HP Vertica Analytics Platform 7.0.x SQL Reference Manual
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SQL Reference Manual
HP Vertica Analytic Database
Software Version: 7.0.x
Document Release Date: 2/24/2014
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HP Vertica Analytic Database (7.0.x)
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Contents
Contents
3
SQL Overview
39
HP Vertica Support for ANSI SQL Standards
39
Support for Historical Queries
39
Joins
39
Transactions
39
System Limits
41
SQL Language Elements
43
Keywords and Reserved Words
44
Keywords
44
Reserved Words
47
Identifiers
48
Non-ASCII Characters
Literals
48
51
Number-Type Literals
51
String Literals
54
Character String Literals
54
Single Quotes in a String
54
Standard Conforming Strings and Escape Characters
55
Dollar-Quoted String Literals
56
Unicode String Literals
57
Using Standard Conforming Strings
57
VARBINARY String Literals
58
Extended String Literals
59
Standard Conforming Strings and Escape Characters
60
Identifying Strings That Are Not Standard Conforming
61
Doubled Single Quotes
62
Date/Time Literals
Time Zone Values
HP Vertica Analytic Database (7.0.x)
64
64
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Contents
Day of the Week Names
65
Month Names
65
Interval Values
66
Interval-Literal
67
Interval-Qualifier
70
Operators
72
Binary Operators
72
Boolean Operators
74
Comparison Operators
75
Data Type Coercion Operators (CAST)
75
Date/Time Operators
78
Mathematical Operators
79
NULL Operators
81
String Concatenation Operators
81
Expressions
83
Operator Precedence
83
Expression Evaluation Rules
84
Aggregate Expressions
84
CASE Expressions
85
Column References
87
Comments
88
Date/Time Expressions
88
NULL Value
91
Numeric Expressions
91
Predicates
93
BETWEEN-predicate
93
Boolean-Predicate
93
Column-Value-Predicate
94
IN-predicate
95
INTERPOLATE
95
Join-Predicate
99
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Contents
LIKE-predicate
100
NULL-predicate
104
SQL Data Types
107
Binary Data Types
109
Boolean Data Type
113
Character Data Types
114
The Difference Between NULL and NUL
Date/Time Data Types
115
117
Time Zone Abbreviations for Input
117
DATE
118
DATETIME
119
INTERVAL
120
Displaying or Omitting Interval Units in Output
120
Specifying Units on Input
122
How the Interval-Qualifier Affects Output Units
123
Specifying Precision
124
Casting with Intervals
125
Processing Signed Intervals
126
Processing Interval-Literals Without Units
127
Using INTERVALYM for INTERVAL YEAR TO MONTH
128
Operations with Intervals
129
Fractional Seconds in Interval Units
129
Interval-Literal
132
Interval-Qualifier
134
SMALLDATETIME
135
TIME
136
TIME AT TIME ZONE
137
TIMESTAMP
139
TIMESTAMP AT TIME ZONE
144
Long Data Types
146
Numeric Data Types
149
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DOUBLE PRECISION (FLOAT)
150
INTEGER
152
NUMERIC
153
Numeric Data Type Overflow
156
Data Type Coercion
158
Data Type Coercion Chart
163
SQL Functions
Aggregate Functions
167
168
APPROXIMATE_COUNT_DISTINCT
168
APPROXIMATE_COUNT_DISTINCT_OF_SYNOPSIS
170
APPROXIMATE_COUNT_DISTINCT_SYNOPSIS
173
AVG [Aggregate]
174
BIT_AND
175
BIT_OR
176
BIT_XOR
178
CORR
179
COUNT [Aggregate]
180
COVAR_POP
184
COVAR_SAMP
185
MAX [Aggregate]
185
MIN [Aggregate]
186
REGR_AVGX
187
REGR_AVGY
188
REGR_COUNT
188
REGR_INTERCEPT
189
REGR_R2
189
REGR_SLOPE
190
REGR_SXX
190
REGR_SXY
191
REGR_SYY
192
STDDEV [Aggregate]
192
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STDDEV_POP [Aggregate]
193
STDDEV_SAMP [Aggregate]
194
SUM [Aggregate]
196
SUM_FLOAT [Aggregate]
197
VAR_POP [Aggregate]
198
VAR_SAMP [Aggregate]
198
VARIANCE [Aggregate]
200
Analytic Functions
202
Analytic Function Syntax
202
Analytic Syntactic Construct
202
window_partition_clause
204
window_order_clause
204
window_frame_clause
206
Window Aggregates
209
named_windows
211
AVG [Analytic]
212
CONDITIONAL_CHANGE_EVENT [Analytic]
213
CONDITIONAL_TRUE_EVENT [Analytic]
214
COUNT [Analytic]
216
CUME_DIST [Analytic]
218
DENSE_RANK [Analytic]
219
EXPONENTIAL_MOVING_AVERAGE [Analytic]
221
FIRST_VALUE [Analytic]
224
LAG [Analytic]
227
LAST_VALUE [Analytic]
231
LEAD [Analytic]
233
MAX [Analytic]
236
MEDIAN [Analytic]
237
MIN [Analytic]
238
NTILE [Analytic]
240
PERCENT_RANK [Analytic]
241
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PERCENTILE_CONT [Analytic]
243
PERCENTILE_DISC [Analytic]
246
RANK [Analytic]
248
ROW_NUMBER [Analytic]
250
STDDEV [Analytic]
252
STDDEV_POP [Analytic]
254
STDDEV_SAMP [Analytic]
255
SUM [Analytic]
256
VAR_POP [Analytic]
258
VAR_SAMP [Analytic]
259
VARIANCE [Analytic]
260
Date/Time Functions
263
Usage
263
Daylight Savings Time Considerations
263
Date/Time Functions in Transactions
263
ADD_MONTHS
264
AGE_IN_MONTHS
265
AGE_IN_YEARS
267
CLOCK_TIMESTAMP
268
CURRENT_DATE
269
CURRENT_TIME
269
CURRENT_TIMESTAMP
270
DATE_PART
271
DATE
280
DATE_TRUNC
280
DATEDIFF
282
DAY
289
DAYOFMONTH
290
DAYOFWEEK
290
DAYOFWEEK_ISO
291
DAYOFYEAR
292
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DAYS
293
EXTRACT
294
GETDATE
301
GETUTCDATE
302
HOUR
303
ISFINITE
304
JULIAN_DAY
304
LAST_DAY
305
LOCALTIME
306
LOCALTIMESTAMP
306
MICROSECOND
307
MIDNIGHT_SECONDS
308
MINUTE
309
MONTH
309
MONTHS_BETWEEN
310
NEW_TIME
312
NEXT_DAY
314
NOW [Date/Time]
315
OVERLAPS
316
QUARTER
317
ROUND [Date/Time]
318
SECOND
319
STATEMENT_TIMESTAMP
320
SYSDATE
321
TIME_SLICE
322
TIMEOFDAY
327
TIMESTAMPADD
328
TIMESTAMPDIFF
330
TIMESTAMP_ROUND
332
TIMESTAMP_TRUNC
333
TRANSACTION_TIMESTAMP
335
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TRUNC [Date/Time]
335
WEEK
337
WEEK_ISO
337
YEAR
339
YEAR_ISO
339
Formatting Functions
341
TO_BITSTRING
341
TO_CHAR
342
TO_DATE
345
TO_HEX
346
TO_TIMESTAMP
347
TO_TIMESTAMP_TZ
349
TO_NUMBER
351
Template Patterns for Date/Time Formatting
353
Template Pattern Modifiers for Date/Time Formatting
Template Patterns for Numeric Formatting
Geospatial Package SQL Functions
355
356
357
To Install the Geospatial package:
357
Contents of the Geospatial Package
357
Using Geospatial Package SQL Functions
357
Using Built-In HP Vertica Functions for Geospatial Analysis
358
Geospatial SQL Functions
358
WGS-84 SQL Functions
358
Earth Radius, Radius of Curvature, and Bearing SQL Functions
359
ECEF Conversion SQL Functions
359
Bounding Box SQL Functions
360
Miles/Kilometer Conversion SQL Functions
360
BB_WITHIN
360
BEARING
361
CHORD_TO_ARC
362
DWITHIN
363
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Contents
ECEF_CHORD
364
ECEF_x
365
ECEF_y
366
ECEF_z
367
ISLEFT
367
KM2MILES
368
LAT_WITHIN
369
LL_WITHIN
370
LLD_WITHIN
371
LON_WITHIN
372
MILES2KM
373
RADIUS_LON
374
RADIUS_M
374
RADIUS_N
375
RADIUS_R
376
RADIUS_Ra
377
RADIUS_Rc
377
RADIUS_Rv
378
RADIUS_SI
379
RAYCROSSING
379
WGS84_a
381
WGS84_b
382
WGS84_e2
382
WGS84_f
383
WGS84_if
383
WGS84_r1
384
IP Conversion Functions
385
INET_ATON
385
INET_NTOA
386
V6_ATON
387
V6_NTOA
388
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Contents
V6_SUBNETA
389
V6_SUBNETN
390
V6_TYPE
392
Mathematical Functions
394
ABS
394
ACOS
394
ASIN
395
ATAN
396
ATAN2
396
CBRT
397
CEILING (CEIL)
398
COS
398
COT
399
DEGREES
400
DISTANCE
401
DISTANCEV
401
EXP
402
FLOOR
403
HASH
404
LN
405
LOG
406
MOD
406
MODULARHASH
408
PI
409
POWER (or POW)
409
RADIANS
410
RANDOM
411
RANDOMINT
411
ROUND
412
SIGN
414
SIN
415
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SQRT
415
TAN
416
TRUNC
416
WIDTH_BUCKET
417
NULL-handling Functions
420
COALESCE
420
IFNULL
421
ISNULL
422
NULLIF
424
NULLIFZERO
425
NVL
426
NVL2
428
ZEROIFNULL
429
Pattern Matching Functions
431
EVENT_NAME
431
MATCH_ID
433
PATTERN_ID
434
Regular Expression Functions
436
ISUTF8
436
REGEXP_COUNT
437
REGEXP_INSTR
439
REGEXP_LIKE
442
REGEXP_REPLACE
445
REGEXP_SUBSTR
448
Sequence Functions
451
NEXTVAL
451
CURRVAL
453
LAST_INSERT_ID
455
String Functions
459
ASCII
459
BIT_LENGTH
460
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SQL Reference Manual
Contents
BITCOUNT
461
BITSTRING_TO_BINARY
462
BTRIM
463
CHARACTER_LENGTH
464
CHR
465
CONCAT
466
DECODE
466
GREATEST
468
GREATESTB
469
HEX_TO_BINARY
471
HEX_TO_INTEGER
472
INET_ATON
473
INET_NTOA
474
INITCAP
475
INITCAPB
476
INSERT
477
INSTR
478
INSTRB
481
ISUTF8
482
LEAST
483
LEASTB
484
LEFT
486
LENGTH
487
LOWER
488
LOWERB
489
LPAD
490
LTRIM
490
MD5
491
OCTET_LENGTH
492
OVERLAY
493
OVERLAYB
494
HP Vertica Analytic Database (7.0.x)
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POSITION
496
POSITIONB
498
QUOTE_IDENT
498
QUOTE_LITERAL
499
REPEAT
500
REPLACE
501
RIGHT
502
RPAD
503
RTRIM
504
SPACE
505
SPLIT_PART
506
SPLIT_PARTB
507
STRPOS
508
STRPOSB
509
SUBSTR
510
SUBSTRB
511
SUBSTRING
512
TO_BITSTRING
514
TO_HEX
515
TRANSLATE
516
TRIM
516
UPPER
518
UPPERB
519
V6_ATON
519
V6_NTOA
521
V6_SUBNETA
522
V6_SUBNETN
523
V6_TYPE
524
System Information Functions
527
CURRENT_DATABASE
527
CURRENT_SCHEMA
527
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Contents
CURRENT_USER
529
DBNAME (function)
529
HAS_TABLE_PRIVILEGE
530
SESSION_USER
532
USER
533
USERNAME
533
VERSION
534
Timeseries Functions
535
TS_FIRST_VALUE
535
TS_LAST_VALUE
536
URI Encode/Decode Functions
539
URI_PERCENT_DECODE
539
URI_PERCENT_ENCODE
540
HP Vertica Meta-Functions
Alphabetical List of HP Vertica Meta-Functions
ADD_LOCATION
Storage Location Subdirectories
541
542
542
543
ADVANCE_EPOCH
544
ALTER_LOCATION_USE
545
USER Storage Location Restrictions
546
Monitoring Storage Locations
546
ALTER_LOCATION_LABEL
546
ANALYZE_CONSTRAINTS
548
Detecting Constraint Violations During a Load Process
549
Understanding Function Failures
550
ANALYZE_CORRELATIONS
556
ANALYZE_HISTOGRAM
558
ANALYZE_STATISTICS
561
ANALYZE_WORKLOAD
564
AUDIT
568
AUDIT_FLEX
572
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Contents
AUDIT_LICENSE_SIZE
574
AUDIT_LICENSE_TERM
575
BUILD_FLEXTABLE_VIEW
575
CANCEL_REBALANCE_CLUSTER
579
CANCEL_REFRESH
579
CHANGE_CURRENT_STATEMENT_RUNTIME_PRIORITY
580
CHANGE_RUNTIME_PRIORITY
581
CLEAR_CACHES
582
CLEAR_DATA_COLLECTOR
583
CLEAR_PROFILING
584
CLEAR_PROJECTION_REFRESHES
585
CLEAR_RESOURCE_REJECTIONS
586
CLEAR_OBJECT_STORAGE_POLICY
587
CLOSE_SESSION
588
Controlling Sessions
590
CLOSE_ALL_SESSIONS
592
Controlling Sessions
594
COMPUTE_FLEXTABLE_KEYS
595
COMPUTE_FLEXTABLE_KEYS_AND_BUILD_VIEW
597
CURRENT_SCHEMA
598
DATA_COLLECTOR_HELP
599
DISABLE_DUPLICATE_KEY_ERROR
601
DISABLE_ELASTIC_CLUSTER
604
DISABLE_LOCAL_SEGMENTS
605
DISABLE_PROFILING
605
DISPLAY_LICENSE
606
DO_TM_TASK
607
DROP_LICENSE
609
DROP_LOCATION
610
Retiring or Dropping a Storage Location
610
Storage Locations with Temp and Data Files
610
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DROP_PARTITION
611
DROP_STATISTICS
614
DUMP_CATALOG
616
DUMP_LOCKTABLE
617
DUMP_PARTITION_KEYS
618
DUMP_PROJECTION_PARTITION_KEYS
619
DUMP_TABLE_PARTITION_KEYS
620
ENABLE_ELASTIC_CLUSTER
622
ENABLE_LOCAL_SEGMENTS
622
ENABLE_PROFILING
623
EVALUATE_DELETE_PERFORMANCE
624
EXPORT_CATALOG
627
EXPORT_OBJECTS
628
EXPORT_STATISTICS
630
EXPORT_TABLES
632
FLUSH_DATA_COLLECTOR
633
GET_AHM_EPOCH
634
GET_AHM_TIME
635
GET_AUDIT_TIME
636
GET_COMPLIANCE_STATUS
636
GET_CURRENT_EPOCH
637
GET_DATA_COLLECTOR_POLICY
638
GET_LAST_GOOD_EPOCH
639
GET_NUM_ACCEPTED_ROWS
639
GET_NUM_REJECTED_ROWS
640
GET_PROJECTION_STATUS
640
GET_PROJECTIONS, GET_TABLE_PROJECTIONS
642
HAS_ROLE
644
IMPORT_STATISTICS
646
INTERRUPT_STATEMENT
647
INSTALL_LICENSE
650
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Contents
LAST_INSERT_ID
651
MAKE_AHM_NOW
653
MARK_DESIGN_KSAFE
655
MATERIALIZE_FLEXTABLE_COLUMNS
657
MEASURE_LOCATION_PERFORMANCE
659
MERGE_PARTITIONS
661
MOVE_PARTITIONS_TO_TABLE
662
PARTITION_PROJECTION
663
PARTITION_TABLE
665
PURGE
667
PURGE_PARTITION
667
PURGE_PROJECTION
669
PURGE_TABLE
670
REALIGN_CONTROL_NODES
672
REBALANCE_CLUSTER
672
REENABLE_DUPLICATE_KEY_ERROR
674
REFRESH
674
RELEASE_ALL_JVM_MEMORY
677
RELEASE_JVM_MEMORY
678
RELOAD_SPREAD
678
RESET_LOAD_BALANCE_POLICY
680
RESTORE_LOCATION
680
Effects of Restoring a Previously Retired Location
681
Monitoring Storage Locations
681
RESTORE_FLEXTABLE_DEFAULT_KEYS_TABLE_AND_VIEW
681
RETIRE_LOCATION
683
Effects of Retiring a Storage Location
683
Monitoring Storage Locations
684
SET_AHM_EPOCH
684
SET_AHM_TIME
686
SET_AUDIT_TIME
688
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Contents
SET_CONTROL_SET_SIZE
689
SET_DATA_COLLECTOR_POLICY
690
SET_DATA_COLLECTOR_TIME_POLICY
692
SET_LOAD_BALANCE_POLICY
695
SET_LOCATION_PERFORMANCE
696
SET_SCALING_FACTOR
697
SET_OBJECT_STORAGE_POLICY
697
New Storage Policy
698
Existing Storage Policy
698
Forcing Existing Data Storage to a New Storage Location
698
SHUTDOWN
699
SLEEP
701
START_REBALANCE_CLUSTER
702
START_REFRESH
703
SYNCH_WITH_HCATALOG_SCHEMA
704
Catalog Management Functions
707
DROP_LICENSE
707
DUMP_CATALOG
707
EXPORT_CATALOG
708
EXPORT_OBJECTS
709
INSTALL_LICENSE
711
MARK_DESIGN_KSAFE
711
SYNCH_WITH_HCATALOG_SCHEMA
714
Client Connection Management Functions
715
SET_LOAD_BALANCE_POLICY
715
RESET_LOAD_BALANCE_POLICY
716
Cluster Management Functions
717
SET_CONTROL_SET_SIZE
717
REALIGN_CONTROL_NODES
718
RELOAD_SPREAD
719
REBALANCE_CLUSTER
720
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Cluster Scaling Functions
722
CANCEL_REBALANCE_CLUSTER
722
DISABLE_ELASTIC_CLUSTER
722
DISABLE_LOCAL_SEGMENTS
723
ENABLE_ELASTIC_CLUSTER
723
ENABLE_LOCAL_SEGMENTS
724
REBALANCE_CLUSTER
725
SET_SCALING_FACTOR
726
START_REBALANCE_CLUSTER
727
Constraint Management Functions
729
ANALYZE_CONSTRAINTS
729
Detecting Constraint Violations During a Load Process
730
Understanding Function Failures
731
ANALYZE_CORRELATIONS
737
DISABLE_DUPLICATE_KEY_ERROR
739
LAST_INSERT_ID
742
REENABLE_DUPLICATE_KEY_ERROR
744
Data Collector Functions
746
Related Topics
746
CLEAR_DATA_COLLECTOR
746
DATA_COLLECTOR_HELP
747
FLUSH_DATA_COLLECTOR
749
GET_DATA_COLLECTOR_POLICY
750
SET_DATA_COLLECTOR_POLICY
751
SET_DATA_COLLECTOR_TIME_POLICY
753
Database Designer Functions
756
DESIGNER_ADD_DESIGN_QUERIES
757
DESIGNER_ADD_DESIGN_QUERIES_FROM_RESULTS
760
DESIGNER_ADD_DESIGN_QUERY
762
DESIGNER_ADD_DESIGN_TABLES
763
DESIGNER_CANCEL_POPULATE_DESIGN
765
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DESIGNER_CREATE_DESIGN
766
DESIGNER_DESIGN_PROJECTION_ENCODINGS
768
DESIGNER_DROP_ALL_DESIGNS
769
DESIGNER_DROP_DESIGN
770
DESIGNER_OUTPUT_ALL_DESIGN_PROJECTIONS
771
DESIGNER_OUTPUT_DEPLOYMENT_SCRIPT
773
DESIGNER_RESET_DESIGN
774
DESIGNER_RUN_POPULATE_DESIGN_AND_DEPLOY
775
DESIGNER_SET_ANALYZE_CORRELATIONS_MODE
777
DESIGNER_SET_DESIGN_KSAFETY
779
DESIGNER_SET_DESIGN_TYPE
781
DESIGNER_SET_OPTIMIZATION_OBJECTIVE
782
DESIGNER_SET_PROPOSE_UNSEGMENTED_PROJECTIONS
784
DESIGNER_WAIT_FOR_DESIGN
785
Database Management Functions
787
CLEAR_RESOURCE_REJECTIONS
787
DUMP_LOCKTABLE
787
DUMP_PARTITION_KEYS
788
EXPORT_TABLES
790
HAS_ROLE
791
SET_CONFIG_PARAMETER
793
SHUTDOWN
794
Epoch Management Functions
797
ADVANCE_EPOCH
797
GET_AHM_EPOCH
797
GET_AHM_TIME
798
GET_CURRENT_EPOCH
799
GET_LAST_GOOD_EPOCH
799
MAKE_AHM_NOW
800
SET_AHM_EPOCH
801
SET_AHM_TIME
803
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Contents
Flex Table Functions
805
COMPUTE_FLEXTABLE_KEYS
805
COMPUTE_FLEXTABLE_KEYS
807
COMPUTE_FLEXTABLE_KEYS_AND_BUILD_VIEW
809
MATERIALIZE_FLEXTABLE_COLUMNS
810
RESTORE_FLEXTABLE_DEFAULT_KEYS_TABLE_AND_VIEW
812
License Management Functions
814
AUDIT
814
AUDIT_FLEX
818
AUDIT_LICENSE_SIZE
820
AUDIT_LICENSE_TERM
821
GET_AUDIT_TIME
821
GET_COMPLIANCE_STATUS
822
DISPLAY_LICENSE
823
SET_AUDIT_TIME
824
Partition Management Functions
825
DROP_PARTITION
825
DUMP_PROJECTION_PARTITION_KEYS
828
DUMP_TABLE_PARTITION_KEYS
829
MERGE_PARTITIONS
830
MOVE_PARTITIONS_TO_TABLE
832
PARTITION_PROJECTION
833
PARTITION_TABLE
835
PURGE_PARTITION
836
Profiling Functions
839
CLEAR_PROFILING
839
DISABLE_PROFILING
839
ENABLE_PROFILING
840
Projection Management Functions
842
EVALUATE_DELETE_PERFORMANCE
842
GET_PROJECTION_STATUS
845
HP Vertica Analytic Database (7.0.x)
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Contents
GET_PROJECTIONS, GET_TABLE_PROJECTIONS
846
REFRESH
848
START_REFRESH
851
Purge Functions
853
PURGE
853
PURGE_PARTITION
854
PURGE_PROJECTION
855
PURGE_TABLE
856
Session Management Functions
859
CANCEL_REFRESH
859
CLOSE_ALL_SESSIONS
860
Controlling Sessions
862
CLOSE_SESSION
Controlling Sessions
863
866
GET_NUM_ACCEPTED_ROWS
867
GET_NUM_REJECTED_ROWS
868
INTERRUPT_STATEMENT
868
RELEASE_ALL_JVM_MEMORY
872
RELEASE_JVM_MEMORY
873
Statistic Management Functions
874
ANALYZE_HISTOGRAM
874
ANALYZE_STATISTICS
877
DROP_STATISTICS
881
EXPORT_STATISTICS
883
IMPORT_STATISTICS
885
Storage Management Functions
887
ADD_LOCATION
Storage Location Subdirectories
ALTER_LOCATION_USE
887
888
889
USER Storage Location Restrictions
890
Monitoring Storage Locations
890
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Contents
ALTER_LOCATION_LABEL
890
CLEAR_CACHES
892
CLEAR_OBJECT_STORAGE_POLICY
893
DROP_LOCATION
894
Retiring or Dropping a Storage Location
894
Storage Locations with Temp and Data Files
894
MEASURE_LOCATION_PERFORMANCE
895
RESTORE_LOCATION
897
Effects of Restoring a Previously Retired Location
897
Monitoring Storage Locations
897
RETIRE_LOCATION
898
Effects of Retiring a Storage Location
898
Monitoring Storage Locations
899
SET_LOCATION_PERFORMANCE
899
SET_OBJECT_STORAGE_POLICY
901
New Storage Policy
901
Existing Storage Policy
901
Forcing Existing Data Storage to a New Storage Location
902
Tuple Mover Functions
903
DO_TM_TASK
903
Workload Management Functions
906
ANALYZE_WORKLOAD
906
CHANGE_CURRENT_STATEMENT_RUNTIME_PRIORITY
909
CHANGE_RUNTIME_PRIORITY
910
CLEAR_CACHES
911
SLEEP
912
SQL Statements
915
ALTER DATABASE
915
ALTER FAULT GROUP
916
ALTER FUNCTION
917
ALTER LIBRARY
920
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Contents
ALTER NODE
921
ALTER NETWORK INTERFACE
922
ALTER PROJECTION RENAME
922
ALTER PROFILE
923
ALTER PROFILE RENAME
926
ALTER RESOURCE POOL
927
ALTER ROLE RENAME
938
ALTER SCHEMA
938
ALTER SEQUENCE
940
ALTER SUBNET
943
ALTER TABLE
944
Table Behavior After Alteration
955
Changing a Data Type for a Column Specified in a SEGMENTED BY Clause
955
Locked Tables
956
Adding and Changing Constraints on Columns Using ALTER TABLE
956
Adding and Dropping NOT NULL Column Constraints
957
Table-Constraint
958
Specifying Primary and Foreign Keys
959
Adding Constraints to Views
959
ALTER USER
Database Account Changes Users Can Make
959
959
ALTER VIEW
963
BEGIN
963
COMMENT ON Statements
966
COMMENT ON COLUMN
966
COMMENT ON CONSTRAINT
968
COMMENT ON FUNCTION
969
COMMENT ON LIBRARY
971
COMMENT ON NODE
972
COMMENT ON PROJECTION
974
COMMENT ON SCHEMA
975
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Contents
COMMENT ON SEQUENCE
976
COMMENT ON TABLE
978
COMMENT ON TRANSFORM FUNCTION
979
COMMENT ON VIEW
981
COMMIT
982
CONNECT
983
Connection Details
COPY
984
984
COPY Option Summary
995
Setting vsql Variables
997
Using Compressed Data and Named Pipes
998
COPY LOCAL
999
How Copy Local Works
1000
Viewing Copy Local Operations in a Query Plan
1000
COPY FROM VERTICA
Source and Destination Column Mapping
1000
1003
CREATE EXTERNAL TABLE AS COPY
1004
CREATE FAULT GROUP
1006
CREATE FLEX TABLE
1008
Unsupported CREATE Options for Flex Tables
1009
Default Flex Table and Keys Table Projections
1009
CREATE FLEX EXTERNAL TABLE AS COPY
1010
CREATE FUNCTION Statements
1013
About Creating User Defined Transform Functions (UDTFs)
1014
CREATE AGGREGATE FUNCTION
1014
CREATE ANALYTIC FUNCTION
1017
CREATE FILTER
1018
CREATE FUNCTION (SQL Functions)
1020
CREATE FUNCTION (UDF)
1024
CREATE PARSER
1026
CREATE SOURCE
1029
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Contents
CREATE TRANSFORM FUNCTION
UDTF Query Restrictions
1031
1032
CREATE HCATALOG SCHEMA
1033
CREATE LIBRARY
1035
CREATE LOCAL TEMPORARY VIEW
1037
CREATE NETWORK INTERFACE
1040
CREATE PROCEDURE
1040
CREATE PROFILE
1042
CREATE PROJECTION
1045
Projections and Superprojections
1051
Checking Column Constraints
1051
Updating the Projection Using Refresh
1051
Creating Unsegmented Projections with the ALL NODES Option
1052
Encoding-Type
1053
ENCODING AUTO (default)
1053
ENCODING BLOCK_DICT
1053
ENCODING BLOCKDICT_COMP
1053
ENCODING COMMONDELTA_COMP
1054
ENCODING DELTARANGE_COMP
1054
ENCODING DELTAVAL
1054
ENCODING GCDDELTA
1054
ENCODING RLE
1055
ENCODING NONE
1055
Hash-Segmentation-Clause
1055
CREATE RESOURCE POOL
1057
Permissions
1065
Built-In Pools
1067
Built-In Pool
1067
Settings
1067
Upgrade From Earlier Versions of HP Vertica
1069
Built-In Pool Configuration
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Contents
GENERAL
1070
SYSQUERY
1071
SYSDATA
1072
WOSDATA
1073
TM
1073
REFRESH
1074
RECOVERY
1074
DBD
1075
JVM
1075
CREATE ROLE
1076
CREATE SCHEMA
1076
CREATE SEQUENCE
1078
Incrementing and Obtaining Sequence Values
1080
Removing a Sequence
1081
CREATE SUBNET
1083
CREATE TABLE
1084
Automatic Projection Creation
1090
Partition Clauses
1090
Column-Definition (table)
1095
Column-Name-List (table)
1096
Column-Constraint
1099
Table-Constraint
1106
Specifying Primary and Foreign Keys
1107
Adding Constraints to Views
1107
Hash-Segmentation-Clause (table)
1108
CREATE TEMPORARY TABLE
1109
Column-Definition (temp table)
1115
Column-Name-List (temp table)
1117
Hash-Segmentation-Clause (temp table)
1119
CREATE USER
1121
CREATE VIEW
1125
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Contents
Transforming a SELECT Query to Use a View
1126
Dropping a View
1127
Renaming a View
1127
DELETE
Using the DELETE Statement
1128
1129
DISCONNECT
1130
DROP AGGREGATE FUNCTION
1131
DROP FAULT GROUP
1133
DROP FUNCTION
1134
DROP SOURCE
1135
DROP FILTER
1137
DROP PARSER
1138
DROP LIBRARY
1139
DROP NETWORK INTERFACE
1140
DROP PROCEDURE
1141
DROP PROFILE
1142
DROP PROJECTION
1143
DROP RESOURCE POOL
1144
Transferring Resource Requests
1145
DROP ROLE
1145
DROP SCHEMA
1146
DROP SEQUENCE
1147
DROP SUBNET
1149
DROP TABLE
1149
DROP TRANSFORM FUNCTION
1151
DROP USER
1152
DROP VIEW
1154
END
1154
EXPLAIN
1155
EXPORT TO VERTICA
1156
Source and Destination Column Mapping
HP Vertica Analytic Database (7.0.x)
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Contents
GRANT Statements
1160
GRANT (Database)
1160
GRANT (Procedure)
1161
GRANT (Resource Pool)
1163
GRANT (Role)
1163
Creating Roles
1164
Activating a Role
1164
Granting One Role To Another
1165
Checking for Circular References
1165
Granting Administrative Privileges
1165
GRANT (Schema)
1166
GRANT (Sequence)
1167
GRANT (Storage Location)
1168
GRANT (Table)
1170
GRANT (User Defined Extension)
1172
GRANT (View)
1174
INSERT
1176
MERGE
1178
Using Named Sequences
1183
Improving MERGE Performance
1183
PROFILE
1183
Real-Time Profiling Example
1184
How to Use the Linux watch Command
1185
How to Find Out Which Counters are Available
1185
RELEASE SAVEPOINT
1186
REVOKE Statements
1188
REVOKE (Database)
1188
REVOKE (Procedure)
1189
REVOKE (Resource Pool)
1191
REVOKE (Role)
1191
REVOKE (Schema)
1192
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Contents
REVOKE (Sequence)
1194
REVOKE (Storage Location)
1195
REVOKE (Table)
1196
REVOKE (User Defined Extension)
1198
REVOKE (View)
1200
ROLLBACK
1201
ROLLBACK TO SAVEPOINT
1202
SAVEPOINT
1203
SELECT
1205
Parameters
1206
EXCEPT Clause
1208
FROM Clause
1212
Table-Reference
1212
Table-Primary
1213
Joined-Table
1213
GROUP BY Clause
1213
HAVING Clause
1215
INTERSECT Clause
1216
INTO Clause
1220
LIMIT Clause
1221
MATCH Clause
1222
Pattern Semantic Evaluation
1225
MINUS Clause
1227
OFFSET Clause
1227
ORDER BY Clause
1228
TIMESERIES Clause
1230
UNION Clause
1233
WHERE Clause
1236
WINDOW Clause
1238
WITH Clause
1238
SET DATESTYLE
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Contents
SET ESCAPE_STRING_WARNING
1241
SET INTERVALSTYLE
1242
Output Intervals with Units
1243
Output Intervals Without Units
1243
Displaying the Current Interval OUTPUT Style
1243
SET LOCALE
1244
SET ROLE
1248
SET SEARCH_PATH
1249
SET SESSION AUTOCOMMIT
1251
SET SESSION CHARACTERISTICS
1251
Understanding READ COMMITTED and Snapshot Isolation
1252
Using SERIALIZABLE Transaction Isolation
1253
Setting READ ONLY Transaction Mode
1253
SET SESSION MEMORYCAP
1253
SET SESSION RESOURCE_POOL
1254
SET SESSION RUNTIMECAP
1255
SET SESSION TEMPSPACECAP
1257
SET STANDARD_CONFORMING_STRINGS
1259
SET TIME ZONE
1260
Time Zone Names for Setting TIME ZONE
SHOW
1261
1263
How to Display All Current Run-Time Parameter Settings
1264
Displaying Current Search Path Settings
1265
Displaying the Transaction Isolation Level
1265
START TRANSACTION
1265
TRUNCATE TABLE
1269
UPDATE
1270
HP Vertica System Tables
V_CATALOG Schema
1275
1276
ALL_TABLES
1276
CLUSTER_LAYOUT
1277
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Contents
COLUMNS
1278
COMMENTS
1281
CONSTRAINT_COLUMNS
1282
DATABASES
1283
DUAL
1284
ELASTIC_CLUSTER
1285
EPOCHS
1286
FAULT_GROUPS
1287
FOREIGN_KEYS
1288
GRANTS
1290
HCATALOG_COLUMNS
1294
HCATALOG_SCHEMATA
1298
HCATALOG_TABLES
1300
HCATALOG_TABLE_LIST
1302
LARGE_CLUSTER_CONFIGURATION_STATUS
1304
LICENSE_AUDITS
1304
LICENSES
1305
MATERIALIZE_FLEXTABLE_COLUMNS_RESULTS
1306
NODES
1307
ODBC_COLUMNS
1308
PASSWORDS
1310
PRIMARY_KEYS
1310
PROFILE_PARAMETERS
1311
PROFILES
1312
PROJECTION_CHECKPOINT_EPOCHS
1313
PROJECTION_COLUMNS
1314
PROJECTION_DELETE_CONCERNS
1321
PROJECTIONS
1321
RESOURCE_POOL_DEFAULTS
1324
RESOURCE_POOLS
1325
ROLES
1331
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Contents
SCHEMATA
1332
SEQUENCES
1333
STORAGE_LOCATIONS
1336
SYSTEM_COLUMNS
1340
SYSTEM_TABLES
1341
TABLE_CONSTRAINTS
1342
TABLES
1344
TYPES
1346
USER_AUDITS
1348
USER_FUNCTIONS
1349
USER_PROCEDURES
1350
USER_TRANSFORMS
1351
USERS
1352
VIEW_COLUMNS
1354
VIEWS
1356
V_MONITOR Schema
1358
ACTIVE_EVENTS
1358
COLUMN_STORAGE
1360
CONFIGURATION_CHANGES
1363
CONFIGURATION_PARAMETERS
1365
CPU_USAGE
1366
CRITICAL_HOSTS
1366
CRITICAL_NODES
1367
CURRENT_SESSION
1367
DATA_COLLECTOR
1373
DATABASE_BACKUPS
1378
DATABASE_CONNECTIONS
1379
DATABASE_SNAPSHOTS
1380
DELETE_VECTORS
1381
DEPLOY_STATUS
1382
DEPLOYMENT_PROJECTION_STATEMENTS
1383
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Contents
DEPLOYMENT_PROJECTIONS
1385
DESIGN_QUERIES
1387
DESIGN_STATUS
1389
DESIGN_TABLES
1391
DESIGNS
1392
DISK_RESOURCE_REJECTIONS
1394
DISK_STORAGE
1395
ERROR_MESSAGES
1400
EVENT_CONFIGURATIONS
1402
EXECUTION_ENGINE_PROFILES
1403
HOST_RESOURCES
1415
IO_USAGE
1418
LOAD_STREAMS
1418
LOCK_USAGE
1420
LOCKS
1423
LOGIN_FAILURES
1427
MEMORY_USAGE
1429
MONITORING_EVENTS
1430
NETWORK_INTERFACES
1432
NETWORK_USAGE
1434
NODE_RESOURCES
1434
NODE_STATES
1436
OUTPUT_DEPLOYMENT_STATUS
1437
OUTPUT_EVENT_HISTORY
1439
PARTITION_REORGANIZE_ERRORS
1442
PARTITION_STATUS
1443
PARTITIONS
1444
PROCESS_SIGNALS
1445
PROJECTION_RECOVERIES
1446
PROJECTION_REFRESHES
1449
PROJECTION_STORAGE
1453
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Contents
PROJECTION_USAGE
1456
QUERY_EVENTS
1458
QUERY_METRICS
1463
QUERY_PLAN_PROFILES
1464
QUERY_PROFILES
1466
QUERY_REQUESTS
1469
REBALANCE_PROJECTION_STATUS
1472
REBALANCE_TABLE_STATUS
1474
RECOVERY_STATUS
1476
RESOURCE_ACQUISITIONS
1477
RESOURCE_POOL_STATUS
1480
RESOURCE_QUEUES
1485
RESOURCE_REJECTION_DETAILS
1485
RESOURCE_REJECTIONS
1487
RESOURCE_USAGE
1490
SESSION_PROFILES
1492
SESSIONS
1494
STORAGE_CONTAINERS
1498
STORAGE_POLICIES
1502
STORAGE_TIERS
1503
STORAGE_USAGE
1504
STRATA
1506
STRATA_STRUCTURES
1508
SYSTEM
1511
SYSTEM_RESOURCE_USAGE
1512
SYSTEM_SERVICES
1514
SYSTEM_SESSIONS
1516
TRANSACTIONS
1519
TUNING_RECOMMENDATIONS
1521
TUPLE_MOVER_OPERATIONS
1523
UDX_FENCED_PROCESSES
1526
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Contents
USER_LIBRARIES
1527
USER_LIBRARY_MANIFEST
1528
USER_SESSIONS
1529
WOS_CONTAINER_STORAGE
1532
Appendix: Compatibility with Other RDBMS
Data Type Mappings Between HP Vertica and Oracle
We appreciate your feedback!
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SQL Overview
SQL Overview
An abbreviation for Structured Query Language, SQL is a widely-used, industry standard data
definition and data manipulation language for relational databases.
Note: In HP Vertica, use a semicolon to end a statement or to combine multiple statements on
one line.
HP Vertica Support for ANSI SQL Standards
HP Vertica SQL supports a subset of ANSI SQL-99.
See BNF Grammar for SQL-99
Support for Historical Queries
Unlike most databases, the DELETE command in HP Vertica does not delete data; it marks
records as deleted. The UPDATE command performs an INSERT and a DELETE. This behavior is
necessary for historical queries. See Historical (Snapshot) Queries in the Programmer's Guide.
Joins
HP Vertica supports typical data warehousing query joins. For details, see Joins in the
Programmer's Guide.
HP Vertica also provides the INTERPOLATE predicate, which allows for a special type of join. The
event series join is an HP Vertica SQL extension that lets you analyze two event series when their
measurement intervals don’t align precisely—such as when timestamps don't match. These joins
provide a natural and efficient way to query misaligned event data directly, rather than having to
normalize the series to the same measurement interval. See Event Series Joins in the
Programmer's Guide for details.
Transactions
Session-scoped isolation levels determine transaction characteristics for transactions within a
specific user session. You set them through the SET SESSION CHARACTERISTICS command.
Specifically, they determine what data a transaction can access when other transactions are
running concurrently. See Transactions in the Concepts Guide.
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SQL Overview
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SQL Reference Manual
System Limits
System Limits
This section describes the system limits on the size and number of objects in an HP Vertica
database. In most cases, computer memory and disk drive are the limiting factors.
Item
Limit
Number of nodes
Maximum 128 (without HP Vertica assistance).
Database size
Approximates the number of files times the file size on a platform, depending
on the maximum disk configuration.
Table size
2^64 rows per node, or 2^63 bytes per column, whichever is smaller.
Row size
32 MB. The row size is approximately the sum of its maximum column
sizes, where, for example, a VARCHAR(80) has a maximum size of 80
bytes.
Key size
Limited only by row size
Number of
tables/projections
per database
Limited by physical RAM, as the catalog must fit in memory.
Number of
concurrent
connections per
node
Default of 50, limited by physical RAM (or threads per process), typically
1024.
Number of
concurrent
connections per
cluster
Limited by physical RAM of a single node (or threads per process), typically
1024.
Number of
columns per table
1600.
Number of rows
per load
2^63.
Number of
partitions
1024.
l
While HP Vertica supports a maximum of 1024 partitions, few, if any,
organizations will need to approach that maximum. Fewer partitions are
likely to meet your business needs, while also ensuring maximum
performance. Many customers, for example, partition their data by month,
bringing their partition count to 12. HP Vertica recommends you keep the
number of partitions between 10 and 20 to achieve excellent
performance.
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System Limits
Item
Limit
Length for a fixed- 65000 bytes.
length column
Length for a
variable-length
column
65000 bytes.
Length of basic
names
128 bytes. Basic names include table names, column names, etc.
Query length
No limit.
Depth of nesting
subqueries
Unlimited in FROM, WHERE, or HAVING clause.
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SQL Reference Manual
SQL Language Elements
SQL Language Elements
This chapter presents detailed descriptions of the language elements and conventions of HP
Vertica SQL.
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SQL Language Elements
Keywords and Reserved Words
Keywords are words that have a specific meaning in the SQL language. Although SQL is not casesensitive with respect to keywords, they are generally shown in uppercase letters throughout this
documentation for readability purposes.
Some SQL keywords are also reserved words that cannot be used in an identifier unless enclosed
in double quote (") characters. Some unreserved keywords can be used in statements by preceding
them with AS. For example, SOURCE is a keyword, but is not reserved, and you can use it as
follows:
VMART=> select my_node AS SOURCE from nodes;
Keywords
Keyword are words that are specially handled by the grammar. Every SQL statement contains one
or more keywords.
Begins
with
Keyword
A
ABORT, ABSOLUTE, ACCESS, ACCESRANK, ACCOUNT, ACTION, ADD,
ADMIN, AFTER, AGGREGATE, ALL, ALSO, ALTER, ANALYSE, ANALYZE, AND,
ANY, ARRAY, AS, ASC, ASSERTION, ASSIGNMENT, AT, AUTHORIZATION,
AUTO, AUTO_INCREMENT, AVAILABLE
B
BACKWARD, BEFORE, BEGIN, BETWEEN, BIGINT, BINARY, BIT, BLOCK_
DICT, BLOCKDICT_COMP, BOOLEAN, BOTH, BY, BYTEA, BZIP
C
CACHE, CALLED, CASCADE, CASE, CAST, CATALOGPATH, CHAIN, CHAR,
CHAR_LENGTH, CHARACTER, CHARACTER_LENGTH, CHARACTERISTICS,
CHARACTERS, CHECK, CHECKPOINT, CLASS, CLOSE, CLUSTER, COLLATE,
COLUMN, COLUMNS_COUNT, COMMENT, COMMIT, COMMITTED,
COMMONDELTA_COMP, CONNECT, CONSTRAINT, CONSTRAINTS, COPY,
CORRELATION, CREATE, CREATEDB, CREATEUSER, CROSS, CSV,
CURRENT, CURRENT_DATABASE, CURRENT_DATE, CURRENT_SCHEMA,
CURRENT_TIME, CURRENT_TIMESTAMP, CURRENT_USER, CURSOR,
CYCLE
D
DATA, DATABASE, DATAPATH, DATE, DATEDIFF, DATETIME, DAY,
DEALLOCATE, DEC, DECIMAL, DECLARE, DECODE, DEFAULT, DEFAULTS,
DEFERRABLE, DEFERRED, DEFINE, DEFINER, DELETE, DELIMITER,
DELIMITERS, DELTARANGE_COMP, DELTARANGE_COMP_SP, DELTAVAL,
DESC, DETERMINES, DIRECT, DIRECTCOLS, DIRECTGROUPED,
DIRECTPROJ, DISABLE, DISCONNECT, DISTINCT, DISTVALINDEX, DO,
DOMAIN, DOUBLE, DROP, DURABLE
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SQL Language Elements
Begins
with
Keyword
E
EACH, ELSE, ENABLE, ENABLED, ENCLOSED, ENCODED, ENCODING,
ENCRYPTED, END, ENFORCELENGTH, EPHEMERAL, EPOCH, ERROR,
ESCAPE, EVENT, EVENTS, EXCEPT, EXCEPTIONS, EXCLUDE, EXCLUDING,
EXCLUSIVE, EXECUTE, EXISTS, EXPIRE, EXPLAIN, EXPORT, EXTERNAL,
EXTRACT
F
FAILED_LOGIN_ATTEMPTS, FALSE, FETCH, FILLER, FIRST, FLOAT,
FOLLOWING, FOR, FORCE, FOREIGN, FORMAT, FORWARD, FREEZE, FROM,
FULL, FUNCTION
G
GCDDELTA, GLOBAL, GRANT, GROUP, GROUPED, GZIP
H
HANDLER, HASH, HAVING, HOLD, HOSTNAME, HOUR, HOURS
I
IDENTIFIED, IDENTITY, IF, IGNORE, ILIKE, ILIKEB, IMMEDIATE, IMMUTABLE,
IMPLICIT, IN, INCLUDING, INCREMENT, INDEX, INHERITS, INITIALLY, INNER,
INOUT, INPUT, INSENSITIVE, INSERT, INSTEAD, INT, INTEGER,
INTERPOLATE, INTERSECT, INTERVAL, INTERVALYM, INTO, INVOKER, IS,
ISNULL, ISOLATION
J
JOIN
K
KEY, KSAFE
L
LANCOMPILER, LANGUAGE, LARGE, LAST, LATEST, LEADING, LEFT, LESS,
LEVEL, LIBRARY, LIKE, LIKEB, LIMIT, LISTEN, LOAD, LOCAL, LOCALTIME,
LOCALTIMESTAMP, LOCATION, LOCK
M
MANAGED, MATCH, MAXCONCURRENCY, MAXMEMORYSIZE, MAXVALUE,
MEMORYCAP, MEMORYSIZE, MERGE, MERGEOUT, MICROSECONDS,
MILLISECONDS, MINUTE, MINUTES, MINVALUE, MODE, MONEY, MONTH,
MOVE, MOVEOUT
N
NAME, NATIONAL, NATIVE, NATURAL, NCHAR, NEW, NEXT, NO,
NOCREATEDB, NOCREATEUSER, NODE, NODES, NONE, NOT, NOTHING,
NOTIFY, NOTNULL, NOWAIT, NULL, NULLCOLS, NULLS, NULLSEQUAL,
NULLIF, NUMBER, NUMERIC
O
OBJECT, OCTETS, OF, OFF, OFFSET, OIDS, OLD, ON, ONLY, OPERATOR,
OPTION, OR, ORDER, OTHERS, OUT, OUTER, OVER, OVERLAPS, OVERLAY,
OWNER
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SQL Language Elements
Begins
with
Keyword
P
PARTIAL, PARTITION, PASSWORD, PASSWORD_GRACE_TIME, PASSWORD_
LIFE_TIME, PASSWORD_LOCK_TIME, PASSWORD_MAX_LENGTH,
PASSWORD_MIN_DIGITS, PASSWORD_MIN_LENGTH, PASSWORD_MIN_
LETTERS, PASSWORD_MIN_LOWERCASE_LETTERS, PASSWORD_MIN_
SYMBOLS, PASSWORD_MIN_UPPERCASE_LETTERS,PASSWORD_REUSE_
MAX, PASSWORD_REUSE_TIME, PATTERN, PERCENT, PERMANENT,
PINNED, PLACING, PLANNEDCONCURRENCY, POOL, POSITION,
PRECEDING, PRECISION, PREPARE, PRESERVE, PREVIOUS, PRIMARY,
PRIOR, PRIORITY, PRIVILEGES, PROCEDURAL, PROCEDURE, PROFILE,
PROJECTION
Q
QUEUETIMEOUT, QUOTE
R
RANGE, RAW, READ, REAL, RECHECK, RECORD, RECOVER, REFERENCES,
REFRESH, REINDEX, REJECTED, REJECTMAX, RELATIVE, RELEASE,
RENAME, REPEATABLE, REPLACE, RESET, RESOURCE, RESTART,
RESTRICT, RESULTS, RETURN, RETURNREJECTED, REVOKE, RIGHT, RLE,
ROLE, ROLES, ROLLBACK, ROW, ROWS, RULE, RUNTIMECAP
S
SAMPLE, SAVEPOINT, SCHEMA, SCROLL, SECOND, SECONDS, SECURITY,
SEGMENTED, SELECT, SEQUENCE, SERIALIZABLE, SESSION, SESSION_
USER, SET, SETOF, SHARE, SHOW, SIMILAR, SIMPLE, SINGLEINITIATOR,
SITE, SITES, SKIP, SMALLDATETIME, SMALLINT, SOME, SOURCE, SPLIT,
STABLE, START, STATEMENT, STATISTICS, STDERR, STDIN, STDOUT,
STORAGE, STREAM, STRENGTH, STRICT, SUBSTRING, SYSDATE, SYSID,
SYSTEM
T
TABLE, TABLESPACE, TEMP, TEMPLATE, TEMPORARY, TEMPSPACECAP,
TERMINATOR, THAN, THEN, TIES, TIME, TIMESERIES, TIMESTAMP,
TIMESTAMPADD, TIMESTAMPDIFF, TIMESTAMPTZ, TIMETZ, TIMEZONE,
TINYINT, TO, TOAST, TRAILING, TRANSACTION, TRANSFORM, TREAT,
TRICKLE, TRIGGER, TRIM, TRUE, TRUNCATE, TRUSTED, TUNING, TYPE
U
UNBOUNDED, UNCOMMITTED, UNCOMPRESSED, UNENCRYPTED, UNION,
UNIQUE, UNKNOWN, UNLIMITED, UNLISTEN, UNLOCK, UNSEGMENTED,
UNTIL, UPDATE, USAGE, USER, USING
V
VACUUM, VALIDATOR, VALINDEX, VALUE, VALUES, VARBINARY, VARCHAR,
VARCHAR2, VARYING, VERBOSE, VERTICA, VIEW, VOLATILE
W
WAIT, WHEN, WHERE, WINDOW, WITH, WITHIN, WITHOUT, WORK, WRITE
Y
YEAR
Z
ZONE
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Reserved Words
Many SQL keywords are also reserved words, but a reserved word is not necessarily a keyword.
For example, a reserved word might be reserved for other/future use. In HP Vertica, reserved words
can be used anywhere identifiers can be used, as long as you double-quote them.
Begins
with
Reserved Word
A
ALL, ANALYSE, ANALYZE, AND, ANY, ARRAY, AS, ASC
B
BINARY, BOTH
C
CASE, CAST, CHECK, COLUMN, CONSTRAINT, CORRELATION, CREATE,
CURRENT_DATABASE, CURRENT_DATE, CURRENT_SCHEMA, CURRENT_
TIME, CURRENT_TIMESTAMP, CURRENT_USER
D
DEFAULT, DEFERRABLE, DESC, DISTINCT, DO
E
ELSE, ENCODED, END, EXCEPT
F
FALSE, FOR, FOREIGN, FROM
G
GRANT, GROUP, GROUPED
H
HAVING
I
IN, INITIALLY, INTERSECT, INTERVAL, INTERVALYM, INTO
J
JOIN
K
KSAFE
L
LEADING, LIMIT, LOCALTIME, LOCALTIMESTAMP
M
MATCH
N
NEW, NOT, NULL, NULLSEQUAL
O
OFF, OFFSET, OLD, ON, ONLY, OR, ORDER
P
PINNED, PLACING, PRIMARY, PROJECTION
R
REFERENCES
S
SCHEMA, SEGMENTED, SELECT, SESSION_USER, SOME, SYSDATE
T
TABLE, THEN, TIMESERIES, TO, TRAILING, TRUE
U
UNBOUNDED, UNION, UNIQUE, UNSEGMENTED, USER, USING
W
WHEN, WHERE, WINDOW, WITH, WITHIN
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Identifiers
Identifiers (names) of objects such as schema, table, projection, column names, and so on, can be
up to 128 bytes in length.
Unquoted Identifiers
Unquoted SQL identifiers must begin with one of the following:
l
Letters such as A–Z or a–z, including letters with diacritical marks and non-Latin letters)
l
Underscore (_)
Subsequent characters in an identifier can be:
l
Letters
l
Digits(0–9)
l
Dollar sign ($). Dollar sign is not allowed in identifiers according to the SQL standard and could
cause application portability problems.
l
Underscore (_)
Quoted Identifiers
Identifiers enclosed in double quote (") characters can contain any character. If you want to include
a double quote, you need a pair of them; for example """". You can use names that would
otherwise be invalid, such as names that include only numeric characters ("123") or contain space
characters, punctuation marks, keywords, and so on; for example:
CREATE SEQUENCE "my sequence!";
Double quotes are required for non-alphanumerics and SQL keywords such as "1time", "Next
week" and "Select".
Note: Identifiers are not case-sensitive. Thus, identifiers "ABC", "ABc", and "aBc" are
synonymous, as are ABC, ABc, and aBc.
Non-ASCII Characters
HP Vertica accepts non-ASCII UTF-8 Unicode characters for table names, column names, and
other Identifiers, extending the cases in which upper/lower case distinctions are ignored (casefolded) to all alphabets, including Latin, Cyrillic, and Greek.
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Identifiers Are Stored As Created
SQL identifiers, such as table and column names, are no longer converted to lowercase. They are
stored as created, and references to them are resolved using case-insensitive compares. It is not
necessary to double quote mixed-case identifiers. For example, The following statement creates
table ALLCAPS.
=> CREATE TABLE ALLCAPS(c1 varchar(30));
=> INSERT INTO ALLCAPS values('upper case');
The following statements are variations of the same query and all return identical results:
=> SELECT * FROM ALLCAPS;
=> SELECT * FROM allcaps;
=> SELECT * FROM "allcaps";
All three commands return the same result:
c1
-----------upper case
(1 row)
Note that the system returns an error if you try to create table AllCaps:
=> CREATE TABLE allcaps(c1 varchar(30));
ROLLBACK: table "AllCaps" already exists
See QUOTE_IDENT for additional information.
Case-Sensitive System Tables
The V_CATALOG.TABLES.TABLE_SCHEMA and TABLE_NAME columns are case sensitive when used
with an equality (=) predicate in queries. For example, given the following sample schema, if you
execute a query using the = predicate, HP Vertica returns 0 rows:
=> CREATE SCHEMA SS;
=> CREATE TABLE SS.TT (c1 int);
=> INSERT INTO ss.tt VALUES (1);
=> SELECT table_schema, table_name FROM v_catalog.tables
WHERE table_schema ='ss';
table_schema | table_name
--------------+-----------(0 rows)
Tip: Use the case-insensitive ILIKE predicate to return the expected results.
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=> SELECT table_schema, table_name FROM v_catalog.tables
WHERE table_schema ILIKE 'ss';
table_schema | table_name
-------------+-----------SS
| TT
(1 row)
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Literals
Literals are numbers or strings used in SQL as constants. Literals are included in the select-list,
along with expressions and built-in functions and can also be constants.
HP Vertica provides support for number-type literals (integers and numerics), string literals,
VARBINARY string literals, and date/time literals. The various string literal formats are discussed
in this section.
Number-Type Literals
There are three types of numbers in HP Vertica: Integers, numerics, and floats.
l
Integers are whole numbers less than 2^63 and must be digits.
l
Numerics are whole numbers larger than 2^63 or that include a decimal point with a precision
and a scale. Numerics can contain exponents. Numbers that begin with 0x are hexadecimal
numerics.
Numeric-type values can also be generated using casts from character strings. This is a more
general syntax. See the Examples section below, as well as Data Type Coercion Operators
(CAST).
Syntax
digits
digits.[digits] | [digits].digits
digits e[+-]digits | [digits].digits e[+-]digits | digits.[digits] e[+-]digits
Parameters
digits
Represents one or more numeric characters (0 through 9).
e
Represents an exponent marker.
Notes
l
At least one digit must follow the exponent marker (e), if e is present.
l
There cannot be any spaces or other characters embedded in the constant.
l
Leading plus (+) or minus (–) signs are not considered part of the constant; they are unary
operators applied to the constant.
