 pw=0 time=961 us starts=1 cost=2 size=7383 card=107) 20-10 Chapter 20 Overview of Cursor Sharing See Also: "Shared Pool Check" 20.1.3 About Literals and Bind Variables Bind variables are essential to cursor sharing in Oracle database applications. This section contains the following topics: • Literals and Cursors When constructing SQL statements, some Oracle applications use literals instead of bind variables. • Bind Variables and Cursors You can develop Oracle applications to use bind variables instead of literals. • Bind Variable Peeking In bind variable peeking (also known as bind peeking), the optimizer looks at the value in a bind variable when the database performs a hard parse of a statement. 20.1.3.1 Literals and Cursors When constructing SQL statements, some Oracle applications use literals instead of bind variables. For example, the statement SELECT SUM(salary) FROM hr.employees WHERE employee_id < 101 uses the literal value 101 for the employee ID. By default, when similar statements do not use bind variables, Oracle Database cannot take advantage of cursor sharing. Thus, Oracle Database sees a statement that is identical except for the value 102, or any other random value, as a completely new statement, requiring a hard parse. The Real-World Performance group has determined that applications that use literals are a frequent cause of performance, scalability, and security problems. In the real world, it is not uncommon for applications to be written quickly, without considering cursor sharing. A classic example is a “screen scraping” application that copies the contents out of a web form, and then concatenates strings to construct the SQL statement dynamically. Major problems that result from using literal values include the following: • Applications that concatenate literals input by an end user are prone to SQL injection attacks. Only rewriting the applications to use bind variables eliminates this threat. • If every statement is hard parsed, then cursors are not shared, and so the database must consume more memory to create the cursors. • Oracle Database must latch the shared pool and library cache when hard parsing. As the number of hard parses increases, so does the number of processes waiting to latch the shared pool. This situation decreases concurrency and increases contention. 20-11 Chapter 20 Overview of Cursor Sharing Video: Video Example 20-6 Literals and Cursor Sharing Consider an application that executes the following statements, which differ only in literals: SELECT SUM(salary) FROM hr.employees WHERE employee_id < 101; SELECT SUM(salary) FROM hr.employees WHERE employee_id < 120; SELECT SUM(salary) FROM hr.employees WHERE employee_id < 165; The following query of V$SQLAREA shows that the three statements require three different parent cursors. As shown by VERSION_COUNT, each parent cursor requires its own child cursor. COL SQL_TEXT FORMAT a30 SELECT SQL_TEXT, SQL_ID, VERSION_COUNT, HASH_VALUE FROM V$SQLAREA WHERE SQL_TEXT LIKE '%mployee%' AND SQL_TEXT NOT LIKE '%SQL_TEXT%'; SQL_TEXT -----------------------------SELECT SUM(salary) FROM hr.emp loyees WHERE employee_id < 165 SELECT SUM(salary) FROM hr.emp loyees WHERE employee_id < 101 SELECT SUM(salary) FROM hr.emp loyees WHERE employee_id < 120 SQL_ID VERSION_COUNT HASH_VALUE ------------- ------------- ---------b1tvfcc5qnczb 1 191509483 cn5250y0nqpym 1 2169198547 au8nag2vnfw67 1 3074912455 See Also: "Do Not Use CURSOR_SHARING = FORCE as a Permanent Fix" to learn about SQL injection 20.1.3.2 Bind Variables and Cursors You can develop Oracle applications to use bind variables instead of literals. A bind variable is a placeholder in a query. For example, the statement SELECT SUM(salary) FROM hr.employees WHERE employee_id < :emp_id uses the bind variable:emp_id for the employee ID. The Real-World Performance group has found that applications that use bind variables perform better, scale better, and are more secure. Major benefits that result from using bind variables include the following: • Applications that use bind variables are not vulnerable to the same SQL injection attacks as applications that use literals. 20-12 Chapter 20 Overview of Cursor Sharing • When identical statements use bind variables, Oracle Database can take advantage of cursor sharing, and share the plan and other information when different values are bound to the same statement. • Oracle Database avoids the overhead of latching the shared pool and library cache required for hard parsing. Video: Video Example 20-7 Bind Variables and Shared Cursors The following example uses the VARIABLE command in SQL*Plus to create the emp_id bind variable, and then executes a query using three different bind values (101, 120, and 165): VARIABLE emp_id NUMBER EXEC :emp_id := 101; SELECT SUM(salary) FROM hr.employees WHERE employee_id < :emp_id; EXEC :emp_id := 120; SELECT SUM(salary) FROM hr.employees WHERE employee_id < :emp_id; EXEC :emp_id := 165; SELECT SUM(salary) FROM hr.employees WHERE employee_id < :emp_id; The following query of V$SQLAREA shows one unique SQL statement: COL SQL_TEXT FORMAT a34 SELECT SQL_TEXT, SQL_ID, VERSION_COUNT, HASH_VALUE FROM V$SQLAREA WHERE SQL_TEXT LIKE '%mployee%' AND SQL_TEXT NOT LIKE '%SQL_TEXT%'; SQL_TEXT SQL_ID VERSION_COUNT HASH_VALUE ---------------------------------- ------------- ------------- ---------SELECT SUM(salary) FROM hr.employe 4318cbskba8yh 1 615850960 es WHERE employee_id < :emp_id The VERSION_COUNT value of 1 indicates that the database reused the same child cursor rather than creating three separate child cursors. Using a bind variable made this reuse possible. 20.1.3.3 Bind Variable Peeking In bind variable peeking (also known as bind peeking), the optimizer looks at the value in a bind variable when the database performs a hard parse of a statement. The optimizer does not look at the bind variable values before every parse. Rather, the optimizer peeks only when the optimizer is first invoked, which is during the hard parse. When a query uses literals, the optimizer can use the literal values to find the best plan. However, when a query uses bind variables, the optimizer must select the best plan without the presence of literals in the SQL text. This task can be extremely difficult. By peeking at bind values during the initial hard parse, the optimizer can 20-13 Chapter 20 Overview of Cursor Sharing determine the cardinality of a WHERE clause condition as if literals had been used, thereby improving the plan. Because the optimizer only peeks at the bind value during the hard parse, the plan may not be optimal for all possible bind values. The following examples illustrate this principle. Example 20-8 Literals Result in Different Execution Plans Assume that you execute the following statements, which execute three different statements using different literals (101, 120, and 165), and then display the execution plans for each: SET LINESIZE 167 SET PAGESIZE 0 SELECT SUM(salary) FROM hr.employees WHERE employee_id < 101; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR()); SELECT SUM(salary) FROM hr.employees WHERE employee_id < 120; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR()); SELECT SUM(salary) FROM hr.employees WHERE employee_id < 165; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR()); The database hard parsed all three statements, which were not identical. The DISPLAY_CURSOR output, which has been edited for clarity, shows that the optimizer chose the same index range scan plan for the first two statements, but a full table scan plan for the statement using literal 165: SQL_ID cn5250y0nqpym, child number 0 ------------------------------------SELECT SUM(salary) FROM hr.employees WHERE employee_id < 101 Plan hash value: 2410354593 ------------------------------------------------------------------------------------|Id| Operation | Name |Rows|Bytes|Cost(%CPU)|Time| ------------------------------------------------------------------------------------| 0| SELECT STATEMENT | | | |2 (100)| | | 1| SORT AGGREGATE | |1 | 8 | | | | 2| TABLE ACCESS BY INDEX ROWID BATCHED| EMPLOYEES |1 | 8 |2 (0) | 00:00:01 | |*3| INDEX RANGE SCAN | EMP_EMP_ID_PK |1 | |1 (0) | 00:00:01 | ------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------3 - access("EMPLOYEE_ID"<101) SQL_ID au8nag2vnfw67, child number 0 ------------------------------------SELECT SUM(salary) FROM hr.employees WHERE employee_id < 120 Plan hash value: 2410354593 ------------------------------------------------------------------------------------|Id| Operation | Name |Rows|Bytes|Cost(%CPU)|Time| ------------------------------------------------------------------------------------| 0| SELECT STATEMENT | | | |2 (100)| | | 1| SORT AGGREGATE | |1 | 8 | | | | 2| TABLE ACCESS BY INDEX ROWID BATCHED| EMPLOYEES |20|160|2 (0) | 00:00:01 | |*3| INDEX RANGE SCAN | EMP_EMP_ID_PK |20| |1 (0) | 00:00:01 | ------------------------------------------------------------------------------------- 20-14 Chapter 20 Overview of Cursor Sharing Predicate Information (identified by operation id): --------------------------------------------------3 - access("EMPLOYEE_ID"<120) SQL_ID b1tvfcc5qnczb, child number 0 ------------------------------------SELECT SUM(salary) FROM hr.employees WHERE employee_id < 165 Plan hash value: 1756381138 ------------------------------------------------------------------------| Id | Operation | Name |Rows| Bytes |Cost(%CPU)| Time | ------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | | 2 (100)| | | 1 | SORT AGGREGATE | | 1 | 8 | | | |* 2 | TABLE ACCESS FULL| EMPLOYEES | 66 | 528 | 2 (0)| 00:00:01 | ------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------2 - filter("EMPLOYEE_ID"<165) The preceding output shows that the optimizer considers a full table scan more efficient than an index scan for the query that returns more rows. Example 20-9 Bind Variables Result in Cursor Reuse This example rewrites the queries executed in Example 20-8 to use bind variables instead of literals. You bind the same values (101, 120, and 165) to the bind variable :emp_id, and then display the execution plans for each: VAR emp_id NUMBER EXEC :emp_id := 101; SELECT SUM(salary) FROM hr.employees WHERE employee_id < :emp_id; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR()); EXEC :emp_id := 120; SELECT SUM(salary) FROM hr.employees WHERE employee_id < :emp_id; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR()); EXEC :emp_id := 165; SELECT SUM(salary) FROM hr.employees WHERE employee_id < :emp_id; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR()); The DISPLAY_CURSOR output shows that the optimizer chose exactly the same plan for all three statements: SELECT SUM(salary) FROM hr.employees WHERE employee_id < :emp_id Plan hash value: 2410354593 ------------------------------------------------------------------------------------| Id | Operation | Name |Rows|Bytes|Cost (%CPU)|Time| ------------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | |2 (100)| | | 1 | SORT AGGREGATE | |1|8 | | | | 2 | TABLE ACCESS BY INDEX ROWID BATCHED| EMPLOYEES |1|8 | 2 (0)| 00:00:01 | |* 3 | INDEX RANGE SCAN | EMP_EMP_ID_PK |1| | 1 (0)| 00:00:01 | ------------------------------------------------------------------------------------- 20-15 Chapter 20 Overview of Cursor Sharing Predicate Information (identified by operation id): --------------------------------------------------3 - access("EMPLOYEE_ID"<:EMP_ID) In contrast, when the preceding statements were executed with literals, the optimizer chose a lower-cost full table scan when the employee ID value was 165. This is the problem solved by adaptive cursor sharing. See Also: "Adaptive Cursor Sharing" 20.1.4 About the Life Cycle of Shared Cursors The database allocates a new shared SQL area when the optimizer parses a new SQL statement that is not DDL. The amount of memory required depends on the statement complexity. The database can remove a shared SQL area from the shared pool even if this area corresponds to an open cursor that has been unused for a long time. If the open cursor is later used to run its statement, then the database reparses the statement and allocates a new shared SQL area. The database does not remove cursors whose statements are executing, or whose rows have not been completely fetched. Shared SQL areas can become invalid because of changes to dependent schema objects or to optimizer statistics. Oracle Database uses two techniques to manage the cursor life cycle: invalidation and rolling invalidation. This section contains the following topics: • Cursor Marked Invalid When a shared SQL area is marked invalid, the database can remove it from the shared pool, along with valid cursors that have been unused for some time. • Cursors Marked Rolling Invalid When cursors are marked rolling invalid (V$SQL.IS_ROLLING_INVALID is Y), the database gradually performs hard parses over an extended time. See Also: Oracle Database Concepts for an overview of memory allocation in the shared pool 20.1.4.1 Cursor Marked Invalid When a shared SQL area is marked invalid, the database can remove it from the shared pool, along with valid cursors that have been unused for some time. 20-16 Chapter 20 Overview of Cursor Sharing In some situations, the database must execute a statement that is associated with an invalid shared SQL area in the shared pool. In this case, the database performs a hard parse of the statement before execution. The database immediately marks dependent shared SQL areas invalid when the following conditions are met: • DBMS_STATS gathers statistics for a table, table cluster, or index when the NO_INVALIDATE parameter is FALSE. • A SQL statement references a schema object, which is later modified by a DDL statement that uses immediate cursor invalidation (default). You can manually specify immediate invalidation on statements such as ALTER TABLE ... IMMEDIATE VALIDATION and ALTER INDEX ... IMMEDIATE VALIDATION, or set the CURSOR_INVALIDATION initialization parameter to IMMEDIATE at the session or system level. Note: A DDL statement using the DEFERRED VALIDATION clause overrides the IMMEDIATE setting of the CURSOR_INVALIDATION initialization parameter. When the preceding conditions are met, the database reparses the affected statements at next execution. When the database invalidates a cursor, the V$SQL.INVALIDATIONS value increases (for example, from 0 to 1), and V$SQL.OBJECT_STATUS shows INVALID_UNAUTH. Example 20-10 Forcing Cursor Invalidation by Setting NO_INVALIDATE=FALSE This example logs in as user sh, who has been granted administrator privileges. The example queries sales, and then gathers statistics for this table with NO_INVALIDATE=FALSE. Afterwards, the V$SQL.INVALIDATIONS value changes from 0 to 1 for the cursor, indicating that the database flagged the cursor as invalid. SQL> SELECT COUNT(*) FROM sales; COUNT(*) ---------918843 SQL> SELECT PREV_SQL_ID SQL_ID FROM V$SESSION WHERE SID = SYS_CONTEXT('userenv', 'SID'); SQL_ID ------------1y17j786c7jbh SQL> SELECT CHILD_NUMBER, EXECUTIONS, 2 PARSE_CALLS, INVALIDATIONS, OBJECT_STATUS 3 FROM V$SQL WHERE SQL_ID = '1y17j786c7jbh'; CHILD_NUMBER EXECUTIONS PARSE_CALLS INVALIDATIONS OBJECT_STATUS ------------ ---------- ----------- ------------- ------------0 1 1 0 VALID 20-17 Chapter 20 Overview of Cursor Sharing SQL> EXEC DBMS_STATS.GATHER_TABLE_STATS(null,'sales',no_invalidate => FALSE); PL/SQL procedure successfully completed. SQL> SELECT CHILD_NUMBER, EXECUTIONS, 2 PARSE_CALLS, INVALIDATIONS, OBJECT_STATUS 3 FROM V$SQL WHERE SQL_ID = '1y17j786c7jbh'; CHILD_NUMBER EXECUTIONS PARSE_CALLS INVALIDATIONS OBJECT_STATUS ------------ ---------- ----------- ------------- -------------0 1 1 1 INVALID_UNAUTH See Also: • "About Optimizer Initialization Parameters" • Oracle Database SQL Language Reference to learn more about ALTER TABLE ... IMMEDIATE VALIDATION and other DDL statements that permit immediate validation • Oracle Database Reference to learn more about V$SQL and V$SQLAREA dynamic views • Oracle Database Reference to learn more about the CURSOR_INVALIDATION initialization parameter 20.1.4.2 Cursors Marked Rolling Invalid When cursors are marked rolling invalid (V$SQL.IS_ROLLING_INVALID is Y), the database gradually performs hard parses over an extended time. Note: When V$SQL.IS_ROLLING_REFRESH_INVALID is Y, the underlying object has changed, but recompilation of the cursor is not required. The database updates metadata in the cursor. Purpose of Rolling Invalidation Because a sharp increase in hard parses can significantly degrade performance, rolling invalidation—also called deferred invalidation—is useful for workloads that simultaneously invalidate many cursors. The database assigns each invalid cursor a randomly generated time period. SQL areas invalidated at the same time typically have different time periods. A hard parse occurs only if a query accessing the cursor executes after the time period has expired. In this way, the database diffuses the overhead of hard parsing over time. 20-18 Chapter 20 Overview of Cursor Sharing Note: If parallel SQL statements are marked rolling invalid, then the database performs a hard parse at next execution, regardless of whether the time period has expired. In an Oracle Real Application Clusters (Oracle RAC) environment, this technique ensures consistency between execution plans of parallel execution servers and the query coordinator. An analogy for rolling invalidation might be the gradual replacement of worn-out office furniture. Instead of replacing all the furniture at once, forcing a substantial financial outlay, a company assigns each piece a different expiration date. Over the course of a year, a piece stays in use until it is replaced, at which point a cost is incurred. Specification of Deferred Invalidation By default, DDL specifies that statements accessing the object use immediate cursor invalidation. For example, if you create a table or an index, then cursors that reference this table or index use immediate invalidation. If a DDL statement supports deferred cursor invalidation, then you can override the default behavior by using statements such as ALTER TABLE ... DEFERRED INVALIDATION. The options depend on the DDL statement. For example, ALTER INDEX only supports DEFERRED INVALIDATION when the UNUSABLE or REBUILD option is also specified. An alternative to DDL is setting the CURSOR_INVALIDATION initialization parameter to DEFERRED at the session or system level. A DDL statement using the IMMEDIATE INVALIDATION clause overrides the DEFERRED setting of the CURSOR_INVALIDATION initialization parameter. When Rolling Invalidation Occurs If the DEFERRED INVALIDATION attribute applies to an object, either as a result of DDL or an initialization parameter setting, then statements that access the object may be subject to deferred invalidation. The database marks shared SQL areas as rolling invalid in either of the following circumstances: • DBMS_STATS gathers statistics for a table, table cluster, or index when the NO_INVALIDATE parameter is set to DBMS_STATS.AUTO_INVALIDATE. This is the default setting. • One of the following statements is issued with DEFERRED INVALIDATION in circumstances that do not prevent the use of deferred invalidation: – ALTER TABLE on partitioned tables – ALTER TABLE ... PARALLEL – ALTER INDEX ... UNUSABLE – ALTER INDEX ... REBUILD – CREATE INDEX – DROP INDEX – TRUNCATE TABLE on partitioned tables A subset of DDL statements require immediate cursor invalidation for DML (INSERT, UPDATE, DELETE, or MERGE) but not SELECT statements. Many factors relating to the 20-19 Chapter 20 CURSOR_SHARING and Bind Variable Substitution specific DDL statements and affected cursors determine whether Oracle Database uses deferred invalidation. See Also: • "About Optimizer Initialization Parameters" • Oracle Database SQL Language Reference to learn more about ALTER TABLE ... DEFERRED INVALIDATION and other DDL statements that permit deferred invalidation • Oracle Database Reference to learn more about V$SQL and V$SQLAREA dynamic views • Oracle Database Reference to learn more about the CURSOR_INVALIDATION initialization parameter 20.2 CURSOR_SHARING and Bind Variable Substitution This topic explains what the CURSOR_SHARING initialization parameter is, and how setting it to different values affects how Oracle Database uses bind variables. This section contains the following topics: • CURSOR_SHARING Initialization Parameter The CURSOR_SHARING initialization parameter controls how the database processes statements with bind variables. • Parsing Behavior When CURSOR_SHARING = FORCE When SQL statements use literals rather than bind variables, setting the CURSOR_SHARING initialization parameter to FORCE enables the database to replace literals with system-generated bind variables. Using this technique, the database can sometimes reduce the number of parent cursors in the shared SQL area. 20.2.1 CURSOR_SHARING Initialization Parameter The CURSOR_SHARING initialization parameter controls how the database processes statements with bind variables. In Oracle Database 12c, the parameter supports the following values: • EXACT This is the default value. The database enables only textually identical statements to share a cursor. The database does not attempt to replace literal values with system-generated bind variables. In this case, the optimizer generates a plan for each statement based on the literal value. • FORCE The database replaces all literals with system-generated bind variables. For statements that are identical after the bind variables replace the literals, the optimizer uses the same plan. 20-20 Chapter 20 CURSOR_SHARING and Bind Variable Substitution Note: The SIMILAR value for CURSOR_SHARING is deprecated. You can set CURSOR_SHARING at the system or session level, or use the CURSOR_SHARING_EXACT hint at the statement level. See Also: "Do Not Use CURSOR_SHARING = FORCE as a Permanent Fix" 20.2.2 Parsing Behavior When CURSOR_SHARING = FORCE When SQL statements use literals rather than bind variables, setting the CURSOR_SHARING initialization parameter to FORCE enables the database to replace literals with system-generated bind variables. Using this technique, the database can sometimes reduce the number of parent cursors in the shared SQL area. Note: If a statement uses an ORDER BY clause, then the database does not perform literal replacement in the clause because it is not semantically correct to consider the constant column number as a literal. The column number in the ORDER BY clause affects the query plan and execution, so the database cannot share two cursors having different column numbers. When CURSOR_SHARING is set to FORCE, the database performs the following steps during the parse: 1. Copies all literals in the statement to the PGA, and replaces them with systemgenerated bind variables For example, an application could process the following statement: SELECT SUBSTR(last_name, 1, 4), SUM(salary) FROM hr.employees WHERE employee_id < 101 GROUP BY last_name The optimizer replaces literals, including the literals in the SUBSTR function, as follows: SELECT SUBSTR(last_name, :"SYS_B_0", :"SYS_B_1"), SUM(salary) FROM hr.employees WHERE employee_id < :"SYS_B_2" GROUP BY last_name 2. Searches for an identical statement (same SQL hash value) in the shared pool If an identical statement is not found, then the database performs a hard parse. Otherwise, the database proceeds to the next step. 3. Performs a soft parse of the statement 20-21 Chapter 20 CURSOR_SHARING and Bind Variable Substitution As the preceding steps indicate, setting the CURSOR_SHARING initialization parameter to FORCE does not reduce the parse count. Rather, in some cases, FORCE enables the database to perform a soft parse instead of a hard parse. Also, FORCE does not the prevent against SQL injection attacks because Oracle Database binds the values after any injection has already occurred. Example 20-11 Replacement of Literals with System Bind Variables This example sets CURSOR_SHARING to FORCE at the session level, executes three statements containing literals, and displays the plan for each statement: ALTER SESSION SET CURSOR_SHARING=FORCE; SET LINESIZE 170 SET PAGESIZE 0 SELECT SUM(salary) FROM hr.employees WHERE employee_id < 101; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR()); SELECT SUM(salary) FROM hr.employees WHERE employee_id < 120; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR()); SELECT SUM(salary) FROM hr.employees WHERE employee_id < 165; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR()); The following DISPLAY_CURSOR output, edited for readability, shows that all three statements used the same plan. The optimizer chose the plan, an index range scan, because it peeked at the first value (101) bound to the system bind variable, and picked this plan as the best for all values. In fact, this plan is not the best plan for all values. When the value is 165, a full table scan is more efficient. SQL_ID cxx8n1cxr9khn, child number 0 ------------------------------------SELECT SUM(salary) FROM hr.employees WHERE employee_id < :"SYS_B_0" Plan hash value: 2410354593 ------------------------------------------------------------------------------------| Id | Operation | Name |Rows|Bytes|Cost(%CPU)|Time| ------------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | |2 (100)| | | 1 | SORT AGGREGATE | |1 | 8 | | | | 2 | TABLE ACCESS BY INDEX ROWID BATCHED| EMPLOYEES |1 | 8 |2 (0) |00:00:01| |* 3 | INDEX RANGE SCAN | EMP_EMP_ID_PK |1 | |1 (0) |00:00:01| ------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------3 - access("EMPLOYEE_ID"<101) A query of V$SQLAREA confirms that Oracle Database replaced with the literal with system bind variable :”SYS_B_0”, and created one parent and one child cursor (VERSION_COUNT=1) for all three statements, which means that all executions shared the same plan. COL SQL_TEXT FORMAT a36 SELECT SQL_TEXT, SQL_ID, VERSION_COUNT, HASH_VALUE FROM V$SQLAREA WHERE SQL_TEXT LIKE '%mployee%' AND SQL_TEXT NOT LIKE '%SQL_TEXT%'; SQL_TEXT SQL_ID VERSION_COUNT HASH_VALUE ------------------------------------ ------------- ------------- ---------- 20-22 Chapter 20 Adaptive Cursor Sharing SELECT SUM(salary) FROM hr.employees cxx8n1cxr9khn WHERE employee_id < :"SYS_B_0" 1 997509652 See Also: • "Private and Shared SQL Areas" for more details on the various checks performed • Oracle Database Reference to learn about the CURSOR_SHARING initialization parameter 20.3 Adaptive Cursor Sharing The adaptive cursor sharing feature enables a single statement that contains bind variables to use multiple execution plans. Cursor sharing is "adaptive" because the cursor adapts its behavior so that the database does not always use the same plan for each execution or bind variable value. This section contains the following topics: • Purpose of Adaptive Cursor Sharing With bind peeking, the optimizer peeks at the values of user-defined bind variables on the first invocation of a cursor. • How Adaptive Cursor Sharing Works: Example Adaptive cursor sharing monitors statements that use bind variables to determine whether a new plan is more efficient. • Bind-Sensitive Cursors A bind-sensitive cursor is a cursor whose optimal plan may depend on the value of a bind variable. • Bind-Aware Cursors A bind-aware cursor is a bind-sensitive cursor that is eligible to use different plans for different bind values. • Cursor Merging If the optimizer creates a plan for a bind-aware cursor, and if this plan is the same as an existing cursor, then the optimizer can perform cursor merging. • Adaptive Cursor Sharing Views You can use the V$ views for adaptive cursor sharing to see selectivity ranges, cursor information (such as whether a cursor is bind-aware or bind-sensitive), and execution statistics. 20.3.1 Purpose of Adaptive Cursor Sharing With bind peeking, the optimizer peeks at the values of user-defined bind variables on the first invocation of a cursor. The optimizer determines the cardinality of any WHERE clause condition as if literals had been used instead of bind variables. If a column in a WHERE clause has skewed data, however, then a histogram may exist on this column. When the optimizer peeks at the value of the user-defined bind variable and chooses a plan, this plan may not be good for all values. 20-23 Chapter 20 Adaptive Cursor Sharing In adaptive cursor sharing, the database monitors data accessed over time for different bind values, ensuring the optimal choice of cursor for a specific bind value. For example, the optimizer might choose one plan for bind value 10 and a different plan for bind value 50. Cursor sharing is "adaptive" because the cursor adapts its behavior so that the optimizer does not always choose the same plan for each execution or bind variable value. Thus, the optimizer automatically detects when different execution of a statement would benefit from different execution plans. Note: Adaptive cursor sharing is independent of the CURSOR_SHARING initialization parameter. Adaptive cursor sharing is equally applicable to statements that contain user-defined and system-generated bind variables. Adaptive cursor sharing does not apply to statements that contain only literals. 20.3.2 How Adaptive Cursor Sharing Works: Example Adaptive cursor sharing monitors statements that use bind variables to determine whether a new plan is more efficient. Assume that an application executes the following statement five times, binding different values every time: SELECT * FROM employees WHERE salary = :sal AND department_id = :dept Also assume in this example that a histogram exists on at least one of the columns in the predicate. The database processes this statement as follows: 1. The application issues the statement for the first time, which causes a hard parse. During the parse, the database performs the following tasks: • Peeks at the bind variables to generate the initial plan. • Marks the cursor as bind-sensitive. A bind-sensitive cursor is a cursor whose optimal plan may depend on the value of a bind variable. To determine whether a different plan is beneficial, the database monitors the behavior of a bind-sensitive cursor that uses different bind values. • Stores metadata about the predicate, including the cardinality of the bound values (in this example, assume that only 5 rows were returned). • Creates an execution plan (in this example, index access) based on the peeked values. 2. The database executes the cursor, storing the bind values and execution statistics in the cursor. 3. The application issues the statement a second time, using different bind variables, causing the database to perform a soft parse, and find the matching cursor in the library cache. 4. The database executes the cursor. 5. The database performs the following post-execution tasks: a. The database compares the execution statistics for the second execution with the first-execution statistics. 20-24 Chapter 20 Adaptive Cursor Sharing b. 6. The database observes the pattern of statistics over all previous executions, and then decides whether to mark the cursor as a bind-aware cursor. In this example, assume that the database decides the cursor is bind-aware. The application issues the statement a third time, using different bind variables, which causes a soft parse. Because the cursor is bind-aware, the database does the following: • Determines whether the cardinality of the new values falls within the same range as the stored cardinality. In this example, the cardinality is similar: 8 rows instead of 5 rows. • Reuses the execution plan in the existing child cursor. 7. The database executes the cursor. 8. The application issues the statement a fourth time, using different bind variables, causing a soft parse. Because the cursor is bind-aware, the database does the following: 9. • Determines whether the cardinality of the new values falls within the same range as the stored cardinality. In this example, the cardinality is vastly different: 102 rows (in a table with 107 rows) instead of 5 rows. • Does not find a matching child cursor. The database performs a hard parse. As a result, the database does the following: • Creates a new child cursor with a second execution plan (in this example, a full table scan) • Stores metadata about the predicate, including the cardinality of the bound values, in the cursor 10. The database executes the new cursor. 11. The database stores the new bind values and execution statistics in the new child cursor. 12. The application issues the statement a fifth time, using different bind variables, which causes a soft parse. Because the cursor is bind-aware, the database does the following: • Determines whether the cardinality of the new values falls within the same range as the stored cardinality. In this example, the cardinality is 20. • Does not find a matching child cursor. 13. The database performs a hard parse. As a result, the database does the following: a. Creates a new child cursor with a third execution plan (in this example, index access) b. Determines that this index access execution plan is the same as the index access execution plan used for the first execution of the statement c. Merges the two child cursors containing index access plans, which involves storing the combined cardinality statistics into one child cursor, and deleting the other one 14. The database executes the cursor using the index access execution plan. 20-25 Chapter 20 Adaptive Cursor Sharing 20.3.3 Bind-Sensitive Cursors A bind-sensitive cursor is a cursor whose optimal plan may depend on the value of a bind variable. The database has examined the bind value when computing cardinality, and considers the query “sensitive” to plan changes based on different bind values. The database monitors the behavior of a bind-sensitive cursor that uses different bind values to determine whether a different plan is beneficial. The optimizer uses the following criteria to decide whether a cursor is bind-sensitive: • The optimizer has peeked at the bind values to generate cardinality estimates. • The bind is used in an equality or a range predicate. For each execution of the query with a new bind value, the database records the execution statistics for the new value and compares them to the execution statistics for the previous value. If execution statistics vary greatly, then the database marks the cursor bind-aware. Example 20-12 Column with Significant Data Skew This example assumes that the hr.employees.department_id column has significant data skew. SYSTEM executes the following setup code, which adds 100,000 employees in department 50 to the employees table in the sample schema, for a total of 100,107 rows, and then gathers table statistics: DELETE FROM hr.employees WHERE employee_id > 999; ALTER TABLE hr.employees DISABLE NOVALIDATE CONSTRAINT emp_email_uk; DECLARE v_counter NUMBER(7) := 1000; BEGIN FOR i IN 1..100000 LOOP INSERT INTO hr.employees VALUES (v_counter,null,'Doe','Doe@example.com',null,'07JUN-02','AC_ACCOUNT',null,null,null,50); v_counter := v_counter + 1; END LOOP; END; / COMMIT; EXEC DBMS_STATS.GATHER_TABLE_STATS ( ownname => 'hr', tabname => 'employees'); ALTER SYSTEM FLUSH SHARED_POOL; The following query shows a histogram on the employees.department_id column: COL TABLE_NAME FORMAT a15 COL COLUMN_NAME FORMAT a20 COL HISTOGRAM FORMAT a9 SELECT FROM WHERE AND AND TABLE_NAME, COLUMN_NAME, HISTOGRAM DBA_TAB_COLS OWNER = 'HR' TABLE_NAME = 'EMPLOYEES' COLUMN_NAME = 'DEPARTMENT_ID'; 20-26 Chapter 20 Adaptive Cursor Sharing TABLE_NAME COLUMN_NAME HISTOGRAM --------------- -------------------- --------EMPLOYEES DEPARTMENT_ID FREQUENCY Example 20-13 Low-Cardinality Query This example continues the example in Example 20-12. The following query shows that the value 10 has extremely low cardinality for the column department_id, occupying .00099% of the rows: VARIABLE dept_id NUMBER EXEC :dept_id := 10; SELECT COUNT(*), MAX(employee_id) FROM hr.employees WHERE department_id = :dept_id; COUNT(*) MAX(EMPLOYEE_ID) ---------- ---------------1 200 The optimizer chooses an index range scan, as expected for such a low-cardinality query: SQL> SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR); PLAN_TABLE_OUTPUT ------------------------------------SQL_ID a9upgaqqj7bn5, child number 0 ------------------------------------select COUNT(*), MAX(employee_id) FROM hr.employees WHERE department_id = :dept_id Plan hash value: 1642965905 ------------------------------------------------------------------------------------| Id| Operation | Name |Rows|Bytes|Cost (%CPU)|Time | ------------------------------------------------------------------------------------| 0| SELECT STATEMENT | | | |2(100)| | | 1| SORT AGGREGATE | |1 |8 | | | | 2| TABLE ACCESS BY INDEX ROWID BATCHED| EMPLOYEES |1 |8 |2 (0)|00:00:01| |*3| INDEX RANGE SCAN | EMP_DEPARTMENT_IX |1 | |1 (0)|00:00:01| ------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------3 - access("DEPARTMENT_ID"=:DEPT_ID) The following query of V$SQL obtains information about the cursor: COL COL COL COL COL COL COL BIND_AWARE FORMAT a10 SQL_TEXT FORMAT a22 CHILD# FORMAT 99999 EXEC FORMAT 9999 BUFF_GETS FORMAT 999999999 BIND_SENS FORMAT a9 SHARABLE FORMAT a9 SELECT SQL_TEXT, CHILD_NUMBER AS CHILD#, EXECUTIONS AS EXEC, BUFFER_GETS AS BUFF_GETS, IS_BIND_SENSITIVE AS BIND_SENS, IS_BIND_AWARE AS BIND_AWARE, IS_SHAREABLE AS SHARABLE FROM V$SQL WHERE SQL_TEXT LIKE '%mployee%' 20-27 Chapter 20 Adaptive Cursor Sharing AND SQL_TEXT NOT LIKE '%SQL_TEXT%'; SQL_TEXT CHILD# EXEC BUFF_GETS BIND_SENS BIND_AWARE SHARABLE ---------------------- ------ ----- ---------- --------- ---------- -------SELECT COUNT(*), MAX(e 0 1 196 Y N Y mployee_id) FROM hr.em ployees WHERE departme nt_id = :dept_id The preceding output shows one child cursor that has been executed once for the lowcardinality query. The cursor has been marked bind-sensitive because the optimizer believes the optimal plan may depend on the value of the bind variable. When a cursor is marked bind-sensitive, Oracle Database monitors the behavior of the cursor using different bind values, to determine whether a different plan for different bind values is more efficient. The database marked this cursor bind-sensitive because the optimizer used the histogram on the department_id column to compute the selectivity of the predicate WHERE department_id = :dept_id. Because the presence of the histogram indicates that the column is skewed, different values of the bind variable may require different plans. Example 20-14 High-Cardinality Query This example continues the example in Example 20-13. The following code reexecutes the same query using the value 50, which occupies 99.9% of the rows: EXEC :dept_id := 50; SELECT COUNT(*), MAX(employee_id) FROM hr.employees WHERE department_id = :dept_id; COUNT(*) MAX(EMPLOYEE_ID) ---------- ---------------100045 100999 Even though such an unselective query would be more efficient with a full table scan, the optimizer chooses the same index range scan used for department_id=10. This reason is that the database assumes that the existing plan in the cursor can be shared: SQL> SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR); PLAN_TABLE_OUTPUT ------------------------------------SQL_ID a9upgaqqj7bn5, child number 0 ------------------------------------SELECT COUNT(*), MAX(employee_id) FROM hr.employees WHERE department_id = :dept_id Plan hash value: 1642965905 ------------------------------------------------------------------------------------| Id| Operation | Name |Rows|Bytes|Cost (%CPU)|Time | ------------------------------------------------------------------------------------| 0| SELECT STATEMENT | | | |2(100)| | | 1| SORT AGGREGATE | |1 |8 | | | | 2| TABLE ACCESS BY INDEX ROWID BATCHED| EMPLOYEES |1 |8 |2 (0)|00:00:01| |*3| INDEX RANGE SCAN | EMP_DEPARTMENT_IX |1 | |1 (0)|00:00:01| ------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------3 - access("DEPARTMENT_ID"=:DEPT_ID) 20-28 Chapter 20 Adaptive Cursor Sharing A query of V$SQL shows that the child cursor has now been executed twice: SELECT SQL_TEXT, CHILD_NUMBER AS CHILD#, EXECUTIONS AS EXEC, BUFFER_GETS AS BUFF_GETS, IS_BIND_SENSITIVE AS BIND_SENS, IS_BIND_AWARE AS BIND_AWARE, IS_SHAREABLE AS SHARABLE FROM V$SQL WHERE SQL_TEXT LIKE '%mployee%' AND SQL_TEXT NOT LIKE '%SQL_TEXT%'; SQL_TEXT CHILD# EXEC BUFF_GETS BIND_SENS BIND_AWARE SHARABLE ---------------------- ------ ----- ---------- --------- ---------- -------SELECT COUNT(*), MAX(e 0 2 1329 Y N Y mployee_id) FROM hr.em ployees WHERE departme nt_id = :dept_id At this stage, the optimizer has not yet marked the cursor as bind-aware. See Also: Oracle Database Reference to learn about V$SQL 20.3.4 Bind-Aware Cursors A bind-aware cursor is a bind-sensitive cursor that is eligible to use different plans for different bind values. After a cursor has been made bind-aware, the optimizer chooses plans for future executions based on the bind value and its cardinality estimate. Thus, “bind-aware” means essentially “best plan for the current bind value.” When a statement with a bind-sensitive cursor executes, the optimizer uses an internal algorithm to determine whether to mark the cursor bind-aware. The decision depends on whether the cursor produces significantly different data access patterns for different bind values, resulting in a performance cost that differs from expectations. If the database marks the cursor bind-aware, then the next time that the cursor executes the database does the following: • Generates a new plan based on the bind value • Marks the original cursor generated for the statement as not sharable (V$SQL.IS_SHAREABLE is N). The original cursor is no longer usable and is eligible to age out of the library cache When the same query repeatedly executes with different bind values, the database adds new bind values to the “signature” of the SQL statement (which includes the optimizer environment, NLS settings, and so on), and categorizes the values. The database examines the bind values, and considers whether the current bind value results in a significantly different data volume, or whether an existing plan is sufficient. The database does not need to create a new plan for each new value. Consider a scenario in which you execute a statement with 12 distinct bind values (executing each distinct value twice), which causes the database to trigger 5 hard parses, and create 2 additional plans. Because the database performs 5 hard parses, it creates 5 new child cursors, even though some cursors have the same execution 20-29 Chapter 20 Adaptive Cursor Sharing plan as existing cursors. The database marks the superfluous cursors as not usable, which means these cursors eventually age out of the library cache. During the initial hard parses, the optimizer is essentially mapping out the relationship between bind values and the appropriate execution plan. After this initial period, the database eventually reaches a steady state. Executing with a new bind value results in picking the best child cursor in the cache, without requiring a hard parse. Thus, the number of parses does not scale with the number of different bind values. Example 20-15 Bind-Aware Cursors This example continues the example in "Bind-Sensitive Cursors". The following code issues a second query employees with the bind variable set to 50: EXEC :dept_id := 50; SELECT COUNT(*), MAX(employee_id) FROM hr.employees WHERE department_id = :dept_id; COUNT(*) MAX(EMPLOYEE_ID) ---------- ---------------100045 100999 During the first two executions, the database was monitoring the behavior of the queries, and determined that the different bind values caused the queries to differ significantly in cardinality. Based on this difference, the database adapts its behavior so that the same plan is not always shared for this query. Thus, the optimizer generates a new plan based on the current bind value, which is 50: SQL> SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR); PLAN_TABLE_OUTPUT ------------------------------------SQL_ID a9upgaqqj7bn5, child number 1 ------------------------------------SELECT COUNT(*), MAX(employee_id) FROM hr.employees WHERE department_id = :dept_id Plan hash value: 1756381138 ----------------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost(%CPU)| Time | ----------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | |254 (100)| | | 1 | SORT AGGREGATE | | 1 | 8 | | | |* 2 | TABLE ACCESS FULL| EMPLOYEES | 100K | 781K |254 (15)| 00:00:01 | ----------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------2 - filter("DEPARTMENT_ID"=:DEPT_ID) The following query of V$SQL obtains information about the cursor: SELECT SQL_TEXT, CHILD_NUMBER AS CHILD#, EXECUTIONS AS EXEC, BUFFER_GETS AS BUFF_GETS, IS_BIND_SENSITIVE AS BIND_SENS, IS_BIND_AWARE AS BIND_AWARE, IS_SHAREABLE AS SHAREABLE FROM V$SQL WHERE SQL_TEXT LIKE '%mployee%' AND SQL_TEXT NOT LIKE '%SQL_TEXT%'; SQL_TEXT CHILD# EXEC BUFF_GETS BIND_SENS BIND_AWARE SHAREABLE ---------------------- ------ ----- ---------- --------- ---------- --------- 20-30 Chapter 20 Adaptive Cursor Sharing SELECT COUNT(*), MAX(e mployee_id) FROM hr.em ployees WHERE departme nt_id = :dept_id 0 2 1329 Y N N SELECT COUNT(*), MAX(e mployee_id) FROM hr.em ployees WHERE departme nt_id = :dept_id 1 1 800 Y Y Y The preceding output shows that the database created an additional child cursor (CHILD# of 1). Cursor 0 is now marked as not shareable. Cursor 1 shows a number of buffers gets lower than cursor 0, and is marked both bind-sensitive and bind-aware. A bind-aware cursor may use different plans for different bind values, depending on the selectivity of the predicates containing the bind variable. Example 20-16 Bind-Aware Cursors: Choosing the Optimal Plan This example continues the example in "Example 20-15". The following code executes the same employees query with the value of 10, which has extremely low cardinality (only one row): EXEC :dept_id := 10; SELECT COUNT(*), MAX(employee_id) FROM hr.employees WHERE department_id = :dept_id; COUNT(*) MAX(EMPLOYEE_ID) ---------- ---------------1 200 The following output shows that the optimizer picked the best plan, which is an index scan, based on the low cardinality estimate for the current bind value of 10: SQL> SELECT * from TABLE(DBMS_XPLAN.DISPLAY_CURSOR); PLAN_TABLE_OUTPUT ------------------------------------SQL_ID a9upgaqqj7bn5, child number 2 ------------------------------------select COUNT(*), MAX(employee_id) FROM hr.employees WHERE department_id = :dept_id Plan hash value: 1642965905 ------------------------------------------------------------------------------------| Id| Operation | Name |Rows|Bytes|Cost (%CPU)|Time | ------------------------------------------------------------------------------------| 0| SELECT STATEMENT | | | |2(100)| | | 1| SORT AGGREGATE | |1 |8 | | | | 2| TABLE ACCESS BY INDEX ROWID BATCHED| EMPLOYEES |1 |8 |2 (0)|00:00:01| |*3| INDEX RANGE SCAN | EMP_DEPARTMENT_IX | 1| |1 (0)|00:00:01| ------------------------------------------------------------------------------------Predicate Information (identified by operation id): --------------------------------------------------3 - access("DEPARTMENT_ID"=:DEPT_ID) The V$SQL output now shows that three child cursors exist: SELECT SQL_TEXT, CHILD_NUMBER AS CHILD#, EXECUTIONS AS EXEC, BUFFER_GETS AS BUFF_GETS, IS_BIND_SENSITIVE AS BIND_SENS, IS_BIND_AWARE AS BIND_AWARE, IS_SHAREABLE AS SHAREABLE 20-31 Chapter 20 Adaptive Cursor Sharing FROM V$SQL WHERE SQL_TEXT LIKE '%mployee%' AND SQL_TEXT NOT LIKE '%SQL_TEXT%'; SQL_TEXT CHILD# EXEC BUFF_GETS BIND_SENS BIND_AWARE SHAREABLE ---------------------- ------ ----- ---------- --------- ---------- --------SELECT COUNT(*), MAX(e 0 2 1329 Y N N mployee_id) FROM hr.em ployees WHERE departme nt_id = :dept_id SELECT COUNT(*), MAX(e mployee_id) FROM hr.em ployees WHERE departme nt_id = :dept_id 1 1 800 Y Y Y SELECT COUNT(*), MAX(e mployee_id) FROM hr.em ployees WHERE departme nt_id = :dept_id 2 1 3 Y Y Y The database discarded the original cursor (CHILD# of 0) when the cursor switched to bind-aware mode. This is a one-time overhead. The database marked cursor 0 as not shareable (SHAREABLE is N), which means that this cursor is unusable and will be among the first to age out of the cursor cache. See Also: Oracle Database Reference to learn about V$SQL 20.3.5 Cursor Merging If the optimizer creates a plan for a bind-aware cursor, and if this plan is the same as an existing cursor, then the optimizer can perform cursor merging. In this case, the database merges cursors to save space in the library cache. The database increases the selectivity range for the cursor to include the selectivity of the new bind value. When a query uses a new bind variable, the optimizer tries to find a cursor that it thinks is a good fit based on similarity in the selectivity of the bind value. If the database cannot find such a cursor, then it creates a new one. If the plan for the new cursor is the same as one of the existing cursors, then the database merges the two cursors to save space in the library cache. The merge results in the database marking one cursor as not sharable. If the library cache is under space pressure, then the database ages out the non-sharable cursor first. See Also: "Example 20-12" 20-32 Chapter 20 Real-World Performance Guidelines for Cursor Sharing 20.3.6 Adaptive Cursor Sharing Views You can use the V$ views for adaptive cursor sharing to see selectivity ranges, cursor information (such as whether a cursor is bind-aware or bind-sensitive), and execution statistics. Specifically, use the following views: • V$SQL shows whether a cursor is bind-sensitive or bind-aware. • V$SQL_CS_HISTOGRAM shows the distribution of the execution count across a three- bucket execution history histogram. • V$SQL_CS_SELECTIVITY shows the selectivity ranges stored for every predicate containing a bind variable if the selectivity was used to check cursor sharing. It contains the text of the predicates, and the low and high values for the selectivity ranges. • V$SQL_CS_STATISTICS summarizes the information that the optimizer uses to determine whether to mark a cursor bind-aware. For a sample of executions, the database tracks the rows processed, buffer gets, and CPU time. The PEEKED column shows YES when the bind set was used to build the cursor; otherwise, the value is NO. See Also: Oracle Database Reference to learn about V$SQL and its related views 20.4 Real-World Performance Guidelines for Cursor Sharing The Real-World Performance team has created guidelines for how to optimize cursor sharing in Oracle database applications. This section contains the following topics: • Develop Applications with Bind Variables for Security and Performance The Real-World Performance group strongly suggests that all enterprise applications use bind variables. • Do Not Use CURSOR_SHARING = FORCE as a Permanent Fix The best practice is to write sharable SQL and use the default of EXACT for CURSOR_SHARING. • Establish Coding Conventions to Increase Cursor Reuse By default, any variation in the text of two SQL statements prevents the database from sharing a cursor, including the names of bind variables. Also, changes in the size of bind variables can cause cursor mismatches. For this reason, using bind variables in application code is not enough to guarantee cursor sharing. • Minimize Session-Level Changes to the Optimizer Environment A best practice is to prevent users of the application from changing the optimization approach and goal for their individual sessions. Any changes to the optimizer environment can prevent otherwise identical statements from sharing cursors. 20-33 Chapter 20 Real-World Performance Guidelines for Cursor Sharing 20.4.1 Develop Applications with Bind Variables for Security and Performance The Real-World Performance group strongly suggests that all enterprise applications use bind variables. Oracle Database applications were intended to be written with bind variables. Avoid application designs that result in large numbers of users issuing dynamic, unshared SQL statements. Whenever Oracle Database fails to find a match for a statement in the library cache, it must perform a hard parse. Despite the dangers of developing applications with literals, not all real-world applications use bind variables. Developers sometimes find that it is faster and easier to write programs that use literals. However, decreased development time does not lead to better performance and security after deployment. Video: Video The primary benefits of using bind variables are as follows: • Resource efficiency Compiling a program before every execution does not use resources efficiently, but this is essentially what Oracle Database does when it performs a hard parse. The database server must expend significant CPU and memory to create cursors, generate and evaluate execution plans, and so on. By enabling the database to share cursors, soft parsing consumes far fewer resources. If an application uses literals instead of bind variables, but executes only a few queries each day, then DBAs may not perceive the extra overhead as a performance problem. However, if an application executes hundreds or thousands of queries per second, then the extra resource overhead can easily degrade performance to unacceptable levels. Using bind variables enables the database to perform a hard parse only once, no matter how many times the statement executes. • Scalability When the database performs a hard parse, the database spends more time acquiring and holding latches in the shared pool and library cache. Latches are low-level serialization devices. The longer and more frequently the database latches structures in shared memory, the longer the queue for these latches becomes. When multiple statements share the same execution plan, the requests for latches and the durations of latches go down. This behavior increases scalability. • Throughput and response time When the database avoids constantly reparsing and creating cursors, more of its time is spent in user space. The Real-World Performance group has found that changing literals to use binds often leads to orders of magnitude improvements in throughput and user response time. 20-34 Chapter 20 Real-World Performance Guidelines for Cursor Sharing Video: Video • Security The only way to prevent SQL injection attacks is to use bind variables. Malicious users can exploit application that concatenate strings by “injecting” code into the application. See Also: Oracle Database PL/SQL Language Reference for an example of an application that fixes a security vulnerability created by literals 20.4.2 Do Not Use CURSOR_SHARING = FORCE as a Permanent Fix The best practice is to write sharable SQL and use the default of EXACT for CURSOR_SHARING. However, for applications with many similar statements, setting CURSOR_SHARING to FORCE can sometimes significantly improve cursor sharing. The replacement of literals with system-generated bind values can lead to reduced memory usage, faster parses, and reduced latch contention. However, FORCE is not meant to be a permanent development solution. As a general guideline, the Real-World Performance group recommends against setting CURSOR_SHARING to FORCE exception in rare situations, and then only when all of the following conditions are met: • Statements in the shared pool differ only in the values of literals. • Response time is suboptimal because of a very high number of library cache misses. • Your existing code has a serious security and scalability bug—the absence of bind variables—and you need a temporary band-aid until the source code can be fixed. • You set this initialization parameter at the session level and not at the instance level. Setting CURSOR_SHARING to FORCE has the following drawbacks: • It indicates that the application does not use user-defined bind variables, which means that it is open to SQL injection. Setting CURSOR_SHARING to FORCE does not fix SQL injection bugs or render the code any more secure. The database binds values only after any malicious SQL text has already been injected. 20-35 Chapter 20 Real-World Performance Guidelines for Cursor Sharing Video: Video • The database must perform extra work during the soft parse to find a similar statement in the shared pool. • The database removes every literal, which means that it can remove useful information. For example, the database strips out literal values in SUBSTR and TO_DATE functions. The use of system-generated bind variables where literals are more optimal can have a negative impact on execution plans. • There is an increase in the maximum lengths (as returned by DESCRIBE) of any selected expressions that contain literals in a SELECT statement. However, the actual length of the data returned does not change. • Star transformation is not supported. See Also: • "CURSOR_SHARING and Bind Variable Substitution" • Oracle Database Reference to learn about the CURSOR_SHARING initialization parameter 20.4.3 Establish Coding Conventions to Increase Cursor Reuse By default, any variation in the text of two SQL statements prevents the database from sharing a cursor, including the names of bind variables. Also, changes in the size of bind variables can cause cursor mismatches. For this reason, using bind variables in application code is not enough to guarantee cursor sharing. The Real-World Performance group recommends that you standardize spacing and capitalization conventions for SQL statements and PL/SQL blocks. Also establish conventions for the naming and definitions of bind variables. If the database does not share cursors as expected, begin your diagnosis by querying V$SQL_SHARED_CURSOR. Example 20-17 Variations in SQL Text In this example, an application that uses bind variables executes 7 statements using the same bind variable value, but the statements are not textually identical: VARIABLE emp_id NUMBER EXEC :emp_id := 101; SELECT SELECT SELECT SELECT select Select Select SUM(salary) SUM(salary) SUM(salary) SUM(salary) sum(salary) sum(salary) sum(salary) FROM hr.employees WHERE employee_id < :emp_id; FROM hr.employees WHERE employee_id < :EMP_ID; FROM hr.employees WHERE employee_id < :Emp_Id; FROM hr.employees WHERE employee_id < :emp_id; from hr.employees where employee_id < :emp_id; From hr.employees Where employee_id < :emp_id; From hr.employees Where employee_id< :emp_id; 20-36 Chapter 20 Real-World Performance Guidelines for Cursor Sharing A query of V$SQLAREA shows that no cursor sharing occurred: COL SQL_TEXT FORMAT a35 SELECT SQL_TEXT, SQL_ID, VERSION_COUNT, HASH_VALUE FROM V$SQLAREA WHERE SQL_TEXT LIKE '%mployee%' AND SQL_TEXT NOT LIKE '%SQL_TEXT%'; SQL_TEXT ----------------------------------SELECT SUM(salary) FROM hr.employee s WHERE employee_id < :EMP_ID SELECT SUM(salary) FROM hr.employee s WHERE employee_id < :Emp_Id Select sum(salary) From hr.employee s Where employee_id< :emp_id SELECT SUM(salary) FROM hr.employe es WHERE employee_id < :emp_id SELECT SUM(salary) FROM hr.employee s WHERE employee_id < :emp_id select sum(salary) from hr.employee s where employee_id < :emp_id Select sum(salary) From hr.employee s Where employee_id < :emp_id SQL_ID VERSION_COUNT HASH_VALUE ------------- ------------- ---------bkrfu3ggu5315 1 3751971877 70mdtwh7xj9gv 1 265856507 18tt4ny9u5wkt 1 2476929625 b6b21tbyaf8aq 1 4238811478 4318cbskba8yh 1 615850960 633zpx3xm71kj 1 4214457937 1mqbbbnsrrw08 1 830205960 7 rows selected. Example 20-18 Bind Length Mismatch The following code defines a bind variable with different lengths, and then executes textually identical statements with the same bind values: VARIABLE lname VARCHAR2(20) EXEC :lname := 'Taylor'; SELECT SUM(salary) FROM hr.employees WHERE last_name = :lname; VARIABLE lname VARCHAR2(100) EXEC :lname := 'Taylor'; SELECT SUM(salary) FROM hr.employees WHERE last_name = :lname; The following query shows that the database did not share the cursor: COL SQL_TEXT FORMAT a35 SELECT SQL_TEXT, SQL_ID, VERSION_COUNT, HASH_VALUE FROM V$SQLAREA WHERE SQL_TEXT LIKE '%mployee%' AND SQL_TEXT NOT LIKE '%SQL_TEXT%'; SQL_TEXT SQL_ID VERSION_COUNT HASH_VALUE ----------------------------------- ------------- ------------- ---------SELECT SUM(salary) FROM hr.employee buh8j4557r0h1 2 1249608193 s WHERE last_name = :lname The reason is because of the bind lengths: COL BIND_LENGTH_UPGRADEABLE FORMAT a15 SELECT s.SQL_TEXT, s.CHILD_NUMBER, c.BIND_LENGTH_UPGRADEABLE FROM V$SQL s, V$SQL_SHARED_CURSOR c WHERE SQL_TEXT LIKE '%employee%' AND SQL_TEXT NOT LIKE '%SQL_TEXT%' AND s.CHILD_ADDRESS = c.CHILD_ADDRESS; 20-37 Chapter 20 Real-World Performance Guidelines for Cursor Sharing SQL_TEXT CHILD_NUMBER BIND_LENGTH_UPG ----------------------------------- ------------ --------------SELECT SUM(salary) FROM hr.employee 0 N s WHERE last_name = :lname SELECT SUM(salary) FROM hr.employee 1 Y s WHERE last_name = :lname 20.4.4 Minimize Session-Level Changes to the Optimizer Environment A best practice is to prevent users of the application from changing the optimization approach and goal for their individual sessions. Any changes to the optimizer environment can prevent otherwise identical statements from sharing cursors. Example 20-19 Environment Mismatches This example shows two textually identical statements that nevertheless do not share a cursor: VARIABLE emp_id NUMBER EXEC :emp_id := 110; ALTER SESSION SELECT salary ALTER SESSION SELECT salary SET OPTIMIZER_MODE = FIRST_ROWS; FROM hr.employees WHERE employee_id < :emp_id; SET OPTIMIZER_MODE = ALL_ROWS; FROM hr.employees WHERE employee_id < :emp_id; A query of V$SQL_SHARED_CURSOR shows a mismatch in the optimizer modes: SELECT S.SQL_TEXT, S.CHILD_NUMBER, s.CHILD_ADDRESS, C.OPTIMIZER_MODE_MISMATCH FROM V$SQL S, V$SQL_SHARED_CURSOR C WHERE SQL_TEXT LIKE '%employee%' AND SQL_TEXT NOT LIKE '%SQL_TEXT%' AND S.CHILD_ADDRESS = C.CHILD_ADDRESS; SQL_TEXT CHILD_NUMBER CHILD_ADDRESS O ----------------------------------- ------------ ---------------- SELECT salary FROM hr.employees WHE 0 0000000080293040 N RE employee_id < :emp_id SELECT salary FROM hr.employees WHE 1 000000008644E888 Y RE employee_id < :emp_id 20-38 Part VII Monitoring and Tracing SQL Use DBMS_MONITOR to track database operations, SQL Test Case Builder to package information relating to a performance problem, and SQL Trace to generate diagnostic data for problem SQL statements. This part contains the following chapters: • Monitoring Database Operations This chapter describes how to monitor SQL and PL/SQL. • Gathering Diagnostic Data with SQL Test Case Builder SQL Test Case Builder is a tool that automatically gathers information needed to reproduce the problem in a different database instance. • Performing Application Tracing This chapter explains what end-to-end application tracing is, and how to generate and read trace files. 21 Monitoring Database Operations This chapter describes how to monitor SQL and PL/SQL. This chapter contains the following topics: • About Monitoring Database Operations A database operation is a user-defined logical object that includes session activity between two points in time. • Enabling and Disabling Monitoring of Database Operations Use initialization parameters to enable or disable monitoring. • Creating a Database Operation Creating a database operation involves supplying a name and defining its beginning and end times. • Monitoring SQL Executions Using Cloud Control By default, AWR automatically captures SQL monitoring reports in XML format. 21.1 About Monitoring Database Operations A database operation is a user-defined logical object that includes session activity between two points in time. A database operation contains a set of database tasks. A typical task might be a batch job or Extraction, Transformation, and Loading (ETL) processing job. A database operation is uniquely identified by its name and execution ID. Each operation can be executed many times. Each execution of an operation is uniquely identifiable. Real-Time Database Operations provides the ability to monitor composite operations automatically. The database automatically monitors parallel queries, DML, and DDL statements as soon as execution begins. By default, Real-Time SQL Monitoring automatically starts when a SQL statement runs in parallel, or when it has consumed at least 5 seconds of CPU or I/O time in a single execution. The SQL monitoring feature is enabled by default when the STATISTICS_LEVEL initialization parameter is either set to TYPICAL (the default value) or ALL. Using the DBMS_SQL_MONITOR package, you can start and stop, monitor, and report on database operations. This section contains the following topics: • Purpose of Monitoring Database Operations Real-Time SQL Monitoring enables you to monitor a single SQL statement or PL/SQL program unit. • Database Operation Monitoring Concepts The DBMS_SQL_MONITOR package is the key component of the architecture for database operations. 21-1 Chapter 21 About Monitoring Database Operations • User Interfaces for Database Operations Monitoring You can monitor database operations from the command line or by using Cloud Control. • Basic Tasks in Database Operations Monitoring This section explains the basic tasks in database operations monitoring. See Also: Oracle Database Concepts for a brief conceptual overview of database operations 21.1.1 Purpose of Monitoring Database Operations Real-Time SQL Monitoring enables you to monitor a single SQL statement or PL/SQL program unit. Database operations extend Real-Time SQL Monitoring by enabling you to treat a set of statements or procedures as a named, uniquely identified, and re-executable unit. In general, monitoring database operations is useful for the following users: • DBAs whose responsibilities include identifying expensive (high response time) SQL statements and PL/SQL functions • DBAs who manage batch jobs in a data warehouse or OLTP system • Application or database developers who need to monitor the activities related to particular operations, for example, Oracle Data Pump operations Monitoring database operations is useful for performing the following tasks: • Tracking and reporting Tracking requires first defining a database operation, for example, though DBMS_SQL_MONITOR, OCI, or JDBC APIs. You can define an operation from a different session from the one that you are currently using. The database infrastructure determines what to track on behalf of the defined operation. You can generate reports on the operation. For example, your tuning task may involve determining which SQL statements run on behalf of a specific batch job, what their execution statistics were, what was occurring in the database when the operation was executing, and so on. • Monitoring execution progress This task involves monitoring a currently executing database operation. The information is particularly useful when you are investigating why an operation is taking a long time to complete. • Monitoring resource usage You may want to detect when a SQL execution uses excessive CPU, issues an excessive amount of I/O, or takes a long time to complete. With Oracle Database Resource Manager (the Resource Manager), you can configure thresholds for each consumer group that specify the maximum resource usage for all SQL executions in the group. When a SQL operation reaches a specified threshold, the Resource Manager can switch the operation into a lower-priority consumer group, 21-2 Chapter 21 About Monitoring Database Operations terminate the session or call, or log the event. You can then monitor these SQL operations. • Tuning for response time When tuning a database operation, you typically aim to improve the response time. Often the database operation performance issues are mainly SQL performance issues. This section contains the following topics: • Simple Database Operation Use Cases For simple operations, Real-Time SQL Monitoring helps determine where a currently executing SQL statement is in its execution plan and where the statement is spending its time. • Composite Database Operation Use Cases In OLTP and data warehouse environments, a job often logically groups related SQL statements. The job can span multiple concurrent sessions. See Also: • "Monitoring SQL Executions Using Cloud Control" • Oracle Database Administrator’s Guide 21.1.1.1 Simple Database Operation Use Cases For simple operations, Real-Time SQL Monitoring helps determine where a currently executing SQL statement is in its execution plan and where the statement is spending its time. You can also see the breakdown of time and resource usage for recently completed statements. In this way, you can better determine why a particular operation is expensive. Typical use cases for Real-Time SQL Monitoring include the following: • A frequently executed SQL statement is executing more slowly than normal. You must identify the root cause of this problem. • A database session is experiencing slow performance. • A parallel SQL statement is taking a long time. You want to determine how the server processes are dividing the work. 21.1.1.2 Composite Database Operation Use Cases In OLTP and data warehouse environments, a job often logically groups related SQL statements. The job can span multiple concurrent sessions. Typical use cases for monitoring composite operations include the following: • A periodic batch job containing many SQL statements must complete in a certain number of hours, but took twice as long as expected. 21-3 Chapter 21 About Monitoring Database Operations • After a database upgrade, the execution time of an important batch job doubled. To resolve this problem, you must collect enough relevant statistical data from the batch job before and after the upgrade, compare the two sets of data, and then identify the changes. • Packing a SQL tuning set (STS) took far longer than anticipated. To diagnose the problem, you need to know what was being executed over time. Because this issue cannot be easily reproduced, you need to monitor the process while it is running. See Also: "About SQL Tuning Sets" 21.1.2 Database Operation Monitoring Concepts The DBMS_SQL_MONITOR package is the key component of the architecture for database operations. This section contains the following topics: • About the Architecture of Real-Time SQL Monitoring Real-Time SQL Monitoring is a built-in database infrastructure that helps you identify performance problems with long-running and parallel SQL statements. • When the Database Monitors Operations Monitoring of operations depends on whether the database operation is simple or composite. • Attributes of Database Operations The DBMS_SQL_MONITOR.BEGIN_OPERATION function defines a database operation. 21.1.2.1 About the Architecture of Real-Time SQL Monitoring Real-Time SQL Monitoring is a built-in database infrastructure that helps you identify performance problems with long-running and parallel SQL statements. Real-Time SQL Monitoring is a feature of the Oracle Database Tuning Pack. Database operations are enabled when the CONTROL_MANAGEMENT_PACK_ACCESS initialization parameter is set to DIAGNOSTIC+TUNING (default). The following figure gives an overview of the architecture for Real-Time SQL Monitoring. 21-4 Chapter 21 About Monitoring Database Operations Figure 21-1 Architecture for Real-Time SQL Monitoring DBMS_SQL_MONITOR BEGIN OPERATION ... END_OPERATION Database Operations User-Defined Operations AWR V$SQL_MONITOR V$SQL_PLAN_MONITOR V$SQL_MONITOR_SESSTAT DBMS_SQL_MONITOR REPORT_SQL_MONITOR Oracle Enterprise Manager Real-Time SQL Monitoring User CONTROL_MANAGEMENT_PACK_ACCESS As shown in the preceding graphic, the DBMS_SQL_MONITOR PL/SQL package defines database operations. After monitoring is initiated, the database stores metadata about the database operations in AWR, and the data itself in both AWR and ASH. The database refreshes monitoring statistics in close to real time as each monitored statement executes, typically once every second. The database stores the operational data (the statements and metadata about the statements) in the SGA, and then periodically writes it to disk. Every monitored database operation has an entry in the V$SQL_MONITOR view. This entry tracks key performance metrics collected for the execution, including the elapsed time, CPU time, number of reads and writes, I/O wait time, and various other wait times. The V$SQL_PLAN_MONITOR view includes monitoring statistics for each operation in the execution plan of the SQL statement being monitored. You can access reports by using DBMS_SQL_MONITOR.REPORT_SQL_MONITOR, Oracle Enterprise Manager Cloud Control (Cloud Control), or EM Express. 21-5 Chapter 21 About Monitoring Database Operations See Also: • "Monitoring SQL Executions Using Cloud Control" • Oracle Database Reference to learn about V$SQL_MONITOR, V$SQL_PLAN_MONITOR, and CONTROL_MANAGEMENT_PACK_ACCESS • Oracle Database PL/SQL Packages and Types Reference to learn about DBMS_SQLTUNE and DBMS_SQL_MONITOR packages 21.1.2.2 When the Database Monitors Operations Monitoring of operations depends on whether the database operation is simple or composite. This section contains the following topics: • Simple Database Operations A simple database operation is a single SQL statement or PL/SQL subprogram. • Composite Database Operations A composite database operation is activity between two points in time in a database session, with each session defining its own beginning and end points. 21.1.2.2.1 Simple Database Operations A simple database operation is a single SQL statement or PL/SQL subprogram. Oracle Database monitors simple database operations when any of the following conditions is true: • SQL statements execute in parallel. • Tracking for SQL statements is forced by the /*+ MONITOR */ hint. • SQL statements or PL/SQL subprograms have consumed at least 5 seconds of CPU or I/O time in a single execution. For simple operations, Real-Time SQL Monitoring helps determine where a currently executing SQL statement is in its execution plan and where the statement is spending its time. You can also see the breakdown of time and resource usage for recently completed statements. In this way, you can better determine why a particular operation is expensive. Typical use cases for Real-Time SQL Monitoring include the following: • A frequently executed SQL statement is executing more slowly than normal. You must identify the root cause of this problem. • A database session is experiencing slow performance. • A parallel SQL statement is taking a long time. You want to determine how the server processes are dividing the work. 21.1.2.2.2 Composite Database Operations A composite database operation is activity between two points in time in a database session, with each session defining its own beginning and end points. 21-6 Chapter 21 About Monitoring Database Operations SQL statements or PL/SQL subprograms that execute within these two points in time are part of the composite operation. A session can participate in at most one composite database operation at a time. Oracle Database monitors composite database operations when either of the following conditions is true: • You started an operation with DBMS_SQL_MONITOR.BEGIN_OPERATION, and the operation has consumed at least 5 seconds of CPU or I/O time. • Tracking for the operations is forced by setting FORCE_TRACKING to Y in BEGIN_OPERATION. In OLTP and data warehouse environments, a job often logically groups related SQL statements. The job can span multiple concurrent sessions. Typical use cases for monitoring composite operations include the following: • A periodic batch job containing many SQL statements must complete in a certain number of hours, but took longer than expected. • After a database upgrade, the execution time of an important batch job increased. To resolve this problem, you must collect enough relevant statistical data from the batch job before and after the upgrade, compare the two sets of data, and then identify the changes. • Packing a SQL tuning set (STS) took far longer than anticipated. To diagnose the problem, you need to know what was being executed over time. Because this issue cannot be easily reproduced, you need to monitor the process while it is running. See Also: • "About SQL Tuning Sets" • Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_SQL_MONITOR 21.1.2.3 Attributes of Database Operations The DBMS_SQL_MONITOR.BEGIN_OPERATION function defines a database operation. A database operation is uniquely identified by the following information: • Database operation name This is a user-created name such as daily_sales_report. The operation name is the same for a job even if it is executed concurrently by different sessions or on different databases. Database operation names do not reside in different namespaces. • Database operation execution ID Two or more occurrences of the same database operation can run at the same time, with the same name but different execution IDs. This numeric ID uniquely identifies different executions of the same database operation. 