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l
In most cases a numeric-type constant is automatically coerced to the most appropriate type
depending on context. When necessary, you can force a numeric value to be interpreted as a
specific data type by casting it as described in Data Type Coercion Operators (CAST).
l
Floating point literals are not supported. If you specifically need to specify a float, you can cast
as described in Data Type Coercion Operators (CAST).
l
HP Vertica follows the IEEE specification for floating point, including NaN (not a number) and
Infinity (Inf).
l
A NaN is not greater than and at the same time not less than anything, even itself. In other
words, comparisons always return false whenever a NaN is involved. See Numeric Expressions
for examples.
l
Dividing INTEGERS (x / y) yields a NUMERIC result. You can use the // operator to truncate
the result to a whole number.
Examples
The following are examples of number-type literals:
42
3.5
4.
.001
5e2
1.925e-3
Scientific notation:
=> SELECT NUMERIC '1e10';
?column?
------------10000000000
(1 row)
BINARY scaling:
=> SELECT NUMERIC '1p10';
?column?
---------1024
(1 row)
=> SELECT FLOAT 'Infinity';
?column?
---------Infinity
(1 row)
The following examples illustrated using the / and // operators to divide integers:
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VMart=> SELECT 40/25;
?column?
---------------------1.600000000000000000
(1 row)
VMart=> SELECT 40//25;
?column?
---------1
(1 row)
See Also
l
Data Type Coercion
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String Literals
String literals are string values surrounded by single or double quotes. Double-quoted strings are
subject to the backslash, but single-quoted strings do not require a backslash, except for \' and \\.
You can embed single quotes and backslashes into single-quoted strings.
To include other backslash (escape) sequences, such as \t (tab), you must use the double-quoted
form.
Precede single-quoted strings with a space between the string and its preceding word, since single
quotes are allowed in identifiers.
See Also
l
SET STANDARD_CONFORMING_STRINGS
l
SET ESCAPE_STRING_WARNING
l
Internationalization Parameters
l
Implement Locales for International Data Sets
Character String Literals
Character string literals are a sequence of characters from a predefined character set and are
enclosed by single quotes. If the single quote is part of the sequence, it must be doubled as "''".
Syntax
'characters'
Parameters
characters
Arbitrary sequence of characters bounded by single quotes (')
Single Quotes in a String
The SQL standard way of writing a single-quote character within a string literal is to write two
adjacent single quotes. for example:
=> SELECT 'Chester''s gorilla';
?column?
------------------Chester's gorilla
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(1 row)
Standard Conforming Strings and Escape Characters
HP Vertica uses standard conforming strings as specified in the SQL standard, which means that
backslashes are treated as string literals, not escape characters.
Note: Earlier versions of HP Vertica did not use standard conforming strings, and backslashes
were always considered escape sequences. To revert to this older behavior, set the
StandardConformingStrings parameter to '0', as described in Configuration Parameters in
the Administrator's Guide.
Examples
=> SELECT 'This is a string';
?column?
-----------------This is a string
(1 row)
=> SELECT 'This \is a string';
WARNING: nonstandard use of escape in a string literal at character 8
HINT: Use the escape string syntax for escapes, e.g., E'\r\n'.
?column?
-----------------This is a string
(1 row)
vmartdb=> SELECT E'This \is a string';
?column?
-----------------This is a string
=> SELECT E'This is a \n new line';
?column?
---------------------This is a
new line
(1 row)
=> SELECT 'String''s characters';
?column?
-------------------String's characters
(1 row)
See Also
l
SET STANDARD_CONFORMING_STRINGS
l
SET ESCAPE_STRING_WARNING
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l
Internationalization Parameters
l
Implement Locales for International Data Sets
Dollar-Quoted String Literals
Dollar-quoted string literals are rarely used, but are provided here for your convenience.
The standard syntax for specifying string literals can be difficult to understand. To allow more
readable queries in such situations, HP Vertica SQL provides dollar quoting. Dollar quoting is
not part of the SQL standard, but it is often a more convenient way to write complicated string
literals than the standard-compliant single quote syntax.
Syntax
$$characters$$
Parameters
characters
Arbitrary sequence of characters bounded by paired dollar signs ($$)
Dollar-quoted string content is treated as a literal. Single quote, backslash, and dollar sign
characters have no special meaning within a dollar-quoted string.
Notes
A dollar-quoted string that follows a keyword or identifier must be separated from the preceding
word by whitespace; otherwise, the dollar-quoting delimiter is taken as part of the preceding
identifier.
Examples
=> SELECT $$Fred's\n car$$;
?column?
------------------Fred's\n car
(1 row)
=> SELECT 'SELECT 'fact';';
ERROR: syntax error at or near "';'" at character 21
LINE 1: SELECT 'SELECT 'fact';';
=> SELECT 'SELECT $$fact';$$;
?column?
--------------SELECT $$fact
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(1 row)
=> SELECT 'SELECT ''fact'';';
?column?
---------------SELECT 'fact';
(1 row)
Unicode String Literals
Syntax
U&'characters' [ UESCAPE '' ]
Parameters
characters
Arbitrary sequence of UTF-8 characters bounded by single quotes (')
Unicode escape character
A single character from the source language character set other than
a hexit, plus sign (+), quote ('), double quote (''), or white space
Using Standard Conforming Strings
With StandardConformingStrings enabled, HP Vertica supports SQL standard Unicode
character string literals (the character set is UTF-8 only).
Before you enter a Unicode character string literal, enable standard conforming strings in one of the
following ways.
l
To enable for all sessions, update the StandardConformingStrings configuration parameter.
See Configuration Parameters in the Administrator's Guide.
l
To treat backslashes as escape characters for the current session, use the SET STANDARD_
CONFORMING_STRINGS statement.
See also Extended String Literals.
Examples
To enter a Unicode character in hexadecimal, such as the Russian phrase for "thank you, use the
following syntax:
=> SET STANDARD_CONFORMING_STRINGS TO ON;
=> SELECT U&'\0441\043F\0430\0441\0438\0431\043E' as 'thank you';
thank you
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----------спасибо
(1 row)
To enter the German word mude (where u is really u-umlaut) in hexadecimal:
=> SELECT U&'m\00fcde';
?column?
---------müde
(1 row)
=> SELECT 'ü';
?column?
---------ü
(1 row)
To enter the LINEAR B IDEOGRAM B240 WHEELED CHARIOT in hexadecimal:
=> SELECT E'\xF0\x90\x83\x8C';
?column?
---------(wheeled chariot character)
(1 row)
Note: Not all fonts support the wheeled chariot character.
See Also
l
SET STANDARD_CONFORMING_STRINGS
l
SET ESCAPE_STRING_WARNING
l
Internationalization Parameters
l
Implement Locales for International Data Sets
VARBINARY String Literals
VARBINARY string literals allow you to specify hexadecimal or binary digits in a string literal.
Syntax
X''B''
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Parameters
X
Specifies hexadecimal digits. The string must be enclosed in single
quotes (').
B
Specifies binary digits. The string must be enclosed in single quotes (').
Examples
=> SELECT X'abcd';
?column?
---------\253\315
(1 row)
=> SELECT B'101100';
?column?
---------,
(1 row)
Extended String Literals
Syntax
E'characters'
Parameters
characters
Arbitrary sequence of characters bounded by single quotes (')
You can use C-style backslash sequence in extended string literals, which are an extension to the
SQL standard. You specify an extended string literal by writing the letter E as a prefix (before the
opening single quote); for example:
E'extended character string\n'
Within an extended string, the backslash character (\) starts a C-style backslash sequence, in
which the combination of backslash and following character or numbers represent a special byte
value, as shown in the following list. Any other character following a backslash is taken literally; for
example, to include a backslash character, write two backslashes (\\).
l
\\ is a backslash
l
\b is a backspace
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l
\f is a form feed
l
\n is a newline
l
\r is a carriage return
l
\t is a tab
l
\x##,where ## is a 1 or 2-digit hexadecimal number; for example \x07 is a tab
l
\###, where ### is a 1, 2, or 3-digit octal number representing a byte with the corresponding
code.
When an extended string literal is concatenated across lines, write only E before the first opening
quote:
=> SELECT E'first part o'
->
'f a long line';
?column?
--------------------------first part of a long line
(1 row)
Two adjacent single quotes are used as one single quote:
=> SELECT 'Aren''t string literals fun?';
?column?
----------------------------Aren't string literals fun?
(1 row)
Standard Conforming Strings and Escape Characters
When interpreting commands, such as those entered in vsql or in queries passed via JDBC or
ODBC, HP Vertica uses standard conforming strings as specified in the SQL standard. In standard
conforming strings, backslashes are treated as string literals (ordinary characters), not escape
characters.
Note: Text read in from files or streams (such as the data inserted using the COPY statement)
are not treated as literal strings. The COPY command defines its own escape characters for
the data it reads. See the COPY statement documentation for details.
In HP Vertica databases prior to 4.0, the standard conforming strings was not on by default, and
backslashes were considered escape sequences. After 4.0, escape sequences, including
Windows path names, did not work as they had previously. For example, the TAB character '\t' is
two characters: '\' and 't'.
E'...' is the Extended character string literal format, so to treat backslashes as escape
characters, use E'\t'.
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The following options are available, but HP recommends that you migrate your application to use
standard conforming strings at your earliest convenience, after warnings have been addressed.
l
To revert to pre 4.0 behavior, set the StandardConformingStrings parameter to '0', as
described in Configuration Parameters in the Administrator's Guide.
l
To enable standard conforming strings permanently, set the StandardConformingStrings
parameter to '1', as described in the procedure in the section, "Identifying Strings that are not
Standard Conforming," below.
l
To enable standard conforming strings per session, use SET STANDARD_CONFORMING_
STRING TO ON, which treats backslashes as escape characters for the current session only.
The two sections that follow help you identify issues between HP Vertica 3.5 and 4.0.
Identifying Strings That Are Not Standard Conforming
The following procedure can be used to identify non-standard conforming strings in your application
so that you can convert them into standard conforming strings:
1. Be sure the StandardConformingStrings parameter is off, as described in
Internationalization Parameters in the Administrator's Guide.
=> SELECT SET_CONFIG_PARAMETER ('StandardConformingStrings' ,'0');
Note: HP recommends that you migrate your application to use Standard Conforming
Strings at your earliest convenience.
2. Turn on the EscapeStringWarning parameter. (ON is the default in HP Vertica Version 4.0 and
later.)
=> SELECT SET_CONFIG_PARAMETER ('EscapeStringWarning','1');
HP Vertica now returns a warning each time it encounters an escape string within a string
literal. For example, HP Vertica interprets the \n in the following example as a new line:
=> SELECT 'a\nb';
WARNING: nonstandard use of escape in a string literal at character 8
HINT: Use the escape string syntax for escapes, e.g., E'\r\n'.
?column?
---------a
b
(1 row)
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When StandardConformingStrings is ON, the string is interpreted as four characters: a \ n
b.
Modify each string that HP Vertica flags by extending it as in the following example:
E'a\nb'
Or if the string has quoted single quotes, double them; for example, 'one'' double'.
3. Turn on the StandardConformingStrings parameter for all sessions:
SELECT SET_CONFIG_PARAMETER ('StandardConformingStrings' ,'1');
Doubled Single Quotes
This section discusses vsql inputs that are not passed on to the server.
HP Vertica recognizes two consecutive single quotes within a string literal as one single quote
character. For example, the following inputs, 'You''re here!' ignored the second consecutive
quote and returns the following:
=> SELECT 'You''re here!';
?column?
-------------You're here!
(1 row)
This is the SQL standard representation and is preferred over the form, 'You\'re here!', because
backslashes are not parsed as before. You need to escape the backslash:
=> SELECT (E'You\'re here!');
?column?
-------------You're here!
(1 row)
This behavior change introduces a potential incompatibility in the use of the vsql \set command,
which automatically concatenates its arguments. For example, the following works in both HP
Vertica 3.5 and 4.0:
\set file
'\''
`pwd`
'/file.txt'
'\''\echo :file
vsql takes the four arguments and outputs the following:
'/home/vertica/file.txt'
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In HP Vertica 3.5 the above \set file command could be written all with the arguments run
together, but in 4.0 the adjacent single quotes are now parsed differently:
\set file '\''`pwd`'/file.txt''\''\echo :file
'/home/vertica/file.txt''
Note the extra single quote at the end. This is due to the pair of adjacent single quotes together with
the backslash-quoted single quote.
The extra quote can be resolved either as in the first example above, or by combining the literals as
follows:
\set file '\''`pwd`'/file.txt'''\echo :file
'/home/vertica/file.txt'
In either case the backslash-quoted single quotes should be changed to doubled single quotes as
follows:
\set file '''' `pwd` '/file.txt'''
Additional Examples
=> SELECT 'This \is a string';
?column?
-----------------This \is a string
(1 row)
=> SELECT E'This \is a string';
?column?
-----------------This is a string
=> SELECT E'This is a \n new line';
?column?
---------------------This is a
new line
(1 row)
=> SELECT 'String''s characters';
?column?
-------------------String's characters
(1 row)
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Date/Time Literals
Date or time literal input must be enclosed in single quotes. Input is accepted in almost any
reasonable format, including ISO 8601, SQL-compatible, traditional POSTGRES, and others.
HP Vertica is more flexible in handling date/time input than the SQL standard requires.The exact
parsing rules of date/time input and for the recognized text fields including months, days of the
week, and time zones are described in Date/Time Expressions.
Time Zone Values
HP Vertica attempts to be compatible with the SQL standard definitions for time zones. However,
the SQL standard has an odd mix of date and time types and capabilities. Obvious problems are:
l
Although the DATE type does not have an associated time zone, the TIME type can. Time
zones in the real world have little meaning unless associated with a date as well as a time, since
the offset can vary through the year with daylight-saving time boundaries.
l
HP Vertica assumes your local time zone for any data type containing only date or time.
l
The default time zone is specified as a constant numeric offset from UTC. It is therefore not
possible to adapt to daylight-saving time when doing date/time arithmetic across DST
boundaries.
To address these difficulties, HP recommends using Date/Time types that contain both date and
time when you use time zones. HP recommends that you do not use the type TIME WITH TIME
ZONE, even though it is supported it for legacy applications and for compliance with the SQL
standard.
Time zones and time-zone conventions are influenced by political decisions, not just earth
geometry. Time zones around the world became somewhat standardized during the 1900's, but
continue to be prone to arbitrary changes, particularly with respect to daylight-savings rules.
HP Vertica currently supports daylight-savings rules over the time period 1902 through 2038,
corresponding to the full range of conventional UNIX system time. Times outside that range are
taken to be in "standard time" for the selected time zone, no matter what part of the year in which
they occur.
Example Description
PST
Pacific Standard Time
-8:00
ISO-8601 offset for PST
-800
ISO-8601 offset for PST
-8
ISO-8601 offset for PST
zulu
Military abbreviation for UTC
z
Short form of zulu
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Day of the Week Names
The following tokens are recognized as names of days of the week:
Day
Abbreviations
SUNDAY
SUN
MONDAY
MON
TUESDAY
TUE, TUES
WEDNESDAY WED, WEDS
THURSDAY
THU, THUR, THURS
FRIDAY
FRI
SATURDAY
SAT
Month Names
The following tokens are recognized as names of months:
Month
Abbreviations
JANUARY
JAN
FEBRUARY
FEB
MARCH
MAR
APRIL
APR
MAY
MAY
JUNE
JUN
JULY
JUL
AUGUST
AUG
SEPTEMBER
SEP, SEPT
OCTOBER
OCT
NOVEMBER
NOV
DECEMBER
DEC
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Interval Values
An interval value represents the duration between two points in time.
Syntax
[ @ ] quantity unit [ quantity unit... ] [ AGO ]
Parameters
@
(at sign) is optional and ignored
quantity
Is an integer numeric constant
unit
Is one of the following units or abbreviations or plurals of the following units:
MILLISECONDSECOND
MINUTE
HOUR
AGO
DAYWEEK
MONTH
YEAR
DECADECENTURY
MILLENNIUM
[Optional] specifies a negative interval value (an interval going back in time). 'AGO' is
a synonym for '-'.
The amounts of different units are implicitly added up with appropriate sign accounting.
Notes
l
Quantities of days, hours, minutes, and seconds can be specified without explicit unit markings.
For example:
'1 12:59:10' is read the same as '1 day 12 hours 59 min 10 sec'
l
l
l
The boundaries of an interval constant are:
n
'9223372036854775807 usec' to '9223372036854775807 usec ago'
n
296533 years 3 mons 21 days 04:00:54.775807 to -296533 years -3 mons -21 days 04:00:54.775807
The range of an interval constant is +/– 263 – 1 (plus or minus two to the sixty-third minus one)
microseconds.
In HP Vertica, the interval fields are additive and accept large floating-point numbers.
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Examples
=> SELECT INTERVAL '1 12:59:10';
?column?
-----------1 12:59:10
(1 row)
=> SELECT INTERVAL '9223372036854775807 usec';
?column?
--------------------------106751991 04:00:54.775807
(1 row)
=> SELECT INTERVAL '-9223372036854775807 usec';
?column?
----------------------------106751991 04:00:54.775807
(1 row)
=> SELECT INTERVAL '-1 day 48.5 hours';
?column?
----------3 00:30
(1 row)
=> SELECT TIMESTAMP 'Apr 1, 07' - TIMESTAMP 'Mar 1, 07';
?column?
---------31
(1 row)
=> SELECT TIMESTAMP 'Mar 1, 07' - TIMESTAMP 'Feb 1, 07';
?column?
---------28
(1 row)
=> SELECT TIMESTAMP 'Feb 1, 07' + INTERVAL '29 days';
?column?
--------------------03/02/2007 00:00:00
(1 row)
=> SELECT TIMESTAMP WITHOUT TIME ZONE '1999-10-01 00:00:01' +
INTERVAL '1 month - 1 second' AS "Oct 31";
Oct 31
--------------------1999-10-31 00:00:00
(1 row)
Interval-Literal
The following table lists the units allowed for the required interval-literal parameter.
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Unit
Description
a
Julian year, 365.25 days exactly
ago
Indicates negative time offset
c, cent, century
Century
centuries
Centuries
d, day
Day
days
Days
dec, decade
Decade
decades, decs
Decades
h, hour, hr
Hour
hours, hrs
Hours
ka
Julian kilo-year, 365250 days exactly
m
Minute or month for year/month, depending on context. See
Notes below this table.
microsecond
Microsecond
microseconds
Microseconds
mil, millennium
Millennium
millennia, mils
Millennia
millisecond
Millisecond
milliseconds
Milliseconds
min, minute, mm
Minute
mins, minutes
Minutes
mon, month
Month
mons, months
Months
ms, msec, millisecond
Millisecond
mseconds, msecs
Milliseconds
q, qtr, quarter
Quarter
qtrs, quarters
Quarters
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Unit
Description
s, sec, second
Second
seconds, secs
Seconds
us, usec
Microsecond
microseconds, useconds, usecs
Microseconds
w, week
Week
weeks
Weeks
y, year, yr
Year
years, yrs
Years
Processing the Input Unit 'm'
The input unit 'm' can represent either 'months' or 'minutes,' depending on the context. For
instance, the following command creates a one-column table with an interval value:
=> CREATE TABLE int_test(i INTERVAL YEAR TO MONTH);
In the first INSERT statement, the values are inserted as 1 year, six months:
=> INSERT INTO int_test VALUES('1 year 6 months');
The second INSERT statement results in an error from specifying minutes for a YEAR TO MONTH
interval. At runtime, the result will be a NULL:
=> INSERT INTO int_test VALUES('1 year 6 minutes');
ERROR: invalid input syntax for type interval year to month: "1 year 6 minutes"
In the third INSERT statement, the 'm' is processed as months (not minutes), because DAY TO
SECOND is truncated:
=> INSERT INTO int_test VALUES('1 year 6 m');
-- the m counts as months
The table now contains two identical values, with no minutes:
=> SELECT * FROM int_test;
i
----1 year 6 months
1 year 6 months
(2 rows)
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In the following command, the 'm' counts as minutes, because the DAY TO SECOND interval-qualifier
extracts day/time values from the input:
=> SELECT INTERVAL '1y6m' DAY TO SECOND;
?column?
----------365 days 6 mins
(1 row)
Interval-Qualifier
The following table lists the optional interval qualifiers. Values in INTERVAL fields, other than
SECOND, are integers with a default precision of 2 when they are not the first field.
You cannot combine day/time and year/month qualifiers. For example, the following intervals are
not allowed:
l
DAY TO YEAR
l
HOUR TO MONTH
Interval
Type
Day/time
intervals
Units
Valid interval-literal entries
DAY
Unconstrained.
DAY TO HOUR
An interval that represents a span of days and hours.
DAY TO MINUTE
An interval that represents a span of days and minutes.
DAY TO SECOND
(Default) interval that represents a span of days, hours,
minutes, seconds, and fractions of a second if subtype
unspecified.
HOUR
Hours within days.
HOUR TO MINUTE
An interval that represents a span of hours and minutes.
HOUR TO SECOND
An interval that represents a span of hours and seconds.
MINUTE
Minutes within hours.
MINUTE TO SECOND
An interval that represents a span of minutes and seconds.
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Interval
Type
Units
Valid interval-literal entries
SECOND
Seconds within minutes.
Note: The SECOND field can have an interval fractional seconds
precision, which indicates the number of decimal digits
maintained following the decimal point in the SECONDS value.
When SECOND is not the first field, it has a precision of 2 places
before the decimal point.
Year/month MONTH
intervals
Months within year.
YEAR
Unconstrained.
YEAR TO MONTH
An interval that represents a span of years and months.
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Operators
Operators are logical, mathematical, and equality symbols used in SQL to evaluate, compare, or
calculate values.
Binary Operators
Each of the functions in the following table works with BINARY and VARBINARY data types.
Operator Function
Description
'='
binary_eq
Equal to
'<>'
binary_ne
Not equal to
'<'
binary_lt
Less than
'<='
binary_le
Less than or equal to
'>'
binary_gt
Greater than
binary_ge
Greater than or equal to
'&'
binary_and
And
'~'
binary_not
Not
'|'
binary_or
Or
'#'
binary_xor
Either or
'||'
binary_cat
Concatenate
'>='
Notes
l
If the arguments vary in length binary operators treat the values as though they are all equal in
length by right-extending the smaller values with the zero byte to the full width of the column
(except when using the binary_cat function). For example, given the values 'ff' and 'f', the
value 'f' is treated as 'f0'.
l
Operators are strict with respect to nulls. The result is null if any argument is null. For example,
null <> 'a'::binary returns null.
l
To apply the OR ('|') operator to a VARBINARY type, explicitly cast the arguments; for
example:
=> SELECT '1'::VARBINARY | '2'::VARBINARY;
?column?
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---------3
(1 row)
Similarly, to apply the LENGTH, REPEAT, TO_HEX, and SUBSTRING functions to a BINARY
type, explicitly cast the argument; for example:
=> SELECT LENGTH('\\001\\002\\003\\004'::varbinary(4));
LENGTH
-------4
(1 row)
When applying an operator or function to a column, the operator's or function's argument type is
derived from the column type.
Examples
In the following example, the zero byte is not removed from column cat1 when values are
concatenated:
=> SELECT 'ab'::BINARY(3) || 'cd'::BINARY(2) AS cat1,
'ab'::VARBINARY(3) ||
'cd'::VARBINARY(2) AS cat2;
cat1
| cat2
----------+-----ab\000cd | abcd
(1 row)
When the binary value 'ab'::binary(3) is translated to varbinary, the result is equivalent to
'ab\\000'::varbinary(3); for example:
=> SELECT 'ab'::binary(3); binary
-------ab\000
(1 row)
The following example performs a bitwise AND operation on the two input values (see also BIT_
AND):
=> SELECT '10001' & '011' as AND;
AND
----1
(1 row)
The following example performs a bitwise OR operation on the two input values (see also BIT_OR):
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=> SELECT '10001' | '011' as OR;
OR
------10011
(1 row)
The following example concatenates the two input values:
=> SELECT '10001' || '011' as CAT;
CAT
---------10001011
(1 row)
Boolean Operators
Syntax
[ AND | OR | NOT ]
Parameters
SQL uses a three-valued Boolean logic where the null value represents "unknown."
a
b
a AND b a OR b
TRUE
TRUE
TRUE
TRUE
FALSE FALSE
TRUE
TRUE
NULL
TRUE
NULL
TRUE
FALSE FALSE FALSE
FALSE
FALSE NULL
FALSE
NULL
NULL
NULL
NULL
NULL
a
NOT a
TRUE
FALSE
FALSE TRUE
NULL
NULL
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Notes
l
The operators AND and OR are commutative, that is, you can switch the left and right operand
without affecting the result. However, the order of evaluation of subexpressions is not defined.
When it is essential to force evaluation order, use a CASE construct.
l
Do not confuse Boolean operators with the Boolean-Predicate or the Boolean data type, which
can have only two values: true and false.
Comparison Operators
Comparison operators are available for all data types where comparison makes sense. All
comparison operators are binary operators that return values of True, False, or NULL.
Syntax and Parameters
<
less than
>
greater than
<=
less than or equal to
>=
greater than or equal to
= or <=>
equal
<> or !=
not equal
Notes
l
The != operator is converted to <> in the parser stage. It is not possible to implement != and <>
operators that do different things.
l
The comparison operators return NULL (signifying "unknown") when either operand is null.
l
The <=> operator performs an equality comparison like the = operator, but it returns true, instead
of NULL, if both operands are NULL, and false, instead of NULL, if one operand is NULL.
Data Type Coercion Operators (CAST)
Data type coercion (casting) passes an expression value to an input conversion routine for a
specified data type, resulting in a constant of the indicated type.
Syntax
SELECT CAST ( expression AS data_type )
SELECT expression::data_type
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SELECT data_type 'string'
Parameters
expression
An expression of any type
data_type
Converts the value of expression to one of the following data types:
BINARY
BOOLEAN
CHARACTER
DATE/TIME
NUMERIC
Notes
l
In HP Vertica, data type coercion (casting) can be invoked by an explicit cast request. It must
use one of the following constructs:
=> SELECT CAST ( expression AS data_type )
=> SELECT expression::data_type
=> SELECT data_type 'string'
l
The explicit type cast can be omitted if there is no ambiguity as to the type the constant must be.
For example, when a constant is assigned directly to a column, it is automatically coerced to the
column's data type.
l
If a binary value is cast (implicitly or explicitly) to a binary type with a smaller length, the value is
silently truncated. For example:
=> SELECT 'abcd'::BINARY(2);
?column?
---------ab
(1 row)
l
Similarly, if a character value is cast (implicitly or explicitly) to a character value with a smaller
length, the value is silently truncated. For example:
=> SELECT 'abcd'::CHAR(3);
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?column?
---------abc
l
l
HP Vertica supports only casts and resize operations as follows:
n
BINARY to and from VARBINARY
n
VARBINARY to and from LONG VARBINARY
n
BINARY to and from LONG VARBINARY
On binary data that contains a value with fewer bytes than the target column, values are rightextended with the zero byte '\0' to the full width of the column. Trailing zeros on variable-length
binary values are not right-extended:
=> SELECT 'ab'::BINARY(4), 'ab'::VARBINARY(4), 'ab'::LONG VARBINARY(4);
?column? | ?column? | ?column?
------------+----------+---------ab\000\000 | ab
| ab
(1 row)
Examples
=> SELECT CAST((2 + 2) AS VARCHAR);
?column?
---------4
(1 row)
=> SELECT (2 + 2)::VARCHAR;
?column?
---------4
(1 row)
=> SELECT INTEGER '123';
?column?
---------123
(1 row)
=> SELECT (2 + 2)::LONG VARCHAR
?column?
---------4
(1 row)
=> SELECT '2.2' + 2;
ERROR: invalid input syntax for integer: "2.2"
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=> SELECT FLOAT '2.2' + 2;
?column?
---------4.2
(1 row)
See Also
l
Data Type Conversions
l
Data Type Coercion Chart
Date/Time Operators
Syntax
[ + | – | * | / ]
Parameters
+
–
*
/
Addition
Subtraction
Multiplication
Division
Notes
l
The operators described below that take TIME or TIMESTAMP inputs actually come in two
variants: one that takes TIME WITH TIME ZONE or TIMESTAMP WITH TIME ZONE, and one that
takes TIME WITHOUT TIME ZONE or TIMESTAMP WITHOUT TIME ZONE. For brevity, these
variants are not shown separately.
l
The + and * operators come in commutative pairs (for example both DATE + INTEGER and
INTEGER + DATE); only one of each such pair is shown.
Example
Result Type
Result
DATE '2001-09-28' + INTEGER '7'
DATE
'2001-10-05'
DATE '2001-09-28' + INTERVAL '1 HOUR'
TIMESTAMP
'2001-09-28 01:00:00'
DATE '2001-09-28' + TIME
'03:00'
TIMESTAMP
'2001-09-28 03:00:00'
INTERVAL '1 DAY' + INTERVAL
'1 HOUR'
INTERVAL
'1 DAY 01:00:00'
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Example
Result Type
Result
TIMESTAMP '2001-09-28 01:00'
+ INTERVAL '23 HOURS'
TIMESTAMP
'2001-09-29 00:00:00'
TIME '01:00' + INTERVAL
'3 HOURS'
TIME
'04:00:00'
- INTERVAL '23 HOURS'
INTERVAL
'-23:00:00'
DATE '2001-10-01' – DATE
'2001-09-28'
INTEGER
'3'
DATE '2001-10-01' – INTEGER '7'
DATE
'2001-09-24'
DATE '2001-09-28' – INTERVAL
'1 HOUR'
TIMESTAMP
'2001-09-27 23:00:00'
TIME '05:00' – TIME '03:00'
INTERVAL
'02:00:00'
TIME '05:00'
'2 HOURS'
TIME
'03:00:00'
TIMESTAMP '2001-09-28 23:00'
– INTERVAL '23 HOURS'
TIMESTAMP
'2001-09-28 00:00:00'
INTERVAL '1 DAY' – INTERVAL
'1 HOUR'
INTERVAL
'1 DAY -01:00:00'
TIMESTAMP '2001-09-29 03:00'
– TIMESTAMP '2001-09-27 12:00'
INTERVAL
'1 DAY 15:00:00'
900 * INTERVAL '1 SECOND'
INTERVAL
'00:15:00'
21 * INTERVAL '1 DAY'
INTERVAL
'21 DAYS'
DOUBLE PRECISION '3.5'
* INTERVAL '1 HOUR'
INTERVAL
'03:30:00'
INTERVAL '1 HOUR' /
DOUBLE PRECISION '1.5'
INTERVAL
INTERVAL
'00:40:00'
Mathematical Operators
Mathematical operators are provided for many data types.
Operator Description
Example
Result
!
Factorial
5 !
120
+
Addition
2 + 3
5
–
Subtraction
2 – 3
–1
*
Multiplication
2 * 3
6
/
Division (integer division produces NUMERIC results).
4 / 2
2.00...
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Operator Description
Example
Result
//
With integer division, returns an INTEGER rather than a
NUMERIC.
117.32 // 2.5
46
%
Modulo (remainder)
5 % 4
1
^
Exponentiation
2.0 ^ 3.0
8
|/
Square root
|/ 25.0
5
||/
Cube root
||/ 27.0
3
!!
Factorial (prefix operator)
!! 5
120
@
Absolute value
@ -5.0
5
&
Bitwise AND
91 & 15
11
|
Bitwise OR
32 | 3
35
#
Bitwise XOR
17 # 5
20
~
Bitwise NOT
~1
-2
<<
Bitwise shift left
1 << 4
16
>>
Bitwise shift right
8 >> 2
2
Notes
l
The bitwise operators work only on integer data types, whereas the others are available for all
numeric data types.
l
HP Vertica supports the use of the factorial operators on positive and negative floating point
(DOUBLE PRECISION) numbers as well as integers. For example:
=> SELECT 4.98!;
?column?
-----------------115.978600750905
(1 row)
l
Factorial is defined in term of the gamma function, where (-1) = Infinity and the other negative
integers are undefined. For example:
(–4)! = NaN
–! = –(4!) = –24
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l
Factorial is defined as z! = gamma(z+1) for all complex numbers z. See the Handbook of
Mathematical Functions (1964) Section 6.1.5.
l
See MOD() for details about the behavior of %.
NULL Operators
To check whether a value is or is not NULL, use the constructs:
expression IS NULL expression IS NOT NULL
Alternatively, use equivalent, but nonstandard, constructs:
expression ISNULL expression NOTNULL
Do not write expression = NULL because NULL represents an unknown value, and two unknown
values are not necessarily equal. This behavior conforms to the SQL standard.
Note: Some applications might expect that expression = NULL returns true if expression
evaluates to null. HP Vertica strongly recommends that these applications be modified to
comply with the SQL standard.
String Concatenation Operators
To concatenate two strings on a single line, use the concatenation operator (two consecutive
vertical bars).
Syntax
string || string
Parameters
string
Is an expression of type CHAR or VARCHAR
Notes
l
|| is used to concatenate expressions and constants. The expressions are cast to VARCHAR if
possible, otherwise to VARBINARY, and must both be one or the other.
l
Two consecutive strings within a single SQL statement on separate lines are automatically
concatenated
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Examples
The following example is a single string written on two lines:
=> SELECT E'xx'-> '\\';
?column?
---------xx\
(1 row)
The following examples show two strings concatenated:
=> SELECT E'xx' ||-> '\\';
?column?
---------xx\\
(1 row)
=> SELECT 'auto' || 'mobile';
?column?
---------automobile
(1 row)
=> SELECT 'auto'-> 'mobile';
?column?
---------automobile
(1 row)
=> SELECT 1 || 2;
?column?
---------12
(1 row)
=> SELECT '1' || '2';
?column?
---------12
(1 row)=> SELECT '1'-> '2';
?column?
---------12
(1 row)
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Expressions
SQL expressions are the components of a query that compare a value or values against other
values. They can also perform calculations. Expressions found inside any SQL command are
usually in the form of a conditional statement.
Operator Precedence
The following table shows operator precedence in decreasing (high to low) order.
Note: When an expression includes more than one operator, HP recommends that you specify
the order of operation using parentheses, rather than relying on operator precedence.
Operator/Element Associativity Description
.
left
table/column name separator
::
left
typecast
[ ]
left
array element selection
-
right
unary minus
^
left
exponentiation
* / %
left
multiplication, division, modulo
+ -
left
addition, subtraction
IS
IS TRUE, IS FALSE, IS UNKNOWN, IS NULL
IN
set membership
BETWEEN
range containment
OVERLAPS
time interval overlap
LIKE
string pattern matching
< >
less than, greater than
=
right
equality, assignment
NOT
right
logical negation
AND
left
logical conjunction
OR
left
logical disjunction
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Expression Evaluation Rules
The order of evaluation of subexpressions is not defined. In particular, the inputs of an operator or
function are not necessarily evaluated left-to-right or in any other fixed order. To force evaluation in
a specific order, use a CASE construct. For example, this is an untrustworthy way of trying to avoid
division by zero in a WHERE clause:
=> SELECT x, y WHERE x <> 0 AND y/x > 1.5;
But this is safe:
=> SELECT x, y
WHERE
CASE
WHEN x <> 0 THEN y/x > 1.5
ELSE false
END;
A CASE construct used in this fashion defeats optimization attempts, so use it only when
necessary. (In this particular example, it would be best to avoid the issue by writing y > 1.5*x
instead.)
Aggregate Expressions
An aggregate expression represents the application of an aggregate function across the rows or
groups of rows selected by a query.
Using AVG() as an example, the syntax of an aggregate expression is one of the following:
l
Invokes the aggregate across all input rows for which the given expression yields a non-null
value:
AVG (expression)
l
Is the same as AVG(expression), because ALL is the default:
AVG (ALL expression)
l
Invokes the AVG() function across all input rows for all distinct, non-null values of the
expression, where expression is any value expression that does not itself contain an aggregate
expression.
AVG (DISTINCT expression)
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An aggregate expression only can appear in the select list or HAVING clause of a SELECT statement.
It is forbidden in other clauses, such as WHERE, because those clauses are evaluated before the
results of aggregates are formed.
CASE Expressions
The CASE expression is a generic conditional expression that can be used wherever an expression
is valid. It is similar to case and if/then/else statements in other languages.
Syntax (form 1)
CASE
WHEN condition THEN result
[ WHEN condition THEN result ]
...
[ ELSE result ]
END
Parameters
condition
Is an expression that returns a boolean (true/false) result. If the result is false,
subsequent WHEN clauses are evaluated in the same manner.
result
Specifies the value to return when the associated condition is true.
ELSE result
If no condition is true then the value of the CASE expression is the result in the
ELSE clause. If the ELSE clause is omitted and no condition matches, the result is
null.
Syntax (form 2)
CASE expression
WHEN value THEN result
[ WHEN value THEN result ]
...
[ ELSE result ]
END
Parameters
expression
An expression that is evaluated and compared to all the value specifications in the
WHEN clauses until one is found that is equal.
value
Specifies a value to compare to the expression.
result
Specifies the value to return when the expression is equal to the specified value.
ELSE result
Specifies the value to return when the expression is not equal to any value; if no
ELSE clause is specified, the value returned is null.
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Notes
The data types of all the result expressions must be convertible to a single output type.
Examples
The following examples show two uses of the CASE statement.
=> SELECT * FROM test;
a
--1
2
3
=> SELECT a,
CASE WHEN a=1 THEN 'one'
WHEN a=2 THEN 'two'
ELSE 'other'
END
FROM test;
a | case
---+------1 | one
2 | two
3 | other
=> SELECT a,
CASE a WHEN 1 THEN 'one'
WHEN 2 THEN 'two'
ELSE 'other'
END
FROM test;
a | case
---+------1 | one
2 | two
3 | other
Special Example
A CASE expression does not evaluate subexpressions that are not needed to determine the result.
You can use this behavior to avoid division-by-zero errors:
=> SELECT x FROM T1 WHERE
CASE WHEN x <> 0 THEN y/x > 1.5
ELSE false
END;
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Column References
Syntax
[ [ [db-name.]schema. ] tablename. ] columnname
Parameters
[ [db-name.]schema. ]
[Optional] Specifies the database name and optional schema name.
Using a database name identifies objects that are not unique within the
current search path (see Setting Search Paths). You must be connected
to the database you specify, and you cannot change objects in other
databases.
Specifying different database objects lets you qualify database objects
as explicitly as required. For example, you can use a database and a
schema name (mydb.myschema).
tablename.
columnname
Is one of:
l
The name of a table
l
An alias for a table defined by means of a FROM clause in a query
Is the name of a column that must be unique across all the tables being
used in a query
Notes
There are no space characters in a column reference.
If you do not specify a schema, HP Vertica searches the existing schemas according to the order
defined in the SET SEARCH_PATH command.
Example
This example uses the schema from the VMart database. See Introducing the VMart Example
Database.
In the following command, transaction_type and transaction_time are the unique column
references, store is the name of the schema, and store_sales_fact is the table name:
=> SELECT transaction_type, transaction_time
FROM store.store_sales_fact
ORDER BY transaction_time;
transaction_type | transaction_time
------------------+------------------
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purchase
purchase
purchase
purchase
purchase
purchase
purchase
return
return
purchase
purchase
purchase
purchase
purchase
purchase
purchase
purchase
purchase
return
purchase
(20 rows)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
00:00:23
00:00:32
00:00:54
00:00:54
00:01:15
00:01:30
00:01:50
00:03:34
00:03:35
00:03:39
00:05:13
00:05:20
00:05:23
00:05:27
00:05:30
00:05:35
00:05:35
00:05:42
00:06:36
00:06:39
Comments
A comment is an arbitrary sequence of characters beginning with two consecutive hyphen
characters and extending to the end of the line. For example:
-- This is a standard SQL comment
A comment is removed from the input stream before further syntax analysis and is effectively
replaced by white space.
Alternatively, C-style block comments can be used where the comment begins with /* and extends
to the matching occurrence of */.
/* multiline comment
* with nesting: /* nested block comment */
*/
These block comments nest, as specified in the SQL standard. Unlike C, you can comment out
larger blocks of code that might contain existing block comments.
Date/Time Expressions
HP Vertica uses an internal heuristic parser for all date/time input support. Dates and times are
input as strings, and are broken up into distinct fields with a preliminary determination of what kind
of information might be in the field. Each field is interpreted and either assigned a numeric value,
ignored, or rejected. The parser contains internal lookup tables for all textual fields, including
months, days of the week, and time zones.
The date/time type inputs are decoded using the following procedure.
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Break the input string into tokens and categorize each token as a string, time, time zone, or
number.
l
If the numeric token contains a colon (:), this is a time string. Include all subsequent digits and
colons.
l
If the numeric token contains a dash (-), slash (/), or two or more dots (.), this is a date string
which might have a text month.
l
If the token is numeric only, then it is either a single field or an ISO 8601 concatenated date (for
example, 19990113 for January 13, 1999) or time (for example, 141516 for 14:15:16).
l
If the token starts with a plus (+) or minus (–), then it is either a time zone or a special field.
l
If the token is a text string, match up with possible strings.
l
Do a binary-search table lookup for the token as either a special string (for example, today), day
(for example, Thursday), month (for example, January), or noise word (for example, at, on).
l
Set field values and bit mask for fields. For example, set year, month, day for today, and
additionally hour, minute, second for now.
l
If not found, do a similar binary-search table lookup to match the token with a time zone.
l
If still not found, throw an error.
l
When the token is a number or number field:
l
If there are eight or six digits, and if no other date fields have been previously read, then interpret
as a "concatenated date" (for example, 19990118 or 990118). The interpretation is YYYYMMDD or
YYMMDD.
l
If the token is three digits and a year has already been read, then interpret as day of year.
l
If four or six digits and a year has already been read, then interpret as a time (HHMM or HHMMSS).
l
If three or more digits and no date fields have yet been found, interpret as a year (this forces yymm-dd ordering of the remaining date fields).
l
Otherwise the date field ordering is assumed to follow the DateStyle setting: mm-dd-yy, ddmm-yy, or yy-mm-dd. Throw an error if a month or day field is found to be out of range.
l
If BC has been specified, negate the year and add one for internal storage. (There is no year zero
in the our implementation, so numerically 1 BC becomes year zero.)
l
If BC was not specified, and if the year field was two digits in length, then adjust the year to four
digits. If the field is less than 70, then add 2000, otherwise add 1900.
Tip: Gregorian years AD 1–99 can be entered by using 4 digits with leading zeros (for example,
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0099 is AD 99).
Month Day Year Ordering
For some formats, ordering of month, day, and year in date input is ambiguous and there is support
for specifying the expected ordering of these fields.
Special Date/Time Values
HP Vertica supports several special date/time values for convenience, as shown below. All of
these values need to be written in single quotes when used as constants in SQL statements.
The values INFINITY and -INFINITY are specially represented inside the system and are displayed
the same way. The others are simply notational shorthands that are converted to ordinary date/time
values when read. (In particular, NOW and related strings are converted to a specific time value as
soon as they are read.)
String
Valid Data Types
Description
epoch
DATE, TIMESTAMP
1970-01-01 00:00:00+00 (UNIX SYSTEM TIME ZERO)
INFINITY
TIMESTAMP
Later than all other time stamps
-INFINITY
TIMESTAMP
Earlier than all other time stamps
NOW
DATE, TIME, TIMESTAMP
Current transaction's start time
Note: NOW is not the same as the NOW function.
TODAY
DATE, TIMESTAMP
Midnight today
TOMORROW
DATE, TIMESTAMP
Midnight tomorrow
YESTERDAY
DATE, TIMESTAMP
Midnight yesterday
ALLBALLS
TIME
00:00:00.00 UTC
The following SQL-compatible functions can also be used to obtain the current time value for the
corresponding data type:
l
CURRENT_DATE
l
CURRENT_TIME
l
CURRENT_TIMESTAMP
l
LOCALTIME
l
LOCALTIMESTAMP
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The latter four accept an optional precision specification. (See Date/Time Functions.) However,
these functions are SQL functions and are not recognized as data input strings.
NULL Value
NULL is a reserved keyword used to indicate that a data value is unknown.
Be very careful when using NULL in expressions. NULL is not greater than, less than, equal to, or not
equal to any other expression. Use the Boolean-Predicate for determining whether an expression
value is NULL.
Notes
l
HP Vertica stores data in projections, which are sorted in a specific way. All columns are
stored in ASC (ascending) order. For columns of data type NUMERIC, INTEGER, DATE, TIME,
TIMESTAMP, and INTERVAL, NULL values are placed at the beginning of sorted projections (NULLS
FIRST), while for columns of data type FLOAT, STRING, and BOOLEAN, NULL values are placed at
the end (NULLS LAST). For details, see Analytics Null Placement and Minimizing Sort
Operations in the Programmer's Guide.
l
HP Vertica also accepts NUL characters ('\0') in constant strings and no longer removes null
characters from VARCHAR fields on input or output. NUL is the ASCII abbreviation for the NULL
character.
l
You can write queries with expressions that contain the <=> operator for NULL=NULL joins. See
Equi-joins and Non Equi-Joins in the Programmer's Guide.
See Also
l
NULL-handling Functions
Numeric Expressions
HP Vertica follows the IEEE specification for floating point, including NaN.
A NaN is not greater than and at the same time not less than anything, even itself. In other words,
comparisons always return false whenever a NaN is involved.
Examples
=> SELECT CBRT('Nan'); -- cube root
CBRT
-----NaN
(1 row)
=> SELECT 'Nan' > 1.0;
?column?
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---------f
(1 row)
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Predicates
Predicates are truth-tests. If the predicate test is true, it returns a value. Each predicate is
evaluated per row, so that when the predicate is part of an entire table SELECT statement, the
statement can return multiple results.
Predicates consist of a set of parameters and arguments. For example, in the following example
WHERE clause:
WHERE name = 'Smith';
l
name = 'Smith' is the predicate
l
'Smith' is an expression
BETWEEN-predicate
The special BETWEEN predicate is available as a convenience.
Syntax
a BETWEEN x AND y
Notes
a BETWEEN x AND y
is equivalent to:
a >= x AND a <= y
Similarly:
a NOT BETWEEN x AND y
is equivalent to:
a < x OR a > y
Boolean-Predicate
Retrieves rows where the value of an expression is true, false, or unknown (null).
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Syntax
expression IS [NOT] TRUE
expression IS [NOT] FALSE
expression IS [NOT] UNKNOWN
Notes
l
A null input is treated as the value UNKNOWN.
l
IS UNKNOWN and IS NOT UNKNOWN are effectively the same as the NULL-predicate, except that
the input expression does not have to be a single column value. To check a single column value
for NULL, use the NULL-predicate.
l
Do not confuse the boolean-predicate with Boolean Operators or the Boolean data type, which
can have only two values: true and false.
Column-Value-Predicate
Syntax
column-name comparison-op constant-expression
Parameters
column-name
A single column of one the tables specified in the FROM Clause.
comparison-op
A Comparison Operators.
constant-expression
A constant value of the same data type as the column-name.
Notes
To check a column value for NULL, use the NULL-predicate.
Examples
table.column1 = 2
table.column2 = 'Seafood'
table.column3 IS NULL
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IN-predicate
Syntax
column-expression [ NOT ] IN ( list-expression )
Parameters
column-expression
One or more columns from the tables specified in the FROM Clause.
list-expression
Comma-separated list of constant values matching the data type of the
column-expression
Examples
x, y IN ((1,2), (3, 4)), OR x, y IN
(SELECT a, b FROM table)x IN (5, 6, 7)
INTERPOLATE
Used to join two event series together using some ordered attribute, event series joins let you
compare values from two series directly, rather than having to normalize the series to the same
measurement interval.
Syntax
expression1 INTERPOLATE PREVIOUS VALUE expression2
Parameters
expression1expression2
olumn-reference from one the tables specified in the FROM Clause.
The column-reference can be any data type, but DATE/TIME types are
the most useful, especially TIMESTAMP,since you are joining data
that represents an event series.
PREVIOUS VALUE
Pads the non-preserved side with the previous values from relation
when there is no match.
Input rows are sorted in ascending logical order of the join column.
Note: An ORDER BY clause, if used, does not determine the input
order but only determines query output order.
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Notes
l
An event series join is an extension of a regular outer join. Instead of padding the non-preserved
side with null values when there is no match, the event series join pads the non-preserved side
with the previous values from the table.
l
The difference between expressing a regular outer join and an event series join is the
INTERPOLATE predicate, which is used in the ON clause. See the Examples section below
Notes and Restrictions. See also Event Series Joins in the Programmer's Guide.
l
Data is logically partitioned on the table in which it resides, based on other ON clause equality
predicates.
l
Interpolated values come from the table that contains the null, not from the other table.
l
HP Vertica does not guarantee that there will be no null values in the output. If there is no
previous value for a mismatched row, that row will be padded with nulls.
l
Event series join requires that both tables be sorted on columns in equality predicates, in any
order, followed by the INTERPOLATED column. If data is already sorted in this order, then an
explicit sort is avoided, which can improve query performance. For example, given the following
tables:
ask: exchange, stock, ts, pricebid: exchange,
stock, ts, price
In the query that follows
n
ask is sorted on exchange, stock (or the reverse), ts
n
bid is sorted on exchange, stock (or the reverse), ts
SELECT ask.price - bid.price, ask.ts, ask.stock, ask.exchange
FROM ask FULL OUTER JOIN bid
ON ask.stock = bid.stock AND ask.exchange =
bid.exchange AND ask.ts INTERPOLATE PREVIOUS
VALUE bid.ts;
Restrictions
l
Only one INTERPOLATE expression is allowed per join.
l
INTERPOLATE expressions are used only with ANSI SQL-99 syntax (the ON clause), which is
already true for full outer joins.
l
INTERPOLATE can be used with equality predicates only.
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l
The AND operator is supported but not the OR and NOT operators.
l
Expressions and implicit or explicit casts are not supported, but subqueries are allowed.
Example
The examples that follow use this simple schema.
CREATE TABLE t(x TIME);
CREATE TABLE t1(y TIME);
INSERT INTO t VALUES('12:40:23');
INSERT INTO t VALUES('14:40:25');
INSERT INTO t VALUES('14:45:00');
INSERT INTO t VALUES('14:49:55');
INSERT INTO t1 VALUES('12:40:23');
INSERT INTO t1 VALUES('14:00:00');
COMMIT;
Normal Full Outer Join
=> SELECT * FROM t FULL OUTER JOIN t1 ON t.x = t1.y;
Notice the null rows from the non-preserved table:
x
|
y
----------+---------12:40:23 | 12:40:23
14:40:25 |
14:45:00 |
14:49:55 |
| 14:00:00
(5 rows)
Full Outer Join with Interpolation
=> SELECT * FROM t FULL OUTER JOIN t1 ON t.x INTERPOLATE
PREVIOUS VALUE t1.y;
In this case, the rows with no entry point are padded with values from the previous row.
x
|
y
----------+---------12:40:23 | 12:40:23
12:40:23 | 14:00:00
14:40:25 | 14:00:00
14:45:00 | 14:00:00
14:49:55 | 14:00:00
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(5 rows)
Normal Left Outer Join
=> SELECT * FROM t LEFT OUTER JOIN t1 ON t.x = t1.y;
Again, there are nulls in the non-preserved table
x
|
y
----------+---------12:40:23 | 12:40:23
14:40:25 |
14:45:00 |
14:49:55 |
(4 rows)
Left Outer Join with Interpolation
=> SELECT * FROM t LEFT OUTER JOIN t1 ON t.x INTERPOLATE
PREVIOUS VALUE t1.y;
Nulls padded with interpolated values.
x
|
y
----------+---------12:40:23 | 12:40:23
14:40:25 | 14:00:00
14:45:00 | 14:00:00
14:49:55 | 14:00:00
(4 rows)
Inner Joins
For inner joins, there is no difference between a regular inner join and an event series inner join.
Since null values are eliminated from the result set, there is nothing to interpolate.
A regular inner join returns only the single matching row at 12:40:23:
=> SELECT * FROM t INNER JOIN t1 ON t.x = t1.y;
x
|
y
----------+---------12:40:23 | 12:40:23
(1 row)
An event series inner join finds the same single-matching row at 12:40:23:
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=> SELECT * FROM t INNER JOIN t1 ON t.x INTERPOLATE
PREVIOUS VALUE t1.y;
x
|
y
----------+---------12:40:23 | 12:40:23
(1 row)
Semantics
When you write an event series join in place of normal join, values are evaluated as follows (using
the schema in the above examples):
l
t is the outer, preserved table
l
t1 is the inner, non-preserved table
l
For each row in outer table t, the ON clause predicates are evaluated for each combination of
each row in the inner table t1.
l
If the ON clause predicates evaluate to true for any combination of rows, those combination
rows are produced at the output.
l
If the ON clause is false for all combinations, a single output row is produced with the values of
the row from t along with the columns of t1 chosen from the row in t1 with the greatest t1.y
value such that t1.y < t.x; If no such row is found, pad with nulls.
Note: t LEFT OUTER JOIN t1 is equivalent to t1 RIGHT OUTER JOIN t.
In the case of a full outer join, all values from both tables are preserved.
See Also
l
Join-Predicate
Combines records from two or more tables in a database.
Syntax
column-reference
= column-reference
Parameters
column-reference
Refers to a column of one the tables specified in the FROM Clause.
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LIKE-predicate
Retrieves rows where the string value of a column matches a specified pattern. The pattern can
contain one or more wildcard characters. ILIKE is equivalent to LIKE except that the match is caseinsensitive (non-standard extension).
Syntax
string [ NOT ]{ LIKE | ILIKE | LIKEB | ILIKEB }
... pattern
[ESCAPE 'escape-character' ]
Parameters
string
(CHAR, VARCHAR, BINARY, VARBINARY) is the column value to be compared to
the pattern.
NOT
Returns true if LIKE returns false, and the reverse; equivalent to NOT string
LIKE pattern.
pattern
Specifies a string containing wildcard characters.
ESCAPE
l
Underscore (_) matches any single character.
l
Percent sign (%) matches any string of zero or more characters.
Specifies an escape-character. An ESCAPE character can be used to
escape itself, underscore (_), and % only. This is enforced only for non-default
collations.
To match the ESCAPE character itself, use two consecutive escape
characters. The default ESCAPE character is the backslash (\) character,
although standard SQL specifies no default ESCAPE character. ESCAPE works
for CHAR and VARCHAR strings only.
escape-character
Causes character to be treated as a literal, rather than a wildcard, when
preceding an underscore or percent sign character in the pattern.
Notes
l
The LIKE predicate is compliant with the SQL standard.
l
In the default locale, LIKE and ILIKE handle UTF-8 character-at-a-time, locale-insensitive
comparisons. ILIKE handles language-independent case-folding.
Note: In non-default locales, LIKE and ILIKE do locale-sensitive string comparisons,
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including some automatic normalization, using the same algorithm as the "=" operator on
VARCHAR types.
l
The LIKEB and ILIKEB predicates do byte-at-a-time ASCII comparisons, providing access to
HP Vertica 4.0 functionality.
l
LIKE and ILIKE are stable for character strings, but immutable for binary strings, while LIKEB
and ILIKEB are both immutable.
l
For collation=binary settings, the behavior is similar to HP Vertica 4.0. For other collations,
LIKE operates on UTF-8 character strings, with the exact behavior dependent on collation
parameters, such as strength. In particular, ILIKE works by setting S=2 (ignore case) in the
current session locale. See Locale Specification in the Administrator's Guide.
l
Although the SQL standard specifies no default ESCAPE character, in HP Vertica the default is
the backslash (\) and works for CHAR and VARCHAR strings only.
Tip: HP recommends that you specify an explicit escape character in all cases, to avoid
problems should this behavior change. To use a backslash character as a literal, either
specify a different escape character or use two backslashes.
l
ESCAPE expressions evaluate to exactly one octet—or one UTF-8 character for non-default
locales.
l
An ESCAPE character can be used only to escape itself, _, and %. This is enforced only for nondefault collations.
l
LIKE requires that the entire string expression match the pattern. To match a sequence of
characters anywhere within a string, the pattern must start and end with a percent sign.
l
The LIKE predicate does not ignore trailing "white space" characters. If the data values that you
want to match have unknown numbers of trailing spaces, tabs, etc., terminate each LIKE
predicate pattern with the percent sign wildcard character.
l
To use binary data types, you must use a valid binary character as the escape character, since
backslash is not a valid BINARY character.
l
The following symbols are substitutes for the actual keywords:
~~
~#
~~*
~#*
!~~
!~#
LIKE
LIKEB
ILIKE
ILIKEB
NOT LIKE
NOT LIKEB
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!~~*
!~#*
NOT ILIKE
NOT IILIKEB
The ESCAPE keyword is not valid for the above symbols.
l
HP Vertica extends support for single-row subqueries as the pattern argument for LIKEB and
ILIKEB; for example:
SELECT * FROM t1 WHERE t1.x LIKEB (SELECT MAX (t2.a)
FROM t2);
Querying Case-Sensitive Data in System Tables
The V_CATALOG.TABLES.TABLE_SCHEMA and TABLE_NAME columns are case sensitive when used
with an equality (=) predicate in queries. For example, given the following sample schema, if you
execute a query using the = predicate, HP Vertica returns 0 rows:
=> CREATE SCHEMA SS;
=> CREATE TABLE SS.TT (c1 int);
=> INSERT INTO ss.tt VALUES (1);
=> SELECT table_schema, table_name FROM v_catalog.tables
WHERE table_schema ='ss';
table_schema | table_name
--------------+-----------(0 rows)
Tip: Use the case-insensitive ILIKE predicate to return the expected results.
=> SELECT table_schema, table_name FROM v_catalog.tables
WHERE table_schema ILIKE 'ss';
table_schema | table_name
-------------+-----------SS
| TT
(1 row)
Examples
'abc' LIKE 'abc' true'abc' LIKE 'a%'
'abc' LIKE '_b_' true
'abc' LIKE 'c'
false
'abc' LIKE 'ABC' false
'abc' ILIKE 'ABC' true
'abc' not like 'abc' false
not 'abc' like 'abc' false
true
The following example illustrates pattern matching in locales.
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\locale default=> CREATE TABLE src(c1 VARCHAR(100));
=> INSERT INTO src VALUES (U&'\00DF'); --The sharp s (ß)
=> INSERT INTO src VALUES ('ss');
=> COMMIT;
Querying the src table in the default locale returns both ss and sharp s.
=> SELECT * FROM src;
c1
---ß
ss
(2 rows)
The following query combines pattern-matching predicates to return the results from column c1:
=> SELECT c1, c1 = 'ss' AS equality, c1 LIKE 'ss'
AS LIKE, c1 ILIKE 'ss' AS ILIKE FROM src;
c1 | equality | LIKE | ILIKE
----+----------+------+------ß | f
| f
| f
ss | t
| t
| t
(2 rows)
The next query specifies unicode format for c1:
=> SELECT c1, c1 = U&'\00DF' AS equality,
c1 LIKE U&'\00DF' AS LIKE,
c1 ILIKE U&'\00DF' AS ILIKE from src;
c1 | equality | LIKE | ILIKE
----+----------+------+------ß | t
| t
| t
ss | f
| f
| f
(2 rows)
Now change the locale to German with a strength of 1 (ignore case and accents):
\locale LDE_S1
=> SELECT c1, c1 = 'ss' AS equality,
c1 LIKE 'ss' as LIKE, c1 ILIKE 'ss' AS ILIKE from src;
c1 | equality | LIKE | ILIKE
----+----------+------+------ß | t
| t
| t
ss | t
| t
| t
(2 rows)
This example illustrates binary data types with pattern-matching predicates:
=> CREATE TABLE t (c BINARY(1));
=> INSERT INTO t values(HEX_TO_BINARY('0x00'));
=> INSERT INTO t values(HEX_TO_BINARY('0xFF'));
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=> SELECT TO_HEX(c) from t;
TO_HEX
-------00
ff
(2 rows)
select * from t;
c
-----\000
\377
(2 rows)
=> SELECT c, c = '\000', c LIKE '\000', c ILIKE '\000' from t;
c
| ?column? | ?column? | ?column?
------+----------+----------+---------\000 | t
| t
| t
\377 | f
| f
| f
(2 rows)
=> SELECT c, c = '\377', c LIKE '\377', c ILIKE '\377' from t;
c
| ?column? | ?column? | ?column?
------+----------+----------+---------\000 | f
| f
| f
\377 | t
| t
| t
(2 rows)
NULL-predicate
Tests for null values.
Syntax
value_expression IS [ NOT ] NULL
Parameters
value_expression
A column name, literal, or function.
Examples
Column name:
=> SELECT date_key FROM date_dimension WHERE date_key IS NOT NULL;
date_key
---------1
366
1462
1097
2
3
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6
7
8
...
Function:
=> SELECT MAX(household_id) IS NULL FROM customer_dimension;
?column?
---------f
(1 row)
Literal:
=> SELECT 'a' IS NOT NULL;
?column?
---------t
(1 row)
See Also
l
NULL Value
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SQL Data Types
The following tables summarize the data types that HP Vertica supports. It also shows the default
placement of null values in projections. The Size column is listed as uncompressed bytes.
Size
(bytes)
Description
NULL
Sorting
BINARY
1 to 65000
Fixed-length binary string
NULLS LAST
VARBINARY
1 to 65000
Variable-length binary string
NULLS LAST
LONG VARBINARY
1 to
Long variable-length binary string
32,000,000
NULLS LAST
BYTEA
1 to 65000
Variable-length binary string (synonym for
VARBINARY)
NULLS LAST
RAW
1 to 65000
Variable-length binary string (synonym for
VARBINARY)
NULLS LAST
1
True or False or NULL
NULLS LAST
CHAR
1 to 65000
Fixed-length character string
NULLS LAST
VARCHAR
1 to 65000
Variable-length character string
NULLS LAST
LONG VARCHAR
1 to
Long variable-length character string
32,000,000
NULLS LAST
DATE
8
Represents a month, day, and year
NULLS FIRST
DATETIME
8
Represents a date and time with or without
timezone (synonym for TIMESTAMP)
NULLS FIRST
SMALLDATETIME
8
Represents a date and time with or without
timezone (synonym for TIMESTAMP)
NULLS FIRST
TIME
8
Represents a time of day without timezone
NULLS FIRST
TIME WITHTIMEZONE
8
Represents a time of day with timezone
NULLS FIRST
TIMESTAMP
8
Represents a date and time without
timezone
NULLS FIRST
Type
Binary types
Boolean types
BOOLEAN
Character types
Date/time types
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Type
Size
(bytes)
Description
NULL
Sorting
TIMESTAMP WITHTIMEZONE
8
Represents a date and time with timezone
NULLS FIRST
INTERVAL
8
Measures the difference between two
points in time
NULLS FIRST
Approximate numeric types
DOUBLE PRECISION
8
Signed 64-bit IEEE floating point number,
requiring 8 bytes of storage
NULLS LAST
FLOAT
8
Signed 64-bit IEEE floating point number,
requiring 8 bytes of storage
NULLS LAST
FLOAT(n)
8
Signed 64-bit IEEE floating point number,
requiring 8 bytes of storage
NULLS LAST
FLOAT8
8
Signed 64-bit IEEE floating point number,
requiring 8 bytes of storage
NULLS LAST
REAL
8
Signed 64-bit IEEE floating point number,
requiring 8 bytes of storage
NULLS LAST
INTEGER
8
Signed 64-bit integer, requiring 8 bytes of
storage
NULLS FIRST
INT
8
Signed 64-bit integer, requiring 8 bytes of
storage
NULLS FIRST
BIGINT
8
Signed 64-bit integer, requiring 8 bytes of
storage
NULLS FIRST
INT8
8
Signed 64-bit integer, requiring 8 bytes of
storage
NULLS FIRST
SMALLINT
8
Signed 64-bit integer, requiring 8 bytes of
storage
NULLS FIRST
TINYINT
8
Signed 64-bit integer, requiring 8 bytes of
storage
NULLS FIRST
DECIMAL
8+
8 bytes for the first 18 digits of precision,
plus 8 bytes for each additional 19 digits
NULLS FIRST
NUMERIC
8+
8 bytes for the first 18 digits of precision,
plus 8 bytes for each additional 19 digits
NULLS FIRST
NUMBER
8+
8 bytes for the first 18 digits of precision,
plus 8 bytes for each additional 19 digits
NULLS FIRST
Exact numeric types
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Type
Size
(bytes)
MONEY
8+
Description
8 bytes for the first 18 digits of precision,
plus 8 bytes for each additional 19 digits
NULL
Sorting
NULLS FIRST
Binary Data Types
Store raw-byte data, such as IP addresses, up to 65000 bytes.
Syntax
BINARY ( length ){ VARBINARY | BINARY VARYING | BYTEA | RAW } ( max-length )
Parameters
length | max-length
Specifies the length of the string (column width, declared in bytes (octets),
in CREATE TABLE statements).
Notes
l
BYTEA and RAW are synonyms for VARBINARY.
l
The data types BINARY and BINARY VARYING (VARBINARY) are collectively referred to as binary
string types and the values of binary string types are referred to as binary strings.
l
A binary string is a sequence of octets, or bytes. Binary strings store raw-byte data, while
character strings store text.
l
A binary value value of NULL appears last (largest) in ascending order.
l
The binary data types, BINARY and VARBINARY, are similar to the Character Data Types, CHAR
and VARCHAR, respectively, except that binary data types contain byte strings, rather than
character strings.
l
BINARY—A fixed-width string of length bytes, where the number of bytes is declared as an
optional specifier to the type. If length is omitted, the default is 1. Where necessary, values are
right-extended to the full width of the column with the zero byte. For example:
=> SELECT TO_HEX('ab'::BINARY(4));
to_hex
---------61620000
l
VARBINARY—A variable-width string up to a length of max-length bytes, where the maximum
number of bytes is declared as an optional specifier to the type. The default is the default
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attribute size, which is 80, and the maximum length is 65000 bytes. VARBINARY values are not
extended to the full width of the column. For example:
=> SELECT TO_HEX('ab'::VARBINARY(4));
to_hex
-------6162
l
You can use several formats when working with binary values, but the hexadecimal format is
generally the most straightforward and is emphasized in HP Vertica documentation.
l
Binary operands &, ~, | and # have special behavior for binary data types, as described in
Binary Operators.
l
On input, strings are translated from:
n
Hexadecimal representation to a binary value using the HEX_TO_BINARY function
n
Bitstring representation to a binary value using the BITSTRING_TO_BINARY function.
Both functions take a VARCHAR argument and return a VARBINARY value. See the Examples
section below.
l
Binary values can also be represented in octal format by prefixing the value with a backslash
'\'.
Note: If you use vsql, you must use the escape character (\) when you insert another
backslash on input; for example, input '\141' as '\\141'.
You can also input values represented by printable characters. For example, the hexadecimal
value '0x61' can also be represented by the symbol '.
See Bulk Loading Data in the Administrator's Guide.
l
Like the input format the output format is a hybrid of octal codes and printable ASCII characters.
A byte in the range of printable ASCII characters (the range [0x20, 0x7e]) is represented by
the corresponding ASCII character, with the exception of the backslash ('\'), which is escaped
as '\\'. All other byte values are represented by their corresponding octal values. For example,
the bytes {97,92,98,99}, which in ASCII are {a,\,b,c}, are translated to text as 'a\\bc'.
l
The following aggregate functions are supported for binary data types:
n
BIT_AND
n
BIT_OR
n
BIT_XOR
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n
MAX
n
MIN
BIT_AND, BIT_OR, and BIT_XOR are bitwise operations that are applied to each non-null value in a
group, while MAX and MIN are bytewise comparisons of binary values.
l
Like their binary operator counterparts, if the values in a group vary in length, the aggregate
functions treat the values as though they are all equal in length by extending shorter values with
zero bytes to the full width of the column. For example, given a group containing the values
'ff', null, and 'f', a binary aggregate ignores the null value and treats the value 'f' as
'f0'. Also, like their binary operator counterparts, these aggregate functions operate on
VARBINARY types explicitly and operate on BINARY types implicitly through casts. See Data Type
Coercion Operators (CAST).
Examples
The following example shows VARBINARY HEX_TO_BINARY(VARCHAR) and VARCHAR TO_HEX
(VARBINARY) usage.
Table t and and its projection are created with binary columns:
=> CREATE TABLE t (c BINARY(1));
=> CREATE PROJECTION t_p (c) AS SELECT c FROM t;
Insert minimum byte and maximum byte values:
=> INSERT INTO t values(HEX_TO_BINARY('0x00'));
=> INSERT INTO t values(HEX_TO_BINARY('0xFF'));
Binary values can then be formatted in hex on output using the TO_HEX function:
=> SELECT TO_HEX(c) FROM t;
to_hex
-------00
ff
(2 rows)
The BIT_AND, BIT_OR, and BIT_XORfunctions are interesting when operating on a group of values.
For example, create a sample table and projections with binary columns:
This example uses the following schema, which creates table t with a single column of VARBINARY
data type:
=>
=>
=>
=>
CREATE
INSERT
INSERT
INSERT
TABLE t ( c VARBINARY(2) );
INTO t values(HEX_TO_BINARY('0xFF00'));
INTO t values(HEX_TO_BINARY('0xFFFF'));
INTO t values(HEX_TO_BINARY('0xF00F'));
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Query table t to see column c output:
=> SELECT TO_HEX(c) FROM t;
TO_HEX
-------ff00
ffff
f00f
(3 rows)
Now issue the bitwise AND operation. Because these are aggregate functions, an implicit GROUP
BY operation is performed on results using (ff00&(ffff)&f00f):
=> SELECT TO_HEX(BIT_AND(c)) FROM t;
TO_HEX
-------f000
(1 row)
Issue the bitwise OR operation on (ff00|(ffff)|f00f):
=> SELECT TO_HEX(BIT_OR(c)) FROM t;
TO_HEX
-------ffff
(1 row)
Issue the bitwise XOR operation on (ff00#(ffff)#f00f):
=> SELECT TO_HEX(BIT_XOR(c)) FROM t;
TO_HEX
-------f0f0
(1 row)
See Also
l
BIT_AND
l
BIT_OR
l
BIT_XOR
l
MAX [Aggregate]
l
MIN [Aggregate]
l
Binary Operators
l
COPY
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l
Data Type Coercion Operators (CAST)
l
INET_ATON
l
INET_NTOA
l
V6_ATON
l
V6_NTOA
l
V6_SUBNETA
l
V6_SUBNETN
l
V6_TYPE
l
BITCOUNT
l
BITSTRING_TO_BINARY
l
HEX_TO_BINARY
l
LENGTH
l
REPEAT
l
SUBSTRING
l
TO_HEX
l
TO_BITSTRING
Boolean Data Type
HP Vertica provides the standard SQL type BOOLEAN, which has two states: true and false. The
third state in SQL boolean logic is unknown, which is represented by the NULL value.
Syntax
BOOLEAN
Parameters
Valid literal data values for input are:
TRUE
't'
'true'
'y'
'yes'
'1'
1
FALSE
'f'
'false'
'n'
'no'
'0'
0
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Notes
l
Do not confuse the BOOLEAN data type with Boolean Operators or the Boolean-Predicate.
l
The keywords TRUE and FALSE are preferred and are SQL-compliant.
l
A Boolean value of NULL appears last (largest) in ascending order.
l
All other values must be enclosed in single quotes.
l
Boolean values are output using the letters t and f.
See Also
l
NULL Value
l
Data Type Coercion Chart
Character Data Types
Stores strings of letters, numbers, and symbols.
Character data can be stored as fixed-length or variable-length strings. Fixed-length strings are
right-extended with spaces on output; variable-length strings are not extended.
Syntax
[ CHARACTER | CHAR ] ( octet_length )[ VARCHAR | CHARACTER VARYING ] ( octet_length )
Parameters
octet_length
Specifies the length of the string (column width, declared in bytes (octets), in
CREATE TABLE statements).
Notes
l
The data types CHARACTER (CHAR) and CHARACTER VARYING (VARCHAR) are collectively referred to
as character string types, and the values of character string types are known as character
strings.
l
CHAR is conceptually a fixed-length, blank-padded string. Any trailing blanks (spaces) are
removed on input, and only restored on output. The default length is 1, and the maximum length
is 65000 octets (bytes).
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l
VARCHAR is a variable-length character data type. The default length is 80, and the maximum
length is 65000 octets. Values can include trailing spaces.
l
When you define character columns, specify the maximum size of any string to be stored in a
column. For example, to store strings up to 24 octets in length, use either of the following
definitions:
CHAR(24)
l
/* fixed-length */VARCHAR(24) /* variable-length */
The maximum length parameter for VARCHAR and CHAR data type refers to the number of octets
that can be stored in that field, not the number of characters (Unicode code points). When using
multibyte UTF-8 characters, the fields must be sized to accommodate from 1 to 4 octets per
character, depending on the data. If the data loaded into a VARCHAR/CHAR column exceeds the
specified maximum size for that column, data is truncated on UTF-8 character boundaries to fit
within the specified size. See COPY.
Note: Remember to include the extra octets required for multibyte characters in the columnwidth declaration, keeping in mind the 65000 octet column-width limit.
l
String literals in SQL statements must be enclosed in single quotes.
l
Due to compression in HP Vertica, the cost of overestimating the length of these fields is
incurred primarily at load time and during sorts.
l
NULL appears last (largest) in ascending order. See also GROUP BY Clause for additional
information about NULL ordering.
The Difference Between NULL and NUL
NUL represents a character whose ASCII/Unicode code is 0, sometimes qualified "ASCII NUL".
NULL means no value, and is true of a field (column) or constant, not of a character.
CHAR, LONG VARCHAR, and VARCHAR string data types accept ASCII NULs.
The following example casts the input string containing NUL values to VARCHAR:
=> SELECT 'vert\0ica'::CHARACTER VARYING AS VARCHAR;
VARCHAR
--------vert\0ica
(1 row)
The result contains 9 characters:
=> SELECT LENGTH('vert\0ica'::CHARACTER VARYING);
length
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-------9
(1 row)
If you use an extended string literal, the length is 8 characters:
=> SELECT E'vert\0ica'::CHARACTER VARYING AS VARCHAR;
VARCHAR
--------vertica
(1 row)
=> SELECT LENGTH(E'vert\0ica'::CHARACTER VARYING);
LENGTH
-------8
(1 row)
See Also
l
Data Type Coercion
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Date/Time Data Types
HP Vertica supports the full set of SQL date and time data types. In most cases, a combination of
DATE, DATETIME, SMALLDATETIME, TIME, TIMESTAMP WITHOUT TIME ZONE, and TIMESTAMP WITH
TIME ZONE, and INTERVAL provides a complete range of date/time functionality required by any
application.
In compliance with the SQL standard, HP Vertica also supports the TIME WITH TIME ZONE data
type.
The following table lists the characteristics about the date/time data types. All these data types
have a size of 8 bytes.
Name
Description
Low Value
High Value
Resolut
ion
DATE
Dates only
(no time of d
ay)
~ 25e+15 BC
~ 25e+15 AD
1 day
TIME [(p]
Time of day o
nly
(no date)
00:00:00.00
23:59:60.999999
1 μs
TIMETZ [(p)]
Time of day o
nly,
with time zon
e
00:00:00.00+14
23:59:59.999999-14
1 μs
TIMESTAMP [(p)]
Both date and
time,
without time
zone
290279-12-22 19:59:05.2241
94 BC
294277-01-09 04:00:54:7758
06 AD
1 μs
TIMESTAMPTZ [(p)]
Both date and
time,
with time zon
e
290279-12-22 19:59:05.2241
94 BC UTC
294277-01-09 04:00:54:7758
06 AD UTC
1 μs
INTERVAL [(p)]DAY T
O SECOND
Time intervals -106751991 days 04:00:54.7
75807
+-106751991 days 04:00:54.
775807
1 μs
INTERVAL [(p)]YEAR
TO MONTH
Time intervals ~ -768e15 yrs
~ 768e15 yrs
1 month
Time Zone Abbreviations for Input
HP Vertica recognizes the files in /opt/vertica/share/timezonesets as date/time input values
and defines the default list of strings accepted in the AT TIME ZONE zone parameter. The names
are not necessarily used for date/time output—output is driven by the official time zone
abbreviations associated with the currently selected time zone parameter setting.
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Notes
l
In HP Vertica, TIME ZONE is a synonym for TIMEZONE.
l
HP Vertica uses Julian dates for all date/time calculations, which can correctly predict and
calculate any date more recent than 4713 BC to far into the future, based on the assumption that
the average length of the year is 365.2425 days.
l
All date/time types are stored in eight bytes.
l
A date/time value of NULL appears first (smallest) in ascending order.
l
All the date/time data types accept the special literal value NOW to specify the current date and
time. For example:
=> SELECT TIMESTAMP 'NOW';
?column?
---------------------------2012-03-13 11:42:22.766989
(1 row)
l
In HP Vertica, the INTERVAL data type is SQL:2008 compliant and allows modifiers, called
interval qualifiers, that divide the INTERVAL type into two primary subtypes, DAY TO SECOND
(the default) and YEAR TO MONTH. You use the SET INTERVALSTYLE command to change the
intervalstyle run-time parameter for the current session.
Intervals are represented internally as some number of microseconds and printed as up to 60
seconds, 60 minutes, 24 hours, 30 days, 12 months, and as many years as necessary. Fields
can be positive or negative.
See Also
l
TZ Environment Variable
l
Using Time Zones With HP Vertica
l
Sources for Time Zone and Daylight Saving Time Data
DATE
Consists of a month, day, and year.
Syntax
DATE
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Parameters/Limits
Low Value
High Value Resolution
~ 25e+15 BC
~ 25e+15 AD
1 DAY
See SET DATESTYLE for information about ordering.
Example
Description
January 8, 1999
Unambiguous in any datestyle input mode
1999-01-08
ISO 8601; January 8 in any mode (recommended format)
1/8/1999
January 8 in MDY mode; August 1 in DMY mode
1/18/1999
January 18 in MDY mode; rejected in other modes
01/02/03
January 2, 2003 in MDY mode
February 1, 2003 in DMY mode
February 3, 2001 in YMD mode
1999-Jan-08
January 8 in any mode
Jan-08-1999
January 8 in any mode
08-Jan-1999
January 8 in any mode
99-Jan-08
January 8 in YMD mode, else error
08-Jan-99
January 8, except error in YMD mode
Jan-08-99
January 8, except error in YMD mode
19990108
ISO 8601; January 8, 1999 in any mode
990108
ISO 8601; January 8, 1999 in any mode
1999.008
Year and day of year
J2451187
Julian day
January 8, 99 BC
Year 99 before the Common Era
DATETIME
DATETIME is an alias for TIMESTAMP.
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INTERVAL
Measures the difference between two points in time. The INTERVAL data type is divided into two
major subtypes:
l
DAY TO SECOND (day/time, in microseconds)
l
YEAR TO MONTH (year/month, in months)
A day/time interval represents a span of days, hours, minutes, seconds, and fractional seconds. A
year/month interval represents a span of years and months. Intervals can be positive or negative.
Syntax
INTERVAL [ (p) ] [ - ] 'Interval-Literal' [ Interval-Qualifier ]
Parameters
(p)
[Optional] Specifies the precision for the number of digits retained in the
seconds field. Enter the precision value in parentheses (). The interval
precision can range from 0 to 6. The default is 6.
-
[Optional] Indicates a negative interval.
'interval-literal'
Indicates a literal character string expressing a specific interval.
interval-qualifier
[Optional] Specifies a range of interval subtypes with optional precision
specifications. If omitted, the default is DAY TO SECOND(6). Sometimes
referred to as subtype in this topic.
Within the single quotes of an interval-literal, units can be plural, but
outside the quotes, the interval-qualifier must be singular.
Limits
Name
Low Value
High Value
Resolution
INTERVAL [(p)] DAY TO SECOND
–106751991 days
+/–106751991 days
1 microsecond
04:00:54.775807
04:00:54.775807
~/ –768e15 yrs
~ 768e15 yrs
INTERVAL [(p)] YEAR TO MONTH
1 month
Displaying or Omitting Interval Units in Output
To display or omit interval units from the output of a SELECT INTERVAL query, use the
INTERVALSTYLE and DATESTYLE settings. These settings affect only the interval output format, not
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the interval input format.
To omit interval units from the output, set INTERVALSTYLE to PLAIN. This is the default value, and it
follows the SQL:2008 standard (ISO):
=> SET INTERVALSTYLE TO PLAIN;
SET
=> SELECT INTERVAL '3 2';
?column?
---------3 02:00
When INTERVALSTYLE is set to PLAIN, units are omitted from the output, even if you specify the
units in the query:
=> SELECT INTERVAL '3 days 2 hours';
?column?
---------3 02:00
To display interval units in the output, set INTERVALSTYLE to UNITS:
=> SET INTERVALSTYLE TO UNITS;
SET
=> SELECT INTERVAL '3 2';
?column?
---------------3 days 2 hours
When INTERVALSTYLE is set to UNITS to display units in the result, the DATESTYLE setting controls
the format of the units in the output.
If you set DATESTYLE to SQL, interval units are omitted from the output, even if you set
INTERVALSTYLE to UNITS:
=> SET INTERVALSTYLE TO UNITS;
SET
=> SET DATESTYLE TO SQL;
SET
=> SELECT INTERVAL '3 2';
?column?
---------3 02:00
To display interval units on output, set DATESTYLE to ISO:
=> SET INTERVALSTYLE TO UNITS;
SET
=> SET DATESTYLE TO ISO;
SET
=> SELECT INTERVAL '3 2';
?column?
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---------------3 days 2 hours
To check the INTERVALSTYLE or DATESTYLE setting, use the SHOW command:
=> SHOW INTERVALSTYLE;
name
| setting
---------------+--------intervalstyle | units
=> SHOW DATESTYLE;
name
| setting
-----------+---------datestyle | ISO, MDY
Specifying Units on Input
You can specify interval units in the interval-literal:
=> SELECT INTERVAL '3 days 2 hours';
?column?
-------------3 days 2 hours
The following command uses the same interval-literal as the previous example, but specifies a
MINUTE interval-qualifier to so that the results are displayed only in minutes:
=> SELECT INTERVAL '3 days 2 hours' MINUTE;
?column?
----------4440 mins
HP Vertica allows combinations of units in the interval-qualifier, as in the next three examples:
=> SELECT INTERVAL '1 second 1 millisecond' DAY TO SECOND;
?column?
-------------1.001 secs
=> SELECT INTERVAL '28 days 3 hours 65 min' HOUR TO MINUTE;
?column?
----------676 hours 5 mins
Units less than a month are not valid for YEAR TO MONTH interval-qualifiers:
=> SELECT INTERVAL '1 Y 30 DAYS' YEAR TO MONTH;
ERROR: invalid input syntax for type interval year to month: "1 Y 30 DAYS"
If you replace DAYS in the interval-literal with M to represent months, HP Vertica returns the correct
information of 1 year, 3 months:
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=> SELECT INTERVAL '1 Y 3 M' YEAR TO MONTH;
?column?
---------1 year 3 months
in the previous example, M was used as the interval-literal, representing months. If you specify a
DAY TO SECOND interval-qualifier, HP Vertica knows that M represents minutes, as in the following
example:
=> SELECT INTERVAL '1 D 3 M' DAY TO SECOND;
?column?
---------1 day 3 mins
The next two examples use units in the input to return microseconds:
=> SELECT INTERVAL '4:5 1 2 34us';
?column?
------------------1 day 04:05:02.000034
=> SELECT INTERVAL '4:5 1d 2 34us' HOUR TO SECOND;
?column?
----------------28 hours 5 mins 2.000034 secs
How the Interval-Qualifier Affects Output Units
The interval-qualifier specifies a range of interval subtypes to apply to the interval-literal. You can
also specify the precision in the interval-qualifier.
If an interval-qualifier is not specified, the default subtype is DAY TO SECOND(6), regardless of what
is inside the quotes. For example, as an extension to SQL:2008, both of the following commands
return 910 days:
=> SELECT INTERVAL '2-6'
; ?column?
----------------910 days
=> SELECT INTERVAL '2 years 6 months';
?column?
----------------910 days
However, if you change the interval-qualifier to YEAR TO MONTH, you get the following results:
=> SELECT INTERVAL '2 years 6 months' YEAR TO MONTH;
?column?
----------------2 years 6 months
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An interval-qualifier can extract other values from the input parameters. For example, the following
command extracts the HOUR value from the input parameters:
=> SELECT INTERVAL '3 days 2 hours' HOUR;
?column?
---------74 hours
When specifying intervals that use subtype YEAR TO MONTH, the returned value is kept as months:
=> SELECT INTERVAL '2 years 6 months' YEAR TO MONTH;
?column?
----------2 years 6 months
The primary day/time (DAY TO SECOND) and year/month (YEAR TO MONTH) subtype ranges can be
restricted to more specific range of types by an interval-qualifier. For example, HOUR TO MINUTE is
a limited form of day/time interval, which can be used to express time zone offsets.
=> SELECT INTERVAL '1 3' HOUR to MINUTE;
?column?
--------------01:03
The formats hh:mm:ss and hh:mm are used only when at least two of the fields specified in the
interval-qualifier are non-zero and there are no more than 23 hours or 59 minutes:
=> SELECT INTERVAL '2 days 12 hours 15 mins' DAY TO MINUTE;
?column?
-------------2 days 12:15
=> SELECT INTERVAL '15 mins 20 sec' MINUTE TO SECOND;
?column?
---------00:15:20
=> SELECT INTERVAL '1 hour 15 mins 20 sec' MINUTE TO SECOND;
?column?
----------------75 mins 20 secs
Specifying Precision
SQL:2008 allows you to specify precision for the interval output by entering the precision value in
parentheses after the INTERVAL keyword or the interval-qualifier. HP Vertica rounds the input to the
number of decimal places specified. SECOND(2) and SECOND (2) produce the same result:
If you specify two different precisions, HP Vertica picks the lesser of the two:
=> SELECT INTERVAL(1) '1.2467' SECOND(2);
?column?
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---------1.2 secs
When you specify a precision inside an interval-literal, HP Vertica processes the precision by
removing the parentheses. In this example, (3) is processed as 3 minutes, the first omitted field:
=> SELECT INTERVAL '28 days 3 hours 1.234567 second(3)';
?column?
-------------------28 days 03:03:01.234567
The following command specifies that the day field can hold 4 digits, the hour field 2 digits, the
minutes field 2 digits, the seconds field 2 digits, and the fractional seconds field 6 digits:
=> SELECT INTERVAL '1000 12:00:01.123456' DAY(4) TO SECOND(6);
?column?
--------------------------1000 days 12:00:01.123456
AN HP Vertica extension lets you specify the seconds precision on the INTERVAL keyword. The
result is the same:
=> SELECT INTERVAL(6) '1000 12:00:01.123456' DAY(4) TO SECOND;
1000 days 12:00:01.123456
Casting with Intervals
You can cast a string to an interval:
=> SELECT CAST('3700 sec' AS INTERVAL);
?column?
---------01:01:40
You can cast an interval to a string:
=> SELECT CAST((SELECT INTERVAL '3700 seconds') AS VARCHAR(20));
?column?
---------01:01:40
You can cast intervals within the day/time or the year/month subtypes but not between them. Use
CAST to convert interval types:
=> SELECT CAST(INTERVAL '4440' MINUTE as INTERVAL);
?column?
---------3 days 2 hours
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=> SELECT CAST(INTERVAL -'01:15' as INTERVAL MINUTE);
?column?
----------75 mins
Processing Signed Intervals
In the SQL:2008 standard, a minus sign before an interval-literal or as the first character of the
interval-literal negates the entire literal, not just the first component. In HP Vertica, a leading minus
sign negates the entire interval, not just the first component. The following commands both return
the same value:
=> SELECT INTERVAL '-1 month - 1 second';
?column?
----------29 days 23:59:59
=> SELECT INTERVAL -'1 month - 1 second';
?column?
----------29 days 23:59:59
Use one of the following commands instead to return the intended result:
=> SELECT INTERVAL -'1 month 1 second';
?column?
----------30 days 1 sec
=> SELECT INTERVAL -'30 00:00:01';
?column?
----------30 days 1 sec
Two negatives together return a positive:
=> SELECT INTERVAL -'-1 month - 1 second';
?column?
---------29 days 23:59:59
=> SELECT INTERVAL -'-1 month 1 second';
?column?
---------30 days 1 sec
You can use the year-month syntax with no spaces. HP Vertica allows the input of negative months
but requires two negatives when paired with years.
=> SELECT INTERVAL '3-3' YEAR TO MONTH;
?column?
----------
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3 years 3 months
=> SELECT INTERVAL '3--3' YEAR TO MONTH;
?column?
---------2 years 9 months
When the interval-literal looks like a year/month type, but the type is day/second, or vice versa, HP
Vertica reads the interval-literal from left to right, where number-number is years-months, and
number is whatever the units specify. HP Vertica processes the
following command as (–) 1 year 1 month = (–) 365 + 30 = –395 days:
=> SELECT INTERVAL '-1-1' DAY TO HOUR;
?column?
----------395 days
If you insert a space in the interval-literal, HP Vertica processes it based on the subtype DAY TO
HOUR: (–) 1 day – 1 hour = (–) 24 – 1 = –23 hours:
=> SELECT INTERVAL '-1 -1' DAY TO HOUR;
?column?
----------23 hours
Two negatives together returns a positive, so HP Vertica processes the following command as (–) 1
year – 1 month = (–) 365 – 30 = –335 days:
=> SELECT INTERVAL '-1--1' DAY TO HOUR;
?column?
----------335 days
If you omit the value after the hyphen, HP Vertica assumes 0 months and processes the following
command as 1 year 0 month –1 day = 365 + 0 – 1 = –364 days:
=> SELECT INTERVAL '1- -1' DAY TO HOUR;
?column?
---------364 days
Processing Interval-Literals Without Units
You can specify quantities of days, hours, minutes, and seconds without explicit units. HP Vertica
recognizes colons in interval-literals as part of the timestamp:
=> SELECT INTERVAL '1 4 5 6';
?column?
------------
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1 day 04:05:06
=> SELECT INTERVAL '1 4:5:6';
?column?
-----------1 day 04:05:06
=> SELECT INTERVAL '1 day 4 hour 5 min 6 sec';
?column?
-----------1 day 04:05:06
If HP Vertica cannot determine the units, it applies the quantity to any missing units based on the
interval-qualifier. In the next two examples, HP Vertica uses the default interval-qualifier (DAY TO
SECOND(6)) and assigns the trailing 1 to days, since it has already processed hours, minutes, and
seconds in the output:
=> SELECT INTERVAL '4:5:6 1';
?column?
-----------1 day 04:05:06
=> SELECT INTERVAL '1 4:5:6';
?column?
-----------1 day 04:05:06
In the next two examples, HP Vertica recognizes 4:5 as hours:minutes. The remaining values in
the interval-literal are assigned to the missing units; 1 is assigned to days and 2 is assigned to
seconds:
SELECT INTERVAL '4:5 1 2';
?column?
-----------1 day 04:05:02
=> SELECT INTERVAL '1 4:5 2';
?column?
-----------1 day 04:05:02
Specifying the interval-qualifier can change how HP Vertica interprets 4:5:
=> SELECT INTERVAL '4:5' MINUTE TO SECOND;
?column?
-----------00:04:05
Using INTERVALYM for INTERVAL YEAR TO MONTH
INTERVALYM is an alias for the INTERVAL YEAR TO MONTH subtypes and is used only on input:
=> SELECT INTERVALYM '1 2';
?column?
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-----------1 year 2 months
Operations with Intervals
If you divide an interval by an interval, you get a FLOAT:
=> SELECT INTERVAL '28 days 3 hours' HOUR(4) / INTERVAL '27 days 3 hours' HOUR(4);
?column?
-----------1.036866359447
An INTERVAL divided by FLOAT returns an INTERVAL:
=> SELECT INTERVAL '3' MINUTE / 1.5;
?column?
-----------2 mins
INTERVAL MODULO (remainder) INTERVAL returns an INTERVAL:
=> SELECT INTERVAL '28 days 3 hours' HOUR % INTERVAL '27 days 3 hours' HOUR;
?column?
-----------24 hours
If you add INTERVAL and TIME, the result is TIME, modulo 24 hours:
=> SELECT INTERVAL '1' HOUR + TIME '1:30';
?column?
-----------02:30:00
Fractional Seconds in Interval Units
HP Vertica supports intervals in milliseconds (hh:mm:ss:ms), where 01:02:03:25 represents 1
hour, 2 minutes, 3 seconds, and 025 milliseconds. Milliseconds are converted to fractional seconds
as in the following example, which returns 1 day, 2 hours, 3 minutes, 4 seconds, and 25.5
milliseconds:
=> SELECT INTERVAL '1 02:03:04:25.5';
?column?
-----------1 day 02:03:04.0255
HP Vertica allows fractional minutes. The fractional minutes are rounded into seconds:
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=> SELECT INTERVAL '10.5 minutes';
?column?
-----------00:10:30
=> select interval '10.659 minutes';
?column?
------------00:10:39.54
=> select interval '10.3333333333333 minutes';
?column?
---------00:10:20
Notes
l
The HP Vertica INTERVAL data type is SQL:2008 compliant, with extensions. On HP Vertica
databases created prior to version 4.0, all INTERVAL columns are interpreted as INTERVAL DAY
TO SECOND, as in the previous releases.
l
An INTERVAL can include only the subset of units that you need; however, year/month intervals
represent calendar years and months with no fixed number of days, so year/month interval
values cannot include days, hours, minutes. When year/month values are specified for day/time
intervals, the intervals extension assumes 30 days per month and 365 days per year. Since the
length of a given month or year varies, day/time intervals are never output as months or years,
only as days, hours, minutes, and so on.
l
Day/time and year/month intervals are logically independent and cannot be combined with or
compared to each other. In the following example, an interval-literal that contains DAYS cannot be
combined with the YEAR TO MONTH type:
=> SELECT INTERVAL '1 2 3' YEAR TO MONTH;
ERROR 3679: Invalid input syntax for interval year to month: "1 2 3"
l
HP Vertica accepts intervals up to 2^63 – 1 microseconds or months (about 18 digits).
l
INTERVAL YEAR TO MONTH can be used in an analytic RANGE window when the ORDER BY
column type is TIMESTAMP/TIMESTAMP WITH TIMEZONE, or DATE. Using TIME/TIME WITH
TIMEZONE are not supported.
l
You can use INTERVAL DAY TO SECOND when the ORDER BY column type is
TIMESTAMP/TIMESTAMP WITH TIMEZONE, DATE, and TIME/TIME WITH TIMEZONE.
Examples
The table in this section contains additional interval examples. The INTERVALSTYLE is set to PLAIN
(omitting units on output) for brevity.
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Note: If you omit the Interval-Qualifier, the interval type defaults to DAY TO SECOND(6).
Command
Result
SELECT INTERVAL '00:2500:00';
1 17:40
SELECT INTERVAL '2500' MINUTE TO SECOND;
2500
SELECT INTERVAL '2500' MINUTE;
2500
SELECT INTERVAL '28 days 3 hours' HOUR TO SECOND;
675:00
SELECT INTERVAL(3) '28 days 3 hours';
28 03:00
SELECT INTERVAL(3) '28 days 3 hours 1.234567';
28 03:01:14.0
74
SELECT INTERVAL(3) '28 days 3 hours 1.234567 sec';
28 03:00:01.2
35
SELECT INTERVAL(3) '28 days 3.3 hours' HOUR TO SECOND;
675:18
SELECT INTERVAL(3) '28 days 3.35 hours' HOUR TO SECOND;
675:21
SELECT INTERVAL(3) '28 days 3.37 hours' HOUR TO SECOND;
675:22:12
SELECT INTERVAL '1.234567 days' HOUR TO SECOND;
29:37:46.5888
SELECT INTERVAL '1.23456789 days' HOUR TO SECOND;
29:37:46.6656
96
SELECT INTERVAL(3) '1.23456789 days' HOUR TO SECOND;
29:37:46.666
SELECT INTERVAL(3) '1.23456789 days' HOUR TO SECOND(2);
29:37:46.67
SELECT INTERVAL(3) '01:00:01.234567' as "one hour+";
01:00:01.235
SELECT INTERVAL(3) '01:00:01.234567' = INTERVAL(3) '01:00:01.234567';
t
SELECT INTERVAL(3) '01:00:01.234567' = INTERVAL '01:00:01.234567';
f
SELECT INTERVAL(3) '01:00:01.234567' = INTERVAL '01:00:01.234567' HOUR TO SE
COND(3);
t
SELECT INTERVAL(3) '01:00:01.234567' = INTERVAL '01:00:01.234567'MINUTE TO S
ECOND(3);
t
SELECT INTERVAL '255 1.1111' MINUTE TO SECOND(3);
255:01.111
SELECT INTERVAL '@ - 5 ago';
5
SELECT INTERVAL '@ - 5 minutes ago';
00:05
SELECT INTERVAL '@ 5 minutes ago';
-00:05
SELECT INTERVAL '@ ago -5 minutes';
00:05
SELECT DATE_PART('month', INTERVAL '2-3' YEAR TO MONTH);
3
SELECT FLOOR((TIMESTAMP '2005-01-17 10:00' - TIMESTAMP '2005-01-01') / INTER
VAL '7');
2
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See Also
l
Interval Values
l
SET INTERVALSTYLE
l
SET DATESTYLE
l
AGE_IN_MONTHS
l
AGE_IN_YEARS
Interval-Literal
The following table lists the units allowed for the required interval-literal parameter.
Unit
Description
a
Julian year, 365.25 days exactly
ago
Indicates negative time offset
c, cent, century
Century
centuries
Centuries
d, day
Day
days
Days
dec, decade
Decade
decades, decs
Decades
h, hour, hr
Hour
hours, hrs
Hours
ka
Julian kilo-year, 365250 days exactly
m
Minute or month for year/month, depending on context. See
Notes below this table.
microsecond
Microsecond
microseconds
Microseconds
mil, millennium
Millennium
millennia, mils
Millennia
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Unit
Description
millisecond
Millisecond
milliseconds
Milliseconds
min, minute, mm
Minute
mins, minutes
Minutes
mon, month
Month
mons, months
Months
ms, msec, millisecond
Millisecond
mseconds, msecs
Milliseconds
q, qtr, quarter
Quarter
qtrs, quarters
Quarters
s, sec, second
Second
seconds, secs
Seconds
us, usec
Microsecond
microseconds, useconds, usecs
Microseconds
w, week
Week
weeks
Weeks
y, year, yr
Year
years, yrs
Years
Processing the Input Unit 'm'
The input unit 'm' can represent either 'months' or 'minutes,' depending on the context. For
instance, the following command creates a one-column table with an interval value:
=> CREATE TABLE int_test(i INTERVAL YEAR TO MONTH);
In the first INSERT statement, the values are inserted as 1 year, six months:
=> INSERT INTO int_test VALUES('1 year 6 months');
The second INSERT statement results in an error from specifying minutes for a YEAR TO MONTH
interval. At runtime, the result will be a NULL:
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=> INSERT INTO int_test VALUES('1 year 6 minutes');
ERROR: invalid input syntax for type interval year to month: "1 year 6 minutes"
In the third INSERT statement, the 'm' is processed as months (not minutes), because DAY TO
SECOND is truncated:
=> INSERT INTO int_test VALUES('1 year 6 m');
-- the m counts as months
The table now contains two identical values, with no minutes:
=> SELECT * FROM int_test;
i
----1 year 6 months
1 year 6 months
(2 rows)
In the following command, the 'm' counts as minutes, because the DAY TO SECOND interval-qualifier
extracts day/time values from the input:
=> SELECT INTERVAL '1y6m' DAY TO SECOND;
?column?
----------365 days 6 mins
(1 row)
Interval-Qualifier
The following table lists the optional interval qualifiers. Values in INTERVAL fields, other than
SECOND, are integers with a default precision of 2 when they are not the first field.
You cannot combine day/time and year/month qualifiers. For example, the following intervals are
not allowed:
l
DAY TO YEAR
l
HOUR TO MONTH
Interval
Type
Day/time
intervals
Units
Valid interval-literal entries
DAY
Unconstrained.
DAY TO HOUR
An interval that represents a span of days and hours.
DAY TO MINUTE
An interval that represents a span of days and minutes.
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Interval
Type
Units
Valid interval-literal entries
DAY TO SECOND
(Default) interval that represents a span of days, hours,
minutes, seconds, and fractions of a second if subtype
unspecified.
HOUR
Hours within days.
HOUR TO MINUTE
An interval that represents a span of hours and minutes.
HOUR TO SECOND
An interval that represents a span of hours and seconds.
MINUTE
Minutes within hours.
MINUTE TO SECOND
An interval that represents a span of minutes and seconds.
SECOND
Seconds within minutes.
Note: The SECOND field can have an interval fractional seconds
precision, which indicates the number of decimal digits
maintained following the decimal point in the SECONDS value.
When SECOND is not the first field, it has a precision of 2 places
before the decimal point.
Year/month MONTH
intervals
Months within year.
YEAR
Unconstrained.
YEAR TO MONTH
An interval that represents a span of years and months.
SMALLDATETIME
SMALLDATETIME is an alias for TIMESTAMP.
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TIME
Consists of a time of day with or without a time zone.
Syntax
TIME [ (p) ] [ { WITH | WITHOUT } TIME ZONE ] | TIMETZ
[ AT TIME ZONE ]
Parameters
p
(Precision) specifies the number of fractional digits retained in the seconds
field. By default, there is no explicit bound on precision. The allowed range 0
to 6.
WITH TIME ZONE
Specifies that valid values must include a time zone
WITHOUT TIME ZONE
Specifies that valid values do not include a time zone (default). If a time zone
is specified in the input it is silently ignored.
TIMETZ
This is the same as TIME WITH TIME ZONE with no precision
Limits
Name
Low Value
High Value
Resolution
TIME [p]
00:00:00.00
23:59:60.999999
1 µs
TIME [p] WITH TIME ZONE
00:00:00.00+14 23:59:59.999999-14 1 µs
Example
Description
04:05:06.789
ISO 8601
04:05:06
ISO 8601
04:05
ISO 8601
040506
ISO 8601
04:05 AM
Same as 04:05; AM does not affect value
04:05 PM
Same as 16:05; input hour must be <= 12
04:05:06.789-8 ISO 8601
04:05:06-08:00 ISO 8601
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Example
Description
04:05-08:00
ISO 8601
040506-08
ISO 8601
04:05:06 PST
Time zone specified by name
Notes
l
HP Vertica permits coercion from TIME and TIME WITH TIME ZONE types to TIMESTAMP or
TIMESTAMP WITH TIME ZONE or INTERVAL (Day to Second).
l
HP Vertica supports adding milliseconds to a TIME or TIMETZ value.
=>
=>
=>
=>
=>
CREATE TABLE temp (datecol TIME);
INSERT INTO temp VALUES (TIME '12:47:32.62');
INSERT INTO temp VALUES (TIME '12:55:49.123456');
INSERT INTO temp VALUES (TIME '01:08:15.12374578');
SELECT * FROM temp;
datecol
----------------12:47:32.62
12:55:49.123456
01:08:15.123746
(3 rows)
See Also
l
Data Type Coercion Chart
TIME AT TIME ZONE
The TIME AT TIME ZONE construct converts TIMESTAMP and TIMESTAMP WITH ZONE types to
different time zones.
TIME ZONE is a synonym for TIMEZONE. Both are allowed in HP Vertica syntax.
Syntax
timestamp AT TIME ZONE zone
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Parameters
timestamp
zone
TIMESTAMP
Converts UTC to local time in given time
zone
TIMESTAMP WITH TIME ZONE
Converts local time in given time zone to
UTC
TIME WITH TIME ZONE
Converts local time across time zones
Desired time zone specified either as a text string (for example: 'PST') or as an
interval (for example: INTERVAL '-08:00'). In the text case, the available zone
names are abbreviations.
The files in /opt/vertica/share/timezonesets define the default list of strings
accepted in the zone parameter
Examples
The local time zone is PST8PDT. The first example takes a zone-less timestamp and interprets it as
MST time (UTC-7) to produce a UTC timestamp, which is then rotated to PST (UTC-8) for display:
=> SELECT TIMESTAMP '2001-02-16 20:38:40' AT TIME ZONE 'MST';
timezone
-----------------------2001-02-16 22:38:40-05
(1 row)
The second example takes a timestamp specified in EST (UTC-5) and converts it to local time in
MST (UTC-7):
=> SELECT TIMESTAMP WITH TIME ZONE '2001-02-16 20:38:40-05' AT TIME ZONE 'MST';
timezone
--------------------2001-02-16 18:38:40
(1 row)
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TIMESTAMP
Consists of a date and a time with or without a time zone and with or without a historical epoch (AD
or BC).
Syntax
TIMESTAMP [ (p) ] [ { WITH | WITHOUT } TIME ZONE ] | TIMESTAMPTZ[
AT TIME ZONE
]
Parameters
p
Optional precision value that specifies the number of fractional digits
retained in the seconds field. By default, there is no explicit bound on
precision. The allowed range of p is 0 to 6.
WITH TIME ZONE
Specifies that valid values must include a time zone. All TIMESTAMP WITH
TIME ZONE values are stored internally in UTC.
They are converted to local time in the zone specified by the time zone
configuration parameter before being displayed to the client.
WITHOUT TIME ZONE
Specifies that valid values do not include a time zone (default). If a time zone
is specified in the input it is silently ignored.
TIMESTAMPTZ
This is the same as TIMESTAMP WITH TIME ZONE.
Limits
In the following table, values are rounded. See Date/Time Data Types for additional detail.
Name
Low Value High Value Resolution
TIMESTAMP [ (p) ] [ WITHOUT TIME ZONE ]
290279 BC
294277 AD
1 µs
TIMESTAMP [ (p) ] WITH TIME ZONE
290279 BC
294277 AD
1 µs
Notes
l
TIMESTAMP is an alias for DATETIMEand SMALLDATETIME.
l
Valid input for TIMESTAMP types consists of a concatenation of a date and a time, followed by an
optional time zone, followed by an optional AD or BC.
l
AD/BC can appear before the time zone, but this is not the preferred ordering.
l
The SQL standard differentiates TIMESTAMP WITHOUT TIME ZONE and TIMESTAMP WITH TIME
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ZONE literals by the existence of a "+"; or "-". Hence, according to the standard:
TIMESTAMP '2004-10-19 10:23:54' is a TIMESTAMP WITHOUT TIME ZONE.
TIMESTAMP '2004-10-19 10:23:54+02' is a TIMESTAMP WITH TIME ZONE.
Note: HP Vertica differs from the standard by requiring that TIMESTAMP WITH TIME ZONE
literals be explicitly typed:
TIMESTAMP WITH TIME ZONE '2004-10-19 10:23:54+02'
l
If a literal is not explicitly indicated as being of TIMESTAMP WITH TIME ZONE, HP Vertica silently
ignores any time zone indication in the literal. That is, the resulting date/time value is derived
from the date/time fields in the input value, and is not adjusted for time zone.
l
For TIMESTAMP WITH TIME ZONE, the internally stored value is always in UTC. An input value
that has an explicit time zone specified is converted to UTC using the appropriate offset for that
time zone. If no time zone is stated in the input string, then it is assumed to be in the time zone
indicated by the system's TIME ZONE parameter, and is converted to UTC using the offset for
the TIME ZONE zone.
l
When a TIMESTAMP WITH TIME ZONE value is output, it is always converted from UTC to the
current TIME ZONE zone and displayed as local time in that zone. To see the time in another time
zone, either change TIME ZONE or use the AT TIME ZONE construct.
l
Conversions between TIMESTAMP WITHOUT TIME ZONE and TIMESTAMP WITH TIME ZONE
normally assume that the TIMESTAMP WITHOUT TIME ZONE value are taken or given as TIME
ZONE local time. A different zone reference can be specified for the conversion using AT TIME
ZONE.
l
TIMESTAMPTZ and TIMETZ are not parallel SQL constructs. TIMESTAMPTZ records a time and date
in GMT, converting from the specified TIME ZONE. TIMETZ records the specified time and the
specified time zone, in minutes, from GMT.timezone
l
The following list represents typical date/time input variations:
n
1999-01-08 04:05:06
n
1999-01-08 04:05:06 -8:00
n
January 8 04:05:06 1999 PST
l
HP Vertica supports adding a floating-point (in days) to a TIMESTAMP or TIMESTAMPTZ
value.
l
HP Vertica supports adding milliseconds to a TIMESTAMP or TIMESTAMPTZ value.
l
In HP Vertica, intervals are represented internally as some number of microseconds and printed
as up to 60 seconds, 60 minutes, 24 hours, 30 days, 12 months, and as many years as
necessary. Fields are either positive or negative.
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Examples
You can return infinity by specifying 'infinity':
=> SELECT TIMESTAMP 'infinity';
timestamp
----------infinity
(1 row)
To use the minimum TIMESTAMP value lower than the minimum rounded value:
=> SELECT '-infinity'::timestamp;
timestamp
-----------infinity
(1 row)
TIMESTAMP/TIMESTAMPTZ has +/-infinity values.
AD/BC can be placed almost anywhere within the input string; for example:
SELECT TIMESTAMPTZ 'June BC 1, 2000 03:20 PDT';
timestamptz
--------------------------2000-06-01 05:20:00-05 BC
(1 row)
Notice the results are the same if you move the BC after the 1:
SELECT TIMESTAMPTZ 'June 1 BC, 2000 03:20 PDT';
timestamptz
--------------------------2000-06-01 05:20:00-05 BC
(1 row)
And the same if you place the BC in front of the year:
SELECT TIMESTAMPTZ 'June 1, BC 2000 03:20 PDT';
timestamptz
--------------------------2000-06-01 05:20:00-05 BC
(1 row);
The following example returns the year 45 before the Common Era:
=> SELECT TIMESTAMP 'April 1, 45 BC';
timestamp
-----------------------0045-04-01 00:00:00 BC
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(1 row)
If you omit the BC from the date input string, the system assumes you want the year 45 in the
current century:
=> SELECT TIMESTAMP 'April 1, 45';
timestamp
--------------------2045-04-01 00:00:00
(1 row)
In the following example, HP Vertica returns results in years, months, and days, whereas other
RDBMS might return results in days only:
=> SELECT TIMESTAMP WITH TIME ZONE '02/02/294276'- TIMESTAMP WITHOUT TIME ZONE '02/20/200
9' AS result;
result
-----------------------------292266 years 11 mons 12 days
(1 row)
To specify a specific time zone, add it to the statement, such as the use of 'ACST' in the following
example:
=> SELECT T1 AT TIME ZONE 'ACST', t2 FROM test;
timezone
| t2
---------------------+------------2009-01-01 04:00:00 | 02:00:00-07
2009-01-01 01:00:00 | 02:00:00-04
2009-01-01 04:00:00 | 02:00:00-06
You can specify a floating point in days:
=> SELECT 'NOW'::TIMESTAMPTZ + INTERVAL '1.5 day' AS '1.5 days from now';
1.5 days from now
------------------------------2009-03-18 21:35:23.633-04
(1 row)
The following example illustrates the difference between TIMESTAMPTZ with and without a
precision specified:
=> SELECT TIMESTAMPTZ(3) 'now', TIMESTAMPTZ 'now';
timestamptz
timestamptz
----------------------------+------------------------------2009-02-24 11:40:26.177-05 | 2009-02-24 11:40:26.177368-05
(1 row)
|
The following statement returns an error because the TIMESTAMP is out of range:
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=> SELECT TIMESTAMP '294277-01-09 04:00:54.775808';
ERROR: date/time field value out of range: "294277-01-09 04:00:54.775808"
There is no 0 AD, so be careful when you subtract BC years from AD years:
=> SELECT EXTRACT(YEAR FROM TIMESTAMP '2001-02-16 20:38:40');
date_part
----------2001
(1 row)
The following commands create a table with a TIMESTAMP column that contains milliseconds:
CREATE TABLE temp (datecol TIMESTAMP);
INSERT INTO temp VALUES (TIMESTAMP '2010-03-25 12:47:32.62');
INSERT INTO temp VALUES (TIMESTAMP '2010-03-25 12:55:49.123456');
INSERT INTO temp VALUES (TIMESTAMP '2010-03-25 01:08:15.12374578');
SELECT * FROM temp;
datecol
---------------------------2010-03-25 12:47:32.62
2010-03-25 12:55:49.123456
2010-03-25 01:08:15.123746
(3 rows)
Additional Examples
Command
Result
select (timestamp '2005-01-17 10:00' - timestamp '2005-01-01');
16 10:10
select (timestamp '2005-01-17 10:00' - timestamp '2005-01-01') / 7;
2 08:17:08.571429
select (timestamp '2005-01-17 10:00' - timestamp '2005-01-01') day;
16
select cast((timestamp '2005-01-17 10:00' - timestamp '2005-01-01') d
ay as integer) / 7;
2
select floor((timestamp '2005-01-17 10:00' - timestamp '2005-01-01')
/ interval '7');
2
select timestamptz '2009-05-29 15:21:00.456789';
2009-05-2915:21:00.4
56789-04
select timestamptz '2009-05-28';
2009-05-2800:00:00-0
4
select timestamptz '2009-05-29 15:21:00.456789'-timestamptz '2009-0528';
1 15:21:00.456789
select (timestamptz '2009-05-29 15:21:00.456789'-timestamptz '2009-0
5-28');
1 15:21:00.456789
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Command
Result
select (timestamptz '2009-05-29 15:21:00.456789'-timestamptz '2009-0
5-28')(3);
1 15:21:00.457
select (timestamptz '2009-05-29 15:21:00.456789'-timestamptz '2009-0
5-28')second;
141660.456789
select (timestamptz '2009-05-29 15:21:00.456789'-timestamptz '2009-0
5-28') year;
0
select (timestamptz '2009-05-29 15:21:00.456789'-timestamptz '2007-0
1-01') month;
28
select (timestamptz '2009-05-29 15:21:00.456789'-timestamptz '2007-0
1-01') year;
2
select (timestamptz '2009-05-29 15:21:00.456789'-timestamptz '2007-0
1-01') year to month;
2-4
select (timestamptz '2009-05-29 15:21:00.456789'-timestamptz '2009-0
5-28') second(3);
141660.457
select (timestamptz '2009-05-29 15:21:00.456789'-timestamptz '2009-0
5-28') minute(3);
2361
select (timestamptz '2009-05-29 15:21:00.456789'-timestamptz '2009-0
5-28') minute;
2361
select (timestamptz '2009-05-29 15:21:00.456789'-timestamptz '2009-0
5-28') minute to second(3);
2361:00.457
select (timestamptz '2009-05-29 15:21:00.456789'-timestamptz '2009-0
5-28') minute to second;
2361:00.456789
TIMESTAMP AT TIME ZONE
The TIMESTAMP AT TIME ZONE (or TIMEZONE) construct converts TIMESTAMP and TIMESTAMP WITH
TIMEZONE intervals to different time zones.