21-7 Chapter 21 About Monitoring Database Operations The database automatically creates an execution ID when you begin a database operation. You can also specify a user-created execution ID. Optionally, you can specify the session ID and session serial number in which to start the database operations. Thus, one database session can start a database operation defined in a different database session. The database uses the following triplet of values to identify each SQL and PL/SQL statement monitored in the V$SQL_MONITOR view, regardless of whether the statement is included in a database operation: • SQL identifier to identify the SQL statement (SQL_ID) • Start execution timestamp (SQL_EXEC_START) • An internally generated identifier to ensure that this primary key is truly unique (SQL_EXEC_ID) You can use zero or more additional attributes to describe and identify the characteristics of a database operation. Each attribute has a name and value. For example, for operation daily_sales_report, you might define the attribute db_name and assign it the value prod. See Also: • Oracle Database Reference to learn about the V$SQL_MONITOR view • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SQL_MONITOR.BEGIN_OPERATION function 21.1.3 User Interfaces for Database Operations Monitoring You can monitor database operations from the command line or by using Cloud Control. This section contains the following topics: • Monitored SQL Executions Page in Cloud Control The Monitored SQL Executions page in Cloud Control, also known as SQL Monitor, displays details of SQL execution. SQL Monitor is the recommended interface for reporting on database operations. • DBMS_SQL_MONITOR Package The DBMS_SQL_MONITOR package defines the beginning and ending of a database operation, and generates a report of the database operations. • Views for Monitoring and Reporting on Database Operations You can obtain the statistics for database operations using several V$ and data dictionary view. 21-8 Chapter 21 About Monitoring Database Operations 21.1.3.1 Monitored SQL Executions Page in Cloud Control The Monitored SQL Executions page in Cloud Control, also known as SQL Monitor, displays details of SQL execution. SQL Monitor is the recommended interface for reporting on database operations. Statistics at each step of the execution plan are tracked by key performance metrics, including elapsed time, CPU time, number of reads and writes, I/O wait time, and various other wait times. These metrics enable DBAs to analyze SQL execution in depth and decide on the most appropriate tuning strategies for monitored SQL statements. SQL Monitor Active Reports provide a flash-based interactive report that enables you to save data in an HTML file. You can save this file and view it offline. This section contains the following topics: • Accessing the Monitored SQL Executions Page The Monitored SQL Executions shows information such as the SQL ID, database time, and I/O requests. 21.1.3.1.1 Accessing the Monitored SQL Executions Page The Monitored SQL Executions shows information such as the SQL ID, database time, and I/O requests. To access the Monitored SQL Executions page: 1. Log in to Cloud Control with the appropriate credentials. 2. Under the Targets menu, select Databases. 3. In the list of database targets, select the target for the Oracle Database instance that you want to administer. 4. If prompted for database credentials, then enter the minimum credentials necessary for the tasks you intend to perform. 5. From the Performance menu, select SQL Monitoring. The Monitored SQL Executions page appears. Figure 21-2 Monitored SQL Executions 21.1.3.2 DBMS_SQL_MONITOR Package The DBMS_SQL_MONITOR package defines the beginning and ending of a database operation, and generates a report of the database operations. 21-9 Chapter 21 About Monitoring Database Operations Table 21-1 DBMS_SQL_MONITOR Subprogram Description BEGIN_OPERATION This function starts a composite database operation in the current session. This function associates a session with a database operation. Starting in Oracle Database 12c Release 2 (12.2), you can use session_id and session_num to indicate the session in which to start monitoring. END_OPERATION This function ends a composite database operation in the current session. If the specified composite database operation does not exist, then this function has no effect. REPORT_SQL_MONIT This function builds a detailed report with monitoring information for a simple or a composite database operation. OR For each operation, it gives key information and associated global statistics. Use this function to get detailed monitoring information for a simple or a composite database operation. The target database operation for this report can be: • The last database operation monitored by Oracle Database (default, no parameter). • The last database operation executed in the specified session and monitored by Oracle Database. The session is identified by its session ID and optionally its serial number (-1 is current session). • The last execution of a specific database operation identified by its sql_id. • A specific execution of a database operation identified by the combination sql_id, sql_exec_start, and sql_exec_id. • The last execution of a specific composite database operation identified by dbop_name. • The specific execution of a composite database operation identified by the combination dbop_name, dbop_exec_id. Use the type parameter to specify the output type: TEXT (default), HTML, ACTIVE, or XML. REPORT_SQL_MONIT This function is identical to the REPORT_SQL_MONITOR function, except that the return type is XMLType. OR_XML REPORT_SQL_MONIT This function builds a report for all or a subset of database operations that have been monitored by Oracle Database. OR_LIST REPORT_SQL_MONIT This function is identical to the REPORT_SQL_MONITOR_LIST function, except that it returns XMLType. OR_LIST_XML See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SQL_MONITOR package 21.1.3.3 Views for Monitoring and Reporting on Database Operations You can obtain the statistics for database operations using several V$ and data dictionary view. The following table summarizes these views. 21-10 Chapter 21 About Monitoring Database Operations Table 21-2 Views for Database Operations Monitoring View Description DBA_HIST_REPORTS This view displays metadata about XML reports captured into Automatic Workload Repository (AWR). Each XML report contains details about some activity of a component. For example, a SQL Monitor report contains a detailed report about a particular database operation. Important columns include: • • • The REPORT_SUMMARY column contains the summary of the report. The COMPONENT_NAME column accepts the value sqlmonitor. The REPORT_ID column provides the ID of the report, which you can specify in the RID parameter of DBMS_AUTO_REPORT.REPORT_REPOSITORY_DETAIL. DBA_HIST_REPORTS_DETAILS This view displays details about each report captured in AWR. Metadata for each report appears in the DBA_HIST_REPORTS view, whereas the actual report is available in the DBA_HIST_REPORTS_DETAILS view. V$SQL_MONITOR This view contains global, high-level information about simple and composite database operations. For simple database operations, monitoring statistics are not cumulative over several executions. In this case, one entry in V$SQL_MONITOR is dedicated to a single execution of a SQL statement. If the database monitors two executions of the same SQL statement, then each execution has a separate entry in V$SQL_MONITOR. For simple database operations, V$SQL_MONITOR has one entry for the parallel execution coordinator process and one entry for each parallel execution server process. Each entry has corresponding entries in V$SQL_PLAN_MONITOR. Because the processes allocated for the parallel execution of a SQL statement are cooperating for the same execution, these entries share the same execution key (the combination of SQL_ID, SQL_EXEC_START, and SQL_EXEC_ID). For composite database operations, each row contains an operation whose statistics are accumulated over the SQL statements and PL/SQL subprograms that run in the same session as part of the operation. The primary key is the combination of the columns DBOP_NAME and DBOP_EXEC_ID. V$SQL_MONITOR_SESSTAT This view contains the statistics for all sessions involved in the database operation. Most of the statistics are cumulative. The database stores the statistics in XML format instead of using each column for each statistic. This view is primarily intended for the report generator. Oracle recommends that you use V$SESSTAT instead of V$SQL_MONITOR_SESSTAT. 21-11 Chapter 21 Enabling and Disabling Monitoring of Database Operations Table 21-2 (Cont.) Views for Database Operations Monitoring View Description V$SQL_PLAN_MONITOR This view contains monitoring statistics for each step in the execution plan of the monitored SQL statement. The database updates statistics in V$SQL_PLAN_MONITOR every second while the SQL statement is executing. Multiple entries exist in V$SQL_PLAN_MONITOR for every monitored SQL statement. Each entry corresponds to a step in the execution plan of the statement. You can use the preceding V$ views with the following views to get additional information about the monitored execution: • V$ACTIVE_SESSION_HISTORY • V$SESSION • V$SESSION_LONGOPS • V$SQL • V$SQL_PLAN See Also: Oracle Database Reference to learn about the V$ views for database operations monitoring 21.1.4 Basic Tasks in Database Operations Monitoring This section explains the basic tasks in database operations monitoring. Basic tasks are as follows: • "Enabling and Disabling Monitoring of Database Operations" This task explains how you can enable automatic monitoring of database operations at the system and statement level. • "Creating a Database Operation" This section explains how you can define the beginning and end of a database operation using PL/SQL. • "Monitoring SQL Executions Using Cloud Control" This section explains how you can generate and interpret reports on a database operation. 21.2 Enabling and Disabling Monitoring of Database Operations Use initialization parameters to enable or disable monitoring. 21-12 Chapter 21 Enabling and Disabling Monitoring of Database Operations This section contains the following topics: • Enabling Monitoring of Database Operations at the System Level The SQL monitoring feature is enabled by default when the STATISTICS_LEVEL initialization parameter is either set to TYPICAL (the default value) or ALL. SQL monitoring starts automatically for all long-running queries. • Enabling and Disabling Monitoring of Database Operations at the Statement Level When the CONTROL_MANAGEMENT_PACK_ACCESS initialization parameter is set to DIAGNOSTIC+TUNING, you can use hints to enable or disable monitoring of specific SQL statements. 21.2.1 Enabling Monitoring of Database Operations at the System Level The SQL monitoring feature is enabled by default when the STATISTICS_LEVEL initialization parameter is either set to TYPICAL (the default value) or ALL. SQL monitoring starts automatically for all long-running queries. Prerequisites Because SQL monitoring is a feature of the Oracle Database Tuning Pack, the CONTROL_MANAGEMENT_PACK_ACCESS initialization parameter must be set to DIAGNOSTIC +TUNING (the default value). Assumptions This tutorial assumes the following: • The STATISTICS_LEVEL initialization parameter is set to BASIC. • You want to enable automatic monitoring of database operations. To enable monitoring of database operations: 1. Connect SQL*Plus to the database with the appropriate privileges, and then query the current database operations settings. For example, run the following SQL*Plus command: SQL> SHOW PARAMETER statistics_level NAME TYPE VALUE ----------------------------------- ----------- ----statistics_level string BASIC 2. Set the statistics level to TYPICAL. For example, run the following SQL statement: SQL> ALTER SYSTEM SET STATISTICS_LEVEL='TYPICAL'; See Also: Oracle Database Reference to learn about the STATISTICS_LEVEL and CONTROL_MANAGEMENT_PACK_ACCESS initialization parameter 21-13 Chapter 21 Creating a Database Operation 21.2.2 Enabling and Disabling Monitoring of Database Operations at the Statement Level When the CONTROL_MANAGEMENT_PACK_ACCESS initialization parameter is set to DIAGNOSTIC +TUNING, you can use hints to enable or disable monitoring of specific SQL statements. The database monitors SQL statements or PL/SQL subprograms automatically when they have consumed at least 5 seconds of CPU or I/O time in a single execution. The MONITOR hint is useful to enforce monitoring of statements or subprograms that do not meet the time criteria. Two statement-level hints are available to force or prevent the database from monitoring a SQL statement. To force SQL monitoring, use the MONITOR hint: SELECT /*+ MONITOR */ SYSDATE FROM DUAL; This hint is effective only when the CONTROL_MANAGEMENT_PACK_ACCESS parameter is set to DIAGNOSTIC+TUNING. To prevent the hinted SQL statement from being monitored, use the NO_MONITOR reverse hint. Assumptions This tutorial assumes the following: • Database monitoring is currently enabled at the system level. • You want to disable automatic monitoring for the statement SELECT * FROM sales ORDER BY time_id. To disable monitoring of database operations for a SQL statement: 1. Execute the query with the NO_MONITOR hint. For example, run the following statement: SQL> SELECT * /*+NO_MONITOR*/ FROM sales ORDER BY time_id; See Also: Oracle Database SQL Language Reference for information about using the MONITOR and NO_MONITOR hints 21.3 Creating a Database Operation Creating a database operation involves supplying a name and defining its beginning and end times. Start a database operation by using the DBMS_SQL_MONITOR.BEGIN_OPERATION function, and end it by using the DBMS_SQL_MONITOR.END_OPERATION procedure. To begin the operation in a different session, specify the combination of session_id and serial_num. The BEGIN_OPERATION function returns the database operation execution ID. If dbop_exec_id is null, then the database generates a unique value. 21-14 Chapter 21 Creating a Database Operation A single namespace exists for database operations, which means that name collisions are possible. Oracle recommends the following naming convention: component_name.subcomponent_name.operation name. For operations inside the database, Oracle recommends using ORA for the component name. For example, a materialized view refresh could be named ORA.MV.refresh. An E-Business Suite payroll function could be named EBIZ.payroll. To create a database operation in the current session: 1. In SQL*Plus or SQL Developer, log in to the database as a user with the necessary privileges. 2. Start the operation by using DBMS_SQL_MONITOR.BEGIN_OPERATION. This function returns the database operation execution ID. The following example creates the operation named ORA.sales.agg, and stores the execution ID in a SQL*Plus variable: VARIABLE exec_id NUMBER; BEGIN :exec_id := DBMS_SQL_MONITOR.BEGIN_OPERATION ( dbop_name => 'ORA.sales.agg' ); END; / 3. Execute the SQL statements or PL/SQL programs that you want to monitor. 4. End the operation by using DBMS_SQL_MONITOR.END_OPERATION. The following example ends operation ORA.sales.agg: BEGIN DBMS_SQL_MONITOR.END_OPERATION ( dbop_name => 'ORA.sales.agg', dbop_eid => :exec_id ); END; / Example 21-1 Creating a Database Operation The following example illustrates how to use the DBMS_SQL_MONITOR package to begin and end a database operation in a different session. This example assumes the following: Table 21-3 • You are an administrator and want to monitor statements in a session started by user sh. • You want to monitor queries of the sh.sales table and sh.customers table. • You want these two queries to be monitored as a database operation named sh_count. Creating a Database Operation SYSTEM Session SQL> CONNECT SYSTEM Enter password: ********* Connected. SH Session DESCRIPTION n/a Start SQL*Plus and connect as a user with the administrator privileges. 21-15 Chapter 21 Creating a Database Operation Table 21-3 (Cont.) Creating a Database Operation SYSTEM Session n/a SELECT SID, SERIAL# FROM V$SESSION WHERE USERNAME = 'SH'; SH Session DESCRIPTION SQL> CONNECT sh Enter password: ****** Connected. In a different terminal, start SQL*Plus and connect as a user as user sh. n/a In the SYSTEM session, query the session ID and serial number of the sh session. n/a In the SYSTEM session, begin a database operation, specifying the session ID and serial number for the sh session. SID SERIAL# ---------- ---------121 13397 VARIABLE eid NUMBER BEGIN :eid:=DBMS_SQL_MONITOR.BEGIN_OPERATION ('sh_count', null, null, null, '121', '13397'); END; / PRINT eid EID ---------2 n/a SELECT count(*) FROM sh.sales; COUNT(*) ---------918843 In the sh session, query the sales and customers tables. These SQL queries are part of the sh_count operation. SELECT COUNT(*) FROM sh.customers; COUNT(*) ---------55500 BEGIN DBMS_SQL_MONITOR.END_OPERATION ('sh_count',:eid); END; / n/a End the database operation by specifying the operation name and execution ID. 21-16 Chapter 21 Monitoring SQL Executions Using Cloud Control Table 21-3 (Cont.) Creating a Database Operation SYSTEM Session COL DBOP_NAME FORMAT a10 COL STATUS FORMAT a10 COL ID FORMAT 999 SH Session DESCRIPTION n/a Query the metadata for the sh_count database operation. The status of the operation is EXECUTING because the session has not picked up the new session status. SELECT DBOP_NAME, DBOP_EXEC_ID AS ID, STATUS, CPU_TIME, BUFFER_GETS FROM V$SQL_MONITOR WHERE DBOP_NAME IS NOT NULL ORDER BY DBOP_EXEC_ID; DBOP_NAME ID STATUS CPU_TIME GETS ---------- -- ---------- -------- ---sh_count 1 EXECUTING 24997 65 n/a SELECT SYSDATE FROM DUAL; COL DBOP_NAME FORMAT a10 COL STATUS FORMAT a10 COL ID FORMAT 999 n/a To collect changed session information, execute a query that performs a round trip to the database. The status of the operation is now updated to DONE. SELECT DBOP_NAME, DBOP_EXEC_ID AS ID, STATUS, CPU_TIME, BUFFER_GETS FROM V$SQL_MONITOR WHERE DBOP_NAME IS NOT NULL ORDER BY DBOP_EXEC_ID; DBOP_NAME ID STATUS CPU_TIME GETS ---------- -- ---------- -------- ---sh_count 1 DONE 24997 65 See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SQL_MONITOR package 21.4 Monitoring SQL Executions Using Cloud Control By default, AWR automatically captures SQL monitoring reports in XML format. The reports capture only SQL statements that are not executing or queued and have finished execution since the last capture cycle. AWR captures reports only for the most expensive statements according to elapsed execution time. 21-17 Chapter 21 Monitoring SQL Executions Using Cloud Control The Monitored SQL Executions page in Enterprise Manager Cloud Control (Cloud Control) summarizes the activity for monitored statements. You can use this page to drill down and obtain additional details about particular statements. The Monitored SQL Executions Details page uses data from several views, including the following: • GV$SQL_MONITOR • GV$SQL_PLAN_MONITOR • GV$SQL_MONITOR_SESSTAT • GV$SQL • GV$SQL_PLAN • GV$ACTIVE_SESSION_HISTORY • GV$SESSION_LONGOPS • DBA_HIST_REPORTS • DBA_HIST_REPORTS_DETAILS Assumptions This tutorial assumes the following: • The user sh is executing the following long-running parallel query of the sales made to each customer: SELECT c.cust_id, c.cust_last_name, c.cust_first_name, s.prod_id, p.prod_name, s.time_id FROM sales s, customers c, products p WHERE s.cust_id = c.cust_id AND s.prod_id = p.prod_id ORDER BY c.cust_id, s.time_id; • You want to ensure that the preceding query does not consume excessive resources. While the statement executes, you want to determine basic statistics about the database operation, such as the level of parallelism, the total database time, and number of I/O requests. • You use Cloud Control to monitor statement execution. Note: To generate the SQL monitor report from the command line, run the REPORT_SQL_MONITOR function in the DBMS_SQLTUNE package, as in the following sample SQL*Plus script: VARIABLE my_rept CLOB BEGIN :my_rept :=DBMS_SQLTUNE.REPORT_SQL_MONITOR(); END; / PRINT :my_rept To monitor SQL executions: 1. Access the Monitored SQL Executions page, as described in "Monitored SQL Executions Page in Cloud Control". 21-18 Chapter 21 Monitoring SQL Executions Using Cloud Control In the following graphic, the top row shows the parallel query. In this example, the query has been executing for 1.4 minutes. 2. Click the value in the SQL ID column to see details about the statement. The Monitored SQL Details page appears. The preceding report shows the execution plan and statistics relating to statement execution. For example, the Timeline column shows when each step of the execution plan was active. Times are shown relative to the beginning and end of the statement execution. The Executions column shows how many times an operation was executed. 3. In the Overview section, click the link next to the SQL text. A message shows the full text of the SQL statement. 21-19 Chapter 21 Monitoring SQL Executions Using Cloud Control 4. In the Time & Wait Statistics section, next to Database Time, move the cursor over the largest portion on the bar graph. A message shows that user I/O is consuming over half of database time. Database Time measures the amount of time the database has spent working on this SQL statement. This value includes CPU and wait times, such as I/O time. The bar graph is divided into several color-coded portions to highlight CPU resources, user I/O resources, and other resources. You can move the cursor over any portion to view the percentage value of the total. 5. In the Details section, in the IO Requests column, move the cursor over the I/O requests bar to view the percentage value of the total. A message appears. In the preceding graphic, the IO Requests message shows the total number of read requests issued by the monitored SQL. The message shows that read requests form 80% of the total I/O requests. 21-20 Chapter 21 Monitoring SQL Executions Using Cloud Control See Also: • Cloud Control Online Help for descriptions of the elements on the Monitored SQL Executions Details page, and for complete descriptions of all statistics in the report. • Oracle Database Reference to learn about V$SQL_MONITOR and related views for database operations monitoring 21-21 22 Gathering Diagnostic Data with SQL Test Case Builder SQL Test Case Builder is a tool that automatically gathers information needed to reproduce the problem in a different database instance. A SQL test case is a set of information that enables a developer to reproduce the execution plan for a specific SQL statement that has encountered a performance problem. This chapter contains the following topics: • Purpose of SQL Test Case Builder SQL Test Case Builder automates the sometimes difficult and time-consuming process of gathering and reproducing information about a problem and the environment in which it occurred. • Concepts for SQL Test Case Builder Key concepts for SQL Test Case Builder include SQL incidents, types of information recorded, and the form of the output. • User Interfaces for SQL Test Case Builder You can access SQL Test Case Builder either through Cloud Control or using PL/SQL on the command line. • Running SQL Test Case Builder You can run SQL Test Case Builder using Cloud Control. 22.1 Purpose of SQL Test Case Builder SQL Test Case Builder automates the sometimes difficult and time-consuming process of gathering and reproducing information about a problem and the environment in which it occurred. The output of SQL Test Case Builder is a set of scripts in a predefined directory. These scripts contain the commands required to re-create all the necessary objects and the environment. After the test case is ready, you can create a zip file of the directory and move it to another database, or upload the file to Oracle Support. 22.2 Concepts for SQL Test Case Builder Key concepts for SQL Test Case Builder include SQL incidents, types of information recorded, and the form of the output. This section contains the following topics: • SQL Incidents In the fault diagnosability infrastructure of Oracle Database, an incident is a single occurrence of a problem. 22-1 Chapter 22 Concepts for SQL Test Case Builder • What SQL Test Case Builder Captures SQL Test Case Builder captures permanent information about a SQL query and its environment. • Output of SQL Test Case Builder The output of the SQL Test Case Builder is a set of files that contains commands required to re-create the environment and all necessary objects. 22.2.1 SQL Incidents In the fault diagnosability infrastructure of Oracle Database, an incident is a single occurrence of a problem. A SQL incident is a SQL-related problem. When a problem (critical error) occurs multiple times, the database creates an incident for each occurrence. Incidents are timestamped and tracked in the Automatic Diagnostic Repository (ADR). Each incident has a numeric incident ID, which is unique within the ADR. SQL Test Case Builder is accessible any time on the command line. In Oracle Enterprise Manager Cloud Control (Cloud Control), the SQL Test Case pages are only available after a SQL incident is found. See Also: • Oracle Database Concepts for a conceptual overview of ADR • Oracle Database Administrator’s Guide to learn how to investigate, report, and resolve a problem 22.2.2 What SQL Test Case Builder Captures SQL Test Case Builder captures permanent information about a SQL query and its environment. The information includes the query being executed, table and index definitions (but not the actual data), PL/SQL packages and program units, optimizer statistics, SQL plan baselines, and initialization parameter settings. Starting in Oracle Database 12c, SQL Test Case Builder also captures and replays transient information, including information only available as part of statement execution. SQL Test Case Builder supports the following: • Adaptive plans SQL Test Case Builder captures inputs to the decisions made regarding adaptive plans, and replays them at each decision point. For adaptive plans, the final statistics value at each buffering statistics collector is sufficient to decide on the final plan. • Automatic memory management The database automatically handles the memory requested for each SQL operation. Actions such as sorting can affect performance significantly. SQL Test Case Builder keeps track of the memory activities, for example, where the database allocated memory and how much it allocated. 22-2 Chapter 22 Concepts for SQL Test Case Builder • Dynamic statistics Regathering dynamic statistics on a different database does not always generate the same results, for example, when data is missing. To reproduce the problem, SQL Test Case Builder exports the dynamic statistics result from the source database. In the testing database, SQL Test Case Builder reuses the same values captured from the source database instead of regathering dynamic statistics. • Multiple execution support SQL Test Case Builder can capture dynamic information accumulated during multiple executions of the query. This capability is important for automatic reoptimization. • Compilation environment and bind values replay The compilation environment setting is an important part of the query optimization context. SQL Test Case Builder captures nondefault settings altered by the user when running the problem query in the source database. If any nondefault parameter values are used, SQL Test Case Builder re-establishes the same values before running the query. • Object statistics history The statistics history for objects is helpful to determine whether a plan change was caused by a change in statistics values. DBMS_STATS stores the history in the data dictionary. SQL Test Case Builder stores this statistics data into a staging table during export. During import, SQL Test Case Builder automatically reloads the statistics history data into the target database from the staging table. • Statement history The statement history is important for diagnosing problems related to adaptive cursor sharing, statistics feedback, and cursor sharing bugs. The history includes execution plans and compilation and execution statistics. See Also: • "Adaptive Query Plans" • "Supplemental Dynamic Statistics" • "Automatic Reoptimization" • "Managing SQL Plan Baselines" • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS package 22.2.3 Output of SQL Test Case Builder The output of the SQL Test Case Builder is a set of files that contains commands required to re-create the environment and all necessary objects. By default, SQL Test Case Builder stores the files in the following location, where incnum refers to the incident number and runnum refers to the run number: $ADR_HOME/incident/incdir_incnum/SQLTCB_runnum 22-3 Chapter 22 User Interfaces for SQL Test Case Builder For example, a valid output file name could be as follows: $ORACLE_HOME/log/diag/rdbms/dbsa/dbsa/incident/incdir_2657/SQLTCB_1 You can specify a nondefault location by creating an Oracle directory and invoking DBMS_SQLDIAG.EXPORT_SQL_TESTCASE, as in the following example: CREATE OR REPLACE DIRECTORY my_tcb_dir_exp '/tmp'; BEGIN DBMS_SQLDIAG.EXPORT_SQL_TESTCASE ( directory => 'my_tcb_dir_exp' , sql_text => 'SELECT COUNT(*) FROM sales' , testcase => tco ); END; See Also: Oracle Database Administrator’s Guide to learn about the structure of the ADR repository 22.3 User Interfaces for SQL Test Case Builder You can access SQL Test Case Builder either through Cloud Control or using PL/SQL on the command line. This section contains the following topics: • Graphical Interface for SQL Test Case Builder Within Cloud Control, you can access SQL Test Case Builder from the Incident Manager page or the Support Workbench page. • Command-Line Interface for SQL Test Case Builder The DBMS_SQLDIAG package performs tasks relating to SQL Test Case Builder. 22.3.1 Graphical Interface for SQL Test Case Builder Within Cloud Control, you can access SQL Test Case Builder from the Incident Manager page or the Support Workbench page. This section contains the following topics: • Accessing the Incident Manager From the Incidents and Problems section on the Database Home page, you can navigate to the Incident Manager. • Accessing the Support Workbench From the Oracle Database menu, you can navigate to the Support Workbench. 22-4 Chapter 22 User Interfaces for SQL Test Case Builder 22.3.1.1 Accessing the Incident Manager From the Incidents and Problems section on the Database Home page, you can navigate to the Incident Manager. To access the Incident Manager: 1. Log in to Cloud Control with the appropriate credentials. 2. Under the Targets menu, select Databases. 3. In the list of database targets, select the target for the Oracle Database instance that you want to administer. 4. If prompted for database credentials, then enter the minimum credentials necessary for the tasks you intend to perform. 5. In the Incidents and Problems section, locate the SQL incident to be investigated. In the following example, the ORA 600 error is a SQL incident. 6. Click the summary of the incident. The Problem Details page of the Incident Manager appears. The Support Workbench page appears, with the incidents listed in a table. 22-5 Chapter 22 User Interfaces for SQL Test Case Builder See Also: • Oracle Database Administrator’s Guide to learn how to view problems with the Cloud Control Support Workbench • Online help for Cloud Control 22.3.1.2 Accessing the Support Workbench From the Oracle Database menu, you can navigate to the Support Workbench. To access the Support Workbench: 1. Log in to Cloud Control with the appropriate credentials. 2. Under the Targets menu, select Databases. 3. In the list of database targets, select the target for the Oracle Database instance that you want to administer. 4. If prompted for database credentials, then enter the minimum credentials necessary for the tasks you intend to perform. 5. From the Oracle Database menu, select Diagnostics, then Support Workbench. The Support Workbench page appears, with the incidents listed in a table. See Also: Online help for Cloud Control 22.3.2 Command-Line Interface for SQL Test Case Builder The DBMS_SQLDIAG package performs tasks relating to SQL Test Case Builder. This package consists of various subprograms for the SQL Test Case Builder, some of which are listed in the following table. Table 22-1 SQL Test Case Functions in DBMS_SQLDIAG Procedure Description EXPORT_SQL_TESTCASE Exports a SQL test case to a user-specified directory EXPORT_SQL_TESTCASE_DIR_BY_INC Exports a SQL test case corresponding to the incident ID passed as an argument EXPORT_SQL_TESTCASE_DIR_BY_TXT Exports a SQL test case corresponding to the SQL text passed as an argument IMPORT_SQL_TESTCASE Imports a SQL test case into a schema 22-6 Chapter 22 Running SQL Test Case Builder See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about the DBMS_SQLDIAG package 22.4 Running SQL Test Case Builder You can run SQL Test Case Builder using Cloud Control. Assumptions This tutorial assumes the following: • You ran the following EXPLAIN PLAN statement as user sh, which causes an internal error: EXPLAIN PLAN FOR SELECT unit_cost, sold FROM costs c, ( SELECT /*+ merge */ p.prod_id, SUM(quantity_sold) AS sold FROM products p, sales s WHERE p.prod_id = s.prod_id GROUP BY p.prod_id ) v WHERE c.prod_id = v.prod_id; • In the Incidents and Problems section on the Database Home page, a SQL incident generated by the internal error appears. • You access the Incident Details page, as explained in "Accessing the Incident Manager". To run SQL Test Case Builder: 1. Click the Incidents tab. The Problem Details page appears. 2. Click the summary for the incident. The Incident Details page appears. 22-7 Chapter 22 Running SQL Test Case Builder 3. In Guided Resolution, click View Diagnostic Data. The Incident Details: incident_number page appears. 4. In the Application Information section, click Additional Diagnostics. The Additional Diagnostics subpage appears. 22-8 Chapter 22 Running SQL Test Case Builder 5. Select SQL Test Case Builder, and then click Run. The Run User Action page appears. 6. Select a sampling percentage (optional), and then click Submit. After processing completes, the Confirmation page appears. 7. Access the SQL Test Case files in the location described in "Output of SQL Test Case Builder". See Also: Online help for Cloud Control 22-9 23 Performing Application Tracing This chapter explains what end-to-end application tracing is, and how to generate and read trace files. This chapter contains the following topics: • Overview of End-to-End Application Tracing End-to-end application tracing can identify the source of an excessive database workload, such as a high load SQL statement, by client identifier, service, module, action, session, instance, or an entire database. • Enabling Statistics Gathering for End-to-End Tracing To gather the appropriate statistics using PL/SQL, you must enable statistics gathering for client identifier, service, module, or action using procedures in DBMS_MONITOR. • Enabling End-to-End Application Tracing To enable tracing for client identifier, service, module, action, session, instance or database, execute the appropriate procedures in the DBMS_MONITOR package. • Generating Output Files Using SQL Trace and TKPROF This section explains the basic procedure for using SQL Trace and TKPROF. • Guidelines for Interpreting TKPROF Output While TKPROF provides a useful analysis, the most accurate measure of efficiency is the performance of the application. At the end of the TKPROF output is a summary of the work that the process performed during the period that the trace was running. See Also: SQL*Plus User's Guide and Reference to learn about the use of Autotrace to trace and tune SQL*Plus statements 23.1 Overview of End-to-End Application Tracing End-to-end application tracing can identify the source of an excessive database workload, such as a high load SQL statement, by client identifier, service, module, action, session, instance, or an entire database. In multitier environments, the middle tier routes a request from an end client to different database sessions, making it difficult to track a client across database sessions. End-to-end application tracing is an infrastructure that uses a client ID to uniquely trace a specific end-client through all tiers to the database and provides information about the operation that an end client is performing in the database. This section contains the following topics: 23-1 Chapter 23 Overview of End-to-End Application Tracing • Purpose of End-to-End Application Tracing End-to-end application tracing simplifies diagnosing performance problems in multitier environments. • End-to-End Application Tracing in a Multitenant Environment Starting in Oracle Database 12c Release 2 (12.2), when you are connected to a container in a CDB, new V$ views enable read access to trace files that are specific to the container. • Tools for End-to-End Application Tracing The SQL Trace facility and TKPROF are two basic performance diagnostic tools that can help you accurately assess the efficiency of the SQL statements an application runs. 23.1.1 Purpose of End-to-End Application Tracing End-to-end application tracing simplifies diagnosing performance problems in multitier environments. For example, you can identify the source of an excessive database workload, such as a high-load SQL statement, and contact the user responsible. Also, a user having problems can contact you. You can then identify what this user session is doing at the non-CDB or PDB level End-to-end application tracing also simplifies management of application workloads by tracking specific modules and actions in a service. The module and action names are set by the application developer. For example, you would use the SET_MODULE and SET_ACTION procedures in the DBMS_APPLICATION_INFO package to set these values in a PL/SQL program. End-to-end application tracing can identify workload problems in a database for: • Client identifier Specifies an end user based on the logon ID, such as HR.HR • Service Specifies a group of applications with common attributes, service level thresholds, and priorities; or a single application, such as ACCTG for an accounting application • Module Specifies a functional block, such as Accounts Receivable or General Ledger, of an application • Action Specifies an action, such as an INSERT or UPDATE operation, in a module • Session Specifies a session based on a given database session identifier (SID), on the local instance • Instance Specifies a given database instance based on the instance name • Container Specifies the container in a CDB 23-2 Chapter 23 Overview of End-to-End Application Tracing 23.1.2 End-to-End Application Tracing in a Multitenant Environment Starting in Oracle Database 12c Release 2 (12.2), when you are connected to a container in a CDB, new V$ views enable read access to trace files that are specific to the container. The primary use cases are as follows: • CDB administrators must view traces from a specific PDB. The V$DIAG_TRACE_FILE view lists all trace files in the ADR trace directory that contain trace data from a specific PDB. V$DIAG_TRACE_FILE_CONTENTS displays the contents of the trace files. • PDB administrators must view traces from a specific PDB. You can use SQL Trace to collect diagnostic data for the SQL statements executing in a PDB application. The trace data includes SQL tracing (event 10046) and optimizer tracing (event 10053). Using V$ views, developers can access only SQL or optimizer trace records without accessing the entire trace file. To enable users and tools to determine which PDB is associated with a file or a part of a file, PDB annotations exist in trace files, incident dumps, and log files. The PDB information is part of the structured metadata that is stored in the .trm file for each trace file. Each record captures the following attributes: • CON_ID, which is the ID of the container associated with the data • CON_UID, which is the unique ID of the container • NAME, which is the name of the container See Also: "Views for Application Tracing" 23.1.3 Tools for End-to-End Application Tracing The SQL Trace facility and TKPROF are two basic performance diagnostic tools that can help you accurately assess the efficiency of the SQL statements an application runs. For best results, use these tools with EXPLAIN PLAN rather than using EXPLAIN PLAN alone. After tracing information is written to files, you can consolidate this data with the TRCSESS utility, and then diagnose it with TKPROF or SQL Trace. The recommended interface for end-to-end application tracing is Oracle Enterprise Manager Cloud Control (Cloud Control). Using Cloud Control, you can view the top consumers for each consumer type, and enable or disable statistics gathering and SQL tracing for specific consumers. If Cloud Control is unavailable, then you can manage this feature using the DBMS_MONITOR APIs. This section contains the following topics: 23-3 Chapter 23 Overview of End-to-End Application Tracing • Overview of the SQL Trace Facility The SQL Trace facility provides performance information on individual SQL statements. • Overview of TKPROF To format the contents of the trace file and place the output into a readable output file, run the TKPROF program. See Also: Oracle Database PL/SQL Packages and Types Reference for information about the DBMS_MONITOR, DBMS_SESSION, DBMS_SERVICE, and DBMS_APPLICATION_INFO packages 23.1.3.1 Overview of the SQL Trace Facility The SQL Trace facility provides performance information on individual SQL statements. SQL Trace generates the following statistics for each statement: • Parse, execute, and fetch counts • CPU and elapsed times • Physical reads and logical reads • Number of rows processed • Misses on the library cache • User name under which each parse occurred • Each commit and rollback • Wait event data for each SQL statement, and a summary for each trace file If the cursor for the SQL statement is closed, then SQL Trace also provides row source information that includes: • Row operations showing the actual execution plan of each SQL statement • Number of rows, number of consistent reads, number of physical reads, number of physical writes, and time elapsed for each operation on a row Although you can enable the SQL Trace facility for a session or an instance, Oracle recommends that you use the DBMS_SESSION or DBMS_MONITOR packages instead. When the SQL Trace facility is enabled for a session or for an instance, performance statistics for all SQL statements executed in a user session or in the instance are placed into trace files. Using the SQL Trace facility can affect performance and may result in increased system overhead, excessive CPU usage, and inadequate disk space. The TRCSESS command-line utility consolidates tracing information from several trace files based on specific criteria, such as session or client ID. 23-4 Chapter 23 Enabling Statistics Gathering for End-to-End Tracing See Also: • "TRCSESS" • "Enabling End-to-End Application Tracing" to learn how to use the DBMS_SESSION or DBMS_MONITOR packages to enable SQL tracing for a session or an instance 23.1.3.2 Overview of TKPROF To format the contents of the trace file and place the output into a readable output file, run the TKPROF program. TKPROF can also do the following: • Create a SQL script that stores the statistics in the database • Determine the execution plans of SQL statements TKPROF reports each statement executed with the resources it has consumed, the number of times it was called, and the number of rows which it processed. This information enables you to locate those statements that are using the greatest resource. With baselines available, you can assess whether the resources used are reasonable given the work done. 23.2 Enabling Statistics Gathering for End-to-End Tracing To gather the appropriate statistics using PL/SQL, you must enable statistics gathering for client identifier, service, module, or action using procedures in DBMS_MONITOR. The default level is the session-level statistics gathering. Statistics gathering is global for the database and continues after a database instance is restarted. You can gather statistics by the following criteria: • Enabling Statistics Gathering for a Client ID The procedure CLIENT_ID_STAT_ENABLE enables statistics gathering for a given client ID, whereas the procedure CLIENT_ID_STAT_DISABLE disables it. • Enabling Statistics Gathering for Services, Modules, and Actions The procedure SERV_MOD_ACT_STAT_ENABLE enables statistic gathering for a combination of service, module, and action, whereas the procedure SERV_MOD_ACT_STAT_DISABLE disables statistic gathering for a combination of service, module, and action. 23.2.1 Enabling Statistics Gathering for a Client ID The procedure CLIENT_ID_STAT_ENABLE enables statistics gathering for a given client ID, whereas the procedure CLIENT_ID_STAT_DISABLE disables it. You can view client identifiers in the CLIENT_IDENTIFIER column in V$SESSION. 23-5 Chapter 23 Enabling Statistics Gathering for End-to-End Tracing Assumptions This tutorial assumes that you want to enable and then disable statistics gathering for the client with the ID oe.oe. To enable and disable statistics gathering for a client identifier: 1. Start SQL*Plus, and then connect to the database with the appropriate privileges. 2. Enable statistics gathering for oe.oe. For example, run the following command: EXECUTE DBMS_MONITOR.CLIENT_ID_STAT_ENABLE(client_id => 'OE.OE'); 3. Disable statistics gathering for oe.oe. For example, run the following command: EXECUTE DBMS_MONITOR.CLIENT_ID_STAT_DISABLE(client_id => 'OE.OE'); 23.2.2 Enabling Statistics Gathering for Services, Modules, and Actions The procedure SERV_MOD_ACT_STAT_ENABLE enables statistic gathering for a combination of service, module, and action, whereas the procedure SERV_MOD_ACT_STAT_DISABLE disables statistic gathering for a combination of service, module, and action. When you change the module or action using the preceding DBMS_MONITOR procedures, the change takes effect when the next user call is executed in the session. For example, if a module is set to module1 in a session, and if the module is reset to module2 in a user call in the session, then the module remains module1 during this user call. The module is changed to module2 in the next user call in the session. Assumptions This tutorial assumes that you want to gather statistics as follows: • For the ACCTG service • For all actions in the PAYROLL module • For the INSERT ITEM action within the GLEDGER module To enable and disable statistics gathering for a service, module, and action: 1. Start SQL*Plus, and then connect to the database with the appropriate privileges. 2. Enable statistics gathering for the desired service, module, and action. For example, run the following commands: BEGIN DBMS_MONITOR.SERV_MOD_ACT_STAT_ENABLE( service_name => 'ACCTG' , module_name => 'PAYROLL' ); END; BEGIN DBMS_MONITOR.SERV_MOD_ACT_STAT_ENABLE( service_name => 'ACCTG' 23-6 Chapter 23 Enabling End-to-End Application Tracing , module_name => 'GLEDGER' , action_name => 'INSERT ITEM' ); END; 3. Disable statistic gathering for the previously specified combination of service, module, and action. For example, run the following command: BEGIN DBMS_MONITOR.SERV_MOD_ACT_STAT_DISABLE( service_name => 'ACCTG' , module_name => 'GLEDGER' , action_name => 'INSERT ITEM' ); END; 23.3 Enabling End-to-End Application Tracing To enable tracing for client identifier, service, module, action, session, instance or database, execute the appropriate procedures in the DBMS_MONITOR package. With the criteria that you provide, specific trace information is captured in a set of trace files and combined into a single output trace file. You can enable tracing for specific diagnosis and workload management by the following criteria: • Enabling Tracing for a Client Identifier To enable tracing globally for the database for a specified client identifier, use the DBMS_MONITOR.CLIENT_ID_TRACE_ENABLE procedure. • Enabling Tracing for a Service, Module, and Action The DBMS_MONITOR.SERV_MOD_ACT_TRACE_ENABLE procedure enables SQL tracing for a specified combination of service name, module, and action globally for a database, unless the procedure specifies a database instance name. • Enabling Tracing for a Session The SESSION_TRACE_ENABLE procedure enables the trace for a given database session identifier (SID) on the local instance. • Enabling Tracing for the Instance or Database The DBMS_MONITOR.DATABASE_TRACE_ENABLE procedure overrides all other sessionlevel traces, but is complementary to the client identifier, service, module, and action traces. Tracing is enabled for all current and future sessions. See Also: Oracle Database Administrator’s Guide for information about how to locate trace files 23.3.1 Enabling Tracing for a Client Identifier To enable tracing globally for the database for a specified client identifier, use the DBMS_MONITOR.CLIENT_ID_TRACE_ENABLE procedure. The CLIENT_ID_TRACE_DISABLE procedure disables tracing globally for the database for a given client identifier. 23-7 Chapter 23 Enabling End-to-End Application Tracing Assumptions This tutorial assumes the following: • OE.OE is the client identifier for which SQL tracing is to be enabled. • You want to include wait information in the trace. • You want to exclude bind information from the trace. To enable and disable tracing for a client identifier: 1. Start SQL*Plus, and then connect to the database with the appropriate privileges. 2. Enable tracing for the client. For example, execute the following program: BEGIN DBMS_MONITOR.CLIENT_ID_TRACE_ENABLE( client_id => 'OE.OE' , waits => true , binds => false ); END; 3. Disable tracing for the client. For example, execute the following command: EXECUTE DBMS_MONITOR.CLIENT_ID_TRACE_DISABLE(client_id => 'OE.OE'); 23.3.2 Enabling Tracing for a Service, Module, and Action The DBMS_MONITOR.SERV_MOD_ACT_TRACE_ENABLE procedure enables SQL tracing for a specified combination of service name, module, and action globally for a database, unless the procedure specifies a database instance name. The SERV_MOD_ACT_TRACE_DISABLE procedure disables the trace at all enabled instances for a given combination of service name, module, and action name globally. Assumptions This tutorial assumes the following: • You want to enable tracing for the service ACCTG. • You want to enable tracing for all actions for the combination of the ACCTG service and the PAYROLL module. • You want to include wait information in the trace. • You want to exclude bind information from the trace. • You want to enable tracing only for the inst1 instance. To enable and disable tracing for a service, module, and action: 1. Start SQL*Plus, and then connect to the database with the appropriate privileges. 2. Enable tracing for the service, module, and actions. For example, execute the following command: 23-8 Chapter 23 Enabling End-to-End Application Tracing BEGIN DBMS_MONITOR.SERV_MOD_ACT_TRACE_ENABLE( service_name => 'ACCTG' , module_name => 'PAYROLL' , waits => true , binds => false , instance_name => 'inst1' ); END; 3. Disable tracing for the service, module, and actions. For example, execute the following command: BEGIN DBMS_MONITOR.SERV_MOD_ACT_TRACE_DISABLE( service_name => 'ACCTG' , module_name => 'PAYROLL' , instance_name => 'inst1' ); END; 23.3.3 Enabling Tracing for a Session The SESSION_TRACE_ENABLE procedure enables the trace for a given database session identifier (SID) on the local instance. Whereas the DBMS_MONITOR package can only be invoked by a user with the DBA role, users can also enable SQL tracing for their own session by invoking the DBMS_SESSION.SESSION_TRACE_ENABLE procedure, as in the following example: BEGIN DBMS_SESSION.SESSION_TRACE_ENABLE( waits => true , binds => false); END; Assumptions This tutorial assumes the following: • You want to log in to the database with administrator privileges. • User OE has one active session. • You want to temporarily enable tracing for the OE session. • You want to include wait information in the trace. • You want to exclude bind information from the trace. To enable and disable tracing for a session: 1. Start SQL*Plus, and then log in to the database with the administrator privileges. 2. Determine the session ID and serial number values for the session to trace. For example, query V$SESSION as follows: SELECT SID, SERIAL#, USERNAME FROM V$SESSION WHERE USERNAME = 'OE'; SID SERIAL# USERNAME ---------- ---------- -----------------------------27 60 OE 23-9 Chapter 23 Enabling End-to-End Application Tracing 3. Use the values from the preceding step to enable tracing for a specific session. For example, execute the following program to enable tracing for the OE session, where the true argument includes wait information in the trace and the false argument excludes bind information from the trace: BEGIN DBMS_MONITOR.SESSION_TRACE_ENABLE( session_id => 27 , serial_num => 60 , waits => true , binds => false); END; 4. Disable tracing for the session. The SESSION_TRACE_DISABLE procedure disables the trace for a given database session identifier (SID) and serial number. For example: BEGIN DBMS_MONITOR.SESSION_TRACE_DISABLE( session_id => 27 , serial_num => 60); END; 23.3.4 Enabling Tracing for the Instance or Database The DBMS_MONITOR.DATABASE_TRACE_ENABLE procedure overrides all other session-level traces, but is complementary to the client identifier, service, module, and action traces. Tracing is enabled for all current and future sessions. All new sessions inherit the wait and bind information specified by this procedure until you call the DATABASE_TRACE_DISABLE procedure. When you invoke this procedure with the instance_name parameter, the procedure resets the session-level SQL trace for the named instance. If you invoke this procedure without the instance_name parameter, then the procedure resets the session-level SQL trace for the entire database. Prerequisites You must have administrative privileges to execute the DATABASE_TRACE_ENABLE procedure. Assumptions This tutorial assumes the following: • You want to enable tracing for all SQL the inst1 instance. • You want wait information to be in the trace. • You do not want bind information in the trace. To enable and disable tracing for a session: 1. Start SQL*Plus, and then log in to the database with the necessary privileges. 2. Call the DATABASE_TRACE_ENABLE procedure to enable SQL tracing for a given instance or an entire database. For example, execute the following program, where the true argument specifies that wait information is in the trace, and the false argument specifies that no bind information is in the trace: 23-10 Chapter 23 Generating Output Files Using SQL Trace and TKPROF BEGIN DBMS_MONITOR.DATABASE_TRACE_ENABLE( waits => true , binds => false , instance_name => 'inst1' ); END; 3. Disable tracing for the session. The SESSION_TRACE_DISABLE procedure disables the trace. For example, the following program disables tracing for inst1: EXECUTE DBMS_MONITOR.DATABASE_TRACE_DISABLE(instance_name => 'inst1'); To disable the session-level SQL tracing for an entire database, invoke the DATABASE_TRACE_DISABLE procedure without specifying the instance_name parameter: EXECUTE DBMS_MONITOR.DATABASE_TRACE_DISABLE(); 23.4 Generating Output Files Using SQL Trace and TKPROF This section explains the basic procedure for using SQL Trace and TKPROF. The procedure for generating output files is as follows: 1. Set initialization parameters for trace file management. See "Step 1: Setting Initialization Parameters for Trace File Management". 2. Enable the SQL Trace facility for the desired session, and run the application. This step produces a trace file containing statistics for the SQL statements issued by the application. See "Step 2: Enabling the SQL Trace Facility". 3. Run TKPROF to translate the trace file created in Step 2 into a readable output file. This step can optionally create a SQL script that you can use to store the statistics in a database. See "Step 3: Generating Output Files with TKPROF". 4. Optionally, run the SQL script produced in Step 3 to store the statistics in the database. See "Step 4: Storing SQL Trace Facility Statistics". This section contains the following topics: • Step 1: Setting Initialization Parameters for Trace File Management To enable trace files, you must ensure that specific initialization parameters are set. • Step 2: Enabling the SQL Trace Facility You can enable the SQL Trace facility at the instance or session level. • Step 3: Generating Output Files with TKPROF TKPROF accepts as input a trace file produced by the SQL Trace facility, and it produces a formatted output file. TKPROF can also generate execution plans. 23-11 Chapter 23 Generating Output Files Using SQL Trace and TKPROF • Step 4: Storing SQL Trace Facility Statistics You might want to keep a history of the statistics generated by the SQL Trace facility for an application, and compare them over time. 23.4.1 Step 1: Setting Initialization Parameters for Trace File Management To enable trace files, you must ensure that specific initialization parameters are set. When the SQL Trace facility is enabled for a session, Oracle Database generates a trace file containing statistics for traced SQL statements for that session. When the SQL Trace facility is enabled for an instance, Oracle Database creates a separate trace file for each process. To set initialization parameters for trace file management: 1. Check the settings of the TIMED_STATISTICS, MAX_DUMP_FILE_SIZE, and DIAGNOSTIC_DEST initialization parameters, as indicated in "Table 23-1". Table 23-1 Initialization Parameters to Check Before Enabling SQL Trace Parameter Description DIAGNOSTIC_DEST Specifies the location of the Automatic Diagnostic Repository (ADR) Home. The diagnostic files for each database instance are located in this dedicated directory. MAX_DUMP_FILE_SIZE When the SQL Trace facility is enabled at the database instance level, every call to the database writes a text line in a file in the operating system's file format. The maximum size of these files in operating system blocks is limited by this initialization parameter. The default is UNLIMITED. TIMED_STATISTICS Enables and disables the collection of timed statistics, such as CPU and elapsed times, by the SQL Trace facility, and the collection of various statistics in the V$ views. If STATISTICS_LEVEL is set to TYPICAL or ALL, then the default value of TIMED_STATISTICS is true. If STATISTICS_LEVEL is set to BASIC, then the default value of TIMED_STATISTICS is false. Enabling timing causes extra timing calls for low-level operations. This is a dynamic parameter. It is also a session parameter. 2. Devise a way of recognizing the resulting trace file. Be sure you know how to distinguish the trace files by name. You can tag trace files by including in your programs a statement such as SELECT program_name' FROM DUAL. You can then trace each file back to the process that created it. You can also set the TRACEFILE_IDENTIFIER initialization parameter to specify a custom identifier that becomes part of the trace file name. For example, you can add my_trace_id to subsequent trace file names for easy identification with the following: ALTER SESSION SET TRACEFILE_IDENTIFIER = 'my_trace_id'; 23-12 Chapter 23 Generating Output Files Using SQL Trace and TKPROF 3. If the operating system retains multiple versions of files, then ensure that the version limit is high enough to accommodate the number of trace files you expect the SQL Trace facility to generate. 4. If the generated trace files can be owned by an operating system user other than yourself, then ensure that you have the necessary permissions to use TKPROF to format them. See Also: • Oracle Database Reference to learn about the DIAGNOSTIC_DEST, STATISTICS_LEVEL, TIMED_STATISTICS, and TRACEFILE_IDENTIFIER initialization parameters • Oracle Database Administrator’s Guide to learn how to control the trace file size 23.4.2 Step 2: Enabling the SQL Trace Facility You can enable the SQL Trace facility at the instance or session level. The package to use depends on the level: • Database instance Use DBMS_MONITOR.DATABASE_TRACE_ENABLE procedure to enable tracing, and DBMS_MONITOR.DATABASE_TRACE_DISABLE procedure to disable tracing. • Database session Use DBMS_SESSION.SET_SQL_TRACE procedure to enable tracing (true) or disable tracing (false). Note: Because running the SQL Trace facility increases system overhead, enable it only when tuning SQL statements, and disable it when you are finished. To enable and disable tracing at the database instance level: 1. Start SQL*Plus, and connect to the database with administrator privileges. 2. Enable tracing at the database instance level. The following example enables tracing for the orcl instance: EXEC DBMS_MONITOR.DATABASE_TRACE_ENABLE(INSTANCE_NAME => 'orcl'); 3. Execute the statements to be traced. 4. Disable tracing for the database instance. The following example disables tracing for the orcl instance: EXEC DBMS_MONITOR.DATABASE_TRACE_DISABLE(INSTANCE_NAME => 'orcl'); 23-13 Chapter 23 Generating Output Files Using SQL Trace and TKPROF To enable and disable tracing at the session level: 1. Start SQL*Plus, and connect to the database with the desired credentials. 2. Enable tracing for the current session. The following example enables tracing for the current session: EXEC DBMS_SESSION.SET_SQL_TRACE(sql_trace => true); 3. Execute the statements to be traced. 4. Disable tracing for the current session. The following example disables tracing for the current session: EXEC DBMS_SESSION.SET_SQL_TRACE(sql_trace => false); See Also: Oracle Database PL/SQL Packages and Types Reference to learn about DBMS_MONITOR.DATABASE_TRACE_ENABLE 23.4.3 Step 3: Generating Output Files with TKPROF TKPROF accepts as input a trace file produced by the SQL Trace facility, and it produces a formatted output file. TKPROF can also generate execution plans. After the SQL Trace facility has generated trace files, you can: • Run TKPROF on each individual trace file, producing several formatted output files, one for each session. • Concatenate the trace files, and then run TKPROF on the result to produce a formatted output file for the entire instance. • Run the TRCSESS command-line utility to consolidate tracing information from several trace files, then run TKPROF on the result. TKPROF does not report COMMIT and ROLLBACK statements recorded in the trace file. Note: The following SQL statements are truncated to 25 characters in the SQL Trace file: SET ROLE GRANT ALTER USER ALTER ROLE CREATE USER CREATE ROLE 23-14 Chapter 23 Generating Output Files Using SQL Trace and TKPROF Example 23-1 TKPROF Output SELECT * FROM emp, dept WHERE emp.deptno = dept.deptno; call count cpu elapsed disk query current rows ---- ------- ------- --------- -------- -------- ------- -----Parse 1 0.16 0.29 3 13 0 0 Execute 1 0.00 0.00 0 0 0 0 Fetch 1 0.03 0.26 2 2 4 14 Misses in library cache during parse: 1 Parsing user id: (8) SCOTT Rows Execution Plan ------- --------------------------------------------------- 14 MERGE JOIN 4 SORT JOIN 4 TABLE ACCESS (FULL) OF 'DEPT' 14 SORT JOIN 14 TABLE ACCESS (FULL) OF 'EMP' For this statement, TKPROF output includes the following information: • The text of the SQL statement • The SQL Trace statistics in tabular form • The number of library cache misses for the parsing and execution of the statement. • The user initially parsing the statement. • The execution plan generated by EXPLAIN PLAN. TKPROF also provides a summary of user level statements and recursive SQL calls for the trace file. 23.4.4 Step 4: Storing SQL Trace Facility Statistics You might want to keep a history of the statistics generated by the SQL Trace facility for an application, and compare them over time. TKPROF can generate a SQL script that creates a table and inserts rows of statistics into it. This script contains the following: • A CREATE TABLE statement that creates an output table named TKPROF_TABLE. • INSERT statements that add rows of statistics, one for each traced SQL statement, to TKPROF_TABLE. After running TKPROF, run this script to store the statistics in the database. This section contains the following topics: • Generating the TKPROF Output SQL Script When you run TKPROF, use the INSERT parameter to specify the name of the generated SQL script. • Editing the TKPROF Output SQL Script After TKPROF has created the SQL script, you might want to edit the script before running it. 23-15 Chapter 23 Generating Output Files Using SQL Trace and TKPROF • Querying the Output Table After you have created the output table, query using a SELECT statement. 23.4.4.1 Generating the TKPROF Output SQL Script When you run TKPROF, use the INSERT parameter to specify the name of the generated SQL script. If you omit the INSERT parameter, then TKPROF does not generate a script. 23.4.4.2 Editing the TKPROF Output SQL Script After TKPROF has created the SQL script, you might want to edit the script before running it. If you have created an output table for previously collected statistics, and if you want to add new statistics to this table, then remove the CREATE TABLE statement from the script. The script then inserts the new rows into the existing table. If you have created multiple output tables, perhaps to store statistics from different databases in different tables, then edit the CREATE TABLE and INSERT statements to change the name of the output table. 23.4.4.3 Querying the Output Table After you have created the output table, query using a SELECT statement. The following CREATE TABLE statement creates the TKPROF_TABLE: CREATE TABLE TKPROF_TABLE ( DATE_OF_INSERT CURSOR_NUM DEPTH USER_ID PARSE_CNT PARSE_CPU PARSE_ELAP PARSE_DISK PARSE_QUERY PARSE_CURRENT PARSE_MISS EXE_COUNT EXE_CPU EXE_ELAP EXE_DISK EXE_QUERY EXE_CURRENT EXE_MISS EXE_ROWS FETCH_COUNT FETCH_CPU FETCH_ELAP FETCH_DISK FETCH_QUERY FETCH_CURRENT FETCH_ROWS CLOCK_TICKS SQL_STATEMENT DATE, NUMBER, NUMBER, NUMBER, NUMBER, NUMBER, NUMBER, NUMBER, NUMBER, NUMBER, NUMBER, NUMBER, NUMBER, NUMBER, NUMBER, NUMBER, NUMBER, NUMBER, NUMBER, NUMBER, NUMBER, NUMBER, NUMBER, NUMBER, NUMBER, NUMBER, NUMBER, LONG); 23-16 Chapter 23 Generating Output Files Using SQL Trace and TKPROF Most output table columns correspond directly to the statistics that appear in the formatted output file. For example, the PARSE_CNT column value corresponds to the count statistic for the parse step in the output file. The columns in the following table help you identify a row of statistics. Table 23-2 TKPROF_TABLE Columns for Identifying a Row of Statistics Column Description SQL_STATEMENT This is the SQL statement for which the SQL Trace facility collected the row of statistics. Because this column has data type LONG, you cannot use it in expressions or WHERE clause conditions. DATE_OF_INSERT This is the date and time when the row was inserted into the table. This value is different from the time when the SQL Trace facility collected the statistics. DEPTH This indicates the level of recursion at which the SQL statement was issued. For example, a value of 0 indicates that a user issued the statement. A value of 1 indicates that Oracle Database generated the statement as a recursive call to process a statement with a value of 0 (a statement issued by a user). A value of n indicates that Oracle Database generated the statement as a recursive call to process a statement with a value of n-1. USER_ID This identifies the user issuing the statement. This value also appears in the formatted output file. CURSOR_NUM Oracle Database uses this column value to keep track of the cursor to which each SQL statement was assigned. The output table does not store the statement's execution plan. The following query returns the statistics from the output table. These statistics correspond to the formatted output shown in "Example 23-7". SELECT * FROM TKPROF_TABLE; Sample output appears as follows: DATE_OF_INSERT CURSOR_NUM DEPTH USER_ID PARSE_CNT PARSE_CPU PARSE_ELAP -------------- ---------- ----- ------- --------- --------- ---------21-DEC-2017 1 0 8 1 16 22 PARSE_DISK PARSE_QUERY PARSE_CURRENT PARSE_MISS EXE_COUNT EXE_CPU ---------- ----------- ------------- ---------- --------- ------3 11 0 1 1 0 EXE_ELAP EXE_DISK EXE_QUERY EXE_CURRENT EXE_MISS EXE_ROWS FETCH_COUNT -------- -------- --------- ----------- -------- -------- ----------0 0 0 0 0 0 1 FETCH_CPU FETCH_ELAP FETCH_DISK FETCH_QUERY FETCH_CURRENT FETCH_ROWS --------- ---------- ---------- ----------- ------------- ---------2 20 2 2 4 10 SQL_STATEMENT --------------------------------------------------------------------SELECT * FROM EMP, DEPT WHERE EMP.DEPTNO = DEPT.DEPTNO 23-17 Chapter 23 Guidelines for Interpreting TKPROF Output 23.5 Guidelines for Interpreting TKPROF Output While TKPROF provides a useful analysis, the most accurate measure of efficiency is the performance of the application. At the end of the TKPROF output is a summary of the work that the process performed during the period that the trace was running. This section contains the following topics: • Guideline for Interpreting the Resolution of Statistics Timing statistics have a resolution of one hundredth of a second. Therefore, any operation on a cursor that takes a hundredth of a second or less might not be timed accurately. • Guideline for Recursive SQL Statements Recursive SQL is additional SQL that Oracle Database must issue to execute a SQL statement issued by a user. • Guideline for Deciding Which Statements to Tune You must determine which SQL statements use the most CPU or disk resource. • Guidelines for Avoiding Traps in TKPROF Interpretation When interpreting TKPROF output, it helps to be aware of common traps. 23.5.1 Guideline for Interpreting the Resolution of Statistics Timing statistics have a resolution of one hundredth of a second. Therefore, any operation on a cursor that takes a hundredth of a second or less might not be timed accurately. Keep the time limitation in mind when interpreting statistics. In particular, be careful when interpreting the results from simple queries that execute very quickly. 23.5.2 Guideline for Recursive SQL Statements Recursive SQL is additional SQL that Oracle Database must issue to execute a SQL statement issued by a user. Conceptually, recursive SQL is “side-effect SQL.” For example, if a session inserts a row into a table that has insufficient space to hold that row, then the database makes recursive SQL calls to allocate the space dynamically. The database also generates recursive calls when data dictionary information is not available in memory and so must be retrieved from disk. If recursive calls occur while the SQL Trace facility is enabled, then TKPROF produces statistics for the recursive SQL statements and marks them clearly as recursive SQL statements in the output file. You can suppress the listing of Oracle Database internal recursive calls (for example, space management) in the output file by setting the SYS command-line parameter to NO. The statistics for a recursive SQL statement are included in the listing for that statement, not in the listing for the SQL statement that caused the recursive call. So, when you are calculating the total resources required to process a SQL statement, consider the statistics for that statement and those for recursive calls caused by that statement. 23-18 Chapter 23 Guidelines for Interpreting TKPROF Output Note: Recursive SQL statistics are not included for SQL-level operations. 23.5.3 Guideline for Deciding Which Statements to Tune You must determine which SQL statements use the most CPU or disk resource. If the TIMED_STATISTICS parameter is enabled, then you can find high CPU activity in the CPU column. If TIMED_STATISTICS is not enabled, then check the QUERY and CURRENT columns. With the exception of locking problems and inefficient PL/SQL loops, neither the CPU time nor the elapsed time is necessary to find problem statements. The key is the number of block visits, both query (that is, subject to read consistency) and current (that is, not subject to read consistency). Segment headers and blocks that are going to be updated are acquired in current mode, but all query and subquery processing requests the data in query mode. These are precisely the same measures as the instance statistics CONSISTENT GETS and DB BLOCK GETS. You can find high disk activity in the disk column. The following listing shows TKPROF output for one SQL statement as it appears in the output file: SELECT * FROM emp, dept WHERE emp.deptno = dept.deptno; call count cpu elapsed disk query current rows ---- ------- ------- --------- -------- -------- ------- -----Parse 11 0.08 0.18 0 0 0 0 Execute 11 0.23 0.66 0 3 6 0 Fetch 35 6.70 6.83 100 12326 2 824 -----------------------------------------------------------------total 57 7.01 7.67 100 12329 8 826 Misses in library cache during parse: 0 If it is acceptable to have 7.01 CPU seconds and to retrieve 824 rows, then you need not look any further at this trace output. In fact, a major use of TKPROF reports in a tuning exercise is to eliminate processes from the detailed tuning phase. The output indicates that 10 unnecessary parse call were made (because 11 parse calls exist for this single statement) and that array fetch operations were performed. More rows were fetched than there were fetches performed. A large gap between CPU and elapsed timings indicates Physical I/Os. See Also: "Example 23-4" 23-19 Chapter 23 Guidelines for Interpreting TKPROF Output 23.5.4 Guidelines for Avoiding Traps in TKPROF Interpretation When interpreting TKPROF output, it helps to be aware of common traps. This section contains the following topics: • Guideline for Avoiding the Argument Trap If you are not aware of the values being bound at run time, then it is possible to fall into the argument trap. • Guideline for Avoiding the Read Consistency Trap Without knowing that an uncommitted transaction had made a series of updates to a column, it is difficult to see why so many block visits would be incurred. • Guideline for Avoiding the Schema Trap In some cases, an apparently straightforward indexed query looks at many database blocks and accesses them in current mode. • Guideline for Avoiding the Time Trap In some cases, a query takes an inexplicably long time. 23.5.4.1 Guideline for Avoiding the Argument Trap If you are not aware of the values being bound at run time, then it is possible to fall into the argument trap. EXPLAIN PLAN cannot determine the type of a bind variable from the text of SQL statements, and it always assumes that the type is VARCHAR. If the bind variable is actually a number or a date, then TKPROF can cause implicit data conversions, which can cause inefficient plans to be executed. To avoid this situation, experiment with different data types in the query, and perform the conversion yourself. 23.5.4.2 Guideline for Avoiding the Read Consistency Trap Without knowing that an uncommitted transaction had made a series of updates to a column, it is difficult to see why so many block visits would be incurred. Such cases are not normally repeatable. If the process were run again, it is unlikely that another transaction would interact with it in the same way. SELECT name_id FROM cq_names WHERE name = 'FLOOR'; call count -------Parse 1 Execute 1 Fetch 1 cpu --0.10 0.00 0.11 elapsed ------0.18 0.00 0.21 disk ---0 0 2 query current ----- ------0 0 0 0 101 0 rows ---0 0 1 Misses in library cache during parse: 1 Parsing user id: 01 (USER1) Rows e---0 1 2 Execution Plan --------- ---SELECT STATEMENT TABLE ACCESS (BY ROWID) OF 'CQ_NAMES' INDEX (RANGE SCAN) OF 'CQ_NAMES_NAME' (NON_UNIQUE) 23-20 Chapter 23 Guidelines for Interpreting TKPROF Output 23.5.4.3 Guideline for Avoiding the Schema Trap In some cases, an apparently straightforward indexed query looks at many database blocks and accesses them in current mode. The following example shows an extreme (and thus easily detected) example of the schema trap: SELECT name_id FROM cq_names WHERE name = 'FLOOR'; call count -------- ------Parse 1 Execute 1 Fetch 1 cpu -------0.06 0.02 0.23 elapsed disk query current rows --------- ------- ------ ------- ---0.10 0 0 0 0 0.02 0 0 0 0 0.30 31 31 3 1 Misses in library cache during parse: 0 Parsing user id: 02 (USER2) Rows Execution Plan ------- --------------------------------------------------0 SELECT STATEMENT 2340 TABLE ACCESS (BY ROWID) OF 'CQ_NAMES' 0 INDEX (RANGE SCAN) OF 'CQ_NAMES_NAME' (NON-UNIQUE) Two statistics suggest that the query might have been executed with a full table scan: the current mode block visits, and the number of rows originating from the Table Access row source in the plan. The explanation is that the required index was built after the trace file had been produced, but before TKPROF had been run. Generating a new trace file gives the following data: SELECT name_id FROM cq_names WHERE name = 'FLOOR'; call count cpu elapsed disk query current ----- ------ ------ -------- ----- ------ ------Parse 1 0.01 0.02 0 0 0 Execute 1 0.00 0.00 0 0 0 Fetch 1 0.00 0.00 0 2 0 rows ----0 0 1 Misses in library cache during parse: 0 Parsing user id: 02 (USER2) Rows Execution Plan ------- --------------------------------------------------0 SELECT STATEMENT 1 TABLE ACCESS (BY ROWID) OF 'CQ_NAMES' 2 INDEX (RANGE SCAN) OF 'CQ_NAMES_NAME' (NON-UNIQUE) In the correct version, the parse call took 10 milliseconds of CPU time and 20 milliseconds of elapsed time, but the query apparently took no time to execute and perform the fetch. These anomalies arise because the clock tick of 10 milliseconds is too long relative to the time taken to execute and fetch the data. In such cases, it is important to get many executions of the statements, so that you have statistically valid numbers. 23-21 Chapter 23 Application Tracing Utilities 23.5.4.4 Guideline for Avoiding the Time Trap In some cases, a query takes an inexplicably long time. For example, the following update of 7 rows executes in 19 seconds: UPDATE cq_names SET ATTRIBUTES = lower(ATTRIBUTES) WHERE ATTRIBUTES = :att call count cpu elapsed disk query current rows -------- ------- -------- --------- -------- -------- ------- ---------Parse 1 0.06 0.24 0 0 0 0 Execute 1 0.62 19.62 22 526 12 7 Fetch 0 0.00 0.00 0 0 0 0 Misses in library cache during parse: 1 Parsing user id: 02 (USER2) Rows ------0 2519 Execution Plan --------------------------------------------------UPDATE STATEMENT TABLE ACCESS (FULL) OF 'CQ_NAMES' The explanation is interference from another transaction. In this case, another transaction held a shared lock on the table cq_names for several seconds before and after the update was issued. It takes experience to diagnose that interference effects are occurring. On the one hand, comparative data is essential when the interference is contributing only a short delay (or a small increase in block visits in the previous example). However, if the interference contributes only modest overhead, and if the statement is essentially efficient, then its statistics may not require analysis. 23.6.1 Application Tracing Utilities The Oracle tracing utilities are TKPROF and TRCSESS. This section describes the syntax and semantics for the following utilities: • TRCSESS The TRCSESS utility consolidates trace output from selected trace files based on user-specified criteria. • TKPROF The TKPROF program formats the contents of the trace file and places the output into a readable output file. 23.6.1.1 TRCSESS The TRCSESS utility consolidates trace output from selected trace files based on user-specified criteria. After TRCSESS merges the trace information into a single output file, TKPROF can process the output file. This section contains the following topics: • Purpose TRCSESS is useful for consolidating the tracing of a particular session for performance or debugging purposes. 23-22 Chapter 23 Application Tracing Utilities • Guidelines You must specify one of the session, clientid, service, action, or module options. • Syntax • Options TRCSESS supports a number of command-line options. • Examples This section demonstrates common TRCSESS use cases. 23.6.1.1.1 Purpose TRCSESS is useful for consolidating the tracing of a particular session for performance or debugging purposes. Tracing a specific session is usually not a problem in the dedicated server model because one process serves a session during its lifetime. You can see the trace information for the session from the trace file belonging to the server process. However, in a shared server configuration, a user session is serviced by different processes over time. The trace for the user session is scattered across different trace files belonging to different processes, which makes it difficult to get a complete picture of the life cycle of a session. 23.6.1.1.2 Guidelines You must specify one of the session, clientid, service, action, or module options. If you specify multiple options, then TRCSESS consolidates all trace files that satisfy the specified criteria into the output file. 23.6.1.1.3 Syntax trcsess [output=output_file_name] [session=session_id] [clientid=client_id] [service=service_name] [action=action_name] [module=module_name] [trace_files] 23.6.1.1.4 Options TRCSESS supports a number of command-line options. Argument Description output Specifies the file where the output is generated. If this option is not specified, then the utility writes to standard output. session Consolidates the trace information for the session specified. The session identifier is a combination of session index and session serial number, such as 21.2371. You can locate these values in the V$SESSION view. clientid Consolidates the trace information for the specified client ID. service Consolidates the trace information for the specified service name. action Consolidates the trace information for the specified action name. 23-23 Chapter 23 Application Tracing Utilities Argument Description module Consolidates the trace information for the specified module name. trace_files Lists the trace file names, separated by spaces, in which TRCSESS should look for trace information. You can use the wildcard character (*) to specify the trace file names. If you do not specify trace files, then TRCSESS uses all files in the current directory as input. 23.6.1.1.5 Examples This section demonstrates common TRCSESS use cases. Example 23-2 Tracing a Single Session This sample output of TRCSESS shows the container of traces for a particular session. In this example, the session index and serial number equals 21.2371. All files in current directory are taken as input. trcsess session=21.2371 Example 23-3 Specifying Multiple Trace Files The following example specifies two trace files: trcsess session=21.2371 main_12359.trc main_12995.trc The sample output is similar to the following: [PROCESS ID = 12359] *** 2014-04-02 09:48:28.376 PARSING IN CURSOR #1 len=17 dep=0 uid=27 oct=3 lid=27 tim=868373970961 hv=887450622 ad='22683fb4' select * from cat END OF STMT PARSE #1:c=0,e=339,p=0,cr=0,cu=0,mis=0,r=0,dep=0,og=4,tim=868373970944 EXEC #1:c=0,e=221,p=0,cr=0,cu=0,mis=0,r=0,dep=0,og=4,tim=868373971411 FETCH #1:c=0,e=791,p=0,cr=7,cu=0,mis=0,r=1,dep=0,og=4,tim=868373972435 FETCH #1:c=0,e=1486,p=0,cr=20,cu=0,mis=0,r=6,dep=0,og=4,tim=868373986238 *** 2014-04-02 10:03:58.058 XCTEND rlbk=0, rd_only=1 STAT #1 id=1 cnt=7 pid=0 pos=1 obj=0 op='FILTER ' STAT #1 id=2 cnt=7 pid=1 pos=1 obj=18 op='TABLE ACCESS BY INDEX ROWID OBJ$ ' STAT #1 id=3 cnt=7 pid=2 pos=1 obj=37 op='INDEX RANGE SCAN I_OBJ2 ' STAT #1 id=4 cnt=0 pid=1 pos=2 obj=4 op='TABLE ACCESS CLUSTER TAB$J2 ' STAT #1 id=5 cnt=6 pid=4 pos=1 obj=3 op='INDEX UNIQUE SCAN I_OBJ# ' [PROCESS ID=12995] *** 2014-04-02 10:04:32.738 Archiving is disabled 23.6.1.2 TKPROF The TKPROF program formats the contents of the trace file and places the output into a readable output file. TKPROF can also do the following: • Create a SQL script that stores the statistics in the database • Determine the execution plans of SQL statements 23-24 Chapter 23 Application Tracing Utilities Note: If the cursor for a SQL statement is not closed, then TKPROF output does not automatically include the actual execution plan of the SQL statement. In this situation, use the EXPLAIN option with TKPROF to generate an execution plan. TKPROF reports each statement executed with the resources it has consumed, the number of times it was called, and the number of rows which it processed. This section contains the following topics: • Purpose TKPROF can locate statements that are consuming the greatest resources. • Guidelines The input and output files are the only required arguments. • Syntax • Options TKPROF supports a number of command-line options. • Output This section explains the TKPROF output. • Examples This section demonstrates common TKPROF use cases. 23.6.1.2.1 Purpose TKPROF can locate statements that are consuming the greatest resources. With baselines available, you can assess whether the resources used are reasonable given the work performed. 23.6.1.2.2 Guidelines The input and output files are the only required arguments. If you invoke TKPROF without arguments, then the tool displays online help. 23.6.1.2.3 Syntax tkprof input_file output_file [ waits=yes|no ] [ sort=option ] [ print=n ] [ aggregate=yes|no ] [ insert=filename3 ] [ sys=yes|no ] [ table=schema.table ] [ explain=user/password ] [ record=filename4 ] [ width=n ] 23-25 Chapter 23 Application Tracing Utilities 23.6.1.2.4 Options TKPROF supports a number of command-line options. Table 23-3 TKPROF Arguments Argument Description input_file Specifies the input file, a trace file containing statistics produced by the SQL Trace facility. This file can be either a trace file produced for a single session, or a file produced by concatenating individual trace files from multiple sessions. output_file Specifies the file to which TKPROF writes its formatted output. WAITS Specifies whether to record summary for any wait events found in the trace file. Valid values are YES (default) and NO. SORT Sorts traced SQL statements in descending order of specified sort option before listing them in the output file. If multiple options are specified, then the output is sorted in descending order by the sum of the values specified in the sort options. If you omit this parameter, then TKPROF lists statements into the output file in order of first use. Sort options are listed as follows: • • • • • • • • • • • • • • • • • • • • • • • PRSCNT - Number of times parsed PRSCPU - CPU time spent parsing PRSELA - Elapsed time spent parsing PRSDSK - Number of physical reads from disk during parse PRSQRY - Number of consistent mode block reads during parse PRSCU - Number of current mode block reads during parse PRSMIS - Number of library cache misses during parse EXECNT - Number of executions EXECPU - CPU time spent executing EXEELA - Elapsed time spent executing EXEDSK - Number of physical reads from disk during execute EXEQRY - Number of consistent mode block reads during execute EXECU - Number of current mode block reads during execute EXEROW - Number of rows processed during execute EXEMIS - Number of library cache misses during execute FCHCNT - Number of fetches FCHCPU - CPU time spent fetching FCHELA - Elapsed time spent fetching FCHDSK - Number of physical reads from disk during fetch FCHQRY - Number of consistent mode block reads during fetch FCHCU - Number of current mode block reads during fetch FCHROW - Number of rows fetched USERID - Userid of user that parsed the cursor PRINT Lists only the first integer sorted SQL statements from the output file. If you omit this parameter, then TKPROF lists all traced SQL statements. This parameter does not affect the optional SQL script. The SQL script always generates insert data for all traced SQL statements. AGGREGATE If you specify AGGREGATE = NO, then TKPROF does not aggregate multiple users of the same SQL text. 23-26 Chapter 23 Application Tracing Utilities Table 23-3 (Cont.) TKPROF Arguments Argument Description INSERT Creates a SQL script that stores the trace file statistics in the database. TKPROF creates this script with the name filename3. This script creates a table and inserts a row of statistics for each traced SQL statement into the table. SYS Enables and disables the listing of SQL statements issued by the user SYS, or recursive SQL statements, into the output file. The default value of YES causes TKPROF to list these statements. The value of NO causes TKPROF to omit them. This parameter does not affect the optional SQL script. The SQL script always inserts statistics for all traced SQL statements, including recursive SQL statements. TABLE Specifies the schema and name of the table into which TKPROF temporarily places execution plans before writing them to the output file. If the specified table exists, then TKPROF deletes all rows in the table, uses it for the EXPLAIN PLAN statement (which writes more rows into the table), and then deletes those rows. If this table does not exist, then TKPROF creates it, uses it, and then drops it. The specified user must be able to issue INSERT, SELECT, and DELETE statements against the table. If the table does not exist, then the user must also be able to issue CREATE TABLE and DROP TABLE statements. This option enables multiple individuals to run TKPROF concurrently with the same user in the EXPLAIN value. These individuals can specify different TABLE values and avoid destructively interfering with each other's processing on the temporary plan table. TKPROF supports the following combinations: • The EXPLAIN parameter without the TABLE parameter • TKPROF uses the table PROF$PLAN_TABLE in the schema of the user specified by the EXPLAIN parameter The TABLE parameter without the EXPLAIN parameter TKPROF ignores the TABLE parameter. If no plan table exists, then TKPROF creates the table PROF$PLAN_TABLE and then drops it at the end. EXPLAIN Determines the execution plan for each SQL statement in the trace file and writes these execution plans to the output file. TKPROF also displays the number of rows processed by each step of the execution plan. TKPROF determines execution plans by issuing the EXPLAIN PLAN statement after connecting to Oracle Database with the user and password specified in this parameter. The specified user must have CREATE SESSION system privileges. TKPROF takes longer to process a large trace file if the EXPLAIN option is used. Note: Trace files generated immediately after instance startup contain data that reflects the activity of the startup process. In particular, they reflect a disproportionate amount of I/O activity as caches in the system global area (SGA) are filled. For the purposes of tuning, ignore such trace files. RECORD Creates a SQL script with the specified filename with all of the nonrecursive SQL in the trace file. You can use this script to replay the user events from the trace file. WIDTH An integer that controls the output line width of some TKPROF output, such as the explain plan. This parameter is useful for post-processing of TKPROF output. 23-27 Chapter 23 Application Tracing Utilities 23.6.1.2.5 Output This section explains the TKPROF output. This section contains the following topics: • Identification of User Issuing the SQL Statement in TKPROF TKPROF lists the user ID of the user issuing each SQL statement. • Tabular Statistics in TKPROF TKPROF lists the statistics for a SQL statement returned by the SQL Trace facility in rows and columns. • Library Cache Misses in TKPROF TKPROF also lists the number of library cache misses resulting from parse and execute steps for each SQL statement. • Row Source Operations in TKPROF In the TKPROF output, row source operations show the number of rows processed for each operation executed on the rows, and additional row source information, such as physical reads and writes. • Wait Event Information in TKPROF If wait event information exists, then the TKPROF output includes a section on wait events. 23.6.1.2.5.1 Identification of User Issuing the SQL Statement in TKPROF TKPROF lists the user ID of the user issuing each SQL statement. If the SQL Trace input file contained statistics from multiple users, and if the statement was issued by multiple users, then TKPROF lists the ID of the last user to parse the statement. The user ID of all database users appears in the data dictionary in the column ALL_USERS.USER_ID. 23.6.1.2.5.2 Tabular Statistics in TKPROF TKPROF lists the statistics for a SQL statement returned by the SQL Trace facility in rows and columns. Each row corresponds to one of three steps of SQL statement processing. Statistics are identified by the value of the CALL column. See Table 23-4. Table 23-4 CALL Column Values CALL Value Meaning PARSE Translates the SQL statement into an execution plan, including checks for proper security authorization and checks for the existence of tables, columns, and other referenced objects. EXECUTE Actual execution of the statement by Oracle Database. For INSERT, UPDATE, DELETE, and MERGE statements, this modifies the data. For SELECT statements, this identifies the selected rows. FETCH Retrieves rows returned by a query. Fetches are only performed for SELECT statements. 23-28 Chapter 23 Application Tracing Utilities The other columns of the SQL Trace facility output are combined statistics for all parses, executions, and fetches of a statement. The sum of query and current is the total number of buffers accessed, also called Logical I/Os (LIOs). See Table 23-5. Table 23-5 SQL Trace Statistics for Parses, Executes, and Fetches. SQL Trace Statistic Meaning COUNT Number of times a statement was parsed, executed, or fetched. CPU Total CPU time in seconds for all parse, execute, or fetch calls for the statement. This value is zero (0) if TIMED_STATISTICS is not enabled. ELAPSED Total elapsed time in seconds for all parse, execute, or fetch calls for the statement. This value is zero (0) if TIMED_STATISTICS is not enabled. DISK Total number of data blocks physically read from the data files on disk for all parse, execute, or fetch calls. QUERY Total number of buffers retrieved in consistent mode for all parse, execute, or fetch calls. Usually, buffers are retrieved in consistent mode for queries. CURRENT Total number of buffers retrieved in current mode. Buffers are retrieved in current mode for statements such as INSERT, UPDATE, and DELETE. Statistics about the processed rows appear in the ROWS column. The column shows the number of rows processed by the SQL statement. This total does not include rows processed by subqueries of the SQL statement. For SELECT statements, the number of rows returned appears for the fetch step. For UPDATE, DELETE, and INSERT statements, the number of rows processed appears for the execute step. Note: The row source counts are displayed when a cursor is closed. In SQL*Plus, there is only one user cursor, so each statement executed causes the previous cursor to be closed; therefore, the row source counts are displayed. PL/SQL has its own cursor handling and does not close child cursors when the parent cursor is closed. Exiting (or reconnecting) causes the counts to be displayed. 23.6.1.2.5.3 Library Cache Misses in TKPROF TKPROF also lists the number of library cache misses resulting from parse and execute steps for each SQL statement. These statistics appear on separate lines following the tabular statistics. If the statement resulted in no library cache misses, then TKPROF does not list the statistic. In "Examples", the statement resulted in one library cache miss for the parse step and no misses for the execute step. 23-29 Chapter 23 Application Tracing Utilities 23.6.1.2.5.4 Row Source Operations in TKPROF In the TKPROF output, row source operations show the number of rows processed for each operation executed on the rows, and additional row source information, such as physical reads and writes. Table 23-6 Row Source Operations Row Source Operation Meaning cr Consistent reads performed by the row source. r Physical reads performed by the row source w Physical writes performed by the row source time Time in microseconds In the following sample TKPROF output, note the cr, r, w, and time values under the Row Source Operation column: Rows Row Source Operation ------- --------------------------------------------------0 DELETE (cr=43141 r=266947 w=25854 time=60235565 us) 28144 HASH JOIN ANTI (cr=43057 r=262332 w=25854 time=48830056 us) 51427 TABLE ACCESS FULL STATS$SQLTEXT (cr=3465 r=3463 w=0 time=865083 us) 647529 INDEX FAST FULL SCAN STATS$SQL_SUMMARY_PK (cr=39592 r=39325 w=0 time=10522877 us) (object id 7409) 23.6.1.2.5.5 Wait Event Information in TKPROF If wait event information exists, then the TKPROF output includes a section on wait events. Output looks similar to the following: Elapsed times include waiting on following events: Event waited on Times Max. Wait Total Waited ---------------------------------------- Waited ---------- -----------db file sequential read 8084 0.12 5.34 direct path write 834 0.00 0.00 direct path write temp 834 0.00 0.05 db file parallel read 8 1.53 5.51 db file scattered read 4180 0.07 1.45 direct path read 7082 0.00 0.05 direct path read temp 7082 0.00 0.44 rdbms ipc reply 20 0.00 0.01 SQL*Net message to client 1 0.00 0.00 SQL*Net message from client 1 0.00 0.00 In addition, wait events are summed for the entire trace file at the end of the file. To ensure that wait events information is written to the trace file for the session, run the following SQL statement: ALTER SESSION SET EVENTS '10046 trace name context forever, level 8'; 23-30 Chapter 23 Application Tracing Utilities 23.6.1.2.6 Examples This section demonstrates common TKPROF use cases. Example 23-4 Printing the Most Resource-Intensive Statements If you are processing a large trace file using a combination of SORT parameters and the PRINT parameter, then you can produce a TKPROF output file containing only the highest resource-intensive statements. The following statement prints the 10 statements in the trace file that have generated the most physical I/O: TKPROF ora53269.trc ora53269.prf SORT = (PRSDSK, EXEDSK, FCHDSK) PRINT = 10 Example 23-5 Generating a SQL Script This example runs TKPROF, accepts a trace file named examp12_jane_fg_sqlplus_007.trc, and writes a formatted output file named outputa.prf: TKPROF examp12_jane_fg_sqlplus_007.trc OUTPUTA.PRF EXPLAIN=hr TABLE=hr.temp_plan_table_a INSERT=STOREA.SQL SYS=NO SORT=(EXECPU,FCHCPU) This example is likely to be longer than a single line on the screen, and you might need to use continuation characters, depending on the operating system. Note the other parameters in this example: • The EXPLAIN value causes TKPROF to connect as the user hr and use the EXPLAIN PLAN statement to generate the execution plan for each traced SQL statement. You can use this to get access paths and row source counts. Note: If the cursor for a SQL statement is not closed, then TKPROF output does not automatically include the actual execution plan of the SQL statement. In this situation, you can use the EXPLAIN option with TKPROF to generate an execution plan. • The TABLE value causes TKPROF to use the table temp_plan_table_a in the schema scott as a temporary plan table. • The INSERT value causes TKPROF to generate a SQL script named STOREA.SQL that stores statistics for all traced SQL statements in the database. • The SYS parameter with the value of NO causes TKPROF to omit recursive SQL statements from the output file. In this way, you can ignore internal Oracle Database statements such as temporary table operations. • The SORT value causes TKPROF to sort the SQL statements in order of the sum of the CPU time spent executing and the CPU time spent fetching rows before writing them to the output file. For greatest efficiency, always use SORT parameters. Example 23-6 TKPROF Header This example shows a sample header for the TKPROF report. 23-31 Chapter 23 Application Tracing Utilities TKPROF: Release 12.1.0.0.2 Copyright (c) 1982, 2012, Oracle and/or its affiliates. All rights reserved. Trace file: /disk1/oracle/log/diag/rdbms/orcla/orcla/trace/orcla_ora_917.trc Sort options: default ******************************************************************************** count = number of times OCI procedure was executed cpu = cpu time in seconds executing elapsed = elapsed time in seconds executing disk = number of physical reads of buffers from disk query = number of buffers gotten for consistent read current = number of buffers gotten in current mode (usually for update) rows = number of rows processed by the fetch or execute call ******************************************************************************** Example 23-7 TKPROF Body This example shows a sample body for a TKPROF report. call count cpu elapsed disk query current rows ------- ------ -------- ---------- ---------- ---------- ---------- ---------Parse 1 0.01 0.00 0 0 0 0 Execute 1 0.00 0.00 0 0 0 0 Fetch 0 0.00 0.00 0 0 0 0 ------- ------ -------- ---------- ---------- ---------- ---------- ---------total 2 0.01 0.00 0 0 0 0 Misses in library cache during parse: 1 Optimizer mode: FIRST_ROWS Parsing user id: 44 Elapsed times include waiting on following events: Event waited on Times Max. Wait Total Waited ---------------------------------------- Waited ---------- -----------SQL*Net message to client 1 0.00 0.00 SQL*Net message from client 1 28.59 28.59 ******************************************************************************** select condition from cdef$ where rowid=:1 call count cpu elapsed disk query current rows ------- ------ -------- ---------- ---------- ---------- ---------- ---------Parse 1 0.00 0.00 0 0 0 0 Execute 1 0.00 0.00 0 0 0 0 Fetch 1 0.00 0.00 0 2 0 1 ------- ------ -------- ---------- ---------- ---------- ---------- ---------total 3 0.00 0.00 0 2 0 1 Misses in library cache during parse: 1 Optimizer mode: CHOOSE Parsing user id: SYS (recursive depth: 1) Rows Row Source Operation ------- --------------------------------------------------1 TABLE ACCESS BY USER ROWID OBJ#(31) (cr=1 r=0 w=0 time=151 us) ******************************************************************************** 23-32 Chapter 23 Application Tracing Utilities SELECT last_name, job_id, salary FROM employees WHERE salary = (SELECT max(salary) FROM employees) call count cpu elapsed disk query current rows ------- ------ -------- ---------- ---------- ---------- ---------- ---------Parse 1 0.02 0.01 0 0 0 0 Execute 1 0.00 0.00 0 0 0 0 Fetch 2 0.00 0.00 0 15 0 1 ------- ------ -------- ---------- ---------- ---------- ---------- ---------total 4 0.02 0.01 0 15 0 1 Misses in library cache during parse: 1 Optimizer mode: FIRST_ROWS Parsing user id: 44 Rows Row Source Operation ------- --------------------------------------------------1 TABLE ACCESS FULL EMPLOYEES (cr=15 r=0 w=0 time=1743 us) 1 SORT AGGREGATE (cr=7 r=0 w=0 time=777 us) 107 TABLE ACCESS FULL EMPLOYEES (cr=7 r=0 w=0 time=655 us) Elapsed times include waiting on following events: Event waited on Times Max. Wait Total Waited ---------------------------------------- Waited ---------- -----------SQL*Net message to client 2 0.00 0.00 SQL*Net message from client 2 9.62 9.62 ******************************************************************************** ******************************************************************************** delete from stats$sqltext st where (hash_value, text_subset) not in (select --+ hash_aj hash_value, text_subset from stats$sql_summary ss where ( ( snap_id < :lo_snap or snap_id > :hi_snap ) and dbid = :dbid and instance_number = :inst_num ) or ( dbid != :dbid or instance_number != :inst_num) ) call count cpu elapsed disk query current rows ------- ------ -------- ---------- ---------- ---------- ---------- ---------Parse 1 0.00 0.00 0 0 0 0 Execute 1 29.60 60.68 266984 43776 131172 28144 Fetch 0 0.00 0.00 0 0 0 0 ------- ------ -------- ---------- ---------- ---------- ---------- ---------total 2 29.60 60.68 266984 43776 131172 28144 Misses in library cache during parse: 1 Misses in library cache during execute: 1 Optimizer mode: CHOOSE Parsing user id: 22 23-33 Chapter 23 Application Tracing Utilities Rows Row Source Operation ------- --------------------------------------------------0 DELETE (cr=43141 r=266947 w=25854 time=60235565 us) 28144 HASH JOIN ANTI (cr=43057 r=262332 w=25854 time=48830056 us) 51427 TABLE ACCESS FULL STATS$SQLTEXT (cr=3465 r=3463 w=0 time=865083 us) 647529 INDEX FAST FULL SCAN STATS$SQL_SUMMARY_PK (cr=39592 r=39325 w=0 time=10522877 us) (object id 7409) Elapsed times include waiting on following events: Event waited on Times Max. Wait Total Waited ---------------------------------------- Waited ---------- -----------db file sequential read 8084 0.12 5.34 direct path write 834 0.00 0.00 direct path write temp 834 0.00 0.05 db file parallel read 8 1.53 5.51 db file scattered read 4180 0.07 1.45 direct path read 7082 0.00 0.05 direct path read temp 7082 0.00 0.44 rdbms ipc reply 20 0.00 0.01 SQL*Net message to client 1 0.00 0.00 SQL*Net message from client 1 0.00 0.00 ******************************************************************************** Example 23-8 TKPROF Summary This example that shows a summary for the TKPROF report. OVERALL TOTALS FOR ALL NON-RECURSIVE STATEMENTS call count cpu elapsed disk query current rows ------- ------ -------- ---------- ---------- ---------- ---------- ---------Parse 4 0.04 0.01 0 0 0 0 Execute 5 0.00 0.04 0 0 0 0 Fetch 2 0.00 0.00 0 15 0 1 ------- ------ -------- ---------- ---------- ---------- ---------- ---------total 11 0.04 0.06 0 15 0 1 Misses in library cache during parse: 4 Misses in library cache during execute: 1 Elapsed times include waiting on following events: Event waited on Times Max. Wait Total Waited ---------------------------------------- Waited ---------- -----------SQL*Net message to client 6 0.00 0.00 SQL*Net message from client 5 77.77 128.88 OVERALL TOTALS FOR ALL RECURSIVE STATEMENTS call count cpu elapsed disk query current rows ------- ------ -------- ---------- ---------- ---------- ---------- ---------Parse 1 0.00 0.00 0 0 0 0 Execute 1 0.00 0.00 0 0 0 0 Fetch 1 0.00 0.00 0 2 0 1 ------- ------ -------- ---------- ---------- ---------- ---------- ---------total 3 0.00 0.00 0 2 0 1 Misses in library cache during parse: 1 5 user SQL statements in session. 1 internal SQL statements in session. 6 SQL statements in session. ******************************************************************************** Trace file: main_ora_27621.trc 23-34 Chapter 23 Views for Application Tracing Trace file compatibility: 9.00.01 Sort options: default 1 session in tracefile. 5 user SQL statements in trace file. 1 internal SQL statements in trace file. 6 SQL statements in trace file. 6 unique SQL statements in trace file. 76 lines in trace file. 128 elapsed seconds in trace file. 23.7.1 Views for Application Tracing You can use data dictionary and V$ views to monitor tracing. This section includes the following topics: • Views Relevant for Trace Statistics You can display the statistics that have been gathered with the following V$ and DBA views. • Views Related to Enabling Tracing A Cloud Control report or the DBA_ENABLED_TRACES view can display outstanding traces. 23.7.1.1 Views Relevant for Trace Statistics You can display the statistics that have been gathered with the following V$ and DBA views. Table 23-7 Diagnostic Views View Description DBA_ENABLED_AGGREGATIONS Accumulated global statistics for the currently enabled statistics V$CLIENT_STATS Accumulated statistics for a specified client identifier V$SERVICE_STATS Accumulated statistics for a specified service V$SERV_MOD_ACT_STATS Accumulated statistics for a combination of specified service, module, and action V$SERVICEMETRIC Accumulated statistics for elapsed time of database calls and for CPU use V$DIAG_TRACE_FILE Information about all trace files in ADR for the current container V$DIAG_APP_TRACE_FILE Information about all trace files that contain application trace data (SQL_TRACE or OPTIMIZER_TRACE event data) in ADR for the current container V$DIAG_TRACE_FILE_CONTENTS Trace data in the trace files in ADR V$DIAG_SQL_TRACE_RECORDS SQL_TRACE data in the trace files in ADR V$DIAG_OPT_TRACE_RECORDS Optimizer trace event data in the trace files in ADR V$DIAG_SESS_SQL_TRACE_RECORDS SQL_TRACE data in the trace files in ADR for the current user session V$DIAG_SESS_OPT_TRACE_RECORDS Optimizer trace event data in the trace files in ADR for the current user session V$DIAG_ALERT_EXT Contents of the XML-based alert log in ADR for the current container 23-35 Chapter 23 Views for Application Tracing See Also: Oracle Database Reference for information about V$ and data dictionary views 23.7.1.2 Views Related to Enabling Tracing A Cloud Control report or the DBA_ENABLED_TRACES view can display outstanding traces. In the DBA_ENABLED_TRACES view, you can determine detailed information about how a trace was enabled, including the trace type. The trace type specifies whether the trace is enabled for client identifier, session, service, database, or a combination of service, module, and action. 23-36 Part VIII Automatic SQL Tuning SQL Tuning Advisor and SQL Access Advisor are built-in tools that provide SQL tuning recommendations. This part contains the following chapters: • Managing SQL Tuning Sets You can use SQL tuning sets to group statements and related metadata into a single object, which you can use as input to SQL tuning tools. • Analyzing SQL with SQL Tuning Advisor Use SQL Tuning Advisor to obtain recommendations for improving performance of high-load SQL statements, and prevent regressions by only executing optimal plans. • Optimizing Access Paths with SQL Access Advisor SQL Access Advisor is diagnostic software that identifies and helps resolve SQL performance problems by recommending indexes, materialized views, materialized view logs, or partitions to create, drop, or retain. 24 Managing SQL Tuning Sets You can use SQL tuning sets to group statements and related metadata into a single object, which you can use as input to SQL tuning tools. This chapter contains the following topics: • About SQL Tuning Sets A SQL tuning set (STS) is a database object that you can use as input to tuning tools. • Creating a SQL Tuning Set Use the DBMS_SQLTUNE.CREATE_SQLSET procedure to create an empty STS in the database. • Loading a SQL Tuning Set To load an STS with SQL statements, execute the DBMS_SQLTUNE.LOAD_SQLSET procedure. • Displaying the Contents of a SQL Tuning Set After an STS has been created and populated, execute the DBMS_SQLTUNE.SELECT_SQLSET function to read the contents of the STS, optionally using filtering criteria. • Modifying a SQL Tuning Set Use the DBMS_SQLTUNE.DELETE_SQLSET procedure to delete SQL statements from an STS. • Transporting a SQL Tuning Set You can transport an STS to any database created in Oracle Database 10g Release 2 (10.2) or later. This technique is useful when using SQL Performance Analyzer to tune regressions on a test database. • Dropping a SQL Tuning Set To drop an STS from the database, execute the DBMS_SQLTUNE.DROP_SQLSET procedure. 24.1 About SQL Tuning Sets A SQL tuning set (STS) is a database object that you can use as input to tuning tools. The database stores SQL tuning sets in a database-provided schema. An STS includes: • A set of SQL statements • Associated execution context, such as user schema, application module name and action, list of bind values, and the environment for SQL compilation of the cursor • Associated basic execution statistics, such as elapsed time, CPU time, buffer gets, disk reads, rows processed, cursor fetches, the number of executions, the number of complete executions, optimizer cost, and the command type 24-1 Chapter 24 About SQL Tuning Sets • Associated execution plans and row source statistics for each SQL statement (optional) Note: Data visibility and privilege requirements may differ when using an STS with pluggable databases. See Oracle Database Administrator’s Guide for a table that summarizes how manageability features work in a container database (CDB). This section contains the following topics: • Purpose of SQL Tuning Sets An STS enables you to group SQL statements and related metadata in a single database object, which you can use to meet your tuning goals. • Concepts for SQL Tuning Sets To create an STS, you must load SQL statements into an STS from a source. • User Interfaces for SQL Tuning Sets You can use either Oracle Enterprise Manager Cloud Control (Cloud Control) or PL/SQL packages to manage SQL tuning sets. Oracle recommends Cloud Control. • Basic Tasks for SQL Tuning Sets DBMS_SQLTUNE provides the procedures necessary for creating, using, and deleting SQL tuning sets. 24.1.1 Purpose of SQL Tuning Sets An STS enables you to group SQL statements and related metadata in a single database object, which you can use to meet your tuning goals. Specifically, SQL tuning sets achieve the following goals: • Providing input to the performance tuning advisors You can use an STS as input to multiple database advisors, including SQL Tuning Advisor, SQL Access Advisor, and SQL Performance Analyzer. • Transporting SQL between databases You can export SQL tuning sets from one database to another, enabling transfer of SQL workloads between databases for remote performance diagnostics and tuning. When suboptimally performing SQL statements occur on a production database, developers may not want to investigate and tune directly on the production database. The DBA can transport the problematic SQL statements to a test database where the developers can safely analyze and tune them. 24.1.2 Concepts for SQL Tuning Sets To create an STS, you must load SQL statements into an STS from a source. As shown in Figure 24-1, the source can be Automatic Workload Repository (AWR), the shared SQL area, customized SQL provided by the user, trace files, or another STS. 24-2 Chapter 24 About SQL Tuning Sets Figure 24-1 SQL Tuning Sets SQL Tuning Advisor Custom SQL SQL Access Advisor AWR Shared SQL Area Filter SQL Trace Files SQL Performance Analyzer STS Transport SQL tuning sets can do the following: • Filter SQL statements using the application module name and action, or any execution statistics • Rank SQL statements based on any combination of execution statistics • Serve as input to the advisors or transport it to a different database See Also: Oracle Database Performance Tuning Guide to learn about AWR 24-3 Chapter 24 About SQL Tuning Sets 24.1.3 User Interfaces for SQL Tuning Sets You can use either Oracle Enterprise Manager Cloud Control (Cloud Control) or PL/SQL packages to manage SQL tuning sets. Oracle recommends Cloud Control. This section contains the following topics: • Accessing the SQL Tuning Sets Page in Cloud Control The SQL Tuning Sets page in Cloud Control is the starting page from which you can perform most operations relating to SQL tuning sets. • Command-Line Interface to SQL Tuning Sets On the command line, you can use the DBMS_SQLTUNE package to manage SQL tuning sets. 24.1.3.1 Accessing the SQL Tuning Sets Page in Cloud Control The SQL Tuning Sets page in Cloud Control is the starting page from which you can perform most operations relating to SQL tuning sets. To access the SQL Tuning Sets page: 1. Log in to Cloud Control with the appropriate credentials. 2. Under the Targets menu, select Databases. 3. In the list of database targets, select the target for the Oracle Database instance that you want to administer. 4. If prompted for database credentials, then enter the minimum credentials necessary for the tasks you intend to perform. 5. From the Performance menu, select SQL, then SQL Tuning Sets. The SQL Tuning Sets page appears, as shown in Figure 24-2. Figure 24-2 SQL Tuning Sets See Also: Oracle Database 2 Day + Performance Tuning Guide 24-4 Chapter 24 About SQL Tuning Sets 24.1.3.2 Command-Line Interface to SQL Tuning Sets On the command line, you can use the DBMS_SQLTUNE package to manage SQL tuning sets. You must have the ADMINISTER SQL TUNING SET system privilege to manage SQL tuning sets that you own, or the ADMINISTER ANY SQL TUNING SET system privilege to manage any SQL tuning sets. See Also: Oracle Database PL/SQL Packages and Types Reference to learn about DBMS_SQLTUNE 24.1.4 Basic Tasks for SQL Tuning Sets DBMS_SQLTUNE provides the procedures necessary for creating, using, and deleting SQL tuning sets. The following graphic shows the basic workflow. Figure 24-3 SQL Tuning Sets APIs CREATE_SQLSET Create STS Populate STS with SQL LOAD_SQLSET DELETE_SQLSET UPDATE SQLSET Create a Tuning Task Modify STS Contents Display Contents of STS SELECT_SQLSET CREATE_STGTAB_SQLSET PACK_STGTAB_SQLSET Optionally, transport STS to different database Transport STS UNPACK_STGTAB_SQLSET DROP_SQLSET Drop STS Typically, you perform STS operations in the following sequence: 24-5 Chapter 24 Creating a SQL Tuning Set 1. Create a new STS. "Creating a SQL Tuning Set" describes this task. 2. Load the STS with SQL statements and associated metadata. "Loading a SQL Tuning Set" describes this task. 3. Optionally, display the contents of the STS. "Displaying the Contents of a SQL Tuning Set" describes this task. 4. Optionally, update or delete the contents of the STS. "Modifying a SQL Tuning Set" describes this task. 5. Create a tuning task with the STS as input. 6. Optionally, transport the STS to another database. "Transporting a SQL Tuning Set" describes this task. 7. Drop the STS when finished. "Dropping a SQL Tuning Set" describes this task. 24.2 Creating a SQL Tuning Set Use the DBMS_SQLTUNE.CREATE_SQLSET procedure to create an empty STS in the database. Using the function instead of the procedure causes the database to generate a name for the STS. The following table describes some procedure parameters. Table 24-1 DBMS_SQLTUNE.CREATE_SQLSET Parameters Parameter Description sqlset_name Name of the STS description Optional description of the STS Assumptions This tutorial assumes that you want to create an STS named SQLT_WKLD_STS. To create an STS: 1. Connect SQL*Plus to the database with the appropriate privileges, and then run the DBMS_SQLTUNE.CREATE_SQLSET procedure. For example, execute the following PL/SQL program: BEGIN DBMS_SQLTUNE.CREATE_SQLSET ( sqlset_name => 'SQLT_WKLD_STS' , description => 'STS to store SQL from the private SQL area' ); END; 2. Optionally, confirm that the STS was created. The following example queries the status of all SQL tuning sets owned by the current user: 24-6 Chapter 24 Loading a SQL Tuning Set COLUMN NAME FORMAT a20 COLUMN COUNT FORMAT 99999 COLUMN DESCRIPTION FORMAT a11 SELECT NAME, STATEMENT_COUNT AS "SQLCNT", DESCRIPTION FROM USER_SQLSET; Sample output appears below: NAME SQLCNT DESCRIPTION -------------------- ------ ----------SQLT_WKLD_STS 2 SQL Cache See Also: Oracle Database PL/SQL Packages and Types Reference for complete reference information 24.3 Loading a SQL Tuning Set To load an STS with SQL statements, execute the DBMS_SQLTUNE.LOAD_SQLSET procedure. The standard sources for populating an STS are AWR, another STS, or the shared SQL area. For both the workload repository and SQL tuning sets, predefined table functions can select columns from the source to populate a new STS. Table 24-2 describes some DBMS_SQLTUNE.LOAD_SQLSET procedure parameters. Table 24-2 DBMS_SQLTUNE.LOAD_SQLSET Parameters Parameter Description populate_cursor Specifies the cursor reference from which to populate the STS. load_option Specifies how the statements are loaded into the STS. The possible values are INSERT (default), UPDATE, and MERGE. The DBMS_SQLTUNE.SELECT_CURSOR_CACHE function collects SQL statements from the shared SQL area according to the specified filter. This function returns one SQLSET_ROW per SQL ID or PLAN_HASH_VALUE pair found in each data source. Use the CAPTURE_CURSOR_CACHE_SQLSET function to repeatedly poll the shared SQL area over a specified interval. This function is more efficient than repeatedly calling the SELECT_CURSOR_CACHE and LOAD_SQLSET procedures. This function effectively captures the entire workload, as opposed to the AWR, which only captures the workload of highload SQL statements, or the LOAD_SQLSET procedure, which accesses the data source only once. Prerequisites This tutorial has the following prerequisites: • Filters provided to the SELECT_CURSOR_CACHE function are evaluated as part of SQL statements run by the current user. As such, they are executed with that user's 24-7 Chapter 24 Displaying the Contents of a SQL Tuning Set security privileges and can contain any constructs and subqueries that user can access, but no more. • The current user must have privileges on the shared SQL area views. Assumptions This tutorial assumes that you want to load the SQL tuning set named SQLT_WKLD_STS with statements from the shared SQL area. To load an STS: 1. Connect SQL*Plus to the database as a user with the appropriate privileges. 2. Run the DBMS_SQLTUNE.LOAD_SQLSET procedure. For example, execute the following PL/SQL program to populate a SQL tuning set with all cursor cache statements that belong to the sh schema: DECLARE c_sqlarea_cursor DBMS_SQLTUNE.SQLSET_CURSOR; BEGIN OPEN c_sqlarea_cursor FOR SELECT VALUE(p) FROM TABLE( DBMS_SQLTUNE.SELECT_CURSOR_CACHE( ' module = ''SQLT_WKLD'' AND parsing_schema_name = ''SH'' ') ) p; -- load the tuning set DBMS_SQLTUNE.LOAD_SQLSET ( sqlset_name => 'SQLT_WKLD_STS' , populate_cursor => c_sqlarea_cursor ); END; / See Also: Oracle Database PL/SQL Packages and Types Reference for complete reference information. 24.4 Displaying the Contents of a SQL Tuning Set After an STS has been created and populated, execute the DBMS_SQLTUNE.SELECT_SQLSET function to read the contents of the STS, optionally using filtering criteria. You select the output of SELECT_SQLSET using a PL/SQL pipelined table function, which accepts a collection of rows as input. You invoke the table function as the operand of the table operator in the FROM list of a SELECT statement. The following table describes some SELECT_SQLSET function parameters. 24-8 Chapter 24 Displaying the Contents of a SQL Tuning Set Table 24-3 DBMS_SQLTUNE.SELECT_SQLSET Parameters Parameter Description basic_filter The SQL predicate to filter the SQL from the STS defined on attributes of the SQLSET_ROW object_filter Specifies the objects that exist in the object list of selected SQL from the shared SQL area Table 24-4 describes some attributes of the SQLSET_ROW object. These attributes appears as columns when you query TABLE(DBMS_SQLTUNE.SELECT_SQLSET()). Table 24-4 SQLSET_ROW Attributes Parameter Description parsing_schema_name Schema in which the SQL is parsed elapsed_time Sum of the total number of seconds elapsed for this SQL statement buffer_gets Total number of buffer gets (number of times the database accessed a block) for this SQL statement Assumptions This tutorial assumes that you want to display the contents of an STS named SQLT_WKLD_STS. To display the contents of an STS: 1. In SQL*Plus or SQL Developer, log in to the database as a user with the necessary privileges. 2. Query the STS contents using the TABLE function. For example, execute the following query: COLUMN SQL_TEXT FORMAT a30 COLUMN SCH FORMAT a3 COLUMN ELAPSED FORMAT 999999999 SELECT SQL_ID, PARSING_SCHEMA_NAME AS "SCH", SQL_TEXT, ELAPSED_TIME AS "ELAPSED", BUFFER_GETS FROM TABLE( DBMS_SQLTUNE.SELECT_SQLSET( 'SQLT_WKLD_STS' ) ); Sample output appears below: SQL_ID SCH SQL_TEXT ELAPSED BUFFER_GETS ------------- --- ------------------------------ ---------- ----------79f8shn041a1f SH select * from sales where quan 8373148 24016 tity_sold < 5 union select * f rom sales where quantity_sold > 500 2cqsw036j5u7r SH select promo_name, count(*) c from promotions p, sales s whe re s.promo_id = p.promo_id and p.promo_category = 'internet' group by p.promo_name order b 3557373 309 24-9 Chapter 24 Modifying a SQL Tuning Set y c desc 3. fudq5z56g642p SH select sum(quantity_sold) from sales s, products p where s.p rod_id = p.prod_id and s.amoun t_sold > 20000 and p.prod_name = 'Linen Big Shirt' 4787891 12118 bzmnj0nbvmz8t SH select * from sales where amou nt_sold = 4 442355 15281 Optionally, filter the results based on user-specific criteria. The following example displays statements with a disk reads to buffer gets ratio greater than or equal to 50%: COLUMN COLUMN COLUMN COLUMN COLUMN SELECT SQL_TEXT FORMAT a30 SCH FORMAT a3 BUF_GETS FORMAT 99999999 DISK_READS FORMAT 99999999 %_DISK FORMAT 9999.99 sql_id, parsing_schema_name as "SCH", sql_text, buffer_gets as "B_GETS", disk_reads, ROUND(disk_reads/buffer_gets*100,2) "%_DISK" FROM TABLE( DBMS_SQLTUNE.SELECT_SQLSET( 'SQLT_WKLD_STS', '(disk_reads/buffer_gets) >= 0.50' ) ); Sample output appears below: SQL_ID SCH SQL_TEXT B_GETS DISK_READS %_DISK ------------- --- ------------------------------ ------ ---------- ------79f8shn041a1f SH select * from sales where quan 24016 17287 71.98 tity_sold < 5 union select * f rom sales where quantity_sold > 500 fudq5z56g642p SH select sum(quantity_sold) from 12118 sales s, products p where s.p rod_id = p.prod_id and s.amoun t_sold > 20000 and p.prod_name = 'Linen Big Shirt' 6355 52.44 See Also: Oracle Database PL/SQL Packages and Types Reference for complete reference information 24.5 Modifying a SQL Tuning Set Use the DBMS_SQLTUNE.DELETE_SQLSET procedure to delete SQL statements from an STS. You can use the UPDATE_SQLSET procedure to update the attributes of SQL statements (such as PRIORITY or OTHER) in an existing STS identified by STS name and SQL ID. 24-10 Chapter 24 Modifying a SQL Tuning Set Assumptions This tutorial assumes that you want to modify SQLT_WKLD_STS as follows: • You want to delete all SQL statements with fetch counts over 100. • You want to change the priority of the SQL statement with ID fudq5z56g642p to 1. You can use priority as a ranking criteria when running SQL Tuning Advisor. To modify the contents of an STS: 1. Connect SQL*Plus to the database with the appropriate privileges, and then optionally query the STS contents using the TABLE function. For example, execute the following query: SELECT SQL_ID, ELAPSED_TIME, FETCHES, EXECUTIONS FROM TABLE(DBMS_SQLTUNE.SELECT_SQLSET('SQLT_WKLD_STS')); Sample output appears below: SQL_ID ELAPSED_TIME FETCHES EXECUTIONS ------------- ------------ ---------- ---------2cqsw036j5u7r 3407459 2 1 79f8shn041a1f 9453965 61258 1 bzmnj0nbvmz8t 401869 1 1 fudq5z56g642p 5300264 1 1 2. Delete SQL statements based on user-specified criteria. Use the basic_filter predicate to filter the SQL from the STS defined on attributes of the SQLSET_ROW. The following example deletes all statements in the STS with fetch counts over 100: BEGIN DBMS_SQLTUNE.DELETE_SQLSET ( sqlset_name => 'SQLT_WKLD_STS' , basic_filter => 'fetches > 100' ); END; / 3. Set attribute values for SQL statements. The following example sets the priority of statement 2cqsw036j5u7r to 1: BEGIN DBMS_SQLTUNE.UPDATE_SQLSET ( sqlset_name => 'SQLT_WKLD_STS' , sql_id => '2cqsw036j5u7r' , attribute_name => 'PRIORITY' , attribute_value => 1 ); END; / 4. Optionally, query the STS to confirm that the intended modifications were made. For example, execute the following query: SELECT SQL_ID, ELAPSED_TIME, FETCHES, EXECUTIONS, PRIORITY FROM TABLE(DBMS_SQLTUNE.SELECT_SQLSET('SQLT_WKLD_STS')); Sample output appears below: 24-11 Chapter 24 Transporting a SQL Tuning Set SQL_ID ELAPSED_TIME FETCHES EXECUTIONS PRIORITY ------------- ------------ ---------- ---------- ---------2cqsw036j5u7r 3407459 2 1 1 bzmnj0nbvmz8t 401869 1 1 fudq5z56g642p 5300264 1 1 See Also: Oracle Database PL/SQL Packages and Types Reference for more information 24.6 Transporting a SQL Tuning Set You can transport an STS to any database created in Oracle Database 10g Release 2 (10.2) or later. This technique is useful when using SQL Performance Analyzer to tune regressions on a test database. This section contains the following topics: • About Transporting SQL Tuning Sets Transporting SQL tuning sets between databases means copying the SQL tuning sets to and from a staging table, and then using other tools to move the staging table to the destination database. The most common tools are Oracle Data Pump or a database link. • Transporting SQL Tuning Sets with DBMS_SQLTUNE You can transport SQL tuning sets using three subprograms in the DBMS_SQLTUNE package. 24.6.1 About Transporting SQL Tuning Sets Transporting SQL tuning sets between databases means copying the SQL tuning sets to and from a staging table, and then using other tools to move the staging table to the destination database. The most common tools are Oracle Data Pump or a database link. This section contains the following topics: • Basic Steps for Transporting SQL Tuning Sets Transporting SQL tuning sets requires exporting the STS, transporting the dump file, and then importing the dump file. • Basic Steps for Transporting SQL Tuning Sets When the CON_DBID Values Differ When transporting an STS, you must remap the con_dbid of each SQL statement in the STS when the con_dbid of the source database and the destination database are different. 24.6.1.1 Basic Steps for Transporting SQL Tuning Sets Transporting SQL tuning sets requires exporting the STS, transporting the dump file, and then importing the dump file. The following graphic shows the process using Oracle Data Pump and ftp. 24-12 Chapter 24 Transporting a SQL Tuning Set Figure 24-4 Transporting SQL Tuning Sets Production Database Test Database System-Supplied Schema PACK_STGTAB_SQLSET System-Supplied Schema UNPACK_STGTAB_SQLSET Staging Table Staging Table Data Pump Export Data Pump Import .dmp file Transport ftp, nfs .dmp file As shown in Figure 24-4, the steps are as follows: 1. In the production database, pack the STS into a staging table using DBMS_SQLTUNE.PACK_STGTAB_SQLSET. 2. Export the STS from the staging table to a .dmp file using Oracle Data Pump. 3. Transfer the .dmp file from the production host to the test host using a transfer tool such as ftp. 4. In the test database, import the STS from the .dmp file to a staging table using Oracle Data Pump. 5. Unpack the STS from the staging table using DBMS_SQLTUNE.UNPACK_STGTAB_SQLSET. 24.6.1.2 Basic Steps for Transporting SQL Tuning Sets When the CON_DBID Values Differ When transporting an STS, you must remap the con_dbid of each SQL statement in the STS when the con_dbid of the source database and the destination database are different. Situations that cause the con_dbid value to differ include the following: • A single-instance database whose instance has been restarted • Different instances of an Oracle RAC database • Different PDBs • A non-CDB and a CDB The basic steps for remapping are as follows: 1. Pack the STS into a staging table using DBMS_SQLTUNE.PACK_STGTAB_SQLSET. 2. Remap each con_dbid in the staging table using DBMS_SQLTUNE.REMAP_STGTAB_SQLSET. 3. Export the STS. 24-13 Chapter 24 Transporting a SQL Tuning Set 4. Unpack the STS in the destination CDB. Example 24-1 Remapping a CON_DBID When Transporting an STS from a NonCDB to a CDB In this example, you intend to transport an STS named STS_for_transport from a nonCDB to a CDB. On the source non-CDB, you have already packed the STS into source staging table src_stg_tbl using the DBMS_SQLTUNE.PACK_STGTAB_SQLSET procedure. The container ID of the destination CDB is 12345. In the source non-CDB, you execute the following commands: VARIABLE con_dbid_src NUMBER; EXEC SELECT UNIQUE con_dbid INTO :con_dbid_src FROM src_stg_tbl; BEGIN DBMS_SQLTUNE.REMAP_STGTAB_SQLSET ( staging_table_name => 'src_stg_tbl' , staging_schema_owner => 'dba1' , old_sqlset_name => 'STS_for_transport' , old_con_dbid => :con_dbid_src , new_con_dbid => 12345); END; You can now export the contents of the staging table, and then continue using the normal transport procedure. See Also: Oracle Database PL/SQL Packages and Types Reference to learn about REMAP_STGTAB_SQLSET 24.6.2 Transporting SQL Tuning Sets with DBMS_SQLTUNE You can transport SQL tuning sets using three subprograms in the DBMS_SQLTUNE package. The following table describes the DBMS_SQLTUNE procedures relevant for transporting SQL tuning sets. Table 24-5 DBMS_SQLTUNE Procedures for Transporting SQL Tuning Sets Procedure Description CREATE_STGTAB_SQLSET Create a staging table to hold the exported SQL tuning sets PACK_STGTAB_SQLSET Populate a staging table with SQL tuning sets UNPACK_STGTAB_SQLSET Copy the SQL tuning sets from the staging table into a database Assumptions This tutorial assumes the following: 24-14 Chapter 24 Transporting a SQL Tuning Set • An STS with regressed SQL resides in a production database created in the current release. • You run SQL Performance Analyzer trials on a remote test database created in Oracle Database 11g Release 2 (11.2). • You want to copy the STS from the production database to the test database and tune the regressions from the SQL Performance Analyzer trials. • You want to use Oracle Database Pump to transfer the SQL tuning sets between database hosts. To transport an STS: 1. Connect SQL*Plus to the production database with administrator privileges. 2. Use the CREATE_STGTAB_SQLSET procedure to create a staging table to hold the exported SQL tuning sets. The following example creates my_11g_staging_table in the dba1 schema and specifies the format of the staging table as 11.2: BEGIN DBMS_SQLTUNE.CREATE_STGTAB_SQLSET ( table_name => 'my_10g_staging_table' , schema_name => 'dba1' , db_version => DBMS_SQLTUNE.STS_STGTAB_11_2_VERSION ); END; / 3. Use the PACK_STGTAB_SQLSET procedure to populate the staging table with SQL tuning sets. The following example populates dba1.my_11g_staging_table with the STS my_sts owned by hr: BEGIN DBMS_SQLTUNE.PACK_STGTAB_SQLSET ( sqlset_name => 'sqlt_wkld_sts' , sqlset_owner => 'sh' , staging_table_name => 'my_11g_staging_table' , staging_schema_owner => 'dba1' , db_version => DBMS_SQLTUNE.STS_STGTAB_11_2_VERSION ); END; / 4. If necessary, remap the container ID values for the statements in the STS as described in "Basic Steps for Transporting SQL Tuning Sets When the CON_DBID Values Differ". 5. Use Oracle Data Pump to export the contents of the staging table. For example, run the expdp command at the operating system prompt: expdp dba1 DIRECTORY=dpump_dir1 DUMPFILE=sts.dmp TABLES=my_11g_staging_table 6. Transfer the dump file to the test database host. 7. Log in to the test host as an administrator, and then use Oracle Data Pump to import the contents of the staging table. For example, run the impdp command at the operating system prompt: 24-15 Chapter 24 Dropping a SQL Tuning Set impdp dba1 DIRECTORY=dpump_dir1 DUMPFILE=sts.dmp TABLES=my_11g_staging_table 8. On the test database, execute the UNPACK_STGTAB_SQLSET procedure to copy the SQL tuning sets from the staging table into the database. The following example shows how to unpack the SQL tuning sets: BEGIN DBMS_SQLTUNE.UNPACK_STGTAB_SQLSET ( sqlset_name => '%' , replace => true , staging_table_name => 'my_11g_staging_table'); END; / See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_SQLTUNE.UNPACK_STGTAB_SQLSET 24.7 Dropping a SQL Tuning Set To drop an STS from the database, execute the DBMS_SQLTUNE.DROP_SQLSET procedure. Prerequisites Ensure that no tuning task is currently using the STS to be dropped. If an existing tuning task is using this STS, then drop the task before dropping the STS. Otherwise, the database issues an ORA-13757 error. Assumptions This tutorial assumes that you want to drop an STS named SQLT_WKLD_STS. To drop an STS: 1. Start SQL*Plus, and then log in to the database with the appropriate privileges. 2. Run the DBMS_SQLTUNE.DROP_SQLSET procedure. For example, execute the following PL/SQL program: BEGIN DBMS_SQLTUNE.DROP_SQLSET( sqlset_name => 'SQLT_WKLD_STS' ); END; / 3. Optionally, confirm that the STS was deleted. The following example counts the number of SQL tuning sets named SQLT_WKLD_STS owned by the current user (sample output included): SELECT COUNT(*) FROM USER_SQLSET WHERE NAME = 'SQLT_WKLD_STS'; COUNT(*) ---------0 24-16 Chapter 24 Dropping a SQL Tuning Set See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the STS procedures in DBMS_SQLTUNE 24-17 25 Analyzing SQL with SQL Tuning Advisor Use SQL Tuning Advisor to obtain recommendations for improving performance of high-load SQL statements, and prevent regressions by only executing optimal plans. This chapter contains the following topics: • About SQL Tuning Advisor SQL Tuning Advisor is SQL diagnostic software in the Oracle Database Tuning Pack. • Managing the Automatic SQL Tuning Task When your goal is to identify SQL performance problems proactively, configuring SQL Tuning Advisor as an automated task is a simple solution. The task processes selected high-load SQL statements from AWR that qualify as tuning candidates. • Running SQL Tuning Advisor On Demand You can run SQL Tuning Advisor on demand. 25.1 About SQL Tuning Advisor SQL Tuning Advisor is SQL diagnostic software in the Oracle Database Tuning Pack. You can submit one or more SQL statements as input to the advisor and receive advice or recommendations for how to tune the statements, along with a rationale and expected benefit. This section contains the following topics: • Purpose of SQL Tuning Advisor SQL Tuning Advisor is a mechanism for resolving problems related to suboptimally performing SQL statements. • SQL Tuning Advisor Architecture Automatic Tuning Optimizer is the central tool used by SQL Tuning Advisor. The advisor can receive SQL statements as input from multiple sources, analyze these statements using the optimizer, and then make recommendations. • SQL Tuning Advisor Operation You can run SQL Tuning Advisor automatically or on demand. You can also run the advisor on a local or remote database. 25.1.1 Purpose of SQL Tuning Advisor SQL Tuning Advisor is a mechanism for resolving problems related to suboptimally performing SQL statements. Use SQL Tuning Advisor to obtain recommendations for improving performance of high-load SQL statements, and prevent regressions by only executing optimal plans. Tuning recommendations include: 25-1 Chapter 25 About SQL Tuning Advisor • Collection of object statistics • Creation of indexes • Rewriting SQL statements • Creation of SQL profiles • Creation of SQL plan baselines The recommendations generated by SQL Tuning Advisor help you achieve the following specific goals: • Avoid labor-intensive manual tuning Identifying and tuning high-load SQL statements is challenging even for an expert. SQL Tuning Advisor uses the optimizer to tune SQL for you. • Generate recommendations and implement SQL profiles automatically You can configure an Automatic SQL Tuning task to run nightly in maintenance windows. When invoked in this way, the advisor can generate recommendations and also implement SQL profiles automatically. • Analyze database-generated statistics to achieve optimal plans The database contains a vast amount of statistics about its own operations. SQL Tuning Advisor can perform deep mining and analysis of internal information to improve execution plans. • Enable developers to tune SQL on a test system instead of the production system When suboptimally performing SQL statements occur on a production database, developers may not want to investigate and tune directly on the production database. The DBA can transport the problematic SQL statements to a test database where the developers can safely analyze and tune them. When tuning multiple statements, SQL Tuning Advisor does not recognize interdependencies between the statements. Instead, SQL Tuning Advisor offers a convenient way to get tuning recommendations for many statements. Note: Data visibility and privilege requirements may differ when using SQL Tuning Advisor with pluggable databases. The advisor can tune a query in the current pluggable database (PDB), and in other PDBs in which this query has been executed. In this way, a container database (CDB) administrator can tune the same query in many PDBs at the same time, whereas a PDB administrator can only tune a single PDB. See Also: • "Managing SQL Plan Baselines" to learn about SQL plan management • Oracle Database Administrator’s Guide for a table that summarizes how manageability features work in a CDB 25-2 Chapter 25 About SQL Tuning Advisor 25.1.2 SQL Tuning Advisor Architecture Automatic Tuning Optimizer is the central tool used by SQL Tuning Advisor. The advisor can receive SQL statements as input from multiple sources, analyze these statements using the optimizer, and then make recommendations. Invoking Automatic Tuning Optimizer for every hard parse consumes significant time and resources. Tuning mode is meant for complex and high-load SQL statements that significantly affect database performance. Manageability advisors such as SQL tuning advisor use a common infrastructure called the advisor framework. This framework provides a common schema and interface for storing task objects. An advisor schema is a set of tables to store the data from advisors. SQL Tuning Advisor receives tuning input, and then writes to the advisor schemas by means of the advisor framework. SQL Tuning Advisor reads data from advisor schema when it produces its reports. The following figure shows the basic architecture of SQL Tuning Advisor. Figure 25-1 SQL Tuning Advisor Architecture Optimizer ADDM Automatic Tuning Optimizer AWR Shared Pool SQL Tuning Advisor Recommendations Library Cache Inplementation of SQL Profiles (Automatic Only) Shared SQL Area SELECT * FROM employees SQL Tuning Set AUTOTASK This section contains the following topics: • Input to SQL Tuning Advisor Input for SQL Tuning Advisor can come from several sources, including ADDM, AWR, the shared SQL area, and SQL tuning sets. 25-3 Chapter 25 About SQL Tuning Advisor • Output of SQL Tuning Advisor After analyzing the SQL statements, SQL Tuning Advisor publishes recommendations. • Automatic Tuning Optimizer Analyses In tuning mode, the optimizer has more time to consider options and gather statistics. For example, Automatic Tuning Optimizer can use dynamic statistics and partial statement execution. See Also: "SQL Parsing" 25.1.2.1 Input to SQL Tuning Advisor Input for SQL Tuning Advisor can come from several sources, including ADDM, AWR, the shared SQL area, and SQL tuning sets. SQL Tuning Advisor uses its input sources as follows: • Automatic Database Diagnostic Monitor (ADDM) The primary input source for SQL Tuning Advisor is ADDM (pronounced Adam). By default, ADDM runs proactively once every hour. To identify performance problems involving high-load SQL statements, ADDM analyzes key statistics gathered by Automatic Workload Repository (AWR) over the last hour . If a highload SQL statement is identified, then ADDM recommends running SQL Tuning Advisor on the SQL. • AWR AWR takes regular snapshots of system activity, including high-load SQL statements ranked by relevant statistics, such as CPU consumption and wait time. You can view the AWR and manually identify high-load SQL statements. You can run SQL Tuning Advisor on these statements, although Oracle Database automatically performs this work as part of automatic SQL tuning. By default, AWR retains data for the last eight days. You can locate and tune any high-load SQL that ran within the retention period of AWR using this technique. • Shared SQL area The database uses the shared SQL area to tune recent SQL statements that have yet to be captured in AWR. The shared SQL area and AWR provide the capability to identify and tune high-load SQL statements from the current time going as far back as the AWR retention allows, which by default is at least 8 days. • SQL tuning set A SQL tuning set (STS) is a database object that stores SQL statements along with their execution context. An STS can include SQL statements that are yet to be deployed, with the goal of measuring their individual performance, or identifying the ones whose performance falls short of expectation. When a set of SQL statements serve as input, the database must first construct and use an STS. 25-4 Chapter 25 About SQL Tuning Advisor See Also: • "About SQL Tuning Sets" • Oracle Database Performance Tuning Guide to learn about ADDM • Oracle Database Performance Tuning Guide to learn about AWR • Oracle Database Concepts to learn about the shared SQL area 25.1.2.2 Output of SQL Tuning Advisor After analyzing the SQL statements, SQL Tuning Advisor publishes recommendations. Specifically, SQL Tuning Advisor produces the following types of output: • Advice on optimizing the execution plan • Rationale for the proposed optimization • Estimated performance benefit • SQL statement to implement the advice The benefit percentage shown for each recommendation is calculated using the following formula: abnf% = (time_old - time_new)/(time_old) For example, assume that before tuning the execution time was 100 seconds, and after implementing the recommendation the new execution time is expected to be 33 seconds. This benefit calculation for this performance improvement is as follows: 67% = (100 - 33)/(100) You choose whether to accept the recommendations to optimize the SQL statements. Depending on how it is configured, Automatic SQL Tuning Advisor can implement the SQL profile recommendations to tune the statement without user intervention. When invoked on demand, SQL Tuning Advisor can recommend that the user implement a SQL profile, but can never implement it automatically. 25.1.2.3 Automatic Tuning Optimizer Analyses In tuning mode, the optimizer has more time to consider options and gather statistics. For example, Automatic Tuning Optimizer can use dynamic statistics and partial statement execution. The following graphic depicts the different types of analysis that Automatic Tuning Optimizer performs. 25-5 Chapter 25 About SQL Tuning Advisor Figure 25-2 Automatic Tuning Optimizer Optimizer Automatic Tuning Optimizer Normal Mode Tuning Mode SQL Tuning Advisor Statistical Analysis Access Path Analysis Alternative Plan Analysis SQL Structure Analysis SQL Profiling This section contains the following topics: • Statistical Analysis The optimizer relies on object statistics to generate execution plans. • SQL Profiling SQL profiling is the verification by the Automatic Tuning Optimizer of its own estimates. • Access Path Analysis An access path is the means by which the database retrieves data. • SQL Structural Analysis During structural analysis, Automatic Tuning Optimizer tries to identify syntactic, semantic, or design problems that can lead to suboptimal performance. The goal is to identify poorly written SQL statements and to advise you how to restructure them. • Alternative Plan Analysis While tuning a SQL statement, SQL Tuning Advisor searches real-time and historical performance data for alternative execution plans for the statement. See Also: "Query Optimizer Concepts " 25-6 Chapter 25 About SQL Tuning Advisor 25.1.2.3.1 Statistical Analysis The optimizer relies on object statistics to generate execution plans. If these statistics are stale or missing, then the optimizer can generate suboptimal plans. Automatic Tuning Optimizer checks each query object for missing or stale statistics, and recommends gathering fresh statistics if needed. The following graphic depicts the process of statistical analysis. Figure 25-3 Statistical Analysis by Automatic Tuning Optimizer SELECT . . . SQL Tuning Advisor Optimizer Automatic Tuning Optimizer Customers Table Stale Statistics Customers Table Recommended collecting object-level statistics Absent Statistics 25.1.2.3.2 SQL Profiling SQL profiling is the verification by the Automatic Tuning Optimizer of its own estimates. By reviewing execution history and testing the SQL, the optimizer can ensure that it has the most accurate information available to generate execution plans. SQL profiling is related to but distinct from the steps of generating SQL Tuning Advisor recommendations and implementing these recommendations. The following graphic shows SQL Tuning Advisor recommending a SQL profile and automatically implementing it. After creating the profile, the optimizer can use it as additional input when generating execution plans. 25-7 Chapter 25 About SQL Tuning Advisor Figure 25-4 SQL Profile SQL Tuning Advisor SQL Profile Submit Optimizer (Tuning Mode) Create Use Database Users No application code change Optimizer (Normal Mode) Output GB HJ Well-Tuned Plan HJ This section contains the following topics: • How SQL Profiling Works The database can profile some DML and DDL statements. • SQL Profile Implementation If the optimizer generates auxiliary information during statistical analysis or SQL profiling, then the optimizer recommends implementing a SQL profile. See Also: "About SQL Profiles" 25.1.2.3.2.1 How SQL Profiling Works The database can profile some DML and DDL statements. Specifically, SQL Tuning Advisor can profile the following types of statement: • DML statements (SELECT, INSERT with a SELECT clause, UPDATE, DELETE, and the update or insert operations of MERGE) • CREATE TABLE statements (only with the AS SELECT clause) 25-8 Chapter 25 About SQL Tuning Advisor After performing its analysis, SQL Tuning Advisor either recommends or does not recommend implementing a SQL profile. The following graphic shows the SQL profiling process. Figure 25-5 SQL Profiling SELECT . . . SQL Tuning Advisor Optimizer Automatic Tuning Optimizer SQL Profiling * Reviews past execution history to adjust settings * Performs sampling or partial execution No Recommendation Recommendation to Implement SQL Profile During SQL profiling, the optimizer verifies cost, selectivity, and cardinality for a statement. The optimizer uses either of the following methods: • Samples the data and applies appropriate predicates to the sample The optimizer compares the new estimate to the regular estimate and, if the difference is great enough, applies a correction factor. • Executes a fragment of the SQL statement This method is more efficient than the sampling method when the predicates provide efficient access paths. The optimizer uses the past statement execution history to determine correct settings. For example, if the history indicates that a SQL statement is usually executed only partially, then the optimizer uses FIRST_ROWS instead of ALL_ROWS optimization. See Also: "Choosing an Optimizer Goal" 25-9 Chapter 25 About SQL Tuning Advisor 25.1.2.3.2.2 SQL Profile Implementation If the optimizer generates auxiliary information during statistical analysis or SQL profiling, then the optimizer recommends implementing a SQL profile. As shown in Figure 25-6, the following options are possible: • When SQL Tuning Advisor is run on demand, the user must choose whether to implement the SQL profile. • When the Automatic SQL Tuning task is configured to implement SQL profiles automatically, advisor behavior depends on the setting of the ACCEPT_SQL_PROFILE tuning task parameter: – If set to true, then the advisor implements SQL profiles automatically. – If set to false, then user intervention is required. – If set to AUTO (default), then the setting is true when at least one SQL statement exists with a SQL profile, and false when this condition is not satisfied. Note: The Automatic SQL Tuning task cannot automatically create SQL plan baselines or add plans to them. Figure 25-6 Implementing SQL Profiles On Demand SQL Tuning Advisor Recommends Implementing SQL profile User must choose whether to implement No Autoimplementation Autotask Autoimplementation Implements SQL profile At any time during or after automatic SQL tuning, you can view a report. This report describes in detail the SQL statements that were analyzed, the recommendations generated, and any SQL profiles that were automatically implemented. 