Note: TIME ZONE is a synonym for TIMEZONE. Both are allowed in HP Vertica syntax.
Syntax
timestamp AT TIME ZONE zone
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Parameters
timestamp
zone
TIMESTAMP
Converts UTC to local time in the given
time zone
TIMESTAMP WITH TIME ZONE
Converts local time in given time zone to
UTC
TIME
Converts local time.
TIME WITH TIME ZONE
Converts local time across time zones
Specifies the time zone either as a text string, (such as 'America/Chicago') or as
an interval (INTERVAL '-08:00'). The preferred way to express a time zone is in the
format 'America/Chicago'.
For a list of time zone text strings, see TZ Environment Variable in the Installation
Guide.
To view the default list of acceptable strings for the zone parameter, see the files in:
/opt/vertica/share/timezonesets
Examples
If you indicate a TIME interval timezone (such as America/Chicago in the following example), the
interval function converts the interval to the timezone you specify and includes the UTC offset
value (-05 here):
=> select time '10:00' at time zone 'America/Chicago';
?column?
--------------------09:00:00-05
(1 row)
Casting a TIMESTAMPTZ interval to a TIMESTAMP without a zone depends on the local time
zone.
=> select (varchar '2013-03-31 5:10 AMERICA/CHICAGO')::timestamp;
?column?
--------------------2013-03-31 06:10:00
(1 row)
Note: For a complete list of valid time zone definitions, see Wikipedia - tz database time
zones.
Casting a TIME (or TIMETZ) interval to a TIMESTAMP returns the local date and time, without the
UTC offset:
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=> select (time '3:01am')::timestamp;
?column?
--------------------2012-08-30 03:01:00
(1 row)
=> select (timetz '3:01am')::timestamp;
?column?
--------------------2012-08-22 03:01:00
(1 row)
Casting the same interval (TIME or TIMETZ) to a TIMESTAMPTZ returns the local date and time
appended with the UTC offset (-04 here):
=> select (time '3:01am')::timestamptz;
?column?
--------------------2012-08-30 03:01:00-04
(1 row)
Long Data Types
Store data up to 32,000,000 bytes:
l
LONG VARBINARY—Variable-length raw-byte data, such as IP addresses. LONG VARBINARY
values are not extended to the full width of the column.
l
LONG VARCHAR—Variable-length strings of letters, numbers, and symbols. LONG VARCHAR values
are not extended to the full width of the column.
The maximum size for the LONG data types is 32,000,000 bytes. Use the LONG data types only when
you need to store data greater than 65,000 bytes, which is the maximum size for VARBINARY and
VARCHAR data types. Such data might include unstructured data, online comments or posts, or small
log files.
Syntax
LONG VARBINARY ( max_length )
LONG VARCHAR ( octet_length )
Parameters
max_length
Specifies the length of the byte string (column width, declared in bytes (octets)), in
CREATE TABLE statements). Maximum value: 32,000,000.
octet_length
Specifies the length of the string (column width, declared in bytes (octets)), in
CREATE TABLE statements). Maximum value: 32,000,000.
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Notes
Data type coercion for the LONG data types is limited. HP Vertica only supports coercion from LONG
VARBINARY to VARCHAR and vice versa. Converting a data type from a shorter type to a longer type is
implicit; to convert from a longer type to a shorter type requires an explicit cast.
For optimal performance of LONG data types, HP Vertica recommends that you:
l
Use the LONG data types as "storage only" containers; HP Vertica does not support operations
on their content.
l
Use the VARBINARY and VARCHAR data types, instead of their LONG counterparts, whenever
possible. The VARBINARY and VARCHAR data types are more flexible and have a wider range of
operations.
l
Use efficient encoding formats for LONG data types, even if the decoded value is less than
32,000,000. For example, HP Vertica returns an error if you attempt to load a 32,000,000-byte
LONG VARBINARY value encoded in octal format, because the octal encoding quadruples the size
of the value; each byte is converted into a backslash followed by three-digit values.
l
Do not sort, segment, or partition projections on LONG data type columns.
l
Do not add constraints, such as primary key, to any LONG VARBINARY or LONG VARCHAR
columns.
l
Do not join or aggregate any LONG data type columns.
Example
The following example creates a table user_comments with a LONG VARCHAR column and inserts
data into it:
=> CREATE TABLE user_comments VALUES
(
id INTEGER,
username VARCHAR(200)
time_posted TIMESTAMP,
comment_text LONG VARCHAR(200000)
);
=> INSERT INTO user_comments VALUES
(
1,
'User1',
TIMESTAMP '2013-06-25 12:47:32.62',
'The weather tomorrow will be cold and rainy and then
on the day after, the sun will come and the temperature
will rise dramatically.'
);
=> INSERT INTO user_comments VALUES
(
2,
'User2',
TIMESTAMP '2013-06-25 12:55:49.123456',
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'To get to the main library entrance, take Exit 21,
turn left at the end of the exit ramp onto Rt. 333,
travel four miles, and the parking lot will be on the
left-hand side of the street.'
);
=> INSERT INTO user_comments VALUES (
(
3,
'User3',
TIMESTAMP '2013-06-25 01:08:15.12374578',
'To purchase your tickets for this event, contact
the box office. Tickets will be sold on a first-come,
first-serve basis, and are nonrefundable.'
);
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Numeric Data Types
Numeric data types are numbers stored in database columns. These data types are typically
grouped by:
l
Exact numeric types, values where the precision and scale need to be preserved. The exact
numeric types are BIGINT, DECIMAL, INTEGER, NUMERIC, NUMBER, and MONEY.
l
Approximate numeric types, values where the precision needs to be preserved and the scale
can be floating. The approximate numeric types are DOUBLE PRECISION, FLOAT, and REAL.
Implicit casts from INTEGER, FLOAT, and NUMERIC to VARCHAR are not supported. If you need that
functionality, write an explicit cast using one of the following forms:
CAST(x AS data-type-name) or x::data-type-name
The following example casts a float to an integer:
=> SELECT(FLOAT '123.5')::INT;
?column?
---------124
(1 row)
String-to-numeric data type conversions accept formats of quoted constants for scientific notation,
binary scaling, hexadecimal, and combinations of numeric-type literals:
l
Scientific notation:
=> SELECT FLOAT '1e10';
?column?
------------10000000000
(1 row)
l
BINARY scaling:
=> SELECT NUMERIC '1p10';
?column?
---------1024
(1 row)
l
Hexadecimal:
=> SELECT NUMERIC '0x0abc';
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?column?
---------2748
(1 row)
DOUBLE PRECISION (FLOAT)
HP Vertica supports the numeric data type DOUBLE PRECISION, which is the IEEE-754 8-byte
floating point type, along with most of the usual floating point operations.
Syntax
[ DOUBLE PRECISION | FLOAT | FLOAT(n) | FLOAT8 | REAL ]
Parameters
Note: On a machine whose floating-point arithmetic does not follow IEEE-754, these values
probably do not work as expected.
Double precision is an inexact, variable-precision numeric type. In other words, some values
cannot be represented exactly and are stored as approximations. Thus, input and output operations
involving double precision might show slight discrepancies.
l
All of the DOUBLE PRECISION data types are synonyms for 64-bit IEEE FLOAT.
l
The n in FLOAT(n) must be between 1 and 53, inclusive, but a 53-bit fraction is always used.
See the IEEE-754 standard for details.
l
For exact numeric storage and calculations (money for example), use NUMERIC.
l
Floating point calculations depend on the behavior of the underlying processor, operating
system, and compiler.
l
Comparing two floating-point values for equality might not work as expected.
Values
COPY accepts floating-point data in the following format:
l
Optional leading white space
l
An optional plus ("+") or minus sign ("-")
l
A decimal number, a hexadecimal number, an infinity, a NAN, or a null value
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A decimal number consists of a non-empty sequence of decimal digits possibly containing a radix
character (decimal point "."), optionally followed by a decimal exponent. A decimal exponent
consists of an "E" or "e", followed by an optional plus or minus sign, followed by a non-empty
sequence of decimal digits, and indicates multiplication by a power of 10.
A hexadecimal number consists of a "0x" or "0X" followed by a non-empty sequence of
hexadecimal digits possibly containing a radix character, optionally followed by a binary exponent.
A binary exponent consists of a "P" or "p", followed by an optional plus or minus sign, followed by a
non-empty sequence of decimal digits, and indicates multiplication by a power of 2. At least one of
radix character and binary exponent must be present.
An infinity is either INF or INFINITY, disregarding case.
A NaN (Not A Number) is NAN (disregarding case) optionally followed by a sequence of characters
enclosed in parentheses. The character string specifies the value of NAN in an implementationdependent manner. (The HP Vertica internal representation of NAN is 0xfff8000000000000LL on
x86 machines.)
When writing infinity or NAN values as constants in a SQL statement, enclose them in single
quotes. For example:
=> UPDATE table SET x = 'Infinity'
Note: HP Vertica follows the IEEE definition of NaNs (IEEE 754). The SQL standards do not
specify how floating point works in detail.
IEEE defines NaNs as a set of floating point values where each one is not equal to anything,
even to itself. A NaN is not greater than and at the same time not less than anything, even
itself. In other words, comparisons always return false whenever a NaN is involved.
However, for the purpose of sorting data, NaN values must be placed somewhere in the result.
The value generated 'NaN' appears in the context of a floating point number matches the NaN
value generated by the hardware. For example, Intel hardware generates
(0xfff8000000000000LL), which is technically a Negative, Quiet, Non-signaling NaN.
HP Vertica uses a different NaN value to represent floating point NULL (0x7ffffffffffffffeLL).
This is a Positive, Quiet, Non-signaling NaN and is reserved by HP Vertica
The load file format of a null value is user defined, as described in the COPY command. The HP
Vertica internal representation of a null value is 0x7fffffffffffffffLL. The interactive format is
controlled by the vsql printing option null. For example:
\pset null '(null)'
The default option is not to print anything.
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Rules
l
-0 == +0
l
1/0 = Infinity
l
0/0 == Nan
l
NaN != anything (even NaN)
To search for NaN column values, use the following predicate:
... WHERE column != column
This is necessary because WHERE column = 'Nan' cannot be true by definition.
Sort Order (Ascending)
l
NaN
l
-Inf
l
numbers
l
+Inf
l
NULL
Notes
l
NULL appears last (largest) in ascending order.
l
All overflows in floats generate +/-infinity or NaN, per the IEEE floating point standard.
INTEGER
A signed 8-byte (64-bit) data type.
Syntax
[ INTEGER | INT | BIGINT | INT8 | SMALLINT | TINYINT ]
Parameters
INT, INTEGER, INT8, SMALLINT, TINYINT, and BIGINT are all synonyms for the same signed 64-bit
integer data type. Automatic compression techniques are used to conserve disk space in cases
where the full 64 bits are not required.
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Notes
l
The range of values is –2^63+1 to 2^63-1.
l
2^63 = 9,223,372,036,854,775,808 (19 digits).
l
The value –2^63 is reserved to represent NULL.
l
NULL appears first (smallest) in ascending order.
l
HP Vertica does not have an explicit 4-byte (32-bit integer) or smaller types. HP Vertica's
encoding and compression automatically eliminate the storage overhead of values that fit in less
than 64 bits.
Restrictions
l
The JDBC type INTEGER is 4 bytes and is not supported by HP Vertica. Use BIGINT instead.
l
HP Vertica does not support the SQL/JDBC types NUMERIC, SMALLINT, or TINYINT.
l
HP Vertica does not check for overflow (positive or negative) except in the aggregate function
SUM(). If you encounter overflow when using SUM, use SUM_FLOAT(), which converts to floating
point.
See Also
Data Type Coercion Chart
NUMERIC
Numeric data types store numeric data. For example, a money value of $123.45 can be stored in a
NUMERIC(5,2) field.
Syntax
NUMERIC | DECIMAL | NUMBER | MONEY [ ( precision [ , scale ] ) ]
Parameters
precision
The total number of significant digits that the data type stores. precision must be
positive and <= 1024. If you assign a value that exceeds the precision value, an error
occurs.
scale
The maximum number of digits to the right of the decimal point that the data type
stores. scale must be non-negative and less than or equal to precision. If you omit the
scale parameter, the scale value is set to 0. If you assign a value with more decimal
digits than scale, the value is rounded to scale digits.
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Notes
l
l
NUMERIC, DECIMAL, NUMBER, and MONEY are all synonyms that return NUMERIC types. However,
the default values NUMBER and MONEY are different.
Type
Precision
Scale
NUMERIC
37
15
DECIMAL
37
15
NUMBER
38
0
MONEY
18
4
NUMERIC data types support exact representations of numbers that can be expressed with a
number of digits before and after a decimal point. This contrasts slightly with existing HP Vertica
data types:
n
DOUBLE PRECISION (FLOAT) types support ~15 digits, variable exponent, and represent
numeric values approximately.
n
INTEGER (and similar) types support ~18 digits, whole numbers only.
l
NUMERIC data types are generally called exact numeric data types because they store numbers
of a specified precision and scale. The approximate numeric data types, such as DOUBLE
PRECISION, use floating points and are less precise.
l
Supported numeric operations include the following:
n
Basic math: +, –, *, /
n
Aggregation: SUM, MIN, MAX, COUNT
n
Comparison operators: <, <=, =, <=>, <>, >, >=
l
NUMERIC divide operates directly on numeric values, without converting to floating point. The
result has at least 18 decimal places and is rounded.
l
NUMERIC mod (including %) operates directly on numeric values, without converting to floating
point. The result has the same scale as the numerator and never needs rounding.
l
NULL appears first (smallest) in ascending order.
l
COPY accepts a DECIMAL data type with a decimal point ('.'), prefixed by – or +(optional).
l
LZO, RLE, and BLOCK_DICT are supported encoding types. Anything that can be used on an
INTEGER can also be used on a NUMERIC, as long as the precision is <= 18.
l
The NUMERIC data type is preferred for non-integer constants, because it is always exact. For
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example:
=> SELECT 1.1 + 2.2 = 3.3;
?column?
---------t
(1 row)
=> SELECT 1.1::float + 2.2::float = 3.3::float;
?column?
---------f
(1 row)
l
Performance of the NUMERIC data type has been fine tuned for the common case of 18 digits of
precision.
l
Some of the more complex operations used with NUMERIC data types result in an implicit cast to
FLOAT. When using SQRT, STDDEV, transcendental functions such as LOG, and TO_CHAR/TO_
NUMBER formatting, the result is always FLOAT.
Examples
The following series of commands creates a table that contains a NUMERIC data type and then
performs some mathematical operations on the data:
=> CREATE TABLE num1 (id INTEGER, amount NUMERIC(8,2));
Insert some values into the table:
=> INSERT INTO num1 VALUES (1, 123456.78);
Query the table:
=> SELECT * FROM num1;
id | amount
------+----------1 | 123456.78
(1 row)
The following example returns the NUMERIC column, amount, from table num1:
=> SELECT amount FROM num1;
amount
----------123456.78
(1 row)
The following syntax adds one (1) to the amount:
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=> SELECT amount+1 AS 'amount' FROM num1;
amount
----------123457.78
(1 row)
The following syntax multiplies the amount column by 2:
=> SELECT amount*2 AS 'amount' FROM num1;
amount
----------246913.56
(1 row)
The following syntax returns a negative number for the amount column:
=> SELECT -amount FROM num1;
?column?
------------123456.78
(1 row)
The following syntax returns the absolute value of the amount argument:
=> SELECT ABS(amount) FROM num1;
ABS
----------123456.78
(1 row)
The following syntax casts the NUMERIC amount as a FLOAT data type:
=> SELECT amount::float FROM num1;
amount
----------123456.78
(1 row)
See Also
Mathematical Functions
Numeric Data Type Overflow
HP Vertica does not check for overflow (positive or negative) except in the aggregate function SUM
(). If you encounter overflow when using SUM, use SUM_FLOAT() which converts to floating point.
Dividing zero by zero returns zero:
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=> select 0/0;
?column?
---------------------0.000000000000000000
(1 row)
=> select 0.0/0;
?column?
----------------------0.0000000000000000000
=> select 0 // 0;
?column?
---------0
Dividing zero as a FLOAT by zero returns NaN:
=> select 0.0::float/0;
?column?
---------NaN
=> select 0.0::float//0;
?column?
---------NaN
Dividing a non-zero FLOAT by zero returns Infinity:
=> select 2.0::float/0;
?column?
---------Infinity
=> select 200.0::float//0;
?column?
---------Infinity
All other division-by-zero operations return an error:
=> select 1/0;
ERROR 3117: Division by
=> select 200/0;
ERROR 3117: Division by
=> select 200.0/0;
ERROR 3117: Division by
=> select 116.43 // 0;
ERROR 3117: Division by
zero
zero
zero
zero
Add, subtract, and multiply operations ignore overflow. Sum and average operations use 128-bit
arithmetic internally. SUM() reports an error if the final result overflows, suggesting the use of SUM_
FLOAT(INT), which converts the 128-bit sum to a FLOAT8. For example:
=> CREATE TEMP TABLE t (i INT);
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=>
=>
=>
=>
=>
=>
INSERT INTO t VALUES (1<<62);
INSERT INTO t VALUES (1<<62);
INSERT INTO t VALUES (1<<62);
INSERT INTO t VALUES (1<<62);
INSERT INTO t VALUES (1<<62);
SELECT SUM(i) FROM t;
ERROR: sum() overflowed
HINT: try sum_float() instead
=> SELECT SUM_FLOAT(i) FROM t;
sum_float
--------------------2.30584300921369e+19
Data Type Coercion
HP Vertica supports two types of data type casting:
l
Implicit casting—Occurs when the expression automatically converts the data from one type to
another.
l
Explicit casting—Occurs when you write a SQL statement that specifies the target data type for
the conversion.
The ANSI SQL-92 standard supports implicit casting between similar data types:
l
Number types
l
CHAR, VARCHAR, LONG VARCHAR
l
BINARY, VARBINARY, LONG VARBINARY
HP Vertica supports two types of non-standard implicit casts:
l
From CHAR to FLOAT, to match the one from VARCHAR to FLOAT. The following example
converts the CHAR '3' to a FLOAT so it can add the number 334 to the FLOAT result of the
second expression:
=> SELECT '3' + 4.33::NUMERIC(3,2);
?column?
---------7.33
(1 row)
l
Between DATE and TIMESTAMP. The following example DATE to a TIMESTAMP and
calculates the time 6 hours, 6 minutes, and 6 seconds back from 12:00 AM:
=> SELECT DATE('now') - INTERVAL '6:6:6';
?column?
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--------------------2013-07-30 17:53:54
(1 row)
When there is no ambiguity about the data type of an expression value, it is implicitly coerced to
match the expected data type. In the following command, the quoted string constant '2' is implicitly
coerced into an INTEGER value so that it can be the operand of an arithmetic operator (addition):
=> SELECT 2 + '2';
?column?
---------4
(1 row)
A concatenate operation explicitly takes arguments of any data type. In the following example, the
concatenate operation implicitly coerces the arithmetic expression 2 + 2 and the INTEGER
constant 2 to VARCHAR values so that they can be concatenated.
=> SELECT 2 + 2 || 2;
?column?
---------42
(1 row)
Another example is to first get today's date:
=> SELECT DATE 'now';
?column?
-----------2013-07-31
(1 row)
The following command converts DATE to a TIMESTAMP and adds a day and a half to the results
by using INTERVAL:
=> SELECT DATE 'now' + INTERVAL '1 12:00:00';
?column?
--------------------2013-07-31 12:00:00
(1 row)
Most implicit casts stay within their relational family and go in one direction, from less detailed to
more detailed. For example:
l
DATE to TIMESTAMP/TZ
l
INTEGER to NUMERIC to FLOAT
l
CHAR to FLOAT
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l
CHAR to VARCHAR
l
CHAR and/or VARCHAR to FLOAT
l
CHAR to LONG VARCHAR
l
VARCHAR to LONG VARCHAR
l
BINARY to VARBINARY
l
BINARY to LONG VARBINARY
l
VARBINARY to LONG VARBINARY
More specifically, data type coercion works in this manner in HP Vertica:
Type
Direction Type
Notes
INT8
>
FLOAT8
Implicit, can lose significance
FLOAT8
>
INT8
Explicit, rounds
VARCHAR
<->
CHAR
Implicit, adjusts trailing spaces
VARBINARY <->
BINARY
Implicit, adjusts trailing NULs
VARCHAR
LONG VARCHAR
Implicit, adjusts trailing spaces
>
VARBINARY >
LONG VARBINARY Implicit, adjusts trailing NULs
No other types cast to or from LONGVARBINARY, VARBINARY, or BINARY. In the following list,
means one these types: INT8, FLOAT8, DATE, TIME, TIMETZ, TIMESTAMP,
TIMESTAMPTZ, INTERVAL.
l
-> VARCHAR—implicit
l
VARCHAR -> —explicit, except that VARCHAR->FLOAT is implicit
l
<-> CHAR—explicit
l
DATE -> TIMESTAMP/TZ—implicit
l
TIMESTAMP/TZ -> DATE—explicit, loses time-of-day
l
TIME -> TIMETZ—implicit, adds local timezone
l
TIMETZ -> TIME—explicit, loses timezone
l
TIME -> INTERVAL—implicit, day to second with days=0
l
INTERVAL -> TIME—explicit, truncates non-time parts
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l
TIMESTAMP <-> TIMESTAMPTZ—implicit, adjusts to local timezone
l
TIMESTAMP/TZ -> TIME—explicit, truncates non-time parts
l
TIMESTAMPTZ -> TIMETZ—explicit
l
VARBINARY -> LONG VARBINARY—implicit
l
LONG VARBINARY -> VARBINARY—explicit
l
VARCHAR -> LONG VARCHAR—implicit
l
LONG VARCHAR -> VARCHAR—explicit
Important: Implicit casts from INTEGER, FLOAT, and NUMERIC to VARCHAR are not
supported. If you need that functionality, write an explicit cast:
CAST(x AS data-type-name)
or
x::data-type-name
The following example casts a FLOAT to an INTEGER:
=> SELECT(FLOAT '123.5')::INT;
?column?
---------124
(1 row)
String-to-numeric data type conversions accept formats of quoted constants for scientific notation,
binary scaling, hexadecimal, and combinations of numeric-type literals:
l
Scientific notation:
=> SELECT FLOAT '1e10';
?column?
------------10000000000
(1 row)
l
BINARY scaling:
=> SELECT NUMERIC '1p10';
?column?
---------1024
(1 row)
l
Hexadecimal:
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=> SELECT NUMERIC '0x0abc';
?column?
---------2748
(1 row)
Examples
The following example casts three strings as NUMERICs:
=> SELECT NUMERIC '12.3e3', '12.3p10'::NUMERIC, CAST('0x12.3p-10e3' AS NUMERIC);
?column? | ?column? |
?column?
----------+----------+------------------12300 | 12595.2 | 17.76123046875000
(1 row)
This example casts a VARBINARY string into a LONG VARBINARY data type:
=> SELECT B'101111000'::LONG VARBINARY;
?column?
---------\001x
(1 row)
The following example concatenates a CHAR with a LONG VARCHAR, resulting in a LONG
VARCHAR:
=> \set s ''''`cat longfile.txt`''''
=> SELECT length ('a' || :s ::LONG VARCHAR);
length
---------65002
(1 row)
The following example casts a combination of NUMERIC and INTEGER data into a NUMERIC
result:
=> SELECT (18. + 3./16)/1024*1000;
?column?
----------------------------------------17.761230468750000000000000000000000000
(1 row)
Note: In SQL expressions, pure numbers between (–2^63–1) and (2^63–1) are INTEGERs.
Numbers with decimal points are NUMERIC.
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See Also
l
Data Type Coercion Chart
l
Data Type Coercion Operators (CAST)
Data Type Coercion Chart
Conversion Types
The following table defines all possible type conversions that HP Vertica supports. The values
across the top row are the data types you want, and the values down the first column on the left are
the data types that you have.
Conversion Types
Data Types
Implicit
Explicit
BOOLEAN
Assignment
Assignment
without numeric
meaning
Conversion
without explicit
casting
INTEGER
LONG VARCHAR
VARCHAR
CHAR
INTEGER
BOOLEAN
NUMERIC
FLOAT
INTERVAL
DAY/SECOND
INTERVAL
YEAR/MONTH
LONG VARCHAR
VARCHAR
CHAR
NUMERIC
FLOAT
INTEGER
LONG VARCHAR
VARCHAR
CHAR
INTEGER
NUMERIC
LONG VARCHAR
VARCHAR
CHAR
FLOAT
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Conversion Types
Conversion
without explicit
casting
Data Types
Implicit
Explicit
LONG VARCHAR
FLOAT
CHAR
BOOLEAN
INTEGER
NUMERIC
VARCHAR
TIMESTAMP
TIMESTAMP
WITH TIME
ZONE
DATE
TIME
TIME WITH
TIME ZONE
INTERVAL
DAY/SECOND
INTERVAL
YEAR/MONTH
LONG
VARBINARY
LONG VARCHAR
VARCHAR
FLOAT
LONG VARCHAR
CHAR
BOOLEAN
INTEGER
NUMERIC
TIMESTAMP
TIMESTAMP
WITH TIME
ZONE
DATE
TIME
TIME WITH
TIME ZONE
INTERVAL
DAY/SECOND
INTERVAL
YEAR/MONTH
VARCHAR
CHAR
FLOAT
LONG VARCHAR
VARCHAR
BOOLEAN
INTEGER
NUMERIC
TIMESTAMP
TIMESTAMP
WITH TIME
ZONE
DATE
TIME
TIME WITH
TIME ZONE
INTERVAL
DAY/SECOND
INTERVAL
YEAR/MONTH
CHAR
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Assignment
without numeric
meaning
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Conversion Types
Explicit
Assignment
Assignment
without numeric
meaning
Conversion
without explicit
casting
Data Types
Implicit
TIMESTAMP
TIMESTAMP
WITH TIME
ZONE
LONG CHAR
VARCHAR
CHAR
DATE
TIME
TIMESTAMP
TIMESTAMP
WITH TIME
ZONE
TIMESTAMP
LONG CHAR
VARCHAR
CHAR
DATE
TIME
TIME WITH
TIME ZONE
TIMESTAMP WITH
TIME ZONE
DATE
TIMESTAMP
LONG CHAR
VARCHAR
CHAR
TIMESTAMP
WITH TIME
ZONE
TIME
TIME WITH
TIME ZONE
TIMESTAMP
TIMESTAMP
WITH TIME
ZONE
INTERVAL
DAY/SECOND
LONG CHAR
VARCHAR
CHAR
TIME
TIME WITH
TIME ZONE
TIMESTAMP
TIMESTAMP
WITH TIME
ZONE
LONG CHAR
VARCHAR
CHAR
TIME
TIME WITH TIME
ZONE
INTERVAL
DAY/SECOND
TIME
INTEGER
LONG CHAR
VARCHAR
CHAR
INTERVAL
DAY/SECOND
INTEGER
LONG CHAR
VARCHAR
CHAR
INTERVAL
YEAR/MONTH
INTERVAL
YEAR/MONTH
LONG
VARBINARY
VARBINARY
LONG VARBINARY
VARBINARY
LONG
VARBINARY
BINARY
VARBINARY
BINARY
VARBINARY
BINARY
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Notes
l
Implicit conversion converts the source data to the target column's data type when what needs
to be converted is clear. For example, with "INT + NUMERIC -> NUMERIC", the integer is
implicitly cast to numeric(18,0); another precision/scale conversion may occur as part of the
add.
l
In an Assignment conversion, coercion implicitly occurs when values are assigned to database
columns in an INSERT or UPDATE..SET command. For example, in a statement that includes
INSERT ... VALUES('2.5'), where the target column is NUMERIC(18,5), a cast from
VARCHAR to NUMERIC(18,5) is inferred.
l
In Explicit conversion, the source data requires explicit casting to the target column's data type.
l
HP Vertica supports a conversion of data types without explicit casting, such as NUMERIC
(10,6) -> NUMERIC(18,4).
l
In an assignment without numeric meaning, the value is subject to CHAR/VARCHAR/LONG
VARCHAR comparisons.
See Also
l
Data Type Coercion
l
Data Type Coercion Operators (CAST)
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SQL Functions
Functions return information from the database and are allowed anywhere an expression is allowed.
The exception is HP Vertica-specific functions, which are not allowed everywhere.
Some functions could produce different results on different invocations with the same set of
arguments. The following three categories of functions are defined based on their behavior:
When run with a given set of arguments, immutable functions always produce the same result.
The function is independent of any environment or session settings, such as locale. For example,
2+2 always equals 4. Another immutable function is AVG(). Some immutable functions can take
an optional stable argument; in this case they are treated as stable functions.
l
Immutable (invariant):
When run with a given set of arguments, stable functions produce the same result within a single
query or scan operation. However, a stable function could produce different results when issued
under a different environment, such as a change of locale and time zone. Expressions that could
give different results in the future are also stable, for example SYSDATE() or 'today'.
l
Stable:
Regardless of the arguments or environment, volatile functions can return different results on
multiple invocations. RANDOM() is one example.
l
Volatile:
This chapter describes the functions that HP Vertica supports.
l
Each function is annotated with behavior type as immutable, stable or volatile.
l
All HP Vertica-specific functions can be assumed to be volatile and are not annotated
individually.
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Aggregate Functions
Note: All functions in this section that have an analytic function counterpart are appended with
[Aggregate] to avoid confusion between the two.
Aggregate functions summarize data over groups of rows from a query result set. The groups are
specified using the GROUP BY clause. They are allowed only in the select list and in the HAVING
and ORDER BY clauses of a SELECT statement (as described in Aggregate Expressions).
Notes
l
Except for COUNT, these functions return a null value when no rows are selected. In particular,
SUM of no rows returns NULL, not zero.
l
In some cases you can replace an expression that includes multiple aggregates with an single
aggregate of an expression. For example SUM(x) + SUM(y) can be expressed as as SUM(x+y)
(where x and y are NOT NULL).
l
HP Vertica does not support nested aggregate functions.
You can also use some of the simple aggregate functions as analytic (window) functions. See
Analytic Functions for details. See also Using SQL Analytics in the Programmer's Guide.
APPROXIMATE_COUNT_DISTINCT
Returns the number of rows in the data set that have distinct non-NULL values.
Behavior Type
Immutable
Syntax
APPROXIMATE_COUNT_DISTINCT ( expr [, error_tolerance ] )
Parameters
expr
Value to be evaluated using any data type that supports equality comparison.
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error_tolerance
NUMERIC that represents the desired error tolerance, distributed around the
exact COUNT(DISTINCT) value.
l
Default value: 1.0. This value typically gives a 0.4% chance of a getting a
count within three standard deviations of the exact count.
l
Minimum value: 0.078.
l
There is no maximum value, but anything greater than or equal to 5 is
implemented with 5% accuracy.
For detailed information about the error tolerance, see the following Notes
section.
Notes
l
The expected value that APPROXIMATE_COUNT_DISTINCT(x [, error_tolerance]) returns is
equal to COUNT(DISTINCT x), with an error that is lognormally distributed with standard
deviation s. You can control the standard deviation directly by setting the error_tolerance.
The error_tolerance is defined as 2.17 standard deviations, which corresponds to a 97%
confidence interval. For example, setting the error_tolerance to 1% (the default) corresponds to a
standard deviation s = (1 / 100) / 2.17 = 0.0046. If you specify an error_tolerance of 1,
APPROXIMATE_COUNT_DISTINCT(x) returns a value between COUNT(DISTINCT x) / 1.01
and 1.01 * COUNT(DISTINCT x), 97% of the time.
Similarly, specifying error_tolerance = 5 (percent) constrains the value returned by
APPROXIMATE_COUNT_DISTINCT(x) to be between COUNT(DISTINCT x) / 1.05 and 1.05 *
COUNT(DISTINCT x) 97% of the time. The remaining 3% of the time, the errors are larger than
the specified error_tolerance.
A 99% confidence interval corresponds to s = 2.58 standard deviations. To set an error_
tolerance corresponding to a 99% confidence level (instead of a 97% confidence level), multiply
the error_tolerance by 2.17 / 2.58 = 0.841. For example, if you want APPROXIMATE_COUNT_
DISTINCT(x) to return a value between COUNT(DISTINCT x) / 1.05 and 1.05 * COUNT
(DISTINCT x) 99% of the time, specify the error_tolerance as 5 * 0.841 = 4.2.
l
The maximum number of distinct values is in the range of 0 to approximately 2^47, or 1.4*10^14.
l
APPROXIMATE_COUNT_DISTINCT cannot appear in the same query block with DISTINCT
aggregates.
Examples
The following query counts total number of distinct values in the product_key column of the
store.store_sales_fact table:
=> \timing
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Timing is on.
=> SELECT COUNT(DISTINCT product_key) FROM store.store_sales_fact;
COUNT
------19982
(1 row)
Time: First fetch (1 row): 16.839 ms. All rows formatted: 16.866 ms
The next query counts the approximate number of distinct values in the product_key column with
various error tolerances. The smaller the error_tolerance, the closer the approximation.
=> \timing
Timing is on.
=> SELECT APPROXIMATE_COUNT_DISTINCT(product_key,5) AS five_pct_accuracy,
APPROXIMATE_COUNT_DISTINCT(product_key,1) AS one_pct_accuracy,
APPROXIMATE_COUNT_DISTINCT(product_key,.1) AS point_one_pct_accuracy
FROM store.store_sales_fact;
five_pct_accuracy | one_pct_accuracy | point_one_pct_accuracy
-------------------+------------------+-----------------------19431 |
19921 |
19980
(1 row)
Time: First fetch (1 row): 262.580 ms. All rows formatted: 262.616 ms
The following query counts the distinct values in the date_key and product_key columns.
=> \timing
Timing is on.
=> SELECT COUNT (DISTINCT date_key),
COUNT (DISTINCT product_key)
FROM store.store_sales_fact;
count | count
-------+------1826 | 19982
(1 row)
Time: First fetch (1 row): 207.431 ms. All rows formatted: 207.468 ms
See Also
l
APPROXIMATE_COUNT_DISTINCT_SYNOPSIS
l
APPROXIMATE_COUNT_DISTINCT_OF_SYNOPSIS
l
COUNT [Aggregate]
APPROXIMATE_COUNT_DISTINCT_OF_SYNOPSIS
Returns the number of rows in the synopsis object created by APPROXIMATE_COUNT_
DISTINCT_SYNOPSIS that have distinct non-NULL values.
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Behavior Type
Immutable
Syntax
APPROXIMATE_COUNT_DISTINCT_OF_SYNOPSIS ( expr [, maximum_error_percent ] )
Parameters
expr
Value to be evaluated using any data type that
supports equality comparison.
error_
NUMERIC that represents the desired error tolerance,
distributed around the exact COUNT(DISTINCT)
value.
l
Default value: 1.0. This value typically gives a
0.4% chance of a getting a count within three
standard deviations of the exact count.
l
Minimum value: 0.078.
l
There is no maximum value, but anything greater
than or equal to 5 is implemented with 5%
accuracy.
For detailed information about the error tolerance, see
the following Notes section.
Notes
l
The expected value that APPROXIMATE_COUNT_DISTINCT(x [, error_tolerance]) returns is
equal to COUNT(DISTINCT x), with an error that is lognormally distributed with standard
deviation s. You can control the standard deviation directly by setting the error_tolerance.
The error_tolerance is defined as 2.17 standard deviations, which corresponds to a 97%
confidence interval. For example, setting the error_tolerance to 1% (the default) corresponds to a
standard deviation s = (1 / 100) / 2.17 = 0.0046. If you specify an error_tolerance of 1,
APPROXIMATE_COUNT_DISTINCT(x) returns a value between COUNT(DISTINCT x) / 1.01
and 1.01 * COUNT(DISTINCT x), 97% of the time.
Similarly, specifying error_tolerance = 5 (percent) constrains the value returned by
APPROXIMATE_COUNT_DISTINCT(x) to be between COUNT(DISTINCT x) / 1.05 and 1.05 *
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COUNT(DISTINCT x) 97% of the time. The remaining 3% of the time, the errors are larger than
the specified error_tolerance.
A 99% confidence interval corresponds to s = 2.58 standard deviations. To set an error_
tolerance corresponding to a 99% confidence level (instead of a 97% confidence level), multiply
the error_tolerance by 2.17 / 2.58 = 0.841. For example, if you want APPROXIMATE_COUNT_
DISTINCT(x) to return a value between COUNT(DISTINCT x) / 1.05 and 1.05 * COUNT
(DISTINCT x) 99% of the time, specify the error_tolerance as 5 * 0.841 = 4.2.
l
The maximum number of distinct values is in the range of 0 to approximately 2^47, or 1.4*10^14.
l
APPROXIMATE_COUNT_DISTINCT cannot appear in the same query block with DISTINCT
aggregates.
Examples
The following example creates the synopsis and then calculates an approximate count of distinct
values in the synopsis.
=> \timing
Timing is on.
=> SELECT product_version,
APPROXIMATE_COUNT_DISTINCT(product_key)
FROM store.store_sales_fact
GROUP BY product_version;
product_version | ApproxCountDistinct
-----------------+--------------------1
|
19921
2
|
15958
3
|
11895
4
|
7935
5
|
3993
(5 rows)
Time: First fetch (5 rows): 2826.318 ms. All rows formatted: 2826.358 ms
=> CREATE TABLE my_summary AS
SELECT product_version,
APPROXIMATE_COUNT_DISTINCT_SYNOPSIS (product_key) syn
FROM store.store_sales_fact
GROUP BY product_version;
CREATE TABLE
=> SELECT APPROXIMATE_COUNT_DISTINCT_OF_SYNOPSIS
FROM my_summary;
ApproxCountDistinctOfSynopsis
------------------------------19963
(1 row)
Time: First fetch (1 row): 42.994 ms. All rows formatted: 43.021 ms
=>
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See Also
l
APPROXIMATE_COUNT_DISTINCT
l
APPROXIMATE_COUNT_DISTINCT_SYNOPSIS
l
COUNT [Aggregate]
APPROXIMATE_COUNT_DISTINCT_SYNOPSIS
Returns a subset of the data set, known as a synopsis, as a VARBINARY or LONG VARBINARY.
Save the synopsis as an HP Vertica table for use by APPROXIMATE_COUNT_DISTINCT_OF_
SYNOPSIS.
Behavior Type
Immutable
Syntax
APPROXIMATE_COUNT_DISTINCT_SYNOPSIS ( expr )
Parameters
expr
Value to be evaluated using any data type that supports
equality comparison.
Notes
l
The maximum number of distinct values is in the range of 0 to approximately 2^47, or 1.4*10^14.
l
APPROXIMATE_COUNT_DISTINCT_SYNOPSIS cannot appear in the same query block with
DISTINCT aggregates.
Example
In the following example, the query creates a table that contains the synopsis of the product_key
data. The my_summary table is used in the example for APPROXIMATE_COUNT_DISTINCT_OF_
SYNOPSIS.
=> CREATE TABLE my_summary AS
SELECT tender_type,
APPROXIMATE_COUNT_DISTINCT_SYNOPSIS(product_key) syn
FROM store.store_sales_fact
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GROUP BY tender_type;
CREATE TABLE
Time: First fetch (0 rows): 3216.056 ms. All rows formatted: 3216.069 ms
See Also
l
APPROXIMATE_COUNT_DISTINCT
l
APPROXIMATE_COUNT_DISTINCT_OF_SYNOPSIS
l
COUNT [Aggregate]
AVG [Aggregate]
Computes the average (arithmetic mean) of an expression over a group of rows. It returns a DOUBLE
PRECISION value for a floating-point expression. Otherwise, the return value is the same as the
expression data type.
Behavior Type
Immutable
Syntax
AVG ( [ ALL | DISTINCT ] expression )
Parameters
ALL
Invokes the aggregate function for all rows in the group (default).
DISTINCT
Invokes the aggregate function for all distinct non-null values of the expression
found in the group.
expression
The value whose average is calculated over a set of rows. Can be any expression
resulting in DOUBLE PRECISION.
Notes
The AVG() aggregate function is different from the AVG() analytic function, which computes an
average of an expression over a group of rows within a window.
Examples
The following example returns the average income from the customer table:
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=> SELECT AVG(annual_income) FROM customer_dimension;
avg
-------------2104270.6485
(1 row)
See Also
l
AVG [Analytic]
l
COUNT [Aggregate]
l
SUM [Aggregate]
l
Numeric Data Types
BIT_AND
Takes the bitwise AND of all non-null input values. If the input parameter is NULL, the return value is
also NULL.
Behavior Type
Immutable
Syntax
BIT_AND ( expression )
Parameters
expression
The [BINARY |VARBINARY] input value to be evaluated. BIT_AND() operates on
VARBINARY types explicitly and on BINARY types implicitly through casts.
Notes
l
The function returns the same value as the argument data type.
l
For each bit compared, if all bits are 1, the function returns 1; otherwise it returns 0.
l
If the columns are different lengths, the return values are treated as though they are all equal in
length and are right-extended with zero bytes. For example, given a group containing the hex
values 'ff', null, and 'f', the function ignores the null value and extends the value 'f' to
'f0'..
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Example
This example uses the following schema, which creates table t with a single column of VARBINARY
data type:
=>
=>
=>
=>
CREATE
INSERT
INSERT
INSERT
TABLE t ( c VARBINARY(2) );
INTO t values(HEX_TO_BINARY('0xFF00'));
INTO t values(HEX_TO_BINARY('0xFFFF'));
INTO t values(HEX_TO_BINARY('0xF00F'));
Query table t to see column c output:
=> SELECT TO_HEX(c) FROM t;
TO_HEX
-------ff00
ffff
f00f
(3 rows)
Query table t to get the AND value for column c:
SELECT TO_HEX(BIT_AND(c)) FROM t;
TO_HEX
-------f000
(1 row)
The function is applied pairwise to all values in the group, resulting in f000, which is determined as
follows:
1. ff00 (record 1) is compared with ffff (record 2), which results in ff00.
2. The result from the previous comparison is compared with f00f (record 3), which results in
f000.
See Also
l
Binary Data Types
BIT_OR
Takes the bitwise OR of all non-null input values. If the input parameter is NULL, the return value is
also NULL.
Behavior Type
Immutable
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Syntax
BIT_OR ( expression )
Parameters
expression
The [BINARY |VARBINARY] input value to be evaluated. BIT_OR() operates on
VARBINARY types explicitly and on BINARY types implicitly through casts.
Notes
l
The function returns the same value as the argument data type.
l
For each bit compared, if any bit is 1, the function returns 1; otherwise it returns 0.
l
If the columns are different lengths, the return values are treated as though they are all equal in
length and are right-extended with zero bytes. For example, given a group containing the hex
values 'ff', null, and 'f', the function ignores the null value and extends the value 'f' to
'f0'.
Example
This example uses the following schema, which creates table t with a single column of VARBINARY
data type:
=>
=>
=>
=>
CREATE
INSERT
INSERT
INSERT
TABLE t ( c VARBINARY(2) );
INTO t values(HEX_TO_BINARY('0xFF00'));
INTO t values(HEX_TO_BINARY('0xFFFF'));
INTO t values(HEX_TO_BINARY('0xF00F'));
Query table t to see column c output:
=> SELECT TO_HEX(c) FROM t;
TO_HEX
-------ff00
ffff
f00f
(3 rows)
Query table t to get the OR value for column c:
SELECT TO_HEX(BIT_OR(c)) FROM t;
TO_HEX
-------ffff
(1 row)
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The function is applied pairwise to all values in the group, resulting in ffff, which is determined as
follows:
1. ff00 (record 1) is compared with ffff, which results in ffff.
2. The ff00 result from the previous comparison is compared with f00f (record 3), which results
in ffff.
See Also
l
Binary Data Types
BIT_XOR
Takes the bitwise XOR of all non-null input values. If the input parameter is NULL, the return value is
also NULL.
Behavior Type
Immutable
Syntax
BIT_XOR ( expression )
Parameters
expression
The [BINARY | VARBINARY] input value to be evaluated. BIT_XOR() operates on
VARBINARY types explicitly and on BINARY types implicitly through casts.
Notes
l
The function returns the same value as the argument data type.
l
For each bit compared, if there are an odd number of arguments with set bits, the function
returns 1; otherwise it returns 0.
l
If the columns are different lengths, the return values are treated as though they are all equal in
length and are right-extended with zero bytes. For example, given a group containing the hex
values 'ff', null, and 'f', the function ignores the null value and extends the value 'f' to
'f0'.
Example
First create a sample table and projections with binary columns:
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This example uses the following schema, which creates table t with a single column of VARBINARY
data type:
=>
=>
=>
=>
CREATE
INSERT
INSERT
INSERT
TABLE t ( c VARBINARY(2) );
INTO t values(HEX_TO_BINARY('0xFF00'));
INTO t values(HEX_TO_BINARY('0xFFFF'));
INTO t values(HEX_TO_BINARY('0xF00F'));
Query table t to see column c output:
=> SELECT TO_HEX(c) FROM t;
TO_HEX
-------ff00
ffff
f00f
(3 rows)
Query table t to get the XOR value for column c:
SELECT TO_HEX(BIT_XOR(c)) FROM t;
TO_HEX
-------f0f0
(1 row)
See Also
l
Binary Data Types
CORR
Returns the coefficient of correlation of a set of expression pairs (expression1 and expression2).
The return value is of type DOUBLE PRECISION. The function eliminates expression pairs where
either expression in the pair is NULL. If no rows remain, the function returns NULL.Syntax
SELECT CORR (expression1,expression2)
Parameters
expression1
The dependent expression. Is of type DOUBLE PRECISION.
expression2
The independent expression. Is of type DOUBLE PRECISION.
Example
=> SELECT CORR (Annual_salary, Employee_age) FROM employee_dimension;
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CORR
----------------------0.00719153413192422
(1 row)
COUNT [Aggregate]
Returns the number of rows in each group of the result set for which the expression is not NULL. The
return value is a BIGINT.
The COUNT() aggregate function is different from the COUNT() analytic function. The COUNT()
analytic function returns the number over a group of rows within a window.
When an approximate count of the number of distinct values is sufficient, use the
APPROXIMATE_COUNT_DISTINCT function. If you want to combine the data in different ways,
use APPROXIMATE_COUNT_DISTINCT_SYNOPSIS together with APPROXIMATE_COUNT_
DISTINCT_OF_SYNOPSIS.
Behavior Type
Immutable
Syntax
COUNT ( [ * ] [ ALL | DISTINCT ] expression )
Parameters
*
Indicates that the count does not apply to any specific column or expression in the
select list. Requires a FROM Clause.
ALL
Invokes the aggregate function for all rows in the group (default).
DISTINCT
Invokes the aggregate function for all distinct non-null values of the expression
found in the group.
expression
Returns the number of rows in each group for which the expression is not null. Can
be any expression resulting in BIGINT.
Examples
The following query returns the number of distinct values in the primary_key column of the date_
dimension table:
=> SELECT COUNT (DISTINCT date_key) FROM date_dimension;
COUNT
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------1826
(1 row)
This example returns all distinct values of evaluating the expression x+y for all records of fact.
=> SELECT COUNT (DISTINCT date_key + product_key)
FROM inventory_fact;
COUNT
------21560
(1 row)
You can create an equivalent query using the LIMIT keyword to restrict the number of rows
returned:
=> SELECT COUNT(date_key + product_key) FROM inventory_fact
GROUP BY date_key LIMIT 10;
COUNT
------173
31
321
113
286
84
244
238
145
202
(10 rows)
This query returns the number of distinct values of date_key in all records with the specific distinct
product_key value.
=> SELECT product_key, COUNT (DISTINCT date_key)
FROM inventory_fact GROUP BY product_key LIMIT 10;
product_key | count
-------------+------1 |
12
2 |
18
3 |
13
4 |
17
5 |
11
6 |
14
7 |
13
8 |
17
9 |
15
10 |
12
(10 rows)
This query counts each distinct product_key value in inventory_fact table with the constant 1.
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=> SELECT product_key, COUNT (DISTINCT product_key)
FROM inventory_fact GROUP BY product_key LIMIT 10;
product_key | count
-------------+------1 |
1
2 |
1
3 |
1
4 |
1
5 |
1
6 |
1
7 |
1
8 |
1
9 |
1
10 |
1
(10 rows)
This query selects each distinct date_key value and counts the number of distinct product_key
values for all records with the specific product_key value. It then sums the qty_in_stock values
in all records with the specific product_key value and groups the results by date_key.
=> SELECT date_key, COUNT (DISTINCT product_key),
SUM(qty_in_stock) FROM inventory_fact
GROUP BY date_key LIMIT 10;
date_key | count | sum
----------+-------+-------1 |
173 | 88953
2 |
31 | 16315
3 |
318 | 156003
4 |
113 | 53341
5 |
285 | 148380
6 |
84 | 42421
7 |
241 | 119315
8 |
238 | 122380
9 |
142 | 70151
10 |
202 | 95274
(10 rows)
This query selects each distinct product_key value and then counts the number of distinct date_
key values for all records with the specific product_key value. It also counts the number of distinct
warehouse_key values in all records with the specific product_key value.
=> SELECT product_key, COUNT (DISTINCT date_key),
COUNT (DISTINCT warehouse_key) FROM inventory_fact
GROUP BY product_key LIMIT 15;
product_key | count | count
-------------+-------+------1 |
12 |
12
2 |
18 |
18
3 |
13 |
12
4 |
17 |
18
5 |
11 |
9
6 |
14 |
13
7 |
13 |
13
8 |
17 |
15
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9
10
11
12
13
14
15
|
|
|
|
|
|
|
15
12
11
13
9
13
18
|
|
|
|
|
|
|
14
12
11
12
7
13
17
(15 rows)
This query selects each distinct product_key value, counts the number of distinct date_key and
warehouse_key values for all records with the specific product_key value, and then sums all qty_
in_stock values in records with the specific product_key value. It then returns the number of
product_version values in records with the specific product_key value.
=> SELECT product_key, COUNT (DISTINCT date_key),
COUNT (DISTINCT warehouse_key),
SUM (qty_in_stock),
COUNT (product_version)
FROM inventory_fact GROUP BY product_key
LIMIT 15;
product_key | count | count | sum | count
-------------+-------+-------+-------+------1 |
12 |
12 | 5530 |
12
2 |
18 |
18 | 9605 |
18
3 |
13 |
12 | 8404 |
13
4 |
17 |
18 | 10006 |
18
5 |
11 |
9 | 4794 |
11
6 |
14 |
13 | 7359 |
14
7 |
13 |
13 | 7828 |
13
8 |
17 |
15 | 9074 |
17
9 |
15 |
14 | 7032 |
15
10 |
12 |
12 | 5359 |
12
11 |
11 |
11 | 6049 |
11
12 |
13 |
12 | 6075 |
13
13 |
9 |
7 | 3470 |
9
14 |
13 |
13 | 5125 |
13
15 |
18 |
17 | 9277 |
18
(15 rows)
The following example returns the number of warehouses from the warehouse dimension table:
=> SELECT COUNT(warehouse_name) FROM warehouse_dimension;
COUNT
------100
(1 row)
This next example returns the total number of vendors:
=> SELECT COUNT(*) FROM vendor_dimension;
COUNT
------50
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(1 row)
See Also
l
Analytic Functions
l
AVG [Aggregate]
l
SUM [Aggregate]
l
Using SQL Analytics
l
APPROXIMATE_COUNT_DISTINCT
l
APPROXIMATE_COUNT_DISTINCT_SYNOPSIS
l
APPROXIMATE_COUNT_DISTINCT_OF_SYNOPSIS
COVAR_POP
Returns the population covariance for a set of expression pairs (expression1 and expression2). The
return value is of type DOUBLE PRECISION. The function eliminates expression pairs where
either expression in the pair is NULL. If no rows remain, the function returns NULL.
Syntax
SELECT COVAR_POP (expression1,expression2)
Parameters
expression1
The dependent expression, type DOUBLE PRECISION.
expression2
The independent expression, type DOUBLE PRECISION.
Example
=> SELECT COVAR_POP (Annual_salary, Employee_age)
FROM employee_dimension;
COVAR_POP
-------------------9032.34810730019
(1 row)
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COVAR_SAMP
Returns the sample covariance for a set of expression pairs (expression1 and expression2). The
return value is of type DOUBLE PRECISION. The function eliminates expression pairs where
either expression in the pair is NULL. If no rows remain, the function returns NULL.
Syntax
COVAR_SAMP (expression1,expression2)
Parameters
expression1
The dependent expression, type DOUBLE PRECISION.
expression2
The independent expression, type DOUBLE PRECISION.
Example
=> SELECT COVAR_SAMP (Annual_salary, Employee_age)
FROM employee_dimension;
COVAR_SAMP
-------------------9033.25143244343
(1 row)
MAX [Aggregate]
Returns the greatest value of an expression over a group of rows. The return value is the same as
the expression data type.
Behavior Type
Immutable
Syntax
MAX ( [ ALL | DISTINCT ] expression )
Parameters
ALL | DISTINCT
These parameters have no meaning in this context.
expression
Any expression for which the maximum value is calculated, typically a column
reference.
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Notes
The MAX() aggregate function is different from the MAX() analytic function, which returns the
maximum value of an expression over a group of rows within a window.
Example
This example returns the largest value (dollar amount) of the sales_dollar_amount column.
=> SELECT MAX(sales_dollar_amount) AS highest_sale FROM store.store_sales_fact;
highest_sale
-------------600
(1 row)
See Also
l
Analytic Functions
l
MIN [Aggregate]
MIN [Aggregate]
Returns the smallest value of an expression over a group of rows. The return value is the same as
the expression data type.
Behavior Type
Immutable
Syntax
MIN ( [ ALL | DISTINCT ] expression )
Parameters
ALL | DISTINCT
Are meaningless in this context.
expression
Any expression for which the minimum value is calculated, typically a column
reference.
Notes
The MIN() aggregate function is different from the MIN() analytic function, which returns the
minimum value of an expression over a group of rows within a window.
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Example
This example returns the lowest salary from the employee dimension table.
=> SELECT MIN(annual_salary) AS lowest_paid FROM employee_dimension;
lowest_paid
------------1200
(1 row)
See Also
l
Analytic Functions
l
MAX [Aggregate]
l
Using SQL Analytics
REGR_AVGX
Returns the average of the independent expression in an expression pair (expression1 and
expression2). The return value is of type DOUBLE PRECISION. The function eliminates
expression pairs where either expression in the pair is NULL. If no rows remain, the function returns
NULL.
Syntax
SELECT REGR_AVGX (expression1,expression2)
Parameters
expression1
The dependent expression. Is of type DOUBLE PRECISION.
expression2
The independent expression. Is of type DOUBLE PRECISION.
Example
=> SELECT REGR_AVGX (Annual_salary, Employee_age)
FROM employee_dimension;
REGR_AVGX
----------39.321
(1 row)
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REGR_AVGY
Returns the average of the dependent expression in an expression pair (expression1 and
expression2). The return value is of type DOUBLE PRECISION. The function eliminates
expression pairs where either expression in the pair is NULL. If no rows remain, the function returns
NULL.
Syntax
REGR_AVGY (expression1,expression2)
Parameters
expression1
The dependent expression, type DOUBLE PRECISION.
expression2
The independent expression, type DOUBLE PRECISION.
Example
=> SELECT REGR_AVGY (Annual_salary, Employee_age)
FROM employee_dimension;
REGR_AVGY
-----------58354.4913
(1 row)
REGR_COUNT
Returns the number of expression pairs (expression1 and expression2). The return value is of type
INTEGER. The function eliminates expression pairs where either expression in the pair is NULL. If
no rows remain, the function returns 0.
Syntax
SELECT REGR_COUNT (expression1, expression2)
Parameters
expression1
The dependent expression, type DOUBLE PRECISION.
expression2
The independent expression, type DOUBLE PRECISION.
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Example
=> SELECT REGR_COUNT (Annual_salary, Employee_age) FROM employee_dimension;
REGR_COUNT
-----------10000
(1 row)
REGR_INTERCEPT
Returns the y-intercept of the regression line determined by a set of expression pairs (expression1
and expression2). The return value is of type DOUBLE PRECISION. The function eliminates
expression pairs where either expression in the pair is NULL. If no rows remain, the function returns
NULL.
Syntax
SELECT REGR_INTERCEPT (expression1,expression2)
Parameters
expression1
The dependent expression, type DOUBLE PRECISION.
expression2
The independent expression, type DOUBLE PRECISION.