25-10 Chapter 25 About SQL Tuning Advisor See Also: • "Configuring the Automatic SQL Tuning Task Using the Command Line" • "Plan Evolution" • "About SQL Profiles" • Oracle Database PL/SQL Packages and Types Reference to learn more about ACCEPT_SQL_PROFILE 25.1.2.3.3 Access Path Analysis An access path is the means by which the database retrieves data. For example, a query using an index and a query using a full table scan use different access paths. In some cases, indexes can greatly enhance the performance of a SQL statement by eliminating full table scans. The following graphic illustrates access path analysis. Figure 25-7 Access Path Analysis SELECT . . . SQL Tuning Advisor Optimizer Automatic Tuning Optimizer Recommends Workload SQL Access Advisor Index Creation Comprehensive Analysis Automatic Tuning Optimizer explores whether a new index can significantly enhance query performance and recommends either of the following: • Creating an index 25-11 Chapter 25 About SQL Tuning Advisor Index recommendations are specific to the SQL statement processed by SQL Tuning Advisor. Sometimes a new index provides a quick solution to the performance problem associated with a single SQL statement. • Running SQL Access Advisor Because the Automatic Tuning Optimizer does not analyze how its index recommendation can affect the entire SQL workload, it also recommends running SQL Access Advisor on the SQL statement along with a representative SQL workload. SQL Access Advisor examines the effect of creating an index on the SQL workload before making recommendations. 25.1.2.3.4 SQL Structural Analysis During structural analysis, Automatic Tuning Optimizer tries to identify syntactic, semantic, or design problems that can lead to suboptimal performance. The goal is to identify poorly written SQL statements and to advise you how to restructure them. The following graphic illustrates structural analysis. Figure 25-8 Structural Analysis SELECT . . . UNION SQL Tuning Advisor Optimizer Automatic Tuning Optimizer SQL Constructors (NOT IN, UNION) Data Type Mismatches Design Mistakes (No WHERE Clause) Restructured SELECT . . . UNION ALL Some syntax variations negatively affect performance. In structural analysis, the automatic tuning optimizer evaluates statements against a set of rules, identifies inefficient coding techniques, and recommends an alternative statement if possible. As shown in Figure 25-8, Automatic Tuning Optimizer identifies the following categories of structural problems: • Inefficient use of SQL constructors A suboptimally performing statement may be using NOT IN instead of NOT EXISTS, or UNION instead of UNION ALL. The UNION operator, as opposed to the UNION ALL operator, uses a unique sort to ensure that no duplicate rows are in the result set. If you know that two queries do not return duplicates, then use UNION ALL. 25-12 Chapter 25 About SQL Tuning Advisor • Data type mismatches If the indexed column and the compared value have a data type mismatch, then the database does not use the index because of the implicit data type conversion. Also, the database must expend additional resources converting data types, and some SQL statements may fail because data values do not convert correctly. Common mistakes include columns that contain numeric data but are never used for arithmetic operations: telephone numbers, credit card numbers, and check numbers. To avoid poor cardinality estimates, suboptimal plans, and ORA-01722 errors, developers must ensure that bind variables are type VARCHAR2 and not numbers. • Design mistakes A classic example of a design mistake is a missing join condition. If n is the number of tables in a query block, then n-1 join conditions must exist to avoid a Cartesian product. In each case, Automatic Tuning Optimizer makes relevant suggestions to restructure the statements. The suggested alternative statement is similar, but not equivalent, to the original statement. For example, the suggested statement may use UNION ALL instead of UNION. You can then determine if the advice is sound. 25.1.2.3.5 Alternative Plan Analysis While tuning a SQL statement, SQL Tuning Advisor searches real-time and historical performance data for alternative execution plans for the statement. If plans other than the original plan exist, then SQL Tuning Advisor reports an alternative plan finding. The follow graphic shows SQL Tuning Advisor finding two alternative plans and generating an alternative plan finding. 25-13 Chapter 25 About SQL Tuning Advisor Figure 25-9 Alternative Plan Analysis Optimizer Automatic Tuning Optimizer Real-Time Performance Data GB Origin: Cursor Cache HJ HJ SQL Tuning Advisor Searches Produces AWR Alternative Plan Finding Performance Summary Recommendations GB Origin: STS HJ HJ SQL Tuning Advisor validates the alternative execution plans and notes any plans that are not reproducible. When reproducible alternative plans are found, you can create a SQL plan baseline to instruct the optimizer to choose these plans in the future. Example 25-1 Alternative Plan Finding The following example shows an alternative plan finding for a SELECT statement: 2- Alternative Plan Finding --------------------------Some alternative execution plans for this statement were found by searching the system's real-time and historical performance data. The following table lists these plans ranked by their average elapsed time. See section "ALTERNATIVE PLANS SECTION" for detailed information on each plan. id -1 2 plan hash ---------1378942017 2842999589 last seen elapsed (s) origin note -------------------- ------------ --------------- ---------------2009-02-05/23:12:08 0.000 Cursor Cache original plan 2009-02-05/23:12:08 0.002 STS Information ----------- The Original Plan appears to have the best performance, based on the elapsed time per execution. However, if you know that one alternative plan is better than the Original Plan, you can create a SQL plan baseline for it. This will instruct the Oracle optimizer to pick it over any other choices in the future. 25-14 Chapter 25 About SQL Tuning Advisor execute dbms_sqltune.create_sql_plan_baseline(task_name => 'TASK_XXXXX', object_id => 2, task_owner => 'SYS', plan_hash => xxxxxxxx); The preceding example shows that SQL Tuning Advisor found two plans, one in the shared SQL area and one in a SQL tuning set. The plan in the shared SQL area is the same as the original plan. SQL Tuning Advisor only recommends an alternative plan if the elapsed time of the original plan is worse than alternative plans. In this case, SQL Tuning Advisor recommends that users create a SQL plan baseline on the plan with the best performance. In Example 25-1, the alternative plan did not perform as well as the original plan, so SQL Tuning Advisor did not recommend using the alternative plan. Example 25-2 Alternative Plans Section In this example, the alternative plans section of the SQL Tuning Advisor output includes both the original and alternative plans and summarizes their performance. The most important statistic is elapsed time. The original plan used an index, whereas the alternative plan used a full table scan, increasing elapsed time by .002 seconds. Plan 1 -----Plan Origin Plan Hash Value Executions Elapsed Time CPU Time Buffer Gets Disk Reads Disk Writes :Cursor Cache :1378942017 :50 :0.000 sec :0.000 sec :0 :0 :0 Notes: 1. Statistics shown are averaged over multiple executions. 2. The plan matches the original plan. -------------------------------------------| Id | Operation | Name | -------------------------------------------| 0 | SELECT STATEMENT | | | 1 | SORT AGGREGATE | | | 2 | MERGE JOIN | | | 3 | INDEX FULL SCAN | TEST1_INDEX | | 4 | SORT JOIN | | | 5 | TABLE ACCESS FULL| TEST | -------------------------------------------Plan 2 -----Plan Origin Plan Hash Value Executions Elapsed Time CPU Time Buffer Gets Disk Reads Disk Writes :STS :2842999589 :10 :0.002 sec :0.002 sec :3 :0 :0 Notes: 1. Statistics shown are averaged over multiple executions. 25-15 Chapter 25 About SQL Tuning Advisor ------------------------------------| Id | Operation | Name | ------------------------------------| 0 | SELECT STATEMENT | | | 1 | SORT AGGREGATE | | | 2 | HASH JOIN | | | 3 | TABLE ACCESS FULL| TEST | | 4 | TABLE ACCESS FULL| TEST1 | ------------------------------------- To adopt an alternative plan regardless of whether SQL Tuning Advisor recommends it, call DBMS_SQLTUNE.CREATE_SQL_PLAN_BASELINE. You can use this procedure to create a SQL plan baseline on any existing reproducible plan. See Also: "Differences Between SQL Plan Baselines and SQL Profiles" 25.1.3 SQL Tuning Advisor Operation You can run SQL Tuning Advisor automatically or on demand. You can also run the advisor on a local or remote database. This section contains the following topics: • Automatic and On-Demand SQL Tuning Configure SQL Tuning Advisor to run automatically using DBMS_AUTO_SQLTUNE, or on demand using DBMS_SQLTUNE. • Local and Remote SQL Tuning SQL Tuning Advisor can either analyze a workload on a local database or a remote database. 25.1.3.1 Automatic and On-Demand SQL Tuning Configure SQL Tuning Advisor to run automatically using DBMS_AUTO_SQLTUNE, or on demand using DBMS_SQLTUNE. The methods of invocation differ as follows: • Automatically You can configure SQL Tuning Advisor to run during nightly system maintenance windows. When run by AUTOTASK, the advisor is known as Automatic SQL Tuning Advisor and performs automatic SQL tuning. • On-Demand In on-demand SQL tuning, you manually invoke SQL Tuning Advisor to diagnose and fix SQL-related performance problems after they have been discovered. Oracle Enterprise Manager Cloud Control (Cloud Control) is the preferred interface for tuning SQL on demand, but you can also use the DBMS_SQLTUNE PL/SQL package. SQL Tuning Advisor uses Automatic Tuning Optimizer to perform its analysis. This optimization is "automatic" because the optimizer analyzes the SQL instead of the 25-16 Chapter 25 About SQL Tuning Advisor user. Do not confuse Automatic Tuning Optimizer with automatic SQL tuning, which in this document refers only to the work performed by the Automatic SQL Tuning task. See Also: • "Running SQL Tuning Advisor On Demand" • "Managing the Automatic SQL Tuning Task" • Oracle Database PL/SQL Packages and Types Reference to learn about DBMS_SQLTUNE 25.1.3.2 Local and Remote SQL Tuning SQL Tuning Advisor can either analyze a workload on a local database or a remote database. In the simplest case, SQL Tuning Advisor accepts input, executes, and stores results within a single database. Local mode is appropriate for databases in which the performance overhead of SQL Tuning Advisor execution is acceptable. In remote tuning, the database on which you initiate a tuning task differs from the database in which the tuning process executes or in which results are stored. For example, a standby database can have its own workload of queries, some of which may require tuning. You can issue SQL Tuning Advisor statements on a standby database. A standby-to-primary database link enables DBMS_SQLTUNE to write data to and read data from the primary database. The link is necessary because the standby database, which is read-only, cannot write the SQL tuning data. The following figure illustrates the general setup for tuning a standby database workload on a primary database. This technique requires a standby-to-primary database link. Figure 25-10 Tuning a Standby Workload on a Primary Database Primary Database Standby Database Standby-to-Primary Database Link To tune a standby workload on a primary database, specify the database_link_to parameter in DBMS_SQLTUNE procedures. By default, the database_link_to parameter is null, which means that tuning is local. 25-17 Chapter 25 Managing the Automatic SQL Tuning Task The database_link_to parameter must specify a private database link. This link must be owned by SYS and accessed by the default privileged user SYS$UMF. The following sample statement creates a link named lnk_to_pri: CREATE DATABASE LINK lnk_to_pri CONNECT TO SYS$UMF IDENTIFIED BY password USING 'inst1'; The following table illustrates a typical remote tuning session. You issue all statements on the standby database. DBMS_SQLTUNE uses the database link both to fetch data from the primary database, and store data in the primary database. Table 25-1 Tuning a Standby Database Workload Using a Database Link to the Primary Database Step Statement Issued on Standby Database Result 1 CREATE_TUNING_TASK DBMS_SQLTUNE creates the task data in the primary database using the standby-to-primary database link. 2 EXECUTE_TUNING_TASK DBMS_SQLTUNE uses the database link to read the SQL Tuning Advisor task data stored in the primary database. The tuning analysis occurs on the standby database, but DBMS_SQLTUNE writes the results remotely to the primary database. 3 REPORT_TUNING_TASK DBMS_SQLTUNE uses the database link to read the SQL Tuning Advisor report data from the primary database, and then constructs the report locally on the standby database. 4 ACCEPT_SQL_PROFILE DBMS_SQLTUNE uses the database link to write the SQL profile data remotely to the primary database. See Also: • "Configuring a SQL Tuning Task" • Oracle Data Guard Concepts and Administration to learn how to perform remote tuning in a Data Guard environment • Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_SQLTUNE.CREATE_TUNING_TASK 25.2 Managing the Automatic SQL Tuning Task When your goal is to identify SQL performance problems proactively, configuring SQL Tuning Advisor as an automated task is a simple solution. The task processes selected high-load SQL statements from AWR that qualify as tuning candidates. This section contains the following topics: • About the Automatic SQL Tuning Task By default, the Automatic SQL Tuning task runs for in a nightly maintenance window. 25-18 Chapter 25 Managing the Automatic SQL Tuning Task • Enabling and Disabling the Automatic SQL Tuning Task You can enable or disable the Automatic SQL Tuning task using Cloud Control (preferred) or a command-line interface. • Configuring the Automatic SQL Tuning Task You can configure the Automatic SQL Tuning task using Cloud Control or the command line. • Viewing Automatic SQL Tuning Reports At any time during or after the running of the Automatic SQL Tuning task, you can view a tuning report. See Also: Oracle Database Administrator’s Guide to learn more about automated maintenance tasks 25.2.1 About the Automatic SQL Tuning Task By default, the Automatic SQL Tuning task runs for in a nightly maintenance window. This section contains the following topics: • Purpose of Automatic SQL Tuning Configuring automatic SQL tuning instead of tuning manually decreases cost and increases manageability • Automatic SQL Tuning Concepts Oracle Scheduler uses the automated maintenance tasks infrastructure (known as AutoTask) to schedules tasks to run automatically. • Command-Line Interface to SQL Tuning Advisor On the command line, you can use PL/SQL packages to perform SQL tuning tasks. • Basic Tasks for Automatic SQL Tuning This section explains the basic tasks in running SQL Tuning Advisor as an automatic task. See Also: Oracle Database Administrator’s Guide to learn about SQL Tuning Advisor in a multitenant environment 25.2.1.1 Purpose of Automatic SQL Tuning Configuring automatic SQL tuning instead of tuning manually decreases cost and increases manageability Many DBAs do not have the time needed for the intensive analysis required for SQL tuning. Even when they do, SQL tuning involves several manual steps. Because 25-19 Chapter 25 Managing the Automatic SQL Tuning Task several different SQL statements may be high load on any given day, DBAs may have to expend considerable effort to monitor and tune them. . The automated SQL tuning task does not process the following types of SQL: • Ad hoc SQL statements or SQL statements that do not repeat within a week • Parallel queries • Queries that take too long to run after being SQL profiled, so that it is not practical for SQL Tuning Advisor to test execution • Recursive SQL You can run SQL Tuning Advisor on demand to tune the preceding types of SQL statements. 25.2.1.2 Automatic SQL Tuning Concepts Oracle Scheduler uses the automated maintenance tasks infrastructure (known as AutoTask) to schedules tasks to run automatically. By default, the Automatic SQL Tuning task runs for at most one hour in a nightly maintenance window. You can customize attributes of the maintenance windows, including start and end time, frequency, and days of the week. See Also: • Oracle Database Administrator’s Guide to learn about Oracle Scheduler • Oracle Database PL/SQL Packages and Types Reference to learn about DBMS_AUTO_TASK_ADMIN 25.2.1.3 Command-Line Interface to SQL Tuning Advisor On the command line, you can use PL/SQL packages to perform SQL tuning tasks. The following table describes the most relevant packages. Table 25-2 SQL Tuning Advisor Packages Package Description DBMS_AUTO_SQLTUNE Enables you run SQL Tuning Advisor, manage SQL profiles, manage SQL tuning sets, and perform real-time SQL performance monitoring. To use this API, you must have the ADVISOR privilege. DBMS_AUTO_TASK_ADMIN Provides an interface to AUTOTASK. You can use this interface to enable and disable the Automatic SQL Tuning task. 25-20 Chapter 25 Managing the Automatic SQL Tuning Task See Also: Oracle Database PL/SQL Packages and Types Reference to learn about DBMS_SQLTUNE ad DBMS_AUTO_TASK_ADMIN 25.2.1.4 Basic Tasks for Automatic SQL Tuning This section explains the basic tasks in running SQL Tuning Advisor as an automatic task. The following graphic shows the basic workflow. Figure 25-11 Automatic SQL Tuning APIs DBMS_AUTO_TASK_ADMIN.ENABLE Enable the Automatic SQL Tuning Task Configure the Automatic SQL Tuning Task DBMS_SQLTUNE. SET_TUNING_TASK_PARAMETER DBMS_AUTO_TASK_ADMIN.DISABLE Report on the SQL Tuning Task DBMS_SQLTUNE. REPORT_AUTO_TUNING_TASK Disable the Automatic SQL Tuning Task As shown in Figure 25-12, the basic procedure is as follows: 1. Enable the Automatic SQL Tuning task. See "Enabling and Disabling the Automatic SQL Tuning Task". 2. Optionally, configure the Automatic SQL Tuning task. See "Configuring the Automatic SQL Tuning Task". 3. Display the results of the Automatic SQL Tuning task. See "Viewing Automatic SQL Tuning Reports". 4. Disable the Automatic SQL Tuning task. See "Enabling and Disabling the Automatic SQL Tuning Task". 25-21 Chapter 25 Managing the Automatic SQL Tuning Task 25.2.2 Enabling and Disabling the Automatic SQL Tuning Task You can enable or disable the Automatic SQL Tuning task using Cloud Control (preferred) or a command-line interface. This section contains the following topics: • Enabling and Disabling the Automatic SQL Tuning Task Using Cloud Control You can enable and disable all automatic maintenance tasks, including the Automatic SQL Tuning task, using Cloud Control. • Enabling and Disabling the Automatic SQL Tuning Task from the Command Line If you do not use Cloud Control to enable and disable the Automatic SQL Tuning task, then you must use the command line. 25.2.2.1 Enabling and Disabling the Automatic SQL Tuning Task Using Cloud Control You can enable and disable all automatic maintenance tasks, including the Automatic SQL Tuning task, using Cloud Control. To enable or disable the Automatic SQL Tuning task using Cloud Control: 1. Log in to Cloud Control with the appropriate credentials. 2. Under the Targets menu, select Databases. 3. In the list of database targets, select the target for the Oracle Database instance that you want to administer. 4. If prompted for database credentials, then enter the minimum credentials necessary for the tasks you intend to perform. 5. From the Administration menu, select Oracle Scheduler, then Automated Maintenance Tasks. The Automated Maintenance Tasks page appears. This page shows the predefined tasks. You access each task by clicking the corresponding link to get more information about the task. 6. Click Automatic SQL Tuning. The Automatic SQL Tuning Result Summary page appears. The Task Status section shows whether the Automatic SQL Tuning Task is enabled or disabled. In the following graphic, the task is disabled: 7. In Automatic SQL Tuning, click Configure. The Automated Maintenance Tasks Configuration page appears. 25-22 Chapter 25 Managing the Automatic SQL Tuning Task By default, Automatic SQL Tuning executes in all predefined maintenance windows in MAINTENANCE_WINDOW_GROUP. 8. Perform the following steps: a. In the Task Settings for Automatic SQL Tuning, select either Enabled or Disabled to enable or disable the automated task. b. To disable Automatic SQL Tuning for specific days in the week, check the appropriate box next to the window name. c. To change the characteristics of a window, click Edit Window Group. d. Click Apply. 25.2.2.2 Enabling and Disabling the Automatic SQL Tuning Task from the Command Line If you do not use Cloud Control to enable and disable the Automatic SQL Tuning task, then you must use the command line. You have the following options: • Run the ENABLE or DISABLE procedure in the DBMS_AUTO_TASK_ADMIN PL/SQL package. This package is the recommended command-line technique. For both the ENABLE and DISABLE procedures, you can specify a particular maintenance window with the window_name parameter. • Set the STATISTICS_LEVEL initialization parameter to BASIC to disable collection of all advisories and statistics, including Automatic SQL Tuning Advisor. Because monitoring and many automatic features are disabled, Oracle strongly recommends that you do not set STATISTICS_LEVEL to BASIC. To enable or disable Automatic SQL Tuning using DBMS_AUTO_TASK_ADMIN: 1. Connect SQL*Plus to the database with administrator privileges, and then do one of the following: • To enable the automated task, execute the following PL/SQL block: BEGIN DBMS_AUTO_TASK_ADMIN.ENABLE ( client_name => 'sql tuning advisor' , operation => NULL , window_name => NULL 25-23 Chapter 25 Managing the Automatic SQL Tuning Task ); END; / • To disable the automated task, execute the following PL/SQL block: BEGIN DBMS_AUTO_TASK_ADMIN.DISABLE ( client_name => 'sql tuning advisor' , operation => NULL , window_name => NULL ); END; / 2. Query the data dictionary to confirm the change. For example, query DBA_AUTOTASK_CLIENT as follows (sample output included): COL CLIENT_NAME FORMAT a20 SELECT CLIENT_NAME, STATUS FROM DBA_AUTOTASK_CLIENT WHERE CLIENT_NAME = 'sql tuning advisor'; CLIENT_NAME STATUS -------------------- -------sql tuning advisor ENABLED To disable collection of all advisories and statistics: 1. Connect SQL*Plus to the database with administrator privileges, and then query the current statistics level setting. The following SQL*Plus command shows that STATISTICS_LEVEL is set to ALL: sys@PROD> SHOW PARAMETER statistics_level NAME TYPE VALUE ------------------------------------ ----------- ----statistics_level string ALL 2. Set STATISTICS_LEVEL to BASIC as follows: sys@PROD> ALTER SYSTEM SET STATISTICS_LEVEL ='BASIC'; System altered. See Also: Oracle Database PL/SQL Packages and Types Reference for complete reference information 25.2.3 Configuring the Automatic SQL Tuning Task You can configure the Automatic SQL Tuning task using Cloud Control or the command line. This section contains the following topics: 25-24 Chapter 25 Managing the Automatic SQL Tuning Task • Configuring the Automatic SQL Tuning Task Using Cloud Control You can enable and disable all automatic maintenance tasks, including the Automatic SQL Tuning task, using Cloud Control. You must perform the operation as SYS or have the EXECUTE privilege on the PL/SQL package DBMS_AUTO_SQLTUNE. • Configuring the Automatic SQL Tuning Task Using the Command Line The DBMS_AUTO_SQLTUNE package enables you to configure automatic SQL tuning by specifying the task parameters using the SET_AUTO_TUNING_TASK_PARAMETER procedure. 25.2.3.1 Configuring the Automatic SQL Tuning Task Using Cloud Control You can enable and disable all automatic maintenance tasks, including the Automatic SQL Tuning task, using Cloud Control. You must perform the operation as SYS or have the EXECUTE privilege on the PL/SQL package DBMS_AUTO_SQLTUNE. To configure the Automatic SQL Tuning task using Cloud Control: 1. Log in to Cloud Control with the appropriate credentials. 2. Under the Targets menu, select Databases. 3. In the list of database targets, select the target for the Oracle Database instance that you want to administer. 4. If prompted for database credentials, then enter the minimum credentials necessary for the tasks you intend to perform. 5. From the Administration menu, click Oracle Scheduler, then Automated Maintenance Tasks. The Automated Maintenance Tasks page appears. This page shows the predefined tasks. You access each task by clicking the corresponding link to get more information about the task itself. 6. Click Automatic SQL Tuning. The Automatic SQL Tuning Result Summary page appears. 7. Under Task Settings, click Configure next to Automatic SQL Tuning (SYS_AUTO_SQL_TUNING_TASK). The Automated Maintenance Tasks Configuration page appears. 8. Under Task Settings, click Configure next to Automatic SQL Tuning. The Automatic SQL Tuning Settings page appears. 25-25 Chapter 25 Managing the Automatic SQL Tuning Task 9. Make the desired changes and click Apply. 25.2.3.2 Configuring the Automatic SQL Tuning Task Using the Command Line The DBMS_AUTO_SQLTUNE package enables you to configure automatic SQL tuning by specifying the task parameters using the SET_AUTO_TUNING_TASK_PARAMETER procedure. Because the task is owned by SYS, only SYS can set task parameters. The ACCEPT_SQL_PROFILE tuning task parameter specifies whether to implement SQL profiles automatically (true) or require user intervention (false). The default is AUTO, which means true if at least one SQL statement exists with a SQL profile and false if this condition is not satisfied. Note: When automatic implementation is enabled, the advisor only implements recommendations to create SQL profiles. Recommendations such as creating new indexes, gathering optimizer statistics, and creating SQL plan baselines are not automatically implemented. Assumptions This tutorial assumes the following: • You want the database to implement SQL profiles automatically, but to implement no more than 50 SQL profiles per execution, and no more than 50 profiles total on the database. • You want the task to time out after 1200 seconds per execution. To set Automatic SQL Tuning task parameters: 1. Connect SQL*Plus to the database with the appropriate privileges, and then optionally query the current task settings. For example, connect SQL*Plus to the database with administrator privileges and execute the following query: COL PARAMETER_NAME FORMAT a25 COL VALUE FORMAT a10 SELECT PARAMETER_NAME, PARAMETER_VALUE AS "VALUE" FROM DBA_ADVISOR_PARAMETERS WHERE ( (TASK_NAME = 'SYS_AUTO_SQL_TUNING_TASK') AND ( (PARAMETER_NAME LIKE '%PROFILE%') OR (PARAMETER_NAME = 'LOCAL_TIME_LIMIT') OR (PARAMETER_NAME = 'EXECUTION_DAYS_TO_EXPIRE') ) ); Sample output appears as follows: PARAMETER_NAME ------------------------EXECUTION_DAYS_TO_EXPIRE LOCAL_TIME_LIMIT ACCEPT_SQL_PROFILES VALUE ---------30 1000 FALSE 25-26 Chapter 25 Managing the Automatic SQL Tuning Task MAX_SQL_PROFILES_PER_EXEC 20 MAX_AUTO_SQL_PROFILES 10000 2. Set parameters using PL/SQL code of the following form: BEGIN DBMS_SQLTUNE.SET_TUNING_TASK_PARAMETER ( task_name => 'SYS_AUTO_SQL_TUNING_TASK' , parameter => parameter_name , value => value ); END; / See Also: Oracle Database PL/SQL Packages and Types Reference for complete reference information for DBMS_AUTO_SQLTUNE Example 25-3 Setting SQL Tuning Task Parameters The following PL/SQL block sets a time limit to 20 minutes, and also automatically implements SQL profiles and sets limits for these profiles: BEGIN DBMS_SQLTUNE.SET_TUNING_TASK_PARAMETER('SYS_AUTO_SQL_TUNING_TASK', 'LOCAL_TIME_LIMIT', 1200); DBMS_SQLTUNE.SET_TUNING_TASK_PARAMETER('SYS_AUTO_SQL_TUNING_TASK', 'ACCEPT_SQL_PROFILES', 'true'); DBMS_SQLTUNE.SET_TUNING_TASK_PARAMETER('SYS_AUTO_SQL_TUNING_TASK', 'MAX_SQL_PROFILES_PER_EXEC', 50); DBMS_SQLTUNE.SET_TUNING_TASK_PARAMETER('SYS_AUTO_SQL_TUNING_TASK', 'MAX_AUTO_SQL_PROFILES', 10002); END; / 25.2.4 Viewing Automatic SQL Tuning Reports At any time during or after the running of the Automatic SQL Tuning task, you can view a tuning report. The tuning report contains information about all executions of the automatic SQL tuning task. Depending on the sections that were included in the report, you can view information in the following sections: • General information This section has a high-level description of the automatic SQL tuning task, including information about the inputs given for the report, the number of SQL statements tuned during the maintenance, and the number of SQL profiles created. • Summary This section lists the SQL statements (by their SQL identifiers) that were tuned during the maintenance window and the estimated benefit of each SQL profile, or the execution statistics after performing a test execution of the SQL statement with the SQL profile. 25-27 Chapter 25 Managing the Automatic SQL Tuning Task • Tuning findings This section contains the following information about each SQL statement analyzed by SQL Tuning Advisor: • – All findings associated with each SQL statement – Whether the profile was implemented on the database, and why – Whether the SQL profile is currently enabled on the database – Detailed execution statistics captured when testing the SQL profile Explain plans This section shows the old and new explain plans used by each SQL statement analyzed by SQL Tuning Advisor. • Errors This section lists all errors encountered by the automatic SQL tuning task. This section contains the following topics: • Viewing Automatic SQL Tuning Reports Using the Command Line To generate a SQL tuning report as a CLOB, execute the DBMS_SQLTUNE.REPORT_AUTO_TUNING_TASK function. 25.2.4.1 Viewing Automatic SQL Tuning Reports Using the Command Line To generate a SQL tuning report as a CLOB, execute the DBMS_SQLTUNE.REPORT_AUTO_TUNING_TASK function. You can store the CLOB in a variable and then print the variable to view the report. Assumptions This section assumes that you want to show all SQL statements that were analyzed in the most recent execution, including recommendations that were not implemented. To create and access an Automatic SQL Tuning Advisor report: 1. Connect SQL*Plus to the database with administrator privileges, and then execute the DBMS_SQLTUNE.REPORT_AUTO_TUNING_TASK function. The following example generates a text report to show all SQL statements that were analyzed in the most recent execution, including recommendations that were not implemented: VARIABLE my_rept CLOB; BEGIN :my_rept :=DBMS_SQLTUNE.REPORT_AUTO_TUNING_TASK ( begin_exec => NULL , end_exec => NULL , type => 'TEXT' , level => 'TYPICAL' , section => 'ALL' , object_id => NULL , result_limit => NULL ); END; / 25-28 Chapter 25 Managing the Automatic SQL Tuning Task PRINT :my_rept 2. Read the general information section for an overview of the tuning execution. The following sample shows the Automatic SQL Tuning task analyzed 17 SQL statements in just over 7 minutes: MY_REPT --------------------------------------------------------------------------GENERAL INFORMATION SECTION --------------------------------------------------------------------------Tuning Task Name : SYS_AUTO_SQL_TUNING_TASK Tuning Task Owner : SYS Workload Type : Automatic High-Load SQL Workload Execution Count : 6 Current Execution : EXEC_170 Execution Type : TUNE SQL Scope : COMPREHENSIVE Global Time Limit(seconds) : 3600 Per-SQL Time Limit(seconds) : 1200 Completion Status : COMPLETED Started at : 04/16/2012 10:00:00 Completed at : 04/16/2012 10:07:11 Number of Candidate SQLs : 17 Cumulative Elapsed Time of SQL (s) : 8 3. Look for findings and recommendations. If SQL Tuning Advisor makes a recommendation, then weigh the pros and cons of accepting it. The following example shows that SQL Tuning Advisor found a plan for a statement that is potentially better than the existing plan. The advisor recommends implementing a SQL profile. --------------------------------------------------------------------------SQLs with Findings Ordered by Maximum (Profile/Index) Benefit, Object ID --------------------------------------------------------------------------ob ID SQL ID statistics profile(benefit) index(benefit) restructure ------ ------------- ---------- ---------------- -------------- ----------82 dqjcc345dd4ak 58.03% 72 51bbkcd9zwsjw 2 81 03rxjf8gb18jg --------------------------------------------------------------------------DETAILS SECTION --------------------------------------------------------------------------Statements with Results Ordered by Max (Profile/Index) Benefit, Object ID --------------------------------------------------------------------------Object ID : 82 Schema Name: DBA1 SQL ID : dqjcc345dd4ak SQL Text : SELECT status FROM dba_autotask_client WHERE client_name=:1 --------------------------------------------------------------------------FINDINGS SECTION (1 finding) --------------------------------------------------------------------------1- SQL Profile Finding (see explain plans section below) -------------------------------------------------------A potentially better execution plan was found for this statement. The SQL profile was not automatically created because the verified 25-29 Chapter 25 Running SQL Tuning Advisor On Demand benefit was too low. Recommendation (estimated benefit: 58.03%) ------------------------------------------ Consider accepting the recommended SQL profile. execute dbms_sqltune.accept_sql_profile(task_name => 'SYS_AUTO_SQL_TUNING_TASK', object_id => 82, replace => TRUE); Validation results -----------------The SQL profile was tested by executing both its plan and the original plan and measuring their respective execution statistics. A plan may have been only partially executed if the other could be run to completion in less time. Original Plan With SQL Profile % Improved ------------- ---------------- ---------Completion Status: COMPLETE COMPLETE Elapsed Time(us): 26963 8829 67.25 % CPU Time(us): 27000 9000 66.66 % User I/O Time(us): 25 14 44 % Buffer Gets: 905 380 58.01 % Physical Read Requests: 0 0 Physical Write Requests: 0 0 Physical Read Bytes: 0 0 Physical Write Bytes: 7372 7372 0 % Rows Processed: 1 1 Fetches: 1 1 Executions: 1 1 Notes ----1. The original plan was first executed to warm the buffer cache. 2. Statistics for original plan were averaged over next 9 executions. 3. The SQL profile plan was first executed to warm the buffer cache. 4. Statistics for the SQL profile plan were averaged over next 9 executions. See Also: Oracle Database PL/SQL Packages and Types Reference for complete reference information. 25.3 Running SQL Tuning Advisor On Demand You can run SQL Tuning Advisor on demand. This section contains the following topics: • About On-Demand SQL Tuning On-demand SQL tuning is defined as any invocation of SQL Tuning Advisor that does not result from the Automatic SQL Tuning task. • Creating a SQL Tuning Task To create a SQL tuning task execute the DBMS_SQLTUNE.CREATE_TUNING_TASK function. 25-30 Chapter 25 Running SQL Tuning Advisor On Demand • Configuring a SQL Tuning Task To change the parameters of a tuning task after it has been created, execute the DBMS_SQLTUNE.SET_TUNING_TASK_PARAMETER function. • Executing a SQL Tuning Task To execute a SQL tuning task, use the DBMS_SQLTUNE.EXECUTE_TUNING_TASK function. The most important parameter is task_name. • Monitoring a SQL Tuning Task When you create a SQL tuning task in Cloud Control, no separate monitoring step is necessary. Cloud Control displays the status page automatically. • Displaying the Results of a SQL Tuning Task To report the results of a tuning task, use the DBMS_SQLTUNE.REPORT_TUNING_TASK function. 25.3.1 About On-Demand SQL Tuning On-demand SQL tuning is defined as any invocation of SQL Tuning Advisor that does not result from the Automatic SQL Tuning task. This section contains the following topics: • Purpose of On-Demand SQL Tuning Typically, you invoke SQL Tuning Advisor to run ADDM proactively, or to tune SQL statement reactively when users complain about suboptimal performance. • User Interfaces for On-Demand SQL Tuning The recommended user interface for running SQL Tuning Advisor manually is Cloud Control. • Basic Tasks in On-Demand SQL Tuning This section explains the basic tasks in running SQL Tuning Advisor using the DBMS_SQLTUNE package. 25.3.1.1 Purpose of On-Demand SQL Tuning Typically, you invoke SQL Tuning Advisor to run ADDM proactively, or to tune SQL statement reactively when users complain about suboptimal performance. In both the proactive and reactive scenarios, running SQL Tuning Advisor is usually the quickest way to fix unexpected SQL performance problems. 25.3.1.2 User Interfaces for On-Demand SQL Tuning The recommended user interface for running SQL Tuning Advisor manually is Cloud Control. This section contains the following topics: • Accessing the SQL Tuning Advisor Using Cloud Control Automatic Database Diagnostic Monitor (ADDM) automatically identifies high-load SQL statements. If ADDM identifies such statements, then click Schedule/Run SQL Tuning Advisor on the Recommendation Detail page to run SQL Tuning Advisor. • Command-Line Interface to On-Demand SQL Tuning If Cloud Control is unavailable, then you can run SQL Tuning Advisor using procedures in the DBMS_SQLTUNE package. 25-31 Chapter 25 Running SQL Tuning Advisor On Demand 25.3.1.2.1 Accessing the SQL Tuning Advisor Using Cloud Control Automatic Database Diagnostic Monitor (ADDM) automatically identifies high-load SQL statements. If ADDM identifies such statements, then click Schedule/Run SQL Tuning Advisor on the Recommendation Detail page to run SQL Tuning Advisor. To tune SQL statements manually using SQL Tuning Advisor: 1. Log in to Cloud Control with the appropriate credentials. 2. Under the Targets menu, select Databases. 3. In the list of database targets, select the target for the Oracle Database instance that you want to administer. 4. If prompted for database credentials, then enter the minimum credentials necessary for the tasks you intend to perform. 5. From the Performance menu, click SQL, then SQL Tuning Advisor. The Schedule SQL Tuning Advisor page appears. See Also: Oracle Database 2 Day + Performance Tuning Guide to learn how to configure and run SQL Tuning Advisor using Cloud Control. 25-32 Chapter 25 Running SQL Tuning Advisor On Demand 25.3.1.2.2 Command-Line Interface to On-Demand SQL Tuning If Cloud Control is unavailable, then you can run SQL Tuning Advisor using procedures in the DBMS_SQLTUNE package. To use the APIs, the user must have the ADVISOR privilege. See Also: Oracle Database PL/SQL Packages and Types Reference for complete reference information 25.3.1.3 Basic Tasks in On-Demand SQL Tuning This section explains the basic tasks in running SQL Tuning Advisor using the DBMS_SQLTUNE package. The following graphic shows the basic workflow when using the PL/SQL APIs. Figure 25-12 SQL Tuning Advisor APIs STS create_tuning_task set_tuning_task_parameter execute_tuning_task Monitor Task report_tuning_task Implement Recommendations Gather optimizer statistics Create SQL Profile Create Index Restructure SQL Create SQL Plan Baseline As shown in Figure 25-12, the basic procedure is as follows: 25-33 Chapter 25 Running SQL Tuning Advisor On Demand 1. 2. Prepare or create the input to SQL Tuning Advisor. The input can be either: • The text of a single SQL statement • A SQL tuning set that contains one or more statements Create a SQL tuning task. See "Creating a SQL Tuning Task". 3. Optionally, configure the SQL tuning task that you created. See "Configuring a SQL Tuning Task". 4. Execute a SQL tuning task. See "Executing a SQL Tuning Task". 5. Optionally, check the status or progress of a SQL tuning task. "Monitoring a SQL Tuning Task". 6. Display the results of a SQL tuning task. "Displaying the Results of a SQL Tuning Task". 7. Implement recommendations as appropriate. See Also: Oracle Database 2 Day + Performance Tuning Guide to learn how to tune SQL using Cloud Control 25.3.2 Creating a SQL Tuning Task To create a SQL tuning task execute the DBMS_SQLTUNE.CREATE_TUNING_TASK function. You can create tuning tasks from any of the following: • The text of a single SQL statement • A SQL tuning set containing multiple statements • A SQL statement selected by SQL identifier from the shared SQL area • A SQL statement selected by SQL identifier from AWR The scope parameter is one of the most important for this function. You can set this parameter to the following values: • LIMITED SQL Tuning Advisor produces recommendations based on statistical checks, access path analysis, and SQL structure analysis. SQL profile recommendations are not generated. • COMPREHENSIVE SQL Tuning Advisor carries out all the analysis it performs under limited scope plus SQL profiling. 25-34 Chapter 25 Running SQL Tuning Advisor On Demand Assumptions This tutorial assumes the following: • You want to tune as user hr, who has the ADVISOR privilege. • You want to tune the following query: SELECT FROM WHERE AND AND /*+ ORDERED */ * employees e, locations l, departments d e.department_id = d.department_id l.location_id = d.location_id e.employee_id < :bnd; • You want to pass the bind variable 100 to the preceding query. • You want SQL Tuning Advisor to perform SQL profiling. • You want the task to run no longer than 60 seconds. To create a SQL tuning task: 1. Connect SQL*Plus to the database with the appropriate privileges, and then run the DBMS_SQLTUNE.CREATE_TUNING_TASK function. For example, execute the following PL/SQL program: DECLARE my_task_name VARCHAR2(30); my_sqltext CLOB; BEGIN my_sqltext := 'SELECT /*+ ORDERED */ * ' 'FROM employees e, locations l, departments d ' 'WHERE e.department_id = d.department_id AND ' 'l.location_id = d.location_id AND ' 'e.employee_id < :bnd'; || || || || my_task_name := DBMS_SQLTUNE.CREATE_TUNING_TASK ( sql_text => my_sqltext bind_list => sql_binds(anydata.ConvertNumber(100)) user_name => 'HR' scope => 'COMPREHENSIVE' time_limit => 60 task_name => 'STA_SPECIFIC_EMP_TASK' description => 'Task to tune a query on a specified employee' , , , , , , ); END; / 2. Optionally, query the status of the task. The following example queries the status of all tasks owned by the current user, which in this example is hr: COL TASK_ID FORMAT 999999 COL TASK_NAME FORMAT a25 COL STATUS_MESSAGE FORMAT a33 SELECT TASK_ID, TASK_NAME, STATUS, STATUS_MESSAGE FROM USER_ADVISOR_LOG; Sample output appears below: 25-35 Chapter 25 Running SQL Tuning Advisor On Demand TASK_ID TASK_NAME STATUS STATUS_MESSAGE ------- ------------------------- ----------- -------------884 STA_SPECIFIC_EMP_TASK INITIAL In the preceding output, the INITIAL status indicates that the task has not yet started execution. See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SQLTUNE.CREATE_TUNING_TASK function 25.3.3 Configuring a SQL Tuning Task To change the parameters of a tuning task after it has been created, execute the DBMS_SQLTUNE.SET_TUNING_TASK_PARAMETER function. This tutorial assumes the following: • You want to tune with user account hr, which has been granted the ADVISOR privilege. • You want to tune the STA_SPECIFIC_EMP_TASK created in "Creating a SQL Tuning Task". • You want to change the maximum time that the SQL tuning task can run to 300 seconds. To configure a SQL tuning task: 1. Connect SQL*Plus to the database with the appropriate privileges, and then run the DBMS_SQLTUNE.SET_TUNING_TASK_PARAMETER function. For example, execute the following PL/SQL program to change the time limit of the tuning task to 300 seconds: BEGIN DBMS_SQLTUNE.SET_TUNING_TASK_PARAMETER ( task_name => 'STA_SPECIFIC_EMP_TASK' , parameter => 'TIME_LIMIT' , value => 300 ); END; / 2. Optionally, verify that the task parameter was changed. The following example queries the values of all used parameters in task STA_SPECIFIC_EMP_TASK: COL PARAMETER_NAME FORMAT a25 COL VALUE FORMAT a15 SELECT PARAMETER_NAME, PARAMETER_VALUE AS "VALUE" FROM USER_ADVISOR_PARAMETERS WHERE TASK_NAME = 'STA_SPECIFIC_EMP_TASK' AND PARAMETER_VALUE != 'UNUSED' ORDER BY PARAMETER_NAME; 25-36 Chapter 25 Running SQL Tuning Advisor On Demand Sample output appears below: PARAMETER_NAME ------------------------DAYS_TO_EXPIRE DEFAULT_EXECUTION_TYPE EXECUTION_DAYS_TO_EXPIRE JOURNALING MODE SQL_LIMIT SQL_PERCENTAGE TARGET_OBJECTS TEST_EXECUTE TIME_LIMIT Example 25-4 VALUE --------------30 TUNE SQL UNLIMITED INFORMATION COMPREHENSIVE -1 1 1 AUTO 300 Tuning a Standby Database Workload Using a Database Link Starting in Oracle Database 12c Release 2 (12.2), you can tune a standby database workload by specifying a database link in the database_link_to parameter. For security reasons, Oracle recommends using a private database link. The link must be owned by SYS and accessed by a privileged user. Oracle Database includes a default privileged user named SYS$UMF. The following program, which is issued on the standby database, shows a sample SQL tuning session for a query of table1. The database_link_to parameter specifies the name of the standby-to-primary database link. VARIABLE tname VARCHAR2(30); VARIABLE query VARCHAR2(500); EXEC :tname := 'my_task'; EXEC :query := 'SELECT /*+ FULL(t)*/ col1 FROM table1 t WHERE col1=9000'; BEGIN :tname := DBMS_SQLTUNE.CREATE_TUNING_TASK( sql_text => :query , task_name => :tname , database_link_to => 'lnk_to_pri' ); END; / EXEC DBMS_SQLTUNE.EXECUTE_TUNING_TASK(:tname); SELECT DBMS_SQLTUNE.REPORT_TUNING_TASK(:tname) FROM DUAL; BEGIN DBMS_SQLTUNE.ACCEPT_SQL_PROFILE( task_name => :tname , name => 'prof' , task_owner => 'SYS' , replace => TRUE , database_link_to => 'lnk_to_pri' ); END; / Note that the bind_list parameter of CREATE_TUNING_TASK is not supported on a standby database. 25-37 Chapter 25 Running SQL Tuning Advisor On Demand See Also: • "Local and Remote SQL Tuning" • Oracle Data Guard Concepts and Administration to learn how to perform remote tuning in a Data Guard environment • Oracle Database PL/SQL Packages and Types Reference for DBMS_SQLTUNE.SET_TUNING_TASK_PARAMETER syntax and semantics 25.3.4 Executing a SQL Tuning Task To execute a SQL tuning task, use the DBMS_SQLTUNE.EXECUTE_TUNING_TASK function. The most important parameter is task_name. Note: You can also execute the automatic tuning task SYS_AUTO_SQL_TUNING_TASK using the EXECUTE_TUNING_TASK API. SQL Tuning Advisor performs the same analysis and actions as it would when run automatically. Assumptions This tutorial assumes the following: • You want to tune as user hr, who has the ADVISOR privilege. • You want to execute the STA_SPECIFIC_EMP_TASK created in "Creating a SQL Tuning Task". To execute a SQL tuning task: 1. Connect SQL*Plus to the database with the appropriate privileges, and then run the DBMS_SQLTUNE.EXECUTE_TUNING_TASK function. For example, execute the following PL/SQL program: BEGIN DBMS_SQLTUNE.EXECUTE_TUNING_TASK(task_name=>'STA_SPECIFIC_EMP_TASK'); END; / 2. Optionally, query the status of the task. The following example queries the status of all tasks owned by the current user, which in this example is hr: COL TASK_ID FORMAT 999999 COL TASK_NAME FORMAT a25 COL STATUS_MESSAGE FORMAT a33 SELECT TASK_ID, TASK_NAME, STATUS, STATUS_MESSAGE FROM USER_ADVISOR_LOG; Sample output appears below: 25-38 Chapter 25 Running SQL Tuning Advisor On Demand TASK_ID TASK_NAME STATUS STATUS_MESSAGE ------- ------------------------- ----------- -------------884 STA_SPECIFIC_EMP_TASK COMPLETED See Also: Oracle Database PL/SQL Packages and Types Reference for complete reference information about the DBMS_SQLTUNE.EXECUTE_TUNING_TASK function 25.3.5 Monitoring a SQL Tuning Task When you create a SQL tuning task in Cloud Control, no separate monitoring step is necessary. Cloud Control displays the status page automatically. If you do not use Cloud Control, then you can monitor currently executing SQL tuning tasks by querying the data dictionary and dynamic performance views. The following table describes the relevant views. Table 25-3 DBMS_SQLTUNE.EXECUTE_TUNING_TASK Parameters View Description USER_ADVISOR_TASKS Displays information about tasks owned by the current user. The view contains one row for each task. Each task has a name that is unique to the owner. Task names are just informational and no uniqueness is enforced within any other namespace. V$ADVISOR_PROGRESS Displays information about the progress of advisor execution. Assumptions This tutorial assumes the following: • You tune as user hr, who has the ADVISOR privilege. • You monitor the STA_SPECIFIC_EMP_TASK that you executed in "Executing a SQL Tuning Task". To monitor a SQL tuning task: 1. Connect SQL*Plus to the database with the appropriate privileges, and then determine whether the task is executing or completed. For example, query the status of STA_SPECIFIC_EMP_TASK as follows: SELECT STATUS FROM USER_ADVISOR_TASKS WHERE TASK_NAME = 'STA_SPECIFIC_EMP_TASK'; The following output shows that the task has completed: STATUS ----------EXECUTING 25-39 Chapter 25 Running SQL Tuning Advisor On Demand 2. Determine the progress of an executing task. The following example queries the status of the task with task ID 884: VARIABLE my_tid NUMBER; EXEC :my_tid := 884 COL ADVISOR_NAME FORMAT a20 COL SOFAR FORMAT 999 COL TOTALWORK FORMAT 999 SELECT TASK_ID, ADVISOR_NAME, SOFAR, TOTALWORK, ROUND(SOFAR/TOTALWORK*100,2) "%_COMPLETE" FROM V$ADVISOR_PROGRESS WHERE TASK_ID = :my_tid; Sample output appears below: TASK_ID ADVISOR_NAME SOFAR TOTALWORK %_COMPLETE ---------- -------------------- ----- --------- ---------884 SQL Tuning Advisor 1 2 50 See Also: Oracle Database Reference to learn about the V$ADVISOR_PROGRESS view 25.3.6 Displaying the Results of a SQL Tuning Task To report the results of a tuning task, use the DBMS_SQLTUNE.REPORT_TUNING_TASK function. The report contains all the findings and recommendations of SQL Tuning Advisor. For each proposed recommendation, the report provides the rationale and benefit along with the SQL statements needed to implement the recommendation. Assumptions This tutorial assumes the following: • You want to tune as user hr, who has the ADVISOR privilege. • You want to access the report for the STA_SPECIFIC_EMP_TASK executed in "Executing a SQL Tuning Task". To view the report for a SQL tuning task: 1. Connect SQL*Plus to the database with the appropriate privileges, and then run the DBMS_SQLTUNE.REPORT_TUNING_TASK function. For example, you run the following statements: SET LONG 1000 SET LONGCHUNKSIZE 1000 SET LINESIZE 100 SELECT DBMS_SQLTUNE.REPORT_TUNING_TASK( 'STA_SPECIFIC_EMP_TASK' ) FROM DUAL; Truncated sample output appears below: 25-40 Chapter 25 Running SQL Tuning Advisor On Demand DBMS_SQLTUNE.REPORT_TUNING_TASK('STA_SPECIFIC_EMP_TASK') --------------------------------------------------------------------------GENERAL INFORMATION SECTION --------------------------------------------------------------------------Tuning Task Name : STA_SPECIFIC_EMP_TASK Tuning Task Owner : HR Workload Type : Single SQL Statement Execution Count : 11 Current Execution : EXEC_1057 Execution Type : TUNE SQL Scope : COMPREHENSIVE Time Limit(seconds): 300 Completion Status : COMPLETED Started at : 04/22/2012 07:35:49 Completed at : 04/22/2012 07:35:50 --------------------------------------------------------------------------Schema Name: HR SQL ID : dg7nfaj0bdcvk SQL Text : SELECT /*+ ORDERED */ * FROM employees e, locations l, departments d WHERE e.department_id = d.department_id AND l.location_id = d.location_id AND e.employee_id < :bnd Bind Variables : 1 - (NUMBER):100 --------------------------------------------------------------------------FINDINGS SECTION (4 findings) ----------------------------------------------2. Interpret the results. See Also: • "Viewing Automatic SQL Tuning Reports Using the Command Line" • Oracle Database PL/SQL Packages and Types Reference for complete reference information 25-41 26 Optimizing Access Paths with SQL Access Advisor SQL Access Advisor is diagnostic software that identifies and helps resolve SQL performance problems by recommending indexes, materialized views, materialized view logs, or partitions to create, drop, or retain. This chapter contains the following topics: • About SQL Access Advisor SQL Access Advisor accepts input from several sources, including SQL tuning sets, and then issues recommendations. • Optimizing Access Paths with SQL Access Advisor: Basic Tasks Basic tasks include creating an STS, loading it, creating a SQL Access Advisor task, and then executing the task. • Performing a SQL Access Advisor Quick Tune To tune a single SQL statement, the DBMS_ADVISOR.QUICK_TUNE procedure accepts as its input a task_name and a single SQL statement. • Using SQL Access Advisor: Advanced Tasks This section describes advanced tasks involving SQL Access Advisor. • SQL Access Advisor Examples Oracle Database provides a script that contains several SQL Access Advisor examples that you can run on a test database. • SQL Access Advisor Reference You can access metadata about SQL Access Advisor using data dictionary views. 26.1 About SQL Access Advisor SQL Access Advisor accepts input from several sources, including SQL tuning sets, and then issues recommendations. Note: Data visibility and privilege requirements may differ when using SQL Access Advisor with pluggable databases. This section contains the following topics: • Purpose of SQL Access Advisor SQL Access Advisor recommends the proper set of materialized views, materialized view logs, partitions, and indexes for a specified workload. 26-1 Chapter 26 About SQL Access Advisor • SQL Access Advisor Architecture Automatic Tuning Optimizer is the central tool used by SQL Access Advisor. • User Interfaces for SQL Access Advisor Oracle recommends that you use SQL Access Advisor through its GUI wizard, which is available in Cloud Control. See Also: Oracle Database Administrator’s Guide for a table that summarizes how manageability features work in a container database (CDB) 26.1.1 Purpose of SQL Access Advisor SQL Access Advisor recommends the proper set of materialized views, materialized view logs, partitions, and indexes for a specified workload. Materialized views, partitions, and indexes are essential when tuning a database to achieve optimum performance for complex, data-intensive queries. SQL Access Advisor takes an actual workload as input, or derives a hypothetical workload from a schema. The advisor then recommends access structures for faster execution path. The advisor provides the following advantages: • Does not require you to have expert knowledge • Makes decisions based on rules that reside in the optimizer • Covers all aspects of SQL access in a single advisor • Provides simple, user-friendly GUI wizards in Cloud Control • Generates scripts for implementation of recommendations See Also: Oracle Database 2 Day + Performance Tuning Guide to learn how to use SQL Access Advisor with Cloud Control 26.1.2 SQL Access Advisor Architecture Automatic Tuning Optimizer is the central tool used by SQL Access Advisor. The advisor can receive SQL statements as input from the sources shown in Figure 26-1, analyze these statements using the optimizer, and then make recommendations. Figure 26-1 shows the basic architecture of SQL Access Advisor. 26-2 Chapter 26 About SQL Access Advisor Figure 26-1 SQL Access Advisor Architecture Filter Options Hypothetical Optimizer Automatic Tuning Optimizer Shared Pool Library Cache Shared SQL Area SELECT * FROM employees Workload SQL Access Advisor Recommendations Indexes Materialized Views Materialized View Logs SQL Tuning Set Partitions DBA This section contains the following topics: • Input to SQL Access Advisor SQL Access Advisor requires a workload, which consists of one or more SQL statements, plus statistics and attributes that fully describe each statement. • Filter Options for SQL Access Advisor You can apply a filter to a workload to restrict what is analyzed. • SQL Access Advisor Recommendations A task recommendation can range from a simple to a complex solution. • SQL Access Advisor Actions In general, each recommendation provides a benefit for a set of queries. • SQL Access Advisor Repository Information required and generated by SQL Access Advisor resides in the Advisor repository, which is in the data dictionary. See Also: "About Automatic Tuning Optimizer" 26-3 Chapter 26 About SQL Access Advisor 26.1.2.1 Input to SQL Access Advisor SQL Access Advisor requires a workload, which consists of one or more SQL statements, plus statistics and attributes that fully describe each statement. A full workload contains all SQL statements from a target business application. A partial workload contains a subset of SQL statements. As shown in Figure 26-1, SQL Access Advisor input can come from the following sources: • Shared SQL area The database uses the shared SQL area to analyze recent SQL statements that are currently in V$SQL. • SQL tuning set A SQL tuning set (STS) is a database object that stores SQL statements along with their execution context. When a set of SQL statements serve as input, the database must first construct and use an STS. Note: For best results, provide a workload as a SQL tuning set. The DBMS_SQLTUNE package provides helper functions that can create SQL tuning sets from common workload sources, such as the SQL cache, a user-defined workload stored in a table, and a hypothetical workload. • Hypothetical workload You can create a hypothetical workload from a schema by analyzing dimensions and constraints. This option is useful when you are initially designing your application. See Also: • "About SQL Tuning Sets" • Oracle Database Concepts to learn about the shared SQL area 26.1.2.2 Filter Options for SQL Access Advisor You can apply a filter to a workload to restrict what is analyzed. For example, specify that the advisor look at only the 30 most resource-intensive statements in the workload, based on optimizer cost. This restriction can generate different sets of recommendations based on different workload scenarios. SQL Access Advisor parameters control the recommendation process and customization of the workload. These parameters control various aspects of the process, such as the type of recommendation required and the naming conventions for what it recommends. 26-4 Chapter 26 About SQL Access Advisor To set these parameters, use the DBMS_ADVISOR.SET_TASK_PARAMETER procedure. Parameters are persistent in that they remain set for the life span of the task. When a parameter value is set using DBMS_ADVISOR.SET_TASK_PARAMETER, the value does not change until you make another call to this procedure. See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_ADVISOR.SET_TASK_PARAMETER procedure 26.1.2.3 SQL Access Advisor Recommendations A task recommendation can range from a simple to a complex solution. The advisor can recommend that you create database objects such as the following: • Indexes SQL Access Advisor index recommendations include bitmap, function-based, and B-tree indexes. A bitmap index offers a reduced response time for many types of ad hoc queries and reduced storage requirements compared to other indexing techniques. B-tree indexes are most commonly used in a data warehouse to index unique or near-unique keys. SQL Access Advisor materialized view recommendations include fast refreshable and full refreshable materialized views, for either general rewrite or exact text match rewrite. • Materialized views SQL Access Advisor, using the TUNE_MVIEW procedure, also recommends how to optimize materialized views so that they can be fast refreshable and take advantage of general query rewrite. • Materialized view logs A materialized view log is a table at the materialized view's master site or master materialized view site that records all DML changes to the master table or master materialized view. A fast refresh of a materialized view is possible only if the materialized view's master has a materialized view log. • Partitions SQL Access Advisor can recommend partitioning on an existing unpartitioned base table to improve performance. Furthermore, it may recommend new indexes and materialized views that are themselves partitioned. While creating new partitioned indexes and materialized view is no different from the unpartitioned case, partition existing base tables with care. This is especially true when indexes, views, constraints, or triggers are defined on the table. To make recommendations, SQL Access Advisor relies on structural statistics about table and index cardinalities of dimension level columns, JOIN KEY columns, and fact table key columns. You can gather exact or estimated statistics with the DBMS_STATS package. Because gathering statistics is time-consuming and full statistical accuracy is not required, it is usually preferable to estimate statistics. Without gathering statistics on a specified table, queries referencing this table are marked as invalid in the workload, 26-5 Chapter 26 About SQL Access Advisor resulting in no recommendations for these queries. It is also recommended that all existing indexes and materialized views have been analyzed. See Also: • "About Manual Statistics Collection with DBMS_STATS" • Oracle Database Data Warehousing Guide to learn more about materialized views • Oracle Database VLDB and Partitioning Guide to learn more about partitions 26.1.2.4 SQL Access Advisor Actions In general, each recommendation provides a benefit for a set of queries. All individual actions in a recommendation must be implemented together to achieve the full benefit. Recommendations can share actions. For example, a CREATE INDEX statement could provide a benefit for several queries, but some queries might benefit from an additional CREATE MATERIALIZED VIEW statement. In that case, the advisor would generate two recommendations: one for the set of queries that require only the index, and another one for the set of queries that require both the index and the materialized view. This section contains the following topics: • Types of Actions SQL Access Advisor makes several different types of recommendations. • Guidelines for Interpreting Partitioning Recommendations When SQL Access Advisor determines that partitioning a base table would improve performance, the advisor adds a partition action to every recommendation containing a query referencing the table. In this way, index and materialized view recommendations are implemented on the correctly partitioned tables. 26.1.2.4.1 Types of Actions SQL Access Advisor makes several different types of recommendations. Recommendations include the following types of actions: • PARTITION BASE TABLE This action partitions an existing unpartitioned base table. • CREATE|DROP|RETAIN {MATERIALIZED VIEW|MATERIALIZED VIEW LOG|INDEX} The CREATE actions corresponds to new access structures. RETAIN recommends keeping existing access structures. SQL Access Advisor only recommends DROP when the WORKLOAD_SCOPE parameter is set to FULL. • GATHER STATS This action generates a call to a DBMS_STATS procedure to gather statistics on a newly generated access structure. 26-6 Chapter 26 About SQL Access Advisor Multiple recommendations may refer to the same action. However, when generating a script for the recommendation, you only see each action once. See Also: • "About Manual Statistics Collection with DBMS_STATS" • "Viewing SQL Access Advisor Task Results" to learn how to view actions and recommendations 26.1.2.4.2 Guidelines for Interpreting Partitioning Recommendations When SQL Access Advisor determines that partitioning a base table would improve performance, the advisor adds a partition action to every recommendation containing a query referencing the table. In this way, index and materialized view recommendations are implemented on the correctly partitioned tables. SQL Access Advisor may recommend partitioning an existing nonpartitioned base table. When the advisor implementation script contains partition recommendations, note the following issues: • Partitioning an existing table is a complex and extensive operation, which may take considerably longer than implementing a new index or materialized view. Sufficient time should be reserved for implementing this recommendation. • While index and materialized view recommendations are easy to reverse by deleting the index or view, a table, after being partitioned, cannot easily be restored to its original state. Therefore, ensure that you back up the database before executing a script containing partition recommendations. • While repartitioning a base table, SQL Access Advisor scripts make a temporary copy of the original table, which occupies the same amount of space as the original table. Therefore, the repartitioning process requires sufficient free disk space for another copy of the largest table to be repartitioned. Ensure that such space is available before running the implementation script. The partition implementation script attempts to migrate dependent objects such as indexes, materialized views, and constraints. However, some object cannot be automatically migrated. For example, PL/SQL stored procedures defined against a repartitioned base table typically become invalid and must be recompiled. • If you decide not to implement a partition recommendation, then all other recommendations on the same table in the same script (such as CREATE INDEX and CREATE MATERIALIZED VIEW recommendations) depend on the partitioning recommendation. To obtain accurate recommendations, do not simply remove the partition recommendation from the script. Rather, rerun the advisor with partitioning disabled, for example, by setting parameter ANALYSIS_SCOPE to a value that does not include the keyword TABLE. 26-7 Chapter 26 About SQL Access Advisor See Also: • Oracle Database SQL Language Reference for CREATE DIRECTORY syntax • Oracle Database PL/SQL Packages and Types Reference for detailed information about the DBMS_ADVISOR.GET_TASK_SCRIPT function 26.1.2.5 SQL Access Advisor Repository Information required and generated by SQL Access Advisor resides in the Advisor repository, which is in the data dictionary. The SQL Access Advisor repository has the following benefits: • Collects a complete workload for SQL Access Advisor • Supports historical data • Is managed by the database 26.1.3 User Interfaces for SQL Access Advisor Oracle recommends that you use SQL Access Advisor through its GUI wizard, which is available in Cloud Control. You can also invoke SQL Access Advisor through the DBMS_ADVISOR package. This chapter explains how to use the API. This section contains the following topics: • Accessing the SQL Access Advisor: Initial Options Page Using Cloud Control The SQL Access Advisor: Initial Options page in Cloud Control is the starting page for a wizard that guides you through the process of obtaining recommendations. • Command-Line Interface to SQL Tuning Sets On the command line, you can use the DBMS_ADVISOR package to manage SQL Tuning Advisor. See Also: • Oracle Database 2 Day + Performance Tuning Guide explains how to use the SQL Access Advisor wizard. • See Oracle Database PL/SQL Packages and Types Reference for complete semantics and syntax. 26-8 Chapter 26 About SQL Access Advisor 26.1.3.1 Accessing the SQL Access Advisor: Initial Options Page Using Cloud Control The SQL Access Advisor: Initial Options page in Cloud Control is the starting page for a wizard that guides you through the process of obtaining recommendations. To access the SQL Access Advisor: Initial Options page: 1. Log in to Cloud Control with the appropriate credentials. 2. Under the Targets menu, select Databases. 3. In the list of database targets, select the target for the Oracle Database instance that you want to administer. 4. If prompted for database credentials, then enter the minimum credentials necessary for the tasks you intend to perform. 5. From the Performance menu, select SQL, then SQL Access Advisor. The SQL Access Advisor: Initial Options page appears., shown in Figure 26-2. Figure 26-2 SQL Access Advisor: Initial Options You can perform most SQL plan management tasks in this page or in pages accessed through this page. See Also: • Cloud Control context-sensitive online help to learn about the options on the SQL Access Advisor: Initial Options page • Oracle Database 2 Day + Performance Tuning Guide to learn how to configure and run SQL Tuning Advisor using Cloud Control 26-9 Chapter 26 Optimizing Access Paths with SQL Access Advisor: Basic Tasks 26.1.3.2 Command-Line Interface to SQL Tuning Sets On the command line, you can use the DBMS_ADVISOR package to manage SQL Tuning Advisor. The DBMS_ADVISOR package consists of a collection of analysis and advisory functions and procedures callable from any PL/SQL program. You must have the ADVISOR privilege to use DBMS_ADVISOR. See Also: Oracle Database PL/SQL Packages and Types Reference to learn about DBMS_ADVISOR 26.2 Optimizing Access Paths with SQL Access Advisor: Basic Tasks Basic tasks include creating an STS, loading it, creating a SQL Access Advisor task, and then executing the task. The following graphic shows the basic workflow for SQL Access Advisor. 26-10 Chapter 26 Optimizing Access Paths with SQL Access Advisor: Basic Tasks Figure 26-3 Using SQL Access Advisor Typically, you use SQL Access Advisor by performing the following steps: 1. Create a SQL tuning set The input workload source for SQL Access Advisor is a SQL tuning set (STS). Use DBMS_SQLTUNE.CREATE_SQLSET to create a SQL tuning set. "Creating a SQL Tuning Set as Input for SQL Access Advisor" describes this task. 2. Load the SQL tuning set SQL Access Advisor performs best when a workload based on actual usage is available. Use DBMS_SQLTUNE.LOAD_SQLSET to populate the SQL tuning set with your workload. "Populating a SQL Tuning Set with a User-Defined Workload" describes this task. 3. Create and configure a task In the task, you define what SQL Access Advisor must analyze and the location of the analysis results. Create a task using the DBMS_ADVISOR.CREATE_TASK procedure. You can then define parameters for the task using the SET_TASK_PARAMETER procedure, and then link the task to an STS by using the DBMS_ADVISOR.ADD_STS_REF procedure. "Creating and Configuring a SQL Access Advisor Task" describes this task. 26-11 Chapter 26 Optimizing Access Paths with SQL Access Advisor: Basic Tasks 4. Execute the task Run the DBMS_ADVISOR.EXECUTE_TASK procedure to generate recommendations. Each recommendation specifies one or more actions. For example, a recommendation could be to create several materialized view logs, create a materialized view, and then analyze it to gather statistics. "Executing a SQL Access Advisor Task" describes this task. 5. View the recommendations You can view the recommendations by querying data dictionary views. "Viewing SQL Access Advisor Task Results" describes this task. 6. Optionally, generate and execute a SQL script that implements the recommendations. "Generating and Executing a Task Script" that describes this task. This section contains the following topics: • Creating a SQL Tuning Set as Input for SQL Access Advisor The input workload source for SQL Access Advisor is an STS. • Populating a SQL Tuning Set with a User-Defined Workload A workload consists of one or more SQL statements, plus statistics and attributes that fully describe each statement. • Creating and Configuring a SQL Access Advisor Task Use the DBMS_ADVISOR.CREATE_TASK procedure to create a SQL Access Advisor task. • Executing a SQL Access Advisor Task The DBMS_ADVISOR.EXECUTE_TASK procedure performs SQL Access Advisor analysis or evaluation for the specified task. • Viewing SQL Access Advisor Task Results You can view each recommendation generated by SQL Access Advisor using several data dictionary views. • Generating and Executing a Task Script You can use the procedure DBMS_ADVISOR.GET_TASK_SCRIPT to create a script of the SQL statements for the SQL Access Advisor recommendations. The script is an executable SQL file that can contain DROP, CREATE, and ALTER statements. 26.2.1 Creating a SQL Tuning Set as Input for SQL Access Advisor The input workload source for SQL Access Advisor is an STS. Because an STS is stored as a separate entity, multiple advisor tasks can share it. Create an STS with the DBMS_SQLTUNE.CREATE_SQLSET procedure. After an advisor task has referenced an STS, you cannot delete or modify the STS until all advisor tasks have removed their dependency on it. A workload reference is removed when a parent advisor task is deleted, or when you manually remove the workload reference from the advisor task. Prerequisites The user creating the STS must have been granted the ADMINISTER SQL TUNING SET privilege. To run SQL Access Advisor on SQL tuning sets owned by other users, the user must have the ADMINISTER ANY SQL TUNING SET privilege. 26-12 Chapter 26 Optimizing Access Paths with SQL Access Advisor: Basic Tasks Assumptions This tutorial assumes the following: • You want to create an STS named MY_STS_WORKLOAD. • You want to use this STS as input for a workload derived from the sh schema. To create an STS : 1. In SQL*Plus, log in to the database as user sh. 2. Set SQL*Plus variables. For example, enter the following commands: SET SERVEROUTPUT ON; VARIABLE task_id NUMBER; VARIABLE task_name VARCHAR2(255); VARIABLE workload_name VARCHAR2(255); 3. Create the SQL tuning set. For example, assign a value to the workload_name variable and create the STS as follows: EXECUTE :workload_name := 'MY_STS_WORKLOAD'; EXECUTE DBMS_SQLTUNE.CREATE_SQLSET(:workload_name, 'test purpose'); See Also: • "About SQL Tuning Sets" • Oracle Database PL/SQL Packages and Types Reference to learn about CREATE_SQLSET 26.2.2 Populating a SQL Tuning Set with a User-Defined Workload A workload consists of one or more SQL statements, plus statistics and attributes that fully describe each statement. A full workload contains all SQL statements from a target business application. A partial workload contains a subset of SQL statements. The difference is that for full workloads SQL Access Advisor may recommend dropping unused materialized views and indexes. You cannot use SQL Access Advisor without a workload. SQL Access Advisor ranks the entries according to a specific statistic, business importance, or combination of the two, which enables the advisor to process the most important SQL statements first. SQL Access Advisor performs best with a workload based on actual usage. You can store multiple workloads in the form of SQL tuning sets, so that you can view the different uses of a real-world data warehousing or OLTP environment over a long period and across the life cycle of database instance startup and shutdown. The following table describes procedures that you can use to populate an STS with a user-defined workload. 