Example
=> SELECT REGR_INTERCEPT (Annual_salary, Employee_age) FROM employee_dimension;
REGR_INTERCEPT
-----------------59929.5490163437
(1 row)
REGR_R2
Returns the square of the correlation coefficient of a set of expression pairs (expression1 and
expression2). The return value is of type DOUBLE PRECISION. The function eliminates
expression pairs where either expression in the pair is NULL. If no rows remain, the function returns
NULL.
Syntax
SELECT REGR_R2 (expression1,expression2)
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Parameters
expression1
The dependent expression, type DOUBLE PRECISION.
expression2
The independent expression, type DOUBLE PRECISION.
Example
=> SELECT REGR_R2 (Annual_salary, Employee_age) FROM employee_dimension;
REGR_R2
---------------------5.17181631706311e-05
(1 row)
REGR_SLOPE
Returns the slope of the regression line, determined by a set of expression pairs (expression1 and
expression2). The return value is of type DOUBLE PRECISION. The function eliminates
expression pairs where either expression in the pair is NULL. If no rows remain, the function returns
NULL.
Syntax
SELECT REGR_SLOPE (expression1,expression2)
Parameters
expression1
The dependent expression, type DOUBLE PRECISION.
expression2
The independent expression, type DOUBLE PRECISION.
Example
=> SELECT REGR_SLOPE (Annual_salary, Employee_age) FROM employee_dimension;
REGR_SLOPE
------------------40.056400303749
(1 row)
REGR_SXX
Returns the sum of squares of the independent expression in an expression pair (expression1 and
expression2). The return value is of type DOUBLE PRECISION. The function eliminates
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expression pairs where either expression in the pair is NULL. If no rows remain, the function returns
NULL.
Syntax
SELECT REGR_SXX (expression1,expression2)
Parameters
expression1
The dependent expression, type DOUBLE PRECISION.
expression2
The independent expression, type DOUBLE PRECISION.
Example
=> SELECT REGR_SXX (Annual_salary, Employee_age) FROM employee_dimension;
REGR_SXX
-----------2254907.59
(1 row)
REGR_SXY
Returns the sum of products of the independent expression multiplied by the dependent expression
in an expression pair (expression1 and expression2). The return value is of type DOUBLE
PRECISION. The function eliminates expression pairs where either expression in the pair is NULL.
If no rows remain, the function returns NULL.
Syntax
SELECT REGR_SXY (expression1,expression2)
Parameters
expression1
The dependent expression, type DOUBLE PRECISION.
expression2
The independent expression, type DOUBLE PRECISION.
Example
=> SELECT REGR_SXY (Annual_salary, Employee_age) FROM employee_dimension;
REGR_SXY
-------------------90323481.0730019
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REGR_SYY
Returns the sum of squares of the dependent expression in an expression pair (expression1 and
expression2). The return value is of type DOUBLE PRECISION. The function eliminates
expression pairs where either expression in the pair is NULL. If no rows remain, the function returns
NULL.
Syntax
SELECT REGR_SYY (expression1,expression2)
Parameters
expression1
The dependent expression, type DOUBLE PRECISION.
expression2
The independent expression, type DOUBLE PRECISION.
Example
=> SELECT REGR_SYY (Annual_salary, Employee_age) FROM employee_dimension;
REGR_SYY
-----------------69956728794707.2
(1 row)
STDDEV [Aggregate]
Note: The non-standard function STDDEV() is provided for compatibility with other databases.
It is semantically identical to STDDEV_SAMP().
Evaluates the statistical sample standard deviation for each member of the group. The STDDEV()
return value is the same as the square root of the VAR_SAMP() function:
STDDEV(expression) = SQRT(VAR_SAMP(expression))
When VAR_SAMP() returns NULL, this function returns NULL.
Behavior Type
Immutable
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Syntax
STDDEV ( expression )
Parameters
expression
Any NUMERIC data type or any non-numeric data type that can be implicitly
converted to a numeric data type. The function returns the same data type as the
numeric data type of the argument.
Notes
The STDDEV() aggregate function is different from the STDDEV() analytic function, which computes
the statistical sample standard deviation of the current row with respect to the group of rows within
a window.
Examples
The following example returns the statistical sample standard deviation for each household ID from
the customer_dimension table of the VMart example database:
=> SELECT STDDEV(household_id) FROM customer_dimension;
STDDEV
----------------8651.5084240071
See Also
l
Analytic Functions
l
STDDEV_SAMP [Aggregate]
l
Using SQL Analytics
STDDEV_POP [Aggregate]
Evaluates the statistical population standard deviation for each member of the group. The STDDEV_
POP() return value is the same as the square root of the VAR_POP() function
STDDEV_POP(expression) = SQRT(VAR_POP(expression))
When VAR_SAMP() returns NULL, this function returns NULL.
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Behavior Type
Immutable
Syntax
STDDEV_POP ( expression )
Parameters
expression
Any NUMERIC data type or any non-numeric data type that can be implicitly
converted to a numeric data type. The function returns the same data type as the
numeric data type of the argument.
Notes
The STDDEV_POP() aggregate function is different from the STDDEV_POP() analytic function, which
evaluates the statistical population standard deviation for each member of the group of rows within
a window.
Examples
The following example returns the statistical population standard deviation for each household ID in
the customer table.
=> SELECT STDDEV_POP(household_id) FROM customer_dimension;
STDDEV_POP
-----------------8651.41895973367
(1 row)
See Also
l
Analytic Functions
l
Using SQL Analytics
STDDEV_SAMP [Aggregate]
Evaluates the statistical sample standard deviation for each member of the group. The STDDEV_
SAMP() return value is the same as the square root of the VAR_SAMP() function:
STDDEV_SAMP(expression) = SQRT(VAR_SAMP(expression))
When VAR_SAMP() returns NULL, this function returns NULL.
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Behavior Type
Immutable
Syntax
STDDEV_SAMP ( expression )
Parameters
expression
Any NUMERIC data type or any non-numeric data type that can be implicitly
converted to a numeric data type. The function returns the same data type as the
numeric data type of the argument.
Notes
l
STDDEV_SAMP() is semantically identical to the non-standard function, STDDEV(), which is
provided for compatibility with other databases.
l
The STDDEV_SAMP() aggregate function is different from the STDDEV_SAMP() analytic function,
which computes the statistical sample standard deviation of the current row with respect to the
group of rows within a window.
Examples
The following example returns the statistical sample standard deviation for each household ID from
the customer dimension table.
=> SELECT STDDEV_SAMP(household_id) FROM customer_dimension;
stddev_samp
-----------------8651.50842400771
(1 row)
See Also
l
Analytic Functions
l
STDDEV [Aggregate]
l
Using SQL Analytics
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SUM [Aggregate]
Computes the sum of an expression over a group of rows. It returns a DOUBLE PRECISION value for
a floating-point expression. Otherwise, the return value is the same as the expression data type.
Behavior Type
Immutable
Syntax
SUM ( [ ALL | DISTINCT ] expression )
Parameters
ALL
Invokes the aggregate function for all rows in the group (default)
DISTINCT
Invokes the aggregate function for all distinct non-null values of the expression
found in the group
expression
Any NUMERIC data type or any non-numeric data type that can be implicitly
converted to a numeric data type. The function returns the same data type as the
numeric data type of the argument.
Notes
l
The SUM() aggregate function is different from the SUM() analytic function, which computes the
sum of an expression over a group of rows within a window.
l
If you encounter data overflow when using SUM(), use SUM_FLOAT() which converts the data to
a floating point.
Example
This example returns the total sum of the product_cost column.
=> SELECT SUM(product_cost) AS cost FROM product_dimension;
cost
--------9042850
(1 row)
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See Also
l
AVG [Aggregate]
l
COUNT [Aggregate]
Numeric Data Types
l
l
SUM_FLOAT [Aggregate]
Computes the sum of an expression over a group of rows. It returns a DOUBLE PRECISION value for
the expression, regardless of the expression type.
Behavior Type
Immutable
Syntax
SUM_FLOAT ( [ ALL | DISTINCT ] expression )
Parameters
ALL
Invokes the aggregate function for all rows in the group (default).
DISTINCT
Invokes the aggregate function for all distinct non-null values of the expression
found in the group.
expression
Any expression whose result is type DOUBLE PRECISION.
Example
The following example returns the floating-point sum of the average price from the product table:
=> SELECT SUM_FLOAT(average_competitor_price) AS cost FROM product_dimension;
cost
---------18181102
(1 row)
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VAR_POP [Aggregate]
Evaluates the population variance for each member of the group. This is defined as the sum of
squares of the difference of expression from the mean of expression, divided by the number of rows
remaining.
(SUM(expression*expression) - SUM(expression)*SUM(expression) /COUNT(expression)) / COUNT
(expression)
Behavior Type
Immutable
Syntax
VAR_POP ( expression )
Parameters
expression
Any NUMERIC data type or any non-numeric data type that can be implicitly
converted to a numeric data type. The function returns the same data type as the
numeric data type of the argument.
Notes
The VAR_POP() aggregate function is different from the VAR_POP() analytic function, which
computes the population variance of the current row with respect to the group of rows within a
window.
Examples
The following example returns the population variance for each household ID in the customer table.
=> SELECT VAR_POP(household_id) FROM customer_dimension;
var_pop
-----------------74847050.0168393
(1 row)
VAR_SAMP [Aggregate]
Evaluates the sample variance for each row of the group. This is defined as the sum of squares of
the difference of expression from the mean of expression, divided by the number of rows remaining
minus 1 (one).
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(SUM(expression*expression) - SUM(expression) *SUM(expression) /COUNT(expression)) / (COU
NT(expression) -1)
Behavior Type
Immutable
Syntax
VAR_SAMP ( expression )
Parameters
expression
Any NUMERIC data type or any non-numeric data type that can be implicitly
converted to a numeric data type. The function returns the same data type as the
numeric data type of the argument.
Notes
l
VAR_SAMP() is semantically identical to the non-standard function, VARIANCE(), which is
provided for compatibility with other databases.
l
The VAR_SAMP() aggregate function is different from the VAR_SAMP() analytic function, which
computes the sample variance of the current row with respect to the group of rows within a
window.
Examples
The following example returns the sample variance for each household ID in the customer table.
=> SELECT VAR_SAMP(household_id) FROM customer_dimension;
var_samp
-----------------74848598.0106764
(1 row)
See Also
Analytic Functions
l
VARIANCE [Aggregate]
l
l
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VARIANCE [Aggregate]
Note: The non-standard function VARIANCE() is provided for compatibility with other
databases. It is semantically identical to VAR_SAMP().
Evaluates the sample variance for each row of the group. This is defined as the sum of squares of
the difference of expression from the mean of expression, divided by the number of rows remaining
minus 1 (one).
(SUM(expression*expression) - SUM(expression) *SUM(expression) /COUNT(expression)) / (COU
NT(expression) -1)
Behavior Type
Immutable
Syntax
VARIANCE ( expression )
Parameters
expression
Any NUMERIC data type or any non-numeric data type that can be implicitly
converted to a numeric data type. The function returns the same data type as the
numeric data type of the argument.
Notes
The VARIANCE() aggregate function is different from the VARIANCE() analytic function, which
computes the sample variance of the current row with respect to the group of rows within a
window.
Examples
The following example returns the sample variance for each household ID in the customer table.
=> SELECT VARIANCE(household_id) FROM customer_dimension;
variance
-----------------74848598.0106764
(1 row)
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See Also
Analytic Functions
l
VAR_SAMP [Aggregate]
l
l
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Analytic Functions
Note: All analytic functions in this section that have an aggregate counterpart are appended
with [Analytics] in the heading to avoid confusion between the two.
HP Vertica analytics are SQL functions based on the ANSI 99 standard. These functions handle
complex analysis and reporting tasks such as:
l
Rank the longest-standing customers in a particular state
l
Calculate the moving average of retail volume over a specified time
l
Find the highest score among all students in the same grade
l
Compare the current sales bonus each salesperson received against his or her previous bonus
Analytic functions return aggregate results but they do not group the result set. They return the
group value multiple times, once per record.
You can sort these group values, or partitions, using a window ORDER BY clause, but the order
affects only the function result set, not the entire query result set. This ordering concept is
described more fully later.
Analytic Function Syntax
ANALYTIC_FUNCTION( argument-1, ..., argument-n )
OVER( [ window_partition_clause ]
[ window_order_clause ]
[ window_frame_clause ] )
Analytic Syntactic Construct
ANALYTIC_FUNCTION()
HP Vertica provides a number of analytic functions that allow
advanced data manipulation and analysis. Each of these functions
takes one or more arguments.
OVER(...)
Specifies partitioning, ordering, and window framing for the function—
important elements that determine what data the analytic function
takes as input with respect to the current row. The OVER() clause is
evaluated after the FROM, WHERE, GROUP BY, and HAVING clauses. The
SQL OVER() clause must follow the analytic function.
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window_partition_clause
Groups the rows in the input table by a given list of columns or
expressions.
The window_partition_clause is optional; if you omit it, the rows are
not grouped, and the analytic function applies to all rows in the input
set as a single partition. See window_partition_clause.
window_order_clause
Sorts the rows specified by the OVER() operator and supplies the
ordered set of rows to the analytic function. If the partition clause is
present, the window_order_clause applies within each partition.
The order clause is optional. If you do not use it, the selection set is
not sorted. See window_order_clause.
window_frame_clause
Used by only some analytic functions. If you include the frame clause
in the OVER() statement, which specifies the beginning and end of the
window relative to the current row, the analytic function applies only to
a subset of the rows defined by the partition clause. This subset
changes as the rows in the partition change (called a moving window).
See window_frame_clause.
Notes
Analytic functions:
l
Require the OVER() clause. However, depending on the function, the window_frame_clause
and window_order_clause might not apply. For example, when used with analytic aggregate
functions like SUM(x), you can use the OVER() clause without supplying any of the windowing
clauses; in this case, the aggregate returns the same aggregated value for each row of the result
set.
l
Are allowed only in the SELECT and ORDER BY clauses.
l
Can be used in a subquery or in the parent query but cannot be nested; for example, the
following query is not allowed:
=> SELECT MEDIAN(RANK() OVER(ORDER BY sal) OVER()).
l
WHERE, GROUP BY and HAVING operators are technically not part of the analytic function;
however, they determine on which rows the analytic functions operate.
See Also
l
Using SQL Analytics
l
Optimizing GROUP BY Queries
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window_partition_clause
Window partitioning is optional. When specified, the window_partition_clause divides the rows
in the input based on user-provided expressions, such as aggregation functions like SUM(x).
Window partitioning is similar to the GROUP BY clause except that it returns only one result row
per input row. If you omit the window_partition_clause, all input rows are treated as a single
partition.
The analytic function is computed per partition and starts over again (resets) at the beginning of
each subsequent partition. The window_partition_clause is specified within the OVER() clause.
Syntax
OVER ( PARTITION BY expression [ , ... ] )
Parameters
expression
Expression on which to sort the partition on. May involve columns, constants, or an
arbitrary expression formed on columns.
For examples, see Window Partitioning in the Programmer's Guide.
window_order_clause
Sorts the rows specified by the OVER() clause and specifies whether data is sorted in ascending or
descending order as well as the placement of null values. For example:
ORDER BY expr_list [ ASC | DESC ] [ NULLS { FIRST | LAST | AUTO ]
The ordering of the data affects the results.
Using ORDER BY in an OVER clause changes the default window to RANGE UNBOUNDED
PRECEDING AND CURRENT ROW, which is described in the window_frame_clause.
The following table shows the default null placement, with bold clauses to indicate what is implicit:
Ordering
Null placement
ORDER BY column1
ORDER BY a ASC NULLS LAST
ORDER BY column1 ASC
ORDER BY a ASC NULLS LAST
ORDER BY column1 DESC
ORDER BY a DESC NULLS FIRST
Because the window_order_clause is different from a query's final ORDER BY clause, window
ordering might not guarantee the final result order; it specifies only the order within a window result
set, supplying the ordered set of rows to the window_frame_clause (if present), to the analytic
function, or to both. Use the SQL ORDER BY clause to guarantee ordering of the final result set.
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Syntax
OVER ( ORDER BY expression [ { ASC | DESC } ]
... [ NULLS { FIRST | LAST | AUTO } ] [, expression ...] )
Parameters
expression
Expression on which to sort the partition, which may involve
columns, constants, or an arbitrary expression formed on
columns.
ASC | DESC
Specifies the ordering sequence as ascending (default) or
descending.
NULLS { FIRST | LAST |
AUTO }
Indicates the position of nulls in the ordered sequence as
either first or last. The order makes nulls compare either high
or low with respect to non-null values.
If the sequence is specified as ascending order,
ASC NULLS FIRST implies that nulls are smaller than other
non-null values. ASC NULLS LAST implies thatnulls are larger
than non-null values. The opposite is true for descending order.
If you specify NULLS AUTO, HP vertica chooses the most
efficient placement of nulls (for example, either
NULLS FIRST or NULLS LAST), based on your query. The
default is ASC NULLS LAST and DESC NULLS FIRST. For more
information, see
l
l
"NULL Placement By Analytic Functions" on page 1
"Designing Tables to Minimize Run-Time Sorting of NULL
Values in Analytic Functions" on page 1
The following analytic functions require the window_order_clause:
l
RANK() / DENSE_RANK()
l
LEAD() / LAG()
l
PERCENT_RANK() / CUME_DIST()
l
NTILE()
You can also use the window_order_clause with aggregation functions, such as SUM(x).
The ORDER BY clause is optional for the ROW_NUMBER() function.
The ORDER BY clause is not allowed with the following functions:
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l
PERCENTILE_CONT() / PERCENTILE_DISC()
l
MEDIAN()
For examples, see Window Ordering in the Programmer's Guide.
window_frame_clause
Allowed for some analytic functions in the analytic OVER() clause, window framing represents a
unique construct, called a moving window. It defines which values in the partition are evaluated
relative to the current row. You specify a window frame in terms of either logical intervals (such as
time) using the RANGE keyword or on a physical number of rows before and/or after the current row
using the ROWS keyword. The CURRENT ROW is the next row for which the analytic function
computes results.
As the current row advances, the window boundaries are recomputed (move) along with it,
determining which rows fall into the current window.
An analytic function with a window frame specification is computed for each row based on the rows
that fall into the window relative to that row.
An analytic function with a window frame specification is computed for each row based on the rows
that fall into the window relative to that row. If you omit the window_frame_clause, the default
window is RANGE UNBOUNDED PRECEDING AND CURRENT ROW.
Syntax
{ ROWS | RANGE }
{
{
BETWEEN
{ UNBOUNDED PRECEDING
| CURRENT ROW
| constant-value { PRECEDING | FOLLOWING }
}
AND
{ UNBOUNDED FOLLOWING
| CURRENT ROW
| constant-value { PRECEDING | FOLLOWING }
}
}
|
{
{ UNBOUNDED PRECEDING
| CURRENT ROW
| constant-value PRECEDING
}
}
}
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Parameters
ROWS | RANGE
The ROWS and RANGE keywords define the window
frame type.
ROWS specifies a window as a physical offset and
defines the window's start and end point by the
number of rows before or after the current row. The
value can be INTEGER data type only.
RANGE specifies the window as a logical offset, such
as time. The range value must match the window_
order_clause data type, which can be NUMERIC,
DATE/TIME, FLOAT or INTEGER.
Note: The value returned by an analytic function
with a logical offset is always deterministic.
However, the value returned by an analytic
function with a physical offset could produce
nondeterministic results unless the ordering
expression results in a unique ordering. You
might have to specify multiple columns in the
window_order_clause to achieve this unique
ordering.
BETWEEN ... AND
Specifies a start point and end point for the window.
The first expression (before AND) defines the start
point and the second expression (after AND) defines
the end point.
Note: If you use the keyword BETWEEN, you must
also use AND.
UNBOUNDED PRECEDING
Within a partition, indicates that the window frame
starts at the first row of the partition. This start-point
specification cannot be used as an end-point
specification, and the default is RANGE
UNBOUNDED PRECEDING AND CURRENT
ROW
UNBOUNDED FOLLOWING
Within a partition, indicates that the window frame
ends at the last row of the partition. This end-point
specification cannot be used as a start-point
specification.
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CURRENT ROW
As a start point, CURRENT ROW specifies that the
window begins at the current row or value, depending
on whether you have specified ROW or RANGE,
respectively. In this case, the end point cannot be
constant-value PRECEDING.
As an end point, CURRENT ROW specifies that the
window ends at the current row or value, depending
on whether you have specified ROW or RANGE,
respectively. In this case the start point cannot be
constant-value FOLLOWING.
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constant-value {PRECEDING | FOLLOWING }
For RANGE or ROW:
l
If constant-value FOLLOWING is the start point, the
end point must be constant-value FOLLOWING.
l
If constant-value PRECEDING is the end point, the
start point must be constant-value PRECEDING.
l
If you specify a logical window that is defined by a
time interval in NUMERIC format, you might need
to use conversion functions.
If you specified ROWS:
l
constant-value is a physical offset. It must be a
constant or expression and must evaluate to an
INTEGER data type value.
l
If constant-value is part of the start point, it must
evaluate to a row before the end point.
If you specified RANGE:
l
constant-value is a logical offset. It must be a
constant or expression that evaluates to a positive
numeric value or an INTERVAL literal.
l
If constant-value evaluates to a NUMERIC value,
the ORDER BY column type must be a NUMERIC
data type..
l
If the constant-value evaluates to an INTERVAL
DAY TO SECOND subtype, the ORDER BY column
type can only be TIMESTAMP, TIME, DATE, or
INTERVAL DAY TO SECOND.
l
If the constant-value evaluates to an INTERVAL
YEAR TO MONTH, the ORDER BY column type can
only be TIMESTAMP, DATE, or INTERVAL YEAR TO
MONTH.
l
You can specify only one expression in the
window_order_clause.
Window Aggregates
Analytic functions that take the window_frame_clause are called window aggregates, and they
return information such as moving averages and cumulative results. To use the following functions
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as window (analytic) aggregates, instead of basic aggregates, specify both an ORDER BY clause
(window_order_clause) and a moving window (window_frame_clause) in the OVER() clause.
Used by only some analytic functions. If you include the frame clause in the OVER() statement,
which specifies the beginning and end of the window relative to the current row, the analytic
function applies only to a subset of the rows defined by the partition clause. This subset changes as
the rows in the partition change (called a moving window). See window_frame_clause.
Sorts the rows specified by the window_partition_clause and supplies an ordered set of rows to
the window_frame_clause (if present), to the analytic function, or to both. The window_order_
clause specifies whether data is returned in ascending or descending order and specifies where
null values appear in the sorted result as either first or last. The ordering of the data affects the
results.
Note: The window_order_clause does not guarantee the order of the SQL result. Use the SQL
ORDER BY clause to guarantee the ordering of the final result set.
Within a partition, UNBOUNDED PRECEDING/FOLLOWING means beginning/end of partition. If
you omit the window_frame_clause but you specify the window_order_clause, the system
provides the default window of RANGE BETWEEN UNBOUNDED PRECEDING AND
CURRENT ROW.
The following analytic functions take the window_frame_clause:
l
AVG()
l
COUNT()
l
MAX() and MIN()
l
SUM()
l
STDDEV(), STDDEV_POP(), and STDDEV_SAMP()
l
VARIANCE(), VAR_POP(), and VAR_SAMP()
Note: FIRST_VALUE and LAST_VALUE functions also accept the window_frame_clause,
but they are analytic functions only and have no aggregate counterpart. EXPONENTIAL_
MOVING_AVERAGE, LAG, and LEAD analytic functions do not take the window_frame_
clause.
If you use a window aggregate with an empty OVER() clause, there is no moving window, and the
function is used as a reporting function, where the entire input is treated as one partition.
The value returned by an analytic function with a logical offset is always deterministic. However,
the value returned by an analytic function with a physical offset could produce nondeterministic
results unless the ordering expression results in a unique ordering. You might have to specify
multiple columns in the window_order_clause to achieve this unique ordering.
See Window Framing in the Programmer's Guide for examples.
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named_windows
You can use the WINDOW clause to name your windows and avoid typing long OVER() clause
syntax.
The window_partition_clause is defined in the named window specification, not in the OVER()
clause, and a window definition cannot contain a window_frame_clause.
Each window defined in the window_definition_clause must have a unique name.
Syntax
WINDOW window_name AS ( window_definition_clause );
Parameters
window_name
User-supplied name of the analytics window.
window_definition_clause
[ window_partition_clause ] [ window_order_clause ]
Examples
In the following example, RANK() and DENSE_RANK() use the partitioning and ordering specifications
in the window definition for a window named w:
=> SELECT RANK() OVER w , DENSE_RANK() OVER w
FROM employee_dimension
WINDOW w AS (PARTITION BY employee_region ORDER by annual_salary);
Though analytic functions can reference a named window to inherit the window_partition_clause,
you can use OVER() to define your own window_order_clause, but only if the window_definition_
clause did not already define it. Because ORDER by annual_salary was already defined in the
WINDOW clause in the previous example, the following query would return an error.
=> SELECT RANK() OVER(w ORDER BY annual_salary ASC),
DENSE_RANK() OVER(w ORDER BY annual_salary DESC)
FROM employee_dimension
WINDOW w AS (PARTITION BY employee_region);
ERROR: cannot override ORDER BY clause of window "w"
You can reference window names within their scope only. For example, because named window w1
in the following query is defined before w2, w2 is within the scope of w1:
=> SELECT RANK() OVER(w1 ORDER BY sal DESC), RANK() OVER w2
FROM EMP
WINDOW w1 AS (PARTITION BY deptno), w2 AS (w1 ORDER BY sal);
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AVG [Analytic]
Computes an average of an expression in a group within a window.
Behavior Type
Immutable
Syntax
AVG
...
...
...
(
[
[
[
expression ) OVER (
window_partition_clause ]
window_order_clause ]
window_frame_clause ] )
Parameters
expression
The value whose average is calculated over a set of rows. Can be any expression
resulting in DOUBLE PRECISION.
OVER(...)
See Analytic Functions.
Notes
AVG() takes as an argument any numeric data type or any non-numeric data type that can be
implicitly converted to a numeric data type. The function returns the same data type as the
argument's numeric data type.
Examples
The following query finds the sales for that calendar month and returns a running/cumulative
average (sometimes called a moving average) using the default window of RANGE UNBOUNDED
PRECEDING AND CURRENT ROW:
=> SELECT calendar_month_number_in_year, SUM(product_price) AS sales,
AVG(SUM(product_price)) OVER (ORDER BY calendar_month_number_in_year)
FROM product_dimension, date_dimension, inventory_fact
WHERE date_dimension.date_key = inventory_fact.date_key
AND product_dimension.product_key = inventory_fact.product_key
GROUP BY calendar_month_number_in_year;
calendar_month_number_in_year | sales
|
?column?
-------------------------------+----------+-----------------1 | 23869547 |
23869547
2 | 19604661 |
21737104
3 | 22877913 | 22117373.6666667
4 | 22901263 |
22313346
5 | 23670676 |
22584812
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6
7
8
9
10
11
12
|
|
|
|
|
|
|
22507600
21514089
24860684
21687795
23648921
21115910
24708317
|
|
|
|
|
|
|
22571943.3333333
22420821.2857143
22725804.125
22610469.7777778
22714314.9
22569005.3636364
22747281.3333333
(12 rows)
To return a moving average that is not a running (cumulative) average, the window should specify
ROWS BETWEEN 2 PRECEDING AND 2 FOLLOWING:
=> SELECT calendar_month_number_in_year, SUM(product_price) AS sales,
AVG(SUM(product_price)) OVER (ORDER BY calendar_month_number_in_year
ROWS BETWEEN 2 PRECEDING AND 2 FOLLOWING)
FROM product_dimension, date_dimension, inventory_fact
WHERE date_dimension.date_key = inventory_fact.date_key
AND product_dimension.product_key = inventory_fact.product_key
GROUP BY calendar_month_number_in_year;
See Also
l
AVG [Aggregate]
l
COUNT [Analytic]
l
SUM [Analytic]
l
Using SQL Analytics
CONDITIONAL_CHANGE_EVENT [Analytic]
Assigns an event window number to each row, starting from 0, and increments by 1 when the result
of evaluating the argument expression on the current row differs from that on the previous row.
Behavior Type
Immutable
Syntax
CONDITIONAL_CHANGE_EVENT ( expression ) OVER (
... [ window_partition_clause ]
... window_order_clause )
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Parameters
expression
SQL scalar expression that is evaluated on an input record. The result of expression
can be of any data type.
OVER(...)
See Analytic Functions.
Notes
The analytic window_order_clause is required but the window_partition_clause is optional.
Example
=> SELECT CONDITIONAL_CHANGE_EVENT(bid)
OVER (PARTITION BY symbol ORDER BY ts) AS cce
FROM TickStore;
The system returns an error when no ORDER BY clause is present:
=> SELECT CONDITIONAL_CHANGE_EVENT(bid)
OVER (PARTITION BY symbol) AS cce
FROM TickStore;
ERROR: conditional_change_event must contain an
ORDER BY clause within its analytic clause
For more examples, see Event-Based Windows in the Programmer's Guide.
See Also
l
CONDITIONAL_TRUE_EVENT [Analytic]
l
ROW_NUMBER [Analytic]
l
Using Time Series Analytics
l
Event-Based Windows
CONDITIONAL_TRUE_EVENT [Analytic]
Assigns an event window number to each row, starting from 0, and increments the number by 1
when the result of the boolean argument expression evaluates true. For example, given a sequence
of values for column a, as follows:
( 1, 2, 3, 4, 5, 6 )
CONDITIONAL_TRUE_EVENT(a > 3) returns 0, 0, 0, 1, 2, 3.
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Behavior Type:
Immutable
Syntax
CONDITIONAL_TRUE_EVENT ( boolean-expression ) OVER
... ( [ window_partition_clause ]
... window_order_clause )
Parameters
boolean-expression
SQL scalar expression that is evaluated on an input record, type
BOOLEAN.
OVER(...)
See Analytic Functions.
Notes
The analytic window_order_clause is required but the window_partition_clause is optional.
Example
> SELECT CONDITIONAL_TRUE_EVENT(bid > 10.6)
OVER(PARTITION BY bid ORDER BY ts) AS cte
FROM Tickstore;
The system returns an error if the ORDER BY clause is omitted:
> SELECT CONDITIONAL_TRUE_EVENT(bid > 10.6)
OVER(PARTITION BY bid) AS cte
FROM Tickstore;
ERROR: conditional_true_event must contain an ORDER BY
clause within its analytic clause
For more examples, see Event-Based Windows in the Programmer's Guide.
See Also
l
CONDITIONAL_CHANGE_EVENT [Analytic]
l
Using Time Series Analytics
l
Event-Based Windows
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COUNT [Analytic]
Counts occurrences within a group within a window. If you specify * or some non-null constant,
COUNT() counts all rows.
Behavior Type
Immutable
Syntax
COUNT
... [
... [
... [
( expression ) OVER (
window_partition_clause ]
window_order_clause ]
window_frame_clause ] )
Parameters
expression
Returns the number of rows in each group for which the expression is not null. Can
be any expression resulting in BIGINT.
OVER(...)
See Analytic Functions.
Example
Using the schema defined in Window Framing in the Programmer's Guide, the following COUNT
function does not specify an order_clause or a frame_clause; otherwise it would be treated as a
window aggregate. Think of the window of reporting aggregates as UNBOUNDED PRECEDING
and UNBOUNDED FOLLOWING.
=> SELECT deptno, sal, empno, COUNT(sal)
OVER (PARTITION BY deptno) AS count FROM emp;
deptno | sal | empno | count
--------+-----+-------+------10 | 101 |
1 |
2
10 | 104 |
4 |
2
20 | 110 |
10 |
6
20 | 110 |
9 |
6
20 | 109 |
7 |
6
20 | 109 |
6 |
6
20 | 109 |
8 |
6
20 | 109 |
11 |
6
30 | 105 |
5 |
3
30 | 103 |
3 |
3
30 | 102 |
2 |
3
Using ORDER BY sal creates a moving window query with default window: RANGE BETWEEN
UNBOUNDED PRECEDING AND CURRENT ROW.
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=> SELECT deptno, sal, empno, COUNT(sal)
OVER (PARTITION BY deptno ORDER BY sal) AS count
FROM emp;
deptno | sal | empno | count
--------+-----+-------+------10 | 101 |
1 |
1
10 | 104 |
4 |
2
20 | 100 |
11 |
1
20 | 109 |
7 |
4
20 | 109 |
6 |
4
20 | 109 |
8 |
4
20 | 110 |
10 |
6
20 | 110 |
9 |
6
30 | 102 |
2 |
1
30 | 103 |
3 |
2
30 | 105 |
5 |
3
Using the VMart schema, the following query finds the number of employees who make less than or
equivalent to the hourly rate of the current employee. The query returns a running/cumulative
average (sometimes called a moving average) using the default window of RANGE UNBOUNDED
PRECEDING AND CURRENT ROW:
=> SELECT employee_last_name AS "last_name", hourly_rate, COUNT(*)
OVER (ORDER BY hourly_rate) AS moving_count from employee_dimension;
last_name | hourly_rate | moving_count
------------+-------------+-------------Gauthier
|
6 |
4
Taylor
|
6 |
4
Jefferson |
6 |
4
Nielson
|
6 |
4
McNulty
|
6.01 |
11
Robinson
|
6.01 |
11
Dobisz
|
6.01 |
11
Williams
|
6.01 |
11
Kramer
|
6.01 |
11
Miller
|
6.01 |
11
Wilson
|
6.01 |
11
Vogel
|
6.02 |
14
Moore
|
6.02 |
14
Vogel
|
6.02 |
14
Carcetti
|
6.03 |
19
...
To return a moving average that is not also a running (cumulative) average, the window should
specify ROWS BETWEEN 2 PRECEDING AND 2 FOLLOWING:
=> SELECT employee_last_name AS "last_name", hourly_rate, COUNT(*)
OVER (ORDER BY hourly_rate ROWS BETWEEN 2 PRECEDING AND 2 FOLLOWING)
AS moving_count from employee_dimension;
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See Also
l
COUNT [Aggregate]
l
AVG [Analytic]
l
SUM [Analytic]
l
Using SQL Analytics
CUME_DIST [Analytic]
Calculates the cumulative distribution, or relative rank, of the current row with regard to other rows
in the same partition within a window.
CUME_DIST() returns a number greater then 0 and less then or equal to 1, where the number
represents the relative position of the specified row within a group of N rows. For a row x (assuming
ASC ordering), the CUME_DIST of x is the number of rows with values lower than or equal to the value
of x, divided by the number of rows in the partition. In a group of three rows, for example, the
cumulative distribution values returned would be 1/3, 2/3, and 3/3.
Note: Because the result for a given row depends on the number of rows preceding that row in
the same partition, HP recommends that you always specify a window_order_clause when
you call this function.
Behavior Type
Immutable
Syntax
CUME_DIST ( ) OVER (
... [ window_partition_clause ]
... window_order_clause )
Parameters
OVER(...)
See Analytic Functions.
Notes
The analytic window_order_clause is required but the window_partition_clause is optional.
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Examples
The following example returns the cumulative distribution of sales for different transaction types
within each month of the first quarter.
=> SELECT calendar_month_name AS month, tender_type, SUM(sales_quantity),
CUME_DIST()
OVER (PARTITION BY calendar_month_name ORDER BY SUM(sales_quantity)) AS
CUME_DIST
FROM store.store_sales_fact JOIN date_dimension
USING(date_key) WHERE calendar_month_name IN ('January','February','March')
AND tender_type NOT LIKE 'Other'
GROUP BY calendar_month_name, tender_type;
month
| tender_type | SUM
| CUME_DIST
----------+-------------+--------+----------March
| Credit
| 469858 |
0.25
March
| Cash
| 470449 |
0.5
March
| Check
| 473033 |
0.75
March
| Debit
| 475103 |
1
January | Cash
| 441730 |
0.25
January | Debit
| 443922 |
0.5
January | Check
| 446297 |
0.75
January | Credit
| 450994 |
1
February | Check
| 425665 |
0.25
February | Debit
| 426726 |
0.5
February | Credit
| 430010 |
0.75
February | Cash
| 430767 |
1
(12 rows)
See Also
l
PERCENT_RANK [Analytic]
l
PERCENTILE_DISC [Analytic]
l
Using SQL Analytics
DENSE_RANK [Analytic]
Computes the relative rank of each row returned from a query with respect to the other rows, based
on the values of the expressions in the window ORDER BY clause.
The data within a group is sorted by the ORDER BY clause and then a numeric ranking is assigned to
each row in turn starting with 1 and continuing from there. The rank is incremented every time the
values of the ORDER BY expressions change. Rows with equal values receive the same rank (nulls
are considered equal in this comparison). A DENSE_RANK() function returns a ranking number
without any gaps, which is why it is called "DENSE."
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Behavior Type
Immutable
Syntax
DENSE_RANK ( ) OVER (
... [ window_partition_clause ]
... window_order_clause )
Parameters
OVER(...)
See Analytic Functions.
Notes
l
The analytic window_order_clause is required but the window_partition_clause is optional.
l
The ranks are consecutive integers beginning with 1. The largest rank value is the number of
unique values returned by the query.
l
The primary difference between DENSE_RANK() and RANK() is that RANK leaves gaps when
ranking records whereas DENSE_RANK leaves no gaps. For example, N records occupy a
particular position (say, a tie for rank X), RANK assigns all those records with rank X and skips
the next N ranks, therefore the next assigned rank is X+N. DENSE_RANK places all the records in
that position only—it does not skip any ranks.
If there is a tie at the third position with two records having the same value, RANK and DENSE_
RANK place both the records in the third position, but RANK places the next record at the fifth
position, while DENSE_RANK places the next record at the fourth position.
l
If you omit NULLS FIRST | LAST | AUTO, the ordering of the NULL values depends on the ASC or
DESC arguments. NULL values are considered larger than any other value. If the ordering
sequence is ASC, then nulls appear last; nulls appear first otherwise. Nulls are considered equal
to other nulls and, therefore, the order in which nulls are presented is non-deterministic.
Example
The following example shows the difference between RANK and DENSE_RANK when ranking
customers by their annual income. Notice that RANK has a tie at 10 and skips 11, while DENSE_RANK
leaves no gaps in the ranking sequence:
=> SELECT customer_name, SUM(annual_income),
RANK () OVER (ORDER BY TO_CHAR(SUM(annual_income),'100000') DESC) rank,
DENSE_RANK () OVER (ORDER BY TO_CHAR(SUM(annual_income),'100000') DESC) dense_rank
FROM customer_dimension GROUP BY customer_name LIMIT 15;
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customer_name
| sum | rank | dense_rank
---------------------+-------+------+-----------Brian M. Garnett
| 99838 |
1 |
1
Tanya A. Brown
| 99834 |
2 |
2
Tiffany P. Farmer
| 99826 |
3 |
3
Jose V. Sanchez
| 99673 |
4 |
4
Marcus D. Rodriguez | 99631 |
5 |
5
Alexander T. Nguyen | 99604 |
6 |
6
Sarah G. Lewis
| 99556 |
7 |
7
Ruth Q. Vu
| 99542 |
8 |
8
Theodore T. Farmer | 99532 |
9 |
9
Daniel P. Li
| 99497 |
10 |
10
Seth E. Brown
| 99497 |
10 |
10
Matt X. Gauthier
| 99402 |
12 |
11
Rebecca W. Lewis
| 99296 |
13 |
12
Dean L. Wilson
| 99276 |
14 |
13
Tiffany A. Smith
| 99257 |
15 |
14
(15 rows)
See Also
l
RANK [Analytic]
l
Using SQL Analytics
EXPONENTIAL_MOVING_AVERAGE [Analytic]
Calculates the exponential moving average of expression E with smoothing factor X.
The exponential moving average (EMA) is calculated by adding the previous EMA value to the
current data point scaled by the smoothing factor, as in the following formula, where:
l
EMA0 is the previous row's EMA value
l
X is the smoothing factor
l
E is the current data point: EMA = EMA0 + (X * (E - EMA0))
EXPONENTIAL_MOVING_AVERAGE() is different from a simple moving average in that it
provides a more stable picture of changes to data over time.
Behavior Type
Immutable
Syntax
EXPONENTIAL_MOVING_AVERAGE ( E , X ) OVER (
... [ window_partition_clause ]
... window_order_clause )
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Parameters
E
The value whose average is calculated over a set of rows. Can be INTEGER, FLOAT or
NUMERIC type and must be a constant.
X
A positive FLOAT value between 0 and 1 that is used as the smoothing factor.
OVER(...)
See Analytic Functions.
Notes
l
The analytic window_order_clause is required but the window_partition_clause is optional.
l
There is no [Aggregate] equivalent of this function because of its unique semantics.
l
The EXPONENTIAL_MOVING_AVERAGE() function also works at the row level; for example, EMA
assumes the data in a given column is sampled at uniform intervals. If the users' data points are
sampled at non-uniform intervals, they should run the time series gap filling and interpolation
(GFI) operations before EMA().
Examples
The following example uses time series gap filling and interpolation (GFI) first in a subquery, and
then performs an EXPONENTIAL_MOVING_AVERAGE operation on the subquery result.
Create a simple four-column table:
=> CREATE TABLE ticker(
time TIMESTAMP,
symbol VARCHAR(8),
bid1 FLOAT,
bid2 FLOAT );
Insert some data, including nulls, so GFI can do its interpolation and gap filling:
=>
=>
=>
=>
=>
=>
=>
=>
=>
=>
=>
INSERT INTO
INSERT INTO
INSERT INTO
INSERT INTO
INSERT INTO
INSERT INTO
INSERT INTO
INSERT INTO
INSERT INTO
INSERT INTO
COMMIT;
ticker
ticker
ticker
ticker
ticker
ticker
ticker
ticker
ticker
ticker
VALUES
VALUES
VALUES
VALUES
VALUES
VALUES
VALUES
VALUES
VALUES
VALUES
('2009-07-12
('2009-07-12
('2009-07-12
('2009-07-12
('2009-07-12
('2009-07-12
('2009-07-12
('2009-07-12
('2009-07-12
('2009-07-12
03:00:00',
03:00:01',
03:00:02',
03:00:03',
03:00:04',
03:00:00',
03:00:01',
03:00:02',
03:00:03',
03:00:04',
'ABC',
'ABC',
'ABC',
'ABC',
'ABC',
'XYZ',
'XYZ',
'XYZ',
'XYZ',
'XYZ',
60.45, 60.44);
60.49, 65.12);
57.78, 59.25);
null, 65.12);
67.88, null);
47.55, 40.15);
44.35, 46.78);
71.56, 75.78);
85.55, 70.21);
45.55, 58.65);
Note: During gap filling and interpolation, HP Vertica takes the closest non null value on either
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side of the time slice and uses that value. For example, if you use a linear interpolation scheme
and you do not specify IGNORE NULLS, and your data has one real value and one null, the result
is null. If the value on either side is null, the result is null. See When Time Series Data Contains
Nulls in the Programmer's Guide for details.
Query the table that you just created to you can see the output:
=> SELECT * FROM ticker;
time
| symbol | bid1 | bid2
---------------------+--------+-------+------2009-07-12 03:00:00 | ABC
| 60.45 | 60.44
2009-07-12 03:00:01 | ABC
| 60.49 | 65.12
2009-07-12 03:00:02 | ABC
| 57.78 | 59.25
2009-07-12 03:00:03 | ABC
|
| 65.12
2009-07-12 03:00:04 | ABC
| 67.88 |
2009-07-12 03:00:00 | XYZ
| 47.55 | 40.15
2009-07-12 03:00:01 | XYZ
| 44.35 | 46.78
2009-07-12 03:00:02 | XYZ
| 71.56 | 75.78
2009-07-12 03:00:03 | XYZ
| 85.55 | 70.21
2009-07-12 03:00:04 | XYZ
| 45.55 | 58.65
(10 rows)
The following query processes the first and last values that belong to each 2-second time slice in
table trades' column a. The query then calculates the exponential moving average of expression fv
and lv with a smoothing factor of 50%:
=> SELECT symbol, slice_time, fv, lv,
EXPONENTIAL_MOVING_AVERAGE(fv, 0.5)
OVER (PARTITION BY symbol ORDER BY slice_time) AS ema_first,
EXPONENTIAL_MOVING_AVERAGE(lv, 0.5)
OVER (PARTITION BY symbol ORDER BY slice_time) AS ema_last
FROM (
SELECT symbol, slice_time,
TS_FIRST_VALUE(bid1 IGNORE NULLS) as fv,
TS_LAST_VALUE(bid2 IGNORE NULLS) AS lv
FROM ticker TIMESERIES slice_time AS '2 seconds'
OVER (PARTITION BY symbol ORDER BY time) ) AS sq;
symbol |
slice_time
| fv
| lv
| ema_first | ema_last
--------+---------------------+-------+-------+-----------+---------ABC
| 2009-07-12 03:00:00 | 60.45 | 65.12 |
60.45 |
65.12
ABC
| 2009-07-12 03:00:02 | 57.78 | 65.12 |
59.115 |
65.12
ABC
| 2009-07-12 03:00:04 | 67.88 | 65.12 |
63.4975 |
65.12
XYZ
| 2009-07-12 03:00:00 | 47.55 | 46.78 |
47.55 |
46.78
XYZ
| 2009-07-12 03:00:02 | 71.56 | 70.21 |
59.555 |
58.495
XYZ
| 2009-07-12 03:00:04 | 45.55 | 58.65 |
52.5525 | 58.5725
(6 rows)
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See Also
l
TIMESERIES Clause
l
Using Time Series Analytics
l
Using SQL Analytics
FIRST_VALUE [Analytic]
Allows the selection of the first value of a table or partition without having to use a self-join. If no
window is specified for the current row, the default window is UNBOUNDED PRECEDING AND
CURRENT ROW.
Behavior Type
Immutable
Syntax
FIRST_VALUE ( expression [ IGNORE NULLS ] ) OVER (
... [ window_partition_clause ]
... [ window_order_clause ]
... [ window_frame_clause ] )
Parameters
expression
Expression to evaluate; for example, a constant, column, nonanalytic function,
function expression, or expressions involving any of these.
IGNORE NULLS
Specifies to return the first non-null value in the set, or NULL if all values are NULL.
OVER(...)
See Analytic Functions.
Notes
l
The FIRST_VALUE() function lets you select a table's first value (determined by the window_
order_clause) without having to use a self join. This function is useful when you want to use
the first value as a baseline in calculations.
l
HP recommends that you use FIRST_VALUE with the window_order_clause to produce
deterministic results.
l
If the first value in the set is null, then the function returns NULL unless you specify IGNORE
NULLS. If you specify IGNORE NULLS, FIRST_VALUE returns the first non-null value in the set, or
NULL if all values are null.
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Examples
The following query, which asks for the first value in the partitioned day of week, illustrates the
potential nondeterministic nature of the FIRST_VALUE function:
=> SELECT calendar_year, date_key, day_of_week, full_date_description,
FIRST_VALUE(full_date_description)
OVER(PARTITION BY calendar_month_number_in_year ORDER BY day_of_week)
AS "first_value"
FROM date_dimension
WHERE calendar_year=2003 AND calendar_month_number_in_year=1;
The first value returned is January 31, 2003; however, the next time the same query is run, the first
value could be January 24 or January 3, or the 10th or 17th. The reason is because the analytic
ORDER BY column (day_of_week) returns rows that contain ties (multiple Fridays). These repeated
values make the ORDER BY evaluation result nondeterministic, because rows that contain ties can
be ordered in any way, and any one of those rows qualifies as being the first value of day_of_week.
calendar_year | date_key | day_of_week | full_date_description |first_value
--------------+----------+-------------+-----------------------+----------2003 |
31 | Friday
| January 31, 2003
| January 31,
2003 |
24 | Friday
| January 24, 2003
| January 31,
2003 |
3 | Friday
| January 3, 2003
| January 31,
2003 |
10 | Friday
| January 10, 2003
| January 31,
2003 |
17 | Friday
| January 17, 2003
| January 31,
2003 |
6 | Monday
| January 6, 2003
| January 31,
2003 |
27 | Monday
| January 27, 2003
| January 31,
2003 |
13 | Monday
| January 13, 2003
| January 31,
2003 |
20 | Monday
| January 20, 2003
| January 31,
2003 |
11 | Saturday
| January 11, 2003
| January 31,
2003 |
18 | Saturday
| January 18, 2003
| January 31,
2003 |
25 | Saturday
| January 25, 2003
| January 31,
2003 |
4 | Saturday
| January 4, 2003
| January 31,
2003 |
12 | Sunday
| January 12, 2003
| January 31,
2003 |
26 | Sunday
| January 26, 2003
| January 31,
2003 |
5 | Sunday
| January 5, 2003
| January 31,
2003 |
19 | Sunday
| January 19, 2003
| January 31,
2003 |
23 | Thursday
| January 23, 2003
| January 31,
2003 |
2 | Thursday
| January 2, 2003
| January 31,
2003 |
9 | Thursday
| January 9, 2003
| January 31,
2003 |
16 | Thursday
| January 16, 2003
| January 31,
2003 |
30 | Thursday
| January 30, 2003
| January 31,
2003 |
21 | Tuesday
| January 21, 2003
| January 31,
2003 |
14 | Tuesday
| January 14, 2003
| January 31,
2003 |
7 | Tuesday
| January 7, 2003
| January 31,
2003 |
28 | Tuesday
| January 28, 2003
| January 31,
2003 |
22 | Wednesday
| January 22, 2003
| January 31,
2003 |
29 | Wednesday
| January 29, 2003
| January 31,
2003 |
15 | Wednesday
| January 15, 2003
| January 31,
2003 |
1 | Wednesday
| January 1, 2003
| January 31,
2003 |
8 | Wednesday
| January 8, 2003
| January 31,
(31 rows)
HP Vertica Analytic Database (7.0.x)
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
2003
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Note: The day_of_week results are returned in alphabetical order because of lexical rules. The
fact that each day does not appear ordered by the 7-day week cycle (for example, starting with
Sunday followed by Monday, Tuesday, and so on) has no affect on results.
To return deterministic results, modify the query so that it performs its analytic ORDER BY operations
on a unique field, such as date_key:
=> SELECT calendar_year, date_key, day_of_week, full_date_description,
FIRST_VALUE(full_date_description) OVER
(PARTITION BY calendar_month_number_in_year ORDER BY date_key) AS "first_value"
FROM date_dimension WHERE calendar_year=2003;
Notice that the results return a first value of January 1 for the January partition and the first value of
February 1 for the February partition. Also, there are no ties in the full_date_description
column:
calendar_year | date_key | day_of_week | full_date_description | first_value
---------------+----------+-------------+-----------------------+-----------2003 |
1 | Wednesday
| January 1, 2003
| January 1, 2003
2003 |
2 | Thursday
| January 2, 2003
| January 1, 2003
2003 |
3 | Friday
| January 3, 2003
| January 1, 2003
2003 |
4 | Saturday
| January 4, 2003
| January 1, 2003
2003 |
5 | Sunday
| January 5, 2003
| January 1, 2003
2003 |
6 | Monday
| January 6, 2003
| January 1, 2003
2003 |
7 | Tuesday
| January 7, 2003
| January 1, 2003
2003 |
8 | Wednesday
| January 8, 2003
| January 1, 2003
2003 |
9 | Thursday
| January 9, 2003
| January 1, 2003
2003 |
10 | Friday
| January 10, 2003
| January 1, 2003
2003 |
11 | Saturday
| January 11, 2003
| January 1, 2003
2003 |
12 | Sunday
| January 12, 2003
| January 1, 2003
2003 |
13 | Monday
| January 13, 2003
| January 1, 2003
2003 |
14 | Tuesday
| January 14, 2003
| January 1, 2003
2003 |
15 | Wednesday
| January 15, 2003
| January 1, 2003
2003 |
16 | Thursday
| January 16, 2003
| January 1, 2003
2003 |
17 | Friday
| January 17, 2003
| January 1, 2003
2003 |
18 | Saturday
| January 18, 2003
| January 1, 2003
2003 |
19 | Sunday
| January 19, 2003
| January 1, 2003
2003 |
20 | Monday
| January 20, 2003
| January 1, 2003
2003 |
21 | Tuesday
| January 21, 2003
| January 1, 2003
2003 |
22 | Wednesday
| January 22, 2003
| January 1, 2003
2003 |
23 | Thursday
| January 23, 2003
| January 1, 2003
2003 |
24 | Friday
| January 24, 2003
| January 1, 2003
2003 |
25 | Saturday
| January 25, 2003
| January 1, 2003
2003 |
26 | Sunday
| January 26, 2003
| January 1, 2003
2003 |
27 | Monday
| January 27, 2003
| January 1, 2003
2003 |
28 | Tuesday
| January 28, 2003
| January 1, 2003
2003 |
29 | Wednesday
| January 29, 2003
| January 1, 2003
2003 |
30 | Thursday
| January 30, 2003
| January 1, 2003
2003 |
31 | Friday
| January 31, 2003
| January 1, 2003
2003 |
32 | Saturday
| February 1, 2003
| February 1, 2003
2003 |
33 | Sunday
| February 2, 2003
| February 1,2003
...
(365 rows)
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See Also
l
LAST_VALUE [Analytic]
l
TIME_SLICE
l
Using SQL Analytics
LAG [Analytic]
Returns the value of the input expression at the given offset before the current row within a
window.
Behavior Type
Immutable
Syntax
LAG ( expression [, offset ] [, default ] ) OVER (
... [ window_partition_clause ]
... window_order_clause )
Parameters
expression
Is the expression to evaluate; for example, a constant, column, non-analytic
function, function expression, or expressions involving any of these.
offset
[Optional] Indicates how great is the lag. The default value is 1 (the previous row).
The offset parameter must be (or can be evaluated to) a constant positive integer.
default
NULL. This optional parameter is the value returned if offset falls outside the bounds
of the table or partition.
Note: The default input argument must be a constant value or an expression
that can be evaluated to a constant; its data type is coercible to that of the first
argument.
OVER(...)
See Analytic Functions.
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Notes
l
The analytic window_order_clause is required but the window_partition_clause is optional.
l
The LAG() function returns values from the row before the current row, letting you access more
than one row in a table at the same time. This is useful for comparing values when the relative
positions of rows can be reliably known. It also lets you avoid the more costly self join, which
enhances query processing speed.
l
See LEAD() for how to get the next rows.
l
Analytic functions, such as LAG(), cannot be nested within aggregate functions.
Examples
This example sums the current balance by date in a table and also sums the previous balance from
the last day. Given the inputs that follow, the data satisfies the following conditions:
l
For each some_id, there is exactly 1 row for each date represented by month_date.
l
For each some_id, the set of dates is consecutive; that is, if there is a row for February 24 and a
row for February 26, there would also be a row for February 25.
l
Each some_id has the same set of dates.
=> CREATE TABLE balances (
month_date DATE,
current_bal INT,
some_id INT);
=>
=>
=>
=>
=>
=>
=>
=>
=>
INSERT
INSERT
INSERT
INSERT
INSERT
INSERT
INSERT
INSERT
INSERT
INTO
INTO
INTO
INTO
INTO
INTO
INTO
INTO
INTO
balances
balances
balances
balances
balances
balances
balances
balances
balances
values
values
values
values
values
values
values
values
values
('2009-02-24',
('2009-02-25',
('2009-02-26',
('2009-02-24',
('2009-02-25',
('2009-02-26',
('2009-02-24',
('2009-02-25',
('2009-02-26',
10,
10,
10,
20,
20,
20,
30,
20,
30,
1);
1);
1);
2);
2);
2);
3);
3);
3);
Now run the LAG() function to sum the current balance for each date and sum the previous balance
from the last day:
=> SELECT month_date,
SUM(current_bal) as current_bal_sum,
SUM(previous_bal) as previous_bal_sum FROM
(SELECT month_date, current_bal,
LAG(current_bal, 1, 0) OVER
(PARTITION BY some_id ORDER BY month_date)
AS previous_bal FROM balances) AS subQ
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GROUP BY month_date ORDER BY month_date;
month_date | current_bal_sum | previous_bal_sum
------------+-----------------+-----------------2009-02-24 |
60 |
0
2009-02-25 |
50 |
60
2009-02-26 |
60 |
50
(3 rows)
Using the same example data, the following query would not be allowed because LAG() is nested
inside an aggregate function:
=> SELECT month_date,
SUM(current_bal) as current_bal_sum,
SUM(LAG(current_bal, 1, 0) OVER
(PARTITION BY some_id ORDER BY month_date)) AS previous_bal_sum
FROM some_table GROUP BY month_date ORDER BY month_date;
In the next example, which uses the VMart example database (see Introducing the VMart Example
Database), the LAG() function first returns the annual income from the previous row, and then it
calculates the difference between the income in the current row from the income in the previous
row. Note: The vmart example database returns over 50,000 rows, so we'll limit the results to 20
records:
=> SELECT occupation, customer_key, customer_name, annual_income,
LAG(annual_income, 1, 0) OVER (PARTITION BY occupation
ORDER BY annual_income) AS prev_income, annual_income LAG(annual_income, 1, 0) OVER (PARTITION BY occupation
ORDER BY annual_income) AS difference
FROM customer_dimension ORDER BY occupation, customer_key LIMIT 20;
occupation | customer_key |
customer_name
| annual_income | prev_income | differe
nce
------------+--------------+----------------------+---------------+-------------+----------Accountant |
15 | Midori V. Peterson
|
692610 |
692535 |
75
Accountant |
43 | Midori S. Rodriguez |
282359 |
280976 |
1
383
Accountant |
93 | Robert P. Campbell
|
471722 |
471355 |
367
Accountant |
102 | Sam T. McNulty
|
901636 |
901561 |
75
Accountant |
134 | Martha B. Overstreet |
705146 |
704335 |
811
Accountant |
165 | James C. Kramer
|
376841 |
376474 |
367
Accountant |
225 | Ben W. Farmer
|
70574 |
70449 |
125
Accountant |
270 | Jessica S. Lang
|
684204 |
682274 |
1
930
Accountant |
273 | Mark X. Lampert
|
723294 |
722737 |
557
Accountant |
295 | Sharon K. Gauthier
|
29033 |
28412 |
621
Accountant |
338 | Anna S. Jackson
|
816858 |
815557 |
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1301
Accountant
277
Accountant
914
Accountant
226
Accountant
244
Accountant
620
Accountant
707
Accountant
383
Accountant
336
Accountant
662
(20 rows)
|
377 | William I. Jones
|
915149 |
914872 |
|
438 | Joanna A. McCabe
|
147396 |
144482 |
2
|
452 | Kim P. Brown
|
126023 |
124797 |
1
|
467 | Meghan K. Carcetti
|
810528 |
810284 |
|
478 | Tanya E. Greenwood
|
639649 |
639029 |
|
511 | Midori P. Vogel
|
187246 |
185539 |
|
525 | Alexander K. Moore
|
677433 |
677050 |
|
550 | Sam P. Reyes
|
735691 |
735355 |
|
577 | Robert U. Vu
|
616101 |
615439 |
1
Continuing with the Vmart database, the next example uses both LEAD() and LAG() to return the
third row after the salary in the current row and fifth salary before the salary in the current row.
=> SELECT hire_date, employee_key, employee_last_name,
LEAD(hire_date, 1) OVER (ORDER BY hire_date) AS "next_hired" ,
LAG(hire_date, 1) OVER (ORDER BY hire_date) AS "last_hired"
FROM employee_dimension ORDER BY hire_date, employee_key;
hire_date | employee_key | employee_last_name | next_hired | last_hired
------------+--------------+--------------------+------------+-----------1956-04-11 |
2694 | Farmer
| 1956-05-12 |
1956-05-12 |
5486 | Winkler
| 1956-09-18 | 1956-04-11
1956-09-18 |
5525 | McCabe
| 1957-01-15 | 1956-05-12
1957-01-15 |
560 | Greenwood
| 1957-02-06 | 1956-09-18
1957-02-06 |
9781 | Bauer
| 1957-05-25 | 1957-01-15
1957-05-25 |
9506 | Webber
| 1957-07-04 | 1957-02-06
1957-07-04 |
6723 | Kramer
| 1957-07-07 | 1957-05-25
1957-07-07 |
5827 | Garnett
| 1957-11-11 | 1957-07-04
1957-11-11 |
373 | Reyes
| 1957-11-21 | 1957-07-07
1957-11-21 |
3874 | Martin
| 1958-02-06 | 1957-11-11
(10 rows)
The following example specifies arguments that use different data types; for example annual_
income(INT) and occupation(VARCHAR). The query returns an error:
=> SELECT customer_key, customer_name, occupation, annual_income,
LAG (annual_income, 1, occupation) OVER
(PARTITION BY occupation ORDER BY customer_key) LAG1
FROM customer_dimension ORDER BY 3, 1;
ERROR: Third argument of lag could not be converted from type character varying to ty
pe int8
HINT: You may need to add explicit type cast.
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See Also
l
LEAD [Analytic]
l
Using SQL Analytics
LAST_VALUE [Analytic]
Returns values of the expression from the last row of a window for the current row. If no window is
specified for the current row, the default window is UNBOUNDED PRECEDING AND CURRENT ROW.
Behavior Type
Immutable
Syntax
LAST_VALUE ( expression [ IGNORE NULLS ] ) OVER ( ... [ window_partition_clause ]
... [ window_order_clause ]
... [ window_frame_clause ] )
Parameters
expression
Is the expression to evaluate; for example, a constant, column, nonanalytic
function, function expression, or expressions involving any of these.
IGNORE NULLS
Returns the last non-null value in the set, or NULL if all values are NULL.
OVER(...)
See Analytic Functions.
Notes
l
The LAST_VALUE() function lets you select a window's last value (determined by the window_
order_clause), without having to use a self join. This function is useful when you want to use the
last value as a baseline in calculations.
l
LAST_VALUE() takes the last record from the partition after the analytic window_order_clause.
The expression is then computed against the last record, and results are returned.
l
HP recommends that you use LAST_VALUE with the window_order_clause to produce
deterministic results.
Tip: Due to default window semantics, LAST_VALUE does not always return the last value
of a partition. If you omit the window_frame_clause from the analytic clause, LAST_VALUE
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operates on this default window. Although results can seem non-intuitive by not returning
the bottom of the current partition, it returns the bottom of the window, which continues to
change along with the current input row being processed. If you want to return the last value
of a partition, use UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING. See
examples below.
l
If the last value in the set is null, then the function returns NULL unless you specify IGNORE
NULLS. If you specify IGNORE NULLS, LAST_VALUE returns the fist non-null value in the set,
or NULL if all values are null.
Example
Using the schema defined in Window Framing in the Programmer's Guide, the following query does
not show the highest salary value by department; instead it shows the highest salary value by
department by salary.
=> SELECT deptno, sal, empno, LAST_VALUE(sal)
OVER (PARTITION BY deptno ORDER BY sal) AS lv
FROM emp;
deptno | sal | empno |
lv
--------+-----+-------+-------10 | 101 |
1 |
101
10 | 104 |
4 |
104
20 | 100 |
11 |
100
20 | 109 |
7 |
109
20 | 109 |
6 |
109
20 | 109 |
8 |
109
20 | 110 |
10 |
110
20 | 110 |
9 |
110
30 | 102 |
2 |
102
30 | 103 |
3 |
103
30 | 105 |
5 |
105
If you include the window_frame clause ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED
FOLLOWING, the LAST_VALUE() function will return the highest salary by department, an accurate
representation of the information.
=> SELECT deptno, sal, empno, LAST_VALUE(sal)
OVER (PARTITION BY deptno ORDER BY sal
ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS lv
FROM emp;
deptno | sal | empno |
lv
--------+-----+-------+-------10 | 101 |
1 |
104
10 | 104 |
4 |
104
20 | 100 |
11 |
110
20 | 109 |
7 |
110
20 | 109 |
6 |
110
20 | 109 |
8 |
110
20 | 110 |
10 |
110
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20
30
30
30
|
|
|
|
110
102
103
105
|
|
|
|
9
2
3
5
|
|
|
|
110
105
105
105
For additional examples, see FIRST_VALUE().
See Also
FIRST_VALUE [Analytic]
l
TIME_SLICE
l
l
LEAD [Analytic]
Returns the value of the input expression at the given offset after the current row within a window.
Behavior Type
Immutable
Syntax
LEAD ( expression [, offset ] [, default ] ) OVER (
... [ window_partition_clause ]
... window_order_clause )
Parameters
expression
Is the expression to evaluate; for example, a constant, column, nonanalytic
function, function expression, or expressions involving any of these.
offset
Is an optional parameter that defaults to 1 (the next row). The offset parameter must
be (or can be evaluated to) a constant positive integer.
default
Is NULL. This optional parameter is the value returned if offset falls outside the
bounds of the table or partition.
Note: The third input argument must be a constant value or an expression that
can be evaluated to a constant; its data type is coercible to that of the first
argument.
OVER(...)
See Analytic Functions.
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Notes
l
The analytic window_order_clause is required but the window_partition_clause is optional.
l
The LEAD() function returns values from the row after the current row, letting you access more
than one row in a table at the same time. This is useful for comparing values when the relative
positions of rows can be reliably known. It also lets you avoid the more costly self join, which
enhances query processing speed.
l
Analytic functions, such as LEAD(), cannot be nested within aggregate functions.
Examples
In this example, the LEAD() function finds the hire date of the employee hired just after the current
row:
=> SELECT employee_region, hire_date, employee_key, employee_last_name,
LEAD(hire_date, 1) OVER (PARTITION BY employee_region ORDER BY hire_date) AS "next_hir
ed"
FROM employee_dimension ORDER BY employee_region, hire_date, employee_key;
employee_region | hire_date | employee_key | employee_last_name | next_hired
-------------------+------------+--------------+--------------------+-----------East
| 1956-04-08 |
9218 | Harris
| 1957-02-06
East
| 1957-02-06 |
7799 | Stein
| 1957-05-25
East
| 1957-05-25 |
3687 | Farmer
| 1957-06-26
East
| 1957-06-26 |
9474 | Bauer
| 1957-08-18
East
| 1957-08-18 |
570 | Jefferson
| 1957-08-24
East
| 1957-08-24 |
4363 | Wilson
| 1958-02-17
East
| 1958-02-17 |
6457 | McCabe
| 1958-06-26
East
| 1958-06-26 |
6196 | Li
| 1958-07-16
East
| 1958-07-16 |
7749 | Harris
| 1958-09-18
East
| 1958-09-18 |
9678 | Sanchez
| 1958-11-10
(10 rows)
The next example uses both LEAD() and LAG() to return the third row after the salary in the current
row and fifth salary before the salary in the current row.
=> SELECT hire_date, employee_key, employee_last_name,
LEAD(hire_date, 1) OVER (ORDER BY hire_date) AS "next_hired" ,
LAG(hire_date, 1) OVER (ORDER BY hire_date) AS "last_hired"
FROM employee_dimension ORDER BY hire_date, employee_key;
hire_date | employee_key | employee_last_name | next_hired | last_hired
------------+--------------+--------------------+------------+-----------1956-04-11 |
2694 | Farmer
| 1956-05-12 |
1956-05-12 |
5486 | Winkler
| 1956-09-18 | 1956-04-11
1956-09-18 |
5525 | McCabe
| 1957-01-15 | 1956-05-12
1957-01-15 |
560 | Greenwood
| 1957-02-06 | 1956-09-18
1957-02-06 |
9781 | Bauer
| 1957-05-25 | 1957-01-15
1957-05-25 |
9506 | Webber
| 1957-07-04 | 1957-02-06
1957-07-04 |
6723 | Kramer
| 1957-07-07 | 1957-05-25
1957-07-07 |
5827 | Garnett
| 1957-11-11 | 1957-07-04
1957-11-11 |
373 | Reyes
| 1957-11-21 | 1957-07-07
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1957-11-21 |
(10 rows)
3874 | Martin
| 1958-02-06 | 1957-11-11
The following example returns employee name and salary, along with the next highest and lowest
salaries.
=> SELECT employee_last_name, annual_salary,
NVL(LEAD(annual_salary) OVER (ORDER BY annual_salary),
MIN(annual_salary) OVER()) "Next Highest",
NVL(LAG(annual_salary) OVER (ORDER BY annual_salary),
MAX(annual_salary) OVER()) "Next Lowest"
FROM employee_dimension;
employee_last_name | annual_salary | Next Highest | Next Lowest
--------------------+---------------+--------------+------------Nielson
|
1200 |
1200 |
995533
Lewis
|
1200 |
1200 |
1200
Harris
|
1200 |
1202 |
1200
Robinson
|
1202 |
1202 |
1200
Garnett
|
1202 |
1202 |
1202
Weaver
|
1202 |
1202 |
1202
Nielson
|
1202 |
1202 |
1202
McNulty
|
1202 |
1204 |
1202
Farmer
|
1204 |
1204 |
1202
Martin
|
1204 |
1204 |
1204
(10 rows)
The next example returns, for each assistant director in the employees table, the hire date of the
director hired just after the director on the current row. For example, Jackson was hired on 2007-1228, and the next director hired was Bauer:
=> SELECT employee_last_name, hire_date,
LEAD(hire_date, 1) OVER (ORDER BY hire_date DESC) as "NextHired"
FROM employee_dimension WHERE job_title = 'Assistant Director';
employee_last_name | hire_date | NextHired
--------------------+------------+-----------Jackson
| 2007-12-28 | 2007-12-26
Bauer
| 2007-12-26 | 2007-12-11
Miller
| 2007-12-11 | 2007-12-07
Fortin
| 2007-12-07 | 2007-11-27
Harris
| 2007-11-27 | 2007-11-15
Goldberg
| 2007-11-15 |
(5 rows)
See Also
l
LAG [Analytic]
l
Using SQL Analytics
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MAX [Analytic]
Returns the maximum value of an expression within a window. The return value is the same as the
expression data type.
Behavior Type
Immutable
Syntax
MAX
...
...
...
(
[
[
[
[ DISTINCT ] expression ) OVER (
window_partition_clause ]
window_order_clause ]
window_frame_clause ] )
Parameters
DISTINCT
This parameter has no meaning in this context.
expression
Any expression for which the maximum value is calculated, typically a column
reference .
OVER(...)
See Analytic Functions.
Example
The following query computes the deviation between the employees' annual salary and the
maximum annual salary in Massachusetts:
=> SELECT employee_state, annual_salary,
MAX(annual_salary)
OVER(PARTITION BY employee_state ORDER BY employee_key) max,
annual_salary- MAX(annual_salary)
OVER(PARTITION BY employee_state ORDER BY employee_key) diff
FROM employee_dimension
WHERE employee_state = 'MA';
employee_state | annual_salary | max
| diff
----------------+---------------+--------+--------MA
|
1918 | 995533 | -993615
MA
|
2058 | 995533 | -993475
MA
|
2586 | 995533 | -992947
MA
|
2500 | 995533 | -993033
MA
|
1318 | 995533 | -994215
MA
|
2072 | 995533 | -993461
MA
|
2656 | 995533 | -992877
MA
|
2148 | 995533 | -993385
MA
|
2366 | 995533 | -993167
MA
|
2664 | 995533 | -992869
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See Also
MAX [Aggregate]
l
MIN [Analytic]
l
l
MEDIAN [Analytic]
A numerical value of an expression in a result set within a window, which separates the higher half
of a sample from the lower half. For example, a query can retrieve the median of a finite list of
numbers by arranging all observations from lowest value to highest value and then picking the
middle one.
If there is an even number of observations, then there is no single middle value; thus, the median is
defined to be the mean (average) of the two middle values
MEDIAN() is an alias for 50% PERCENTILE(); for example:
PERCENTILE_CONT(0.5) WITHIN GROUP(ORDER BY expression)
Behavior Type
Immutable
Syntax
MEDIAN ( expression ) OVER ( [ window_partition_clause ] )
Parameters
expression
Any NUMERIC data type or any non-numeric data type that can be implicitly
converted to a numeric data type. The function returns the middle value or an
interpolated value that would be the middle value once the values are sorted. Null
values are ignored in the calculation.
OVER(...)
See Analytic Functions.
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Notes
l
For each row, MEDIAN() returns the value that would fall in the middle of a value set within each
partition.
l
HP Vertica determines the argument with the highest numeric precedence, implicitly converts
the remaining arguments to that data type, and returns that data type.
l
MEDIAN() does not allow the window_order_clause or window_frame_clause.
Examples
The following query computes the median annual income for first 500 customers in Wisconsin and
in the District of Columbia. The median is reported for every row in each partitioned result set:
=> SELECT customer_state, annual_income,
MEDIAN(annual_income) OVER (PARTITION BY customer_state) AS MEDIAN
FROM customer_dimension
WHERE customer_state IN ('DC','WI')
ORDER BY customer_state;
customer_state | customer_key | annual_income | MEDIAN
----------------+--------------+---------------+---------DC
|
120 |
299768 |
535413
DC
|
113 |
535413 |
535413
DC
|
130 |
848360 |
535413
---------------------------------------------------------WI
|
372 |
34962 |
668147
WI
|
437 |
47128 |
668147
WI
|
435 |
67770 |
668147
WI
|
282 |
638054 |
668147
WI
|
314 |
668147 |
668147
WI
|
128 |
675608 |
668147
WI
|
179 |
825304 |
668147
WI
|
302 |
827618 |
668147
WI
|
29 |
922760 |
668147
(12 rows)
See Also
l
PERCENTILE_CONT [Analytic]
l
Using SQL Analytics
MIN [Analytic]
Returns the minimum value of an expression within a window. The return value is the same as the
expression data type.
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Behavior Type
Immutable
Syntax
MIN
...
...
...
(
[
[
[
[ DISTINCT ] expression ) OVER (
window_partition_clause ]
window_order_clause ]
window_frame_clause ] )
Parameters
DISTINCT
This parameter has no meaning in this context.
expression
Any expression for which the minimum value is calculated, typically a column
reference.
OVER(...)
See Analytic Functions.
Examples
The following query computes the deviation between the employees' annual salary and the
minimum annual salary in Massachusetts:
=> SELECT employee_state, annual_salary,
MIN(annual_salary)
OVER(PARTITION BY employee_state ORDER BY employee_key) min,
annual_salary- MIN(annual_salary)
OVER(PARTITION BY employee_state ORDER BY employee_key) diff
FROM employee_dimension
WHERE employee_state = 'MA';
employee_state | annual_salary | min | diff
----------------+---------------+------+-----MA
|
1918 | 1204 | 714
MA
|
2058 | 1204 | 854
MA
|
2586 | 1204 | 1382
MA
|
2500 | 1204 | 1296
MA
|
1318 | 1204 | 114
MA
|
2072 | 1204 | 868
MA
|
2656 | 1204 | 1452
MA
|
2148 | 1204 | 944
MA
|
2366 | 1204 | 1162
MA
|
2664 | 1204 | 1460
(10 rows)
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See Also
l
MIN [Aggregate]
l
MAX [Analytic]
l
Using SQL Analytics
NTILE [Analytic]
Equally divides an ordered data set (partition) into a {value} number of subsets within a window,
with buckets (subsets) numbered 1 through constant-value. For example, if constant-value = 4,
then each row in the partition is assigned a number from 1 to 4. If the partition contains 20 rows, the
first 5 would be assigned 1, the next 5 would be assigned 2, and so on.
Behavior Type
Immutable
Syntax
NTILE ( constant-value ) OVER (
... [ window_partition_clause ]
... window_order_clause )
Parameters
constant-value
Represents the number of subsets and must resolve to a positive constant for
each partition.
OVER(...)
See Analytic Functions.
Notes
l
The analytic window_order_clause is required but the window_partition_clause is optional.
l
If the number of subsets is greater than the number of rows, then a number of subsets equal to
the number of rows is filled, and the remaining subsets are empty.
l
In the event the cardinality of the partition is not evenly divisible by the number of subsets, the
rows are distributed so no subset has more than 1 row more then any other subset, and the
lowest subsets are the ones that have extra rows. For example, using constant-value = 4 again
and the number of rows = 21, subset = 1 has 6 rows, subset = 2 has 5, and so on.
l
Analytic functions, such as NTILE(), cannot be nested within aggregate functions.
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Examples
The following query assigns each month's sales total into one of four subsets:
=> SELECT calendar_month_name AS MONTH, SUM(sales_quantity),
NTILE(4) OVER (ORDER BY SUM(sales_quantity)) AS NTILE
FROM store.store_sales_fact JOIN date_dimension
USING(date_key)
GROUP BY calendar_month_name
ORDER BY NTILE;
MONTH
| SUM | NTILE
-----------+------+------February | 755 |
1
June
| 842 |
1
September | 849 |
1
January
| 881 |
2
May
| 882 |
2
July
| 894 |
2
August
| 921 |
3
April
| 952 |
3
March
| 987 |
3
October
| 1010 |
4
November | 1026 |
4
December | 1094 |
4
(12 rows)
See Also
l
PERCENTILE_CONT [Analytic]
l
WIDTH_BUCKET
l
Using SQL Analytics
PERCENT_RANK [Analytic]
Calculates the relative rank of a row for a given row in a group within a window by dividing that
row’s rank less 1 by the number of rows in the partition, also less 1. This function always returns
values from 0 to 1 inclusive. The first row in any set has a PERCENT_RANK() of 0. The return value is
NUMBER.
( rank - 1 ) / ( [ rows ] - 1 )
In the preceding formula, rank is the rank position of a row in the group and rows is the total number
of rows in the partition defined by the OVER() clause.
Behavior Type
Immutable
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Syntax
PERCENT_RANK ( ) OVER (
... [ window_partition_clause ]
... window_order_clause )
Parameters
OVER(...)
See Analytic Functions.
Notes
The window_order_clause is required but the window_partition_clause is optional.
Examples
The following example finds the percent rank of gross profit for different states within each month of
the first quarter:
=> SELECT calendar_month_name AS MONTH, store_state,
SUM(gross_profit_dollar_amount),
PERCENT_RANK() OVER (PARTITION BY calendar_month_name
ORDER BY SUM(gross_profit_dollar_amount)) AS PERCENT_RANK
FROM store.store_sales_fact JOIN date_dimension
USING(date_key)
JOIN store.store_dimension
USING (store_key)
WHERE calendar_month_name IN ('January','February','March')
AND store_state IN ('OR','IA','DC','NV','WI')
GROUP BY calendar_month_name, store_state
ORDER BY calendar_month_name, PERCENT_RANK;
MONTH
| store_state | SUM |
PERCENT_RANK
----------+-------------+------+------------------February | OR
|
16 |
0
February | IA
|
47 |
0.25
February | DC
|
94 |
0.5
February | NV
| 113 |
0.75
February | WI
| 119 |
1
January | IA
| -263 |
0
January | OR
|
91 | 0.333333333333333
January | NV
| 372 | 0.666666666666667
January | DC
| 497 |
1
March
| NV
| -141 |
0
March
| OR
| 224 |
1
(11 rows)
The following example calculates, for each employee, the percent rank of the employee's salary by
their job title:
=> SELECT job_title, employee_last_name, annual_salary,
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PERCENT_RANK()
OVER (PARTITION BY job_title ORDER BY annual_salary DESC) AS percent_rank
FROM employee_dimension
ORDER BY percent_rank, annual_salary;
job_title
| employee_last_name | annual_salary |
PERCENT_RANK
--------------------+--------------------+---------------+--------------------CEO
| Campbell
|
963914 |
0
Co-Founder
| Nguyen
|
968625 |
0
Founder
| Overstreet
|
995533 |
0
Greeter
| Peterson
|
3192 | 0.00113895216400911
Greeter
| Greenwood
|
3192 | 0.00113895216400911
Customer Service
| Peterson
|
3190 | 0.00121065375302663
Delivery Person
| Rodriguez
|
3192 | 0.00121065375302663
Shelf Stocker
| Martin
|
3194 | 0.00125786163522013
Shelf Stocker
| Vu
|
3194 | 0.00125786163522013
Marketing
| Li
|
99711 | 0.00190114068441065
Assistant Director | Sanchez
|
99913 | 0.00190839694656489
Branch Manager
| Perkins
|
99901 | 0.00192307692307692
Advertising
| Lampert
|
99809 | 0.00204918032786885
Sales
| Miller
|
99727 | 0.00211416490486258
Shift Manager
| King
|
99904 | 0.00215982721382289
Custodian
| Bauer
|
3196 | 0.00235849056603774
Custodian
| Goldberg
|
3196 | 0.00235849056603774
Customer Service
| Fortin
|
3184 | 0.00242130750605327
Delivery Person
| Greenwood
|
3186 | 0.00242130750605327
Cashier
| Overstreet
|
3178 | 0.00243605359317905
Regional Manager
| McCabe
|
199688 | 0.00306748466257669
VP of Sales
| Li
|
199309 | 0.00313479623824451
Director of HR
| Goldberg
|
199592 | 0.00316455696202532
Head of Marketing | Stein
|
199941 | 0.00317460317460317
VP of Advertising | Goldberg
|
199036 | 0.00323624595469256
Head of PR
| Stein
|
199767 | 0.00323624595469256
Customer Service
| Rodriguez
|
3180 | 0.0036319612590799
Delivery Person
| King
|
3184 | 0.0036319612590799
Cashier
| Dobisz
|
3174 | 0.00365408038976857
Cashier
| Miller
|
3174 | 0.00365408038976857
Marketing
| Dobisz
|
99655 | 0.00380228136882129
Branch Manager
| Gauthier
|
99082 |
0.025
Branch Manager
| Moore
|
98415 |
0.05
...
See Also
l
CUME_DIST [Analytic]
l
Using SQL Analytics
PERCENTILE_CONT [Analytic]
An inverse distribution function where, for each row, PERCENTILE_CONT() returns the value that
would fall into the specified percentile among a set of values in each partition within a window. For
example, if the argument to the function is 0.5, the result of the function is the median of the data
set (the 50th percentile). PERCENTILE_CONT() assumes a continuous distribution data model. Nulls
are ignored.
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Behavior Type
Immutable
Syntax
PERCENTILE_CONT ( %_number ) WITHIN GROUP (
... ORDER BY expression [ ASC | DESC ] ) OVER (
... [ window_partition_clause ] )
Parameters
%_number
Percentile value, which must be a FLOAT constant ranging
from 0 to 1 (inclusive).
WITHIN GROUP(ORDER BYexpression)
Specifies how the data is sorted within each group. ORDER
BY takes only one column/expression that must be INTEGER,
FLOAT, INTERVAL, or NUMERIC data type. Nulls are discarded.
Note: The WITHIN GROUP(ORDER BY) clause does not
guarantee the order of the SQL result. Use the SQL ORDER
BY clause to guarantee the ordering of the final result set.
ASC | DESC
Specifies the ordering sequence as ascending (default) or
descending.
OVER(...)
See Analytic Functions.
Notes
l
HP Vertica computes the percentile by first computing the row number where the percentile row
would exist; for example:
ROW_NUMBER = 1 + PERCENTILE_VALUE * (NUMBER_OF_ROWS_IN_PARTITION -1)
If the CEILING(ROW_NUMBER) = FLOOR(ROW_NUMBER), then the percentile is the value at the
ROW_NUMBER. Otherwise, there is an even number of rows, and HP Vertica must interpolate the
value between the rows. In this case, the percentile CEILING_VAL = the value at the CEILING
(ROW_NUMBER) and FLOOR_VAL = the value at FLOOR(ROW_NUMBER). The interpolation is
determined by (CEILING(ROW_NUMBER) - ROW_NUMBER) * CEILING_VAL + (ROW_NUMBER FLOOR(ROW_NUMBER) * FLOOR_VAL.
If CEIL(num) = FLOOR(num) = num, retrieve the value in that row. Otherwise compute values
at [ CEIL(num) + FLOOR(num) ] / 2
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l
Specifying ASC or DESC in the WITHIN GROUP clause affects results as long as the percentile
parameter is not .5.
l
The MEDIAN() function is a specific case of PERCENTILE_CONT()where the percentile value
defaults to 0.5. For more information, see MEDIAN().
Examples
This query computes the median annual income per group for the first 500 customers in Wisconsin
and the District of Columbia.
=> SELECT customer_state, customer_key, annual_income,
PERCENTILE_CONT(.5) WITHIN GROUP(ORDER BY annual_income)
OVER (PARTITION BY customer_state) AS PERCENTILE_CONT
FROM customer_dimension
WHERE customer_state IN ('DC','WI')
AND customer_key < 300
ORDER BY customer_state, customer_key;
customer_state | customer_key | annual_income | PERCENTILE_CONT
----------------+--------------+---------------+----------------DC
|
104 |
658383 |
658383
DC
|
168 |
417092 |
658383
DC
|
245 |
670205 |
658383
WI
|
106 |
227279 |
458607
WI
|
127 |
703889 |
458607
WI
|
209 |
458607 |
458607
(6 rows)
The median value for DC is 65838, and the median value for WI is 458607. With a %_number of 0.5
in the above query, PERCENTILE_CONT() returns the same result as MEDIAN() in the following
query:
=> SELECT customer_state, customer_key, annual_income,
MEDIAN(annual_income)
OVER (PARTITION BY customer_state) AS MEDIAN
FROM customer_dimension
WHERE customer_state IN ('DC','WI')
AND customer_key < 300
ORDER BY customer_state, customer_key;
customer_state | customer_key | annual_income | MEDIAN
----------------+--------------+---------------+-------DC
|
104 |
658383 | 658383
DC
|
168 |
417092 | 658383
DC
|
245 |
670205 | 658383
WI
|
106 |
227279 | 458607
WI
|
127 |
703889 | 458607
WI
|
209 |
458607 | 458607
(6 rows)
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See Also
l
MEDIAN [Analytic]
l
Using SQL Analytics
PERCENTILE_DISC [Analytic]
An inverse distribution function where, for each row, PERCENTILE_DISC() returns the value that
would fall into the specified percentile among a set of values in each partition within a window.
PERCENTILE_DISC() assumes a discrete distribution data model. Nulls are ignored.
Behavior Type
Immutable
Syntax
PERCENTILE_DISC ( %_number ) WITHIN GROUP (
... ORDER BY expression [ ASC | DESC ] ) OVER (
... [ window_partition_clause ] )
Parameters
%_number
Percentile value, which must be a FLOAT constant ranging
from 0 to 1 (inclusive).
WITHIN GROUP(ORDER BYexpression)
Specifies how the data is sorted within each group. ORDER
BY takes only one column/expression that must be INTEGER,
FLOAT, INTERVAL, or NUMERIC data type. Nulls are
discarded.
Note: The WITHIN GROUP(ORDER BY) clause does not
guarantee the order of the SQL result. Use the SQL
ORDER BY clause to guarantee the ordering of the final
result set.
ASC | DESC
Specifies the ordering sequence as ascending (default) or
descending.
OVER(...)
See Analytic Functions.
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Notes
l
PERCENTILE_DISC(%_number) examines the cumulative distribution values in each group until it
finds one that is greater than or equal to %_number.
l
HP Vertica computes the percentile where, for each row, PERCENTILE_DISC outputs the first
value of the WITHIN GROUP(ORDER BY) column whose CUME_DIST (cumulative distribution)
value is >= the argument FLOAT value (for example, 0.4). Specifically:
PERCENTILE_DIST(.4) WITHIN GROUP (ORDER BY salary) OVER(PARTITION By deptno) ...
If you write, for example:
SELECT CUME_DIST() OVER(ORDER BY salary) FROM table;
The smallest CUME_DIST value that is greater than 0.4 is also the PERCENTILE_DISC.
Example
This query computes the 20th percentile annual income by group for first 500 customers in
Wisconsin and the District of Columbia.
=> SELECT customer_state, customer_key, annual_income,
PERCENTILE_DISC(.2) WITHIN GROUP(ORDER BY annual_income)
OVER (PARTITION BY customer_state) AS PERCENTILE_DISC
FROM customer_dimension
WHERE customer_state IN ('DC','WI')
AND customer_key < 300
ORDER BY customer_state, customer_key;
customer_state | customer_key | annual_income | PERCENTILE_DISC
----------------+--------------+---------------+----------------DC
|
104 |
658383 |
417092
DC
|
168 |
417092 |
417092
DC
|
245 |
670205 |
417092
WI
|
106 |
227279 |
227279
WI
|
127 |
703889 |
227279
WI
|
209 |
458607 |
227279
(6 rows)
See Also
l
CUME_DIST [Analytic]
l
PERCENTILE_CONT [Analytic]
l
Using SQL Analytics
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RANK [Analytic]
Assigns a rank to each row returned from a query with respect to the other ordered rows, based on
the values of the expressions in the window ORDER BY clause. The data within a group is sorted by
the ORDER BY clause and then a numeric ranking is assigned to each row in turn, starting with 1, and
continuing up. Rows with the same values of the ORDER BY expressions receive the same rank;
however, if two rows receive the same rank (a tie), RANK() skips the ties. If, for example, two rows
are numbered 1, RANK() skips number 2 and assigns 3 to the next row in the group. This is in
contrast to DENSE_RANK(), which does not skip values.
Behavior Type
Immutable
Syntax
RANK ( ) OVER (
... [ window_partition_clause ]
... window_order_clause )
Parameters
OVER(...)
See Analytic Functions.
Notes
l
Ranking functions return a rank value for each row in a result set based on the order specified in
the query. For example, a territory sales manager might want to identify the top or bottom
ranking sales associates in a department or the highest/lowest-performing sales offices by
region.
l
RANK() requires an OVER() clause. The window_partition_clause is optional.
l
In ranking functions, OVER() specifies the measures expression on which ranking is done and
defines the order in which rows are sorted in each group (or partition). Once the data is sorted
within each partition, ranks are given to each row starting from 1.
l
The primary difference between RANK and DENSE_RANK is that RANK leaves gaps when ranking
records; DENSE_RANK leaves no gaps. For example, if more than one record occupies a particular
position (a tie), RANK places all those records in that position and it places the next record after a
gap of the additional records (it skips one). DENSE_RANK places all the records in that position
only—it does not leave a gap for the next rank.
If there is a tie at the third position with two records having the same value, RANK and DENSE_
RANK place both the records in the third position only, but RANK has the next record at the fifth
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position—leaving a gap of 1 position—while DENSE_RANK places the next record at the forth
position (no gap).
l
If you omit NULLS FIRST | LAST | AUTO, the ordering of the null values depends on the ASC or
DESC arguments. Null values are considered larger than any other values. If the ordering
sequence is ASC, then nulls appear last; nulls appear first otherwise. Nulls are considered equal
to other nulls and, therefore, the order in which nulls are presented is non-deterministic.
Examples
This example ranks the longest standing customers in Massachusetts. The query first computes
the customer_since column by region, and then partitions the results by customers with
businesses in MA. Then within each region, the query ranks customers over the age of 70.
=> SELECT customer_type, customer_name,
RANK() OVER (PARTITION BY customer_region
ORDER BY customer_since) as rank
FROM customer_dimension
WHERE customer_state = 'MA'
AND customer_age > '70';
customer_type | customer_name | rank
---------------+---------------+-----Company
| Virtadata
|
1
Company
| Evergen
|
2
Company
| Infocore
|
3
Company
| Goldtech
|
4
Company
| Veritech
|
5
Company
| Inishop
|
6
Company
| Intracom
|
7
Company
| Virtacom
|
8
Company
| Goldcom
|
9
Company
| Infostar
|
10
Company
| Golddata
|
11
Company
| Everdata
|
12
Company
| Goldcorp
|
13
(13 rows)
The following example shows the difference between RANK and DENSE_RANK when ranking
customers by their annual income. RANK has a tie at 10 and skips 11, while DENSE_RANK leaves no
gaps in the ranking sequence:
=> SELECT customer_name, SUM(annual_income),
RANK () OVER (ORDER BY TO_CHAR(SUM(annual_income),'100000') DESC) rank,
DENSE_RANK () OVER (ORDER BY TO_CHAR(SUM(annual_income),'100000') DESC) dense_rank
FROM customer_dimension
GROUP BY customer_name
LIMIT 15;
customer_name
| sum | rank | dense_rank
---------------------+-------+------+-----------Brian M. Garnett
| 99838 |
1 |
1
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Tanya A. Brown
Tiffany P. Farmer
Jose V. Sanchez
Marcus D. Rodriguez
Alexander T. Nguyen
Sarah G. Lewis
Ruth Q. Vu
Theodore T. Farmer
Daniel P. Li
Seth E. Brown
Matt X. Gauthier
Rebecca W. Lewis
Dean L. Wilson
Tiffany A. Smith
(15 rows)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
99834
99826
99673
99631
99604
99556
99542
99532
99497
99497
99402
99296
99276
99257
|
|
|
|
|
|
|
|
|
|
|
|
|
|
2
3
4
5
6
7
8
9
10
10
12
13
14
15
|
|
|
|
|
|
|
|
|
|
|
|
|
|
2
3
4
5
6
7
8
9
10
10
11
12
13
14
See Also
l
DENSE_RANK [Analytic]
l
Using SQL Analytics
ROW_NUMBER [Analytic]
Assigns a unique number, sequentially, starting from 1, to each row in a partition within a window.
Behavior Type
Immutable
Syntax
ROW_NUMBER ( ) OVER (
... [ window_partition_clause ]
... window_order_clause )
Parameters
OVER(...)
See Analytic Functions.
Notes
l
ROW_NUMBER() is an HP Vertica extension, not part of the SQL-99 standard. It requires an OVER
() clause. The window_partition_clause is optional.
l
You can use the optional partition clause to group data into partitions before operating on it; for
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example:
SUM OVER (PARTITION BY col1, col2, ...)
l
You can substitute any RANK() example for ROW_NUMBER(). The difference is that ROW_
NUMBERassigns a unique ordinal number, starting with 1, to each row in the ordered set.
Examples
The following query first partitions customers in the customer_dimension table by occupation and
then ranks those customers based on the ordered set specified by the analytic partition_clause.
=> SELECT occupation, customer_key, customer_since, annual_income,
ROW_NUMBER() OVER (PARTITION BY occupation) AS customer_since_row_num
FROM public.customer_dimension
ORDER BY occupation, customer_since_row_num;
occupation
| customer_key | customer_since | annual_income | customer_since_row_
num
--------------------+--------------+----------------+---------------+----------------------Accountant
|
19453 | 1973-11-06
|
602460 |
1
Accountant
|
42989 | 1967-07-09
|
850814 |
2
Accountant
|
24587 | 1995-05-18
|
180295 |
3
Accountant
|
26421 | 2001-10-08
|
126490 |
4
Accountant
|
37783 | 1993-03-16
|
790282 |
5
Accountant
|
39170 | 1980-12-21
|
823917 |
6
Banker
|
13882 | 1998-04-10
|
15134 |
1
Banker
|
14054 | 1989-03-16
|
961850 |
2
Banker
|
15850 | 1996-01-19
|
262267 |
3
Banker
|
29611 | 2004-07-14
|
739016 |
4
Doctor
|
261 | 1969-05-11
|
933692 |
1
Doctor
|
1264 | 1981-07-19
|
593656 |
2
Psychologist
|
5189 | 1999-05-04
|
397431 |
1
Psychologist
|
5729 | 1965-03-26
|
339319 |
2
Software Developer |
2513 | 1996-09-22
|
920003 |
1
Software Developer |
5927 | 2001-03-12
|
633294 |
2
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Software Developer
3
Software Developer
4
Software Developer
5
Software Developer
6
Software Developer
7
Software Developer
8
Software Developer
9
Software Developer
10
Software Developer
11
Stock Broker
1
Stock Broker
2
Stock Broker
3
Stock Broker
4
Stock Broker
5
Writer
1
Writer
2
Writer
3
Writer
4
Writer
5
Writer
6
(39 rows)
|
9125 | 1971-10-06
|
198953 |
|
16097 | 1968-09-02
|
748371 |
|
23137 | 1988-12-07
|
92578 |
|
24495 | 1989-04-16
|
149371 |
|
24548 | 1994-09-21
|
743788 |
|
33744 | 2005-12-07
|
735003 |
|
9684 | 1970-05-20
|
246000 |
|
24278 | 2001-11-14
|
122882 |
|
27122 | 1994-02-05
|
810044 |
|
5950 | 1965-01-20
|
752120 |
|
12517 | 2003-06-13
|
380102 |
|
33010 | 1984-05-07
|
384463 |
|
46196 | 1972-11-28
|
497049 |
|
8710 | 2005-02-11
|
79387 |
|
3149 | 1998-11-17
|
643972 |
|
17124 | 1965-01-18
|
444747 |
|
20100 | 1994-08-13
|
106097 |
|
23317 | 2003-05-27
|
511750 |
|
42845 | 1967-10-23
|
433483 |
|
47560 | 1997-04-23
|
515647 |
See Also
RANK [Analytic]
l
l
STDDEV [Analytic]
Note: The non-standard function STDDEV() is provided for compatibility with other databases.
It is semantically identical to STDDEV_SAMP().
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Computes the statistical sample standard deviation of the current row with respect to the group
within a window. The STDDEV_SAMP() return value is the same as the square root of the variance
defined for the VAR_SAMP() function:
STDDEV(expression) = SQRT(VAR_SAMP(expression))
When VAR_SAMP() returns null, this function returns null.
Behavior Type
Immutable
Syntax
STDDEV ( expression ) OVER (
... [ window_partition_clause ]
... [ window_order_clause ]
... [ window_frame_clause ] )
Parameters
expression
Any NUMERIC data type or any non-numeric data type that can be implicitly
converted to a numeric data type. The function returns the same data type as the
numeric data type of the argument.
OVER(...)
See Analytic Functions.
Example
The following example returns the standard deviations of salaries in the employee dimension table
by job title Assistant Director:
=> SELECT employee_last_name, annual_salary,
STDDEV(annual_salary) OVER (ORDER BY hire_date) as "stddev"
FROM employee_dimension
WHERE job_title = 'Assistant Director';
employee_last_name | annual_salary |
stddev
--------------------+---------------+-----------------Goldberg
|
61859 |
NaN
Miller
|
79582 | 12532.0534829692
Goldberg
|
74236 | 9090.97147357388
Campbell
|
66426 | 7909.9541665339
Moore
|
66630 | 7068.30282316761
Nguyen
|
53530 | 9154.14713486005
Harris
|
74115 | 8773.54346886142
Lang
|
59981 | 8609.60471031374
Farmer
|
60597 | 8335.41158418579
Nguyen
|
78941 | 8812.87941405456
Smith
|
55018 | 9179.7672390773
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...
See Also
l
STDDEV [Aggregate]
l
STDDEV_SAMP [Aggregate]
STDDEV_SAMP [Analytic]
l
l
STDDEV_POP [Analytic]
Computes the statistical population standard deviation and returns the square root of the population
variance within a window. The STDDEV_POP() return value is the same as the square root of the
VAR_POP() function:
STDDEV_POP(expression) = SQRT(VAR_POP(expression))
When VAR_POP returns null, STDDEV_POP returns null.
Behavior Type
Immutable
Syntax
STDDEV_POP ( expression ) OVER (
... [ window_partition_clause ]
... [ window_order_clause ]
... [ window_frame_clause ] )
Parameters
expression
Any NUMERIC data type or any non-numeric data type that can be implicitly
converted to a numeric data type. The function returns the same data type as the
numeric data type of the argument.
OVER(...)
See Analytic Functions.
Examples
The following example returns the population standard deviations of salaries in the employee
dimension table by job title Assistant Director:
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=> SELECT employee_last_name, annual_salary,
STDDEV_POP(annual_salary) OVER (ORDER BY hire_date) as "stddev_pop"
FROM employee_dimension WHERE job_title = 'Assistant Director';
employee_last_name | annual_salary |
stddev_pop
--------------------+---------------+-----------------Goldberg
|
61859 |
0
Miller
|
79582 |
8861.5
Goldberg
|
74236 | 7422.74712548456
Campbell
|
66426 | 6850.22125098891
Moore
|
66630 | 6322.08223926257
Nguyen
|
53530 | 8356.55480080699
Harris
|
74115 | 8122.72288970008
Lang
|
59981 | 8053.54776538731
Farmer
|
60597 | 7858.70140687825
Nguyen
|
78941 | 8360.63150784682
See Also
STDDEV_POP [Aggregate]
l
l
STDDEV_SAMP [Analytic]
Computes the statistical sample standard deviation of the current row with respect to the group
within a window. The STDDEV_SAMP() return value is the same as the square root of the variance
defined for the VAR_SAMP() function:
STDDEV(expression) = SQRT(VAR_SAMP(expression))
When VAR_SAMP() returns null, STDDEV_SAMP returns null.
Behavior Type
Immutable
Syntax
STDDEV_SAMP ( expression ) OVER (
... [ window_partition_clause ]
... [ window_order_clause ]
... [ window_frame_clause ] )
Parameters
expression
Any NUMERIC data type or any non-numeric data type that can be implicitly
converted to a numeric data type. The function returns the same data type as the
numeric data type of the argument..
OVER(...)
See Analytic Functions.
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Notes
STDDEV_SAMP() is semantically identical to the non-standard function, STDDEV().
Examples
The following example returns the sample standard deviations of salaries in the employee
dimension table by job title Assistant Director:
=> SELECT employee_last_name, annual_salary,
STDDEV(annual_salary) OVER (ORDER BY hire_date) as "stddev_samp"
FROM employee_dimension WHERE job_title = 'Assistant Director';
employee_last_name | annual_salary |
stddev_samp
--------------------+---------------+-----------------Goldberg
|
61859 |
NaN
Miller
|
79582 | 12532.0534829692
Goldberg
|
74236 | 9090.97147357388
Campbell
|
66426 | 7909.9541665339
Moore
|
66630 | 7068.30282316761
Nguyen
|
53530 | 9154.14713486005
Harris
|
74115 | 8773.54346886142
Lang
|
59981 | 8609.60471031374
Farmer
|
60597 | 8335.41158418579
Nguyen
|
78941 | 8812.87941405456
...
See Also
l
Analytic Functions
l
STDDEV [Analytic]
l
STDDEV [Aggregate]
STDDEV_SAMP [Aggregate]
l
l
SUM [Analytic]
Computes the sum of an expression over a group of rows within a window. It returns a DOUBLE
PRECISION value for a floating-point expression. Otherwise, the return value is the same as the
expression data type.
Behavior Type
Immutable
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Syntax
SUM
...
...
...
(
[
[
[
expression ) OVER (
window_partition_clause ]
window_order_clause ]
window_frame_clause ] )
Parameters
expression
Any NUMERIC data type or any non-numeric data type that can be implicitly
converted to a numeric data type. The function returns the same data type as the
numeric data type of the argument.
OVER(...)
See Analytic Functions.
Notes
l
If you encounter data overflow when using SUM(), use SUM_FLOAT() which converts data to a
floating point.
l
SUM() returns the sum of values of an expression.
Examples
The following query returns the cumulative sum all of the returns made to stores in January:
=> SELECT calendar_month_name AS month, transaction_type, sales_quantity,
SUM(sales_quantity)
OVER (PARTITION BY calendar_month_name ORDER BY date_dimension.date_key) AS SUM
FROM store.store_sales_fact JOIN date_dimension
USING(date_key) WHERE calendar_month_name IN ('January')
AND transaction_type= 'return';
month | transaction_type | sales_quantity | SUM
---------+------------------+----------------+-----January | return
|
4 | 2338
January | return
|
3 | 2338
January | return
|
1 | 2338
January | return
|
5 | 2338
January | return
|
8 | 2338
January | return
|
3 | 2338
January | return
|
5 | 2338
January | return
|
10 | 2338
January | return
|
9 | 2338
January | return
|
10 | 2338
(10 rows)
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See Also
SUM [Aggregate]
l
Numeric Data Types
l
l
VAR_POP [Analytic]
Returns the statistical population variance of a non-null set of numbers (nulls are ignored) in a group
within a window. Results are calculated by the sum of squares of the difference of expression from
the mean of expression, divided by the number of rows remaining:
(SUM(expression*expression) - SUM(expression)*SUM(expression) /
UNT(expression)
COUNT(expression)) / CO
Behavior Type
Immutable
Syntax
VAR_POP ( expression ) OVER (
... [ window_partition_clause ]
... [ window_order_clause ]
... [ window_frame_clause ] )
Parameters
expression
Any NUMERIC data type or any non-numeric data type that can be implicitly
converted to a numeric data type. The function returns the same data type as the
numeric data type of the argument
OVER(...)
See Analytic Functions.
Examples
The following example calculates the cumulative population in the store orders fact table of sales in
December 2007:
=> SELECT date_ordered,
VAR_POP(SUM(total_order_cost))
OVER (ORDER BY date_ordered) "var_pop"
FROM store.store_orders_fact s
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WHERE date_ordered BETWEEN '2007-12-01' AND '2007-12-31'
GROUP BY s.date_ordered;
date_ordered |
var_pop
--------------+-----------------2007-12-01
|
0
2007-12-02
|
1129564881
2007-12-03
| 1206008121.55542
2007-12-04
| 26353624176.1875
2007-12-05
| 21315288023.4402
2007-12-06
| 21619271028.3333
2007-12-07
| 19867030477.6328
2007-12-08
|
19197735288.5
2007-12-09
| 19100157155.2097
2007-12-10
| 19369222968.0896
(10 rows)
See Also
VAR_POP [Aggregate]
l
l
VAR_SAMP [Analytic]
Returns the sample variance of a non-null set of numbers (nulls in the set are ignored) for each row
of the group within a window. Results are calculated by the sum of squares of the difference of
expression from the mean of expression, divided by the number of rows remaining minus 1:
(SUM(expression*expression) - SUM(expression)*SUM(expression) /
COUNT(expression)) / (COUNT(expression) - 1)
Behavior Type
Immutable
Syntax
VAR_SAMP ( expression ) OVER (
... [ window_partition_clause ]
... [ window_order_clause ]
... [ window_frame_clause ] )
Parameters
expression
Any NUMERIC data type or any non-numeric data type that can be implicitly
converted to a numeric data type. The function returns the same data type as the
numeric data type of the argument
OVER(...)
See Analytic Functions.
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Notes
l
VAR_SAMP() returns the sample variance of a set of numbers after it discards the nulls in the set.
l
If the function is applied to an empty set, then it returns null.
l
This function is similar to VARIANCE(), except that given an input set of one element, VARIANCE
() returns 0 and VAR_SAMP() returns null.
Examples
The following example calculates the sample variance in the store orders fact table of sales in
December 2007:
=> SELECT date_ordered,
VAR_SAMP(SUM(total_order_cost))
OVER (ORDER BY date_ordered) "var_samp"
FROM store.store_orders_fact s
WHERE date_ordered BETWEEN '2007-12-01' AND '2007-12-31'
GROUP BY s.date_ordered;
date_ordered |
var_samp
--------------+-----------------2007-12-01
|
NaN
2007-12-02
|
2259129762
2007-12-03
| 1809012182.33301
2007-12-04
|
35138165568.25
2007-12-05
| 26644110029.3003
2007-12-06
|
25943125234
2007-12-07
| 23178202223.9048
2007-12-08
| 21940268901.1431
2007-12-09
| 21487676799.6108
2007-12-10
| 21521358853.4331
(10 rows)
See Also
VARIANCE [Analytic]
l
VAR_SAMP [Aggregate]
l
l
VARIANCE [Analytic]
Note: The non-standard function VARIANCE() is provided for compatibility with other
databases. It is semantically identical to VAR_SAMP().
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Returns the sample variance of a non-null set of numbers (nulls in the set are ignored) for each row
of the group within a window. Results are calculated by the sum of squares of the difference of
expression from the mean of expression, divided by the number of rows remaining minus 1:
(SUM(expression*expression) - SUM(expression)*SUM(expression) /
OUNT(expression) - 1)
COUNT(expression)) / (C
Behavior Type
Immutable
Syntax
VAR_SAMP ( expression ) OVER (
... [ window_partition_clause ]
... [ window_order_clause ]
... [ window_frame_clause ] )
Parameters
expression
Any NUMERIC data type or any non-numeric data type that can be implicitly
converted to a numeric data type. The function returns the same data type as the
numeric data type of the argument.
OVER(...)
See Analytic Functions.
Notes
l
VARIANCE() returns the variance of expression.
l
The variance of expression is calculated as follows:
n
0 if the number of rows in expression = 1
n
VAR_SAMP() if the number of rows in expression > 1
Examples
The following example calculates the cumulative variance in the store orders fact table of sales in
December 2007:
=> SELECT date_ordered,
VARIANCE(SUM(total_order_cost))
OVER (ORDER BY date_ordered) "variance"
FROM store.store_orders_fact s
WHERE date_ordered BETWEEN '2007-12-01' AND '2007-12-31'
GROUP BY s.date_ordered;
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date_ordered |
variance
--------------+-----------------2007-12-01
|
NaN
2007-12-02
|
2259129762
2007-12-03
| 1809012182.33301
2007-12-04
|
35138165568.25
2007-12-05
| 26644110029.3003
2007-12-06
|
25943125234
2007-12-07
| 23178202223.9048
2007-12-08
| 21940268901.1431
2007-12-09
| 21487676799.6108
2007-12-10
| 21521358853.4331
(10 rows)
See Also
l
VAR_SAMP [Analytic]
l
VARIANCE [Aggregate]
VAR_SAMP [Aggregate]
l
l
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Date/Time Functions
Date and time functions perform conversion, extraction, or manipulation operations on date and
time data types and can return date and time information.
Usage
Functions that take TIME or TIMESTAMP inputs come in two variants:
l
TIME WITH TIME ZONE or TIMESTAMP WITH TIME ZONE
l
TIME WITHOUT TIME ZONE or TIMESTAMP WITHOUT TIME ZONE
For brevity, these variants are not shown separately.
The + and * operators come in commutative pairs; for example, both DATE + INTEGER and INTEGER
+ DATE. We show only one of each such pair.
Daylight Savings Time Considerations
When adding an INTERVAL value to (or subtracting an INTERVAL value from) a TIMESTAMP WITH
TIME ZONE value, the days component advances (or decrements) the date of the TIMESTAMP WITH
TIME ZONE by the indicated number of days. Across daylight saving time changes (with the session
time zone set to a time zone that recognizes DST), this means INTERVAL '1 day' does not
necessarily equal INTERVAL '24 hours'.
For example, with the session time zone set to CST7CDT:
TIMESTAMP WITH TIME ZONE '2005-04-02 12:00-07' + INTERVAL '1 day'
produces
TIMESTAMP WITH TIME ZONE '2005-04-03 12:00-06'
Adding INTERVAL '24 hours' to the same initial TIMESTAMP WITH TIME ZONE produces
TIMESTAMP WITH TIME ZONE '2005-04-03 13:00-06',
This result occurs because there is a change in daylight saving time at 2005-04-03 02:00 in time
zone CST7CDT.
Date/Time Functions in Transactions
CURRENT_TIMESTAMP() and related functions return the start time of the current transaction; their
values do not change during the transaction. The intent is to allow a single transaction to have a
consistent notion of the "current" time, so that multiple modifications within the same transaction
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bear the same timestamp. However, TIMEOFDAY() returns the wall-clock time and advances during
transactions.
See Also
Template Patterns for Date/Time Formatting
l
ADD_MONTHS
Takes a DATE, TIMESTAMP, or TIMESTAMPTZ argument and a number of months and returns a
date. TIMESTAMPTZ arguments are implicitly cast to TIMESTAMP.
Behavior Type
Immutable if called with DATE or TIMESTAMP but stable with TIMESTAMPTZ in that its results
can change based on TIMEZONE settings
Syntax
ADD_MONTHS ( d , n );
Parameters
d
The incoming DATE, TIMESTAMP, or TIMESTAMPTZ. If the start date falls on the last day
of the month, or if the resulting month has fewer days than the given day of the month, then
the result is the last day of the resulting month. Otherwise, the result has the same start day.
n
Any INTEGER.
Examples
The following example's results include a leap year:
SELECT ADD_MONTHS('31-Jan-08', 1) "Months";
Months
-----------2008-02-29
(1 row)
The next example adds four months to January and returns a date in May:
SELECT ADD_MONTHS('31-Jan-08', 4) "Months";
Months
-----------2008-05-31
(1 row)
This example subtracts four months from January, returning a date in September:
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SELECT ADD_MONTHS('31-Jan-08', -4) "Months";
Months
-----------2007-09-30
(1 row)
Because the following example specifies NULL, the result set is empty:
SELECT ADD_MONTHS('31-Jan-03', NULL) "Months";
Months
-------(1 row)
This example provides no date argument, so even though the number of months specified is 1, the
result set is empty:
SELECT ADD_MONTHS(NULL, 1) "Months";
Months
-------(1 row)
In this example, the date field defaults to a timestamp, so the PST is ignored. Even though it is
already the next day in Pacific time, the result falls on the same date in New York (two years later):
SET TIME ZONE 'America/New_York';
SELECT ADD_MONTHS('2008-02-29 23:30 PST', 24);
add_months
-----------2010-02-28
(1 row)
The next example specifies a timestamp with time zone, so the PST is taken into account:
SET TIME ZONE 'America/New_York';
SELECT ADD_MONTHS('2008-02-29 23:30 PST'::TIMESTAMPTZ, 24);
add_months
-----------2010-03-01
(1 row)
AGE_IN_MONTHS
Returns an INTEGER value representing the difference in months between two TIMESTAMP,
DATE or TIMESTAMPTZ values.
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Behavior Type
Stable if second argument is omitted or if either argument is TIMESTAMPTZ. Immutable
otherwise.
Syntax
AGE_IN_MONTHS ( expression1 [ , expression2 ] )
Parameters
expression1
Specifies the beginning of the period.
expression2
Specifies the end of the period. The default is the CURRENT_DATE.
Notes
The inputs can be TIMESTAMP, TIMESTAMPTZ, or DATE.
Examples
The following example returns the age in months of a person born on March 2, 1972 on the date
June 21, 1990, with a time elapse of 18 years, 3 months, and 19 days:
SELECT AGE_IN_MONTHS(TIMESTAMP '1990-06-21', TIMESTAMP '1972-03-02');
AGE_IN_MONTHS
--------------219
(1 row)
The next example shows the age in months of the same person (born March 2, 1972) as of March
16, 2010:
SELECT AGE_IN_MONTHS(TIMESTAMP 'March 16, 2010', TIMESTAMP '1972-03-02');
AGE_IN_MONTHS
--------------456
(1 row)
This example returns the age in months of a person born on November 21, 1939:
SELECT AGE_IN_MONTHS(TIMESTAMP '1939-11-21');
AGE_IN_MONTHS
--------------844
(1 row)
In the above form, the result changes as time goes by.