26-13 Chapter 26 Optimizing Access Paths with SQL Access Advisor: Basic Tasks Table 26-1 Procedures for Loading an STS Procedure Description To Learn More DBMS_SQLTUNE.LOAD_SQLSET Populates the SQL tuning set Oracle Database PL/SQL with a set of selected SQL. Packages and Types You can call the procedure Reference multiple times to add new SQL statements or replace attributes of existing statements. DBMS_ADVISOR.COPY_SQLWKLD_ Copies SQL workload data to Oracle Database PL/SQL a user-designated SQL tuning Packages and Types TO_STS set. The user must have the Reference required SQL tuning set privileges and the required ADVISOR privilege. Assumptions This tutorial assumes that you want to do the following: • Create a table named sh.user_workload to store information about SQL statements • Load the sh.user_workload table with information about three queries of tables in the sh schema • Populate the STS created in "Creating a SQL Tuning Set as Input for SQL Access Advisor" with the workload contained in sh.user_workload To populate an STS with a user-defined workload: 1. Connect SQL*Plus to the database as user sh, and then create the user_workload table. For example, enter the following commands: DROP TABLE user_workload; CREATE TABLE user_workload ( username varchar2(128), /* User who executes statement module varchar2(64), /* Application module name action varchar2(64), /* Application action name elapsed_time number, /* Elapsed time for query cpu_time number, /* CPU time for query buffer_gets number, /* Buffer gets consumed by query disk_reads number, /* Disk reads consumed by query rows_processed number, /* # of rows processed by query executions number, /* # of times query executed optimizer_cost number, /* Optimizer cost for query priority number, /* User-priority (1,2 or 3) last_execution_date date, /* Last time query executed stat_period number, /* Window exec time in seconds sql_text clob /* Full SQL Text ); 2. */ */ */ */ */ */ */ */ */ */ */ */ */ */ Load the user_workload table with information about queries. For example, execute the following statements: 26-14 Chapter 26 Optimizing Access Paths with SQL Access Advisor: Basic Tasks -- aggregation with selection INSERT INTO user_workload (username, module, action, priority, sql_text) VALUES ('SH', 'Example1', 'Action', 2, 'SELECT t.week_ending_day, p.prod_subcategory, SUM(s.amount_sold) AS dollars, s.channel_id, s.promo_id FROM sales s, times t, products p WHERE s.time_id = t.time_id AND s.prod_id = p.prod_id AND s.prod_id > 10 AND s.prod_id < 50 GROUP BY t.week_ending_day, p.prod_subcategory, s.channel_id, s.promo_id') / -- aggregation with selection INSERT INTO user_workload (username, module, action, priority, sql_text) VALUES ('SH', 'Example1', 'Action', 2, 'SELECT t.calendar_month_desc, SUM(s.amount_sold) AS dollars FROM sales s , times t WHERE s.time_id = t.time_id AND s.time_id BETWEEN TO_DATE(''01-JAN-2000'', ''DD-MON-YYYY'') AND TO_DATE(''01-JUL-2000'', ''DD-MON-YYYY'') GROUP BY t.calendar_month_desc') / -- order by INSERT INTO user_workload (username, module, action, priority, sql_text) VALUES ('SH', 'Example1', 'Action', 2, 'SELECT c.country_id, c.cust_city, c.cust_last_name FROM customers c WHERE c.country_id IN (52790, 52789) ORDER BY c.country_id, c.cust_city, c.cust_last_name') / COMMIT; 3. Execute a PL/SQL program that fills a cursor with rows from the user_workload table, and then loads the contents of this cursor into the STS named MY_STS_WORKLOAD. For example, execute the following PL/SQL program: DECLARE sqlset_cur DBMS_SQLTUNE.SQLSET_CURSOR; BEGIN OPEN sqlset_cur FOR SELECT SQLSET_ROW(null,null, SQL_TEXT, null, null, 'SH', module, 'Action', 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, null, 2, 3, sysdate, 0, 0, null, 0, null, null) FROM USER_WORKLOAD; DBMS_SQLTUNE.LOAD_SQLSET('MY_STS_WORKLOAD', sqlset_cur); END; / 26.2.3 Creating and Configuring a SQL Access Advisor Task Use the DBMS_ADVISOR.CREATE_TASK procedure to create a SQL Access Advisor task. In the SQL Access Advisor task, you define what the advisor must analyze and the location of the results. You can create multiple tasks, each with its own specialization. All are based on the same Advisor task model and share the same repository. Configuring the task involves the following steps: 26-15 Chapter 26 Optimizing Access Paths with SQL Access Advisor: Basic Tasks • Defining task parameters At the time the recommendations are generated, you can apply a filter to the workload to restrict what is analyzed. This restriction provides the ability to generate different sets of recommendations based on different workload scenarios. SQL Access Advisor parameters control the recommendation process and customization of the workload. These parameters control various aspects of the process, such as the type of recommendation required and the naming conventions for what it recommends. If parameters are not defined, then the database uses the defaults. You can set task parameters by using the DBMS_ADVISOR.SET_TASK_PARAMETER procedure. Parameters are persistent in that they remain set for the life span of the task. When a parameter value is set using SET_TASK_PARAMETER, it does not change until you make another call to this procedure. • Linking the task to the workload Because the workload is independent, you must link it to a task using the DBMS_ADVISOR.ADD_STS_REF procedure. After this link has been established, you cannot delete or modify the workload until all advisor tasks have removed their dependency on the workload. A workload reference is removed when a user deletes a parent advisor task or manually removes the workload reference from the task by using the DBMS_ADVISOR.DELETE_STS_REF procedure. Prerequisites The user creating the task must have been granted the ADVISOR privilege. Assumptions This tutorial assumes the following: • You want to create a task named MYTASK. • You want to use this task to analyze the workload that you defined in "Populating a SQL Tuning Set with a User-Defined Workload". • You want to terminate the task if it takes longer than 30 minutes to execute. • You want to SQL Access Advisor to only consider indexes. To create and configure a SQL Access Advisor task: 1. Connect SQL*Plus to the database as user sh, and then create the task. For example, enter the following commands: EXECUTE :task_name := 'MYTASK'; EXECUTE DBMS_ADVISOR.CREATE_TASK('SQL Access Advisor', :task_id, :task_name); 2. Set task parameters. For example, execute the following statements: EXECUTE DBMS_ADVISOR.SET_TASK_PARAMETER(:task_name, 'TIME_LIMIT', 30); EXECUTE DBMS_ADVISOR.SET_TASK_PARAMETER(:task_name, 'ANALYSIS_SCOPE', 'ALL'); 3. Link the task to the workload. For example, execute the following statement: EXECUTE DBMS_ADVISOR.ADD_STS_REF(:task_name, 'SH', :workload_name); 26-16 Chapter 26 Optimizing Access Paths with SQL Access Advisor: Basic Tasks See Also: • "Categories for SQL Access Advisor Task Parameters" • "Deleting SQL Access Advisor Tasks" • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_ADVISOR.CREATE_TASK, DBMS_ADVISOR.SET_TASK_PARAMETER, and DBMS_ADVISOR.ADD_STS_REF procedures 26.2.4 Executing a SQL Access Advisor Task The DBMS_ADVISOR.EXECUTE_TASK procedure performs SQL Access Advisor analysis or evaluation for the specified task. Task execution is a synchronous operation, so the database does not return control to the user until the operation has completed, or the database detects a user interrupt. After the return or execution of the task, you can check the DBA_ADVISOR_LOG table for the execution status. Running EXECUTE_TASK generates recommendations. A recommendation includes one or more actions, such as creating a materialized view log or a materialized view. Prerequisites When processing a workload, SQL Access Advisor attempts to validate each statement to identify table and column references. The database achieves validation by processing each statement as if it were being executed by the statement's original user. If the user does not have SELECT privileges to a particular table, then SQL Access Advisor bypasses the statement referencing the table. This behavior can cause many statements to be excluded from analysis. If SQL Access Advisor excludes all statements in a workload, then the workload is invalid. SQL Access Advisor returns the following message: QSM-00774, there are no SQL statements to process for task TASK_NAME To avoid missing critical workload queries, the current database user must have SELECT privileges on the tables targeted for materialized view analysis. For these tables, these SELECT privileges cannot be obtained through a role. Assumptions This tutorial assumes that you want to execute the task you configured in "Creating and Configuring a SQL Access Advisor Task". To create and configure a SQL Access Advisor task: 1. In SQL*Plus or SQL Developer, log in to the database as a user with the necessary privileges. 2. Execute the task. For example, execute the following statement: EXECUTE DBMS_ADVISOR.EXECUTE_TASK(:task_name); 26-17 Chapter 26 Optimizing Access Paths with SQL Access Advisor: Basic Tasks 3. Optionally, query USER_ADVISOR_LOG to check the status of the task. For example, execute the following statements (sample output included): COL TASK_ID FORMAT 999 COL TASK_NAME FORMAT a25 COL STATUS_MESSAGE FORMAT a25 SELECT TASK_ID, TASK_NAME, STATUS, STATUS_MESSAGE FROM USER_ADVISOR_LOG; TASK_ID TASK_NAME STATUS STATUS_MESSAGE ------- ------------------------- ----------- ------------------------103 MYTASK COMPLETED Access advisor execution completed See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about the EXECUTE_TASK procedure and its parameters 26.2.5 Viewing SQL Access Advisor Task Results You can view each recommendation generated by SQL Access Advisor using several data dictionary views. The views are summarized in Table 26-2. However, it is easier to use the DBMS_ADVISOR.GET_TASK_SCRIPT procedure or Cloud Control, which graphically displays the recommendations and provides hyperlinks to quickly see which SQL statements benefit from a recommendation. Each recommendation produced by SQL Access Advisor is linked to the SQL statement it benefits. Each recommendation corresponds to one or more actions. Each action has one or more attributes. Each action has attributes pertaining to the access structure properties. The name and tablespace for each applicable access structure are in the ATTR1 and ATTR2 columns of USER_ADVISOR_ATTRIBUTES. The space occupied by each new access structure is in the NUM_ATTR1 column. Other attributes are different for each action. Table 26-2 Views Showing Task Results Data Dictionary View (DBA, USER) Description DBA_ADVISOR_TASKS Displays information about advisor tasks. To see SQL Access Advisor tasks, select where ADVISOR_NAME = 'SQL Access Advisor'. 26-18 Chapter 26 Optimizing Access Paths with SQL Access Advisor: Basic Tasks Table 26-2 (Cont.) Views Showing Task Results Data Dictionary View (DBA, USER) Description DBA_ADVISOR_RECOMMENDATIONS Displays the results of an analysis of all recommendations in the database. A recommendation can have multiple actions associated with it. The DBA_ADVISOR_ACTIONS view describe the actions. A recommendation also points to a set of rationales that present a justification/reasoning for that recommendation. The DBA_ADVISOR_RATIONALE view describes the rationales. DBA_ADVISOR_ACTIONS Displays information about the actions associated with all recommendations in the database. Each action is specified by the COMMAND and ATTR1 through ATTR6 columns. Each command defines how to use the attribute columns. DBA_ADVISOR_RATIONALE Displays information about the rationales for all recommendations in the database. DBA_ADVISOR_SQLA_WK_STMTS Displays information about all workload objects in the database after a SQL Access Advisor analysis. The precost and postcost numbers are in terms of the estimated optimizer cost (shown in EXPLAIN PLAN) without and with the recommended access structure. Assumptions This tutorial assumes that you want to view results of the task you executed in "Executing a SQL Access Advisor Task". To view the results of a SQL Access Advisor task: 1. Connect SQL*Plus to the database with the appropriate privileges, and then query the advisor recommendations. For example, execute the following statements (sample output included): VARIABLE workload_name VARCHAR2(255); VARIABLE task_name VARCHAR2(255); EXECUTE :task_name := 'MYTASK'; EXECUTE :workload_name := 'MY_STS_WORKLOAD'; SELECT REC_ID, RANK, BENEFIT FROM USER_ADVISOR_RECOMMENDATIONS WHERE TASK_NAME = :task_name ORDER BY RANK; REC_ID RANK BENEFIT ---------- ---------- ---------1 1 236 2 2 356 26-19 Chapter 26 Optimizing Access Paths with SQL Access Advisor: Basic Tasks The preceding output shows the recommendations (rec_id) produced by an SQL Access Advisor run, with their rank and total benefit. The rank is a measure of the importance of the queries that the recommendation helps. The benefit is the total improvement in execution cost (in terms of optimizer cost) of all queries using the recommendation. 2. Identify which query benefits from which recommendation. For example, execute the following query of USER_ADVISOR_SQLA_WK_STMTS (sample output included): SELECT SQL_ID, REC_ID, PRECOST, POSTCOST, (PRECOST-POSTCOST)*100/PRECOST AS PERCENT_BENEFIT FROM USER_ADVISOR_SQLA_WK_STMTS WHERE TASK_NAME = :task_name AND WORKLOAD_NAME = :workload_name ORDER BY percent_benefit DESC; SQL_ID REC_ID PRECOST POSTCOST PERCENT_BENEFIT ------------- ---------- ---------- ---------- --------------fn4bsxdm98w3u 2 578 222 61.5916955 29bbju72rv3t2 1 5750 5514 4.10434783 133ym38r6gbar 0 772 772 0 The precost and postcost numbers are in terms of the estimated optimizer cost (shown in EXPLAIN PLAN) both without and with the recommended access structure changes. 3. Display the number of distinct actions for this set of recommendations. For example, use the following query (sample output included): SELECT 'Action Count', COUNT(DISTINCT action_id) cnt FROM USER_ADVISOR_ACTIONS WHERE TASK_NAME = :task_name; 'ACTIONCOUNT CNT ------------ ---------Action Count 4 4. Display the actions for this set of recommendations. For example, use the following query (sample output included): SELECT REC_ID, ACTION_ID, SUBSTR(COMMAND,1,30) AS command FROM USER_ADVISOR_ACTIONS WHERE TASK_NAME = :task_name ORDER BY rec_id, action_id; REC_ID ACTION_ID COMMAND ---------- ---------- -----------------------------1 1 PARTITION TABLE 1 2 RETAIN INDEX 2 1 PARTITION TABLE 2 3 RETAIN INDEX 2 4 RETAIN INDEX 5. Display attributes of the recommendations. For example, create the following PL/SQL procedure show_recm, and then execute it to see attributes of the actions: CREATE OR REPLACE PROCEDURE show_recm (in_task_name IN VARCHAR2) IS CURSOR curs IS 26-20 Chapter 26 Optimizing Access Paths with SQL Access Advisor: Basic Tasks SELECT DISTINCT action_id, command, attr1, attr2, attr3, attr4 FROM user_advisor_actions WHERE task_name = in_task_name ORDER BY action_id; v_action number; v_command VARCHAR2(32); v_attr1 VARCHAR2(4000); v_attr2 VARCHAR2(4000); v_attr3 VARCHAR2(4000); v_attr4 VARCHAR2(4000); v_attr5 VARCHAR2(4000); BEGIN OPEN curs; DBMS_OUTPUT.PUT_LINE('========================================='); DBMS_OUTPUT.PUT_LINE('Task_name = ' || in_task_name); LOOP FETCH curs INTO v_action, v_command, v_attr1, v_attr2, v_attr3, v_attr4 ; EXIT when curs%NOTFOUND; DBMS_OUTPUT.PUT_LINE('Action ID: ' || v_action); DBMS_OUTPUT.PUT_LINE('Command : ' || v_command); DBMS_OUTPUT.PUT_LINE('Attr1 (name) : ' || SUBSTR(v_attr1,1,30)); DBMS_OUTPUT.PUT_LINE('Attr2 (tablespace): ' || SUBSTR(v_attr2,1,30)); DBMS_OUTPUT.PUT_LINE('Attr3 : ' || SUBSTR(v_attr3,1,30)); DBMS_OUTPUT.PUT_LINE('Attr4 : ' || v_attr4); DBMS_OUTPUT.PUT_LINE('Attr5 : ' || v_attr5); DBMS_OUTPUT.PUT_LINE('----------------------------------------'); END LOOP; CLOSE curs; DBMS_OUTPUT.PUT_LINE('=========END RECOMMENDATIONS============'); END show_recm; / SET SERVEROUTPUT ON SIZE 99999 EXECUTE show_recm(:task_name); The following output shows attributes of actions in the recommendations: ========================================= Task_name = MYTASK Action ID: 1 Command : PARTITION TABLE Attr1 (name) : "SH"."SALES" Attr2 (tablespace): Attr3 : ("TIME_ID") Attr4 : INTERVAL Attr5 : ---------------------------------------Action ID: 2 Command : RETAIN INDEX Attr1 (name) : "SH"."PRODUCTS_PK" Attr2 (tablespace): Attr3 : "SH"."PRODUCTS" Attr4 : BTREE Attr5 : ---------------------------------------Action ID: 3 Command : RETAIN INDEX Attr1 (name) : "SH"."TIMES_PK" Attr2 (tablespace): Attr3 : "SH"."TIMES" 26-21 Chapter 26 Optimizing Access Paths with SQL Access Advisor: Basic Tasks Attr4 : BTREE Attr5 : ---------------------------------------Action ID: 4 Command : RETAIN INDEX Attr1 (name) : "SH"."SALES_TIME_BIX" Attr2 (tablespace): Attr3 : "SH"."SALES" Attr4 : BITMAP Attr5 : ---------------------------------------=========END RECOMMENDATIONS============ See Also: • "Action Attributes in the DBA_ADVISOR_ACTIONS View" • Oracle Database PL/SQL Packages and Types Reference for details regarding Attr5 and Attr6 26.2.6 Generating and Executing a Task Script You can use the procedure DBMS_ADVISOR.GET_TASK_SCRIPT to create a script of the SQL statements for the SQL Access Advisor recommendations. The script is an executable SQL file that can contain DROP, CREATE, and ALTER statements. For new objects, the names of the materialized views, materialized view logs, and indexes are automatically generated by using the user-specified name template. Review the generated SQL script before attempting to execute it. Assumptions This tutorial assumes that you want to save and execute a script that contains the recommendations generated in "Executing a SQL Access Advisor Task". To save and execute a SQL script: 1. Connect SQL*Plus to the database as an administrator. 2. Create a directory object and grant permissions to read and write to it. For example, use the following statements: CREATE DIRECTORY ADVISOR_RESULTS AS '/tmp'; GRANT READ ON DIRECTORY ADVISOR_RESULTS TO PUBLIC; GRANT WRITE ON DIRECTORY ADVISOR_RESULTS TO PUBLIC; 3. Connect to the database as sh, and then save the script to a file. For example, use the following statement: EXECUTE DBMS_ADVISOR.CREATE_FILE(DBMS_ADVISOR.GET_TASK_SCRIPT('MYTASK'), 'ADVISOR_RESULTS', 'advscript.sql'); 4. Use a text editor to view the contents of the script. The following is a fragment of a script generated by this procedure: 26-22 Chapter 26 Performing a SQL Access Advisor Quick Tune Rem Username: SH Rem Task: MYTASK Rem Execution date: Rem Rem Rem Repartitioning table "SH"."SALES" Rem SET SERVEROUTPUT ON SET ECHO ON Rem Rem Creating new partitioned table Rem CREATE TABLE "SH"."SALES1" ( "PROD_ID" NUMBER, "CUST_ID" NUMBER, "TIME_ID" DATE, "CHANNEL_ID" NUMBER, "PROMO_ID" NUMBER, "QUANTITY_SOLD" NUMBER(10,2), "AMOUNT_SOLD" NUMBER(10,2) ) PCTFREE 5 PCTUSED 40 INITRANS 1 MAXTRANS 255 NOCOMPRESS NOLOGGING TABLESPACE "EXAMPLE" PARTITION BY RANGE ("TIME_ID") INTERVAL( NUMTOYMINTERVAL( 1, 'MONTH')) ( PARTITION VALUES LESS THAN (TO_DATE(' 1998-02-01 00:00:00', 'SYYYY-MM-DD HH24:MI:SS', 'NLS_CALENDAR=GREGORIAN')) ); . . . 5. Optionally, in SQL*Plus, run the SQL script. For example, enter the following command: @/tmp/advscript.sql See Also: • Oracle Database SQL Language Reference for CREATE DIRECTORY syntax • Oracle Database PL/SQL Packages and Types Reference to learn more about the GET_TASK_SCRIPT function 26.3 Performing a SQL Access Advisor Quick Tune To tune a single SQL statement, the DBMS_ADVISOR.QUICK_TUNE procedure accepts as its input a task_name and a single SQL statement. The DBMS_ADVISOR.QUICK_TUNE procedure creates a task and workload and executes this task. EXECUTE_TASK and QUICK_TUNE produce the same results. However, QUICK_TUNE is easier when tuning a single SQL statement. 26-23 Chapter 26 Using SQL Access Advisor: Advanced Tasks Assumptions This tutorial assumes the following: • You want to tune a single SQL statement. • You want to name the task MY_QUICKTUNE_TASK. To create a template and base a task on this template: 1. Connect SQL*Plus to the database as user sh, and then initialize SQL*Plus variables for the SQL statement and task name. For example, enter the following commands: VARIABLE t_name VARCHAR2(255); VARIABLE sq VARCHAR2(4000); EXEC :sq := 'SELECT COUNT(*) FROM customers WHERE cust_state_province =''CA'''; EXECUTE :t_name := 'MY_QUICKTUNE_TASK'; 2. Perform the quick tune. For example, the following statement executes MY_QUICKTUNE_TASK: EXEC DBMS_ADVISOR.QUICK_TUNE(DBMS_ADVISOR.SQLACCESS_ADVISOR,:t_name,:sq); See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about the QUICK_TUNE procedure and its parameters 26.4 Using SQL Access Advisor: Advanced Tasks This section describes advanced tasks involving SQL Access Advisor. This section contains the following topics: • Evaluating Existing Access Structures SQL Access Advisor operates in two modes: problem-solving and evaluation. • Updating SQL Access Advisor Task Attributes You can use the DBMS_ADVISOR.UPDATE_TASK_ATTRIBUTES procedure to set attributes for the task. • Creating and Using SQL Access Advisor Task Templates A task template is a saved configuration on which to base future tasks and workloads. • Terminating SQL Access Advisor Task Execution SQL Access Advisor enables you to interrupt the recommendation process or allow it to complete. • Deleting SQL Access Advisor Tasks The DBMS_ADVISOR.DELETE_TASK procedure deletes existing SQL Access Advisor tasks from the repository. 26-24 Chapter 26 Using SQL Access Advisor: Advanced Tasks • Marking SQL Access Advisor Recommendations By default, all SQL Access Advisor recommendations are ready to be implemented. However, you can choose to skip or exclude selected recommendations by using the DBMS_ADVISOR.MARK_RECOMMENDATION procedure. • Modifying SQL Access Advisor Recommendations Using the UPDATE_REC_ATTRIBUTES procedure, SQL Access Advisor names and assigns ownership to new objects such as indexes and materialized views during analysis. 26.4.1 Evaluating Existing Access Structures SQL Access Advisor operates in two modes: problem-solving and evaluation. By default, SQL Access Advisor attempts to solve access method problems by looking for enhancements to index structures, partitions, materialized views, and materialized view logs. For example, a problem-solving run may recommend creating a new index, adding a new column to a materialized view log, and so on. When you set the ANALYSIS_SCOPE parameter to EVALUATION, SQL Access Advisor comments only on which access structures the supplied workload uses. An evaluationonly run may only produce recommendations such as retaining an index, retaining a materialized view, and so on. The evaluation mode can be useful to see exactly which indexes and materialized views a workload is using. SQL Access Advisor does not evaluate the performance impact of existing base table partitioning. To create a task and set it to evaluation mode: 1. Connect SQL*Plus to the database with the appropriate privileges, and then create a task. For example, enter the following statement, where t_name is a SQL*Plus variable set to the name of the task: EXECUTE DBMS_ADVISOR.EXECUTE_TASK(:t_name); 2. Perform the quick tune. For example, the following statement sets the previous task to evaluation mode: EXECUTE DBMS_ADVISOR.SET_TASK_PARAMETER(:t_name,'ANALYSIS_SCOPE','EVALUATION'); See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the SET_TASK_PARAMETER procedure and its parameters 26.4.2 Updating SQL Access Advisor Task Attributes You can use the DBMS_ADVISOR.UPDATE_TASK_ATTRIBUTES procedure to set attributes for the task. You can set the following attributes: • Change the name of a task. • Give a task a description. 26-25 Chapter 26 Using SQL Access Advisor: Advanced Tasks • Set the task to be read-only so it cannot be changed. • Make the task a template upon which you can define other tasks. • Changes various attributes of a task or a task template. Assumptions This tutorial assumes the following: • You want to change the name of existing task MYTASK to TUNING1. • You want to make the task TUNING1 read-only. To update task attributes: 1. Connect SQL*Plus to the database as user sh, and then change the name of the task. For example, use the following statement: EXECUTE DBMS_ADVISOR.UPDATE_TASK_ATTRIBUTES('MYTASK', 'TUNING1'); 2. Set the task to read-only. For example, use the following statement: EXECUTE DBMS_ADVISOR.UPDATE_TASK_ATTRIBUTES('TUNING1', read_only => 'true'); See Also: • "Creating and Using SQL Access Advisor Task Templates" • Oracle Database PL/SQL Packages and Types Reference for more information regarding the UPDATE_TASK_ATTRIBUTES procedure and its parameters 26.4.3 Creating and Using SQL Access Advisor Task Templates A task template is a saved configuration on which to base future tasks and workloads. A template enables you to set up any number of tasks or workloads that can serve as starting points or templates for future task creation. By setting up a template, you can save time when performing tuning analysis. This approach also enables you to custom fit a tuning analysis to the business operation. Physically, there is no difference between a task and a template. However, a template cannot be executed. To create a task from a template, you specify the template to be used when a new task is created. At that time, SQL Access Advisor copies the data and parameter settings from the template into the newly created task. You can also set an existing task to be a template by setting the template attribute when creating the task or later using the UPDATE_TASK_ATTRIBUTE procedure. The following table describes procedures that you can use to manage task templates. 26-26 Chapter 26 Using SQL Access Advisor: Advanced Tasks Table 26-3 DBMS_ADVISOR Procedures for Task Templates Procedure Description CREATE_TASK The template parameter is an optional task name of an existing task or task template. To specify built-in SQL Access Advisor templates, use the template name as described in Table 26-6. is_template is an optional parameter that enables you to set the newly created task as a template. Valid values are true and false. SET_TASK_PARAME The INDEX_NAME_TEMPLATE parameter specifies the method by which new index names are formed. The MVIEW_NAME_TEMPLATE parameter specifies TER the method by which new materialized view names are formed. The PARTITION_NAME_TEMPLATE parameter specifies the method by which new partition names are formed. UPDATE_TASK_ATT is_template marks the task as a template. Physically, there is no difference between a task and a template; however, a template cannot be RIBUTES executed. Possible values are: true and false. If the value is NULL or contains the value ADVISOR_UNUSED, then the setting is not changed. Assumptions This tutorial assumes the following: • You want to create a template named MY_TEMPLATE. • You want to set naming conventions for indexes and materialized views that are recommended by tasks based on MY_TEMPLATE. • You want to create task NEWTASK based on MY_TEMPLATE. To create a template and base a task on this template: 1. Connect SQL*Plus to the database as user sh, and then create a task as a template. For example, create a template named MY_TEMPLATE as follows: VARIABLE template_id NUMBER; VARIABLE template_name VARCHAR2(255); EXECUTE :template_name := 'MY_TEMPLATE'; BEGIN DBMS_ADVISOR.CREATE_TASK ( 'SQL Access Advisor' , :template_id , :template_name , is_template => 'true' ); END; 2. Set template parameters. For example, the following statements set the naming conventions for recommended indexes and materialized views: -- set naming conventions for recommended indexes/mvs BEGIN DBMS_ADVISOR.SET_TASK_PARAMETER ( :template_name , 'INDEX_NAME_TEMPLATE' , 'SH_IDX$$_ yes 18-31 Chapter 18 Basic Tasks for Optimizer Statistics Advisor yes yes yes no yes yes ' 26-27 Chapter 26 Using SQL Access Advisor: Advanced Tasks ); END; BEGIN DBMS_ADVISOR.SET_TASK_PARAMETER ( :template_name , 'MVIEW_NAME_TEMPLATE' , 'SH_MV$$_ ' ); END; 3. Create a task based on a pre-existing template. For example, enter the following commands to create NEWTASK based on MY_TEMPLATE: VARIABLE task_id NUMBER; VARIABLE task_name VARCHAR2(255); EXECUTE :task_name := 'NEWTASK'; BEGIN DBMS_ADVISOR.CREATE_TASK ( 'SQL Access Advisor' , :task_id , :task_name , template=>'MY_TEMPLATE' ); END; See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the CREATE_TASK and SET_TASK_PARAMETER procedures 26.4.4 Terminating SQL Access Advisor Task Execution SQL Access Advisor enables you to interrupt the recommendation process or allow it to complete. An interruption signals SQL Access Advisor to stop processing and marks the task as INTERRUPTED. At that point, you may update recommendation attributes and generate scripts. Intermediate results represent recommendations for the workload contents up to that point in time. If recommendations must be sensitive to the entire workload, then Oracle recommends that you let the task complete. Additionally, recommendations made by the advisor early in the recommendation process do not contain base table partitioning recommendations. The partitioning analysis requires a large part of the workload to be processed before it can determine whether partitioning would be beneficial. Therefore, if SQL Access Advisor detects a benefit, then only later intermediate results contain base table partitioning recommendations. This section describes two ways to terminate SQL Access Advisor task execution: • Interrupting SQL Access Advisor Tasks The DBMS_ADVISOR.INTERRUPT_TASK procedure causes a SQL Access Advisor task execution to terminate as if it had reached its normal end. 26-28 Chapter 26 Using SQL Access Advisor: Advanced Tasks • Canceling SQL Access Advisor Tasks You can stop task execution by calling the DBMS_ADVISOR.CANCEL_TASK procedure and passing in the task name for this recommendation process. 26.4.4.1 Interrupting SQL Access Advisor Tasks The DBMS_ADVISOR.INTERRUPT_TASK procedure causes a SQL Access Advisor task execution to terminate as if it had reached its normal end. Thus, you can see any recommendations that have been formed up to the point of the interruption. An interrupted task cannot be restarted. The syntax is as follows: DBMS_ADVISOR.INTERRUPT_TASK (task_name IN VARCHAR2); Assumptions This tutorial assumes the following: • Long-running task MYTASK is currently executing. • You want to interrupt this task, and then view the recommendations. To interrupt a currently executing task: 1. Connect SQL*Plus to the database as sh, and then interrupt the task. For example, create a template named MY_TEMPLATE as follows: EXECUTE DBMS_ADVISOR.INTERRUPT_TASK ('MYTASK'); See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the INTERRUPT_TASK procedure 26.4.4.2 Canceling SQL Access Advisor Tasks You can stop task execution by calling the DBMS_ADVISOR.CANCEL_TASK procedure and passing in the task name for this recommendation process. SQL Access Advisor may take a few seconds to respond to this request. Because all advisor task procedures are synchronous, to cancel an operation, you must use a separate database session. If you use CANCEL_TASK, then SQL Access Advisor makes no recommendations. A cancel command effective restores the task to its condition before the start of the canceled operation. Therefore, a canceled task or data object cannot be restarted. However, you can reset the task using DBMS_ADVISOR.RESET_TASK, and then execute it again. The CANCEL_TASK syntax is as follows: DBMS_ADVISOR.CANCEL_TASK (task_name IN VARCHAR2); The RESET_TASK procedure resets a task to its initial starting point, which has the effect of removing all recommendations and intermediate data from the task. The task status is set to INITIAL. The syntax is as follows: DBMS_ADVISOR.RESET_TASK (task_name IN VARCHAR2); 26-29 Chapter 26 Using SQL Access Advisor: Advanced Tasks Assumptions This tutorial assumes the following: • Long-running task MYTASK is currently executing. This task is set to make partitioning recommendations. • You want to cancel this task, and then reset it so that the task makes only index recommendations. To cancel a currently executing task: 1. Connect SQL*Plus to the database as user sh, and then cancel the task. For example, create a template named MY_TEMPLATE as follows: EXECUTE DBMS_ADVISOR.CANCEL_TASK ('MYTASK'); 2. Reset the task. For example, execute the RESET_TASK procedure as follows: EXECUTE DBMS_ADVISOR.RESET_TASK('MYTASK'); 3. Set task parameters. For example, change the analysis scope to INDEX as follows: EXECUTE DBMS_ADVISOR.SET_TASK_PARAMETER(:task_name, 'ANALYSIS_SCOPE', 'INDEX'); 4. Execute the task. For example, execute MYTASK as follows: EXECUTE DBMS_ADVISOR.EXECUTE_TASK ('MYTASK'); See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about RESET_TASK and CANCEL_TASK 26.4.5 Deleting SQL Access Advisor Tasks The DBMS_ADVISOR.DELETE_TASK procedure deletes existing SQL Access Advisor tasks from the repository. The syntax for SQL Access Advisor task deletion is as follows: DBMS_ADVISOR.DELETE_TASK (task_name IN VARCHAR2); If a task is linked to an STS workload, and if you want to delete the task or workload, then you must remove the link between the task and the workload using the DELETE_STS_REF procedure. The following example deletes the link between task MYTASK and the current user's SQL tuning set MY_STS_WORKLOAD: EXECUTE DBMS_ADVISOR.DELETE_STS_REF('MYTASK', null, 'MY_STS_WORKLOAD'); 26-30 Chapter 26 Using SQL Access Advisor: Advanced Tasks Assumptions This tutorial assumes the following: • User sh currently owns multiple SQL Access Advisor tasks. • You want to delete MYTASK. • The task MYTASK is currently linked to workload MY_STS_WORKLOAD. To delete a SQL Access Advisor task: 1. Connect SQL*Plus to the database as user sh, and then query existing SQL Access Advisor tasks. For example, query the data dictionary as follows (sample output included): SELECT TASK_NAME FROM USER_ADVISOR_TASKS WHERE ADVISOR_NAME = 'SQL Access Advisor'; TASK_NAME ------------------------MYTASK NEWTASK 2. Delete the link between MYTASK and MY_STS_WORKLOAD. For example, delete the reference as follows: EXECUTE DBMS_ADVISOR.DELETE_STS_REF('MYTASK', null, 'MY_STS_WORKLOAD'); 3. Delete the desired task. For example, delete MYTASK as follows: EXECUTE DBMS_ADVISOR.DELETE_TASK('MYTASK'); See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about the DELETE_TASK procedure and its parameters 26.4.6 Marking SQL Access Advisor Recommendations By default, all SQL Access Advisor recommendations are ready to be implemented. However, you can choose to skip or exclude selected recommendations by using the DBMS_ADVISOR.MARK_RECOMMENDATION procedure. MARK_RECOMMENDATION enables you to annotate a recommendation with a REJECT or IGNORE setting, which causes the GET_TASK_SCRIPT to skip it when producing the implementation procedure. If SQL Access Advisor makes a recommendation to partition one or multiple previously nonpartitioned base tables, then consider carefully before skipping this recommendation. Changing a table's partitioning scheme affects the cost of all queries, indexes, and materialized views defined on the table. Therefore, if you skip the partitioning recommendation, then the advisor's remaining recommendations on 26-31 Chapter 26 Using SQL Access Advisor: Advanced Tasks this table are no longer optimal. To see recommendations on your workload that do not contain partitioning, reset the advisor task and rerun it with the ANALYSIS_SCOPE parameter changed to exclude partitioning recommendations. The syntax is as follows: DBMS_ADVISOR.MARK_RECOMMENDATION ( task_name IN VARCHAR2 id IN NUMBER, action IN VARCHAR2); Assumptions This tutorial assumes the following: • You are reviewing the recommendations as described in tutorial "Viewing SQL Access Advisor Task Results". • You want to reject the first recommendation, which partitions a table. To mark a recommendation: 1. Connect SQL*Plus to the database as user sh, and then mark the recommendation. For example, reject recommendation 1 as follows: EXECUTE DBMS_ADVISOR.MARK_RECOMMENDATION('MYTASK', 1, 'REJECT'); This recommendation and any dependent recommendations do not appear in the script. 2. Generate the script as explained in "Generating and Executing a Task Script". See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about the MARK_RECOMMENDATIONS procedure and its parameters 26.4.7 Modifying SQL Access Advisor Recommendations Using the UPDATE_REC_ATTRIBUTES procedure, SQL Access Advisor names and assigns ownership to new objects such as indexes and materialized views during analysis. SQL Access Advisor may not necessarily choose appropriate names. In this case, you may choose to manually set the owner, name, and tablespace values for new objects. For recommendations referencing existing database objects, owner and name values cannot be changed. The syntax is as follows: DBMS_ADVISOR.UPDATE_REC_ATTRIBUTES ( task_name IN VARCHAR2 rec_id IN NUMBER, action_id IN NUMBER, attribute_name IN VARCHAR2, value IN VARCHAR2); The attribute_name parameter can take the following values: 26-32 Chapter 26 SQL Access Advisor Examples • OWNER Specifies the owner name of the recommended object. • NAME Specifies the name of the recommended object. • TABLESPACE Specifies the tablespace of the recommended object. Assumptions This tutorial assumes the following: • You are reviewing the recommendations as described in tutorial "Viewing SQL Access Advisor Task Results". • You want to change the tablespace for recommendation 1, action 1 to SH_MVIEWS. To mark a recommendation: 1. Connect SQL*Plus to the database as user sh, and then update the recommendation attribute. For example, change the tablespace name to SH_MVIEWS as follows: BEGIN DBMS_ADVISOR.UPDATE_REC_ATTRIBUTES ( 'MYTASK' , 1 , 1 , 'TABLESPACE' , 'SH_MVIEWS' ); END; 2. Generate the script as explained in "Generating and Executing a Task Script". See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about the UPDATE_REC_ATTRIBUTES procedure and its parameters 26.5 SQL Access Advisor Examples Oracle Database provides a script that contains several SQL Access Advisor examples that you can run on a test database. The script is named ORACLE_HOME/rdbms/demo/aadvdemo.sql. 26.6 SQL Access Advisor Reference You can access metadata about SQL Access Advisor using data dictionary views. This section contains the following topics: 26-33 Chapter 26 SQL Access Advisor Reference • Action Attributes in the DBA_ADVISOR_ACTIONS View The DBA_ADVISOR_ACTIONS view displays information about the actions associated with all recommendations in the database. Each action is specified by the COMMAND and ATTR1 through ATTR6 columns. • Categories for SQL Access Advisor Task Parameters SQL Access Advisor task parameters fall into the following categories: workload filtering, task configuration, schema attributes, and recommendation options. • SQL Access Advisor Constants DBMS_ADVISOR provides a number of constants. 26.6.1 Action Attributes in the DBA_ADVISOR_ACTIONS View The DBA_ADVISOR_ACTIONS view displays information about the actions associated with all recommendations in the database. Each action is specified by the COMMAND and ATTR1 through ATTR6 columns. The following table maps SQL Access Advisor actions to attribute columns in the DBA_ADVISOR_ACTIONS view. In the table, MV refers to a materialized view. Table 26-4 SQL Access Advisor Action Attributes Action ATTR1 Column ATTR3 Column ATTR4 Column ATTR5 Column CREATE INDEX Index name Index tablespace Target table BITMAP orBTREE Index column Unused list / expression Storage size in bytes for the index MV tablespace REFRESH COMPLETE, REFRESH FAST,REFRES H FORCE, NEVER REFRESH ENABLE QUERY REWRITE, DISABLE QUERY REWRITE SQL SELECT statement Storage size in bytes for the MV CREATE Target table MV log tablespace MATERIALIZE name D VIEW LOG ROWID PRIMARY KEY,SEQUENC E OBJECT ID INCLUDING NEW VALUES, EXCLUDING NEW VALUES Table column Partitioning list subclauses Unused CREATE Name of Checksum equivalence value REWRITE EQUIVALENCE Unused Unused Source SQL statement Equivalent SQL statement Unused Unused Unused Index columns Unused Storage size in bytes for the index Unused Unused Unused Unused Unused Storage size in bytes for the MV DROP Target table Unused MATERIALIZE name D VIEW LOG Unused Unused Unused Unused Unused CREATE MV name MATERIALIZE D VIEW DROP INDEX ATTR2 Column Index name Unused DROP MV name MATERIALIZE D VIEW ATTR6 Column Unused NUM_ATT R1 Column 26-34 Chapter 26 SQL Access Advisor Reference Table 26-4 (Cont.) SQL Access Advisor Action Attributes Action ATTR1 Column PARTITION TABLE Table name RANGE, INTERVAL, LIST, HASH, RANGE-HASH, RANGE-LIST PARTITION INDEX Index name LOCAL, RANGE, Partition key Unused for HASH partitioning (list of column names) PARTITION MV name ON MATERIALIZE D VIEW RETAIN INDEX ATTR2 Column RANGE, INTERVAL, LIST, HASH, RANGE-HASH, RANGE-LIST Index name Unused RETAIN MV name MATERIALIZE D VIEW Unused RETAIN Target table Unused MATERIALIZE name D VIEW LOG ATTR3 Column ATTR4 Column ATTR5 Column Partition key for partitioning (column name or list of column names) Partition key SQL for PARTITION subpartitionin clause g (column name or list of column names) SQL PARTITION clause ATTR6 Column NUM_ATT R1 Column SQL Unused SUBPARTITIO N clause Unused Unused Partition key for partitioning (column name or list of column names) Partition key SQL for SUBPARTITIO subpartitionin N clause g (column name or list of column names) SQL Unused SUBPARTITIO N clause Target table BITMAP or BTREE Index columns Unused Storage size in bytes for the index REFRESH COMPLETE or REFRESH FAST Unused SQL SELECT statement Unused Storage size in bytes for the MV Unused Unused Unused Unused Unused 26.6.2 Categories for SQL Access Advisor Task Parameters SQL Access Advisor task parameters fall into the following categories: workload filtering, task configuration, schema attributes, and recommendation options. The following table groups the most relevant SQL Access Advisor task parameters into categories. All task parameters for workload filtering are deprecated. Table 26-5 Types of Advisor Task Parameters And Their Uses Workload Filtering Task Configuration Schema Attributes Recommendation Options END_TIME DAYS_TO_EXPIRE DEF_INDEX_OWNER ANALYSIS_SCOPE INVALID_ACTION_LIST JOURNALING DEF_INDEX_TABLESPACE COMPATIBILITY INVALID_MODULE_LIST REPORT_DATE_FORMAT DEF_MVIEW_OWNER CREATION_COST 26-35 Chapter 26 SQL Access Advisor Reference Table 26-5 (Cont.) Types of Advisor Task Parameters And Their Uses Workload Filtering Task Configuration Schema Attributes Recommendation Options INVALID_SQLSTRING_LIMIT DEF_MVIEW_TABLESPACE DML_VOLATILITY INVALID_TABLE_LIST DEF_MVLOG_TABLESPACE LIMIT_PARTITION_SCHEMES INVALID_USERNAME_LIST DEF_PARTITION_TABLESPAC MODE E RANKING_MEASURE INDEX_NAME_TEMPLATE PARTITIONING_TYPES SQL_LIMIT MVIEW_NAME_TEMPLATE REFRESH_MODE START_TIME STORAGE_CHANGE TIME_LIMIT USE_SEPARATE_TABLESPACE S VALID_ACTION_LIST WORKLOAD_SCOPE VALID_MODULE_LIST VALID_SQLSTRING_LIST VALID_TABLE_LIST VALID_USERNAME_LIST 26.6.3 SQL Access Advisor Constants DBMS_ADVISOR provides a number of constants. You can use the constants shown in the following table with SQL Access Advisor. Table 26-6 SQL Access Advisor Constants Constant Description ADVISOR_ALL A value that indicates all possible values. For string parameters, this value is equivalent to the wildcard (%) character. ADVISOR_CURRENT Indicates the current time or active set of elements. Typically, this is used in time parameters. ADVISOR_DEFAULT Indicates the default value. Typically used when setting task or workload parameters. ADVISOR_UNLIMITED A value that represents an unlimited numeric value. ADVISOR_UNUSED A value that represents an unused entity. When a parameter is set to ADVISOR_UNUSED, it has no effect on the current operation. A typical use for this constant is to set a parameter as unused for its dependent operations. SQLACCESS_GENERAL Specifies the name of a default SQL Access general-purpose task template. This template sets the DML_VOLATILITY task parameter to true and ANALYSIS_SCOPE to INDEX, MVIEW. SQLACCESS_OLTP Specifies the name of a default SQL Access OLTP task template. This template sets the DML_VOLATILITY task parameter to true and ANALYSIS_SCOPE to INDEX. 26-36 Chapter 26 SQL Access Advisor Reference Table 26-6 (Cont.) SQL Access Advisor Constants Constant Description SQLACCESS_WAREHOUSE Specifies the name of a default SQL Access warehouse task template. This template sets the DML_VOLATILITY task parameter to false and EXECUTION_TYPE to INDEX, MVIEW. SQLACCESS_ADVISOR Contains the formal name of SQL Access Advisor. You can specify this name when procedures require the Advisor name as an argument. 26-37 Part IX SQL Controls: Profiles and Plan Baselines You can manage SQL profiles and SQL plan baselines, and migrate stored outlines to SQL plan baselines. This part contains the following chapters: • Managing SQL Profiles When warranted, SQL Tuning Advisor recommends a SQL profile. You can use DBMS_SQLTUNE to implement, alter, drop, and transport SQL profiles. • Overview of SQL Plan Management SQL plan management is a preventative mechanism that enables the optimizer to automatically manage execution plans, ensuring that the database uses only known or verified plans. • Managing SQL Plan Baselines This chapter explains the concepts and tasks relating to SQL plan management using the DBMS_SPM package. • Migrating Stored Outlines to SQL Plan Baselines Stored outline migration is the user-initiated process of converting stored outlines to SQL plan baselines. A SQL plan baseline is a set of plans proven to provide optimal performance. 27 Managing SQL Profiles When warranted, SQL Tuning Advisor recommends a SQL profile. You can use DBMS_SQLTUNE to implement, alter, drop, and transport SQL profiles. This chapter contains the following topics: • About SQL Profiles A SQL profile is a database object that contains auxiliary statistics specific to a SQL statement. • Implementing a SQL Profile Implementing a SQL profile means storing it persistently in the database. • Listing SQL Profiles The data dictionary view DBA_SQL_PROFILES stores SQL profiles persistently in the database. • Altering a SQL Profile You can alter attributes of an existing SQL profile using the attribute_name parameter of the ALTER_SQL_PROFILE procedure. • Dropping a SQL Profile You can drop a SQL profile with the DROP_SQL_PROFILE procedure. • Transporting a SQL Profile You can export a SQL profile from the SYS schema in one database to a staging table, and then import it from the staging table into another database. You can transport a SQL profile to any Oracle database created in the same release or later. 27.1 About SQL Profiles A SQL profile is a database object that contains auxiliary statistics specific to a SQL statement. Conceptually, a SQL profile is to a SQL statement what object-level statistics are to a table or index. SQL profiles are created when a DBA invokes SQL Tuning Advisor. This section contains the following topics: • Purpose of SQL Profiles When profiling a SQL statement, SQL Tuning Advisor uses a specific set of bind values as input. • Concepts for SQL Profiles A SQL profile is a collection of auxiliary statistics on a query, including all tables and columns referenced in the query. • User Interfaces for SQL Profiles Oracle Enterprise Manager Cloud Control (Cloud Control) usually handles SQL profiles as part of automatic SQL tuning. 27-1 Chapter 27 About SQL Profiles • Basic Tasks for SQL Profiles Basic tasks include accepting (implementing) a SQL profile, altering it, listing it, and dropping it. See Also: "About SQL Tuning Advisor" 27.1.1 Purpose of SQL Profiles When profiling a SQL statement, SQL Tuning Advisor uses a specific set of bind values as input. The advisor compares the optimizer estimate with values obtained by executing fragments of the statement on a data sample. When significant variances are found, SQL Tuning Advisor bundles corrective actions together in a SQL profile, and then recommends its acceptance. The corrected statistics in a SQL profile can improve optimizer cardinality estimates, which in turn leads the optimizer to select better plans. SQL profiles provide the following benefits over other techniques for improving plans: • Unlike hints and stored outlines, SQL profiles do not tie the optimizer to a specific plan or subplan. SQL profiles fix incorrect estimates while giving the optimizer the flexibility to pick the best plan in different situations. • Unlike hints, no changes to application source code are necessary when using SQL profiles. The use of SQL profiles by the database is transparent to the user. See Also: • "Influencing the Optimizer with Hints" • "Analyzing SQL with SQL Tuning Advisor" 27.1.2 Concepts for SQL Profiles A SQL profile is a collection of auxiliary statistics on a query, including all tables and columns referenced in the query. The profile is stored in the data dictionary, which is viewable using DBA_SQL_PROFILES. The optimizer uses this information during optimization to determine the most optimal plan. Note: The SQL profile contains supplemental statistics for the entire statement, not individual plans. The profile does not itself determine a specific plan. 27-2 Chapter 27 About SQL Profiles A SQL profile contains, among other statistics, a set of cardinality adjustments. The cardinality measure is based on sampling the WHERE clause rather than on statistical projection. A profile uses parts of the query to determine whether the estimated cardinalities are close to the actual cardinalities and, if a mismatch exists, uses the corrected cardinalities. For example, if a SQL profile exists for SELECT * FROM t WHERE x=5 AND y=10, then the profile stores the actual number of rows returned. When choosing plans, the optimizer has the following sources of information: • The environment, which contains the database configuration, bind variable values, optimizer statistics, data set, and so on • The supplemental statistics in the SQL profile The following figure shows the relationship between a SQL statement and the SQL profile for this statement. The optimizer uses the SQL profile and the environment to generate an execution plan. In this example, the plan is in the SQL plan baseline for the statement. Figure 27-1 SQL Profile SQL Statement SQL Profile Optimizer SQL Plan Baseline GB SELECT . . . HJ Environment Optimizer Statistics Bind Variables Data Set Configuration HJ GB NL NL If either the optimizer environment or SQL profile changes, then the optimizer can create a new plan. As tables grow, or as indexes are created or dropped, the plan for a SQL profile can change. The profile continues to be relevant even if the data distribution or access path of the corresponding statement changes. In general, you do not need to refresh SQL profiles. Over time, however, profile content can become outdated. In this case, performance of the SQL statement may degrade. The statement may appear as high-load or top SQL. In this case, the Automatic SQL Tuning task again captures the statement as high-load SQL. You can implement a new SQL profile for the statement. Internally, a SQL profile is implemented using hints that address different types of problems. These hints do not specify any particular plan. Rather, the hints correct errors in the optimizer estimation algorithm that lead to suboptimal plans. For example, a profile may use the TABLE_STATS hint to set object statistics for tables when the statistics are missing or stale. This section contains the following topics: • SQL Profile Recommendations SQL Tuning Advisor invokes Automatic Tuning Optimizer to generate SQL profile recommendations. 27-3 Chapter 27 About SQL Profiles • SQL Profiles and SQL Plan Baselines You can use SQL profiles with or without SQL plan management. See Also: • "Differences Between SQL Plan Baselines and SQL Profiles" • Oracle Database Reference to learn more about DBA_SQL_PROFILES 27.1.2.1 SQL Profile Recommendations SQL Tuning Advisor invokes Automatic Tuning Optimizer to generate SQL profile recommendations. Recommendations to implement SQL profiles occur in a finding, which appears in a separate section of the SQL Tuning Advisor report. When you implement (or accept) a SQL profile, the database creates the profile and stores it persistently in the data dictionary. However, the SQL profile information is not exposed through regular dictionary views. Example 27-1 SQL Profile Recommendation In this example, the database found a better plan for a SELECT statement that uses several expensive joins. The database recommends running DBMS_SQLTUNE.ACCEPT_SQL_PROFILE to implement the profile, which enables the statement to run 98.53% faster. ------------------------------------------------------------------------------FINDINGS SECTION (2 findings) ------------------------------------------------------------------------------1- SQL Profile Finding (see explain plans section below) -------------------------------------------------------A potentially better execution plan was found for this statement. Choose one of the following SQL profiles to implement. Recommendation (estimated benefit: 99.45%) ------------------------------------------ Consider accepting the recommended SQL profile. execute dbms_sqltune.accept_sql_profile(task_name => 'my_task', object_id => 3, task_owner => 'SH', replace => TRUE); Validation results -----------------The SQL profile was tested by executing both its plan and the original plan and measuring their respective execution statistics. A plan may have been only partially executed if the other could be run to completion in less time. Completion Status: Elapsed Time(us): CPU Time(us): User I/O Time(us): Buffer Gets: Disk Reads: Direct Writes: Original Plan With SQL Profile % Improved ------------- ---------------- ---------PARTIAL COMPLETE 15467783 226902 98.53 % 15336668 226965 98.52 % 0 0 3375243 18227 99.45 % 0 0 0 0 27-4 Chapter 27 About SQL Profiles Rows Processed: Fetches: Executions: 0 0 0 109 109 1 Notes ----1. The SQL profile plan was first executed to warm the buffer cache. 2. Statistics for the SQL profile plan were averaged over next 3 executions. Sometimes SQL Tuning Advisor may recommend implementing a profile that uses the Automatic Degree of Parallelism (Auto DOP) feature. A parallel query profile is only recommended when the original plan is serial and when parallel execution can significantly reduce the elapsed time for a long-running query. When it recommends a profile that uses Auto DOP, SQL Tuning Advisor gives details about the performance overhead of using parallel execution for the SQL statement in the report. For parallel execution recommendations, SQL Tuning Advisor may provide two SQL profile recommendations, one using serial execution and one using parallel. The following example shows a parallel query recommendation. In this example, a degree of parallelism of 7 improves response time significantly at the cost of increasing resource consumption by almost 25%. You must decide whether the reduction in database throughput is worth the increase in response time. Recommendation (estimated benefit: 99.99%) ------------------------------------------ Consider accepting the recommended SQL profile to use parallel execution for this statement. execute dbms_sqltune.accept_sql_profile(task_name => 'gfk_task', object_id => 3, task_owner => 'SH', replace => TRUE, profile_type => DBMS_SQLTUNE.PX_PROFILE); Executing this query parallel with DOP 7 will improve its response time 82.22% over the SQL profile plan. However, there is some cost in enabling parallel execution. It will increase the statement's resource consumption by an estimated 24.43% which may result in a reduction of system throughput. Also, because these resources are consumed over a much smaller duration, the response time of concurrent statements might be negatively impacted if sufficient hardware capacity is not available. The following data shows some sampled statistics for this SQL from the past week and projected weekly values when parallel execution is enabled. Past week sampled statistics for this SQL ----------------------------------------Number of executions 0 Percent of total activity .29 Percent of samples with #Active Sessions > 2*CPU 0 Weekly DB time (in sec) 76.51 Weekly DB time (in sec) Projected statistics with Parallel Execution -------------------------------------------95.21 27-5 Chapter 27 About SQL Profiles See Also: • "SQL Profiling" • Oracle Database VLDB and Partitioning Guide to learn more about Auto DOP • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SQLTUNE.ACCEPT_SQL_PROFILE procedure 27.1.2.2 SQL Profiles and SQL Plan Baselines You can use SQL profiles with or without SQL plan management. No strict relationship exists between the SQL profile and a SQL plan baseline. If a statement has multiple plans in a SQL plan baseline, then a SQL profile is useful because it enables the optimizer to choose the lowest-cost plan in the baseline. See Also: "Overview of SQL Plan Management" 27.1.3 User Interfaces for SQL Profiles Oracle Enterprise Manager Cloud Control (Cloud Control) usually handles SQL profiles as part of automatic SQL tuning. On the command line, you can manage SQL profiles with the DBMS_SQLTUNE package. To use the APIs, you must have the ADMINISTER SQL MANAGEMENT OBJECT privilege. See Also: • Oracle Database PL/SQL Packages and Types Reference for information about the DBMS_SQLTUNE package • Oracle Database 2 Day + Performance Tuning Guide to learn how to manage SQL profiles with Cloud Control 27.1.4 Basic Tasks for SQL Profiles Basic tasks include accepting (implementing) a SQL profile, altering it, listing it, and dropping it. The following graphic shows the basic workflow. 27-6 Chapter 27 Implementing a SQL Profile Figure 27-2 Managing SQL Profiles Implement a SQL Profile DBMS_SQLTUNE. ACCEPT_SQL_PROFILE Alter a SQL Profile DBMS_SQLTUNE. ALTER_SQL_PROFILE DBMS_SQLTUNE.DROP_SQL_PROFILE List SQL Profiles DBA_SQL_PROFILES Drop a SQL Profile Typically, you manage SQL profiles in the following sequence: 1. Implement a recommended SQL profile. "Implementing a SQL Profile" describes this task. 2. Obtain information about SQL profiles stored in the database. "Listing SQL Profiles" describes this task. 3. Optionally, modify the implemented SQL profile. "Altering a SQL Profile" describes this task. 4. Drop the implemented SQL profile when it is no longer needed. "Dropping a SQL Profile" describes this task. To tune SQL statements on another database, you can transport both a SQL tuning set and a SQL profile to a separate database. "Transporting a SQL Profile" describes this task. See Also: Oracle Database PL/SQL Packages and Types Reference for information about the DBMS_SQLTUNE package 27.2 Implementing a SQL Profile Implementing a SQL profile means storing it persistently in the database. 27-7 Chapter 27 Implementing a SQL Profile Implementing a profile is the same as accepting it. A profile must be accepted before the optimizer can use it as input when generating plans. This section contains the following topics: • About SQL Profile Implementation As a rule of thumb, implement a SQL profile recommended by SQL Tuning Advisor. • Implementing a SQL Profile To implement a SQL profile, use the DBMS_SQLTUNE.ACCEPT_SQL_PROFILE procedure. 27.2.1 About SQL Profile Implementation As a rule of thumb, implement a SQL profile recommended by SQL Tuning Advisor. If the database recommends both an index and a SQL profile, then either use both or use the SQL profile only. If you create an index, then the optimizer may need the profile to pick the new index. In some situations, SQL Tuning Advisor may find an improved serial plan in addition to an even better parallel plan. In this case, the advisor recommends both a standard and a parallel SQL profile, enabling you to choose between the best serial and best parallel plan for the statement. Implement a parallel plan only if the increase in response time is worth the decrease in throughput. To implement a SQL profile, execute the DBMS_SQLTUNE.ACCEPT_SQL_PROFILE procedure. Some important parameters are as follows: • profile_type Set this parameter to REGULAR_PROFILE for a SQL profile without a change to parallel execution, or PX_PROFLE for a SQL profile with a change to parallel execution. • force_match This parameter controls statement matching. Typically, an accepted SQL profile is associated with the SQL statement through a SQL signature that is generated using a hash function. This hash function changes the SQL statement to upper case and removes all extra whitespace before generating the signature. Thus, the same SQL profile works for all SQL statements in which the only difference is case and white spaces. By setting force_match to true, the SQL profile additionally targets all SQL statements that have the same text after the literal values in the WHERE clause have been replaced by bind variables. This setting may be useful for applications that use only literal values because it enables SQL with text differing only in its literal values to share a SQL profile. If both literal values and bind variables are in the SQL text, or if force_match is set to false (default), then the literal values in the WHERE clause are not replaced by bind variables. See Also: Oracle Database PL/SQL Packages and Types Reference for information about the ACCEPT_SQL_PROFILE procedure 27-8 Chapter 27 Listing SQL Profiles 27.2.2 Implementing a SQL Profile To implement a SQL profile, use the DBMS_SQLTUNE.ACCEPT_SQL_PROFILE procedure. Assumptions This tutorial assumes the following: • The SQL Tuning Advisor task STA_SPECIFIC_EMP_TASK includes a recommendation to create a SQL profile. • The name of the SQL profile is my_sql_profile. • The PL/SQL block accepts a profile that uses parallel execution (profile_type). • The profile uses force matching. To implement a SQL profile: 1. In SQL*Plus or SQL Developer, log in to the database as a user with the necessary privileges. 2. Execute the ACCEPT_SQL_PROFILE function. For example, execute the following PL/SQL: DECLARE my_sqlprofile_name VARCHAR2(30); BEGIN my_sqlprofile_name := DBMS_SQLTUNE.ACCEPT_SQL_PROFILE ( task_name => 'STA_SPECIFIC_EMP_TASK' , name => 'my_sql_profile' , profile_type => DBMS_SQLTUNE.PX_PROFILE , force_match => true ); END; / See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SQLTUNE.ACCEPT_SQL_PROFILE procedure 27.3 Listing SQL Profiles The data dictionary view DBA_SQL_PROFILES stores SQL profiles persistently in the database. The profile statistics are in an Oracle Database internal format, so you cannot query profiles directly. However, you can list profiles. To list SQL profiles: 1. In SQL*Plus or SQL Developer, log in to the database as a user with the necessary privileges. 27-9 Chapter 27 Altering a SQL Profile 2. Query the DBA_SQL_PROFILES view. For example, execute the following query: COLUMN category FORMAT a10 COLUMN sql_text FORMAT a20 SELECT NAME, SQL_TEXT, CATEGORY, STATUS FROM DBA_SQL_PROFILES; Sample output appears below: NAME SQL_TEXT CATEGORY STATUS ------------------------------ -------------------- ---------- -------SYS_SQLPROF_01285f6d18eb0000 select promo_name, c DEFAULT ENABLED ount(*) c from promo tions p, sales s whe re s.promo_id = p.pr omo_id and p.promo_c ategory = 'internet' group by p.promo_na me order by c desc See Also: Oracle Database Reference to learn about the DBA_SQL_PROFILES view 27.4 Altering a SQL Profile You can alter attributes of an existing SQL profile using the attribute_name parameter of the ALTER_SQL_PROFILE procedure. The CATEGORY attribute determines which sessions can apply a profile. View the CATEGORY attribute by querying DBA_SQL_PROFILES.CATEGORY. By default, all profiles are in the DEFAULT category, which means that all sessions in which the SQLTUNE_CATEGORY initialization parameter is set to DEFAULT can use the profile. By altering the category of a SQL profile, you determine which sessions are affected by profile creation. For example, by setting the category to DEV, only sessions in which the SQLTUNE_CATEGORY initialization parameter is set to DEV can use the profile. Other sessions do not have access to the SQL profile and execution plans for SQL statements are not impacted by the SQL profile. This technique enables you to test a profile in a restricted environment before making it available to other sessions. The example in this section assumes that you want to change the category of the SQL profile so it is used only by sessions with the SQL profile category set to TEST, run the SQL statement, and then change the profile category back to DEFAULT. To alter a SQL profile: 1. In SQL*Plus or SQL Developer, log in to the database as a user with the necessary privileges. 2. Use the ALTER_SQL_PROFILE procedure to set the attribute_name. For example, execute the following code to set the attribute CATEGORY to TEST: 27-10 Chapter 27 Dropping a SQL Profile VARIABLE pname my_sql_profile BEGIN DBMS_SQLTUNE.ALTER_SQL_PROFILE ( name => :pname , attribute_name => 'CATEGORY' , value => 'TEST' ); END; 3. Change the initialization parameter setting in the current database session. For example, execute the following SQL: ALTER SESSION SET SQLTUNE_CATEGORY = 'TEST'; 4. Test the profiled SQL statement. 5. Use the ALTER_SQL_PROFILE procedure to set the attribute_name. For example, execute the following code to set the attribute CATEGORY to DEFAULT: VARIABLE pname my_sql_profile BEGIN DBMS_SQLTUNE.ALTER_SQL_PROFILE ( name => :pname , attribute_name => 'CATEGORY' , value => 'DEFAULT' ); END; See Also: • Oracle Database Reference to learn about the SQLTUNE_CATEGORY initialization parameter • Oracle Database PL/SQL Packages and Types Reference to learn about the ALTER_SQL_PROFILE procedure 27.5 Dropping a SQL Profile You can drop a SQL profile with the DROP_SQL_PROFILE procedure. Assumptions This section assumes the following: • You want to drop my_sql_profile. • You want to ignore errors raised if the name does not exist. To drop a SQL profile: 1. In SQL*Plus or SQL Developer, log in to the database as a user with the necessary privileges. 2. Use the DBMS_SQLTUNE.DROP_SQL_PROFILE procedure. The following example drops the profile named my_sql_profile: BEGIN DBMS_SQLTUNE.DROP_SQL_PROFILE ( 27-11 Chapter 27 Transporting a SQL Profile name => 'my_sql_profile' ); END; / See Also: • Oracle Database PL/SQL Packages and Types Reference to learn about the DROP_SQL_PROFILE procedure • Oracle Database Reference to learn about the SQLTUNE_CATEGORY initialization parameter 27.6 Transporting a SQL Profile You can export a SQL profile from the SYS schema in one database to a staging table, and then import it from the staging table into another database. You can transport a SQL profile to any Oracle database created in the same release or later. Table 27-1 shows the main procedures and functions for managing SQL profiles. Table 27-1 APIs for Transporting SQL Profiles Procedure or Function Description CREATE_STGTAB_SQLPROF Creates the staging table used for copying SQL profiles from one system to another. PACK_STGTAB_SQLPROF Moves profile data out of the SYS schema into the staging table. UNPACK_STGTAB_SQLPROF Uses the profile data stored in the staging table to create profiles on this system. The following graphic shows the basic workflow of transporting SQL profiles. Figure 27-3 Transporting SQL Profiles DBMS_SQLTUNE ACCEPT_SQL_PROFILE Accept a SQL Profile CREATE_STGTAB_SQLPROF PACK_STGTAB_SQLPROF Transport SQL Profile Transport to different database UNPACK_STGTAB_SQLPROF 27-12 Chapter 27 Transporting a SQL Profile Assumptions This tutorial assumes the following: • You want to transport my_profile from a production database to a test database. • You want to create the staging table in the dba1 schema. To transport a SQL profile: 1. Connect SQL*Plus to the database with the appropriate privileges, and then use the CREATE_STGTAB_SQLPROF procedure to create a staging table to hold the SQL profiles. The following example creates my_staging_table in the dba1 schema: BEGIN DBMS_SQLTUNE.CREATE_STGTAB_SQLPROF ( table_name => 'my_staging_table' , schema_name => 'dba1' ); END; / 2. Use the PACK_STGTAB_SQLPROF procedure to export SQL profiles into the staging table. The following example populates dba1.my_staging_table with the SQL profile my_profile: BEGIN DBMS_SQLTUNE.PACK_STGTAB_SQLPROF ( profile_name => 'my_profile' , staging_table_name => 'my_staging_table' , staging_schema_owner => 'dba1' ); END; / 3. Move the staging table to the database where you plan to unpack the SQL profiles. Move the table using your utility of choice. For example, use Oracle Data Pump or a database link. 4. On the database where you plan to import the SQL profiles, use UNPACK_STGTAB_SQLPROF to unpack SQL profiles from the staging table. The following example shows how to unpack SQL profiles in the staging table: BEGIN DBMS_SQLTUNE.UNPACK_STGTAB_SQLPROF( replace => true , staging_table_name => 'my_staging_table' ); END; / 27-13 Chapter 27 Transporting a SQL Profile See Also: • Oracle Database PL/SQL Packages and Types Reference for complete reference information about DBMS_SQLTUNE • Oracle Database Utilities to learn how to use Oracle Data Pump 27-14 28 Overview of SQL Plan Management SQL plan management is a preventative mechanism that enables the optimizer to automatically manage execution plans, ensuring that the database uses only known or verified plans. SQL plan management uses a mechanism called a SQL plan baseline. A plan baseline is a set of accepted plans that the optimizer is allowed to use for a SQL statement. In this context, a plan includes all plan-related information (for example, SQL plan identifier, set of hints, bind values, and optimizer environment) that the optimizer needs to reproduce an execution plan. In the typical use case, the database accepts a plan into the plan baseline only after verifying that the plan performs well. The main components of SQL plan management are as follows: • Plan capture This component stores relevant information about plans for a set of SQL statements. • Plan selection This component is the detection by the optimizer of plan changes based on stored plan history, and the use of SQL plan baselines to select appropriate plans to avoid potential performance regressions. • Plan evolution This component is the process of adding new plans to existing SQL plan baselines, either manually or automatically. This chapter contains the following topics: • Purpose of SQL Plan Management The primary goal of SQL plan management is to prevent performance regressions caused by plan changes. A secondary goal is to gracefully adapt to changes such as new optimizer statistics or indexes by verifying and accepting only plan changes that improve performance. • Plan Capture SQL plan capture refers to techniques for capturing and storing relevant information about plans in the SQL Management Base for a set of SQL statements. Capturing a plan means making SQL plan management aware of this plan. • Plan Selection SQL plan selection is the optimizer ability to detect plan changes based on stored plan history, and the use of SQL plan baselines to select plans to avoid potential performance regressions. • Plan Evolution In general, SQL plan evolution is the process by which the optimizer verifies new plans and adds them to an existing SQL plan baseline. 28-1 Chapter 28 Purpose of SQL Plan Management • Storage Architecture for SQL Plan Management The SQL plan management infrastructure records the signatures of parsed statements, and both accepted and unaccepted plans. 28.1 Purpose of SQL Plan Management The primary goal of SQL plan management is to prevent performance regressions caused by plan changes. A secondary goal is to gracefully adapt to changes such as new optimizer statistics or indexes by verifying and accepting only plan changes that improve performance. Note: SQL plan baselines cannot help when an event has caused irreversible execution plan changes, such as dropping an index. This section contains the following topics: • Benefits of SQL Plan Management SQL plan management can improve or preserve SQL performance in database upgrades and system and data changes. • Differences Between SQL Plan Baselines and SQL Profiles Both SQL profiles and SQL plan baselines help improve the performance of SQL statements by ensuring that the optimizer uses only optimal plans. 28.1.1 Benefits of SQL Plan Management SQL plan management can improve or preserve SQL performance in database upgrades and system and data changes. Specifically, benefits include: • A database upgrade that installs a new optimizer version usually results in plan changes for a small percentage of SQL statements. Most plan changes result in either improvement or no performance change. However, some plan changes may cause performance regressions. SQL plan baselines significantly minimize potential regressions resulting from an upgrade. When you upgrade, the database only uses plans from the plan baseline. The database puts new plans that are not in the current baseline into a holding area, and later evaluates them to determine whether they use fewer resources than the current plan in the baseline. If the plans perform better, then the database promotes them into the baseline; otherwise, the database does not promote them. • Ongoing system and data changes can affect plans for some SQL statements, potentially causing performance regressions. SQL plan baselines help minimize performance regressions and stabilize SQL performance. • Deployment of new application modules introduces new SQL statements into the database. 28-2 Chapter 28 Purpose of SQL Plan Management The application software may use appropriate SQL execution plans developed in a standard test configuration for the new statements. If the system configuration is significantly different from the test configuration, then the database can evolve SQL plan baselines over time to produce better performance. See Also: Oracle Database Upgrade Guide to learn how to upgrade an Oracle database 28.1.2 Differences Between SQL Plan Baselines and SQL Profiles Both SQL profiles and SQL plan baselines help improve the performance of SQL statements by ensuring that the optimizer uses only optimal plans. Both profiles and baselines are internally implemented using hints. However, these mechanisms have significant differences, including the following: • In general, SQL plan baselines are proactive, whereas SQL profiles are reactive. Typically, you create SQL plan baselines before significant performance problems occur. SQL plan baselines prevent the optimizer from using suboptimal plans in the future. The database creates SQL profiles when you invoke SQL Tuning Advisor, which you do typically only after a SQL statement has shown high-load symptoms. SQL profiles are primarily useful by providing the ongoing resolution of optimizer mistakes that have led to suboptimal plans. Because the SQL profile mechanism is reactive, it cannot guarantee stable performance as drastic database changes occur. Figure 28-1 SQL Plan Baselines and SQL Profiles Past Present SQL Plan Baseline Suboptimal Plan • Corrects Cause Future Prevents Use Suboptimal Plan SQL Profile SQL plan baselines reproduce a specific plan, whereas SQL profiles correct optimizer cost estimates. A SQL plan baseline is a set of accepted plans. Each plan is implemented using a set of outline hints that fully specify a particular plan. SQL profiles are also implemented using hints, but these hints do not specify any specific plan. Rather, the hints correct miscalculations in the optimizer estimates that lead to suboptimal plans. For example, a hint may correct the cardinality estimate of a table. Because a profile does not constrain the optimizer to any one plan, a SQL profile is more flexible than a SQL plan baseline. For example, changes in initialization parameters and optimizer statistics enable the optimizer to choose a better plan. 28-3 Chapter 28 Plan Capture Oracle recommends that you use SQL Tuning Advisor. In this way, you follow the recommendations made by the advisor for SQL profiles and plan baselines rather than trying to determine which mechanism is best for each SQL statement. See Also: • "About Optimizer Hints" • "Managing SQL Profiles" • "Analyzing SQL with SQL Tuning Advisor" 28.2 Plan Capture SQL plan capture refers to techniques for capturing and storing relevant information about plans in the SQL Management Base for a set of SQL statements. Capturing a plan means making SQL plan management aware of this plan. You can configure initial plan capture to occur automatically by setting an initialization parameter, or you can capture plans manually by using the DBMS_SPM package. This section contains the following topics: • Automatic Initial Plan Capture You enable automatic initial plan capture by setting OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES to true (the default is false). When enabled, the database checks whether executed SQL statements are eligible for automatic capture. • Manual Plan Capture In SQL plan management, manual plan capture refers to the user-initiated bulk load of existing plans into a SQL plan baseline. 28.2.1 Automatic Initial Plan Capture You enable automatic initial plan capture by setting OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES to true (the default is false). When enabled, the database checks whether executed SQL statements are eligible for automatic capture. The settings of the initialization parameters OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES and OPTIMIZER_USE_SQL_PLAN_BASELINES are independent. For example, if OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES is true, then the database creates initial plan baselines regardless of whether OPTIMIZER_USE_SQL_PLAN_BASELINES is true or false. This section contains the following topics: • Eligibility for Automatic Initial Plan Capture To be eligible for automatic plan capture, a statement must be repeatable, and it must pass through any configured capture filters. By default, the database considers all repeatable SQL statements as eligible for capture. 28-4 Chapter 28 Plan Capture • Plan Matching for Automatic Initial Plan Capture If the database executes a repeatable SQL statement, and if this statement passes through the DBMS_SPM.CONFIGURE filters, then the database attempts to match a plan in the SQL plan baseline. See Also: • "Plan Selection" • Oracle Database Reference to learn about the OPTIMIZER_USE_SQL_PLAN_BASELINES initialization parameter 28.2.1.1 Eligibility for Automatic Initial Plan Capture To be eligible for automatic plan capture, a statement must be repeatable, and it must pass through any configured capture filters. By default, the database considers all repeatable SQL statements as eligible for capture. The first check for eligibility is repetition. If a statement is only executed once, then the database does not consider it eligible for a SQL plan baseline. If a statement is executed twice or more, however, then it is by definition repeatable, and so the database considers it eligible for further checking. For repeatable statements, the DBMS_SPM.CONFIGURE procedure enables you to create an automatic capture filter. Thus, you can capture only statements that you want, and exclude noncritical statements, thereby saving space in the SYSAUX tablespace. Noncritical queries often have the following characteristics: • Not executed often enough to be significant • Not resource-intensive • Not sufficiently complex to benefit from SQL plan management For a specified parameter, a filter either includes (allow=>TRUE) or excludes (allow=>FALSE) plans for statements with the specified values. To be eligible for capture, a repeatable statement must not be excluded by any filter. The DBMS_SPM.CONFIGURE procedure supports filters for SQL text, parsing schema name, module, and action. A null value for any parameter removes the filter. By using parameter_value=>'%' in combination with allow=FALSE, you can filter out all values for a parameter, and then create a separate filter to include only specified values. The DBA_SQL_MANAGEMENT_CONFIG view shows the current filters. 28-5 Chapter 28 Plan Capture See Also: • "Configuring Filters for Automatic Plan Capture" • Oracle Database PL/SQL Packages and Types Reference to learn more about the DBMS_SPM.CONFIGURE procedure • Oracle Database Reference to learn more about the DBA_SQL_MANAGEMENT_CONFIG view 28.2.1.2 Plan Matching for Automatic Initial Plan Capture If the database executes a repeatable SQL statement, and if this statement passes through the DBMS_SPM.CONFIGURE filters, then the database attempts to match a plan in the SQL plan baseline. For automatic initial plan capture, the plan matching algorithm is as follows: • If a SQL plan baseline does not exist, then the optimizer creates a plan history and SQL plan baseline for the statement, marking the initial plan for the statement as accepted and adding it to the SQL plan baseline. • If a SQL plan baseline exists, then the optimizer behavior depends on the costbased plan derived at parse time: – If this plan does not match a plan in the SQL plan baseline, then the optimizer marks the new plan as unaccepted and adds it to the SQL plan baseline. – If this plan does match a plan in the SQL plan baseline, then nothing is added to the SQL plan baseline. 28.2.2 Manual Plan Capture In SQL plan management, manual plan capture refers to the user-initiated bulk load of existing plans into a SQL plan baseline. Use Cloud Control or PL/SQL to load the execution plans for SQL statements from AWR, a SQL tuning set (STS), the shared SQL area, a staging table, or a stored outline. 28-6 Chapter 28 Plan Capture Figure 28-2 Loading Plans into a SQL Plan Baseline Shared Pool Library Cache Shared SQL Area SELECT * FROM employees SQL Management Base SQL Statement Log SQL Plan History AWR SQL Plan Baseline Loading Plans into a Plan Baseline GB SQL Tuning Set HJ HJ GB Staging Table accepted enabled HJ accepted enabled HJ Stored Outline /*+ hint */ /*+ hint */ /*+ hint */ The loading behavior varies depending on whether a SQL plan baseline exists for each statement represented in the bulk load: • • If a baseline for the statement does not exist, then the database does the following: 1. Creates a plan history and plan baseline for the statement 2. Marks the initial plan for the statement as accepted 3. Adds the plan to the new baseline If a baseline for the statement exists, then the database does the following: 1. Marks the loaded plan as accepted 2. Adds the plan to the plan baseline for the statement without verifying the plan's performance Manually loaded plans are always marked accepted because the optimizer assumes that any plan loaded manually by the administrator has acceptable performance. You can load plans without enabling them by setting the enabled parameter to NO in the DBMS_SPM.LOAD_PLANS_FROM_% functions. 28-7 Chapter 28 Plan Selection See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about the DBMS_SPM.LOAD_PLANS_FROM_% functions 28.3 Plan Selection SQL plan selection is the optimizer ability to detect plan changes based on stored plan history, and the use of SQL plan baselines to select plans to avoid potential performance regressions. When the database performs a hard parse of a SQL statement, the optimizer generates a best-cost plan. By default, the optimizer then attempts to find a matching plan in the SQL plan baseline for the statement. If no plan baseline exists, then the database runs the statement with the best-cost plan. If a plan baseline exists, then the optimizer behavior depends on whether the newly generated plan is in the plan baseline: • If the new plan is in the baseline, then the database executes the statement using the found plan. • If the new plan is not in the baseline, then the optimizer marks the newly generated plan as unaccepted and adds it to the plan history. Optimizer behavior depends on the contents of the plan baseline: – If fixed plans exist in the plan baseline, then the optimizer uses the fixed plan with the lowest cost. – If no fixed plans exist in the plan baseline, then the optimizer uses the baseline plan with the lowest cost. – If no reproducible plans exist in the plan baseline, which could happen if every plan in the baseline referred to a dropped index, then the optimizer uses the newly generated cost-based plan. 28-8 Chapter 28 Plan Evolution Figure 28-3 Decision Tree for SQL Plan Selection SQL is issued Generate execution plan Does a SQL plan baseline exist? No Execute this plan Yes Is this plan in SQL plan baseline? Yes Execute this plan No Mark plan as unaccepted in plan history Compare costs of accepted plans Execute lowest-cost plan in baseline See Also: "Fixed Plans" 28.4 Plan Evolution In general, SQL plan evolution is the process by which the optimizer verifies new plans and adds them to an existing SQL plan baseline. Specifically, plan evolution consists of the following distinct steps: 1. Verifying that unaccepted plans perform at least as well as accepted plans in a SQL plan baseline (known as plan verification) 2. Adding unaccepted plans to the plan baseline as accepted plans after the database has proved that they perform as well as accepted plans In the standard case of plan evolution, the optimizer performs the preceding steps sequentially, so that a new plan is not usable by SQL plan management until the optimizer verifies plan performance relative to the SQL plan baseline. However, you can configure SQL plan management to perform one step without performing the other. The following graphic shows the possible paths for plan evolution. 28-9 Chapter 28 Plan Evolution Figure 28-4 Plan Evolution Verifying Adding Verifying without adding Adding without verifying This section contains the following topics: • Purpose of Plan Evolution Typically, a SQL plan baseline for a SQL statement starts with a single accepted plan. However, some SQL statements perform well when executed with different plans under different conditions. • PL/SQL Subprograms for Plan Evolution The DBMS_SPM package provides procedures and functions for plan evolution. 28.4.1 Purpose of Plan Evolution Typically, a SQL plan baseline for a SQL statement starts with a single accepted plan. However, some SQL statements perform well when executed with different plans under different conditions. For example, a SQL statement with bind variables whose values result in different selectivities may have several optimal plans. Creating a materialized view or an index or repartitioning a table may make current plans more expensive than other plans. If new plans were never added to SQL plan baselines, then the performance of some SQL statements might degrade. Thus, it is sometimes necessary to evolve newly accepted plans into SQL plan baselines. Plan evolution prevents performance regressions by verifying the performance of a new plan before including it in a SQL plan baseline. 28.4.2 PL/SQL Subprograms for Plan Evolution The DBMS_SPM package provides procedures and functions for plan evolution. These subprograms use the task infrastructure. For example, CREATE_EVOLVE_TASK creates an evolution task, whereas EXECUTE_EVOLVE_TASK executes it. All task evolution subprograms have the string EVOLVE_TASK in the name. Use the evolve procedures on demand, or configure the subprograms to run automatically. The automatic maintenance task SYS_AUTO_SPM_EVOLVE_TASK executes daily in the scheduled maintenance window. The task perform the following actions automatically: 1. Selects and ranks unaccepted plans for verification 2. Accepts each plan if it satisfies the performance threshold 28-10 Chapter 28 Storage Architecture for SQL Plan Management See Also: • "Managing the SPM Evolve Advisor Task" • "Evolving SQL Plan Baselines Manually" • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SPM package 28.5 Storage Architecture for SQL Plan Management The SQL plan management infrastructure records the signatures of parsed statements, and both accepted and unaccepted plans. This section contains the following topics: • SQL Management Base The SQL management base (SMB) is a logical repository in the data dictionary. • SQL Statement Log When automatic SQL plan capture is enabled, the SQL statement log contains the signature of statements that the optimizer has evaluated over time. • SQL Plan History The SQL plan history is the set of captured SQL execution plans. The history contains both SQL plan baselines and unaccepted plans. 28.5.1 SQL Management Base The SQL management base (SMB) is a logical repository in the data dictionary. The SMB contains the following: • SQL statement log, which contains only SQL IDs • SQL plan history, which includes the SQL plan baselines • SQL profiles • SQL patches The SMB stores information that the optimizer can use to maintain or improve SQL performance. The SMB resides in the SYSAUX tablespace and uses automatic segment-space management. Because the SMB is located entirely within the SYSAUX tablespace, the database does not use SQL plan management and SQL tuning features when this tablespace is unavailable. 28-11 Chapter 28 Storage Architecture for SQL Plan Management Figure 28-5 SMB Architecture SYSAUX SQL Management Base SQL Statement Log SQL Profiles SQL Plan History SQL Patches Note: Data visibility and privilege requirements may differ when using the SMB with pluggable databases. See Oracle Database Administrator’s Guide for a table that summarizes how manageability features work in a container database (CDB). See Also: Oracle Database Administrator’s Guide to learn about the SYSAUX tablespace 28.5.2 SQL Statement Log When automatic SQL plan capture is enabled, the SQL statement log contains the signature of statements that the optimizer has evaluated over time. A SQL signature is a numeric hash value computed using a SQL statement text that has been normalized for case insensitivity and white space. When the optimizer parses a statement, it creates signature. During automatic capture, the database matches this signature against the SQL statement log (SQLLOG$) to determine whether the signature has been observed before. If it has not, then the database adds the signature to the log. If the signature is already in the log, then the database has confirmation that the statement is a repeatable SQL statement. Note: If a filter excludes a statement, then its signature is also excluded from the log. 28-12 Chapter 28 Storage Architecture for SQL Plan Management Example 28-1 Logging SQL Statements This example illustrates how the database tracks statements in the statement log and creates baselines automatically for repeatable statements. An initial query of the statement log shows no tracked SQL statements. After a query of hr.jobs for AD_PRES, the log shows one tracked statement. SQL> ALTER SYSTEM SET OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES=true; System altered. SQL> SELECT * FROM SQLLOG$; no rows selected SQL> SELECT job_title FROM hr.jobs WHERE job_id = 'AD_PRES'; JOB_TITLE ----------------------------------President SQL> SELECT * FROM SQLLOG$; SIGNATURE BATCH# ---------- ---------1.8096E+19 1 Now the session executes a different jobs query. The log shows two tracked statements: SQL> SELECT job_title FROM hr.jobs WHERE job_id='PR_REP'; JOB_TITLE ----------------------------------Public Relations Representative SQL> SELECT * FROM SQLLOG$; SIGNATURE BATCH# ---------- ---------1.7971E+19 1 1.8096E+19 1 A query of DBA_SQL_PLAN_BASELINES shows that no baseline for either statement exists because neither statement is repeatable: SQL> SELECT SQL_HANDLE, SQL_TEXT 2 FROM DBA_SQL_PLAN_BASELINES 3 WHERE SQL_TEXT LIKE 'SELECT job_title%'; no rows selected The session executes the query for job_id='PR_REP' a second time. Because this statement is now repeatable, and because automatic SQL plan capture is enabled, the database creates a plan baseline for this statement. The query for job_id='AD_PRES' has only been executed once, so no plan baseline exists for it. SQL> SELECT job_title FROM hr.jobs WHERE job_id='PR_REP'; JOB_TITLE ----------------------------------- 28-13 Chapter 28 Storage Architecture for SQL Plan Management Public Relations Representative SQL> SELECT SQL_HANDLE, SQL_TEXT 2 FROM DBA_SQL_PLAN_BASELINES 3 WHERE SQL_TEXT LIKE 'SELECT job_title%'; SQL_HANDLE SQL_TEXT -------------------- -------------------SQL_f9676a330f972dd5 SELECT job_title FRO M hr.jobs WHERE job_ id='PR_REP' See Also: • "Automatic Initial Plan Capture" • Oracle Database Reference to learn about DBA_SQL_PLAN_BASELINES 28.5.3 SQL Plan History The SQL plan history is the set of captured SQL execution plans. The history contains both SQL plan baselines and unaccepted plans. In SQL plan management, the database detects new SQL execution plans for existing SQL plan baselines and records the new plan in the history so that they can be evolved (verified). Evolution is initiated automatically by the database or manually by the DBA. Starting in Oracle Database 12c, the SMB stores the execution plans for all SQL statements in the SQL plan history. The DBMS_XPLAN.DISPLAY_SQL_PLAN_BASELINE function fetches and displays the plan from the SMB. For plans created before Oracle Database 12c, the function must compile the SQL statement and generate the plan because the SMB does not store it. This section contains the following topics: • Enabled Plans An enabled plan is a plan that is eligible for use by the optimizer. • Accepted Plans An accepted plan is a plan that is in a SQL plan baseline for a SQL statement and thus available for use by the optimizer. An accepted plan contains a set of hints, a plan hash value, and other plan-related information. • Fixed Plans A fixed plan is an accepted plan that is marked as preferred, so that the optimizer considers only the fixed plans in the baseline. Fixed plans influence the plan selection process of the optimizer. 28-14 Chapter 28 Storage Architecture for SQL Plan Management See Also: • "Displaying Plans in a SQL Plan Baseline" • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_XPLAN.DISPLAY_SQL_PLAN_BASELINE function 28.5.3.1 Enabled Plans An enabled plan is a plan that is eligible for use by the optimizer. When plans are loaded with the enabled parameter set to YES (default), the database automatically marks the resulting SQL plan baselines as enabled, even if they are unaccepted. You can manually change an enabled plan to a disabled plan, which means the optimizer can no longer use the plan even if it is accepted. 28.5.3.2 Accepted Plans An accepted plan is a plan that is in a SQL plan baseline for a SQL statement and thus available for use by the optimizer. An accepted plan contains a set of hints, a plan hash value, and other plan-related information. The SQL plan history for a statement contains all plans, both accepted and unaccepted. After the optimizer generates the first accepted plan in a plan baseline, every subsequent unaccepted plan is added to the plan history, awaiting verification, but is not in the SQL plan baseline. 28.5.3.3 Fixed Plans A fixed plan is an accepted plan that is marked as preferred, so that the optimizer considers only the fixed plans in the baseline. Fixed plans influence the plan selection process of the optimizer. Assume that three plans exist in the SQL plan baseline for a statement. You want the optimizer to give preferential treatment to only two of the plans. As shown in the following figure, you mark these two plans as fixed so that the optimizer uses only the best plan from these two, ignoring the other plans. 28-15 Chapter 28 Storage Architecture for SQL Plan Management Figure 28-6 Fixed Plans SQL Plan Baseline GB DBA marks as fixed HJ GB HJ GB HJ fixed accepted enabled HJ Optimizer Considers fixed accepted enabled HJ accepted enabled Ignores unless fixed plans are not reproducible HJ If new plans are added to a baseline that contains at least one enabled fixed plan, then the optimizer cannot use the new plans until you manually declare them as fixed. 28-16 29 Managing SQL Plan Baselines This chapter explains the concepts and tasks relating to SQL plan management using the DBMS_SPM package. This chapter contains the following topics: • About Managing SQL Plan Baselines This topic describes the available interfaces and basic tasks for SQL plan management. • Configuring SQL Plan Management You can configure the capture and use of SQL plan baselines, and the SPM Evolve Advisor task. • Displaying Plans in a SQL Plan Baseline To view the plans stored in the SQL plan baseline for a specific statement, use the DBMS_XPLAN.DISPLAY_SQL_PLAN_BASELINE function. This function uses plan information stored in the plan history to display the plans. • Loading SQL Plan Baselines Using DBMS_SPM, you can initiate the bulk load of a set of existing plans into a SQL plan baseline. • Evolving SQL Plan Baselines Manually You can use PL/SQL or Cloud Control to manually evolve an unaccepted plan to determine whether it performs better than any plan currently in the plan baseline. • Dropping SQL Plan Baselines You can remove some or all plans from a SQL plan baseline. This technique is sometimes useful when testing SQL plan management. • Managing the SQL Management Base The SQL management base is a part of the data dictionary that resides in the SYSAUX tablespace. It stores statement logs, plan histories, SQL plan baselines, and SQL profiles. See Also: • "Migrating Stored Outlines to SQL Plan Baselines" • Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_SPM 29.1 About Managing SQL Plan Baselines This topic describes the available interfaces and basic tasks for SQL plan management. 29-1 Chapter 29 About Managing SQL Plan Baselines This section contains the following topics: • User Interfaces for SQL Plan Management You can access the DBMS_SPM package through Cloud Control or through the command line. • Basic Tasks in SQL Plan Management This topic explains the basic tasks in using SQL plan management to prevent plan regressions and permit the optimizer to consider new execution plans. 29.1.1 User Interfaces for SQL Plan Management You can access the DBMS_SPM package through Cloud Control or through the command line. This section contains the following topics: • Accessing the SQL Plan Baseline Page in Cloud Control The SQL Plan Control page in Cloud Control is a GUI that shows information about SQL profiles, SQL patches, and SQL plan baselines. • DBMS_SPM Package On the command line, use the DBMS_SPM and DBMS_XPLAN PL/SQL packages to perform most SQL plan management tasks. 29.1.1.1 Accessing the SQL Plan Baseline Page in Cloud Control The SQL Plan Control page in Cloud Control is a GUI that shows information about SQL profiles, SQL patches, and SQL plan baselines. To access the SQL Plan Baseline page: 1. Log in to Cloud Control with the appropriate credentials. 2. Under the Targets menu, select Databases. 3. In the list of database targets, select the target for the Oracle Database instance that you want to administer. 4. If prompted for database credentials, then enter the minimum credentials necessary for the tasks you intend to perform. 5. From the Performance menu, select SQL, then SQL Plan Control. The SQL Plan Control page appears. 6. Click Files to view the SQL Plan Baseline subpage, shown in Figure 29-1. 29-2 Chapter 29 About Managing SQL Plan Baselines Figure 29-1 SQL Plan Baseline Subpage You can perform most SQL plan management tasks in this page or in pages accessed through this page. See Also: • Cloud Control context-sensitive online help to learn about the options on the SQL Plan Baseline subpage • "Managing the SPM Evolve Advisor Task" 29.1.1.2 DBMS_SPM Package On the command line, use the DBMS_SPM and DBMS_XPLAN PL/SQL packages to perform most SQL plan management tasks. The following table describes the most relevant DBMS_SPM procedures and functions for creating, dropping, and loading SQL plan baselines. Table 29-1 DBMS_SPM Procedures and Functions Package Procedure or Function Description DBMS_SPM CONFIGURE This procedure changes configuration options for the SMB in name/value format. 29-3 Chapter 29 About Managing SQL Plan Baselines Table 29-1 (Cont.) DBMS_SPM Procedures and Functions Package Procedure or Function Description DBMS_SPM CREATE_STGTAB_BASELINE This procedure creates a staging table that enables you to transport SQL plan baselines from one database to another. DBMS_SPM DROP_SQL_PLAN_BASELINE This function drops some or all plans in a plan baseline. DBMS_SPM LOAD_PLANS_FROM_CURSOR_CACHE This function loads plans in the shared SQL area (also called the cursor cache) into SQL plan baselines. DBMS_SPM LOAD_PLANS_FROM_SQLSET This function loads plans in an STS into SQL plan baselines. DBMS_SPM LOAD_PLANS_FROM_AWR This function loads plans from AWR into SQL plan baselines. DBMS_SPM PACK_STGTAB_BASELINE This function packs SQL plan baselines, which means that it copies them from the SMB into a staging table. DBMS_SPM UNPACK_STGTAB_BASELINE This function unpacks SQL plan baselines, which means that it copies SQL plan baselines from a staging table into the SMB. Also, you can use DBMS_XPLAN.DISPLAY_SQL_PLAN_BASELINE to show one or more execution plans for the SQL statement identified by SQL handle. See Also: • "About the DBMS_SPM Evolve Functions" describes the functions related to SQL plan evolution. • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SPM and DBMS_XPLAN packages 29.1.2 Basic Tasks in SQL Plan Management This topic explains the basic tasks in using SQL plan management to prevent plan regressions and permit the optimizer to consider new execution plans. The tasks are as follows: • Set initialization parameters to control whether the database captures and uses SQL plan baselines, and whether it evolves new plans. See "Configuring SQL Plan Management". • Display plans in a SQL plan baseline. 29-4 Chapter 29 Configuring SQL Plan Management See "Displaying Plans in a SQL Plan Baseline". • Manually load plans into SQL plan baselines. Load plans from AWR, SQL tuning sets, the shared SQL area, a staging table, or stored outlines. See "Loading SQL Plan Baselines". • Manually evolve plans into SQL plan baselines. Use PL/SQL to verify the performance of specified plans and add them to plan baselines. See "Evolving SQL Plan Baselines Manually". • Drop all or some plans in SQL plan baselines. See "Dropping SQL Plan Baselines". • Manage the SMB. Alter disk space limits and change the length of the plan retention policy. See "Managing the SQL Management Base". • Migrate stored outlines to SQL plan baselines. See "Migrating Stored Outlines to SQL Plan Baselines". 29.2 Configuring SQL Plan Management You can configure the capture and use of SQL plan baselines, and the SPM Evolve Advisor task. This section contains the following topics: • Configuring the Capture and Use of SQL Plan Baselines You control SQL plan management with the initialization parameters OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES and OPTIMIZER_USE_SQL_PLAN_BASELINES. • Managing the SPM Evolve Advisor Task SPM Evolve Advisor is a SQL advisor that evolves plans that have recently been added to the SQL plan baseline. The advisor simplifies plan evolution by eliminating the requirement to do it manually. 29.2.1 Configuring the Capture and Use of SQL Plan Baselines You control SQL plan management with the initialization parameters OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES and OPTIMIZER_USE_SQL_PLAN_BASELINES. The default values are as follows: • OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES=false For any repeatable SQL statement that does not already exist in the plan history, the database does not automatically create an initial SQL plan baseline for the statement. If OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES=true, then you can use the DBMS_SPM.CONFIGURE procedure to configure filters that determine which statements are eligible for plan capture. By default, no filters are configured, which means that all repeatable statements are eligible for plan capture. 29-5 Chapter 29 Configuring SQL Plan Management • OPTIMIZER_USE_SQL_PLAN_BASELINES=true For any SQL statement that has an existing SQL plan baseline, the database automatically adds new plans to the SQL plan baseline as unaccepted plans. Note: The settings of the preceding parameters are independent of each other. For example, if OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES is true, then the database creates initial plan baselines for new statements even if OPTIMIZER_USE_SQL_PLAN_BASELINES is false. If the default behavior is what you intend, then skip this section. The following sections explain how to change the default parameter settings from the command line. If you use Cloud Control, then set these parameters in the SQL Plan Baseline subpage. This section contains the following topics: • Enabling Automatic Initial Plan Capture for SQL Plan Management Setting the OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES initialization parameter to true is all that is necessary for the database to automatically create an initial SQL plan baseline for any SQL statement not already in the plan history. • Configuring Filters for Automatic Plan Capture If OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES=true, then you can use the DBMS_SPM.CONFIGURE procedure to create an automatic capture filter for repeatable statements. • Disabling All SQL Plan Baselines When you set the OPTIMIZER_USE_SQL_PLAN_BASELINES initialization parameter to false, the database does not use any plan baselines in the database. See Also: • "Figure 29-1" • "Automatic Initial Plan Capture" • "Plan Selection" 29-6 Chapter 29 Configuring SQL Plan Management 29.2.1.1 Enabling Automatic Initial Plan Capture for SQL Plan Management Setting the OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES initialization parameter to true is all that is necessary for the database to automatically create an initial SQL plan baseline for any SQL statement not already in the plan history. Caution: By default, when automatic baseline capture is enabled, the database creates a SQL plan baseline for every repeatable statement, including all recursive SQL and monitoring SQL. Thus, automatic capture may result in the creation of an extremely large number of plan baselines. To limit the statements that are eligible for plan baselines, configure filters using the DBMS_SPM.CONFIGURE procedure. The OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES parameter does not control the automatic addition of newly discovered plans to a previously created SQL plan baseline. To enable automatic initial plan capture for SQL plan management: 1. In SQL*Plus, log in to the database with the necessary privileges. 2. Show the current settings for SQL plan management. For example, connect SQL*Plus to the database with administrator privileges and execute the following command (sample output included): SHOW PARAMETER SQL_PLAN The following sample output shows that automatic initial plan capture is disabled: NAME -----------------------------------optimizer_capture_sql_plan_baselines optimizer_use_sql_plan_baselines TYPE ----------boolean boolean VALUE ----FALSE TRUE If the parameters are set as you intend, then skip the remaining steps. 3. To enable the automatic recognition of repeatable SQL statements and the generation of SQL plan baselines for these statements, enter the following statement: ALTER SYSTEM SET OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES=true; See Also: Oracle Database Reference to learn more about OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES 29-7 Chapter 29 Configuring SQL Plan Management 29.2.1.2 Configuring Filters for Automatic Plan Capture If OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES=true, then you can use the DBMS_SPM.CONFIGURE procedure to create an automatic capture filter for repeatable statements. An automatic filter enables you to capture only statements that you want, and exclude noncritical statements. This technique saves space in the SYSAUX tablespace. The following table describes the relevant parameters of the DBMS_SPM.CONFIGURE procedure. Table 29-2 DBMS_SPM.CONFIGURE Parameters Parameter Description parameter_name The type of filter for automatic capture. Possible values are AUTO_CAPTURE_SQL_TEXT, AUTO_CAPTURE_PARSING_SCHEMA_NAME, AUTO_CAPTURE_MODULE, and AUTO_CAPTURE_ACTION. parameter_value The search criteria for the automatic capture filter. When parameter_name is set to AUTO_CAPTURE_SQL_TEXT, the search pattern depends on the allow setting: • LIKE • The parameter uses this pattern when allow=>true. NOT LIKE The parameter uses this pattern when allow=>false. For all other non-null parameter_name values, the search pattern depends on the allow setting: • = • The parameter uses this pattern when allow=>true. <> The parameter uses this pattern when allow=>false. A null value removes the filter for parameter_name entirely. allow Whether to include (true) or exclude (false) matching SQL statements and plans. If null, then the procedure ignores the specified parameter. You can configure multiple parameters of different types. Also, you can specify multiple values for the same parameter in separate statements, which the database combines. The settings are additive: one parameter setting does not override a previous setting. For example, the following filter captures SQL in the parsing schema SYS or SYSTEM: EXEC DBMS_SPM.CONFIGURE('AUTO_CAPTURE_PARSING_SCHEMA_NAME','SYS',true); EXEC DBMS_SPM.CONFIGURE('AUTO_CAPTURE_PARSING_SCHEMA_NAME','SYSTEM',true); However, you cannot configure multiple values for the same parameter in the same procedure. For example, you cannot specify multiple SQL text strings for AUTO_CAPTURE_SQL_TEXT. The DBA_SQL_MANAGEMENT_CONFIG view shows the current parameter values. This tutorial assumes the following: 29-8 Chapter 29 Configuring SQL Plan Management • The OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES initialization parameter is set to true. • You want to include only statements parsed in the sh schema to be eligible for baselines. • You want to exclude statements that contain the text TEST_ONLY. To filter out all statements except those parsed in the sh schema: 1. Connect SQL*Plus to the database with the appropriate privileges. 2. To remove any existing filters for parsing schema and SQL text, execute the following PL/SQL programs: EXEC DBMS_SPM.CONFIGURE('AUTO_CAPTURE_PARSING_SCHEMA_NAME',null,true); EXEC DBMS_SPM.CONFIGURE('AUTO_CAPTURE_SQL_TEXT',null,true); 3. Include only statements parsed in the sh schema for consideration for automatic capture: EXEC DBMS_SPM.CONFIGURE('AUTO_CAPTURE_PARSING_SCHEMA_NAME','sh',true); 4. Exclude any statement that contains the text TEST_ONLY from consideration for automatic capture: EXEC DBMS_SPM.CONFIGURE('AUTO_CAPTURE_SQL_TEXT','%TEST_ONLY%',false); 5. Optionally, to confirm the filters, query DBA_SQL_MANAGEMENT_CONFIG. For example, use the following query (sample output included): COL PARAMETER_NAME FORMAT a32 COL PARAMETER_VALUE FORMAT a32 SELECT PARAMETER_NAME, PARAMETER_VALUE FROM DBA_SQL_MANAGEMENT_CONFIG WHERE PARAMETER_NAME LIKE '%AUTO%'; PARAMETER_NAME -------------------------------AUTO_CAPTURE_PARSING_SCHEMA_NAME AUTO_CAPTURE_MODULE AUTO_CAPTURE_ACTION AUTO_CAPTURE_SQL_TEXT PARAMETER_VALUE -------------------------------parsing_schema IN (SH) (sql_text NOT LIKE %TEST_ONLY%) See Also: • "Automatic Initial Plan Capture" • Oracle Database PL/SQL Packages and Types Reference to learn more about the DBMS_SPM.CONFIGURE procedure • Oracle Database Reference to learn more about the DBA_SQL_MANAGEMENT_CONFIG view 29-9 Chapter 29 Configuring SQL Plan Management 29.2.1.3 Disabling All SQL Plan Baselines When you set the OPTIMIZER_USE_SQL_PLAN_BASELINES initialization parameter to false, the database does not use any plan baselines in the database. Typically, you might want to disable one or two plan baselines, but not all of them. A possible use case might be testing the benefits of SQL plan management. To disable all SQL plan baselines in the database: 1. Connect SQL*Plus to the database with the appropriate privileges, and then show the current settings for SQL plan management. For example, connect SQL*Plus to the database with administrator privileges and execute the following command (sample output included): SQL> SHOW PARAMETER SQL_PLAN NAME -----------------------------------optimizer_capture_sql_plan_baselines optimizer_use_sql_plan_baselines TYPE ----------boolean boolean VALUE ----FALSE TRUE If the parameters are set as you intend, then skip the remaining steps. 2. To ignore all existing plan baselines enter the following statement: SQL> ALTER SYSTEM SET OPTIMIZER_USE_SQL_PLAN_BASELINES=false See Also: Oracle Database Reference to learn about the SQL plan baseline initialization parameters 29.2.2 Managing the SPM Evolve Advisor Task SPM Evolve Advisor is a SQL advisor that evolves plans that have recently been added to the SQL plan baseline. The advisor simplifies plan evolution by eliminating the requirement to do it manually. This section contains the following topics: • About the SPM Evolve Advisor Task By default, SYS_AUTO_SPM_EVOLVE_TASK runs daily in the scheduled maintenance window. • Enabling and Disabling the SPM Evolve Advisor Task No separate scheduler client exists for the Automatic SPM Evolve Advisor task. • Configuring the Automatic SPM Evolve Advisor Task Configure automatic plan evolution by specifying the task parameters using the DBMS_SPM.SET_EVOLVE_TASK_PARAMETER procedure. 29-10 Chapter 29 Configuring SQL Plan Management 29.2.2.1 About the SPM Evolve Advisor Task By default, SYS_AUTO_SPM_EVOLVE_TASK runs daily in the scheduled maintenance window. The SPM Evolve Advisor task perform the following operations: 1. Locates unaccepted plans 2. Ranks all unaccepted plans 3. Performs test executions of as many plans as possible during the maintenance window 4. Selects the lowest-cost plan to compare against each unaccepted plan 5. Accepts automatically any unaccepted plan that performs sufficiently better, using a cost-based algorithm, than the existing accepted plan Note: The SPM Evolve Advisor task can accept more than one plan for a single statement. SPM Evolve Advisor locates unaccepted plans that the optimizer added to the SMB during earlier hard parses. In some cases, more optimal plans may reside in other locations. By using the DBMS_SPM.SET_EVOLVE_TASK_PARAMETER procedure, you can configure the automatic task to search the shared SQL area, AWR repository, or SQL tuning sets for plans that are not yet in the SMB plan history. If the advisor finds eligible plans in alternative locations, then it includes them along with the other unaccepted plans. By default, the automatic task does not search alternative locations. See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about the DBMS_SPM.SET_EVOLVE_TASK_PARAMETER procedure 29.2.2.2 Enabling and Disabling the SPM Evolve Advisor Task No separate scheduler client exists for the Automatic SPM Evolve Advisor task. One client controls both Automatic SQL Tuning Advisor and Automatic SPM Evolve Advisor. Thus, the same task enables or disables both. See Also: "Enabling and Disabling the Automatic SQL Tuning Task" to learn how to enable and disable Automatic SPM Evolve Advisor 29-11 Chapter 29 Configuring SQL Plan Management 29.2.2.3 Configuring the Automatic SPM Evolve Advisor Task Configure automatic plan evolution by specifying the task parameters using the DBMS_SPM.SET_EVOLVE_TASK_PARAMETER procedure. Because the SYS_AUTO_SPM_EVOLVE_TASK task is owned by SYS, only SYS can set task parameters. The following table describes some of the procedure parameters. Table 29-3 DBMS_SPM.SET_EVOLVE_TASK_PARAMETER Parameters Parameter Description alternate_plan_source Determines which sources to search for additional plans: CURSOR_CACHE, AUTOMATIC_WORKLOAD_REPOSITORY, or SQL_TUNING_SETS. Combine multiple values with the plus sign (+). The default is CURSOR_CACHE +AUTOMATIC_WORKLOAD_REPOSITORY. alternate_plan_baseline Determines which alternative plans should be loaded. EXISTING, which is the default, loads plans with for statements with existing baselines. NEW loads plans for statements without a baseline, in which case a new baseline is created. Combine multiple values with the plus sign (+). alternate_plan_limit Specifies the maximum number of plans to load. The default is 0. accept_plans Specifies whether to accept recommended plans automatically. When ACCEPT_PLANS is true (default), SQL plan management automatically accepts all plans recommended by the task. When set to false, the task verifies the plans and generates a report if its findings, but does not evolve the plans. The tutorial in this section assumes the following: • You want the database to accept plans automatically. • You want the task to time out after 1200 seconds per execution. • You want the evolve task to look for up to a maximum of 500 plans in the shared SQL area and AWR repository To set automatic evolution task parameters: 1. Connect SQL*Plus to the database as SYS. 2. Query the current parameter settings for SYS_AUTO_SPM_EVOLVE_TASK. For example, connect SQL*Plus to the database with administrator privileges and execute the following query: COL PARAMETER_NAME FORMAT a25 COL VALUE FORMAT a42 SELECT PARAMETER_NAME, PARAMETER_VALUE AS "VALUE" FROM DBA_ADVISOR_PARAMETERS WHERE ( (TASK_NAME = 'SYS_AUTO_SPM_EVOLVE_TASK') AND ( (PARAMETER_NAME = 'ACCEPT_PLANS') OR 29-12 Chapter 29 Configuring SQL Plan Management (PARAMETER_NAME LIKE '%ALT%') OR (PARAMETER_NAME = 'TIME_LIMIT') ) ); Sample output appears as follows: PARAMETER_NAME ------------------------ALTERNATE_PLAN_LIMIT ALTERNATE_PLAN_SOURCE ALTERNATE_PLAN_BASELINE ACCEPT_PLANS TIME_LIMIT 3. VALUE -----------------------------------------0 CURSOR_CACHE+AUTOMATIC_WORKLOAD_REPOSITORY EXISTING true 3600 Set parameters using PL/SQL code of the following form: BEGIN DBMS_SPM.SET_EVOLVE_TASK_PARAMETER( task_name => 'SYS_AUTO_SPM_EVOLVE_TASK' , parameter => parameter_name , value => value ); END; / For example, the following PL/SQL block configures the SYS_AUTO_SPM_EVOLVE_TASK task to automatically accept plans, seek up a maximum of 500 plans in the shared SQL area and AWR repository, and time out after 20 minutes: BEGIN DBMS_SPM.SET_EVOLVE_TASK_PARAMETER( task_name => 'SYS_AUTO_SPM_EVOLVE_TASK' , parameter => 'TIME_LIMIT' , value => '1200' ); DBMS_SPM.SET_EVOLVE_TASK_PARAMETER( task_name => 'SYS_AUTO_SPM_EVOLVE_TASK' , parameter => 'ACCEPT_PLANS' , value => 'true' ); DBMS_SPM.SET_EVOLVE_TASK_PARAMETER( task_name => 'SYS_AUTO_SPM_EVOLVE_TASK' , parameter => 'ALTERNATE_PLAN_LIMIT' , value => '500' ); END; / 4. Optionally, confirm your changes by querying the current parameter settings for SYS_AUTO_SPM_EVOLVE_TASK. For example, execute the following query: SELECT PARAMETER_NAME, PARAMETER_VALUE AS "VALUE" FROM DBA_ADVISOR_PARAMETERS WHERE ( (TASK_NAME = 'SYS_AUTO_SPM_EVOLVE_TASK') AND ( (PARAMETER_NAME = 'ACCEPT_PLANS') OR (PARAMETER_NAME LIKE '%ALT%') OR (PARAMETER_NAME = 'TIME_LIMIT') ) ); Sample output appears as follows: PARAMETER_NAME VALUE ------------------------- ------------------------------------------ 29-13 Chapter 29 Displaying Plans in a SQL Plan Baseline ALTERNATE_PLAN_LIMIT ALTERNATE_PLAN_SOURCE ALTERNATE_PLAN_BASELINE ACCEPT_PLANS TIME_LIMIT 500 CURSOR_CACHE+AUTOMATIC_WORKLOAD_REPOSITORY EXISTING true 1200 See Also: • • Oracle Database PL/SQL Packages and Types Reference for complete reference information for DBMS_SPM.SET_EVOLVE_TASK_PARAMETER Oracle Database Reference to learn more about the DBA_ADVISOR_PARAMETERS view 29.3 Displaying Plans in a SQL Plan Baseline To view the plans stored in the SQL plan baseline for a specific statement, use the DBMS_XPLAN.DISPLAY_SQL_PLAN_BASELINE function. This function uses plan information stored in the plan history to display the plans. The following table describes the relevant parameters for the DBMS_XPLAN.DISPLAY_SQL_PLAN_BASELINE function. Table 29-4 DBMS_XPLAN.DISPLAY_SQL_PLAN_BASELINE Parameters Function Parameter Description sql_handle SQL handle of the statement. Retrieve the SQL handle by joining the V$SQL.SQL_PLAN_BASELINE and DBA_SQL_PLAN_BASELINES views on the PLAN_NAME columns. plan_name Name of the plan for the statement. This section explains how to show plans in a baseline from the command line. If you use Cloud Control, then display plan baselines from the SQL Plan Baseline subpage shown in Figure 29-1. To display plans in a SQL plan baselines: 1. Connect SQL*Plus to the database with the appropriate privileges, and then obtain the SQL ID of the query whose plan you want to display. For example, assume that a SQL plan baseline exists for a SELECT statement with the SQL ID 31d96zzzpcys9. 2. Query the plan by SQL ID. The following query displays execution plans for the statement with the SQL ID 31d96zzzpcys9: SELECT PLAN_TABLE_OUTPUT FROM V$SQL s, DBA_SQL_PLAN_BASELINES b, TABLE( DBMS_XPLAN.DISPLAY_SQL_PLAN_BASELINE(b.sql_handle,b.plan_name,'basic') ) t WHERE s.EXACT_MATCHING_SIGNATURE=b.SIGNATURE 29-14 Chapter 29 Loading SQL Plan Baselines AND AND b.PLAN_NAME=s.SQL_PLAN_BASELINE s.SQL_ID='31d96zzzpcys9'; The sample query results are as follows: PLAN_TABLE_OUTPUT ----------------------------------------------------------------------------------------------------------------------------------------------------SQL handle: SQL_513f7f8a91177b1a SQL text: select * from hr.employees where employee_id=100 ----------------------------------------------------------------------------------------------------------------------------------------------------Plan name: SQL_PLAN_52gvzja8jfysuc0e983c6 Plan id: 3236529094 Enabled: YES Fixed: NO Accepted: YES Origin: AUTO-CAPTURE --------------------------------------------------------------------------Plan hash value: 3236529094 ----------------------------------------------------| Id | Operation | Name | ----------------------------------------------------| 0 | SELECT STATEMENT | | | 1 | TABLE ACCESS BY INDEX ROWID| EMPLOYEES | | 2 | INDEX UNIQUE SCAN | EMP_EMP_ID_PK | ----------------------------------------------------- The results show that the plan for SQL ID 31d96zzzpcys is named SQL_PLAN_52gvzja8jfysuc0e983c6 and was captured automatically. See Also: • "SQL Management Base" • Oracle Database PL/SQL Packages and Types Reference to learn about additional parameters used by the DISPLAY_SQL_PLAN_BASELINE function 29.4 Loading SQL Plan Baselines Using DBMS_SPM, you can initiate the bulk load of a set of existing plans into a SQL plan baseline. This section contains the following topics: • About Loading SQL Plan Baselines The DBMS_SPM package enables you to load plans from multiple sources. • Loading Plans from AWR This topic explains how to load plans from AWR using PL/SQL. • Loading Plans from the Shared SQL Area This topic explains how to load plans from the shared SQL area, also called the cursor cache, using PL/SQL. 29-15 Chapter 29 Loading SQL Plan Baselines • Loading Plans from a SQL Tuning Set A SQL tuning set (STS) is a database object that includes one or more SQL statements, execution statistics, and execution context. • Loading Plans from a Staging Table You may want to transfer optimal plans from a source database to a different destination database. 29.4.1 About Loading SQL Plan Baselines The DBMS_SPM package enables you to load plans from multiple sources. The goal of this task is to load plans from the following sources: • AWR Load plans from Automatic Workload Repository (AWR) snapshots. You must specify the beginning and ending of the snapshot range. Optionally, you can apply a filter to load only plan that meet specified criteria. By default, the optimizer uses the loaded plans the next time that the database executes the SQL statements. • Shared SQL area Load plans for statements directly from the shared SQL area, which is in the shared pool of the SGA. By applying a filter on the module name, the schema, or the SQL ID you identify the SQL statement or set of SQL statements to capture. The optimizer uses the plans the next time that the database executes the SQL statements. Loading plans directly from the shared SQL area is useful when application SQL has been hand-tuned using hints. Because you probably cannot change the SQL to include the hint, populating the SQL plan baseline ensures that the application SQL uses optimal plans. • SQL tuning set (STS) Capture the plans for a SQL workload into an STS, and then load the plans into the SQL plan baselines. The optimizer uses the plans the next time that the database executes the SQL statements. Bulk loading execution plans from an STS is an effective way to prevent plan regressions after a database upgrade. • Staging table Use the DBMS_SPM package to define a staging table, DBMS_SPM.PACK_STGTAB_BASELINE to copy the baselines into a staging table, and Oracle Data Pump to transfer the table to another database. On the destination database, use DBMS_SPM.UNPACK_STGTAB_BASELINE to unpack the plans from the staging table and put the baselines into the SMB. A use case is the introduction of new SQL statements into the database from a new application module. A vendor can ship application software with SQL plan baselines for the new SQL. In this way, the new SQL uses plans that are known to give optimal performance under a standard test configuration. Alternatively, if you develop or test an application in-house, export the correct plans from the test database and import them into the production database. • Stored outline Migrate stored outlines to SQL plan baselines. After the migration, you maintain the same plan stability that you had using stored outlines while being able to use 29-16 Chapter 29 Loading SQL Plan Baselines the more advanced features provided by SQL Plan Management, such as plan evolution. See . See Also: • "Migrating Stored Outlines to SQL Plan Baselines" • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SPM.PACK_STGTAB_BASELINE Function 29.4.2 Loading Plans from AWR This topic explains how to load plans from AWR using PL/SQL. Load plans with the LOAD_PLANS_FROM_AWR function of the DBMS_SPM package. The following table describes some function parameters. Table 29-5 LOAD_PLANS_FROM_AWR Parameters Function Parameter Description begin_snap Number of the beginning snapshot in the range. Required. end_snap Number of the ending snapshot in the range. Required. basic_filter A filter applied to AWR to select only qualifying plans to be loaded. The default null means that all plans in AWR are selected. The filter can take the form of any WHERE clause predicate that can be specified against the column DBA_HIST_SQLTEXT.SQL_TEXT. An example is basic_filter => 'sql_text like ''SELECT /*LOAD_STS*/%'''. fixed Default NO means the loaded plans are used as nonfixed plans. YES means the loaded plans are fixed plans. "Plan Selection" explains that the optimizer chooses a fixed plan in the plan baseline over a nonfixed plan. This section explains how to load plans using the command line. In Cloud Control, go to the SQL Plan Baseline subpage (shown in Figure 29-1) and click Load to load plan baselines from AWR. This tutorial assumes the following: • You want to load plans for the following query into the SMB: SELECT /*LOAD_AWR*/ * FROM sh.sales WHERE quantity_sold > 40 ORDER BY prod_id; • You want the loaded plans to be nonfixed. • The user sh has privileges to query DBA_HIST_SNAPSHOT and DBA_SQL_PLAN_BASELINES, execute DBMS_WORKLOAD_REPOSITORY.CREATE_SNAPSHOT, and execute DBMS_SPM.LOAD_PLANS_FROM_AWR. 29-17 Chapter 29 Loading SQL Plan Baselines To load plans from the shared SQL area: 1. Log in to the database with the appropriate privileges, and then query the most recent 3 AWR snapshots. For example, query DBA_HIST_SNAPSHOT as follows: SELECT * FROM (SELECT SNAP_ID, SNAP_LEVEL, TO_CHAR(BEGIN_INTERVAL_TIME, 'DD/MM/YY HH24:MI:SS') BEGIN FROM DBA_HIST_SNAPSHOT ORDER BY SNAP_ID DESC) WHERE ROWNUM <= 3; SNAP_ID SNAP_LEVEL BEGIN ---------- ---------- ----------------212 1 10/12/15 06:00:02 211 1 10/12/15 05:00:11 210 1 10/12/15 04:00:59 2. Query sh.sales, using the LOAD_AWR tag to identify the SQL statement. For example, use the following query: SELECT /*LOAD_AWR*/ * FROM sh.sales WHERE quantity_sold > 40 ORDER BY prod_id; 3. Take a new AWR snapshot. For example, use the following program: EXEC DBMS_WORKLOAD_REPOSITORY.CREATE_SNAPSHOT; 4. Query the most recent 3 AWR snapshots to confirm that a new snapshot was taken. For example, query DBA_HIST_SNAPSHOT as follows: SELECT * FROM (SELECT SNAP_ID, SNAP_LEVEL, TO_CHAR(BEGIN_INTERVAL_TIME, 'DD/MM/YY HH24:MI:SS') BEGIN FROM DBA_HIST_SNAPSHOT ORDER BY SNAP_ID DESC) WHERE ROWNUM <= 3; SNAP_ID SNAP_LEVEL BEGIN ---------- ---------- ----------------213 1 10/12/15 06:24:53 212 1 10/12/15 06:00:02 211 1 10/12/15 05:00:11 5. Load the plans for the most recent 2 snapshots from AWR. For example, execute the LOAD_PLANS_FROM_AWR function in SQL*Plus to load the plans from snapshot 212 to 213: VARIABLE v_plan_cnt NUMBER EXEC :v_plan_cnt := DBMS_SPM.LOAD_PLANS_FROM_AWR(begin_snap => 212, end_snap => 213); In the preceding example, the variable v_plan_cnt contains the number of plans that were loaded. 29-18 Chapter 29 Loading SQL Plan Baselines 6. Query the data dictionary to ensure that the plans were loaded into the baseline for the LOAD_AWR statement. The following statement queries DBA_SQL_PLAN_BASELINES (sample output included): COL COL COL COL SQL_HANDLE FORMAT a20 SQL_TEXT FORMAT a20 PLAN_NAME FORMAT a30 ORIGIN FORMAT a20 SELECT SQL_HANDLE, SQL_TEXT, PLAN_NAME, ORIGIN, ENABLED, ACCEPTED FROM DBA_SQL_PLAN_BASELINES WHERE SQL_TEXT LIKE '%LOAD_AWR%'; SQL_HANDLE SQL_TEXT PLAN_NAME ORIGIN ENA ACC -------------------- ----------------- ------------------------------ -------------------- --- --SQL_495d29c5f4612cda SELECT /*LOAD_AWR SQL_PLAN_4kr99sru62b6u54bc8843 MANUAL-LOAD-FROM-AWR YES YES */ * FROM sh.sales WHERE quantity_sold > 40 ORDER BY prod_id The output shows that the plan is accepted, which means that it is in the plan baseline for the statement. Also, the origin is MANUAL-LOAD-FROM-AWR, which means that the statement was loaded manually from AWR rather than automatically captured. See Also: • "Fixed Plans" • Oracle Database PL/SQL Packages and Types Reference to learn how to use the DBMS_SPM.LOAD_PLANS_FROM_AWR function • Oracle Database Reference to learn more about the DBA_SQL_PLAN_BASELINES view 29.4.3 Loading Plans from the Shared SQL Area This topic explains how to load plans from the shared SQL area, also called the cursor cache, using PL/SQL. Load plans with the LOAD_PLANS_FROM_CURSOR_CACHE function of the DBMS_SPM package. The following table describes some function parameters. Table 29-6 LOAD_PLANS_FROM_CURSOR_CACHE Parameters Function Parameter Description sql_id SQL statement identifier. Identifies a SQL statement in the shared SQL area. 29-19 Chapter 29 Loading SQL Plan Baselines Table 29-6 (Cont.) LOAD_PLANS_FROM_CURSOR_CACHE Parameters Function Parameter Description fixed Default NO means the loaded plans are used as nonfixed plans. YES means the loaded plans are fixed plans. "Plan Selection" explains that the optimizer chooses a fixed plan in the plan baseline over a nonfixed plan. This section explains how to load plans using the command line. In Cloud Control, go to the SQL Plan Baseline subpage (shown in Figure 29-1) and click Load to load plan baselines from the shared SQL area. This tutorial assumes the following: • You have executed the following query: SELECT /*LOAD_CC*/ * FROM sh.sales WHERE quantity_sold > 40 ORDER BY prod_id; • You want the loaded plans to be nonfixed. To load plans from the shared SQL area: 1. Connect SQL*Plus to the database with the appropriate privileges, and then determine the SQL IDs of the relevant statements in the shared SQL area. For example, query V$SQL for the SQL ID of the sh.sales query (sample output included): SELECT FROM WHERE SQL_ID, CHILD_NUMBER AS "Child Num", PLAN_HASH_VALUE AS "Plan Hash", OPTIMIZER_ENV_HASH_VALUE AS "Opt Env Hash" V$SQL SQL_TEXT LIKE 'SELECT /*LOAD_CC*/%'; SQL_ID Child Num Plan Hash Opt Env Hash ------------- ---------- ---------- -----------27m0sdw9snw59 0 1421641795 3160571937 The preceding output shows that the SQL ID of the statement is 27m0sdw9snw59. 2. Load the plans for the specified statements into the SQL plan baseline. For example, execute the LOAD_PLANS_FROM_CURSOR_CACHE function in SQL*Plus to load the plan for the statement with the SQL ID 27m0sdw9snw59: VARIABLE v_plan_cnt NUMBER EXECUTE :v_plan_cnt := DBMS_SPM.LOAD_PLANS_FROM_CURSOR_CACHE(sql_id => '27m0sdw9snw59'); In the preceding example, the variable v_plan_cnt contains the number of plans that were loaded. 3. Query the data dictionary to ensure that the plans were loaded into the baseline for the statement. The following statement queries DBA_SQL_PLAN_BASELINES (sample output included): 29-20 Chapter 29 Loading SQL Plan Baselines SELECT SQL_HANDLE, SQL_TEXT, PLAN_NAME, ORIGIN, ENABLED, ACCEPTED FROM DBA_SQL_PLAN_BASELINES; SQL_HANDLE SQL_TEXT --------------------- -------------------SQL_a8632bd857a4a25e SELECT /*LOAD_CC*/ * FROM sh.sales WHERE quantity_sold > 40 ORDER BY prod_id PLAN_NAME ORIGIN ENA ACC --------------------- ------------------- --- --SQL_PLAN_gdkvzfhrgkda MANUAL-LOAD-FROM-CC YES YES 71694fc6b The output shows that the plan is accepted, which means that it is in the plan baseline for the statement. Also, the origin is MANUAL-LOAD-FROM-CC, which means that the statement was loaded manually from the shared SQL area rather than automatically captured. See Also: • "Fixed Plans" • Oracle Database PL/SQL Packages and Types Reference to learn how to use the DBMS_SPM.LOAD_PLANS_FROM_CURSOR_CACHE function • Oracle Database Reference to learn more about the DBA_SQL_PLAN_BASELINES view 29.4.4 Loading Plans from a SQL Tuning Set A SQL tuning set (STS) is a database object that includes one or more SQL statements, execution statistics, and execution context. You can load plans with the DBMS_SPM.LOAD_PLANS_FROM_SQLSET function or using Cloud Control. The following table describes some function parameters. Table 29-7 LOAD_PLANS_FROM_SQLSET Parameters Function Parameter Description sqlset_name Name of the STS from which the plans are loaded into SQL plan baselines. basic_filter A filter applied to the STS to select only qualifying plans to be loaded. The filter can take the form of any WHERE clause predicate that can be specified against the view DBA_SQLSET_STATEMENTS. An example is basic_filter => 'sql_text like ''SELECT /*LOAD_STS*/%'''. fixed Default NO means the loaded plans are used as nonfixed plans. YES means the loaded plans are fixed plans. "Plan Selection" explains that the optimizer chooses a fixed plan in the plan baseline over a nonfixed plan. This section explains how to load plans from the command line. In Cloud Control, go to the SQL Plan Baseline subpage (shown in Figure 29-1) and click Load to load plan baselines from SQL tuning sets. 29-21 Chapter 29 Loading SQL Plan Baselines This tutorial assumes the following: • You want the loaded plans to be nonfixed. • You have executed the following query: SELECT /*LOAD_STS*/ * FROM sh.sales WHERE quantity_sold > 40 ORDER BY prod_id; • You have loaded the plan from the shared SQL area into the SQL tuning set named SPM_STS, which is owned by user SPM. To load plans from a SQL tuning set: 1. Connect SQL*Plus to the database with the appropriate privileges, and then verify which plans are in the SQL tuning set. For example, query DBA_SQLSET_STATEMENTS for the STS name (sample output included): SELECT SQL_TEXT FROM DBA_SQLSET_STATEMENTS WHERE SQLSET_NAME = 'SPM_STS'; SQL_TEXT -------------------SELECT /*LOAD_STS*/ * FROM sh.sales WHERE quantity_sold > 40 ORDER BY prod_id The output shows that the plan for the select /*LOAD_STS*/ statement is in the STS. 2. Load the plan from the STS into the SQL plan baseline. For example, in SQL*Plus execute the function as follows: VARIABLE v_plan_cnt NUMBER EXECUTE :v_plan_cnt := DBMS_SPM.LOAD_PLANS_FROM_SQLSET( sqlset_name => 'SPM_STS', basic_filter => 'sql_text like ''SELECT /*LOAD_STS*/%''' ); The basic_filter parameter specifies a WHERE clause that loads only the plans for the queries of interest. The variable v_plan_cnt stores the number of plans loaded from the STS. 3. Query the data dictionary to ensure that the plan was loaded into the baseline for the statement. The following statement queries the DBA_SQL_PLAN_BASELINES view (sample output included). SQL> SELECT SQL_HANDLE, SQL_TEXT, PLAN_NAME, 2 ORIGIN, ENABLED, ACCEPTED 3 FROM DBA_SQL_PLAN_BASELINES; SQL_HANDLE SQL_TEXT PLAN_NAME ORIGIN ENA ACC --------------------- --------------- ---------------- -------------------- --- --SQL_a8632bd857a4a25e SELECT SQL_PLAN_ahstb MANUAL-LOAD-FROM-STS YES YES 29-22 Chapter 29 Loading SQL Plan Baselines /*LOAD_STS*/* FROM sh.sales WHERE quantity_sold > 40 ORDER BY prod_id v1bu98ky1694fc6b The output shows that the plan is accepted, which means that it is in the plan baseline. Also, the origin is MANUAL-LOAD-FROM-STS, which means that the plan was loaded manually from a SQL tuning set rather than automatically captured. 4. Optionally, drop the STS. For example, execute DBMS_SQLTUNE.DROP_SQLSET to drop the SPM_STS tuning set as follows: EXEC SYS.DBMS_SQLTUNE.DROP_SQLSET( sqlset_name => 'SPM_STS', sqlset_owner => 'SPM' ); See Also: • Oracle Database Reference to learn about the DBA_SQL_PLAN_BASELINES view • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SPM.LOAD_PLANS_FROM_SQLSET function 29.4.5 Loading Plans from a Staging Table You may want to transfer optimal plans from a source database to a different destination database. For example, you may have investigated a set of plans on a test database and confirmed that they have performed well. You may then want to load these plans into a production database. A staging table is a table that, for the duration of its existence, stores plans so that the plans do not disappear from the table while you are unpacking them. Use the DBMS_SPM.CREATE_STGTAB_BASELINE procedure to create a staging table. To pack (insert row into) and unpack (extract rows from) the staging table, use the PACK_STGTAB_BASELINE and UNPACK_STGTAB_BASELINE functions of the DBMS_SPM package. Oracle Data Pump Import and Export enable you to copy the staging table to a different database. 29-23 Chapter 29 Loading SQL Plan Baselines Figure 29-2 Loading Plans from a Staging Table Destination Host Source Host Source Database Destination Database Staging Table Staging Table 1 Pack Unpack 2 1 SQL Management Base SQL Plan Baselines Data Pump Export 6 1 SQL Management Base SQL Plan Baselines Data Pump Import 3 Transfer Dump File to Destination Host 4 1 .dump file 5 .dump file Export plans with the PACK_STGTAB_BASELINE function of the DBMS_SPM package, and then import them with UNPACK_STGTAB_BASELINE. The following table describes some function parameters. Table 29-8 PACK_STGTAB_BASELINE and UNPACK_STGTAB_BASELINE Parameters Function Parameter Description table_name Specifies the table to be imported or exported. origin Origin of SQL plan baseline. These procedures accept all possible values of DBA_SQL_PLAN_BASELINES.ORIGIN as the origin argument. For example, the origin parameter permits MANUAL-LOAD-FROM-STS, MANUAL-LOAD-FROM-AWR, and MANUAL-LOAD-FROM-CC. This tutorial assumes the following: • You want to create a staging table named stage1 in the source database. • You want to load all plans owned by user spm into the staging table. • You want to transfer the staging table to a destination database. • You want to load the plans in stage1 as fixed plans. To transfer a set of SQL plan baselines from one database to another: 1. Connect SQL*Plus to the source database with the appropriate privileges, and then create a staging table using the CREATE_STGTAB_BASELINE procedure. The following example creates a staging table named stage1: 29-24 Chapter 29 Loading SQL Plan Baselines BEGIN DBMS_SPM.CREATE_STGTAB_BASELINE ( table_name => 'stage1'); END; / 2. On the source database, pack the SQL plan baselines you want to export from the SQL management base into the staging table. The following example packs enabled plan baselines created by user spm into staging table stage1. Select SQL plan baselines using the plan name (plan_name), SQL handle (sql_handle), or any other plan criteria. The table_name parameter is mandatory. DECLARE v_plan_cnt NUMBER; BEGIN v_plan_cnt := DBMS_SPM.PACK_STGTAB_BASELINE ( table_name => 'stage1' , enabled => 'yes' , creator => 'spm' ); END; / 3. Export the staging table stage1 into a dump file using Oracle Data Pump Export. 4. Transfer the dump file to the host of the destination database. 5. On the destination database, import the staging table stage1 from the dump file using the Oracle Data Pump Import utility. 6. On the destination database, unpack the SQL plan baselines from the staging table into the SQL management base. The following example unpacks all fixed plan baselines stored in the staging table stage1: DECLARE v_plan_cnt NUMBER; BEGIN v_plan_cnt := DBMS_SPM.UNPACK_STGTAB_BASELINE ( table_name => 'stage1' , fixed => 'yes' ); END; / See Also: • • Oracle Database PL/SQL Packages and Types Reference for more information about using the DBMS_SPM package Oracle Database Reference to learn more about the DBA_SQL_PLAN_BASELINES view • Oracle Database Utilities for detailed information about using the Data Pump Export and Import utilities 29-25 Chapter 29 Evolving SQL Plan Baselines Manually 29.5 Evolving SQL Plan Baselines Manually You can use PL/SQL or Cloud Control to manually evolve an unaccepted plan to determine whether it performs better than any plan currently in the plan baseline. This section contains the following topics: • About the DBMS_SPM Evolve Functions This topic describes the most relevant DBMS_SPM functions for managing plan evolution. Execute evolution tasks manually or schedule them to run automatically. • Managing an Evolve Task This topic describes a typical use case in which you create and execute a task, and then implement its recommendations. See Also: "Managing the SPM Evolve Advisor Task" 29.5.1 About the DBMS_SPM Evolve Functions This topic describes the most relevant DBMS_SPM functions for managing plan evolution. Execute evolution tasks manually or schedule them to run automatically. Table 29-9 Tasks DBMS_SPM Functions and Procedures for Managing Plan Evolution Procedure or Function Description ACCEPT_SQL_PLAN_BASELINE This function accepts one recommendation to evolve a single plan into a SQL plan baseline. CREATE_EVOLVE_TASK This function creates an advisor task to prepare the plan evolution of one or more plans for a specified SQL statement. The input parameters can be a SQL handle, plan name or a list of plan names, time limit, task name, and description. EXECUTE_EVOLVE_TASK This function executes an evolution task. The input parameters can be the task name, execution name, and execution description. If not specified, the advisor generates the name, which is returned by the function. IMPLEMENT_EVOLVE_TASK This function implements all recommendations for an evolve task. Essentially, this function is equivalent to using ACCEPT_SQL_PLAN_BASELINE for all recommended plans. Input parameters include task name, plan name, owner name, and execution name. REPORT_EVOLVE_TASK This function displays the results of an evolve task as a CLOB. Input parameters include the task name and section of the report to include. 29-26 Chapter 29 Evolving SQL Plan Baselines Manually Table 29-9 (Cont.) DBMS_SPM Functions and Procedures for Managing Plan Evolution Tasks Procedure or Function Description SET_EVOLVE_TASK_PARAMETER This function updates the value of an evolve task parameter. In this release, the only valid parameter is TIME_LIMIT. Oracle recommends that you configure SPM Evolve Advisor to run automatically. You can also evolve SQL plan baselines manually. The following graphic shows the basic workflow for managing SQL plan management tasks. Figure 29-3 Evolving SQL Plan Baselines DBMS_SPM.CREATE_EVOLVE_TASK DBMS_SPM.SET_EVOLVE_TASK_PARAMETER DBMS_SPM.EXECUTE_EVOLVE_TASK DBMS_SPM.IMPLEMENT_EVOLVE_TASK DBMS_SPM.REPORT_EVOLVE_TASK Typically, you manage SQL plan evolution tasks in the following sequence: 1. Create an evolve task 2. Optionally, set evolve task parameters 3. Execute the evolve task 4. Implement the recommendations in the task 5. Report on the task outcome See Also: • "Configuring the Automatic SPM Evolve Advisor Task") • Oracle Database PL/SQL Packages and Types Reference for more information about the DBMS_SPM package 29-27 Chapter 29 Evolving SQL Plan Baselines Manually 29.5.2 Managing an Evolve Task This topic describes a typical use case in which you create and execute a task, and then implement its recommendations. The following table describes some parameters of the CREATE_EVOLVE_TASK function. Table 29-10 DBMS_SPM.CREATE_EVOLVE_TASK Parameters Function Parameter Description sql_handle SQL handle of the statement. The default NULL considers all SQL statements with unaccepted plans. plan_name Plan identifier. The default NULL means consider all unaccepted plans of the specified SQL handle or all SQL statements if the SQL handle is NULL. time_limit Time limit in number of minutes. The time limit for first unaccepted plan equals the input value. The time limit for the second unaccepted plan equals the input value minus the time spent in first plan verification, and so on. The default DBMS_SPM.AUTO_LIMIT means let the system choose an appropriate time limit based on the number of plan verifications required to be done. task_name User-specified name of the evolution task. This section explains how to evolve plan baselines from the command line. In Cloud Control, from the SQL Plan Baseline subpage, select a plan, and then click Evolve. This tutorial assumes the following: • You do not have the automatic evolve task enabled. • You want to create a SQL plan baseline for the following query: SELECT /* q1_group_by */ prod_name, sum(quantity_sold) FROM products p, sales s WHERE p.prod_id = s.prod_id AND p.prod_category_id =203 GROUP BY prod_name; • You want to create two indexes to improve the query performance, and then evolve the plan that uses these indexes if it performs better than the plan currently in the plan baseline. To evolve a specified plan: 1. Perform the initial setup as follows: a. Connect SQL*Plus to the database with administrator privileges, and then prepare for the tutorial by flushing the shared pool and the buffer cache: ALTER SYSTEM FLUSH SHARED_POOL; ALTER SYSTEM FLUSH BUFFER_CACHE; b. Enable the automatic capture of SQL plan baselines. For example, enter the following statement: ALTER SYSTEM SET OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES=true; 29-28 Chapter 29 Evolving SQL Plan Baselines Manually c. Connect to the database as user sh, and then set SQL*Plus display parameters: CONNECT sh -- enter password SET PAGES 10000 LINES 140 SET SERVEROUTPUT ON COL SQL_TEXT FORMAT A20 COL SQL_HANDLE FORMAT A20 COL PLAN_NAME FORMAT A30 COL ORIGIN FORMAT A12 SET LONGC 60535 SET LONG 60535 SET ECHO ON 2. Execute the SELECT statements so that SQL plan management captures them: a. Execute the SELECT /* q1_group_by */ statement for the first time. Because the database only captures plans for repeatable statements, the plan baseline for this statement is empty. b. Query the data dictionary to confirm that no plans exist in the plan baseline. For example, execute the following query (sample output included): SELECT SQL_HANDLE, SQL_TEXT, PLAN_NAME, ORIGIN, ENABLED, ACCEPTED, FIXED, AUTOPURGE FROM DBA_SQL_PLAN_BASELINES WHERE SQL_TEXT LIKE '%q1_group%'; no rows selected SQL plan management only captures repeatable statements, so this result is expected. c. 3. Execute the SELECT /* q1_group_by */ statement for the second time. Query the data dictionary to ensure that the plans were loaded into the plan baseline for the statement. The following statement queries DBA_SQL_PLAN_BASELINES (sample output included): SELECT SQL_HANDLE, SQL_TEXT, PLAN_NAME, ORIGIN, ENABLED, ACCEPTED, FIXED FROM DBA_SQL_PLAN_BASELINES WHERE SQL_TEXT LIKE '%q1_group%'; SQL_HANDLE SQL_TEXT PLAN_NAME ORIGIN ENA ACC FIX -------------------- ---------------- ------------------------------ ------------ --- --- --SQL_07f16c76ff893342 SELECT /* q1_gro SQL_PLAN_0gwbcfvzskcu242949306 AUTO-CAPTURE YES YES NO up_by */ prod_na me, sum(quantity _sold) FROM products p, sales s WHERE p.prod_id = s.prod_id AND p.prod_category _id =203 GROUP BY prod_name 29-29 Chapter 29 Evolving SQL Plan Baselines Manually The output shows that the plan is accepted, which means that it is in the plan baseline for the statement. Also, the origin is AUTO-CAPTURE, which means that the statement was automatically captured and not manually loaded. 4. Explain the plan for the statement and verify that the optimizer is using this plan. For example, explain the plan as follows, and then display it: EXPLAIN PLAN FOR SELECT /* q1_group_by */ prod_name, sum(quantity_sold) FROM products p, sales s WHERE p.prod_id = s.prod_id AND p.prod_category_id =203 GROUP BY prod_name; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY(null, null, 'basic +note')); Sample output appears below: Plan hash value: 1117033222 -----------------------------------------| Id | Operation | Name | -----------------------------------------| 0 | SELECT STATEMENT | | | 1 | HASH GROUP BY | | | 2 | HASH JOIN | | | 3 | TABLE ACCESS FULL | PRODUCTS | | 4 | PARTITION RANGE ALL| | | 5 | TABLE ACCESS FULL | SALES | -----------------------------------------Note ----- SQL plan baseline "SQL_PLAN_0gwbcfvzskcu242949306" used for this statement The note indicates that the optimizer is using the plan shown with the plan name listed in the previous step. 5. Create two indexes to improve the performance of the SELECT /* q1_group_by */ statement. For example, use the following statements: CREATE INDEX ind_prod_cat_name ON products(prod_category_id, prod_name, prod_id); CREATE INDEX ind_sales_prod_qty_sold ON sales(prod_id, quantity_sold); 6. Execute the select /* q1_group_by */ statement again. Because automatic capture is enabled, the plan baseline is populated with the new plan for this statement. 7. Query the data dictionary to ensure that the plan was loaded into the SQL plan baseline for the statement. The following statement queries DBA_SQL_PLAN_BASELINES (sample output included). SELECT SQL_HANDLE, SQL_TEXT, PLAN_NAME, ORIGIN, ENABLED, ACCEPTED FROM DBA_SQL_PLAN_BASELINES WHERE SQL_HANDLE IN ('SQL_07f16c76ff893342') ORDER BY SQL_HANDLE, ACCEPTED; 29-30 Chapter 29 Evolving SQL Plan Baselines Manually SQL_HANDLE SQL_TEXT PLAN_NAME ORIGIN ENA ACC -------------------- -------------------- ------------------------------ ------------ --- --SQL_07f16c76ff893342 SELECT /* q1_group_b SQL_PLAN_0gwbcfvzskcu20135fd6c AUTO-CAPTURE YES NO y */ prod_name, sum( quantity_sold) FROM products p, s ales s WHERE p.prod_id = s .prod_id AND p.prod_catego ry_id =203 GROUP BY prod_name SQL_07f16c76ff893342 SELECT /* q1_group_b SQL_PLAN_0gwbcfvzskcu242949306 AUTO-CAPTURE YES YES y */ prod_name, sum( quantity_sold) FROM products p, s ales s WHERE p.prod_id = s .prod_id AND p.prod_catego ry_id =203 GROUP BY prod_name The output shows that the new plan is unaccepted, which means that it is in the statement history but not the SQL plan baseline. 8. Explain the plan for the statement and verify that the optimizer is using the original nonindexed plan. For example, explain the plan as follows, and then display it: EXPLAIN PLAN FOR SELECT /* q1_group_by */ prod_name, sum(quantity_sold) FROM products p, sales s WHERE p.prod_id = s.prod_id AND p.prod_category_id =203 GROUP BY prod_name; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY(null, null, 'basic +note')); Sample output appears below: Plan hash value: 1117033222 -----------------------------------------| Id | Operation | Name | -----------------------------------------| 0 | SELECT STATEMENT | | | 1 | HASH GROUP BY | | | 2 | HASH JOIN | | | 3 | TABLE ACCESS FULL | PRODUCTS | | 4 | PARTITION RANGE ALL| | | 5 | TABLE ACCESS FULL | SALES | -----------------------------------------Note ----- SQL plan baseline "SQL_PLAN_0gwbcfvzskcu242949306" used for this statement The note indicates that the optimizer is using the plan shown with the plan name listed in Step 3. 