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AGE_IN_YEARS
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INTERVAL
AGE_IN_YEARS
Returns an INTEGER value representing the difference in years between two TIMESTAMP, DATE
or TIMESTAMPTZ values.
Behavior Type
Stable if second argument is omitted or if either argument is TIMESTAMPTZ. Immutable
otherwise.
Syntax
AGE_IN_YEARS ( expression1 [ , expression2 ] )
Parameters
expression1
Specifies the beginning of the period.
expression2
Specifies the end of the period. The default is the CURRENT_DATE.
Notes
l
The AGE_IN_YEARS() function was previously called AGE. AGE() is not supported.
l
Inputs can be TIMESTAMP, TIMESTAMPTZ, or DATE.
Examples
The following example returns the age in years of a person born on March 2, 1972 on the date June
21, 1990, with a time elapse of 18 years, 3 months, and 19 days:
SELECT AGE_IN_YEARS(TIMESTAMP '1990-06-21', TIMESTAMP '1972-03-02');
AGE_IN_YEARS
-------------18
(1 row)
The next example shows the age in years of the same person (born March 2, 1972) as of February
24, 2009:
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SELECT AGE_IN_YEARS(TIMESTAMP '2009-02-24', TIMESTAMP '1972-03-02');
AGE_IN_YEARS
-------------36
(1 row)
This example returns the age in years of a person born on November 21, 1939:
SELECT AGE_IN_YEARS(TIMESTAMP '1939-11-21');
AGE_IN_YEARS
-------------70
(1 row)
See Also
l
AGE_IN_MONTHS
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INTERVAL
CLOCK_TIMESTAMP
Returns a value of type TIMESTAMP WITH TIMEZONE representing the current system-clock
time.
Behavior Type
Volatile
Syntax
CLOCK_TIMESTAMP()
Notes
This function uses the date and time supplied by the operating system on the server to which you
are connected, which should be the same across all servers. The value changes each time you call
it.
Examples
The following command returns the current time on your system:
SELECT CLOCK_TIMESTAMP() "Current Time";
Current Time
------------------------------
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2010-09-23 11:41:23.33772-04
(1 row)
Each time you call the function, you get a different result. The difference in this example is in
microseconds:
SELECT CLOCK_TIMESTAMP() "Time 1", CLOCK_TIMESTAMP() "Time 2";
Time 1
|
Time 2
-------------------------------+------------------------------2010-09-23 11:41:55.369201-04 | 2010-09-23 11:41:55.369202-04
(1 row)
See Also
l
STATEMENT_TIMESTAMP
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TRANSACTION_TIMESTAMP
CURRENT_DATE
Returns the date (date-type value) on which the current transaction started.
Behavior Type
Stable
Syntax
CURRENT_DATE
Notes
The CURRENT_DATE function does not require parentheses.
Examples
SELECT CURRENT_DATE;
?column?
-----------2010-09-23
(1 row)
CURRENT_TIME
Returns a value of type TIME WITH TIMEZONE representing the time of day.
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Behavior Type
Stable
Syntax
CURRENT_TIME [ ( precision ) ]
Parameters
precision
(INTEGER) causes the result to be rounded to the specified number of fractional
digits in the seconds field.
Notes
l
This function returns the start time of the current transaction; the value does not change during
the transaction. The intent is to allow a single transaction to have a consistent notion of the
current time, so that multiple modifications within the same transaction bear the same
timestamp.
l
The CURRENT_TIME function does not require parentheses.
Examples
SELECT CURRENT_TIME "Current Time";
Current Time
-------------------12:45:12.186089-05
(1 row)
CURRENT_TIMESTAMP
Returns a value of type TIMESTAMP WITH TIME ZONE representing the start of the current
transaction.
Behavior Type
Stable
Syntax
CURRENT_TIMESTAMP [ ( precision ) ]
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Parameters
precision
(INTEGER) causes the result to be rounded to the specified number of fractional
digits in the seconds field. Range of INTEGER is 0-6.
Notes
This function returns the start time of the current transaction; the value does not change during the
transaction. The intent is to allow a single transaction to have a consistent notion of the "current"
time, so that multiple modifications within the same transaction bear the same timestamp.
Examples
SELECT CURRENT_TIMESTAMP;
?column?
------------------------------2010-09-23 11:37:22.354823-04
(1 row)
SELECT CURRENT_TIMESTAMP(2);
?column?
--------------------------2010-09-23 11:37:22.35-04
(1 row)
DATE_PART
Is modeled on the traditional Ingres equivalent to the SQL-standard function EXTRACT. Internally
DATE_PART is used by the EXTRACT function.
Behavior Type
Stable when source is of type TIMESTAMPTZ, Immutable otherwise.
Syntax
DATE_PART ( field , source )
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CENTURY
The century number.
SELECT EXTRACT(CENTURY FROM TIMESTAMP '2000-12-16 12:21:13');
Result: 20
SELECT EXTRACT(CENTURY FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 21
The first century starts at 0001-01-01 00:00:00 AD. This definition applies to all
Gregorian calendar countries. There is no century number 0, you go from –1 to
1.
DAY
The day (of the month) field (1–31).
SELECT EXTRACT(DAY FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 16
SELECT EXTRACT(DAY FROM DATE '2001-02-16');
Result: 16
DECADE
The year field divided by 10.
SELECT EXTRACT(DECADE FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 200
SELECT EXTRACT(DECADE FROM DATE '2001-02-16');
Result: 200
DOQ
The day within the current quarter.
SELECT EXTRACT(DOQ FROM CURRENT_DATE);
Result: 89
The result is calculated as follows: Current date = June 28, current quarter = 2
(April, May, June). 30 (April) + 31 (May) + 28 (June current day) = 89.
DOQ recognizes leap year days.
DOW
The day of the week (0–6; Sunday is 0).
SELECT EXTRACT(DOW FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 5
SELECT EXTRACT(DOW FROM DATE '2001-02-16');
Result: 5
EXTRACT's day of the week numbering is different from that of the TO_CHAR
function.
DOY
The day of the year (1–365/366)
SELECT EXTRACT(DOY FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 47
SELECT EXTRACT(DOY FROM DATE '2001-02-16');
Result: 5
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EPOCH
For DATE and TIMESTAMP values, the number of seconds since 1970-01-01
00:00:00-00 (can be negative); for INTERVAL values, the total number of
seconds in the interval.
SELECT EXTRACT(EPOCH FROM TIMESTAMP WITH TIME ZONE '2001-02-16 20:38:40-0
8');
Result: 982384720
SELECT EXTRACT(EPOCH FROM INTERVAL '5 days 3 hours');
Result: 442800
Here is how you can convert an epoch value back to a timestamp:
SELECT TIMESTAMP WITH TIME ZONE 'epoch' + 982384720 * INTERVAL '1 second';
HOUR
The hour field (0–23).
SELECT EXTRACT(HOUR FROM TIMESTAMP '2001-02-16 20:38:40'); Result: 20
SELECT EXTRACT(HOUR FROM TIME '13:45:59');
Result: 13
ISODOW
The ISO day of the week (1–7; Monday is 1).
By definition, the ISO-8601 week starts on Monday, and the first week of a year
contains January 4 of that year. In other words, the first Thursday of a year is in
week 1 of that year.
Because of this, it is possible for early January dates to be part of the 52nd or
53rd week of the previous year. For example, 2005-01-01 is part of the 53rd
week of year 2004, and 2006-01-01 is part of the 52nd week of year 2005.
SELECT EXTRACT(ISODOW FROM DATE '2010-09-27');
Result: 1
ISOWEEK
The ISO week, which consists of 7 days starting on Monday and ending on
Sunday. The first week of the year is the week that contains January 4.
ISOYEAR
The ISO year, which is 52 or 53 weeks (Monday–Sunday).
SELECT EXTRACT(ISOYEAR FROM DATE '2006-01-01');
Result: 2005
SELECT EXTRACT(ISOYEAR FROM DATE '2006-01-02');
Result: 2006
SELECT EXTRACT(ISOYEAR FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 2001
MICROSECONDS
The seconds field, including fractional parts, multiplied by 1,000,000. This
includes full seconds.
SELECT EXTRACT(MICROSECONDS FROM TIME '17:12:28.5');
Result: 28500000
MILLENNIUM
The millennium number.
SELECT EXTRACT(MILLENNIUM FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 3
Years in the 1900s are in the second millennium. The third millennium starts
January 1, 2001.
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MILLISECONDS
The seconds field, including fractional parts, multiplied by 1000. This includes
full seconds.
SELECT EXTRACT(MILLISECONDS FROM TIME '17:12:28.5');
Result: 28500
MINUTE
The minutes field (0 - 59).
SELECT EXTRACT(MINUTE FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 38
SELECT EXTRACT(MINUTE FROM TIME '13:45:59');
Result: 45
MONTH
For timestamp values, the number of the month within the year (1 - 12) ; for
interval values the number of months, modulo 12 (0 - 11).
SELECT EXTRACT(MONTH FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 2
SELECT EXTRACT(MONTH FROM INTERVAL '2 years 3 months');
Result: 3
SELECT EXTRACT(MONTH FROM INTERVAL '2 years 13 months');
Result: 1
QUARTER
The quarter of the year (1–4) that the day is in (for timestamp values only).
SELECT EXTRACT(QUARTER FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 1
SECOND
The seconds field, including fractional parts (0–59) (60 if leap seconds are
implemented by the operating system).
SELECT EXTRACT(SECOND FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 40
SELECT EXTRACT(SECOND FROM TIME '17:12:28.5');
Result: 28.5
TIME ZONE
The time zone offset from UTC, measured in seconds. Positive values
correspond to time zones east of UTC, negative values to zones west of UTC.
TIMEZONE_HOUR
The hour component of the time zone offset.
TIMEZONE_MINUT
E
The minute component of the time zone offset.
WEEK
The number of the week of the calendar year that the day is in.
SELECT EXTRACT(WEEK FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 7
SELECT EXTRACT(WEEK FROM DATE '2001-02-16');
Result: 7
YEAR
The year field. Keep in mind there is no 0 AD, so subtract BC years from AD
years with care.
SELECT EXTRACT(YEAR FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 2001
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Parameters
field
Single-quoted string value that specifies the field to extract. You must enter the constant
field values (for example, CENTURY, DAY, etc). when specifying the field.
Note: The field parameter values are the same for the EXTRACT function.
source
A date/time expression
Field Values
CENTURY
The century number.
SELECT EXTRACT(CENTURY FROM TIMESTAMP '2000-12-16 12:21:13');
Result: 20
SELECT EXTRACT(CENTURY FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 21
The first century starts at 0001-01-01 00:00:00 AD. This definition applies to all
Gregorian calendar countries. There is no century number 0, you go from –1 to
1.
DAY
The day (of the month) field (1–31).
SELECT EXTRACT(DAY FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 16
SELECT EXTRACT(DAY FROM DATE '2001-02-16');
Result: 16
DECADE
The year field divided by 10.
SELECT EXTRACT(DECADE FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 200
SELECT EXTRACT(DECADE FROM DATE '2001-02-16');
Result: 200
DOQ
The day within the current quarter.
SELECT EXTRACT(DOQ FROM CURRENT_DATE);
Result: 89
The result is calculated as follows: Current date = June 28, current quarter = 2
(April, May, June). 30 (April) + 31 (May) + 28 (June current day) = 89.
DOQ recognizes leap year days.
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DOW
The day of the week (0–6; Sunday is 0).
SELECT EXTRACT(DOW FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 5
SELECT EXTRACT(DOW FROM DATE '2001-02-16');
Result: 5
EXTRACT's day of the week numbering is different from that of the TO_CHAR
function.
DOY
The day of the year (1–365/366)
SELECT EXTRACT(DOY FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 47
SELECT EXTRACT(DOY FROM DATE '2001-02-16');
Result: 5
EPOCH
For DATE and TIMESTAMP values, the number of seconds since 1970-01-01
00:00:00-00 (can be negative); for INTERVAL values, the total number of
seconds in the interval.
SELECT EXTRACT(EPOCH FROM TIMESTAMP WITH TIME ZONE '2001-02-16 20:38:40-0
8');
Result: 982384720
SELECT EXTRACT(EPOCH FROM INTERVAL '5 days 3 hours');
Result: 442800
Here is how you can convert an epoch value back to a timestamp:
SELECT TIMESTAMP WITH TIME ZONE 'epoch' + 982384720 * INTERVAL '1 second';
HOUR
The hour field (0–23).
SELECT EXTRACT(HOUR FROM TIMESTAMP '2001-02-16 20:38:40'); Result: 20
SELECT EXTRACT(HOUR FROM TIME '13:45:59');
Result: 13
ISODOW
The ISO day of the week (1–7; Monday is 1).
By definition, the ISO-8601 week starts on Monday, and the first week of a year
contains January 4 of that year. In other words, the first Thursday of a year is in
week 1 of that year.
Because of this, it is possible for early January dates to be part of the 52nd or
53rd week of the previous year. For example, 2005-01-01 is part of the 53rd
week of year 2004, and 2006-01-01 is part of the 52nd week of year 2005.
SELECT EXTRACT(ISODOW FROM DATE '2010-09-27');
Result: 1
ISOWEEK
The ISO week, which consists of 7 days starting on Monday and ending on
Sunday. The first week of the year is the week that contains January 4.
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ISOYEAR
The ISO year, which is 52 or 53 weeks (Monday–Sunday).
SELECT EXTRACT(ISOYEAR FROM DATE '2006-01-01');
Result: 2005
SELECT EXTRACT(ISOYEAR FROM DATE '2006-01-02');
Result: 2006
SELECT EXTRACT(ISOYEAR FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 2001
MICROSECONDS
The seconds field, including fractional parts, multiplied by 1,000,000. This
includes full seconds.
SELECT EXTRACT(MICROSECONDS FROM TIME '17:12:28.5');
Result: 28500000
MILLENNIUM
The millennium number.
SELECT EXTRACT(MILLENNIUM FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 3
Years in the 1900s are in the second millennium. The third millennium starts
January 1, 2001.
MILLISECONDS
The seconds field, including fractional parts, multiplied by 1000. This includes
full seconds.
SELECT EXTRACT(MILLISECONDS FROM TIME '17:12:28.5');
Result: 28500
MINUTE
The minutes field (0 - 59).
SELECT EXTRACT(MINUTE FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 38
SELECT EXTRACT(MINUTE FROM TIME '13:45:59');
Result: 45
MONTH
For timestamp values, the number of the month within the year (1 - 12) ; for
interval values the number of months, modulo 12 (0 - 11).
SELECT EXTRACT(MONTH FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 2
SELECT EXTRACT(MONTH FROM INTERVAL '2 years 3 months');
Result: 3
SELECT EXTRACT(MONTH FROM INTERVAL '2 years 13 months');
Result: 1
QUARTER
The quarter of the year (1–4) that the day is in (for timestamp values only).
SELECT EXTRACT(QUARTER FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 1
SECOND
The seconds field, including fractional parts (0–59) (60 if leap seconds are
implemented by the operating system).
SELECT EXTRACT(SECOND FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 40
SELECT EXTRACT(SECOND FROM TIME '17:12:28.5');
Result: 28.5
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TIME ZONE
The time zone offset from UTC, measured in seconds. Positive values
correspond to time zones east of UTC, negative values to zones west of UTC.
TIMEZONE_HOUR
The hour component of the time zone offset.
TIMEZONE_MINUT
E
The minute component of the time zone offset.
WEEK
The number of the week of the calendar year that the day is in.
SELECT EXTRACT(WEEK FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 7
SELECT EXTRACT(WEEK FROM DATE '2001-02-16');
Result: 7
YEAR
The year field. Keep in mind there is no 0 AD, so subtract BC years from AD
years with care.
SELECT EXTRACT(YEAR FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 2001
Examples
The following example extracts the day value from the input parameters:
SELECT DATE_PART('day', TIMESTAMP '2009-02-24 20:38:40') "Day";
Day
----24
(1 row)
The following example extracts the month value from the input parameters:
SELECT DATE_PART('month', TIMESTAMP '2009-02-24 20:38:40') "Month";
Month
------2
(1 row)
The following example extracts the year value from the input parameters:
SELECT DATE_PART('year', TIMESTAMP '2009-02-24 20:38:40') "Year";
Year
-----2009
(1 row)
The following example extracts the hours from the input parameters:
SELECT DATE_PART('hour', TIMESTAMP '2009-02-24 20:38:40') "Hour";
Hour
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-----20
(1 row)
The following example extracts the minutes from the input parameters:
SELECT DATE_PART('minutes', TIMESTAMP '2009-02-24 20:38:40') "Minutes";
Minutes
--------38
(1 row)
The following example extracts the seconds from the input parameters:
SELECT DATE_PART('seconds', TIMESTAMP '2009-02-24 20:38:40') "Seconds";
Seconds
--------40
(1 row)
The following example extracts the day of quarter (DOQ) from the input parameters:
SELECT DATE_PART('DOQ', TIMESTAMP '2009-02-24 20:38:40') "DOQ";
DOQ
----55
(1 row)
SELECT DATE_PART('day', INTERVAL '29 days 23 hours');
date_part
----------29
(1 row)
Notice what happens to the above query if you add an hour:
SELECT DATE_PART('day', INTERVAL '29 days 24 hours');
date_part
----------30
(1 row)
The following example returns 0 because an interval in hours is up to 24 only:
SELECT DATE_PART('hour', INTERVAL '24 hours 45 minutes');
date_part
----------0
(1 row)
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EXTRACT
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DATE
Converts a TIMESTAMP, TIMESTAMPTZ, DATE, or VARCHAR to a DATE. You can also use
this function to convert an INTEGER to a DATE. In this case, the resulting date reflects the int
number of days after 0001 AD. (Day 1 is January 1, 0001.)
Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
Syntax
DATE ( d | n )
Parameters
d
TIMESTAMP, TIMESTAMPTZ, VARCHAR, or DATE input value.
n
Integer you want to convert to a DATE.
Examples
=> SELECT DATE (1);
DATE
-----------0001-01-01
(1 row)
=> SELECT DATE (734260);
DATE
-----------2011-05-03
(1 row)
=> SELECT DATE ('TODAY');
DATE
-----------2011-05-31
(1 row)
DATE_TRUNC
Truncates date and time values as indicated. The return value is of type TIME or TIMETZ with all
fields that are less significant than the selected one set to zero (or one, for day and month).
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Behavior Type
Stable.
Syntax
DATE_TRUNC ( field , source )
Parameters
field
String constant that selects the precision to which truncate the input value.
source
Value expression of type TIME or TIMETZ.
Field Values
CENTURY
The century number.
The first century starts at 0001-01-01 00:00:00 AD. This definition applies to all
Gregorian calendar countries. There is no century number 0, you go from –1 to 1.
DAY
The day (of the month) field (1–31).
DECADE
The year field divided by 10.
HOUR
The hour field (0–23).
MICROSECONDS
The seconds field, including fractional parts, multiplied by 1,000,000. This
includes full seconds.
MILLENNIUM
The millennium number.
Years in the 1900s are in the second millennium. The third millennium starts
January 1, 2001.
MILLISECONDS
The seconds field, including fractional parts, multiplied by 1000. Note that this
includes full seconds.
MINUTE
The minutes field (0–59).
MONTH
For timestamp values, the number of the month within the year (1–12) ; for
interval values the number of months, modulo 12 (0–11).
SECOND
The seconds field, including fractional parts (0–59) (60 if leap seconds are
implemented by the operating system).
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WEEK
By definition, the ISO-8601 week starts on Monday, and the first week of a year
contains January 4 of that year. In other words, the first Thursday of a year is in
week 1 of that year.
Because of this, it is possible for early January dates to be part of the 52nd or 53rd
week of the previous year. For example, 2005-01-01 is part of the 53rd week of
year 2004, and 2006-01-01 is part of the 52nd week of year 2005.
The number of the week of the year that the day is in.
YEAR
The year field. Keep in mind there is no 0 AD, so subtract BC years from AD years
with care.
Examples
The following example sets the field value as hour and returns the hour, truncating the minutes and
seconds:
VMart=> select date_trunc('hour', timestamp '2012-02-24 13:38:40') as hour;
hour
--------------------2012-02-24 13:00:00
(1 row)
The following example returns the year from the input timestamptz '2012-02-24 13:38:40'. The
function also defaults the month and day to January 1, truncates the hour:minute:second of the
timestamp, and appends the time zone (-05):
VMart=> select date_trunc('year', timestamptz '2012-02-24 13:38:40') as year;
year
-----------------------2012-01-01 00:00:00-05
(1 row)
The following example returns the year and month and defaults day of month to 1, truncating the
rest of the string:
VMart=> select date_trunc('month', timestamp '2012-02-24 13:38:40') as year;
year
--------------------2012-02-01 00:00:00
(1 row)
DATEDIFF
Returns the difference between two date or time values, based on the specified start and end
arguments.
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Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
Syntax 1
DATEDIFF ( datepart , startdate , enddate);
Syntax 2
DATEDIFF ( datepart , starttime , endtime);
Parameters
datepart
Returns the number of specified datepart boundaries between the specified
startdate and enddate.
Can be an unquoted identifier, a quoted string, or an expression in parentheses,
which evaluates to the datepart as a character string.
The following table lists the valid datepartarguments.
startdate
datepart
Abbreviation
year
yy, yyyy
quarter
qq, q
month
mm, m
day
dd, d, dy, dayofyear, y
week
wk, ww
hour
hh
minute
mi, n
second
ss, s
millisecond
ms
microsecond
mcs, us
Start date for the calculation and is an expression that returns a TIMESTAMP,
DATE, or TIMESTAMPTZ value.
The startdate value is not included in the count.
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enddate
End date for the calculation and is an expression that returns a TIMESTAMP, DATE,
or TIMESTAMPTZ value.
The enddate value is included in the count.
starttime
endtime
Start time for the calculation and is an expression that returns an INTERVAL or TIME
data type.
l
The starttime value is not included in the count.
l
Year, quarter, or month dateparts are not allowed.
End time for the calculation and is an expression that returns an INTERVAL or TIME
data type.
l
The endtime value is included in the count.
l
Year, quarter, or month dateparts are not allowed.
Notes
l
DATEDIFF() is an immutable function with a default type of TIMESTAMP. It also takes DATE.
If TIMESTAMPTZ is specified, the function is stable.
l
HP Vertica accepts statements written in any of the following forms:
=> DATEDIFF(year, s, e);
=> DATEDIFF('year', s, e);
If you use an expression, the expression must be enclosed in parentheses:
=> DATEDIFF((expression), s, e);
l
Starting arguments are not included in the count, but end arguments are included.
The Datepart Boundaries
DATEDIFF calculates results according to ticks—or boundaries—within the date range or time
range. Results are calculated based on the specified datepart. Examine the following statement
and its results:
SELECT DATEDIFF('year', TO_DATE('01-01-2005','MM-DD-YYYY'), TO_DATE('12-31-2008','MM-DD-Y
YYY'));
datediff
---------3
(1 row)
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The previous example specified a datepart of year, a startdate of January 1, 2005 and an
enddate of December 31, 2008. DATEDIFF returns 3 by counting the year intervals as follows:
[1] January 1, 2006 + [2] January 1, 2007 + [3] January 1, 2008 = 3
The function returns 3, and not 4, because startdate (January 1, 2005) is not counted in the
calculation. DATEDIFF also ignores the months between January 1, 2008 and December 31, 2008
because the datepart specified is year and only the start of each year is counted.
Sometimes the enddate occurs earlier in the ending year than the startdate in the starting year.
For example, assume a datepart of year, a startdate of August 15, 2005, and an enddate of
January 1, 2009. In this scenario, less than three years have elapsed, but DATEDIFF counts the
same way it did in the previous example, returning 3 because it returns the number of January 1s
between the limits:
[1] January 1, 2006 + [2] January 1, 2007 + [3] January 1, 2008 = 3
In the following query, HP Vertica recognizes the full year 2005 as the starting year and 2009 as the
ending year.
SELECT DATEDIFF('year', TO_DATE('08-15-2005','MM-DD-YYYY'), TO_DATE('01-01-2009','MM-DD-Y
YYY'));
The count occurs as follows:
[1] January 1, 2006 + [2] January 1, 2007 + [3] January 1, 2008 + [4] January 1, 2009 =
4
Even though August 15 has not yet occurred in the enddate, the function counts the entire enddate
year as one tick or boundary because of the year datepart.
Examples
Year: In this example, the startdateand enddateare adjacent. The difference between the dates
is one time boundary (second) of its datepart, so the result set is 1.
SELECT DATEDIFF('year', TIMESTAMP '2008-12-31 23:59:59',
'2009-01-01 00:00:00');
datediff
---------1
(1 row)
Quarters start on January, April, July, and October.
In the following example, the result is 0 because the difference from January to February in the
same calendar year does not span a quarter:
SELECT DATEDIFF('qq', TO_DATE('01-01-1995','MM-DD-YYYY'),
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TO_DATE('02-02-1995','MM-DD-YYYY'));
datediff
---------0
(1 row)
The next example, however, returns eight quarters because the difference spans two full years. The
extra month is ignored:
SELECT DATEDIFF('quarter', TO_DATE('01-01-1993','MM-DD-YYYY'),
TO_DATE('02-02-1995','MM-DD-YYYY'));
datediff
---------8
(1 row)
Months are based on real calendar months.
The following statement returns 1 because there is a one-month difference between January and
February in the same calendar year:
SELECT DATEDIFF('mm', TO_DATE('01-01-2005','MM-DD-YYYY'),
TO_DATE('02-02-2005','MM-DD-YYYY'));
datediff
---------1
(1 row)
The next example returns a negative value of 1:
SELECT DATEDIFF('month', TO_DATE('02-02-1995','MM-DD-YYYY'),
TO_DATE('01-01-1995','MM-DD-YYYY'));
datediff
----------1
(1 row)
And this third example returns 23 because there are 23 months difference between
SELECT DATEDIFF('m', TO_DATE('02-02-1993','MM-DD-YYYY'),
TO_DATE('01-01-1995','MM-DD-YYYY'));
datediff
---------23
(1 row)
Weeks start on Sunday at midnight.
The first example returns 0 because, even though the week starts on a Sunday, it is not a full
calendar week:
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SELECT DATEDIFF('ww', TO_DATE('02-22-2009','MM-DD-YYYY'),
TO_DATE('02-28-2009','MM-DD-YYYY'));
datediff
---------0
(1 row)
The following example returns 1 (week); January 1, 2000 fell on a Saturday.
SELECT DATEDIFF('week', TO_DATE('01-01-2000','MM-DD-YYYY'),
TO_DATE('01-02-2000','MM-DD-YYYY'));
datediff
---------1
(1 row)
In the next example, DATEDIFF() counts the weeks between January 1, 1995 and February 2,
1995 and returns 4 (weeks):
SELECT DATEDIFF('wk', TO_DATE('01-01-1995','MM-DD-YYYY'),
TO_DATE('02-02-1995','MM-DD-YYYY'));
datediff
---------4
(1 row)
The next example returns a difference of 100 weeks:
SELECT DATEDIFF('ww', TO_DATE('02-02-2006','MM-DD-YYYY'),
TO_DATE('01-01-2008','MM-DD-YYYY'));
datediff
---------100
(1 row)
Days are based on real calendar days.
The first example returns 31, the full number of days in the month of July 2008.
SELECT DATEDIFF('day', 'July 1, 2008', 'Aug 1, 2008'::date);
datediff
---------31
(1 row)
Just over two years of days:
SELECT DATEDIFF('d', TO_TIMESTAMP('01-01-1993','MM-DD-YYYY'),
TO_TIMESTAMP('02-02-1995','MM-DD-YYYY'));
datediff
----------
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762
(1 row)
Hours, minutes, and seconds are based on clock time.
The first example counts backwards from March 2 to February 14 and returns –384 hours:
SELECT DATEDIFF('hour', TO_DATE('03-02-2009','MM-DD-YYYY'),
TO_DATE('02-14-2009','MM-DD-YYYY'));
datediff
----------384
(1 row)
Another hours example:
SELECT DATEDIFF('hh', TO_TIMESTAMP('01-01-1993','MM-DD-YYYY'),
TO_TIMESTAMP('02-02-1995','MM-DD-YYYY'));
datediff
---------18288
(1 row)
This example counts the minutes backwards:
SELECT DATEDIFF('mi', TO_TIMESTAMP('01-01-1993 03:00:45','MM-DD-YYYY HH:MI:SS'),
TO_TIMESTAMP('01-01-1993 01:30:21',' MM-DD-YYYY HH:MI:SS'));
datediff
----------90
(1 row)
And this example counts the minutes forward:
SELECT DATEDIFF('minute', TO_DATE('01-01-1993','MM-DD-YYYY'),
TO_DATE('02-02-1995','MM-DD-YYYY'));
datediff
---------1097280
(1 row)
In the following example, the query counts the difference in seconds, beginning at a start time of
4:44 and ending at 5:55 with an interval of two days:
SELECT DATEDIFF('ss', TIME '04:44:42.315786',
INTERVAL '2 05:55:52.963558');
datediff
---------177070
(1 row)
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See Also
Date/Time Expressions
l
DAY
Extracts the day of the month from a TIMESTAMP, TIMESTAMPTZ, INTEGER, VARCHAR, or
INTERVAL input value. The return value is of type INTEGER.
Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
Syntax
DAY (d )
Parameters
d
TIMESTAMP, TIMESTAMPTZ, INTERVAL, VARCHAR, or INTEGER input value.
Examples
=> SELECT
DAY
----6
(1 row)
=> SELECT
DAY
----22
(1 row)
=> SELECT
DAY
----22
(1 row)
=> SELECT
DAY
----35
(1 row)
DAY (6);
DAY(TIMESTAMP 'sep 22, 2011 12:34');
DAY('sep 22, 2011 12:34');
DAY(INTERVAL '35 12:34');
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DAYOFMONTH
Returns an integer representing the day of the month based on a VARCHAR, DATE, TIMESTAMP,
OR TIMESTAMPTZ input value.
Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
Syntax
DAYOFMONTH ( d )
Parameters
d
VARCHAR, DATE, TIMESTAMP, or TIMESTAMPTZ input value.
Example
=> SELECT DAYOFMONTH (TIMESTAMP 'sep 22, 2011 12:34');
DAYOFMONTH
-----------22
(1 row)
DAYOFWEEK
Returns an INTEGER representing the day of the week based on a TIMESTAMP, TIMESTAMPTZ,
VARCHAR, or DATE input value. Valid return values are:
Integer Week Day
1
Sunday
2
Monday
3
Tuesday
4
Wednesday
5
Thursday
6
Friday
7
Saturday
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Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
Syntax
DAYOFWEEK ( d )
Parameters
d
TIMESTAMP, TIMESTAMPTZ, VARCHAR, or DATE input value.
Example
=> SELECT DAYOFWEEK (TIMESTAMP 'sep 17, 2011 12:34');
DAYOFWEEK
----------7
(1 row)
DAYOFWEEK_ISO
Returns an INTEGER representing the ISO 8061 day of the week based on a VARCHAR, DATE,
TIMESTAMP, or TIMESTAMPTZ input value. Valid return values are:
Integer Week Day
1
Monday
2
Tuesday
3
Wednesday
4
Thursday
5
Friday
6
Saturday
7
Sunday
Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
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Syntax
DAYOFWEEK_ISO ( d )
Parameters
d
VARCHAR, DATE, TIMESTAMP, or TIMESTAMPTZ input value.
Examples
=> SELECT DAYOFWEEK_ISO(TIMESTAMP 'Sep 22, 2011 12:34');
DAYOFWEEK_ISO
--------------4
(1 row)
The following example shows how to combine the DAYOFWEEK_ISO, WEEK_ISO, and YEAR_
ISO functions to find the ISO day of the week, week, and year:
=> SELECT DAYOFWEEK_ISO('Jan 1, 2000'), WEEK_ISO('Jan 1, 2000'),YEAR_ISO('Jan1,2000');
DAYOFWEEK_ISO | WEEK_ISO | YEAR_ISO
---------------+----------+---------6 |
52 |
1999
(1 row)
See Also
l
WEEK_ISO
l
DAYOFWEEK_ISO
l
http://en.wikipedia.org/wiki/ISO_8601
DAYOFYEAR
Returns an INTEGER representing the day of the year based on a TIMESTAMP, TIMESTAMPTZ ,
VARCHAR, or DATE input value. (January 1 is day 1.)
Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
Syntax
DAYOFYEAR ( d )
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Parameters
d
TIMESTAMP, TIMESTAMPTZ, VARCHAR, OR DATE input value.
Example
=> SELECT DAYOFYEAR (TIMESTAMP 'SEPT 22,2011 12:34');
DAYOFYEAR
----------265
(1 row)
DAYS
Converts a DATE, VARCHAR, TIMESTAMP, or TIMESTAMPTZ to an INTEGER, reflecting the
number of days after 0001 AD.
Behavior Type
Immutable
Syntax
DAYS( DATE d )
Parameters
DATE d
VARCHAR, DATE, TIMESTAMP, or TIMESTAMPTZ input value.
Example
=> SELECT DAYS (DATE '2011-01-22');
DAYS
-------734159
(1 row)
=> SELECT DAYS ('1999-12-31');
DAYS
-------730119
(1 row)
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EXTRACT
Retrieves subfields such as year or hour from date/time values and returns values of type
NUMERIC. EXTRACT is primarily intended for computational processing, rather than for
formatting date/time values for display.
Internally EXTRACT uses the DATE_PART function.
Behavior Type
Stable when source is of type TIMESTAMPTZ, Immutable otherwise.
Syntax
EXTRACT ( field FROM source )
CENTURY
The century number.
SELECT EXTRACT(CENTURY FROM TIMESTAMP '2000-12-16 12:21:13');
Result: 20
SELECT EXTRACT(CENTURY FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 21
The first century starts at 0001-01-01 00:00:00 AD. This definition applies to all
Gregorian calendar countries. There is no century number 0, you go from –1 to
1.
DAY
The day (of the month) field (1–31).
SELECT EXTRACT(DAY FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 16
SELECT EXTRACT(DAY FROM DATE '2001-02-16');
Result: 16
DECADE
The year field divided by 10.
SELECT EXTRACT(DECADE FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 200
SELECT EXTRACT(DECADE FROM DATE '2001-02-16');
Result: 200
DOQ
The day within the current quarter.
SELECT EXTRACT(DOQ FROM CURRENT_DATE);
Result: 89
The result is calculated as follows: Current date = June 28, current quarter = 2
(April, May, June). 30 (April) + 31 (May) + 28 (June current day) = 89.
DOQ recognizes leap year days.
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DOW
The day of the week (0–6; Sunday is 0).
SELECT EXTRACT(DOW FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 5
SELECT EXTRACT(DOW FROM DATE '2001-02-16');
Result: 5
EXTRACT's day of the week numbering is different from that of the TO_CHAR
function.
DOY
The day of the year (1–365/366)
SELECT EXTRACT(DOY FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 47
SELECT EXTRACT(DOY FROM DATE '2001-02-16');
Result: 5
EPOCH
For DATE and TIMESTAMP values, the number of seconds since 1970-01-01
00:00:00-00 (can be negative); for INTERVAL values, the total number of
seconds in the interval.
SELECT EXTRACT(EPOCH FROM TIMESTAMP WITH TIME ZONE '2001-02-16 20:38:40-0
8');
Result: 982384720
SELECT EXTRACT(EPOCH FROM INTERVAL '5 days 3 hours');
Result: 442800
Here is how you can convert an epoch value back to a timestamp:
SELECT TIMESTAMP WITH TIME ZONE 'epoch' + 982384720 * INTERVAL '1 second';
HOUR
The hour field (0–23).
SELECT EXTRACT(HOUR FROM TIMESTAMP '2001-02-16 20:38:40'); Result: 20
SELECT EXTRACT(HOUR FROM TIME '13:45:59');
Result: 13
ISODOW
The ISO day of the week (1–7; Monday is 1).
By definition, the ISO-8601 week starts on Monday, and the first week of a year
contains January 4 of that year. In other words, the first Thursday of a year is in
week 1 of that year.
Because of this, it is possible for early January dates to be part of the 52nd or
53rd week of the previous year. For example, 2005-01-01 is part of the 53rd
week of year 2004, and 2006-01-01 is part of the 52nd week of year 2005.
SELECT EXTRACT(ISODOW FROM DATE '2010-09-27');
Result: 1
ISOWEEK
The ISO week, which consists of 7 days starting on Monday and ending on
Sunday. The first week of the year is the week that contains January 4.
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ISOYEAR
The ISO year, which is 52 or 53 weeks (Monday–Sunday).
SELECT EXTRACT(ISOYEAR FROM DATE '2006-01-01');
Result: 2005
SELECT EXTRACT(ISOYEAR FROM DATE '2006-01-02');
Result: 2006
SELECT EXTRACT(ISOYEAR FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 2001
MICROSECONDS
The seconds field, including fractional parts, multiplied by 1,000,000. This
includes full seconds.
SELECT EXTRACT(MICROSECONDS FROM TIME '17:12:28.5');
Result: 28500000
MILLENNIUM
The millennium number.
SELECT EXTRACT(MILLENNIUM FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 3
Years in the 1900s are in the second millennium. The third millennium starts
January 1, 2001.
MILLISECONDS
The seconds field, including fractional parts, multiplied by 1000. This includes
full seconds.
SELECT EXTRACT(MILLISECONDS FROM TIME '17:12:28.5');
Result: 28500
MINUTE
The minutes field (0 - 59).
SELECT EXTRACT(MINUTE FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 38
SELECT EXTRACT(MINUTE FROM TIME '13:45:59');
Result: 45
MONTH
For timestamp values, the number of the month within the year (1 - 12) ; for
interval values the number of months, modulo 12 (0 - 11).
SELECT EXTRACT(MONTH FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 2
SELECT EXTRACT(MONTH FROM INTERVAL '2 years 3 months');
Result: 3
SELECT EXTRACT(MONTH FROM INTERVAL '2 years 13 months');
Result: 1
QUARTER
The quarter of the year (1–4) that the day is in (for timestamp values only).
SELECT EXTRACT(QUARTER FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 1
SECOND
The seconds field, including fractional parts (0–59) (60 if leap seconds are
implemented by the operating system).
SELECT EXTRACT(SECOND FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 40
SELECT EXTRACT(SECOND FROM TIME '17:12:28.5');
Result: 28.5
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TIME ZONE
The time zone offset from UTC, measured in seconds. Positive values
correspond to time zones east of UTC, negative values to zones west of UTC.
TIMEZONE_HOUR
The hour component of the time zone offset.
TIMEZONE_MINUT
E
The minute component of the time zone offset.
WEEK
The number of the week of the calendar year that the day is in.
SELECT EXTRACT(WEEK FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 7
SELECT EXTRACT(WEEK FROM DATE '2001-02-16');
Result: 7
YEAR
The year field. Keep in mind there is no 0 AD, so subtract BC years from AD
years with care.
SELECT EXTRACT(YEAR FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 2001
Parameters
field
Identifier or string that selects what field to extract from the source value. You must
enter the constant field values (i.e. CENTURY, DAY, etc). when specifying the field.
Note: The field parameter is the same for the DATE_PART() function.
source
Expression of type DATE, TIMESTAMP, TIME, or INTERVAL.
Note: Expressions of type DATE are cast to TIMESTAMP.
Field Values
CENTURY
The century number.
SELECT EXTRACT(CENTURY FROM TIMESTAMP '2000-12-16 12:21:13');
Result: 20
SELECT EXTRACT(CENTURY FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 21
The first century starts at 0001-01-01 00:00:00 AD. This definition applies to all
Gregorian calendar countries. There is no century number 0, you go from –1 to
1.
DAY
The day (of the month) field (1–31).
SELECT EXTRACT(DAY FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 16
SELECT EXTRACT(DAY FROM DATE '2001-02-16');
Result: 16
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DECADE
The year field divided by 10.
SELECT EXTRACT(DECADE FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 200
SELECT EXTRACT(DECADE FROM DATE '2001-02-16');
Result: 200
DOQ
The day within the current quarter.
SELECT EXTRACT(DOQ FROM CURRENT_DATE);
Result: 89
The result is calculated as follows: Current date = June 28, current quarter = 2
(April, May, June). 30 (April) + 31 (May) + 28 (June current day) = 89.
DOQ recognizes leap year days.
DOW
The day of the week (0–6; Sunday is 0).
SELECT EXTRACT(DOW FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 5
SELECT EXTRACT(DOW FROM DATE '2001-02-16');
Result: 5
EXTRACT's day of the week numbering is different from that of the TO_CHAR
function.
DOY
The day of the year (1–365/366)
SELECT EXTRACT(DOY FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 47
SELECT EXTRACT(DOY FROM DATE '2001-02-16');
Result: 5
EPOCH
For DATE and TIMESTAMP values, the number of seconds since 1970-01-01
00:00:00-00 (can be negative); for INTERVAL values, the total number of
seconds in the interval.
SELECT EXTRACT(EPOCH FROM TIMESTAMP WITH TIME ZONE '2001-02-16 20:38:40-0
8');
Result: 982384720
SELECT EXTRACT(EPOCH FROM INTERVAL '5 days 3 hours');
Result: 442800
Here is how you can convert an epoch value back to a timestamp:
SELECT TIMESTAMP WITH TIME ZONE 'epoch' + 982384720 * INTERVAL '1 second';
HOUR
The hour field (0–23).
SELECT EXTRACT(HOUR FROM TIMESTAMP '2001-02-16 20:38:40'); Result: 20
SELECT EXTRACT(HOUR FROM TIME '13:45:59');
Result: 13
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ISODOW
The ISO day of the week (1–7; Monday is 1).
By definition, the ISO-8601 week starts on Monday, and the first week of a year
contains January 4 of that year. In other words, the first Thursday of a year is in
week 1 of that year.
Because of this, it is possible for early January dates to be part of the 52nd or
53rd week of the previous year. For example, 2005-01-01 is part of the 53rd
week of year 2004, and 2006-01-01 is part of the 52nd week of year 2005.
SELECT EXTRACT(ISODOW FROM DATE '2010-09-27');
Result: 1
ISOWEEK
The ISO week, which consists of 7 days starting on Monday and ending on
Sunday. The first week of the year is the week that contains January 4.
ISOYEAR
The ISO year, which is 52 or 53 weeks (Monday–Sunday).
SELECT EXTRACT(ISOYEAR FROM DATE '2006-01-01');
Result: 2005
SELECT EXTRACT(ISOYEAR FROM DATE '2006-01-02');
Result: 2006
SELECT EXTRACT(ISOYEAR FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 2001
MICROSECONDS
The seconds field, including fractional parts, multiplied by 1,000,000. This
includes full seconds.
SELECT EXTRACT(MICROSECONDS FROM TIME '17:12:28.5');
Result: 28500000
MILLENNIUM
The millennium number.
SELECT EXTRACT(MILLENNIUM FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 3
Years in the 1900s are in the second millennium. The third millennium starts
January 1, 2001.
MILLISECONDS
The seconds field, including fractional parts, multiplied by 1000. This includes
full seconds.
SELECT EXTRACT(MILLISECONDS FROM TIME '17:12:28.5');
Result: 28500
MINUTE
The minutes field (0 - 59).
SELECT EXTRACT(MINUTE FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 38
SELECT EXTRACT(MINUTE FROM TIME '13:45:59');
Result: 45
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MONTH
For timestamp values, the number of the month within the year (1 - 12) ; for
interval values the number of months, modulo 12 (0 - 11).
SELECT EXTRACT(MONTH FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 2
SELECT EXTRACT(MONTH FROM INTERVAL '2 years 3 months');
Result: 3
SELECT EXTRACT(MONTH FROM INTERVAL '2 years 13 months');
Result: 1
QUARTER
The quarter of the year (1–4) that the day is in (for timestamp values only).
SELECT EXTRACT(QUARTER FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 1
SECOND
The seconds field, including fractional parts (0–59) (60 if leap seconds are
implemented by the operating system).
SELECT EXTRACT(SECOND FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 40
SELECT EXTRACT(SECOND FROM TIME '17:12:28.5');
Result: 28.5
TIME ZONE
The time zone offset from UTC, measured in seconds. Positive values
correspond to time zones east of UTC, negative values to zones west of UTC.
TIMEZONE_HOUR
The hour component of the time zone offset.
TIMEZONE_MINUT
E
The minute component of the time zone offset.
WEEK
The number of the week of the calendar year that the day is in.
SELECT EXTRACT(WEEK FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 7
SELECT EXTRACT(WEEK FROM DATE '2001-02-16');
Result: 7
YEAR
The year field. Keep in mind there is no 0 AD, so subtract BC years from AD
years with care.
SELECT EXTRACT(YEAR FROM TIMESTAMP '2001-02-16 20:38:40');
Result: 2001
Examples
=> SELECT EXTRACT (DAY FROM DATE '2008-12-25');
date_part
----------25
(1 row)
=> SELECT EXTRACT (MONTH FROM DATE '2008-12-25');
date_part
-----------
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12
(1 row
SELECT EXTRACT(DOQ FROM CURRENT_DATE);
date_part
----------89
(1 row)
Remember that internally EXTRACT() uses the DATE_PART() function:
=> SELECT EXTRACT(EPOCH FROM AGE_IN_YEARS(TIMESTAMP '2009-02-24',
2') :: INTERVAL year);
date_part
----------1136073600
(1 row)
TIMESTAMP '1972-03-0
In the above example, AGE_IN_YEARS is 36. The UNIX epoch uses 365.25 days per year:
=> SELECT 1136073600.0/36/(24*60*60);
?column?
---------365.25
(1 row)
You can extract the timezone hour from TIMETZ:
=> SELECT EXTRACT(timezone_hour FROM TIMETZ '10:30+13:30');
date_part
----------13
(1 row)
See Also
l
DATE_PART
GETDATE
Returns the current system date and time as a TIMESTAMP value.
Behavior Type
Stable
Syntax
GETDATE();
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Notes
l
GETDATE is a stable function that requires parentheses but accepts no arguments.
l
This function uses the date and time supplied by the operating system on the server to which
you are connected, which is the same across all servers.
l
GETDATE internally converts STATEMENT_TIMESTAMP() from TIMESTAMPTZ to
TIMESTAMP.
l
This function is identical to SYSDATE().
Example
=> SELECT GETDATE();
GETDATE
---------------------------2011-03-07 13:21:29.497742
(1 row)
See Also
l
Date/Time Expressions
GETUTCDATE
Returns the current system date and time as a TIMESTAMP value relative to UTC.
Behavior Type
Stable
Syntax
GETUTCDATE();
Notes
l
GETUTCDATE is a stable function that requires parentheses but accepts no arguments.
l
This function uses the date and time supplied by the operating system on the server to which
you are connected, which is the same across all servers.
l
GETUTCDATE is internally converted to STATEMENT_TIMESTAMP() at TIME ZONE 'UTC'.
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Example
=> SELECT GETUTCDATE();
GETUTCDATE
---------------------------2011-03-07 20:20:26.193052
(1 row)
See Also
Date/Time Expressions
l
HOUR
Extracts the hour from a DATE, TIMESTAMP, TIMESTAMPTZ, VARCHAR, or INTERVAL value.
The return value is of type INTEGER. (Hour 0 is midnight to 1 a.m.)
Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
Syntax
HOUR ( d )
Parameters
d
Incoming DATE, TIMESTAMP, TIMESTAMPTZ, VARCHAR, or INTERVAL value.
Examples
=> SELECT HOUR (TIMESTAMP 'sep 22, 2011 12:34');
HOUR
-----12
(1 row)
=> SELECT HOUR (INTERVAL '35 12:34');
HOUR
-----12
(1 row)
=> SELECT HOUR ('12:34');
HOUR
-----12
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(1 row)
ISFINITE
Tests for the special TIMESTAMP constant INFINITY and returns a value of type BOOLEAN.
Behavior Type
Immutable
Syntax
ISFINITE ( timestamp )
Parameters
timestamp
Expression of type TIMESTAMP
Examples
SELECT ISFINITE(TIMESTAMP '2009-02-16 21:28:30');
isfinite
---------t
(1 row)
SELECT ISFINITE(TIMESTAMP 'INFINITY');
isfinite
---------f
(1 row)
JULIAN_DAY
Returns an INTEGER representing the Julian day based on an input TIMESTAMP,
TIMESTAMPTZ, VARCHAR, or DATE value.
Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
Syntax
JULIAN_DAY ( d )
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Parameters
d
Is the TIMESTAMP, TIMESTAMPTZ, VARCHAR, or DATE input value.
Example
=> SELECT JULIAN_DAY(TIMESTAMP 'sep 22, 2011 12:34');
JULIAN_DAY
-----------2455827
(1 row)
LAST_DAY
Returns the last day of the month based on a TIMESTAMP. The TIMESTAMP can be supplied as a
DATE or a TIMESTAMPTZ data type.
Behavior Type
Immutable, unless called with TIMESTAMPTZ, in which case it is Stable.
Syntax
LAST_DAY ( date );
Examples
The following example returns the last day of the month, February, as 29 because 2008 was a leap
year:
SELECT LAST_DAY('2008-02-28 23:30 PST') "Last";
Last
-----------2008-02-29
(1 row)
The following example returns the last day of the month in March, after converting the string value
to the specified DATE type:
SELECT LAST_DAY('2003/03/15') "Last";
Last
-----------2003-03-31
(1 row)
The following example returns the last day of February in the specified year (not a leap year):
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SELECT LAST_DAY('2003/02/03') "Last";
Last
-----------2003-02-28
(1 row)
LOCALTIME
Returns a value of type TIME representing the time of day.
Behavior Type
Stable
Syntax
LOCALTIME [ ( precision ) ]
Parameters
precision
Causes the result to be rounded to the specified number of fractional digits in the
seconds field.
Notes
This function returns the start time of the current transaction; the value does not change during the
transaction. The intent is to allow a single transaction to have a consistent notion of the "current"
time, so that multiple modifications within the same transaction bear the same timestamp.
Example
SELECT LOCALTIME;
time
----------------16:16:06.790771
(1 row)
LOCALTIMESTAMP
Returns a value of type TIMESTAMP that represents today's date and time of day.
Behavior Type
Stable
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Syntax
LOCALTIMESTAMP [ ( precision ) ]
Parameters
precision
Causes the result to be rounded to the specified number of fractional digits in the
seconds field.
Notes
This function returns the start time of the current transaction; the value does not change during the
transaction. The intent is to allow a single transaction to have a consistent notion of the "current"
time, so that multiple modifications within the same transaction bear the same timestamp.
Example
SELECT LOCALTIMESTAMP;
timestamp
-------------------------2009-02-24 14:47:48.5951
(1 row)
MICROSECOND
Returns an INTEGER representing the microsecond portion of an input DATE, VARCHAR,
TIMESTAMP, TIMESTAMPTZ, or INTERVAL value.
Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
Syntax
MICROSECOND ( d )
Parameters
d
DATE, VARCHAR, TIMESTAMP, TIMESTAMPTZ, or INTERVAL input value.
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Example
=> SELECT MICROSECOND (TIMESTAMP 'Sep 22, 2011 12:34:01.123456');
MICROSECOND
------------123456
(1 row)
MIDNIGHT_SECONDS
Returns an INTEGER that represents the number of seconds between midnight and the input
value. The input value can be of type VARCHAR, TIME, TIMESTAMP, or TIMESTAMPTZ.
Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
Syntax
MIDNIGHT_SECONDS ( d )
Parameters
d
VARCHAR, TIME, TIMESTAMP, or TIMESTAMPTZ input value.
Example
=> SELECT MIDNIGHT_SECONDS('12:34:00.987654');
MIDNIGHT_SECONDS
-----------------45240
(1 row)
=> SELECT MIDNIGHT_SECONDS(TIME '12:34:00.987654');
MIDNIGHT_SECONDS
-----------------45240
(1 row)
=> SELECT MIDNIGHT_SECONDS (TIMESTAMP 'sep 22, 2011 12:34');
MIDNIGHT_SECONDS
-----------------45240
(1 row)
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MINUTE
Returns an INTEGER that represents the minute value of the input value. The input value can be of
type VARCHAR, DATE, TIMESTAMP, TIMESTAMPTZ, or INTERVAL.
Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
Syntax
MINUTE ( d )
Parameters
d
VARCHAR, DATE, TIMESTAMP, TIMESTAMPTZ, or INTERVAL input value.
Example
=> SELECT MINUTE('12:34:03.456789');
MINUTE
-------34
(1 row)
=>SELECT MINUTE (TIMESTAMP 'sep 22, 2011 12:34');
MINUTE
-------34
(1 row)
=> SELECT MINUTE(INTERVAL '35 12:34:03.456789');
MINUTE
-------34
(1 row)
MONTH
Returns an INTEGER that represents the month portion of the input value. The input value can be
of type VARCHAR, DATE, TIMESTAMP, TIMESTAMPTZ, or INTERVAL.
Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
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Syntax
MONTH( d )
Parameters
d
Incoming VARCHAR, DATE, TIMESTAMP, TIMESTAMPTZ, or INTERVAL value.
Examples
=> SELECT MONTH('6-9');
MONTH
------9
(1 row)
=> SELECT MONTH (TIMESTAMP 'sep 22, 2011 12:34');
MONTH
------9
(1 row)
=> SELECT MONTH(INTERVAL '2-35' year to month);
MONTH
------11
(1 row)
MONTHS_BETWEEN
Returns the number of months between date1 and date2 as a FLOAT8, where the input arguments
can be of TIMESTAMP, DATE, or TIMESTAMPTZ type.
Behavior Type
Immutable for TIMESTAMP and DATE, Stable for TIMESTAMPTZ
Syntax
MONTHS_BETWEEN ( date1 , date2 );
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Parameters
date1, date2
If date1 is later than date2, the result is positive. If date1 is earlier than date2,
then the result is negative.
If date1 and date2 are either the same days of the month or both are the last days
of their respective month, then the result is always an integer. Otherwise
MONTHS_BETWEEN returns a FLOAT8 result based on a 31-day month, which
considers the difference between date1 and date2.
Examples
The following result is an integral number of days because the dates are on the same day of the
month:
=> SELECT MONTHS_BETWEEN('2009-03-07 16:00'::TIMESTAMP,
'2009-04-07 15:00'::TIMESTAMP);
MONTHS_BETWEEN
----------------1
(1 row)
The result from the next example returns an integral number of days because the days fall on the
last day of their respective months:
=> SELECT MONTHS_BETWEEN('29Feb2000', '30Sep2000') "Months";
MONTHS
----------------7
(1 row)
In the next example, and in the example that immediately follows it, MONTHS_BETWEEN() returns the
number of months between date1 and date2 as a fraction because the days do not fall on the same
day or on the last day of their respective months:
=> SELECT MONTHS_BETWEEN(TO_DATE('02-02-1995','MM-DD-YYYY'),
TO_DATE('01-01-1995','MM-DD-YYYY') ) "Months";
Months
-----------------1.03225806451613
(1 row)
=> SELECT MONTHS_BETWEEN(TO_DATE ('2003/01/01', 'yyyy/mm/dd'),
TO_DATE ('2003/03/14', 'yyyy/mm/dd') ) "Months";
Months
-------------------2.41935483870968
(1 row)
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The following two examples use the same date1 and date2 strings, but they are cast to a different
data types (TIMESTAMP and TIMESTAMPTZ). The result is the same for both statements:
SELECT MONTHS_BETWEEN('2008-04-01'::timestamp, '2008-02-29'::timestamp);
months_between
-----------------1.09677419354839
(1 row)
SELECT MONTHS_BETWEEN('2008-04-01'::timestamptz, '2008-02-29'::timestamptz);
months_between
-----------------1.09677419354839
(1 row)
The following two examples show alternate inputs:
SELECT MONTHS_BETWEEN('2008-04-01'::date, '2008-02-29'::timestamp);
months_between
-----------------1.09677419354839
(1 row)
SELECT MONTHS_BETWEEN('2008-02-29'::timestamptz, '2008-04-01'::date);
months_between
-------------------1.09677419354839
(1 row)
NEW_TIME
Converts a TIMESTAMP value between time zones. Intervals are not permitted.
Behavior Type
Immutable
Syntax
NEW_TIME( 'timestamp' , 'timezone1' , 'timezone2')
Returns
TIMESTAMP
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Parameters
timestamp
The TIMESTAMP (or a TIMESTAMPTZ, DATE, or character string which can be
converted to a TIMESTAMP) representing a TIMESTAMP in timezone1 that returns
the equivalent timestamp in timezone2.
timezone1
VARCHAR string of the form required by the TIMESTAMP AT TIMEZONE 'zone'
clause. timezone1 indicates the time zone from which you want to convert
timestamp. It must be a valid timezone, as listed in the field for timezone2 below.
timezone2
VARCHAR string of the form required by the TIMESTAMP AT TIMEZONE 'zone'
clause. timezone2 indicates the time zone into which you want to convert timestamp.