29-31 Chapter 29 Evolving SQL Plan Baselines Manually 9. Connect as an administrator, and then create an evolve task that considers all SQL statements with unaccepted plans. For example, execute the DBMS_SPM.CREATE_EVOLVE_TASK function and then obtain the name of the task: CONNECT / AS SYSDBA VARIABLE cnt NUMBER VARIABLE tk_name VARCHAR2(50) VARIABLE exe_name VARCHAR2(50) VARIABLE evol_out CLOB EXECUTE :tk_name := DBMS_SPM.CREATE_EVOLVE_TASK( sql_handle => 'SQL_07f16c76ff893342', plan_name => 'SQL_PLAN_0gwbcfvzskcu20135fd6c'); SELECT :tk_name FROM DUAL; The following sample output shows the name of the task: :EVOL_OUT -------------------------------------------------------------------------TASK_11 Now that the task has been created and has a unique name, execute the task. 10. Execute the task. For example, execute the DBMS_SPM.EXECUTE_EVOLVE_TASK function (sample output included): EXECUTE :exe_name :=DBMS_SPM.EXECUTE_EVOLVE_TASK(task_name=>:tk_name); SELECT :exe_name FROM DUAL; :EXE_NAME --------------------------------------------------------------------------EXEC_1 11. View the report. For example, execute the DBMS_SPM.REPORT_EVOLVE_TASK function (sample output included): EXECUTE :evol_out := DBMS_SPM.REPORT_EVOLVE_TASK( task_name=>:tk_name, execution_name=>:exe_name ); SELECT :evol_out FROM DUAL; GENERAL INFORMATION SECTION -------------------------------------------------------------------------Task Information: --------------------------------------------Task Name : TASK_11 Task Owner : SYS Execution Name : EXEC_1 Execution Type : SPM EVOLVE Scope : COMPREHENSIVE Status : COMPLETED Started : 01/09/2012 12:21:27 Finished : 01/09/2012 12:21:29 Last Updated : 01/09/2012 12:21:29 Global Time Limit : 2147483646 Per-Plan Time Limit : UNUSED 29-32 Chapter 29 Evolving SQL Plan Baselines Manually Number of Errors : 0 --------------------------------------------------------------------------SUMMARY SECTION --------------------------------------------------------------------------Number of plans processed : 1 Number of findings : 1 Number of recommendations : 1 Number of errors : 0 --------------------------------------------------------------------------DETAILS SECTION --------------------------------------------------------------------------Object ID : 2 Test Plan Name : SQL_PLAN_0gwbcfvzskcu20135fd6c Base Plan Name : SQL_PLAN_0gwbcfvzskcu242949306 SQL Handle : SQL_07f16c76ff893342 Parsing Schema : SH Test Plan Creator : SH SQL Text : SELECT /*q1_group_by*/ prod_name, sum(quantity_sold) FROM products p, sales s WHERE p.prod_id=s.prod_id AND p.prod_category_id=203 GROUP BY prod_name Execution Statistics: ----------------------------Base Plan ---------------------------Elapsed Time (s): .044336 CPU Time (s): .044003 Buffer Gets: 360 Optimizer Cost: 924 Disk Reads: 341 Direct Writes: 0 Rows Processed: 4 Executions: 5 Test Plan -----------------------.012649 .012445 99 891 82 0 2 9 FINDINGS SECTION --------------------------------------------------------------------------Findings (1): ----------------------------1. The plan was verified in 2.18 seconds. It passed the benefit criterion because its verified performance was 2.01 times better than that of the baseline plan. Recommendation: ----------------------------Consider accepting the plan. Execute dbms_spm.accept_sql_plan_baseline(task_name => 'TASK_11', object_id => 2, task_owner => 'SYS'); EXPLAIN PLANS SECTION --------------------------------------------------------------------------Baseline Plan ----------------------------Plan Id : 1 Plan Hash Value : 1117033222 29-33 Chapter 29 Evolving SQL Plan Baselines Manually --------------------------------------------------------------------------| Id| Operation | Name | Rows | Bytes |Cost | Time | --------------------------------------------------------------------------| 0 | SELECT STATEMENT | | 21 | 861 | 924 | 00:00:12| | 1 | HASH GROUP BY | | 21 | 861 | 924 | 00:00:12| | *2| HASH JOIN | |267996|10987836 | 742 | 00:00:09| | *3| TABLE ACCESS FULL | PRODUCTS | 21 | 714 | 2 | 00:00:01| | 4 | PARTITION RANGE ALL | |918843| 6431901 | 662 | 00:00:08| | 5 | TABLE ACCESS FULL | SALES |918843| 6431901 | 662 | 00:00:08| --------------------------------------------------------------------------Predicate Information (identified by operation id): -----------------------------------------* 2 - access("P"."PROD_ID"="S"."PROD_ID") * 3 - filter("P"."PROD_CATEGORY_ID"=203) Test Plan ----------------------------Plan Id : 2 Plan Hash Value : 20315500 --------------------------------------------------------------------------|Id| Operation | Name | Rows | Bytes | Cost| Time | --------------------------------------------------------------------------| 0|SELECT STATEMENT | | 21| 861|891|00:00:11| | 1| SORT GROUP BY NOSORT| | 21| 861|891|00:00:11| | 2| NESTED LOOPS | |267996|10987836|891|00:00:11| |*3| INDEX RANGE SCAN |IND_PROD_CAT_NAME | 21| 714| 1|00:00:01| |*4| INDEX RANGE SCAN |IND_SALES_PROD_QTY| 12762| 89334| 42|00:00:01| --------------------------------------------------------------------------Predicate Information (identified by operation id): -----------------------------------------* 3 - access("P"."PROD_CATEGORY_ID"=203) * 4 - access("P"."PROD_ID"="S"."PROD_ID") This report indicates that the new execution plan, which uses the two new indexes, performs better than the original plan. 12. Implement the recommendations of the evolve task. For example, execute the IMPLEMENT_EVOLVE_TASK function: EXECUTE :cnt := DBMS_SPM.IMPLEMENT_EVOLVE_TASK( task_name=>:tk_name, execution_name=>:exe_name ); 13. Query the data dictionary to ensure that the new plan is accepted. The query provides the following sample output: SELECT SQL_HANDLE, SQL_TEXT, PLAN_NAME, ORIGIN, ENABLED, ACCEPTED FROM DBA_SQL_PLAN_BASELINES WHERE SQL_HANDLE IN ('SQL_07f16c76ff893342') ORDER BY SQL_HANDLE, ACCEPTED; SQL_HANDLE SQL_TEXT PLAN_NAME ORIGIN ENA ACC -------------------- -------------------- ------------------------------ ------------ --- --SQL_07f16c76ff893342 SELECT /* q1_group_b SQL_PLAN_0gwbcfvzskcu20135fd6c AUTO-CAPTURE YES YES y */ prod_name, sum( quantity_sold) FROM products p, s ales s 29-34 Chapter 29 Dropping SQL Plan Baselines WHERE p.prod_id = s .prod_id AND p.prod_catego ry_id =203 GROUP BY prod_name SQL_07f16c76ff893342 SELECT /* q1_group_b SQL_PLAN_0gwbcfvzskcu242949306 AUTO-CAPTURE YES YES y */ prod_name, sum( quantity_sold) FROM products p, s ales s WHERE p.prod_id = s .prod_id AND p.prod_catego ry_id =203 GROUP BY prod_name The output shows that the new plan is accepted. 14. Clean up after the example. For example, enter the following statements: EXEC :cnt := DBMS_SPM.DROP_SQL_PLAN_BASELINE('SQL_07f16c76ff893342'); EXEC :cnt := DBMS_SPM.DROP_SQL_PLAN_BASELINE('SQL_9049245213a986b3'); EXEC :cnt := DBMS_SPM.DROP_SQL_PLAN_BASELINE('SQL_bb77077f5f90a36b'); EXEC :cnt := DBMS_SPM.DROP_SQL_PLAN_BASELINE('SQL_02a86218930bbb20'); DELETE FROM SQLLOG$; CONNECT sh -- enter password DROP INDEX IND_SALES_PROD_QTY_SOLD; DROP INDEX IND_PROD_CAT_NAME; See Also: • "Managing the SPM Evolve Advisor Task" • Oracle Database PL/SQL Packages and Types Reference to learn more about the DBMS_SPM evolve functions 29.6 Dropping SQL Plan Baselines You can remove some or all plans from a SQL plan baseline. This technique is sometimes useful when testing SQL plan management. Drop plans with the DBMS_SPM.DROP_SQL_PLAN_BASELINE function. This function returns the number of dropped plans. The following table describes input parameters. Table 29-11 DROP_SQL_PLAN_BASELINE Parameters Function Parameter Description sql_handle SQL statement identifier. plan_name Name of a specific plan. Default NULL drops all plans associated with the SQL statement identified by sql_handle. 29-35 Chapter 29 Dropping SQL Plan Baselines This section explains how to drop baselines from the command line. In Cloud Control, from the SQL Plan Baseline subpage, select a plan, and then click Drop. This tutorial assumes that you want to drop all plans for the following SQL statement, effectively dropping the SQL plan baseline: SELECT /* repeatable_sql */ COUNT(*) FROM hr.jobs; To drop a SQL plan baseline: 1. Connect SQL*Plus to the database with the appropriate privileges, and then query the data dictionary for the plan baseline. The following statement queries DBA_SQL_PLAN_BASELINES (sample output included): SQL> SELECT SQL_HANDLE, SQL_TEXT, PLAN_NAME, ORIGIN, 2 ENABLED, ACCEPTED 3 FROM DBA_SQL_PLAN_BASELINES 4 WHERE SQL_TEXT LIKE 'SELECT /* repeatable_sql%'; SQL_HANDLE SQL_TEXT PLAN_NAME ORIGIN ENA ACC -------------------- -------------------- ------------------------------ -------------- --- --SQL_b6b0d1c71cd1807b SELECT /* repeatable SQL_PLAN_bdc6jswfd303v2f1e9c20 AUTO-CAPTURE YES YES _sql */ count(*) fro m hr.jobs 2. Drop the SQL plan baseline for the statement. The following example drops the plan baseline with the SQL handle SQL_b6b0d1c71cd1807b, and returns the number of dropped plans. Specify plan baselines using the plan name (plan_name), SQL handle (sql_handle), or any other plan criteria. The table_name parameter is mandatory. DECLARE v_dropped_plans number; BEGIN v_dropped_plans := DBMS_SPM.DROP_SQL_PLAN_BASELINE ( sql_handle => 'SQL_b6b0d1c71cd1807b' ); DBMS_OUTPUT.PUT_LINE('dropped ' || v_dropped_plans || ' plans'); END; / 3. Confirm that the plans were dropped. For example, execute the following query: SELECT SQL_HANDLE, SQL_TEXT, PLAN_NAME, ORIGIN, ENABLED, ACCEPTED FROM DBA_SQL_PLAN_BASELINES WHERE SQL_TEXT LIKE 'SELECT /* repeatable_sql%'; no rows selected See Also: Oracle Database PL/SQL Packages and Types Reference to learn about the DROP_SQL_PLAN_BASELINE function 29-36 Chapter 29 Managing the SQL Management Base 29.7 Managing the SQL Management Base The SQL management base is a part of the data dictionary that resides in the SYSAUX tablespace. It stores statement logs, plan histories, SQL plan baselines, and SQL profiles. This section contains the following topics: • About Managing the SMB Use the DBMS_SPM.CONFIGURE procedure to set configuration options for the SMB and the maintenance of SQL plan baselines. • Changing the Disk Space Limit for the SMB A weekly background process measures the total space occupied by the SMB. • Changing the Plan Retention Policy in the SMB A weekly scheduled purging task manages disk space used by SQL plan management. 29.7.1 About Managing the SMB Use the DBMS_SPM.CONFIGURE procedure to set configuration options for the SMB and the maintenance of SQL plan baselines. The DBA_SQL_MANAGEMENT_CONFIG view shows the current configuration settings for the SMB. The following table describes the parameters in the PARAMETER_NAME column. Table 29-12 Parameters in DBA_SQL_MANAGEMENT_CONFIG.PARAMETER_NAME Parameter Description SPACE_BUDGET_PERCENT Maximum percent of SYSAUX space that the SQL management base can use. The default is 10. The allowable range for this limit is between 1% and 50%. PLAN_RETENTION_WEEKS Number of weeks to retain unused plans before they are purged. The default is 53. AUTO_CAPTURE_PARSING_SCHEMA_N A list of the form (% LIKE a OR % LIKE b ...) AND (% AME NOT LIKE c AND % NOT LIKE d ...), which is the internal representation of the parsing schema name filter. If no parsing schema filters exist, then one side of the outer conjunction will be absent. AUTO_CAPTURE_MODULE A list of the form (% LIKE a OR % LIKE b ...) AND (% NOT LIKE c AND % NOT LIKE d ...), which is the internal representation of the module filter. If no module filters exist, then one side of the outer conjunction will be absent. AUTO_CAPTURE_ACTION A list of the form (% LIKE a OR % LIKE b ...) AND (% NOT LIKE c AND % NOT LIKE d ...), which is the internal representation of the action filter. If no action filters exist, then one side of the outer conjunction will be absent. AUTO_CAPTURE_SQL_TEXT A list of the form (% LIKE a OR % LIKE b ...) AND (% NOT LIKE c AND % NOT LIKE d ...), which is the internal representation of the SQL text filter. If no SQL text filters exist, then one side of the outer conjunction will be absent. 29-37 Chapter 29 Managing the SQL Management Base See Also: • "Eligibility for Automatic Initial Plan Capture" • Oracle Database Reference to learn more about DBA_SQL_MANAGEMENT_CONFIG • Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_SPM.CONFIGURE 29.7.2 Changing the Disk Space Limit for the SMB A weekly background process measures the total space occupied by the SMB. When the defined limit is exceeded, the process writes a warning to the alert log. The database generates alerts weekly until either the SMB space limit is increased, the size of the SYSAUX tablespace is increased, or the disk space used by the SMB is decreased by purging SQL management objects (SQL plan baselines or SQL profiles). This task explains how to change the limit with the DBMS_SPM.CONFIGURE procedure. Assumptions This tutorial assumes the following: • The current SMB space limit is the default of 10%. • You want to change the percentage limit to 30% To change the percentage limit of the SMB: 1. Connect SQL*Plus to the database with the appropriate privileges, and then query the data dictionary to see the current space budget percent. For example, execute the following query (sample output included): SELECT PARAMETER_NAME, PARAMETER_VALUE AS "%_LIMIT", ( SELECT sum(bytes/1024/1024) FROM DBA_DATA_FILES WHERE TABLESPACE_NAME = 'SYSAUX' ) AS SYSAUX_SIZE_IN_MB, PARAMETER_VALUE/100 * ( SELECT sum(bytes/1024/1024) FROM DBA_DATA_FILES WHERE TABLESPACE_NAME = 'SYSAUX' ) AS "CURRENT_LIMIT_IN_MB" FROM DBA_SQL_MANAGEMENT_CONFIG WHERE PARAMETER_NAME = 'SPACE_BUDGET_PERCENT'; PARAMETER_NAME %_LIMIT SYSAUX_SIZE_IN_MB CURRENT_LIMIT_IN_MB -------------------- ---------- ----------------- ------------------SPACE_BUDGET_PERCENT 10 211.4375 21.14375 2. Change the percentage setting. For example, execute the following command to change the setting to 30%: EXECUTE DBMS_SPM.CONFIGURE('space_budget_percent',30); 3. Query the data dictionary to confirm the change. For example, execute the following join (sample output included): SELECT PARAMETER_NAME, PARAMETER_VALUE AS "%_LIMIT", ( SELECT sum(bytes/1024/1024) FROM DBA_DATA_FILES 29-38 Chapter 29 Managing the SQL Management Base WHERE TABLESPACE_NAME = 'SYSAUX' ) AS SYSAUX_SIZE_IN_MB, PARAMETER_VALUE/100 * ( SELECT sum(bytes/1024/1024) FROM DBA_DATA_FILES WHERE TABLESPACE_NAME = 'SYSAUX' ) AS "CURRENT_LIMIT_IN_MB" FROM DBA_SQL_MANAGEMENT_CONFIG WHERE PARAMETER_NAME = 'SPACE_BUDGET_PERCENT'; PARAMETER_NAME %_LIMIT SYSAUX_SIZE_IN_MB CURRENT_LIMIT_IN_MB -------------------- ---------- ----------------- ------------------SPACE_BUDGET_PERCENT 30 211.4375 63.43125 See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about the DBMS_SPM.CONFIGURE procedure 29.7.3 Changing the Plan Retention Policy in the SMB A weekly scheduled purging task manages disk space used by SQL plan management. The task runs as an automated task in the maintenance window. The database purges plans that have not been used for longer than the plan retention period, as identified by the LAST_EXECUTED timestamp stored in the SMB for that plan. The default retention period is 53 weeks. The period can range between 5 and 523 weeks. This task explains how to change the plan retention period with the DBMS_SPM.CONFIGURE procedure. In Cloud Control, set the plan retention policy in the SQL Plan Baseline subpage (shown in Figure 29-1). To change the plan retention period for the SMB: 1. Connect SQL*Plus to the database with the appropriate privileges, and then query the data dictionary to see the current plan retention period. For example, execute the following query (sample output included): SQL> SELECT PARAMETER_NAME, PARAMETER_VALUE 2 FROM DBA_SQL_MANAGEMENT_CONFIG 3 WHERE PARAMETER_NAME = 'PLAN_RETENTION_WEEKS'; PARAMETER_NAME PARAMETER_VALUE ------------------------------ --------------PLAN_RETENTION_WEEKS 53 2. Change the retention period. For example, execute the CONFIGURE procedure to change the period to 105 weeks: EXECUTE DBMS_SPM.CONFIGURE('plan_retention_weeks',105); 3. Query the data dictionary to confirm the change. For example, execute the following query: SQL> SELECT PARAMETER_NAME, PARAMETER_VALUE 2 FROM DBA_SQL_MANAGEMENT_CONFIG 3 WHERE PARAMETER_NAME = 'PLAN_RETENTION_WEEKS'; 29-39 Chapter 29 Managing the SQL Management Base PARAMETER_NAME PARAMETER_VALUE ------------------------------ --------------PLAN_RETENTION_WEEKS 105 See Also: Oracle Database PL/SQL Packages and Types Reference to learn more about the CONFIGURE procedure 29-40 30 Migrating Stored Outlines to SQL Plan Baselines Stored outline migration is the user-initiated process of converting stored outlines to SQL plan baselines. A SQL plan baseline is a set of plans proven to provide optimal performance. Note: Starting in Oracle Database 12c, stored outlines are deprecated. See "Migrating Stored Outlines to SQL Plan Baselines" for an alternative. This chapter explains the concepts and tasks relating to stored outline migration. This chapter contains the following topics: • About Stored Outline Migration A stored outline is a set of hints for a SQL statement. • Preparing for Stored Outline Migration The goal is preparation is determining which stored outlines are eligible for migration. • Migrating Outlines to Utilize SQL Plan Management Features You can migrate stored outline to SQL plan baselines. • Migrating Outlines to Preserve Stored Outline Behavior You can migrate stored outlines to SQL plan baselines and preserve the original behavior of the stored outlines by creating fixed plan baselines. • Performing Follow-Up Tasks After Stored Outline Migration After migrating outlines to SQL plan baselines, you must perform some follow-up work. 30.1 About Stored Outline Migration A stored outline is a set of hints for a SQL statement. The hints direct the optimizer to choose a specific plan for the statement. A stored outline is a legacy technique for providing plan stability. This section contains the following topics: • Purpose of Stored Outline Migration If you rely on stored outlines to maintain plan stability and prevent performance regressions, then you can safely migrate from stored outlines to SQL plan baselines. After the migration, you can maintain the same plan stability while benefiting from the features provided by the SQL Plan Management framework. 30-1 Chapter 30 About Stored Outline Migration • How Stored Outline Migration Works Stored outline migration is a user-initiated process that goes through multiple stages. • User Interface for Stored Outline Migration You can use the DBMS_SPM package to perform the stored outline migration. • Basic Steps in Stored Outline Migration The basic process is to prepare, migrate, and then clean up. 30.1.1 Purpose of Stored Outline Migration If you rely on stored outlines to maintain plan stability and prevent performance regressions, then you can safely migrate from stored outlines to SQL plan baselines. After the migration, you can maintain the same plan stability while benefiting from the features provided by the SQL Plan Management framework. Stored outlines have the following disadvantages: • Stored outlines cannot automatically evolve over time. Consequently, a stored outline may be optimal when you create it, but become a suboptimal plan after a database change, leading to performance degradation. • Hints in a stored outline can become invalid, as with an index hint on a dropped index. In such cases, the database still uses the outlines but excludes the invalid hints, producing a plan that is often worse than the original plan or the current best-cost plan generated by the optimizer. • For a SQL statement, the optimizer can only choose the plan defined in the stored outline in the currently specified category. The optimizer cannot choose from other stored outlines in different categories or the current cost-based plan even if they improve performance. • Stored outlines are a reactive tuning technique, which means that you only use a stored outline to address a performance problem after it has occurred. For example, you may implement a stored outline to correct the plan of a SQL statement that became high-load. In this case, you used stored outlines instead of proactively tuning the statement before it became high-load. The stored outline migration PL/SQL API helps solve the preceding problems in the following ways: • SQL plan baselines enable the optimizer to use the same optimal plan and allow this plan to evolve over time. For a specified SQL statement, you can add new plans as SQL plan baselines after they are verified not to cause performance regressions. • SQL plan baselines prevent plans from becoming unreproducible because of invalid hints. If hints stored in a plan baseline become invalid, then the plan may not be reproducible by the optimizer. In this case, the optimizer selects an alternative reproducible plan baseline or the current best-cost plan generated by optimizer. • For a specific SQL statement, the database can maintain multiple plan baselines. The optimizer can choose from a set of optimal plans for a specific SQL statement instead of being restricted to a single plan per category, as required by stored outlines. 30-2 Chapter 30 About Stored Outline Migration 30.1.2 How Stored Outline Migration Works Stored outline migration is a user-initiated process that goes through multiple stages. This section explains how the database migrates stored outlines to SQL plan baselines. This information is important for performing the task of migrating stored outlines. This section contains the following topics: • Stages of Stored Outline Migration To migrate stored outlines, you specify the stores outlines. The database then creates and updates SQL plan baselines. • Outline Categories and Baseline Modules An outline is a set of hints, whereas a SQL plan baseline is a set of plans. 30.1.2.1 Stages of Stored Outline Migration To migrate stored outlines, you specify the stores outlines. The database then creates and updates SQL plan baselines. Figure 30-1 Stages of Stored Outline Migration outline1 ... outlinen User specifies stored outlines Database creates SQL plan baselines Copy information from stored outlines Reparse hints to generate plans baseline1 ... baselinen Obtain missing information such as bind data Database updates SQL plan baselines at first statement execution The migration process has the following stages: 1. The user invokes a function that specifies which outlines to migrate. 2. The database processes the outlines as follows: a. The database copies information in the outline needed by the plan baseline. The database copies it directly or calculates it based on information in the outline. For example, the text of the SQL statement exists in both schemas, so the database can copy the text from outline to baseline. b. The database reparses the hints to obtain information not in the outline. The plan hash value and plan cost cannot be derived from the existing information in the outline, which necessitates reparsing the hints. 30-3 Chapter 30 About Stored Outline Migration c. 3. The database creates the baselines. The database obtains missing information when it chooses the SQL plan baseline for the first time to execute the SQL statement. The compilation environment and execution statistics are only available during execution when the plan baseline is parsed and compiled. The migration is complete only after the preceding phases complete. 30.1.2.2 Outline Categories and Baseline Modules An outline is a set of hints, whereas a SQL plan baseline is a set of plans. Because they are different technologies, some functionality of outlines does not map exactly to functionality of baselines. For example, a single SQL statement can have multiple outlines, each of which is in a different outline category, but the only category that currently exists for baselines is DEFAULT. The equivalent of a category for an outline is a module for a SQL plan baseline. Table 30-1 explains how outline categories map to modules. Table 30-1 Outline Categories Concept Description Default Value Outline Category Specifies a user-defined DEFAULT grouping for a set of stored outlines. You can use categories to maintain different stored outlines for a SQL statement. For example, a single statement can have an outline in the OLTP category and the DW category. Each SQL statement can have one or more stored outlines. Each stored outline is in one and only one outline category. A statement can have multiple stored outlines in different categories, but only one stored outline exists for each category of each statement. During migration, the database maps each outline category to a SQL plan baseline module. Baseline Module Specifies a high-level function being performed. A SQL plan baseline can belong to one and only one module. After an outline is migrated to a SQL plan baseline, the module name defaults to outline category name. 30-4 Chapter 30 About Stored Outline Migration Table 30-1 (Cont.) Outline Categories Concept Description Default Value Baseline Category Only one SQL plan baseline category exists. This category is named DEFAULT. During stored outline migration, the module name of the SQL plan baseline is set to the category name of the stored outline. DEFAULT A statement can have multiple SQL plan baselines in the DEFAULT category. When migrating stored outlines to SQL plan baselines, Oracle Database maps every outline category to a SQL plan baseline module with the same name. As shown in the following diagram, the outline category OLTP is mapped to the baseline module OLTP. After migration, DEFAULT is a super-category that contains all SQL plan baselines. Figure 30-2 DEFAULT Category Category DEFAULT Category OLTP Module OLTP Outline emp1 Baseline emp1 Category DW Module DW Outline emp2 Baseline emp2 Outline dept Baseline dept SELECT... SELECT... 30.1.3 User Interface for Stored Outline Migration You can use the DBMS_SPM package to perform the stored outline migration. 30-5 Chapter 30 About Stored Outline Migration Table 30-2 DBMS_SPM Functions Relating to Stored Outline Migration DBMS_SPM Function Description MIGRATE_STORED_OUTLINE Migrates existing stored outlines to plan baselines. Use either of the following formats: • • Specify outline name, SQL text, outline category, or all stored outlines. Specify a list of outline names. ALTER_SQL_PLAN_BASELINE Changes an attribute of a single plan or all plans associated with a SQL statement. DROP_MIGRATED_STORED_OUTLINE Drops stored outlines that have been migrated to SQL plan baselines. The function finds stored outlines marked as MIGRATED in the DBA_OUTLINES view, and then drops these outlines from the database. You can control stored outline and plan baseline behavior with initialization and session parameters. Table 30-3 describes the relevant parameters. See Table 30-5 and Table 30-6 for an explanation of how these parameter settings interact. Table 30-3 Parameters Relating to Stored Outline Migration Initialization or Session Parameter Description Parameter Type CREATE_STORED_OUTLINES Determines whether Oracle Database automatically creates and stores an outline for each query submitted during the session. Initialization parameter OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES Enables or disables the automatic recognition of repeatable SQL statement and the generation of SQL plan baselines for these statements. Initialization parameter OPTIMIZER_USE_SQL_PLAN_BASELINES Enables or disables the use of SQL plan Initialization baselines stored in SQL Management parameter Base. USE_STORED_OUTLINES Determines whether the optimizer uses stored outlines to generate execution plans. Session Note: This is a session parameter, not an initialization parameter. You can use database views to access information relating to stored outline migration. Table 30-4 describes the following main views. Table 30-4 Views Relating to Stored Outline Migration View Description DBA_OUTLINES Describes all stored outlines in the database. The MIGRATED column is important for outline migration and shows one of the following values: NOT-MIGRATED and MIGRATED. When MIGRATED, the stored outline has been migrated to a plan baseline and is not usable. 30-6 Chapter 30 Preparing for Stored Outline Migration Table 30-4 (Cont.) Views Relating to Stored Outline Migration View Description DBA_SQL_PLAN_BASELINES Displays information about the SQL plan baselines currently created for specific SQL statements. The ORIGIN column indicates how the plan baseline was created. The value STORED-OUTLINE indicates the baseline was created by migrating an outline. See Also: • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SPM package • Oracle Database Reference to learn about the CREATE_STORED_OUTLINES initialization parameter 30.1.4 Basic Steps in Stored Outline Migration The basic process is to prepare, migrate, and then clean up. The basic steps are as follows: 1. Prepare for stored outline migration. Review the migration prerequisites and determine how you want the migrated plan baselines to behave. See "Preparing for Stored Outline Migration". 2. Perform either of the following tasks: • Migrate to baselines to use SQL Plan Management features. See "Migrating Outlines to Utilize SQL Plan Management Features". • Migrate to baselines while exactly preserving the behavior of the stored outlines. See "Migrating Outlines to Preserve Stored Outline Behavior". 3. Perform post-migration confirmation and cleanup. See "Performing Follow-Up Tasks After Stored Outline Migration". 30.2 Preparing for Stored Outline Migration The goal is preparation is determining which stored outlines are eligible for migration. To prepare for stored outline migration: 1. Connect SQL*Plus to the database with SYSDBA privileges or the EXECUTE privilege on the DBMS_SPM package. 2. Query the stored outlines in the database. 30-7 Chapter 30 Preparing for Stored Outline Migration The following example queries all stored outlines that have not been migrated to SQL plan baselines: SELECT NAME, CATEGORY, SQL_TEXT FROM DBA_OUTLINES WHERE MIGRATED = 'NOT-MIGRATED'; 3. 4. Determine which stored outlines meet the following prerequisites for migration eligibility: • The statement must not be a run-time INSERT AS SELECT statement. • The statement must not reference a remote object. • This statement must not be a private stored outline. Decide whether to migrate all outlines, specified stored outlines, or outlines belonging to a specified outline category. If you do not decide to migrate all outlines, then identify the outlines or categories that you intend to migrate. 5. Decide whether the stored outlines migrated to SQL plan baselines use fixed plans or nonfixed plans: • Fixed plans A fixed plan is frozen. If a fixed plan is reproducible using the hints stored in plan baseline, then the optimizer always chooses the lowest-cost fixed plan baseline over plan baselines that are not fixed. Essentially, a fixed plan baseline acts as a stored outline with valid hints. A fixed plan is reproducible when the database can parse the statement based on the hints stored in the plan baseline and create a plan with the same plan hash value as the one in the plan baseline. If one of more of the hints become invalid, then the database may not be able to create a plan with the same plan hash value. In this case, the plan is nonreproducible. If a fixed plan cannot be reproduced when parsed using its hints, then the optimizer chooses a different plan, which can be either of the following: – Another plan for the SQL plan baseline – The current cost-based plan created by the optimizer In some cases, a performance regression occurs because of the different plan, requiring SQL tuning. • Nonfixed plans If a plan baseline does not contain fixed plans, then SQL Plan Management considers the plans equally when picking a plan for a SQL statement. 6. Before beginning the actual migration, ensure that the Oracle database meets the following prerequisites: • The database must be Enterprise Edition. • The database must be open and not in a suspended state. • The database must not be in restricted access (DBA), read-only, or migrate mode. • Oracle Call Interface (OCI) must be available. 30-8 Chapter 30 Migrating Outlines to Utilize SQL Plan Management Features See Also: • Oracle Database Administrator’s Guide to learn about administrator privileges • Oracle Database Reference to learn about the DBA_OUTLINES views 30.3 Migrating Outlines to Utilize SQL Plan Management Features You can migrate stored outline to SQL plan baselines. The goals of this task are as follows: • To allow SQL Plan Management to select from all plans in a plan baseline for a SQL statement instead of applying the same fixed plan after migration • To allow the SQL plan baseline to evolve in the face of database changes by adding new plans to the baseline Assumptions This tutorial assumes the following: • You migrate all outlines. To migrate specific outlines, use the DBMS_SPM.MIGRATE_STORED_OUTLINE function. • You want the module names of the baselines to be identical to the category names of the migrated outlines. • You do not want the SQL plans to be fixed. By default, generated plans are not fixed and SQL Plan Management considers all plans equally when picking a plan for a SQL statement. This situation permits the advanced feature of plan evolution to capture new plans for a SQL statement, verify their performance, and accept these new plans into the plan baseline. To migrate stored outlines to SQL plan baselines: 1. Connect SQL*Plus to the database with the appropriate privileges. 2. Call PL/SQL function MIGRATE_STORED_OUTLINE. The following sample PL/SQL block migrates all stored outlines to fixed baselines: DECLARE my_report CLOB; BEGIN my_outlines := DBMS_SPM.MIGRATE_STORED_OUTLINE( attribute_name => 'all' ); END; / 30-9 Chapter 30 Migrating Outlines to Preserve Stored Outline Behavior See Also: • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SPM package • Oracle Database SQL Language Reference to learn about the ALTER SYSTEM statement 30.4 Migrating Outlines to Preserve Stored Outline Behavior You can migrate stored outlines to SQL plan baselines and preserve the original behavior of the stored outlines by creating fixed plan baselines. A fixed plan has higher priority over other plans for the same SQL statement. If a plan is fixed, then the plan baseline cannot be evolved. The database does not add new plans to a plan baseline that contains a fixed plan. Assumptions This tutorial assumes the following: • You want to migrate only the stored outlines in the category named firstrow. • You want the module names of the baselines to be identical to the category names of the migrated outlines. To migrate stored outlines to plan baselines: 1. Connect SQL*Plus to the database with the appropriate privileges. 2. Call PL/SQL function MIGRATE_STORED_OUTLINE. The following sample PL/SQL block migrates stored outlines in the category firstrow to fixed baselines: DECLARE my_report CLOB; BEGIN my_outlines := DBMS_SPM.MIGRATE_STORED_OUTLINE( attribute_name => 'category', attribute_value => 'firstrow', fixed => 'YES' ); END; / After migration, the SQL plan baselines is in module firstrow and category DEFAULT. 30-10 Chapter 30 Performing Follow-Up Tasks After Stored Outline Migration See Also: • Oracle Database PL/SQL Packages and Types Reference for syntax and semantics of the DBMS_SPM.MIGRATE_STORED_OUTLINE function • Oracle Database SQL Language Reference to learn about the ALTER SYSTEM statement 30.5 Performing Follow-Up Tasks After Stored Outline Migration After migrating outlines to SQL plan baselines, you must perform some follow-up work. The goals of this task are as follows: • To configure the database to use plan baselines instead of stored outlines for stored outlines that have been migrated to SQL plan baselines • To create SQL plan baselines instead of stored outlines for future SQL statements • To drop the stored outlines that have been migrated to SQL plan baselines This section explains how to set initialization parameters relating to stored outlines and plan baselines. The OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES and CREATE_STORED_OUTLINES initialization parameters determine how and when the database creates stored outlines and SQL plan baselines. Table 30-5 explains the interaction between these parameters. Table 30-5 Creation of Outlines and Baselines CREATE_STORED_ OUTLINES Initialization Parameter OPTIMIZER_CAPTURE _SQL_PLAN_BASELIN ES Initialization Parameter Database Behavior false false When executing a SQL statement, the database does not create stored outlines or SQL plan baselines. false true The automatic recognition of repeatable SQL statements and the generation of SQL plan baselines for these statements is enabled. When executing a SQL statement, the database creates only new SQL plan baselines (if they do not exist) with the category name DEFAULT for the statement. true false Oracle Database automatically creates and stores an outline for each query submitted during the session. When executing a SQL statement, the database creates only new stored outlines (if they do not exist) with the category name DEFAULT for the statement. 30-11 Chapter 30 Performing Follow-Up Tasks After Stored Outline Migration Table 30-5 (Cont.) Creation of Outlines and Baselines CREATE_STORED_ OUTLINES Initialization Parameter OPTIMIZER_CAPTURE _SQL_PLAN_BASELIN ES Initialization Parameter Database Behavior category false When executing a SQL statement, the database creates only new stored outlines (if they do not exist) with the specified category name for the statement. true true Oracle Database automatically creates and stores an outline for each query submitted during the session. The automatic recognition of repeatable SQL statements and the generation of SQL plan baselines for these statements is also enabled. When executing a SQL statement, the database creates both stored outlines and SQL plan baselines with the category name DEFAULT. category Oracle Database automatically creates and stores an outline for each query submitted during the session. The automatic recognition of repeatable SQL statements and the generation of SQL plan baselines for these statements is also enabled. true When executing a SQL statement, the database creates stored outlines with the specified category name and SQL plan baselines with the category name DEFAULT. The USE_STORED_OUTLINES session parameter (it is not an initialization parameter) and OPTIMIZER_USE_SQL_PLAN_BASELINES initialization parameter determine how the database uses stored outlines and plan baselines. Table 30-6 explains how these parameters interact. Table 30-6 Use of Stored Outlines and SQL Plan Baselines USE_STORED_OUTLINES OPTIMIZER_USE_SQL_ Database Behavior Session Parameter PLAN_BASELINES Initialization Parameter false false When choosing a plan for a SQL statement, the database does not use stored outlines or plan baselines. false true When choosing a plan for a SQL statement, the database uses only SQL plan baselines. true false When choosing a plan for a SQL statement, the database uses stored outlines with the category name DEFAULT. 30-12 Chapter 30 Performing Follow-Up Tasks After Stored Outline Migration Table 30-6 (Cont.) Use of Stored Outlines and SQL Plan Baselines USE_STORED_OUTLINES OPTIMIZER_USE_SQL_ Database Behavior Session Parameter PLAN_BASELINES Initialization Parameter category false When choosing a plan for a SQL statement, the database uses stored outlines with the specified category name. If a stored outline with the specified category name does not exist, then the database uses a stored outline in the DEFAULT category if it exists. true true When choosing a plan for a SQL statement, stored outlines take priority over plan baselines. If a stored outline with the category name DEFAULT exists for the statement and is applicable, then the database applies the stored outline. Otherwise, the database uses SQL plan baselines. However, if the stored outline has the property MIGRATED, then the database does not use the outline and uses the corresponding SQL plan baseline instead (if it exists). category true When choosing a plan for a SQL statement, stored outlines take priority over plan baselines. If a stored outline with the specified category name or the DEFAULT category exists for the statement and is applicable, then the database applies the stored outline. Otherwise, the database uses SQL plan baselines. However, if the stored outline has the property MIGRATED, then the database does not use the outline and uses the corresponding SQL plan baseline instead (if it exists). Assumptions This tutorial assumes the following: • You have completed the basic steps in the stored outline migration. • Some stored outlines may have been created before Oracle Database 10g. Hints in releases before Oracle Database 10g use a local hint format. After migration, hints stored in a plan baseline use the global hints format introduced in Oracle Database 10g. 30-13 Chapter 30 Performing Follow-Up Tasks After Stored Outline Migration To place the database in the proper state after the migration: 1. Connect SQL*Plus to the database with the appropriate privileges, and then check that SQL plan baselines have been created as the result of migration. Ensure that the plans are enabled and accepted. For example, enter the following query (partial sample output included): SELECT SQL_HANDLE, PLAN_NAME, ORIGIN, ENABLED, ACCEPTED, FIXED, MODULE FROM DBA_SQL_PLAN_BASELINES; SQL_HANDLE PLAN_NAME ORIGIN ENA ACC FIX MODULE ------------------------- ---------- -------------- --- --- --- -----SYS_SQL_f44779f7089c8fab STMT01 STORED-OUTLINE YES YES NO DEFAULT . . . 2. Optionally, change the attributes of the SQL plan baselines. For example, the following statement changes the status of the baseline for the specified SQL statement to fixed: DECLARE v_cnt PLS_INTEGER; BEGIN v_cnt := DBMS_SPM.ALTER_SQL_PLAN_BASELINE( sql_handle=>'SYS_SQL_f44779f7089c8fab', attribute_name=>'FIXED', attribute_value=>'NO'); DBMS_OUTPUT.PUT_LINE('Plans altered: ' || v_cnt); END; / 3. Check the status of the original stored outlines. For example, enter the following query (partial sample output included): SELECT NAME, OWNER, CATEGORY, USED, MIGRATED FROM DBA_OUTLINES ORDER BY NAME; NAME ---------STMT01 STMT02 . . . 4. OWNER ---------SYS SYS CATEGORY ---------DEFAULT DEFAULT USED -----USED USED MIGRATED -----------MIGRATED MIGRATED Drop all stored outlines that have been migrated to SQL plan baselines. For example, the following statements drops all stored outlines with status MIGRATED in DBA_OUTLINES: DECLARE v_cnt PLS_INTEGER; BEGIN v_cnt := DBMS_SPM.DROP_MIGRATED_STORED_OUTLINE(); DBMS_OUTPUT.PUT_LINE('Migrated stored outlines dropped: ' || v_cnt); END; / 5. Set initialization parameters so that: 30-14 Chapter 30 Performing Follow-Up Tasks After Stored Outline Migration • When executing a SQL statement, the database creates plan baselines but does not create stored outlines. • The database only uses stored outlines when the equivalent SQL plan baselines do not exist. For example, the following SQL statements instruct the database to create SQL plan baselines instead of stored outlines when a SQL statement is executed. The example also instructs the database to apply a stored outline in category allrows or DEFAULT only if it exists and has not been migrated to a SQL plan baseline. In other cases, the database applies SQL plan baselines instead. ALTER SYSTEM SET CREATE_STORED_OUTLINE = false SCOPE = BOTH; ALTER SYSTEM SET OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES = true SCOPE = BOTH; ALTER SYSTEM SET OPTIMIZER_USE_SQL_PLAN_BASELINES = true SCOPE = BOTH; ALTER SESSION SET USE_STORED_OUTLINES = allrows SCOPE = BOTH; See Also: • "Basic Steps in Stored Outline Migration" • Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SPM package • Oracle Database Reference to learn about the CREATE_STORED_OUTLINES initialization parameter 30-15 A Guidelines for Indexes and Table Clusters This appendix provides an overview of data access methods using indexes and clusters that can enhance or degrade performance. This appendix contains the following sections: • Guidelines for Tuning Index Performance Indexes are not a performance cure-all. You must consider carefully when and how to use them. • Guidelines for Using Function-Based Indexes for Performance A function-based index includes columns that are either transformed by a function, such as the UPPER function, or included in an expression, such as col1 + col2. • Guidelines for Using Partitioned Indexes for Performance Similar to partitioned tables, partitioned indexes improve manageability, availability, performance, and scalability. They can be partitioned independently (global indexes) or automatically linked to the table partitioning method (local indexes). • Guidelines for Using Index-Organized Tables for Performance An index-organized table differs from an ordinary table because the data for the table is held in its associated index. • Guidelines for Using Bitmap Indexes for Performance Bitmap indexes can substantially improve performance of specific queries. • Guidelines for Using Bitmap Join Indexes for Performance In addition to a bitmap index on a single table, you can create a bitmap join index, which is a bitmap index for the join of two or more tables. • Guidelines for Using Domain Indexes for Performance Domain indexes are built using the indexing logic supplied by a user-defined indextype. An indextype is an object that specifies the routines that manage a domain (application-specific) index. • Guidelines for Using Table Clusters A table cluster is a group of one or more tables that are physically stored together because they share common columns and usually appear together in SQL statements. • Guidelines for Using Hash Clusters for Performance Hash clusters group table data by applying a hash function to each row's cluster key value. A.1 Guidelines for Tuning Index Performance Indexes are not a performance cure-all. You must consider carefully when and how to use them. This section contains the following topics: A-1 Appendix A Guidelines for Tuning Index Performance • Guidelines for Tuning the Logical Structure Index maintenance can present a significant CPU and I/O resource demand in any write-intensive application. • Guidelines for Choosing Columns and Expressions to Index A key is a column or expression on which you can build an index. • Guidelines for Choosing Composite Indexes A composite index, also called a concatenated index, is an index on multiple columns in a table. • Guidelines for Writing SQL Statements That Use Indexes The optimizer may not choose an access path that uses an index. • Guidelines for Writing SQL Statements That Avoid Using Indexes In some cases, you might want to prevent a SQL statement from using an index access path. For example, you know that the index is not very selective and a full table scan would be more efficient. • Guidelines for Re-Creating Indexes You might want to re-create an index to compact it and minimize fragmented space, or to change the index's storage characteristics. • Guidelines for Compacting Indexes You can coalesce leaf blocks of an index by using the ALTER INDEX statement with the COALESCE option. • Guidelines for Using Nonunique Indexes to Enforce Uniqueness You can use an existing nonunique index on a table to enforce uniqueness, either for UNIQUE constraints or the unique aspect of a PRIMARY KEY constraint. • Guidelines for Using Enabled Novalidated Constraints An enabled novalidated constraint behaves similarly to an enabled validated constraint for new data. A.1.1 Guidelines for Tuning the Logical Structure Index maintenance can present a significant CPU and I/O resource demand in any write-intensive application. Although query optimization helps avoid the use of nonselective indexes within query execution, the SQL engine must continue to maintain all indexes defined against a table, regardless of whether queries use them. Therefore, do not build indexes unless necessary. To maintain optimal performance, drop indexes that an application is not using. You can find indexes that are not being used by using the ALTER INDEX MONITORING USAGE functionality over a period that is representative of your workload. This monitoring feature records whether an index has been used. Make sure you are monitoring a representative workload to avoid dropping an index which is used, but not by the workload you sampled. Also, indexes within an application sometimes have uses that are not immediately apparent from a survey of statement execution plans. An example of this is a foreign key index on a parent table, which prevents share locks from being taken out on a child table. If you are deciding whether to create new indexes to tune statements, then you can also use the EXPLAIN PLAN statement to determine whether the optimizer chooses to A-2 Appendix A Guidelines for Tuning Index Performance use these indexes when the application is run. If you create new indexes to tune a statement that is currently parsed, then Oracle Database invalidates the statement. When the statement is next parsed, the optimizer automatically chooses a new execution plan that could potentially use the new index. If you create new indexes on a remote database to tune a distributed statement, then the optimizer considers these indexes when the statement is next parsed. Creating an index to tune one statement can affect the optimizer's choice of execution plans for other statements. For example, if you create an index to be used by one statement, then the optimizer can choose to use that index for other statements in the application as well. For this reason, reexamine the application's performance and execution plans, and rerun the SQL trace facility after you have tuned those statements that you initially identified for tuning. See Also: • Oracle Database SQL Language Reference for syntax and semantics of the ALTER INDEX MONITORING USAGE statement • Oracle Database Development Guide to learn about foreign keys A.1.2 Guidelines for Choosing Columns and Expressions to Index A key is a column or expression on which you can build an index. Follow these guidelines for choosing keys to index: • Consider indexing keys that appear frequently in WHERE clauses. • Consider indexing keys that frequently join tables in SQL statements. • Choose index keys that are highly selective. The selectivity of an index is the percentage of rows in a table having the same value for the indexed key. An index selectivity is optimal if few rows have the same value. Note: Oracle Database automatically creates indexes, or uses existing indexes, on the keys and expressions of unique and primary keys that you define with integrity constraints. Indexing low selectivity columns can be helpful when the data distribution is skewed so that one or two values occur much less often than other values. • Do not use standard B-tree indexes on keys or expressions with few distinct values. Such keys or expressions are usually unselective and therefore do not optimize performance unless the frequently selected key values appear less frequently than the other key values. You can use bitmap indexes effectively in such cases, unless the index is modified frequently, as in a high concurrency OLTP application. A-3 Appendix A Guidelines for Tuning Index Performance • Do not index frequently modified columns. UPDATE statements that modify indexed columns and INSERT and DELETE statements that modify indexed tables take longer than if there were no index. Such SQL statements must modify data in indexes and data in tables. They also create additional undo and redo. • Do not index keys that appear only in WHERE clauses with functions or operators. A WHERE clause that uses a function, other than MIN or MAX, or an operator with an indexed key does not make available the access path that uses the index except with function-based indexes. • Consider indexing foreign keys of referential integrity constraints in cases in which many concurrent INSERT, UPDATE, and DELETE statements access the parent and child tables. Such an index allows UPDATEs and DELETEs on the parent table without share locking the child table. • When choosing to index a key, consider whether the performance gain for queries is worth the performance loss for INSERT, UPDATE, and DELETE statements and the use of the space required to store the index. You might want to experiment by comparing the processing times of the SQL statements with and without indexes. You can measure processing time with the SQL trace facility. See Also: Oracle Database Concepts for more information about the effects of foreign keys on locking A.1.3 Guidelines for Choosing Composite Indexes A composite index, also called a concatenated index, is an index on multiple columns in a table. Composite indexes can provide additional advantages over single-column indexes: • Improved selectivity Sometimes you can combine two or more columns or expressions, each with low selectivity, to form a composite index with higher selectivity. • Reduced I/O If all columns selected by a query are in a composite index, then Oracle Database can return these values from the index without accessing the table. A SQL statement can use an access path involving a composite index when the statement contains constructs that use a leading portion of the index. Note: This is no longer the case with index skip scans. A leading portion of an index is a set of one or more columns that were specified first and consecutively in the list of columns in the CREATE INDEX statement that created the index. Consider this CREATE INDEX statement: A-4 Appendix A Guidelines for Tuning Index Performance CREATE INDEX comp_ind ON table1(x, y, z); • x, xy, and xyz combinations of columns are leading portions of the index • yz, y, and z combinations of columns are not leading portions of the index This section contains the following topics: • Guidelines for Choosing Keys for Composite Indexes The effectiveness of a composite index depends on whether you choose the correct columns as keys. • Guidelines for Ordering Keys for Composite Indexes In a composite index, the order of the keys can affect query performance. See Also: "Index Skip Scans" A.1.3.1 Guidelines for Choosing Keys for Composite Indexes The effectiveness of a composite index depends on whether you choose the correct columns as keys. Follow these guidelines for choosing keys for composite indexes: • Consider creating a composite index on keys that appear together frequently in WHERE clause conditions combined with AND operators, especially if their combined selectivity is better than the selectivity of either key individually. • If several queries select the same set of keys based on one or more key values, then consider creating a composite index containing all these keys. Consider the guidelines associated with the general performance advantages and trade-offs of indexes described in the previous sections. A.1.3.2 Guidelines for Ordering Keys for Composite Indexes In a composite index, the order of the keys can affect query performance. Follow these guidelines for ordering keys in composite indexes: • Create the index so the keys used in WHERE clauses comprise a leading portion. • If some keys appear in WHERE clauses more frequently, then create the index so that the more frequently selected keys comprise a leading portion to allow the statements that use only these keys to use the index. • If all keys appear in WHERE clauses equally often, but the data is physically ordered on one of the keys, then place this key first in the composite index. A-5 Appendix A Guidelines for Tuning Index Performance A.1.4 Guidelines for Writing SQL Statements That Use Indexes The optimizer may not choose an access path that uses an index. The optimizer chooses an access path only if it contains a construct that makes the access path available. To give the query optimizer the option of using an index access path, ensure that the statement contains a construct that makes such an access path available. A.1.5 Guidelines for Writing SQL Statements That Avoid Using Indexes In some cases, you might want to prevent a SQL statement from using an index access path. For example, you know that the index is not very selective and a full table scan would be more efficient. If the statement contains a construct that makes such an index access path available, then you can force the optimizer to use a full table scan through one of the following methods: • Use the NO_INDEX hint to give the query optimizer maximum flexibility while disallowing the use of a certain index. • Use the FULL hint to instruct the optimizer to choose a full table scan instead of an index scan. • Use the INDEX or INDEX_COMBINE hints to instruct the optimizer to use one index or a set of listed indexes instead of another. Parallel execution uses indexes effectively. It does not perform parallel index range scans, but it does perform parallel index lookups for parallel nested loops join execution. If an index is very selective (few rows correspond to each index entry), then a sequential index lookup might be better than a parallel table scan. See Also: "Influencing the Optimizer " for more information about the NO_INDEX, FULL, INDEX, and INDEX_COMBINE and hints A.1.6 Guidelines for Re-Creating Indexes You might want to re-create an index to compact it and minimize fragmented space, or to change the index's storage characteristics. When creating a new index that is a subset of an existing index, or when rebuilding an existing index with new storage characteristics, Oracle Database might use the existing index instead of the base table to improve the performance of the index build. However, in some cases using the base table instead of the existing index is beneficial. Consider an index on a table on which a lot of DML has been performed. Because of the DML, the size of the index can increase to the point where each block is only 50% full, or even less. If the index refers to most of the columns in the table, A-6 Appendix A Guidelines for Tuning Index Performance then the index could actually be larger than the table. In this case, it is faster to use the base table rather than the index to re-create the index. To reorganize or compact an existing index or to change its storage characteristics, use the ALTER INDEX . . . REBUILD statement. The REBUILD statement uses the existing index as the basis for the new one. All index storage statements are supported, such as STORAGE (for extent allocation), TABLESPACE (to move the index to a new tablespace), and INITRANS (to change the initial number of entries). Usually, ALTER INDEX . . . REBUILD is faster than dropping and re-creating an index, because this statement uses the fast full scan feature. It reads all the index blocks using multiblock I/O, then discards the branch blocks. A further advantage of this approach is that the old index is still available for queries while the rebuild is in progress. See Also: Oracle Database SQL Language Reference for more information about the CREATE INDEX and ALTER INDEX statements and restrictions on rebuilding indexes A.1.7 Guidelines for Compacting Indexes You can coalesce leaf blocks of an index by using the ALTER INDEX statement with the COALESCE option. This option enables you to combine leaf levels of an index to free blocks for reuse. You can also rebuild the index online. See Also: Oracle Database SQL Language Reference and Oracle Database Administrator’s Guide for more information about the syntax for this statement A.1.8 Guidelines for Using Nonunique Indexes to Enforce Uniqueness You can use an existing nonunique index on a table to enforce uniqueness, either for UNIQUE constraints or the unique aspect of a PRIMARY KEY constraint. The advantage of this approach is that the index remains available and valid when the constraint is disabled. Therefore, enabling a disabled UNIQUE or PRIMARY KEY constraint does not require rebuilding the unique index associated with the constraint. This technique can yield significant time savings on enable operations for large tables. Using a nonunique index to enforce uniqueness also enables you to eliminate redundant indexes. You do not need a unique index on a primary key column if that column is included as the prefix of a composite index. You can use the existing index to enable and enforce the constraint. You also save significant space by not duplicating the index. However, if the existing index is partitioned, then the partitioning A-7 Appendix A Guidelines for Tuning Index Performance key of the index must also be a subset of the UNIQUE key; otherwise, Oracle Database creates an additional unique index to enforce the constraint. A.1.9 Guidelines for Using Enabled Novalidated Constraints An enabled novalidated constraint behaves similarly to an enabled validated constraint for new data. Placing a constraint in the enabled novalidated state signifies that any new data entered into the table must conform to the constraint. Existing data is not checked. By placing a constraint in the enabled novalidated state, you enable the constraint without locking the table. If you change a constraint from disabled to enabled, then the table must be locked. No new DML, queries, or DDL can occur, because no mechanism can ensure that operations on the table conform to the constraint during the enable operation. The enabled novalidated state prevents users from performing operations on the table that violate the constraint. The database can validate an enabled novalidated constraint with a parallel, consistent-read query of the table to determine whether any data violates the constraint. The database performs no locking, so the enable operation does not block readers or writers. In addition, the database can validate enabled novalidated constraints in parallel. The database can validate multiple constraints at the same time and check the validity of each constraint using parallel query. To create tables with constraints and indexes: 1. Create the tables with the constraints. NOT NULL constraints can be unnamed and should be created enabled and validated. Name all other constraints (CHECK, UNIQUE, PRIMARY KEY, and FOREIGN KEY) and create them disabled. Note: By default, constraints are created in the ENABLED state. 2. Load old data into the tables. 3. Create all indexes, including indexes needed for constraints. 4. Enable novalidate all constraints. Do this to primary keys before foreign keys. 5. Allow users to query and modify data. 6. With a separate ALTER TABLE statement for each constraint, validate all constraints. Do this to primary keys before foreign keys. For example, CREATE TABLE t (a NUMBER CONSTRAINT apk PRIMARY KEY DISABLE, b NUMBER NOT NULL); CREATE TABLE x (c NUMBER CONSTRAINT afk REFERENCES t DISABLE); Now load data into table t. CREATE UNIQUE INDEX tai ON t (a); CREATE INDEX tci ON x (c); ALTER TABLE t MODIFY CONSTRAINT apk ENABLE NOVALIDATE; ALTER TABLE x MODIFY CONSTRAINT afk ENABLE NOVALIDATE; A-8 Appendix A Guidelines for Using Function-Based Indexes for Performance At this point, users can start performing INSERT, UPDATE, DELETE, and SELECT operations on table t. ALTER TABLE t ENABLE CONSTRAINT apk; ALTER TABLE x ENABLE CONSTRAINT afk; Now the constraints are enabled and validated. See Also: Oracle Database Concepts for an overview of integrity constraints A.2 Guidelines for Using Function-Based Indexes for Performance A function-based index includes columns that are either transformed by a function, such as the UPPER function, or included in an expression, such as col1 + col2. A function-based index is useful when frequently executed SQL statements include transformed columns, or columns in expressions, in a WHERE or ORDER BY clause. If you define a function-based index on the transformed column or expression, then the database can use the index when that function or expression appears in a WHERE clause or an ORDER BY clause. In this way, the database can avoid calculating the expression when processing SELECT and DELETE statements. Oracle Database treats descending indexes as function-based indexes. The columns marked DESC are sorted in descending order. For example, function-based indexes defined with the UPPER(column_name) or LOWER(column_name) keywords allow case-insensitive searches. Assume that you create an index on the following statement: CREATE INDEX uppercase_idx ON employees (UPPER(last_name)); The preceding index facilitates processing queries such as: SELECT * FROM employees WHERE UPPER(last_name) = 'MARKSON'; See Also: • Oracle Database Development Guide and Oracle Database Administrator’s Guide for more information about using function-based indexes • Oracle Database SQL Language Reference for more information about the CREATE INDEX statement A-9 Appendix A Guidelines for Using Partitioned Indexes for Performance A.3 Guidelines for Using Partitioned Indexes for Performance Similar to partitioned tables, partitioned indexes improve manageability, availability, performance, and scalability. They can be partitioned independently (global indexes) or automatically linked to the table partitioning method (local indexes). Oracle Database supports both range and hash-partitioned global indexes. In a rangepartitioned global index, each index partition contains values defined by a partition bound. In a hash-partitioned global index, each partition contains values determined by the Oracle Database hash function. The hash method can improve performance of indexes where a small number leaf blocks in the index have high contention in multiuser OLTP environment. In some OLTP applications, index insertions happen only at the right edge of the index. This situation could occur when the index is defined on monotonically increasing columns. In such situations, the right edge of the index becomes a hot spot because of contention for index pages, buffers, latches for update, and additional index maintenance activity, which results in performance degradation. With hash partitioned global indexes index entries are hashed to different partitions based on partitioning key and the number of partitions. This spreads out contention over number of defined partitions, resulting in increased throughput. Hash-partitioned global indexes would benefit TPC-H refresh functions that are executed as massive PDMLs into huge fact tables because contention for buffer latches would be spread out over multiple partitions. With hash partitioning, an index entry is mapped to a particular index partition based on the hash value generated by Oracle Database. The syntax to create hashpartitioned global index is very similar to hash-partitioned table. Queries involving equality and IN predicates on index partitioning key can efficiently use global hash partitioned index to answer queries quickly. See Also: Oracle Database Concepts and Oracle Database Administrator’s Guide for more information about global indexes tables A.4 Guidelines for Using Index-Organized Tables for Performance An index-organized table differs from an ordinary table because the data for the table is held in its associated index. Changes to the table data, such as adding new rows, updating rows, or deleting rows, result only in updating the index. Because data rows are stored in the index, indexorganized tables provide faster key-based access to table data for queries that involve exact match or range search or both. A-10 Appendix A Guidelines for Using Bitmap Indexes for Performance A parent/child relationship is an example of a situation that may warrant an indexorganized table. For example, a members table has a child table containing phone numbers. Phone numbers for a member are changed and added over time. In a heaporganized table, rows are inserted in data blocks where they fit. However, when you query the members table, you always retrieve the phone numbers from the child table. To make the retrieval more efficient, you can store the phone numbers in an indexorganized table so that phone records for a given member are inserted near each other in the data blocks. In some circumstances, an index-organized table may provide a performance advantage over a heap-organized table. For example, if a query requires fewer blocks in the cache, then the database uses the buffer cache more efficiently. If fewer distinct blocks are needed for a query, then a single physical I/O may retrieve all necessary data, requiring a smaller amount of I/O for each query. Global hash-partitioned indexes are supported for index-organized tables and can provide performance benefits in a multiuser OLTP environment. Index-organized tables are useful when you must store related pieces of data together or physically store data in a specific order. See Also: • Oracle Database Concepts for an overview of index-organized tables • Oracle Database Administrator’s Guide to learn how to manage indexorganized tables A.5 Guidelines for Using Bitmap Indexes for Performance Bitmap indexes can substantially improve performance of specific queries. In general, queries perform best that have all of the following characteristics: • The WHERE clause contains multiple predicates on low- or medium-cardinality columns. • The individual predicates on these low- or medium-cardinality columns select many rows. • The bitmap indexes used in the queries have been created on some or all of these low-cardinality or medium-cardinality columns. • The tables in the queries contain many rows. You can use multiple bitmap indexes to evaluate the conditions on a single table. Bitmap indexes are thus highly advantageous for complex ad hoc queries that contain lengthy WHERE clauses. Bitmap indexes can also provide optimal performance for aggregate queries and for optimizing joins in star schemas. A-11 Appendix A Guidelines for Using Bitmap Join Indexes for Performance See Also: • Oracle Database Concepts for an overview of bitmap indexes • Oracle Database Data Warehousing Guide to learn how to use bitmap indexing in your application A.6 Guidelines for Using Bitmap Join Indexes for Performance In addition to a bitmap index on a single table, you can create a bitmap join index, which is a bitmap index for the join of two or more tables. A bitmap join index is a space-saving way to reduce the volume of data that must be joined by performing restrictions in advance. For each value in a column of a table, a bitmap join index stores the rowids of corresponding rows in another table. In a data warehouse, the join condition is an inner equijoin between the primary key columns of the dimension tables and the foreign key columns in the fact table. Bitmap join indexes are more efficient in storage than materialized join views, an alternative for materializing joins in advance. Materialized join views do not compress the rowids of the fact tables. See Also: Oracle Database Data Warehousing Guide for examples and restrictions of bitmap join indexes A.7 Guidelines for Using Domain Indexes for Performance Domain indexes are built using the indexing logic supplied by a user-defined indextype. An indextype is an object that specifies the routines that manage a domain (application-specific) index. An indextype provides an efficient mechanism to access data that satisfy certain operator predicates. Typically, the user-defined indextype is part of an Oracle Database option, like the Oracle Spatial and Graph option. The cartridge determines the parameters you can specify in creating and maintaining the domain index. Similarly, the performance and storage characteristics of the domain index are presented in the specific cartridge documentation. Refer to the appropriate cartridge documentation for information such as the following: • Which data types can be indexed? • Which indextypes are provided? • Which operators does the indextype support? • How can the domain index be created and maintained? A-12 Appendix A Guidelines for Using Table Clusters • What is the most efficient way to use the operator in queries? • What are the performance characteristics? Note: You can also create index types with the CREATE INDEXTYPE statement. See Also: Oracle Spatial and Graph Developer's Guide to learn more about indexing spatial data A.8 Guidelines for Using Table Clusters A table cluster is a group of one or more tables that are physically stored together because they share common columns and usually appear together in SQL statements. Because the database physically stores related rows together, disk access time improves. To create a table cluster, use the CREATE CLUSTER statement. Consider clustering tables in the following circumstances: • The application frequently accesses the tables in join statements. • In master-detail tables, the application often selects a master record and then the corresponding detail records. Detail records are stored in the same data blocks as the master record, so they are likely still to be in memory when you select them, requiring Oracle Database to perform less I/O. • The application often selects many detail records of the same master. In this case, consider storing a detail table alone in a cluster. This measure improves the performance of queries that select detail records of the same master, but does not decrease the performance of a full table scan on the master table. An alternative is to use an index organized table. Avoid clustering tables in the following circumstances: • The application joins the tables only occasionally or modifies their common column values frequently. Modifying a row's cluster key value takes longer than modifying the value in an nonclustered table, because Oracle Database might need to migrate the modified row to another block to maintain the cluster. • The application often performs full table scans of only one of the tables. A full table scan of a clustered table can take longer than a full table scan of an nonclustered table. Oracle Database is likely to read more blocks because the tables are stored together. • The data from all tables with the same cluster key value exceeds more than one or two data blocks. A-13 Appendix A Guidelines for Using Hash Clusters for Performance To access a row in a clustered table, Oracle Database reads all blocks containing rows with that value. If these rows take up multiple blocks, then accessing a single row could require more reads than accessing the same row in a nonclustered table. • The number of rows for each cluster key value varies significantly. This causes waste of space for the low cardinality key value. It causes collisions for the high cardinality key values. Collisions degrade performance. Consider the benefits and drawbacks of clusters for the application. For example, you might decide that the performance gain for join statements outweighs the performance loss for statements that modify cluster key values. You might want to experiment and compare processing times with the tables both clustered and stored separately. See Also: • Oracle Database Concepts for more information about table clusters • Oracle Database Administrator’s Guide for more information about creating table clusters A.9 Guidelines for Using Hash Clusters for Performance Hash clusters group table data by applying a hash function to each row's cluster key value. All rows with the same cluster key value are stored together on disk. Consider the benefits and drawbacks of hash clusters for the application. You might want to experiment and compare processing times with a particular table in a hash cluster and alone with an index. Follow these guidelines for choosing when to use hash clusters: • Use hash clusters to store tables accessed frequently by SQL statements with WHERE clauses, if the WHERE clauses contain equality conditions that use the same column or combination of columns. Designate this column or combination of columns as the cluster key. • Store a table in a hash cluster if you can determine how much space is required to hold all rows with a given cluster key value, including rows to be inserted immediately and rows to be inserted in the future. • Use sorted hash clusters, where rows corresponding to each value of the hash function are sorted on a specific columns in ascending order, when the database can improve response time on operations with this sorted clustered data. • Do not store a table in a hash cluster in the following cases: – The application often performs full table scans. – You must allocate a great deal of space to the hash cluster in anticipation of the table growing. Full table scans must read all blocks allocated to the hash cluster, even though some blocks might contain few rows. Storing the table alone reduces the number of blocks read by full table scans. A-14 Appendix A Guidelines for Using Hash Clusters for Performance • Do not store a table in a hash cluster if the application frequently modifies the cluster key values. Modifying a row's cluster key value can take longer than modifying the value in an nonclustered table, because Oracle Database might need to migrate the modified row to another block to maintain the cluster. If hashing is appropriate for the table based on the considerations in this list, then storing a single table in a hash cluster can be useful. This is true regardless of whether the table is joined frequently with other tables. See Also: • • Oracle Database Administrator’s Guide to learn how to manage hash clusters Oracle Database SQL Language Reference to learn about the CREATE CLUSTER statement A-15 Glossary accepted plan In the context of SQL plan management, a plan that is in a SQL plan baseline for a SQL statement and thus available for use by the optimizer. An accepted plan contains a set of hints, a plan hash value, and other plan-related information. access path The means by which the database retrieves data from a database. For example, a query using an index and a query using a full table scan use different access paths. adaptive cursor sharing A feature that enables a single statement that contains bind variables to use multiple execution plans. Cursor sharing is "adaptive" because the cursor adapts its behavior so that the database does not always use the same plan for each execution or bind variable value. adaptive dynamic sampling A feature of the adaptive optimizer that enables the automatic adjustment of the dynamic statistics level. adaptive optimizer A feature of the optimizer that enables it to adapt plans based on run-time statistics. adaptive query plan An execution plan that changes after optimization because run-time conditions indicate that optimizer estimates are inaccurate. An adaptive query plan has different built-in plan options. During the first execution, before a specific subplan becomes active, the optimizer makes a final decision about which option to use. The optimizer bases its choice on observations made during the execution up to this point. Thus, an adaptive query plan enables the final plan for a statement to differ from the default plan. adaptive query optimization A set of capabilities that enables the adaptive optimizer to make run-time adjustments to execution plans and discover additional information that can lead to better statistics. Adaptive optimization is helpful when existing statistics are not sufficient to generate an optimal plan. ADDM See Automatic Database Diagnostic Monitor (ADDM). Glossary-1 Glossary antijoin A join that returns rows that fail to match the subquery on the right side. For example, an antijoin can list departments with no employees. Antijoins use the NOT EXISTS or NOT IN constructs. approximate query processing A set of optimization techniques that speed analytic queries by calculating results within an acceptable range of error. automatic capture filter A SQL plan management feature that enables you to specify the eligibility criteria for automatic initial plan capture. Automatic Database Diagnostic Monitor (ADDM) ADDM is self-diagnostic software built into Oracle Database. ADDM examines and analyzes data captured in Automatic Workload Repository (AWR) to determine possible database performance problems. automatic optimizer statistics collection The automatic running of the DBMS_STATS package to collect optimizer statistics for all schema objects for which statistics are missing or stale. automatic initial plan capture The mechanism by which the database automatically creates a SQL plan baseline for any repeatable SQL statement executed on the database. Enable automatic initial plan capture by setting the OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES initialization parameter to true (the default is false). See repeatable SQL statement. automatic reoptimization The ability of the optimizer to automatically change a plan on subsequent executions of a SQL statement. Automatic reoptimization can fix any suboptimal plan chosen due to incorrect optimizer estimates, from a suboptimal distribution method to an incorrect choice of degree of parallelism. automatic SQL tuning The work performed by Automatic SQL Tuning Advisor it runs as an automated task within system maintenance windows. Automatic SQL Tuning Advisor SQL Tuning Advisor when run as an automated maintenance task. Automatic SQL Tuning runs during system maintenance windows as an automated maintenance task, searching for ways to improve the execution plans of high-load SQL statements. See SQL Tuning Advisor. Glossary-2 Glossary Automatic Tuning Optimizer The optimizer when invoked by SQL Tuning Advisor. In SQL tuning mode, the optimizer performs additional analysis to check whether it can further improve the plan produced in normal mode. The optimizer output is not an execution plan, but a series of actions, along with their rationale and expected benefit for producing a significantly better plan. Automatic Workload Repository (AWR) The infrastructure that provides services to Oracle Database components to collect, maintain, and use statistics for problem detection and self-tuning. AWR See Automatic Workload Repository (AWR). AWR snapshot A set of data for a specific time that is used for performance comparisons. The delta values captured by the snapshot represent the changes for each statistic over the time period. Statistics gathered by are queried from memory. You can display the gathered data in both reports and views. band join A special type of nonequijoin in which key values in one data set must fall within the specified range (“band”) of the second data set. base cardinality For a table, the total number of rows in the table. baseline In the context of AWR, the interval between two AWR snapshots that represent the database operating at an optimal level. bind-aware cursor A bind-sensitive cursor that is eligible to use different plans for different bind values. After a cursor has been made bind-aware, the optimizer chooses plans for future executions based on the bind value and its cardinality estimate. bind-sensitive cursor A cursor whose optimal plan may depend on the value of a bind variable. The database monitors the behavior of a bind-sensitive cursor that uses different bind values to determine whether a different plan is beneficial. bind variable A placeholder in a SQL statement that must be replaced with a valid value or value address for the statement to execute successfully. By using bind variables, you can write a SQL statement that accepts inputs or parameters at run time. The following query uses v_empid as a bind variable: Glossary-3 Glossary SELECT * FROM employees WHERE employee_id = :v_empid; bind variable peeking The ability of the optimizer to look at the value in a bind variable during a hard parse. By peeking at bind values, the optimizer can determine the selectivity of a WHERE clause condition as if literals had been used, thereby improving the plan. bitmap join index A bitmap index for the join of two or more tables. bitmap piece A subcomponent of a single bitmap index entry. Each indexed column value may have one or more bitmap pieces. The database uses bitmap pieces to break up an index entry that is large in relation to the size of a block. B-tree index An index organized like an upside-down tree. A B-tree index has two types of blocks: branch blocks for searching and leaf blocks that store values. The leaf blocks contain every indexed data value and a corresponding rowid used to locate the actual row. The "B" stands for "balanced" because all leaf blocks automatically stay at the same depth. bulk load A CREATE TABLE AS SELECT or INSERT INTO ... SELECT operation. bushy join tree A join tree in which the left or the right child of an internal node can be a join node. Nodes in a bushy join tree may have recursive structures in both its descendents. cardinality The number of rows that is expected to be or is returned by an operation in an execution plan. Cartesian join A join in which one or more of the tables does not have any join conditions to any other tables in the statement. The optimizer joins every row from one data source with every row from the other data source, creating the Cartesian product of the two sets. child cursor The cursor containing the plan, compilation environment, and other information for a statement whose text is stored in a parent cursor. The parent cursor is number 0, the first child is number 1, and so on. Child cursors reference the same SQL text as the parent cursor, but are different. For example, two queries with the text SELECT * FROM t use different cursors when they reference two different tables named t. Glossary-4 Glossary cluster scan An access path for a table cluster. In an indexed table cluster, Oracle Database first obtains the rowid of one of the selected rows by scanning the cluster index. Oracle Database then locates the rows based on this rowid. column group A set of columns that is treated as a unit. column group statistics Extended statistics gathered on a group of columns treated as a unit. column statistics Statistics about columns that the optimizer uses to determine optimal execution plans. Column statistics include the number of distinct column values, low value, high value, and number of nulls. complex view merging The merging of views containing the GROUP BY or DISTINCT keywords. composite database operation In a database operation, the activity between two points in time in a database session, with each session defining its own beginning and end points. A session can participate in at most one composite database operation at a time. concurrency Simultaneous access of the same data by many users. A multiuser database management system must provide adequate concurrency controls so that data cannot be updated or changed improperly, compromising data integrity. concurrent statistics gathering mode A mode that enables the database to simultaneously gather optimizer statistics for multiple tables in a schema, or multiple partitions or subpartitions in a table. Concurrency can reduce the overall time required to gather statistics by enabling the database to fully use multiple CPUs. condition A combination of one or more expressions and logical operators that returns a value of TRUE, FALSE, or UNKNOWN. cost A numeric internal measure that represents the estimated resource usage for an execution plan. The lower the cost, the more efficient the plan. cost-based optimizer (CBO) The legacy name for the optimizer. In earlier releases, the cost-based optimizer was an alternative to the rule-based optimizer (RBO). Glossary-5 Glossary cost model The internal optimizer model that accounts for the cost of the I/O, CPU, and network resources that a query is predicted to use. cumulative statistics A count such as the number of block reads. Oracle Database generates many types of cumulative statistics for the system, sessions, and individual SQL statements. cursor A handle or name for a private SQL area in the PGA. Because cursors are closely associated with private SQL areas, the terms are sometimes used interchangeably. cursor cache See shared SQL area. cursor merging Combining cursors to save space in the shared SQL area. If the optimizer creates a plan for a bind-aware cursor, and if this plan is the same as an existing cursor, then the optimizer can merge the cursors. cursor-duration temporary table A temporary, in-memory table that stores query results for the duration of a cursor. For complex operations such as WITH clause queries and star transformations, this optimization enhances the materialization of intermediate results from repetitively used subqueries. In this way, cursor-duration temporary tables improve performance and optimizes I/O. data flow operator (DFO) The unit of work between data redistribution stages in a parallel query. data skew Large variations in the number of duplicate values in a column. database operation A set of database tasks defined by end users or application code, for example, a batch job or ETL processing. default plan For an adaptive plan, the execution plan initially chosen by the optimizer using the statistics from the data dictionary. The default plan can differ from the final plan. disabled plan A plan that a database administrator has manually marked as ineligible for use by the optimizer. Glossary-6 Glossary degree of parallelism (DOP) The number of parallel execution servers associated with a single operation. Parallel execution is designed to effectively use multiple CPUs. Oracle Database parallel execution framework enables you to either explicitly choose a specific degree of parallelism or to rely on Oracle Database to automatically control it. dense key A numeric key that is stored as a native integer and has a range of values. dense grouping key A key that represents all grouping keys whose grouping columns come from a specific fact table or dimension. dense join key A key that represents all join keys whose join columns come from a particular fact table or dimension. density A decimal number between 0 and 1 that measures the selectivity of a column. Values close to 1 indicate that the column is unselective, whereas values close to 0 indicate that this column is more selective. direct path read A single or multiblock read into the PGA, bypassing the SGA. driving table The table to which other tables are joined. An analogy from programming is a for loop that contains another for loop. The outer for loop is the analog of a driving table, which is also called an outer table. dynamic performance view A view created on dynamic performance tables, which are virtual tables that record current database activity. The dynamic performance views are called fixed views because they cannot be altered or removed by the database administrator. They are also called V$ views because they begin with the string V$ (GV$ in Oracle RAC). dynamic plan A set of subplan groups. A subplan group is set of subplans. In an adaptive query plan, the optimizer chooses a subplan at run time depending on the statistics obtained by the statistics collector. dynamic statistics An optimization technique in which the database executes a recursive SQL statement to scan a small random sample of a table's blocks to estimate predicate selectivities. Glossary-7 Glossary dynamic statistics level The level that controls both when the database gathers dynamic statistics, and the size of the sample that the optimizer uses to gather the statistics. Set the dynamic statistics level using either the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter or a statement hint. enabled plan In SQL plan management, a plan that is eligible for use by the optimizer. endpoint number A number that uniquely identifies a bucket in a histogram. In frequency and hybrid histograms, the endpoint number is the cumulative frequency of endpoints. In heightbalanced histograms, the endpoint number is the bucket number. endpoint repeat count In a hybrid histogram, the number of times the endpoint value is repeated, for each endpoint (bucket) in the histogram. By using the repeat count, the optimizer can obtain accurate estimates for almost popular values. endpoint value An endpoint value is the highest value in the range of values in a histogram bucket. equijoin A join whose join condition contains an equality operator. estimator The component of the optimizer that determines the overall cost of a given execution plan. execution plan The combination of steps used by the database to execute a SQL statement. Each step either retrieves rows of data physically from the database or prepares them for the session issuing the statement. You can override execution plans by using a hint. execution tree A tree diagram that shows the flow of row sources from one step to another in an execution plan. expected cardinality For a table, the estimated number of rows the table has after all filter predicates have been applied to the table. expression A combination of one or more values, operators, and SQL functions that evaluates to a value. For example, the expression 2*2 evaluates to 4. In general, expressions assume the data type of their components. Glossary-8 Glossary expression statistics A type of extended statistics that improves optimizer estimates when a WHERE clause has predicates that use expressions. extended statistics A type of optimizer statistics that improves estimates for cardinality when multiple predicates exist or when predicates contain an expression. extensible optimizer An optimizer capability that enables authors of user-defined functions and indexes to create statistics collection, selectivity, and cost functions that the optimizer uses when choosing an execution plan. The optimizer cost model is extended to integrate information supplied by the user to assess CPU and I/O cost. extension A column group or an expression. The statistics collected for column groups and expressions are called extended statistics. external table A read-only table whose metadata is stored in the database but whose data in stored in files outside the database. The database uses the metadata describing external tables to expose their data as if they were relational tables. filter condition A WHERE clause component that eliminates rows from a single object referenced in a SQL statement. final plan In an adaptive plan, the plan that executes to completion. The default plan can differ from the final plan. fixed object A dynamic performance table or its index. The fixed objects are owned by SYS. Fixed object tables have names beginning with X$ and are the base tables for the V$ views. fixed plan An accepted plan that is marked as preferred, so that the optimizer considers only the fixed plans in the SQL plan baseline. You can use fixed plans to influence the plan selection process of the optimizer. frequency histogram A type of histogram in which each distinct column value corresponds to a single bucket. An analogy is sorting coins: all pennies go in bucket 1, all nickels go in bucket 2, and so on. Glossary-9 Glossary full outer join A combination of a left and right outer join. In addition to the inner join, the database uses nulls to preserve rows from both tables that have not been returned in the result of the inner join. In other words, full outer joins join tables together, yet show rows with no corresponding rows in the joined tables. full table scan A scan of table data in which the database sequentially reads all rows from a table and filters out those that do not meet the selection criteria. All data blocks under the high water mark are scanned. global temporary table A special temporary table that stores intermediate session-private data for a specific duration. hard parse The steps performed by the database to build a new executable version of application code. The database must perform a hard parse instead of a soft parse if the parsed representation of a submitted statement does not exist in the shared SQL area. hash cluster A type of table cluster that is similar to an indexed cluster, except the index key is replaced with a hash function. No separate cluster index exists. In a hash cluster, the data is the index. hash collision Hashing multiple input values to the same output value. hash function A function that operates on an arbitrary-length input value and returns a fixed-length hash value. hash join A method for joining large data sets. The database uses the smaller of two data sets to build a hash table on the join key in memory. It then scans the larger data set, probing the hash table to find the joined rows. hash scan An access path for a table cluster. The database uses a hash scan to locate rows in a hash cluster based on a hash value. In a hash cluster, all rows with the same hash value are stored in the same data block. To perform a hash scan, Oracle Database first obtains the hash value by applying a hash function to a cluster key value specified by the statement, and then scans the data blocks containing rows with that hash value. hash table An in-memory data structure that associates join keys with rows in a hash join. For example, in a join of the employees and departments tables, the join key might be the Glossary-10 Glossary department ID. A hash function uses the join key to generate a hash value. This hash value is an index in an array, which is the hash table. hash value In a hash cluster, a unique numeric ID that identifies a bucket. Oracle Database uses a hash function that accepts an infinite number of hash key values as input and sorts them into a finite number of buckets. Each hash value maps to the database block address for the block that stores the rows corresponding to the hash key value (department 10, 20, 30, and so on). hashing A mathematical technique in which an infinite set of input values is mapped to a finite set of output values, called hash values. Hashing is useful for rapid lookups of data in a hash table. heap-organized table A table in which the data rows are stored in no particular order on disk. By default, CREATE TABLE creates a heap-organized table. height-balanced histogram A histogram in which column values are divided into buckets so that each bucket contains approximately the same number of rows. hint An instruction passed to the optimizer through comments in a SQL statement. The optimizer uses hints to choose an execution plan for the statement. histogram A special type of column statistic that provides more detailed information about the data distribution in a table column. hybrid hash distribution technique An adaptive parallel data distribution that does not decide the final data distribution method until execution time. hybrid histogram An enhanced height-based histogram that stores the exact frequency of each endpoint in the sample, and ensures that a value is never stored in multiple buckets. implicit query A component of a DML statement that retrieves data without a subquery. An UPDATE, DELETE, or MERGE statement that does not explicitly include a SELECT statement uses an implicit query to retrieve the rows to be modified. In-Memory scan A table scan that retrieves rows from the In-Memory Column Store (IM column store). Glossary-11 Glossary incremental statistics maintenance The ability of the database to generate global statistics for a partitioned table by aggregating partition-level statistics. index Optional schema object associated with a nonclustered table, table partition, or table cluster. In some cases indexes speed data access. index cluster An table cluster that uses an index to locate data. The cluster index is a B-tree index on the cluster key. index clustering factor A measure of row order in relation to an indexed value such as employee last name. The more scattered the rows among the data blocks, the lower the clustering factor. index fast full scan A scan of the index blocks in unsorted order, as they exist on disk. This scan reads the index instead of the table. index full scan The scan of an entire index in key order. index-organized table A table whose storage organization is a variant of a primary B-tree index. Unlike a heap-organized table, data is stored in primary key order. index range scan An index range scan is an ordered scan of an index that has the following characteristics: • One or more leading columns of an index are specified in conditions. • 0, 1, or more values are possible for an index key. index range scan descending An index range scan in which the database returns rows in descending order. index skip scan An index scan occurs in which the initial column of a composite index is "skipped" or not specified in the query. For example, if the composite index key is (cust_gender,cust_email), then the query predicate does not reference the cust_gender column. index statistics Statistics about indexes that the optimizer uses to determine whether to perform a full table scan or an index scan. Index statistics include B-tree levels, leaf block counts, the index clustering factor, distinct keys, and number of rows in the index. Glossary-12 Glossary index unique scan A scan of an index that returns either 0 or 1 rowid. indextype An object that specifies the routines that manage a domain (application-specific) index. inner join A join of two or more tables that returns only those rows that satisfy the join condition. inner table In a nested loops join, the table that is not the outer table (driving table). For every row in the outer table, the database accesses all rows in the inner table. The outer loop is for every row in the outer table and the inner loop is for every row in the inner table. join A statement that retrieves data from multiple tables specified in the FROM clause of a SQL statement. Join types include inner joins, outer joins, and Cartesian joins. join condition A condition that compares two row sources using an expression. The database combines pairs of rows, each containing one row from each row source, for which the join condition evaluates to true. join elimination The removal of redundant tables from a query. A table is redundant when its columns are only referenced in join predicates, and those joins are guaranteed to neither filter nor expand the resulting rows. join factorization A cost-based transformation that can factorize common computations from branches of a UNION ALL query. Without join factorization, the optimizer evaluates each branch of a UNION ALL query independently, which leads to repetitive processing, including data access and joins. Avoiding an extra scan of a large base table can lead to a huge performance improvement. join group A user-created database object that specifies a group of columns that participate in an join. Join groups are only supported in the In-Memory column store. join method A method of joining a pair of row sources. The possible join methods are nested loop, sort merge, and hash joins. A Cartesian join requires one of the preceding join methods Glossary-13 Glossary join order The order in which multiple tables are joined together. For example, for each row in the employees table, the database can read each row in the departments table. In an alternative join order, for each row in the departments table, the database reads each row in the employees table. To execute a statement that joins more than two tables, Oracle Database joins two of the tables and then joins the resulting row source to the next table. This process continues until all tables are joined into the result. join predicate A predicate in a WHERE or JOIN clause that combines the columns of two tables in a join. key vector A data structure that maps between dense join keys and dense grouping keys. latch A low-level serialization control mechanism used to protect shared data structures in the SGA from simultaneous access. left deep join tree A join tree in which the left input of every join is the result of a previous join. left table In an outer join, the table specified on the left side of the OUTER JOIN keywords (in ANSI SQL syntax). library cache An area of memory in the shared pool. This cache includes the shared SQL areas, private SQL areas (in a shared server configuration), PL/SQL procedures and packages, and control structures such as locks and library cache handles. library cache hit The reuse of SQL statement code found in the library cache. library cache miss During SQL processing, the act of searching for a usable plan in the library cache and not finding it. maintenance window A contiguous time interval during which automated maintenance tasks run. The maintenance windows are Oracle Scheduler windows that belong to the window group named MAINTENANCE_WINDOW_GROUP. manual plan capture The user-initiated bulk load of existing plans into a SQL plan baseline. Glossary-14 Glossary materialized view A schema object that stores a query result. All materialized views are either read-only or updatable. multiblock read An I/O call that reads multiple database blocks. Multiblock reads can significantly speed up full table scans. For example, a data block might be 8 KB, but the operating system can read 1024 KB in a single I/O. For some queries, the optimizer may decide that it is more cost-efficient to read 128 data blocks in one I/O than in 128 sequential I/Os. NDV Number of distinct values. The NDV is important in generating cardinality estimates. nested loops join A type of join method. A nested loops join determines the outer table that drives the join, and for every row in the outer table, probes each row in the inner table. The outer loop is for each row in the outer table and the inner loop is for each row in the inner table. An analogy from programming is a for loop inside of another for loop. nonequijoin A join whose join condition does not contain an equality operator. nonjoin column A predicate in a WHERE clause that references only one table. nonpopular value In a histogram, any value that does not span two or more endpoints. Any value that is not nonpopular is a popular value. noworkload statistics Optimizer system statistics gathered when the database simulates a workload. on-demand SQL tuning The manual invocation of SQL Tuning Advisor. Any invocation of SQL Tuning Advisor that is not the result of an Automatic SQL Tuning task is on-demand tuning. optimization The overall process of choosing the most efficient means of executing a SQL statement. optimizer Built-in database software that determines the most efficient way to execute a SQL statement by considering factors related to the objects referenced and the conditions specified in the statement. Glossary-15 Glossary optimizer cost model The model that the optimizer uses to select an execution plan. The optimizer selects the execution plan with the lowest cost, where cost represents the estimated resource usage for that plan. The optimizer cost model accounts for the I/O, CPU, and network resources that the query will use. optimizer environment The totality of session settings that can affect execution plan generation, such as the work area size or optimizer settings (for example, the optimizer mode). optimizer goal The prioritization of resource usage by the optimizer. Using the OPTIMIZER_MODE initialization parameter, you can set the optimizer goal best throughput or best response time. optimizer statistics Details about the database its object used by the optimizer to select the best execution plan for each SQL statement. Categories include table statistics such as numbers of rows, index statistics such as B-tree levels, system statistics such as CPU and I/O performance, and column statistics such as number of nulls. Optimizer Statistics Advisor A tool that inspects statistics gathering practices, automatically diagnoses problems with these practices, and generates a report of findings and recommendations. Optimizer Statistics Advisor rules System-supplied standards by which Optimizer Statistics Advisor performs its checks. optimizer statistics collection The gathering of optimizer statistics for database objects. The database can collect these statistics automatically, or you can collect them manually by using the systemsupplied DBMS_STATS package. optimizer statistics collector A row source inserted into an execution plan at key points to collect run-time statistics for use in adaptive plans. optimizer statistics preferences The default values of the parameters used by automatic statistics collection and the DBMS_STATS statistics gathering procedures. outer join A join condition using the outer join operator (+) with one or more columns of one of the tables. The database returns all rows that meet the join condition. The database also returns all rows from the table without the outer join operator for which there are no matching rows in the table with the outer join operator. Glossary-16 Glossary outer table See driving table parallel execution The application of multiple CPU and I/O resources to the execution of a single database operation. parallel query A query in which multiple processes work together simultaneously to run a single SQL query. By dividing the work among multiple processes, Oracle Database can run the statement more quickly. For example, four processes retrieve rows for four different quarters in a year instead of one process handling all four quarters by itself. parent cursor The cursor that stores the SQL text and other minimal information for a SQL statement. The child cursor contains the plan, compilation environment, and other information. When a statement first executes, the database creates both a parent and child cursor in the shared pool. parse call A call to Oracle to prepare a SQL statement for execution. The call includes syntactically checking the SQL statement, optimizing it, and then building or locating an executable form of that statement. parsing The stage of SQL processing that involves separating the pieces of a SQL statement into a data structure that can be processed by other routines. A hard parse occurs when the SQL statement to be executed is either not in the shared pool, or it is in the shared pool but it cannot be shared. A soft parse occurs when a session attempts to execute a SQL statement, and the statement is already in the shared pool, and it can be used. partition maintenance operation A partition-related operation such as adding, exchanging, merging, or splitting table partitions. partition-wise join A join optimization that divides a large join of two tables, one of which must be partitioned on the join key, into several smaller joins. pending statistics Unpublished optimizer statistics. By default, the optimizer uses published statistics but does not use pending statistics. Glossary-17 Glossary performance feedback This form of automatic reoptimization helps improve the degree of parallelism automatically chosen for repeated SQL statements when PARALLEL_DEGREE_POLICY is set to ADAPTIVE. pipelined table function A PL/SQL function that accepts a collection of rows as input. You invoke the table function as the operand of the table operator in the FROM list of a SELECT statement. plan evolution The manual change of an unaccepted plan in the SQL plan history into an accepted plan in the SQL plan baseline. plan generator The part of the optimizer that tries different access paths, join methods, and join orders for a given query block to find the plan with the lowest cost. plan selection The optimizer's attempt to find a matching plan in the SQL plan baseline for a statement after performing a hard parse. plan verification Comparing the performance of an unaccepted plan to a plan in a SQL plan baseline and ensuring that it performs better. popular value In a histogram, any value that spans two or more endpoints. Any value that is not popular is an nonpopular value. predicate pushing A transformation technique in which the optimizer "pushes" the relevant predicates from the containing query block into the view query block. For views that are not merged, this technique improves the subplan of the unmerged view because the database can use the pushed-in predicates to access indexes or to use as filters. private SQL area An area in memory that holds a parsed statement and other information for processing. The private SQL area contains data such as bind variable values, query execution state information, and query execution work areas. proactive SQL tuning Using SQL tuning tools to identify SQL statements that are candidates for tuning before users have complained about a performance problem. See reactive SQL tuning, SQL tuning. Glossary-18 Glossary projection view An optimizer-generated view that appear in queries in which a DISTINCT view has been merged, or a GROUP BY view is merged into an outer query block that also contains GROUP BY, HAVING, or aggregates. See simple view merging, complex view merging. query An operation that retrieves data from tables or views. For example, SELECT * FROM employees is a query. query block A top-level SELECT statement, subquery, or unmerged view query optimizer See optimizer. reactive SQL tuning Diagnosing and fixing SQL-related performance problems after users have complained about them. See proactive SQL tuning, SQL tuning. recursive SQL Additional SQL statements that the database must issue to execute a SQL statement issued by a user. The generation of recursive SQL is known as a recursive call. For example, the database generates recursive calls when data dictionary information is not available in memory and so must be retrieved from disk. reoptimization See automatic reoptimization. repeatable SQL statement A statement that the database parses or executes after recognizing that it is tracked in the SQL statement log. response time The time required to complete a unit of work. See throughput. result set In a query, the set of rows generated by the execution of a cursor. right deep join tree A join tree in which the right input of every join is the result of a previous join, and the left child of every internal node of a join tree is a table. Glossary-19 Glossary right table In an outer join, the table specified on the right side of the OUTER JOIN keywords (in ANSI SQL syntax). rowid A globally unique address for a row in a table. row set A set of rows returned by a step in an execution plan. row source An iterative control structure that processes a set of rows in an iterated manner and produces a row set. row source generator Software that receives the optimal plan from the optimizer and outputs the execution plan for the SQL statement. row source tree A collection of row sources produced by the row source generator. The row source tree for a SQL statement shows information such as table order, access methods, join methods, and data operations such as filters and sorts. rule filter The use of DBMS_STATS.CONFIGURE_ADVISOR_RULE_FILTER to restrict an Optimizer Statistics Advisor task to a user-specified set of rules. For example, you might exclude the rule that checks for stale statistics. sample table scan A scan that retrieves a random sample of data from a simple table or a complex SELECT statement, such as a statement involving joins and views. sampling Gathering statistics from a random subset of rows in a table. selectivity A value indicating the proportion of a row set retrieved by a predicate or combination of predicates, for example, WHERE last_name = 'Smith'. A selectivity of 0 means that no rows pass the predicate test, whereas a value of 1 means that all rows pass the test. The adjective selective means roughly "choosy." Thus, a highly selective query returns a low proportion of rows (selectivity close to 0), whereas an unselective query returns a high proportion of rows (selectivity close to 1). semijoin A join that returns rows from the first table when at least one match exists in the second table. For example, you list departments with at least one employee. The Glossary-20 Glossary difference between a semijoin and a conventional join is that rows in the first table are returned at most once. Semijoins use the EXISTS or IN constructs. shared cursor A shared SQL area that is used by multiple SQL statements. shared pool Portion of the SGA that contains shared memory constructs such as shared SQL areas. shared SQL area An area in the shared pool that contains the parse tree and execution plan for a SQL statement. Only one shared SQL area exists for a unique statement. The shared SQL area is sometimes referred to as the cursor cache. simple database operation A database operation consisting of a single SQL statement or PL/SQL procedure or function. simple view merging The merging of select-project-join views. For example, a query joins the employees table to a subquery that joins the departments and locations tables. SMB See SQL management base (SMB). snowflake schema A star schema in which dimension tables reference other tables. snowstorm schema A combination of multiple snowflake schemas. soft parse Any parse that is not a hard parse. If a submitted SQL statement is the same as a reusable SQL statement in the shared pool, then Oracle Database reuses the existing code. This reuse of code is also called a library cache hit. sort merge join A type of join method. The join consists of a sort join, in which both inputs are sorted on the join key, followed by a merge join, in which the sorted lists are merged. SQL Access Advisor SQL Access Advisor is internal diagnostic software that recommends which materialized views, indexes, and materialized view logs to create, drop, or retain. Glossary-21 Glossary SQL compilation In the context of Oracle SQL processing, this term refers collectively to the phases of parsing, optimization, and plan generation. SQL handle A string value derived from the numeric SQL signature. Like the signature, the handle uniquely identifies a SQL statement. It serves as a SQL search key in user APIs. SQL ID For a specific SQL statement, the unique identifier of the parent cursor in the library cache. A hash function applied to the text of the SQL statement generates the SQL ID. The V$SQL.SQL_ID column displays the SQL ID. SQL incident In the fault diagnosability infrastructure of Oracle Database, a single occurrence of a SQL-related problem. When a problem (critical error) occurs multiple times, the database creates an incident for each occurrence. Incidents are timestamped and tracked in the Automatic Diagnostic Repository (ADR). SQL management base (SMB) A logical repository that stores statement logs, plan histories, SQL plan baselines, and SQL profiles. The SMB is part of the data dictionary and resides in the SYSAUX tablespace. SQL plan baseline A set of one or more accepted plans for a repeatable SQL statement. Each accepted plan contains a set of hints, a plan hash value, and other plan-related information. SQL plan management uses SQL plan baselines to record and evaluate the execution plans of SQL statements over time. SQL plan capture Techniques for capturing and storing relevant information about plans in the SQL management base (SMB) for a set of SQL statements. Capturing a plan means making SQL plan management aware of this plan. SQL plan directive Additional information and instructions that the optimizer can use to generate a more optimal plan. For example, a SQL plan directive might instruct the optimizer to collect missing statistics or gather dynamic statistics. SQL plan history The set of captured execution plans. The history contains both SQL plan baselines and unaccepted plans. SQL plan management SQL plan management is a preventative mechanism that records and evaluates the execution plans of SQL statements over time. SQL plan management can prevent Glossary-22 Glossary SQL plan regressions caused by environmental changes such as a new optimizer version, changes to optimizer statistics, system settings, and so on. SQL processing The stages of parsing, optimization, row source generation, and execution of a SQL statement. SQL profile A set of auxiliary information built during automatic tuning of a SQL statement. A SQL profile is to a SQL statement what statistics are to a table. The optimizer can use SQL profiles to improve cardinality and selectivity estimates, which in turn leads the optimizer to select better plans. SQL profiling The verification and validation by the Automatic Tuning Advisor of its own estimates. SQL signature A numeric hash value computed using a SQL statement text that has been normalized for case insensitivity and white space. It uniquely identifies a SQL statement. The database uses this signature as a key to maintain SQL management objects such as SQL profiles, SQL plan baselines, and SQL patches. SQL statement log When automatic SQL plan capture is enabled, a log that contains the SQL ID of SQL statements that the optimizer has evaluated over time. A statement is tracked when it exists in the log. SQL test case A problematic SQL statement and related information needed to reproduce the execution plan in a different environment. A SQL test case is stored in an Oracle Data Pump file. SQL test case builder A database feature that gathers information related to a SQL statement and packages it so that a user can reproduce the problem on a different database. The DBMS_SQLDIAG package is the interface for SQL test case builder. SQL trace file A server-generated file that provides performance information on individual SQL statements. For example, the trace file contains parse, execute, and fetch counts, CPU and elapsed times, physical reads and logical reads, and misses in the library cache. SQL tuning The process of improving SQL statement efficiency to meet measurable goals. SQL Tuning Advisor Built-in database diagnostic software that optimizes high-load SQL statements. Glossary-23 Glossary See Automatic SQL Tuning Advisor. SQL tuning set (STS) A database object that includes one or more SQL statements along with their execution statistics and execution context. star schema A relational schema whose design represents a dimensional data model. The star schema consists of one or more fact tables and one or more dimension tables that are related through foreign keys. statistics feedback A form of automatic reoptimization that automatically improves plans for repeated queries that have cardinality misestimates. The optimizer may estimate cardinalities incorrectly for many reasons, such as missing statistics, inaccurate statistics, or complex predicates. stored outline A set of hints for a SQL statement. The hints in stored outlines direct the optimizer to choose a specific plan for the statement. subplan A portion of an adaptive plan that the optimizer can switch to as an alternative at run time. A subplan can consist of multiple operations in the plan. For example, the optimizer can treat a join method and the corresponding access path as one unit when determining whether to change the plan at run time. subplan group A set of subplans in an adaptive query plan. subquery A query nested within another SQL statement. Unlike implicit queries, subqueries use a SELECT statement to retrieve data. subquery unnesting A transformation technique in which the optimizer transforms a nested query into an equivalent join statement, and then optimizes the join. synopsis A set of auxiliary statistics gathered on a partitioned table when the INCREMENTAL value is set to true. system statistics Statistics that enable the optimizer to use CPU and I/O characteristics. Index statistics include B-tree levels, leaf block counts, clustering factor, distinct keys, and number of rows in the index. Glossary-24 Glossary table cluster A schema object that contains data from one or more tables, all of which have one or more columns in common. In table clusters, the database stores together all the rows from all tables that share the same cluster key. table expansion A transformation technique that enables the optimizer to generate a plan that uses indexes on the read-mostly portion of a partitioned table, but not on the active portion of the table. table statistics Statistics about tables that the optimizer uses to determine table access cost, join cardinality, join order, and so on. Table statistics include row counts, block counts, empty blocks, average free space per block, number of chained rows, average row length, and staleness of the statistics on the table. throughput The amount of work completed in a unit of time. See response time. top frequency histogram A variation of a frequency histogram that ignores nonpopular values that are statistically insignificant, thus producing a better histogram for highly popular values. tuning mode One of the two optimizer modes. When running in tuning mode, the optimizer is known as the Automatic Tuning Optimizer. In tuning mode, the optimizer determines whether it can further improve the plan produced in normal mode. The optimizer output is not an execution plan, but a series of actions, along with their rationale and expected benefit for producing a significantly better plan. unaccepted plan A plan for a statement that is in the SQL plan history but has not been added to the SQL plan management. unselective A relatively large fraction of rows from a row set. A query becomes more unselective as the selectivity approaches 1. For example, a query that returns 999,999 rows from a table with one million rows is unselective. A query of the same table that returns one row is selective. user response time The time between when a user submits a command and receives a response. See throughput. Glossary-25 Glossary V$ view See dynamic performance view. vector I/O A type of I/O in which the database obtains a set of rowids, sends them batched in an array to the operating system, which performs the read. view merging The merging of a query block representing a view into the query block that contains it. View merging can improve plans by enabling the optimizer to consider additional join orders, access methods, and other transformations. workload statistics Optimizer statistics for system activity in a specified time period. Glossary-26 Index A accepted plans, 4-31 access paths, 3-7, 8-1 B-tree indexes, 8-14, 8-15 bitmap index, 8-34 bitmap index range scans, 8-41 bitmap indexes, 8-33, 8-35, 8-43 execution plans, 6-1 full table scans, 8-6, 10-6, 11-2 heap-organized tables, 8-3 In-Memory table scans, 8-12, 8-13 index fast full, 8-28 index full scans, 8-26 index join scans, 8-31 index range scans, 8-22 index skip scans, 8-29 index unique scans, 8-19 optimizing with SQL Access Advisor, 26-10 sample table scans, 8-10, 8-11 table, 8-2 table access by rowid, 8-9 table cluster, 8-44 adaptive query optimization, 4-13 adaptive plans, 22-2 adaptive query plans, 4-14, 7-2, 7-30 controlling, 19-10 dynamic statistics, 10-14 adaptive query plans, 4-14, 7-2, 7-30, 22-2 about, 4-15 bitmap index pruning, 4-22 cardinality misestimates, 4-16 enabling, 4-24 join methods, 4-17 optimizer statistics collector, 4-16 parallel distribution methods, 4-20 subplans, 4-16 adaptive sampling, 13-24 adaptive statistics, 4-24 automatic reoptimization, 4-25 dynamic statistics, 4-24 SQL plan directives, 4-28, 14-1 when enabled, 4-28 ADDM, 1-6 advisors advisors (continued) Optimizer Statistics Advisor, 18-1–18-3 SPM Evolve Advisor, 29-5, 29-10 SQL Access Advisor, 26-1, 26-8 SQL Tuning Advisor, 25-1 ALTER SESSION statement, 23-13 ANALYZE command, 16-2 antijoins, 9-4, 9-40 handling nulls, 9-42 how they work, 9-41 when the optimizer considers, 9-40 APPEND hint, 10-15 applications implementing, 2-3 approximate query processing, 4-29 automatic plan capture creating automatic capture filters, 29-8 enabling, 29-7 automatic reoptimization, 4-13, 4-25, 7-2, 10-22 cardinality misestimates, 4-25 performance feedback, 4-27 statistics feedback, 4-25 automatic statistics collection, 13-2 Automatic Tuning Optimizer, 1-6, 4-13 SQL profiles, 25-7 Automatic Workload Repository (AWR), 1-6 B B-tree indexes, 8-14, 8-15 band joins, 9-29 big bang rollout strategy, 2-4 bind variables, 20-1, 20-2 bind peeking, 20-13 cursors, 20-12 substitution, 20-20 bind-aware cursors, 20-29 bind-sensitive cursors, 20-26 bitmap indexes access paths, 8-34 bitmap merge, 8-43 conversion to rowid, 8-39 inlist iterator, 7-18 on joins, A-12 pruning, 4-22 Index-1 Index bitmap indexes (continued) purpose, 8-35 range scans, 8-41 single value, 8-40 when to use, A-11 Bloom filters, 9-45 controls, 9-46 how they work, 9-46 metadata, 9-47 purpose, 9-45 BOOLEAN data type, 8-22 bulk loads creating histograms, 10-16 gathering index statistics, 10-16 restrictions for online statistics gathering, 10-17 bushy join trees, 9-2 BYTES column PLAN_TABLE table, 7-18, 7-30 C cardinality, 1-6, 4-7, 4-16, 4-17, 4-25, 4-28, 8-35, 10-4, 10-21, 11-2, 11-4, 27-2 CARDINALITY column PLAN_TABLE table, 7-18, 7-30 Cartesian joins, 9-25–9-27 child cursors, 20-4 clusters, table, A-13 sorted hash, A-14 column group statistics, 10-21 automatic and manual, 14-5 column groups, 14-1, 14-6 about, 14-2 creating, 14-9, 14-11 displaying information, 14-12 dropping, 14-13 optimizer statistics, 14-2 why needed, 14-3 columns cardinality, 4-7 to index, A-3 compilation, SQL, 10-19, 10-21, 10-32 composite indexes, A-4 composite partitioning examples of, 7-13 concurrent statistics gathering, 13-15, 13-17, 5 CONFIGURE procedure, 28-5 consistent mode TKPROF, 23-28 constraints, A-8 COST column PLAN_TABLE table, 7-18, 7-30 current mode TKPROF, 23-28 cursor sharing adaptive, 20-23, 20-33 forcing, 20-21 Real-World Performance guidelines, 20-33 CURSOR_NUM column TKPROF_TABLE table, 23-16 CURSOR_SHARING initialization parameter, 20-2, 20-20 cursor-duration temporary tables, 5-19, 5-20 cursors, 3-3 about, 20-1 adaptive sharing, 20-23, 20-33 bind variable peeking, 20-13 bind variables, 20-12 bind-aware, 20-29 bind-sensitive, 20-26 child, 20-4 invalidating, 20-16 literals, 20-11 marked DEFERRED INVALIDATION, 20-18 marked rolling invalid, 20-18 merging, 20-32 parent, 20-4, 20-6 parsing and, 20-7 Real-World Performance guidelines, 20-33 sharing, 20-1, 20-2 temporary tables and, 5-19, 5-20 D data blocks, 3-10 data dictionary cache, 3-4 data modeling, 2-1 Data Pump Export utility statistics on system-generated columns names, 17-2 Import utility copying statistics, 17-2 data skew, 11-2 data types BOOLEAN, 8-22 database operations attributes, 21-7 composite, 1-9, 21-1 concepts, 21-4 definition, 1-9, 21-1 monitoring, 21-1 simple, 1-9, 21-1, 21-3 database operations, monitoring, 1-9, 21-1 composite, 21-6 composite operations, 21-1, 21-3 creating database operations, 21-14 enabling with hints, 21-14 enabling with initialization parameters, 21-13 Index-2 Index database operations, monitoring (continued) Enterprise Manager interface, 21-9 PL/SQL interface, 21-9 purpose, 21-2 real-time SQL, 21-1 simple operations, 21-1 user interfaces, 21-8 using Cloud Control, 21-17 Database Resource Manager, 13-17 DATE_OF_INSERT column TKPROF_TABLE table, 23-16 DB_FILE_MULTIBLOCK_READ_COUNT initialization parameter, 8-7 DBA_ADVISOR_ACTIONS view, 26-34 DBA_ADVISOR_EXECUTIONS view, 18-15 DBA_IND_STATISTICS view, 13-11 DBA_OPTSTAT_OPERATION_TASKS view, 13-20 DBA_OPTSTAT_OPERATIONS view, 13-20 DBA_OUTLINES view, 30-7 DBA_SQL_MANAGEMENT_CONFIG view, 28-5, 29-37 DBA_SQL_PROFILES view, 27-2 DBA_STAT_EXTENSIONS view, 14-16 DBA_TAB_COL_STATISTICS view, 11-5, 14-3 DBA_TAB_MODIFICATIONS view, 13-11 DBA_TAB_PENDING_STATS view, 15-5 DBA_TAB_STATISTICS view, 13-11 DBMS_ADVISOR package, 26-2, 26-25, 26-29 DBMS_AUTO_SQLTUNE package, 25-26 DBMS_AUTO_TASK_ADMIN package, 12-3 DBMS_MONITOR package, 1, 23-8, 23-13 end-to-end application tracing, 23-3 DBMS_SESSION package, 23-13 DBMS_SPM package, 29-1, 29-2, 29-15, 29-28, 29-38, 30-7 DBMS_SQL_MONITOR package, 21-8 DBMS_SQLDIAG package, 22-6 DBMS_SQLTUNE package, 25-28, 25-33, 25-39, 25-40, 27-6 SQL Tuning Advisor, 25-32 DBMS_STATS package, 12-3, 13-10, 13-17, 13-23, 13-35, 13-41, 13-43, 14-1, 14-9, 14-12, 14-16, 15-1, 15-5, 15-6, 16-2, 16-3, 16-5, 16-6, 17-2 DBMS_XPLAN, 7-39 DBMS_XPLAN package, 6-8 DDL (data definition language) processing of, 3-10 deadlocks, 3-3 debugging designs, 2-3 dedicated server, 3-4 deferred invalidation, 20-18 density, histogram, 11-5 DEPTH column DEPTH column (continued) TKPROF_TABLE table, 23-16 designs debugging, 2-3 testing, 2-3 validating, 2-3 development environments, 2-3 DIAGNOSTIC_DEST initialization parameter, 23-12 DISTRIBUTION column PLAN_TABLE table, 7-18, 7-30 domain indexes and EXPLAIN PLAN, 7-18 using, A-12 DROP_SQL_PLAN_DIRECTIVE procedure, 12-16 dynamic statistics, 4-24, 10-14, 10-19, 10-30, 13-14, 22-2 controlling, 12-11 process, 12-12 sampling levels, 12-12 setting statistics level, 12-13 when to use, 12-16 E end-to-end application tracing, 1-10, 1 action and module names, 23-3 creating a service, 23-3 DBMS_APPLICATION_INFO package, 23-3 DBMS_MONITOR package, 23-3 enabling, 23-7, 23-8 gathering statistics, 23-5 multitenant environment, 23-3 overview, 23-1 performing, 23-1 purpose, 23-2 endpoint repeat counts, in histograms, 11-18 ESS See Expression Statistics Store examples EXPLAIN PLAN output, 23-19 EXECUTE_TASK procedure, 26-17 execution plans, 1-9, 3-4 about, 6-1, 6-2 adaptive, 4-14, 7-2, 7-30 displaying, 6-1 generating, 6-1 generation, 4-11 guidelines, 6-4 overview of, 6-1 parallel execution, 7-6 reading, 7-1 reference, 7-29 TKPROF, 23-14, 23-26 3 Index execution plans (continued) V$ views, 7-30 viewing with the utlxpls.sql script, 6-6 execution trees, 3-7 EXPLAIN PLAN statement access paths, 8-11, 8-13 and full partition-wise joins, 7-16 and partial partition-wise joins, 7-14 and partitioned objects, 7-10 basic steps, 6-7 examples of output, 23-19 invoking with the TKPROF program, 23-26 PLAN_TABLE table, 6-6 restrictions, 6-5 viewing the output, 6-6 expression statistics creating, 14-15 dropping, 14-17 managing, 14-13 Expression Statistics Store (ESS), 4-32 expressions, optimizer statistics, 14-13, 14-17 extended statistics, 10-4 extensions, optimizer, 10-20, 14-1 displaying, 14-16 external tables guideline for gathering optimizer statistics, 13-10 F fixed objects gathering statistics for, 12-1, 13-13 FLUSH_DATABASE_MONITORING_INFO procedure, 13-11 FLUSH_SQL_PLAN_DIRECTIVE procedure, 12-16 frequency histograms, 11-6, 11-7, 11-10 full outer joins, 9-36 full partition-wise joins, 7-16 full table scans, 8-6, 10-6, 11-2 function-based indexes, A-9 G global temporary tables, 10-10 statistics, 10-11 H hard parsing, 2-2, 3-4, 3-6, 20-7 hash clusters, 8-47 sorted, A-14 hash joins, 9-16 hash joins (continued) cost-based optimization, 9-4 how they work, 9-17 outer joins, 9-34 hash partitions, 7-10 examples of, 7-11 hashing, A-14 height-balanced histograms, 11-14 high-load SQL tuning, 12-3, 25-32 hints, optimizer, 1-10, 19-11 about, 19-12 APPEND, 10-15 FULL, A-6 GATHER_OPTIMIZER_STATISTICS, 10-18 guidelines, 19-14 INDEX_JOIN, 8-32 multiple, 19-14 NO_GATHER_OPTIMIZER_STATISTICS, 10-18 NO_INDEX, A-6 NO_MONITOR, 21-14 NO_PX_JOIN_FILTER, 9-46 OPTIMIZER_FEATURES_ENABLE, 19-8 ORDERED, 9-27 PQ_DISTRIBUTE, 7-14 PX_JOIN_FILTER, 9-46 USE_NL, 9-14, 9-33 histograms, 11-1 bucket compression, 11-6 cardinality algorithms, 11-4 creation during bulk loads, 10-16 criteria for creation, 11-4 data skew, 11-2 definition, 11-1 density, 11-5 endpoint numbers, 11-5 endpoint repeat counts, 11-18 endpoint values, 11-5 frequency, 11-6, 11-7, 11-10 height-balanced, 11-14 hybrid, 11-18, 11-20 NDV, 11-1 nonpopular values, 11-5 popular values, 11-5 purpose, 11-2 top frequency, 11-10 hybrid histograms, 11-18, 11-20 HyperLogLog algorithm, 13-24 I I/O reducing, A-4 ID column Index-4 Index ID column (continued) PLAN_TABLE table, 7-18, 7-30 In-Memory Aggregation, 5-19 In-Memory Expressions (IMEs) Expression Statistics Store (ESS), 4-32 In-Memory table scans, 8-12 controls, 8-12 example, 8-13 when chosen, 8-12 Incident Manager, accessing, 22-5 incidents, SQL, 22-2 incremental statistics, 13-30, 13-32 maintenance, 13-22 synopses, 13-24 index clustering factor, 10-6 index statistics, 10-5 index clustering factor, 10-6 INDEX_JOIN hint, 8-32 index-organized tables, A-10 indexes avoiding the use of, A-6 B-tree, 8-14, 8-15 bitmap, 4-22, 8-33, 8-35, 8-40, A-11 bitmap join, A-12 choosing columns for, A-3 composite, A-4, A-5 domain, 7-18, A-12 dropping, A-2 enforcing uniqueness, A-7 ensuring the use of, A-6 fast full scans, 8-28 full scans, 8-26 function-based, A-9 guidelines, A-1 improving selectivity, A-4 index-organized tables, A-10 join scans, 8-31 low selectivity, A-6 modifying values of, A-3 non-unique, A-7 partitioned, A-10 range scans, 8-22 re-creating, A-6 selectivity of, A-3 skip scans, 8-29 unique scans, 8-19 inner loop, in nested loops joins, 9-8 invalidation, deferred, 20-18 J join conditions, 9-1 join methods about, 9-5 hash joins, 9-17 join methods (continued) nested loops, 9-6, 9-8, 9-11 sort merge joins, 9-20 join optimizations, 9-44 Bloom filters, 9-45 partition-wise, 9-48, 9-49 join types, 9-27 antijoins, 9-40 inner joins, 9-28 outer joins, 5-30, 9-32–9-34 semijoins, 9-37 joins, 9-1 about, 9-1 antijoins, 9-4, 9-41 band, 9-29 Bloom filters, 9-45 Cartesian, 9-1, 9-25–9-27 conditions, 9-1 full outer, 9-36 hash, 9-16 how the optimizer executes, 9-3 index join scans, 8-31 innert, 9-28 join types, 9-27 nested loops, 3-7, 9-6–9-8 nested loops and cost-based optimization, 9-4 order, 19-15 outer, 5-30, 9-32 partition-wise, 9-48, 9-49 examples of full, 7-16 examples of partial, 7-14 full, 7-16 semijoins, 9-4, 9-37, 9-38 sort merge, 9-21 sort-merge and cost-based optimization, 9-4 trees, 9-2 L latches parsing and, 3-4 left deep join trees, 9-2 library cache misses, 3-4 literals, in cursors, 20-11 M manual plan capture, 28-6 materialized views comparison with bitmap join indexes, A-12 query rewrite, 5-11 modeling, data, 2-1 multiversion read consistency, 3-10 5 Index N NDV, 11-1 nested loops joins, 9-6, 9-7 controls, 9-14 cost-based optimization, 9-4 current implementation, 9-11 how they work, 9-8 nested nested loops, 9-8 original implementation, 9-13 outer joins, 9-33 USE_NL hint, 9-14 NO_INDEX hint, A-6 NO_PX_JOIN_FILTER hint, 9-46 nonpopular values, in histograms, 11-5 NOT IN subquery, 9-4 nulls in antijoins, 9-42 O OBJECT_INSTANCE column PLAN_TABLE table, 7-18, 7-30 OBJECT_NAME column PLAN_TABLE table, 7-18, 7-30 OBJECT_NODE column PLAN_TABLE table, 7-18, 7-30 OBJECT_OWNER column PLAN_TABLE table, 7-18, 7-30 OBJECT_TYPE column PLAN_TABLE table, 7-18, 7-30 OPERATION column PLAN_TABLE table, 7-18, 7-30 optimizer about, 3-6 access paths, 8-1 adaptive, 7-2 concepts, 4-1 cost-based, 4-2 definition, 4-1 environment, 3-4 estimator, 4-7 execution, 3-7 Expression Statistics Store (ESS), 4-32 extensions, 10-20, 14-16 goals, 19-9 plan generation, 4-11 purpose of, 4-2 query transformer, 4-6 row sources, 3-6, 9-5 SQL plan directives, 10-20 SQL processing, 3-1 throughput, 19-9 OPTIMIZER column PLAN_TABLE, 7-18, 7-30 optimizer hints, 1-10, 19-11 about, 19-12 APPEND, 10-15 FULL, A-6 guidelines, 19-14 INDEX_JOIN, 8-32 MONITOR, 21-14 multiple, 19-14 NO_INDEX, A-6 OPTIMIZER_FEATURES_ENABLE, 19-8 ORDERED, 9-27 PQ_DISTRIBUTE, 7-14 USE_NL, 9-33 optimizer statistics, 1, 10-1 adaptive statistics, 4-24 automatic collection, 13-1, 13-2 bulk loads, 10-15 cardinality, 11-2 collection, 12-1 column groups, 10-21, 14-1–14-3, 14-5, 14-9, 14-11 controlling, 15-1 determining staleness, 13-11 dynamic, 10-14, 10-19, 12-11, 13-14, 22-2 exporting and importing, 17-1 expressions, 14-13, 14-17 extended, 10-4, 14-1 gathering, 13-1 gathering concurrently, 13-15, 13-17, 5 gathering during bulk loads, 10-17 gathering manually, 13-6, 13-8 global statistics, 13-23 guideline for gathering external table, 13-10 histograms, 11-1 history, 16-5 how gathered, 10-13 incremental, 13-22, 13-30, 13-32 index, 10-5 locking, 15-1 managing, 16-1 manual collection, 13-7 monitoring collection, 13-20 noworkload, 13-41 Optimizer Statistics Advisor, 18-1–18-3 options for gathering, 12-1 partitioned tables, 13-10, 13-27 pending, 15-3, 15-6 pluggable databases and, 13-2 preferences, 12-3–12-6 publishing, 15-5 purging, 16-6 reporting mode, 13-43 restoring, 16-2, 16-3 retention, 16-4 retention period, 16-5 Index-6 Index optimizer statistics (continued) setting, 15-9, 15-10 SQL plan directives, 10-20, 14-1 system, 13-35, 13-37, 13-41, 13-43 temporary, 10-10 transporting, 17-2 types, 10-3 unlocking, 15-2 when gathered, 10-19 workload, 13-37 Optimizer Statistics Advisor, 18-1 about, 18-4 actions, 18-8 basic tasks, 18-11 benefits, 18-4 components, 18-5 creating a rule filter, 18-19, 18-22 creating a task, 18-14, 18-26 creating an object filter, 18-17 DBA_ADVISOR_EXECUTIONS view, 18-15 DBMS_STATS PL/SQL package, 18-9 executing a task, 18-25 filters, 18-16 findings, 18-7 generating a script, 18-32 implementing actions automatically, 18-30 listing advisor tasks, 18-15 modes of operation, 18-9 problems with script-based approach, 18-3 purpose, 18-2 recommendations, 18-7 rules, 18-5 optimizer statistics collection, 12-1 optimizer statistics collectors, 4-16 OPTIMIZER_ADAPTIVE_FEATURES initialization parameter, xxx OPTIMIZER_CAPTURE_SQL_PLAN_BASELIN ES initialization parameter, 28-4, 29-5 OPTIMIZER_FEATURES_ENABLE initialization parameter, 19-8 OPTIMIZER_USE_PENDING_STATISTICS initialization parameter, 15-5 OPTIMIZER_USE_SQL_PLAN_BASELINES initialization parameter, 28-4, 29-5 OPTIONS column PLAN_TABLE table, 7-18, 7-30 OR expansion, 5-2 ORA$AUTOTASK consumer group, 13-17 ORDERED hint, 9-27 OTHER column PLAN_TABLE table, 7-18, 7-30 OTHER_TAG column PLAN_TABLE table, 7-18, 7-30 outer joins, 9-32, 9-33 hash joins, 9-34 outer joins (continued) multiple left tables, 9-37 outer loops, in nested loops join, 9-8 P parent cursors, 20-4, 20-6 PARENT_ID column PLAN_TABLE table, 7-18, 7-30 parsing, SQL, 3-3 hard, 2-2, 20-7 hard parse, 3-4 parse trees, 3-7 soft, 2-2 soft parse, 3-4 partition maintenance operations, 13-30 PARTITION_ID column PLAN_TABLE table, 7-18, 7-30 PARTITION_START column PLAN_TABLE table, 7-18, 7-30 PARTITION_STOP column PLAN_TABLE table, 7-18, 7-30 partition-wise joins, 9-48 full, 7-16 full, and EXPLAIN PLAN output, 7-16 how they work, 9-49 partial, and EXPLAIN PLAN output, 7-14 purpose, 9-49 partitioned indexes, A-10 partitioned objects and EXPLAIN PLAN statement, 7-10 gathering incremental statistics, 13-22 gathering statistics, 13-27 guideline for gathering statistics, 13-10 partitioning examples of, 7-11 examples of composite, 7-13 hash, 7-10, A-10 range, 7-10, A-10 start and stop columns, 7-11 plan evolution purpose, 28-10 PLAN_TABLE table BYTES column, 7-18, 7-30 CARDINALITY column, 7-18, 7-30 COST column, 7-18, 7-30 creating, 6-6 displaying, 6-8 DISTRIBUTION column, 7-18, 7-30 ID column, 7-18, 7-30 OBJECT_INSTANCE column, 7-18, 7-30 OBJECT_NAME column, 7-18, 7-30 OBJECT_NODE column, 7-18, 7-30 OBJECT_OWNER column, 7-18, 7-30 OBJECT_TYPE column, 7-18, 7-30 7 Index PLAN_TABLE table (continued) OPERATION column, 7-18, 7-30 OPTIMIZER column, 7-18, 7-30 OPTIONS column, 7-18, 7-30 OTHER column, 7-18, 7-30 OTHER_TAG column, 7-18, 7-30 PARENT_ID column, 7-18, 7-30 PARTITION_ID column, 7-18, 7-30 PARTITION_START column, 7-18, 7-30 PARTITION_STOP column, 7-18, 7-30 POSITION column, 7-18, 7-30 REMARKS column, 7-18, 7-30 SEARCH_COLUMNS column, 7-18, 7-30 STATEMENT_ID column, 7-18, 7-30 TIMESTAMP column, 7-18, 7-30 pluggable databases automatic optimizer statistics collection, 13-2 manageability features, 13-2 SQL management base, 28-11 SQL Tuning Advisor, 25-1 SQL tuning sets, 24-1 popular values, in histograms, 11-5 POSITION column PLAN_TABLE table, 7-18, 7-30 PQ_DISTRIBUTE hint, 7-14 preferences, optimizer statistics, 12-4, 12-6 private SQL areas, 20-1, 20-2 parsing and, 3-3 processes dedicated server, 3-4 profiles, SQL about, 27-1 concepts, 27-2 implementing, 27-7 managing, 27-1 programming languages, 2-3 PX_JOIN_FILTER hint, 9-46 Q queries avoiding the use of indexes, A-6 ensuring the use of indexes, A-6 query transformations, 5-1 In-Memory Aggregation, 5-19 join factorization, 5-27 OR expansion, 5-2 predicate pushing, 5-9 purpose of table expansions, 5-22 query rewrite with materialized views, 5-11 star transformation, 5-14 star transformations, 5-12, 8-35 subquery unnesting, 5-10 table expansion, 5-21, 5-22 view merging, 5-3 query transformer, 4-6 R range examples of partitions, 7-11 partitions, 7-10 Real-Time Database Operations, 1-9 Real-Time SQL Monitoring, 1-9, 21-1 architecture, 21-4 use cases, 21-3 Real-World Performance cursor sharing, 20-1 recursive SQL, 3-10, 10-14, 10-32, 23-18 REMARKS column PLAN_TABLE table, 7-18, 7-30 reoptimization, automatic, 4-25, 7-2, 10-22 cardinality misestimates, 4-25 performance feedback, 4-27 statistics feedback, 4-25 restoring optimizer statistics, 16-2 result sets, SQL, 3-6, 3-9 right deep join trees, 9-2 rolling invalid cursors, 20-18 rollout strategies big bang approach, 2-4 trickle approach, 2-4 rowids table access by, 8-9 rows row sets, 3-6 row sources, 3-6, 1, 9-5 rowids used to locate, 8-9 S SAMPLE BLOCK clause, 8-10 SAMPLE clause, 8-10 sample table scans, 8-10 scans In-Memory, 8-12 sample table, 8-10 SEARCH_COLUMNS column PLAN_TABLE table, 7-18, 7-30 SELECT statement SAMPLE clause, 8-10 selectivity creating indexes, A-3 improving for an index, A-4 indexes, A-6 semijoins, 9-4, 9-37 how they work, 9-38 when the optimizer consider, 9-38 SET_EVOLVE_TASK_PARAMETER procedure, 29-11 Index-8 Index shared cursors life cycle, 20-16 shared pool, 10-21, 20-1 parsing check, 3-4 shared SQL areas, 3-4, 20-2 simple database operations, 21-3 SMB See SQL management base soft parsing, 2-2, 3-4 sort merge joins, 9-20 cost-based optimization, 9-4 how they work, 9-21 SPM Evolve Advisor, 29-5, 29-10 SPM Evolve Advisor task about, 29-11 managing, 29-10 scheduling, 29-11 SQL controls, 1 execution, 3-7 operators, 1 optimization, 4-2 parsing, 3-3 processing, 3-1 recursive, 3-10, 10-32, 12-11 result sets, 3-6, 3-9 stages of processing, 8-2, 8-14 test cases, 22-1 SQL Access Advisor, 1-7, 26-2 about, 26-1 actions, 26-6 advanced tasks, 26-24 basic tasks, 26-10 constants, 26-36 creating SQL tuning set, 26-12 creating task templates, 26-26 deleting tasks, 26-30 demo script, 26-33 evaluation mode, 26-25 EXECUTE_TASK procedure, 26-17 filter options, 26-4 input sources, 26-4 marking recommendations, 26-31 modifying recommendations, 26-32 quick tune, 26-23 recommendations, 26-7 reference, 26-33 repository, 26-8 task categories, 26-35 terminating tasks, 26-28 updating task attributes, 26-25 user interfaces, 26-8 SQL compilation, 10-19, 10-21, 10-32 SQL incidents, 22-2 SQL management base, 29-38 SQL management base (continued) changing disk space limits, 29-38 managing, 29-37 plan retention policy, 29-39 pluggable databases and, 28-11 SQL Performance Analyzer, 1-8 SQL plan baselines, 1-7, 4-31, 28-1 automatic plan capture, 28-5 configuring initial capture, 29-5 displaying, 29-14 dropping, 29-35 loading, 29-16 loading from AWR, 29-17 loading from shared SQL area, 29-19 loading using DBMS_SPM, 29-15 managing, 29-1 plan matching, 28-6 plan retention, 29-39 SQL plan capture, 28-4 configuring filters of automatic plan capture, 29-8 enabling automatic, 29-7 SQL plan directives, 4-28, 10-20, 14-1 cardinality misestimates, 10-21 creation, 10-20 managing, 12-16 SQL plan evolution managing an evolve task, 29-28 SQL plan history, 28-14 SQL plan management, 1-7 accepted plans, 4-31 automatic plan capture, 28-4, 29-7 basic tasks, 29-4 benefits, 28-2 configuring, 29-5 DBMS_SPM, 29-3 DBMS_SPM package, 29-2 filters for automatic plan capture, 29-8 introduction, 28-1 loading plans from a staging table, 29-23 loading plans from SQL tuning sets, 29-21 manual plan capture, 28-6 plan capture, 28-1 plan evolution, 28-1, 28-9, 29-28 plan history, 28-14 plan retention, 29-39 plan selection, 28-1, 28-8 plan verification, 28-9 purpose, 28-2 SPM Evolve Advisor, 29-10 SQL management base, 29-37, 29-38 SQL plan baselines, 4-31, 28-1, 29-1, 29-35 SQL plan capture, 28-4 storage architecture, 28-11 user interfaces, 29-2 9 Index SQL processing, 3-1 semantic check, 3-4 shared pool check, 3-4 stages, 3-1 syntax check, 3-3 SQL profiles, 1-6, 25-7 about, 27-1 and SQL plan baselines, 28-3 concepts, 27-2 implementing, 27-7 managing, 27-1 user interface, 27-6 SQL statement log, 28-12 SQL statements avoiding the use of indexes, A-6 ensuring the use of indexes, A-6 execution plans of, 6-1 modifying indexed data, A-3 SQL Test Case Builder, 22-1 accessing the Incident Manager, 22-5 accessing the Support Workbench, 22-6 command-line interface, 22-6 gathering diagnostic data, 22-1 graphical interface, 22-4 key concepts, 22-1 output, 22-3 running, 22-7 SQL incidents, 22-2 user interfaces, 22-4 what it captures, 22-2 SQL trace facility, 1, 23-4, 23-11 enabling, 23-13 generating output, 23-14 output, 23-28 statement truncation, 23-14 trace files, 1-10, 23-12 SQL tuning automation, 1-5 definition, 1-1 introduction, 1-1 manual tools, 1-8 proactive, 1-3 purpose, 1-1 reactive, 1-3 tools overview, 1-2, 1-5 SQL Tuning Advisor, 1-6, 25-1 about, 25-1 administering with APIs, 25-32 analyses, 25-5 architecture, 25-3 automatic, 25-22, 25-28 automatic tuning task, 25-19, 25-26 command-line interface, 25-33 configuring a SQL tuning task, 25-36 controls, 25-16 SQL Tuning Advisor (continued) creating a task, 25-34 DBMS_SQLTUNE, 25-33 displaying tuning results, 25-40 executing a task, 25-38 input sources, 25-4 monitoring tasks, 25-39 pluggable databases and, 25-1 reports, 25-27 running on-demand, 25-30, 25-31 using, 12-3, 25-32 using remotely, 25-17 SQL tuning sets creating as input for SQL Access Advisor, 26-12 loading in SQL plan baselines, 29-21 managing with APIs, 24-1 pluggable databases and, 24-1 SQL_STATEMENT column TKPROF_TABLE, 23-16 star transformations, 5-12, 5-14, 8-35 start columns in partitioning and EXPLAIN PLAN statement, 7-11 STATEMENT_ID column PLAN_TABLE table, 7-18, 7-30 STATISTICS_LEVEL initialization parameter, 16-6 statistics, optimizer, 4-2, 1 adaptive statistics, 4-24 automatic collection, 13-1, 13-2 bulk loads, 10-15 cardinality, 11-2 collection, 12-1 column group, 10-21 column groups, 14-1, 14-3, 14-6, 14-9, 14-11 controlling, 15-1 deleting, 13-43 determining staleness, 13-11 dynamic, 10-14, 10-19, 12-11, 13-14, 22-2 dynamic statistics, 10-30 exporting and importing, 17-1 expressions, 14-13 extended, 10-4, 14-1 gathering concurrently, 13-15 gathering manually, 13-6, 13-8 global statistics, 13-23 global temporary table, 10-11 guideline for external tables, 13-10 history, 16-5 how gathered, 10-13 incremental, 13-22, 13-30, 13-32 index, 10-5 locking, 15-1 managing, 16-1 Index-10 Index statistics, optimizer (continued) manual collection, 13-7 monitoring collection, 13-20 noworkload, 13-41 Optimizer Statistics Advisor, 18-1–18-3 options for gathering, 12-1 partitioned tables, 13-27 pending, 15-3, 15-6 preferences, 12-3, 12-4 publishing, 15-5 purging, 16-6 reporting mode, 13-43 restoring, 16-2, 16-3 retention, 16-4 retention period, 16-5 session-specific, 10-11 setting, 15-9 shared, 10-11 system, 10-12, 13-34, 13-35, 13-37 transporting, 17-2 types, 10-3 unlocking, 15-2 user-defined, 10-12 when gathered, 10-19 workload, 13-37 stop columns in partitioning and EXPLAIN PLAN statement, 7-11 stored outlines about, 30-1 categories, 30-4 how they work, 30-3 purpose of migrating, 30-2 subqueries NOT IN, 9-4 Support Workbench, accessing, 22-6 synopses, 13-24 SYS_AUTO_SQL_TUNING_TASK, 25-38 system statistics, 13-35, 13-37 deleting, 13-43 gathering, 13-39 noworkload, 13-41 T table clusters, A-13 access paths, 8-44 guidelines, A-1 table expansion about, 5-21 how it works, 5-22 purpose, 5-22 scenario, 5-22 table statistics, 10-4 task templates, SQL Access Advisor, 26-26 temporary tables cursor-duration, 5-19, 5-20 global, 10-10 statistics, 10-11 transaction-specific, 10-11 testing designs, 2-3 throughput optimizer goal, 19-9 TIMED_STATISTICS initialization parameter, 23-28 TIMESTAMP column PLAN_TABLE table, 7-18, 7-30 TKPROF program, 23-5, 23-11, 23-22, 23-24 editing the output SQL script, 23-16 example of output, 23-19 generating the output SQL script, 23-16 row source operations, 23-30 timing statistics, 23-18 using the EXPLAIN PLAN statement, 23-26 wait event information, 23-30 TKPROF_TABLE, 23-16 top frequency histograms, 11-10 tracing, 1, 23-1 consolidating with TRCSESS, 23-22 enabling, 23-7, 23-8 enabling statistics gathering, 23-5 generating output files, 23-11 identifying files, 23-12 transaction-specific temporary tables, 10-11 transformations, query In-Memory Aggregation, 5-19 join factorization, 5-27 predicate pushing, 5-9 purpose of table expansions, 5-22 star, 5-12 subquery unnesting, 5-10 table expansion, 5-21, 5-22 view merging, 5-3 transformer, query, 4-6 TRCSESS program, 23-22 trees, join, 9-2 trickle rollout strategy, 2-4 TRUNCATE command, 16-2 tuning logical structure, A-2 U unique index scans, 8-19 USE_NL hint, 9-14, 9-33 USER_ID column, TKPROF_TABLE, 23-16 utlxplp.sql script, 6-8 utlxpls.sql script, 6-8 11 Index V V$SESSION view, 23-5 V$SQL view, 20-33 V$SQL_CS_HISTOGRAM view, 20-33 V$SQL_CS_SELECTIVITY view, 20-33 V$SQL_CS_STATISTICS view, 20-33 V$SQL_PLAN view using to display execution plan, 6-5 V$SQL_PLAN_STATISTICS view using to display execution plan statistics, 6-5 V$SQL_PLAN_STATISTICS view (continued) V$SQL_PLAN_STATISTICS_ALL view using to display execution plan information, 6-5 V$SQLAREA view, 20-6 validating designs, 2-3 view merging, 5-3 W workloads, 2-3 Index-12
Source Exif Data:
File Type : PDF File Type Extension : pdf MIME Type : application/pdf PDF Version : 1.4 Linearized : No Author : Lance AshdownNigel BaylissMaria ColganTom KyteHermann BaerAli CakmakSunil ChakkappenImmanuel ChanDeba ChatterjeeChris ChiappaDinesh DasLeonidas GalanisWilliam EndressMarcus FallenBruce GolbusKatsumi InoueShantanu JoshiAdam KociubesKeith LakerAllison LeeSue LeeDavid McDermidColin McGregorAjit MylavarapuTed PerskyLei ShengEkrem SoylemezHong SuMurali ThiyagarajahRandy UrbanoSahil VaziraniBharath VenkatakrishnanHailing YuJohn Zimmerman Create Date : 2018:03:19 08:05:19-08:00 Modify Date : 2018:03:19 08:05:19-08:00 Creator : AH XSL Formatter V6.0 MR8 for Linux64 : 6.0.9.10681 (2013/04/23 18:22JST) Producer : Antenna House PDF Output Library 6.0.396 (Linux64) Title : SQL Tuning Guide Trapped : False Page Count : 837 Page Mode : UseOutlines Language : ENEXIF Metadata provided by EXIF.tools