Notes
The timezone arguments are character strings of the form required by the TIMESTAMP AT
TIMEZONE 'zone' clause; for example:
AST, ADT
Atlantic Standard Time or Daylight Time
BST, BDT
Bering Standard Time or Daylight Time
CST, CDT
Central Standard Time or Daylight Time
EST, EDT
Eastern Standard Time or Daylight Time
GMT
Greenwich Mean Time
HST
Alaska-Hawaii Standard Time
MST, MDT
Mountain Standard Time or Daylight Time
NST
Newfoundland Standard Time
PST, PDT
Pacific Standard Time or Daylight Time
Examples
The following command converts the specified time from Eastern Standard Time to Pacific
Standard Time:
=> SELECT NEW_TIME('05-24-12 13:48:00', 'EST', 'PST');
NEW_TIME
--------------------2012-05-24 10:48:00
(1 row)
This command converts the time on January 1 from Eastern Standard Time to Pacific Standard
Time. Notice how the time rolls back to the previous year:
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=> SELECT NEW_TIME('01-01-12 01:00:00', 'EST', 'PST');
NEW_TIME
--------------------2011-12-31 22:00:00
(1 row)
Query the current system time:
=> SELECT NOW();
now
------------------------------2012-05-24 08:28:10.155887-04
(1 row)
=> SELECT NEW_TIME('NOW', 'EDT', 'CDT');
NEW_TIME
---------------------------2012-05-24 07:28:10.155887
(1 row)
The following example returns the year 45 before the Common Era in Greenwich Mean Time and
converts it to Newfoundland Standard Time:
=>
SELECT NEW_TIME('April 1, 45 BC', 'GMT', 'NST');
NEW_TIME
-----------------------0045-03-31 20:30:00 BC
(1 row)
=> SELECT NEW_TIME('April 1 2011', 'EDT', 'PDT');
NEW_TIME
--------------------2011-03-31 21:00:00
(1 row)
=> SELECT NEW_TIME('May 24, 2012 10:00', 'Pacific/Kiritamati', 'EDT');
NEW_TIME
--------------------2011-05-23 16:00:00
(1 row)
NEXT_DAY
Returns the date of the first instance of a particular day of the week that follows the specified date.
Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
Syntax
NEXT_DAY( 'date', 'DOW')
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Parameters
date
VARCHAR, TIMESTAMP, TIMESTAMPTZ, or DATE. Only standard English day-names
and day-name abbreviations are accepted.
DOW
Day of week, type CHAR/VARCHAR string or character constant. DOW is not case
sensitive and trailing spaces are ignored.
Examples
The following example returns the date of the next Friday following the specified date. All are
variations on the same query, and all return the same result:
=> SELECT NEXT_DAY('28-MAR-2011','FRIDAY') "NEXT DAY" FROM DUAL;
NEXT DAY
-----------2011-04-01
(1 row)
=> SELECT NEXT_DAY('March 28 2011','FRI') "NEXT DAY" FROM DUAL;
NEXT DAY
-----------2011-04-01
(1 row)
=> SELECT NEXT_DAY('3-29-11','FRI') "NEXT DAY" FROM DUAL;
NEXT DAY
-----------2011-04-01
(1 row)
NOW [Date/Time]
Returns a value of type TIMESTAMP WITH TIME ZONE representing the start of the current
transaction. NOW is equivalent to CURRENT_TIMESTAMP except that it does not accept a
precision parameter.
Behavior Type
Stable
Syntax
NOW()
Notes
This function returns the start time of the current transaction; the value does not change during the
transaction. The intent is to allow a single transaction to have a consistent notion of the "current"
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time, so that multiple modifications within the same transaction bear the same timestamp.
Example
SELECT NOW();
NOW
------------------------------2010-04-01 15:31:12.144584-04
(1 row)
See Also
l
CURRENT_TIMESTAMP
OVERLAPS
Returns true when two time periods overlap, false when they do not overlap.
Behavior Type
Stable when TIMESTAMP and TIMESTAMPTZ are both used, or when TIMESTAMPTZ is used
with INTERVAL, Immutable otherwise.
Syntax
( start, end ) OVERLAPS ( start, end )
( start, interval ) OVERLAPS ( start, interval )
Parameters
start
DATE, TIME, or TIME STAMP value that specifies the beginning of a time period.
end
DATE, TIME, or TIME STAMP value that specifies the end of a time period.
interval
Value that specifies the length of the time period.
Examples
The first command returns true for an overlap in date range of 2007-02-16 through 2007-12-21 with
2007-10-30 through 2008-10-30.
SELECT (DATE '2007-02-16', DATE '2007-12-21')
OVERLAPS (DATE '2007-10-30', DATE '2008-10-30');
OVERLAPS
---------t
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(1 row)
The next command returns false for an overlap in date range of 2007-02-16 through 2007-12-21 with
2008-10-30 through 2008-10-30.
SELECT (DATE '2007-02-16', DATE '2007-12-21')
OVERLAPS (DATE '2008-10-30', DATE '2008-10-30');
OVERLAPS
---------f
(1 row)
The next command returns false for an overlap in date range of 2007-02-16, 22 hours ago with 200710-30, 22 hours ago.
SELECT (DATE '2007-02-16', INTERVAL '1 12:59:10')
OVERLAPS (DATE '2007-10-30', INTERVAL '1 12:59:10');
overlaps
---------f
(1 row)
QUARTER
Returns an INTEGER representing calendar quarter into which the input value falls. The input value
can be of type VARCHAR, DATE, TIMESTAMP or TIMESTAMPTZ.
Syntax
QUARTER( d )
Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
Parameters
d
DATE, VARCHAR, TIMESTAMP, or TIMESTAMPTZ input value.
Example
=> SELECT QUARTER (TIMESTAMP 'sep 22, 2011 12:34');
QUARTER
--------3
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(1 row)
ROUND [Date/Time]
Rounds a TIMESTAMP, TIMESTAMPTZ, or DATE. The return value is of type TIMESTAMP.
Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
Syntax
ROUND([TIMESTAMP | DATE] , format )
Parameters
TIMESTAMP | DATE
TIMESTAMP or DATE input value.
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format
A string constant that selects the precision to which truncate the
input value. Valid values are:
Precision
Valid values
Century
CC, SCC
Year
SYYY, YYYY, YEAR,
YYY, YY,Y
ISO Year
IYYY, IYY, IY, I
Quarter
Q
Month
MONTH, MON, MM,
RM
Same day of the week as the first day
of the year
WW
Same day of the week as the first day
of the ISO year
IW
Same day of the week as the first day
of the month
W
Day
DDD, DD, J
Starting day of the week
DAY, DY, D
Hour
HH, HH12, HH24
Minute
MI
Second
SS
Example
=> SELECT ROUND(TIMESTAMP 'sep 22, 2011 12:34:00', 'dy');
ROUND
--------------------2011-09-18 00:00:00
(1 row)
SECOND
Returns an INTEGER representing the second portion of the input value. The input value can be of
type VARCHAR, TIMESTAMP, TIMESTAMPTZ, or INTERVAL.
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Syntax
SECOND( d )
Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
Parameters
d
VARCHAR, TIMESTAMP, TIMESTAMPTZ, or INTERVAL input value.
Examples
=> SELECT SECOND ('23:34:03.456789');
SECOND
-------3
(1 row)
=> SELECT SECOND (TIMESTAMP 'sep 22, 2011 12:34');
SECOND
-------0
(1 row)
=> SELECT SECOND (INTERVAL '35 12:34:03.456789');
SECOND
-------3
(1 row)
STATEMENT_TIMESTAMP
Is similar to TRANSACTION_TIMESTAMP. It returns a value of type TIMESTAMP WITH TIME
ZONE representing the start of the current statement.
Behavior Type
Stable
Syntax
STATEMENT_TIMESTAMP()
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Notes
This function returns the start time of the current statement; the value does not change during the
statement. The intent is to allow a single statement to have a consistent notion of the "current"
time, so that multiple modifications within the same statement bear the same timestamp.
Example
SELECT STATEMENT_TIMESTAMP();
STATEMENT_TIMESTAMP
------------------------------2010-04-01 15:40:42.223736-04
(1 row)
See Also
l
CLOCK_TIMESTAMP
l
TRANSACTION_TIMESTAMP
SYSDATE
Returns the current system date and time as a TIMESTAMP value.
Behavior Type
Stable
Syntax
SYSDATE();
Notes
l
SYSDATE is a stable function (called once per statement) that requires no arguments.
Parentheses are optional.
l
This function uses the date and time supplied by the operating system on the server to which
you are connected, which must be the same across all servers.
l
In implementation, SYSDATE converts STATEMENT_TIMESTAMP from TIMESTAMPTZ to
TIMESTAMP.
l
This function is identical to GETDATE().
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Example
=> SELECT SYSDATE();
sysdate
---------------------------2011-03-07 13:22:28.295802
(1 row)
See Also
l
Date/Time Expressions
TIME_SLICE
Aggregates data by different fixed-time intervals and returns a rounded-up input TIMESTAMP value
to a value that corresponds with the start or end of the time slice interval.
Given an input TIMESTAMP value, such as '2000-10-28 00:00:01', the start time of a 3-second time
slice interval is '2000-10-28 00:00:00', and the end time of the same time slice is '2000-10-28
00:00:03'.
Behavior Type
Immutable
Syntax
TIME_SLICE(expression, slice_length,
[ time_unit = 'SECOND' ],
[ start_or_end = 'START' ] )
Parameters
expression
Can be either a column of type TIMESTAMP or a (string) constant that can be
parsed into a TIMESTAMP value, such as '2004-10-19 10:23:54'.
HP Vertica evaluates expression on each row.
slice_length
Length of the slice specified in integers. Input must be a positive integer.
time_unit
Time unit of the slice with a default of SECOND.
Domain of possible values: { HOUR, MINUTE, SECOND, MILLISECOND,
MICROSECOND }.
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start_or_end
Indicates whether the returned value corresponds to the start or end time of the
time slice interval. The default is START.
Domain of possible values: { START, END }.
Notes
l
The returned value's data type is TIMESTAMP.
l
The corresponding SQL data type for TIMESTAMP is TIMESTAMP WITHOUT TIME ZONE.
HP Vertica supports TIMESTAMP for TIME_SLICE instead of DATE and TIME data types.
l
TIME_SLICE exhibits the following behavior around nulls:
n
The system returns an error when any one of slice_length, time_unit, or start_or_end
parameters is null.
n
When slice_length, time_unit, and start_or_end contain legal values, and expression is null,
the system returns a NULL value, instead of an error.
Usage
The following command returns the (default) start time of a 3-second time slice:
SELECT TIME_SLICE('2009-09-19 00:00:01', 3);
time_slice
--------------------2009-09-19 00:00:00
(1 row)
The following command returns the end time of a 3-second time slice:
SELECT TIME_SLICE('2009-09-19 00:00:01', 3, 'SECOND', 'END');
time_slice
--------------------2009-09-19 00:00:03
(1 row)
This command returns results in milliseconds, using a 3-second time slice:
SELECT TIME_SLICE('2009-09-19 00:00:01', 3, 'ms');
time_slice
------------------------2009-09-19 00:00:00.999
(1 row)
This command returns results in microseconds, using a 9-second time slice:
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SELECT TIME_SLICE('2009-09-19 00:00:01', 3, 'us');
time_slice
---------------------------2009-09-19 00:00:00.999999
(1 row)
The next example uses a 3-second interval with an input value of '00:00:01'. To focus specifically
on seconds, the example omits date, though all values are implied as being part of the timestamp
with a given input of '00:00:01':
l
'00:00:00' is the start of the 3-second time slice
l
'00:00:03' is the end of the 3-second time slice.
l
'00:00:03' is also the start of the second 3-second time slice. In time slice boundaries, the end
value of a time slice does not belong to that time slice; it starts the next one.
When the time slice interval is not a factor of 60 seconds, such as a given slice length of 9 in the
following example, the slice does not always start or end on 00 seconds:
SELECT TIME_SLICE('2009-02-14 20:13:01', 9);
time_slice
--------------------2009-02-14 20:12:54
(1 row)
This is expected behavior, as the following properties are true for all time slices:
l
Equal in length
l
Consecutive (no gaps between them)
l
Non-overlapping
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To force the above example ('2009-02-14 20:13:01') to start at '2009-02-14 20:13:00', adjust the
output timestamp values so that the remainder of 54 counts up to 60:
SELECT TIME_SLICE('2009-02-14 20:13:01', 9 )+'6 seconds'::INTERVAL AS time;
time
--------------------2009-02-14 20:13:00
(1 row)
Alternatively, you could use a different slice length, which is divisible by 60, such as 5:
SELECT TIME_SLICE('2009-02-14 20:13:01', 5);
time_slice
--------------------2009-02-14 20:13:00
(1 row)
A TIMESTAMPTZ value is implicitly cast to TIMESTAMP. For example, the following two
statements have the same effect.
SELECT TIME_SLICE('2009-09-23 11:12:01'::timestamptz, 3);
TIME_SLICE
--------------------2009-09-23 11:12:00
(1 row)
SELECT TIME_SLICE('2009-09-23 11:12:01'::timestamptz::timestamp, 3);
TIME_SLICE
--------------------2009-09-23 11:12:00
(1 row)
Examples
You can use the SQL analytic functions FIRST_VALUE and LAST_VALUE to find the first/last
price within each time slice group (set of rows belonging to the same time slice). This structure
could be useful if you want to sample input data by choosing one row from each time slice group.
SELECT date_key, transaction_time, sales_dollar_amount,TIME_SLICE(DATE '2000-01-01' + dat
e_key + transaction_time, 3),
FIRST_VALUE(sales_dollar_amount)
OVER (PARTITION BY TIME_SLICE(DATE '2000-01-01' + date_key + transaction_time, 3)
ORDER BY DATE '2000-01-01' + date_key + transaction_time) AS first_value
FROM store.store_sales_fact
LIMIT 20;
date_key | transaction_time | sales_dollar_amount |
time_slice
| first_value
----------+------------------+---------------------+---------------------+------------1 | 00:41:16
|
164 | 2000-01-02 00:41:15 |
164
1 | 00:41:33
|
310 | 2000-01-02 00:41:33 |
310
1 | 15:32:51
|
271 | 2000-01-02 15:32:51 |
271
1 | 15:33:15
|
419 | 2000-01-02 15:33:15 |
419
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1
1
1
2
3
3
3
3
3
3
4
4
4
4
4
5
(20 rows)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
15:33:44
16:36:29
16:36:44
03:11:28
03:55:15
11:58:05
11:58:24
11:58:52
19:01:21
22:15:05
13:36:57
13:37:24
13:37:54
13:38:04
13:38:31
10:21:24
|
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|
|
|
|
|
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|
193
466
250
39
375
369
174
449
201
156
-125
-251
353
426
209
488
|
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|
|
|
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|
|
2000-01-02
2000-01-02
2000-01-02
2000-01-03
2000-01-04
2000-01-04
2000-01-04
2000-01-04
2000-01-04
2000-01-04
2000-01-05
2000-01-05
2000-01-05
2000-01-05
2000-01-05
2000-01-06
15:33:42
16:36:27
16:36:42
03:11:27
03:55:15
11:58:03
11:58:24
11:58:51
19:01:21
22:15:03
13:36:57
13:37:24
13:37:54
13:38:03
13:38:30
10:21:24
|
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|
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193
466
250
39
375
369
174
449
201
156
-125
-251
353
426
209
488
TIME_SLICE rounds the transaction time to the 3-second slice length.
The following example uses the analytic (window) OVER() clause to return the last trading price
(the last row ordered by TickTime) in each 3-second time slice partition:
SELECT DISTINCT TIME_SLICE(TickTime, 3), LAST_VALUE(price)OVER (PARTITION BY TIME_SLICE(T
ickTime, 3)
ORDER BY TickTime ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING);
Note: If you omit the windowing clause from an analytic clause, LAST_VALUE defaults to
RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW. Results can
seem non-intuitive, because instead of returning the value from the bottom of the current
partition, the function returns the bottom of the window, which continues to change along with
the current input row that is being processed. For more information, see Using Time Series
Analytics and Using SQL Analytics in the Programmer's Guide.
In the next example, FIRST_VALUE is evaluated once for each input record and the data is sorted
by ascending values. Use SELECT DISTINCT to remove the duplicates and return only one output
record per TIME_SLICE:
SELECT DISTINCT TIME_SLICE(TickTime, 3), FIRST_VALUE(price)OVER (PARTITION BY TIME_SLICE(
TickTime, 3)
ORDER BY TickTime ASC)
FROM tick_store;
TIME_SLICE
| ?column?
---------------------+---------2009-09-21 00:00:06 |
20.00
2009-09-21 00:00:09 |
30.00
2009-09-21 00:00:00 |
10.00
(3 rows)
The information output by the above query can also return MIN, MAX, and AVG of the trading prices
within each time slice.
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SELECT DISTINCT TIME_SLICE(TickTime, 3),FIRST_VALUE(Price) OVER (PARTITION BY TIME_SLICE(
TickTime, 3)
ORDER BY TickTime ASC),
MIN(price) OVER (PARTITION BY TIME_SLICE(TickTime, 3)),
MAX(price) OVER (PARTITION BY TIME_SLICE(TickTime, 3)),
AVG(price) OVER (PARTITION BY TIME_SLICE(TickTime, 3))
FROM tick_store;
See Also
l
Aggregate Functions
l
FIRST_VALUE [Analytic]
l
LAST_VALUE [Analytic]
l
TIMESERIES Clause
l
TS_FIRST_VALUE
TS_LAST_VALUE
l
l
TIMEOFDAY
Returns a text string representing the time of day.
Behavior Type
Volatile
Syntax
TIMEOFDAY()
Notes
TIMEOFDAY() returns the wall-clock time and advances during transactions.
Example
SELECT TIMEOFDAY();
TIMEOFDAY
------------------------------------Thu Apr 01 15:42:04.483766 2010 EDT
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(1 row)
TIMESTAMPADD
Adds a specified number of intervals to a TIMESTAMP or TIMESTAMPTZ. The return value
depends on the input, as follows:
l
If starttimestamp is TIMESTAMP, the return value is of type TIMESTAMP.
l
If starttimestampis TIMESTAMPTZ, the return value is of type TIMESTAMPTZ.
Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
Syntax
TIMESTAMPADD ( datepart ,interval, starttimestamp );
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Parameters
datepart
(VARCHAR) Returns the number of specified datepart boundaries between
the specified startdate and enddate.
Can be an unquoted identifier, a quoted string, or an expression in parentheses,
which evaluates to the datepart as a character string.
The following table lists the valid datepartarguments.
datepart*
Abbreviation
YEAR
yy, yyyy
QUARTER
qq, q
MONTH
mm, m
DAY
dd, d, dy, dayofyear, y
WEEK
wk, ww
HOUR
hh
MINUTE
mi, n
SECOND
ss, s
MILLISECOND
ms
MICROSECOND
mcs, us
* Each of these dateparts can be prefixed with SQL_TSI_ (i.e. SQL_TSI_
YEAR, SQL_TSI_DAY, and so forth.)
starttimestamp
Start TIMESTAMP or TIMESTAMPTZ for the calculation.
Notes
l
TIMESTAMPDIFF() is an immutable function with a default type of TIMESTAMP. If
TIMESTAMPTZ is specified, the function is stable.
l
HP Vertica accepts statements written in any of the following forms:
TIMESTAMPDIFF(year, s, e);
TIMESTAMPDIFF('year', s, e);
If you use an expression, the expression must be enclosed in parentheses:
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DATEDIFF((expression), s, e);
l
Starting arguments are not included in the count, but end arguments are included.
Example
=> SELECT TIMESTAMPADD (SQL_TSI_MONTH, 2, ('jan 1, 2006'));
TIMESTAMPADD
-----------------------2006-03-01 00:00:00-05
(1 row)
See Also
l
Date/Time Expressions
TIMESTAMPDIFF
Returns the difference between two TIMESTAMP or TIMESTAMPTZ values, based on the
specified start and end arguments.
Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
Syntax
TIMESTAMPDIFF ( datepart , starttimestamp , endtimestamp );
Parameters
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datepart
(VARCHAR) Returns the number of specified datepart boundaries between
the specified startdate and enddate.
Can be an unquoted identifier, a quoted string, or an expression in parentheses,
which evaluates to the datepart as a character string.
The following table lists the valid datepartarguments.
datepart
Abbreviation
year
yy, yyyy
quarter
qq, q
month
mm, m
day
dd, d, dy, dayofyear, y
week
wk, ww
hour
hh
minute
mi, n
second
ss, s
millisecond
ms
microsecond
mcs, us
starttimestamp
Start TIMESTAMP for the calculation.
endtimestamp
End TIMESTAMP for the calculation.
Notes
l
TIMESTAMPDIFF() is an immutable function with a default type of TIMESTAMP. If
TIMESTAMPTZ is specified, the function is stable.
l
HP Vertica accepts statements written in any of the following forms:
TIMESTAMPDIFF(year, s, e);
TIMESTAMPDIFF('year', s, e);
If you use an expression, the expression must be enclosed in parentheses:
TIMESTAMPDIFF((expression), s, e);
l
Starting arguments are not included in the count, but end arguments are included.
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Example
=> SELECT TIMESTAMPDIFF ('YEAR',('jan 1, 2006 12:34:00'), ('jan 1, 2008 12:34:00'));
TIMESTAMPDIFF
--------------2
(1 row)
See Also
l
Date/Time Expressions
TIMESTAMP_ROUND
Rounds a TIMESTAMP to a specified format. The return value is of type TIMESTAMP.
Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
Syntax
TIMESTAMP_ROUND ( timestamp, format )
Parameters
timestamp
TIMESTAMP or TIMESTAMPTZ input value.
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format
String constant that selects the precision to which truncate the input value. Valid
values for format are:
Precision
Valid values
Century
CC, SCC
Year
SYYY, YYYY, YEAR, YYY,
YY,Y
ISO Year
IYYY, IYY, IY, I
Quarter
Q
Month
MONTH, MON, MM, RM
Same day of the week as the first day of the year WW
Same day of the week as the first day of the ISO
year
IW
Same day of the week as the first day of the
month
W
Day
DDD, DD, J
Starting day of the week
DAY, DY, D
Hour
HH, HH12, HH24
Minute
MI
Second
SS
Examples
b=> SELECT TIMESTAMP_ROUND('sep 22, 2011 12:34:00', 'dy');
TIMESTAMP_ROUND
--------------------2011-09-18 00:00:00
(1 row)
TIMESTAMP_TRUNC
Truncates a TIMESTAMP. The return value is of type TIMESTAMP.
Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
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Syntax
TIMESTAMP_TRUNC ( timestamp, format )
Parameters
timestamp
TIMESTAMP or TIMESTAMPTZ input value.
format
String constant that selects the precision to which truncate the input value. Valid
values for format are:
Precision
Valid values
Century
CC, SCC
Year
SYYY, YYYY, YEAR, YYY,
YY,Y
ISO Year
IYYY, IYY, IY, I
Quarter
Q
Month
MONTH, MON, MM, RM
Same day of the week as the first day of the year WW
Same day of the week as the first day of the ISO
year
IW
Same day of the week as the first day of the
month
W
Day
DDD, DD, J
Starting day of the week
DAY, DY, D
Hour
HH, HH12, HH24
Minute
MI
Second
SS
Examples
=> SELECT TIMESTAMP_TRUNC('sep 22, 2011 12:34:00');
TIMESTAMP_TRUNC
--------------------2011-09-22 00:00:00
(1 row)
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=> SELECT TIMESTAMP_TRUNC('sep 22, 2011 12:34:00', 'dy');
TIMESTAMP_TRUNC
--------------------2011-09-18 00:00:00
(1 row)
TRANSACTION_TIMESTAMP
Returns a value of type TIMESTAMP WITH TIME ZONE representing the start of the current
transaction. TRANSACTION_TIMESTAMP is equivalent to CURRENT_TIMESTAMP except that
it does not accept a precision parameter.
Behavior Type
Stable
Syntax
TRANSACTION_TIMESTAMP()
Notes
This function returns the start time of the current transaction; the value does not change during the
transaction. The intent is to allow a single transaction to have a consistent notion of the "current"
time, so that multiple modifications within the same transaction bear the same timestamp.
Example
SELECT TRANSACTION_TIMESTAMP();
TRANSACTION_TIMESTAMP
------------------------------2010-04-01 15:31:12.144584-04
(1 row)
See Also
l
CLOCK_TIMESTAMP
l
STATEMENT_TIMESTAMP
TRUNC [Date/Time]
Truncates a TIMESTAMP, TIMESTAMPTZ, or DATE. The return value is of type TIMESTAMP.
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Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
Syntax
TRUNC ([TIMESTAMP | DATE] , format )
Parameters
TIMESTAMP | DATE
TIMESTAMP or DATE input value.
format
A string constant that selects the precision to which truncate the input value.
Valid values for format are:
Precision
Valid values
Century
CC, SCC
Year
SYYY, YYYY, YEAR,
YYY, YY,Y
ISO Year
IYYY, IYY, IY, I
Quarter
Q
Month
MONTH, MON, MM, RM
Same day of the week as the first day of the WW
year
Same day of the week as the first day of the IW
ISO year
Same day of the week as the first day of the W
month
Day
DDD, DD, J
Starting day of the week
DAY, DY, D
Hour
HH, HH12, HH24
Minute
MI
Second
SS
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Examples
=> SELECT TRUNC(TIMESTAMP 'sep 22, 2011 12:34:00', 'dy');
TRUNC
--------------------2011-09-18 00:00:00
(1 row)
WEEK
Returns an INTEGER representing the week of the year into which the input value falls. A week
starts on Sunday. January 1 is always in the first week of the year.
Syntax
WEEK ( d )
Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
Parameters
d
VARCHAR, DATE, TIMESTAMP, TIMESTAMPTZ input value.
Example
=> SELECT WEEK (TIMESTAMP 'sep 22, 2011 12:34');
WEEK
-----39
(1 row)
WEEK_ISO
Returns an INTEGER from 1–53 that represents the week of the year into which the input value
falls. The return value is based on the ISO 8061 standard.
The ISO week consists of 7 days starting on Monday and ending on Sunday. The first week of the
year is the week that contains January 4.
Syntax
WEEK_ISO ( d )
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Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
Parameters
d
VARCHAR, DATE, TIMESTAMP, or TIMESTAMPTZ input value.
Examples
The following examples illustrate the different results returned by WEEK_ISO. The first shows that
December 28, 2011 falls within week 52 of the ISO calendar:
=> SELECT WEEK_ISO (TIMESTAMP 'Dec 28, 2011 10:00:00');
WEEK_ISO
---------52
(1 row)
The second example shows WEEK_ISO results for January 1, 2012. Note that, since this date falls
on a Sunday, it falls within week 52 of the ISO year:
=> SELECT WEEK_ISO (TIMESTAMP 'Jan 1, 2012 10:00:00');
WEEK_ISO
---------52
(1 row)
The third example shows WEEK_ISO results for January 2, 2012, which occurs on a Monday. This
is the first week of the year that contains a Thursday and contains January 4. The function returns
week 1.
=> SELECT WEEK_ISO (TIMESTAMP 'Jan 2, 2012 10:00:00');
WEEK_ISO
---------1
The last example shows how to combine the DAYOFWEEK_ISO, WEEK_ISO, and YEAR_ISO
functions to find the ISO day of the week, week, and year:
=> SELECT DAYOFWEEK_ISO('Jan 1, 2000'), WEEK_ISO('Jan 1, 2000'),YEAR_ISO('Jan1,2000');
DAYOFWEEK_ISO | WEEK_ISO | YEAR_ISO
---------------+----------+---------6 |
52 |
1999
(1 row)
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See Also
l
YEAR_ISO
l
DAYOFWEEK_ISO
l
http://en.wikipedia.org/wiki/ISO_8601
YEAR
Returns an INTEGER representing the year portion of the input value.
Syntax
YEAR( d )
Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
Parameters
d
VARCHAR, TIMESTAMP, TIMESTAMPTZ, or INTERVAL input value.
Examples
=> SELECT YEAR ('6-9');
YEAR
-----6
(1 row)
=> SELECT YEAR (TIMESTAMP 'sep 22, 2011 12:34');
YEAR
-----2011
(1 row)
=> SELECT YEAR (INTERVAL '2-35' year to month);
YEAR
-----4
(1 row)
YEAR_ISO
Returns an INTEGER representing the year portion of the input value. The return value is based on
the ISO 8061 standard.
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The first week of the ISO year is the week that contains January 4.
Syntax
YEAR_ISO( d )
Behavior Type
Immutable, except for TIMESTAMPTZ arguments where it is Stable.
Parameters
d
VARCHAR, DATE, TIMESTAMP, or TIMESTAMPTZ input value.
Examples
=> SELECT YEAR_ISO (TIMESTAMP 'sep 22, 2011 12:34');
YEAR_ISO
---------2011
(1 row)
The following example shows how to combine the DAYOFWEEK_ISO, WEEK_ISO, and YEAR_
ISO functions to find the ISO day of the week, week, and year:
=> SELECT DAYOFWEEK_ISO('Jan 1, 2000'), WEEK_ISO('Jan 1, 2000'),YEAR_ISO('Jan1,2000');
DAYOFWEEK_ISO | WEEK_ISO | YEAR_ISO
---------------+----------+---------6 |
52 |
1999
(1 row)
See Also
l
WEEK_ISO
l
DAYOFWEEK_ISO
l
http://en.wikipedia.org/wiki/ISO_8601
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Formatting Functions
Formatting functions provide a powerful tool set for converting various data types (DATE/TIME,
INTEGER, FLOATING POINT) to formatted strings and for converting from formatted strings to
specific data types.
These functions all use a common calling convention:
l
The first argument is the value to be formatted.
l
The second argument is a template that defines the output or input format.
Note: The TO_TIMESTAMP function can take a single double precision argument.
TO_BITSTRING
Returns a VARCHAR that represents the given VARBINARY value in bitstring format.
Behavior Type
Immutable
Syntax
TO_BITSTRING ( expression )
Parameters
expression
(VARCHAR) is the string to return.
Notes
VARCHAR TO_BITSTRING(VARBINARY) converts data from binary type to character type
(where the character representation is the bitstring format). This function is the inverse of
BITSTRING_TO_BINARY:
TO_BITSTRING(BITSTRING_TO_BINARY(x)) = x)
BITSTRING_TO_BINARY(TO_BITSTRING(x)) = x)
Examples
SELECT TO_BITSTRING('ab'::BINARY(2));
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to_bitstring
-----------------0110000101100010
(1 row)
SELECT TO_BITSTRING(HEX_TO_BINARY('0x10'));
to_bitstring
-------------00010000
(1 row)
SELECT TO_BITSTRING(HEX_TO_BINARY('0xF0'));
to_bitstring
-------------11110000
(1 row)
See Also
l
BITCOUNT
l
BITSTRING_TO_BINARY
TO_CHAR
Converts various date/time and numeric values into text strings.
Behavior Type
Stable
Syntax
TO_CHAR ( expression [, pattern ] )
Parameters
expression
(TIMESTAMP, TIMESTAMPTZ, TIME, TIMETZ, INTERVAL, INTEGER,
DOUBLE PRECISION) specifies the value to convert.
pattern
[Optional] (CHAR or VARCHAR) specifies an output pattern string using the
Template Patterns for Date/Time Formatting and and/or Template Patterns for
Numeric Formatting.
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Notes
l
TO_CHAR(any) casts any type, except BINARY/VARBINARY, to VARCHAR.
The following example returns an error if you attempt to cast TO_CHAR to a binary data type:
=> SELECT TO_CHAR('abc'::VARBINARY);
ERROR: cannot cast type varbinary to varchar
l
TO_CHAR accepts TIME and TIMETZ data types as inputs if you explicitly cast TIME to
TIMESTAMP and TIMETZ to TIMESTAMPTZ.
=> SELECT TO_CHAR(TIME '14:34:06.4','HH12:MI am');
=> SELECT TO_CHAR(TIMETZ '14:34:06.4+6','HH12:MI am');
You can extract the timezone hour from TIMETZ:
SELECT EXTRACT(timezone_hour FROM TIMETZ '10:30+13:30');
date_part
----------13
(1 row)
l
Ordinary text is allowed in to_char templates and is output literally. You can put a substring in
double quotes to force it to be interpreted as literal text even if it contains pattern key words. For
example, in '"Hello Year "YYYY', the YYYY is replaced by the year data, but the single Y in
Year is not.
l
The TO_CHAR function's day-of-the-week numbering (see the 'D' template pattern) is different
from that of the EXTRACT function.
l
Given an INTERVAL type, TO_CHAR formats HH and HH12 as hours in a single day, while
HH24 can output hours exceeding a single day, for example, >24.
l
To use a double quote character in the output, precede it with a double backslash. This is
necessary because the backslash already has a special meaning in a string constant. For
example: '\\"YYYY Month\\"'
l
TO_CHAR does not support the use of V combined with a decimal point. For example: 99.9V99
is not allowed.
Examples
Expression
SELECT TO_CHAR(CURRENT_TIMESTAMP, 'Day, DD
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HH12:MI:SS');
'Tuesday , 06
05:39:18'
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SELECT TO_CHAR(CURRENT_TIMESTAMP, 'FMDay, FMDD
HH12:MI:SS');
'Tuesday, 6
SELECT TO_CHAR(TIMEtz '14:34:06.4+6','HH12:MI am'); TO_CHAR
04:34 am
SELECT TO_CHAR(-0.1, '99.99');
'
SELECT TO_CHAR(-0.1, 'FM9.99');
'-.1'
SELECT TO_CHAR(0.1, '0.9');
' 0.1'
SELECT TO_CHAR(12, '9990999.9');
'
SELECT TO_CHAR(12, 'FM9990999.9');
'0012.'
SELECT TO_CHAR(485, '999');
' 485'
SELECT TO_CHAR(-485, '999');
'-485'
SELECT TO_CHAR(485, '9 9 9');
' 4 8 5'
SELECT TO_CHAR(1485, '9,999');
' 1,485'
SELECT TO_CHAR(1485, '9G999');
' 1 485'
SELECT TO_CHAR(148.5, '999.999');
' 148.500'
SELECT TO_CHAR(148.5, 'FM999.999');
'148.5'
SELECT TO_CHAR(148.5, 'FM999.990');
'148.500'
SELECT TO_CHAR(148.5, '999D999');
' 148,500'
SELECT TO_CHAR(3148.5, '9G999D999');
' 3 148,500'
SELECT TO_CHAR(-485, '999S');
'485-'
SELECT TO_CHAR(-485, '999MI');
'485-'
SELECT TO_CHAR(485, '999MI');
'485 '
SELECT TO_CHAR(485, 'FM999MI');
'485'
SELECT TO_CHAR(485, 'PL999');
'+485'
SELECT TO_CHAR(485, 'SG999');
'+485'
SELECT TO_CHAR(-485, 'SG999');
'-485'
SELECT TO_CHAR(-485, '9SG99');
'4-85'
SELECT TO_CHAR(-485, '999PR');
'<485>'
SELECT TO_CHAR(485, 'L999');
'DM 485
SELECT TO_CHAR(485, 'RN');
'
SELECT TO_CHAR(485, 'FMRN');
'CDLXXXV'
SELECT TO_CHAR(5.2, 'FMRN');
'V'
SELECT TO_CHAR(482, '999th');
' 482nd'
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SELECT TO_CHAR(485, '"Good number:"999');
'Good number: 485'
SELECT TO_CHAR(485.8, '"Pre:"999" Post:" .999');
'Pre: 485 Post: .800'
SELECT TO_CHAR(12, '99V999');
' 12000'
SELECT TO_CHAR(12.4, '99V999');
' 12400'
SELECT TO_CHAR(12.45, '99V9');
' 125'
SELECT TO_CHAR(-1234.567);
-1234.567
SELECT TO_CHAR('1999-12-25'::DATE);
1999-12-25
SELECT TO_CHAR('1999-12-25 11:31'::TIMESTAMP);
1999-12-25 11:31:00
SELECT TO_CHAR('1999-12-25 11:31 EST'::TIMESTAMPTZ);
1999-12-25 11:31:00-05
SELECT TO_CHAR('3 days 1000.333 secs'::INTERVAL);
3 days 00:16:40.333
TO_DATE
Converts a string value to a DATE type.
Behavior Type
Stable
Syntax
TO_DATE ( expression , pattern )
Parameters
expression
(CHAR or VARCHAR) specifies the value to convert.
pattern
(CHAR or VARCHAR) specifies an output pattern string using the Template
Patterns for Date/Time Formatting and/or Template Patterns for Numeric
Formatting.
Input Value Considerations
The TO_DATE function requires a CHAR or VARCHAR expression. For other input types, use
TO_CHAR to perform an explicit cast to a CHAR or VARCHAR before using this function.
Notes
l
To use a double quote character in the output, precede it with a double backslash. This is
necessary because the backslash already has a special meaning in a string constant. For
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example: '\\"YYYY Month\\"'
n
n
TO_TIMESTAMP, TO_TIMESTAMP_TZ, and TO_DATE skip multiple blank spaces in the
input string if the FX option is not used. FX must be specified as the first item in the template.
For example:
o
For example TO_TIMESTAMP('2000
JUN', 'YYYY MON') is correct.
o
TO_TIMESTAMP('2000 JUN', 'FXYYYY MON') returns an error, because TO_
TIMESTAMP expects one space only.
The YYYY conversion from string to TIMESTAMP or DATE has a restriction if you use a year
with more than four digits. You must use a non-digit character or template after YYYY,
otherwise the year is always interpreted as four digits. For example (with the year 20000):
TO_DATE('200001131', 'YYYYMMDD') is interpreted as a four-digit year
Instead, use a non-digit separator after the year, such as TO_DATE('20000-1131', 'YYYYMMDD') or TO_DATE('20000Nov31', 'YYYYMonDD').
n
In conversions from string to TIMESTAMP or DATE, the CC field is ignored if there is a YYY,
YYYY or Y,YYY field. If CC is used with YY or Y then the year is computed as (CC–1)
*100+YY.
Examples
SELECT TO_DATE('13 Feb 2000', 'DD Mon YYYY');
to_date
-----------2000-02-13
(1 row)
See Also
l
Template Pattern Modifiers for Date/Time Formatting
TO_HEX
Returns a VARCHAR or VARBINARY representing the hexadecimal equivalent of a number.
Behavior Type
Immutable
Syntax
TO_HEX ( number )
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Parameters
number
(INTEGER) is the number to convert to hexadecimal
Notes
VARCHAR TO_HEX(INTEGER) and VARCHAR TO_HEX(VARBINARY) are similar. The
function converts data from binary type to character type (where the character representation is in
hexadecimal format). This function is the inverse of HEX_TO_BINARY.
TO_HEX(HEX_TO_BINARY(x)) = x);
HEX_TO_BINARY(TO_HEX(x)) = x);
Examples
SELECT TO_HEX(123456789);
TO_HEX
--------75bcd15
(1 row)
For VARBINARY inputs, the returned value is not preceded by "0x". For example:
SELECT TO_HEX('ab'::binary(2));
TO_HEX
-------6162
(1 row)
TO_TIMESTAMP
Converts a string value or a UNIX/POSIX epoch value to a TIMESTAMP type.
Behavior Type
Stable
Syntax
TO_TIMESTAMP ( expression, pattern )TO_TIMESTAMP ( unix-epoch )
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Parameters
expression
(CHAR or VARCHAR) is the string to convert
pattern
(CHAR or VARCHAR) specifies an output pattern string using the Template
Patterns for Date/Time Formatting and/or Template Patterns for Numeric
Formatting.
unix-epoch
(DOUBLE PRECISION) specifies some number of seconds elapsed since midnight
UTC of January 1, 1970, not counting leap seconds. INTEGER values are implicitly
cast to DOUBLE PRECISION.
Notes
l
For more information about UNIX/POSIX time, see Wikipedia.
l
Millisecond (MS) and microsecond (US) values in a conversion from string to TIMESTAMP are
used as part of the seconds after the decimal point. For example TO_TIMESTAMP('12:3',
'SS:MS') is not 3 milliseconds, but 300, because the conversion counts it as 12 + 0.3 seconds.
This means for the format SS:MS, the input values 12:3, 12:30, and 12:300 specify the same
number of milliseconds. To get three milliseconds, use 12:003, which the conversion counts as
12 + 0.003 = 12.003 seconds.
Here is a more complex example: TO_TIMESTAMP('15:12:02.020.001230',
'HH:MI:SS.MS.US') is 15 hours, 12 minutes, and 2 seconds + 20 milliseconds + 1230
microseconds = 2.021230 seconds.
l
To use a double quote character in the output, precede it with a double backslash. This is
necessary because the backslash already has a special meaning in a string constant. For
example: '\\"YYYY Month\\"'
l
TZ/tz are not supported patterns for the TO_TIMESTAMP function; for example, the following
command returns an error:
SELECT TO_TIMESTAMP('01-01-01 01:01:01+03:00','DD-MM-YY HH24:MI:SSTZ');
ERROR: "TZ"/"tz" not supported
n
n
TO_TIMESTAMP, TO_TIMESTAMP_TZ, and TO_DATE skip multiple blank spaces in the
input string if the FX option is not used. FX must be specified as the first item in the template.
For example:
o
For example TO_TIMESTAMP('2000
JUN', 'YYYY MON') is correct.
o
TO_TIMESTAMP('2000 JUN', 'FXYYYY MON') returns an error, because TO_
TIMESTAMP expects one space only.
The YYYY conversion from string to TIMESTAMP or DATE has a restriction if you use a year
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with more than four digits. You must use a non-digit character or template after YYYY,
otherwise the year is always interpreted as four digits. For example (with the year 20000):
TO_DATE('200001131', 'YYYYMMDD') is interpreted as a four-digit year
Instead, use a non-digit separator after the year, such as TO_DATE('20000-1131', 'YYYYMMDD') or TO_DATE('20000Nov31', 'YYYYMonDD').
n
In conversions from string to TIMESTAMP or DATE, the CC field is ignored if there is a YYY,
YYYY or Y,YYY field. If CC is used with YY or Y then the year is computed as (CC–1)
*100+YY.
Examples
=> SELECT TO_TIMESTAMP('13 Feb 2009', 'DD Mon YYY');
TO_TIMESTAMP
--------------------1200-02-13 00:00:00
(1 row)
=> SELECT TO_TIMESTAMP(200120400);
TO_TIMESTAMP
--------------------1976-05-05 01:00:00
(1 row)
See Also
l
Template Pattern Modifiers for Date/Time Formatting
TO_TIMESTAMP_TZ
Converts a string value or a UNIX/POSIX epoch value to a TIMESTAMP WITH TIME ZONE type.
Behavior Type
Immutable if single argument form, Stable otherwise.
Syntax
TO_TIMESTAMP_TZ ( expression, pattern )TO_TIMESTAMP ( unix-epoch )
Parameters
expression
(CHAR or VARCHAR) is the string to convert
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pattern
(CHAR or VARCHAR) specifies an output pattern string using the Template
Patterns for Date/Time Formatting and/or Template Patterns for Numeric
Formatting.
unix-epoch
(DOUBLE PRECISION) specifies some number of seconds elapsed since midnight
UTC of January 1, 1970, not counting leap seconds. INTEGER values are implicitly
cast to DOUBLE PRECISION.
Notes
l
For more information about UNIX/POSIX time, see Wikipedia.
l
Millisecond (MS) and microsecond (US) values in a conversion from string to TIMESTAMP are
used as part of the seconds after the decimal point. For example TO_TIMESTAMP('12:3',
'SS:MS') is not 3 milliseconds, but 300, because the conversion counts it as 12 + 0.3 seconds.
This means for the format SS:MS, the input values 12:3, 12:30, and 12:300 specify the same
number of milliseconds. To get three milliseconds, use 12:003, which the conversion counts as
12 + 0.003 = 12.003 seconds.
Here is a more complex example: TO_TIMESTAMP('15:12:02.020.001230',
'HH:MI:SS.MS.US') is 15 hours, 12 minutes, and 2 seconds + 20 milliseconds + 1230
microseconds = 2.021230 seconds.
l
To use a double quote character in the output, precede it with a double backslash. This is
necessary because the backslash already has a special meaning in a string constant. For
example: '\\"YYYY Month\\"'
n
n
TO_TIMESTAMP, TO_TIMESTAMP_TZ, and TO_DATE skip multiple blank spaces in the
input string if the FX option is not used. FX must be specified as the first item in the template.
For example:
o
For example TO_TIMESTAMP('2000
JUN', 'YYYY MON') is correct.
o
TO_TIMESTAMP('2000 JUN', 'FXYYYY MON') returns an error, because TO_
TIMESTAMP expects one space only.
The YYYY conversion from string to TIMESTAMP or DATE has a restriction if you use a year
with more than four digits. You must use a non-digit character or template after YYYY,
otherwise the year is always interpreted as four digits. For example (with the year 20000):
TO_DATE('200001131', 'YYYYMMDD') is interpreted as a four-digit year
Instead, use a non-digit separator after the year, such as TO_DATE('20000-1131', 'YYYYMMDD') or TO_DATE('20000Nov31', 'YYYYMonDD').
n
In conversions from string to TIMESTAMP or DATE, the CC field is ignored if there is a YYY,
YYYY or Y,YYY field. If CC is used with YY or Y then the year is computed as (CC–1)
*100+YY.
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Examples
=> SELECT TO_TIMESTAMP_TZ('13 Feb 2009', 'DD Mon YYY');
TO_TIMESTAMP_TZ
-----------------------1200-02-13 00:00:00-05
(1 row)
=> SELECT TO_TIMESTAMP_TZ(200120400);
TO_TIMESTAMP_TZ
-----------------------1976-05-05 01:00:00-04
(1 row)
See Also
l
Template Pattern Modifiers for Date/Time Formatting
TO_NUMBER
Converts a string value to DOUBLE PRECISION.
Behavior Type
Stable
Syntax
TO_NUMBER ( expression, [ pattern ] )
Parameters
expression
(CHAR or VARCHAR) specifies the string to convert.
pattern
(CHAR or VARCHAR) Optional parameter specifies an output pattern string using
the Template Patterns for Date/Time Formatting and/or Template Patterns for
Numeric Formatting. If omitted, function returns a floating point.
Notes
To use a double quote character in the output, precede it with a double backslash. This is
necessary because the backslash already has a special meaning in a string constant. For example:
'\\"YYYY Month\\"'
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Examples
SELECT TO_CHAR(2009, 'rn'), TO_NUMBER('mmix', 'rn');
TO_CHAR
| TO_NUMBER
-----------------+----------mmix |
2009
(1 row)
It the pattern parameter is omitted, the function returns a floating point.
SELECT TO_NUMBER('-123.456e-01');
TO_NUMBER
-----------12.3456
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Template Patterns for Date/Time Formatting
In an output template string (for TO_CHAR), there are certain patterns that are recognized and
replaced with appropriately-formatted data from the value to be formatted. Any text that is not a
template pattern is copied verbatim. Similarly, in an input template string (for anything other than
TO_CHAR), template patterns identify the parts of the input data string to be looked at and the values
to be found there.
Note: HP Vertica uses the ISO 8601:2004 style for date/time fields in HP Vertica *.log files.
For example,
2008-09-16 14:40:59.123 TM Moveout:0x2aaaac002180 [Txn]
Certain modifiers can be applied to any template pattern to alter its behavior as described in
Template Pattern Modifiers for Date/Time Formatting.
Pattern
Description
HH
Hour of day (00-23)
HH12
Hour of day (01-12)
HH24
Hour of day (00-23)
MI
Minute (00-59)
SS
Second (00-59)
MS
Millisecond (000-999)
US
Microsecond (000000-999999)
SSSS
Seconds past midnight (0-86399)
AM or A.M. or PM or P.M.
Meridian indicator (uppercase)
am or a.m. or pm or p.m.
Meridian indicator (lowercase)
Y,YYY
Year (4 and more digits) with comma
YYYY
Year (4 and more digits)
YYY
Last 3 digits of year
YY
Last 2 digits of year
Y
Last digit of year
IYYY
ISO year (4 and more digits)
IYY
Last 3 digits of ISO year
IY
Last 2 digits of ISO year
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Pattern
Description
I
Last digits of ISO year
BC or B.C. or AD or A.D.
Era indicator (uppercase)
bc or b.c. or ad or a.d.
Era indicator (lowercase)
MONTH
Full uppercase month name (blank-padded to 9 chars)
Month
Full mixed-case month name (blank-padded to 9 chars)
month
Full lowercase month name (blank-padded to 9 chars)
MON
Abbreviated uppercase month name (3 chars)
Mon
Abbreviated mixed-case month name (3 chars)
mon
Abbreviated lowercase month name (3 chars)
MM
Month number (01-12)
DAY
Full uppercase day name (blank-padded to 9 chars)
Day
Full mixed-case day name (blank-padded to 9 chars)
day
full lowercase day name (blank-padded to 9 chars)
DY
Abbreviated uppercase day name (3 chars)
Dy
Abbreviated mixed-case day name (3 chars)
dy
Abbreviated lowercase day name (3 chars)
DDD
Day of year (001-366)
DD
Day of month (01-31) for TIMESTAMP
Note: For INTERVAL, DD is day of year (001-366) because day
of month is undefined.
D
Day of week (1-7; Sunday is 1)
W
Week of month (1-5) (The first week starts on the first day of the
month.)
WW
Week number of year (1-53) (The first week starts on the first day of
the year.)
IW
ISO week number of year (The first Thursday of the new year is in
week 1.)
CC
Century (2 digits)
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Pattern
Description
J
Julian Day (days since January 1, 4712 BC)
Q
Quarter
RM
Month in Roman numerals (I-XII; I=January) (uppercase)
rm
Month in Roman numerals (i-xii; i=January) (lowercase)
TZ
Time-zone name (uppercase)
tz
Time-zone name (lowercase)
Template Pattern Modifiers for Date/Time Formatting
Certain modifiers can be applied to any template pattern to alter its behavior. For example, FMMonth
is the Month pattern with the FM modifier.
Modifier
Description
AM
Time is before 12:00
AT
Ignored
JULIAN, JD, J
Next field is Julian Day
FM prefix
Fill mode (suppress padding blanks and zeros)
For example: FMMonth
Note: The FM modifier suppresses leading zeros and trailing blanks that would
otherwise be added to make the output of a pattern fixed width.
FX prefix
Fixed format global option
For example: FX Month DD Day
ON
Ignored
PM
Time is on or after 12:00
T
Next field is time
TH suffix
Uppercase ordinal number suffix
For example: DDTH
th suffix
Lowercase ordinal number suffix
For example: DDth
TM prefix
Translation mode (print localized day and month names based on lc_messages).
For example: TMMonth
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Template Patterns for Numeric Formatting
Pattern
Description
9
Value with the specified number of digits
0
Value with leading zeros
. (period)
Decimal point
, (comma) Group (thousand) separator
PR
Negative value in angle brackets
S
Sign anchored to number (uses locale)
L
Currency symbol (uses locale)
D
Decimal point (uses locale)
G
Group separator (uses locale)
MI
Minus sign in specified position (if number < 0)
PL
Plus sign in specified position (if number > 0)
SG
Plus/minus sign in specified position
RN
Roman numeral (input between 1 and 3999)
TH or th
Ordinal number suffix
V
Shift specified number of digits (see notes)
EEEE
Scientific notation (not implemented yet)
Usage
l
A sign formatted using SG, PL, or MI is not anchored to the number; for example:
n
TO_CHAR(-12, 'S9999') produces ' -12'
n
TO_CHAR(-12, 'MI9999') produces '- 12'
l
9 results in a value with the same number of digits as there are 9s. If a digit is not available it
outputs a space.
l
TH does not convert values less than zero and does not convert fractional numbers.
l
V effectively multiplies the input values by 10^n, where n is the number of digits following V.
TO_CHAR does not support the use of V combined with a decimal point. For example: 99.9V99
is not allowed.
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Geospatial Package SQL Functions
The HP Vertica Geospatial package contains a suite of geospatial SQL functions you can install to
report on and analyze geographic location data.
To Install the Geospatial package:
Run the install.sh script that appears in the /opt/vertica/packages/geospatial directory.
Note: If you choose to install the Geospatial package in a directory other than the default, be
sure to set the GEOSPATIAL_HOME environment variable to reflect the correct directory.
Contents of the Geospatial Package
When you installed HP Vertica, the RPM saved the Geospatial package files here:
/opt/vertica/packages/geospatial
This directory contains these files:
install.sh Installs the Geospatial package.
readme.txt Contains instructions for installing the package.
This directory also contains these directories:
/src
Contains this file:
l
geospatial.sql—This file contains all the functions that are installed with the
package. The file describes the calculations used for each function, and
provides examples. This file also contains links to helpful sites that provide more
information about standards and calculations.
/examples Contains this file:
l
regions_demo.sql—This file is a demo, intended to illustrate a simple use
case: determine the New England state in which a given point lies.
Using Geospatial Package SQL Functions
For high-level descriptions of all of the functions included in the package, see Geospatial SQL
Functions. For more detailed information about each function and for links to other useful
information, see /opt/vertica/packages/geospatial/src/geospatial.sql.
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Using Built-In HP Vertica Functions for Geospatial
Analysis
Four mathematical functions, automatically installed with HP Vertica, perform geospatial
operations:
l
DEGREES
l
DISTANCE
l
DISTANCEV
l
RADIANS
These functions are not part of the Vertica Geospatial Package; they are installed with HP Vertica.
Geospatial SQL Functions
With the Geospatial Package, HP Vertica provides SQL functions that let you find geographic
constants to use in your calculations and analysis. These functions appear in the file
/opt/vertica/packages/geospatial/src/geospatial.sql.
You can use these functions as they are supplied; you can also edit the geospatial.sql file to
change the calculations according to your needs. If you do modify the geospatial functions, be sure
to save a copy of your changes in a private location so that your changes are not lost if you upgrade
your HP Vertica installation. Note that an upgrade does not overwrite any functions already loaded
in your database; the upgrade only overwrites only the .sql file containing the function definitions.
These functions measure distances in kilometers and angles in fractional degrees, unless stated
otherwise.
Of the several possible definitions of latitude, the geodetic latitude is most commonly used; and this
is what the HP Vertica Geospatial Package uses. Latitude goes from +90 degrees at the North Pole
to –90 at the South Pole. Longitude 0 is near Greenwich, England. It increases going east to +180
degrees, and decreases going west to –180 degrees. True bearings are measured clockwise from
north. For more information, see: http://en.wikipedia.org/wiki/Latitude.
WGS-84 SQL Functions
The following functions return constants determined by the World Geodetic System (WGS)
standard, WGS-84.
l
WGS84_a()
l
WGS84_b()
l
WGS84_e2()
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WGS84_f()
l
WGS84_if()
Earth Radius, Radius of Curvature, and Bearing SQL
Functions
These functions return the earth's radius, radius of curvature, and bearing values.
l
RADIUS_r(lat)
l
WGS84_r1()
l
RADIUS_SI()
l
RADIUS_M(lat)
l
RADIUS_N (lat)
l
RADIUS_Ra (lat)
l
RADIUS_Rv (lat)
l
RADIUS_Rc (lat,bearing)
l
BEARING (lat1,lon1,lat2,lon2)
l
RADIUS_LON (lat)
ECEF Conversion SQL Functions
The following functions convert values to Earth-Centered, Earth-Fixed (ECEF) values. The ECEF
system represents positions on x, y, and z axes in meters. (0,0,0) is the center of the earth; x is
toward latitude 0, longitude 0; y is toward latitude 0, longitude 90 degrees; and z is toward the North
Pole. The height above mean sea level (h) is also in meters.
l
ECEF_x (lat,lon,h)
l
ECEF_y (lat,lon,h)
l
ECEF_z (lat,lon,h)
l
ECEF_chord (lat1,lon1,h1,lat2,lon2,h2)
l
CHORD_TO_ARC (chord)
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Bounding Box SQL Functions
These functions determine whether points are within a bounding box, a rectangular area whose
edges are latitude and longitude lines. Bounding box methods allow you to narrow your focus, and
they work best on HP Vertica projections that are sorted by latitude, or by region (such as swtate)
and then by latitude. These methods also work on projections sorted by longitude.
l
BB_WITHIN (lat,lon,llat,llon,ulat,rlon)
l
LAT_WITHIN (lat,lat0,d)
l
LON_WITHIN (lon,lat0,lon0,d)
l
LL_WITHIN (lat,lon,lat0,lon0,d)
l
DWITHIN (lat,lon,lat0,lon0,d)
l
LLD_WITHIN (lat,lon,lat0,lon0,d)
l
ISLEFT (x0,y0,x1,y1,x2,y2)
l
RAYCROSSING (x0,y0,x1,y1,x2,y2)
Miles/Kilometer Conversion SQL Functions
These functions convert miles to kilometers and kilometers to miles:
l
MILES2KM (miles)
l
KM2MILES (km)
BB_WITHIN
Determines whether a point (lat, lon) falls within a bounding box defined by its lower-left and upperright corners. The return value has the type BOOLEAN.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
BB_WITHIN (lat,lon,llat,llon,ulat,rlon)
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Parameters
lat
A value of type DOUBLE PRECISION indicating the latitude of a given point.
lon
A value of type DOUBLE PRECISION indicating the longitude of a given point.
llat
A value of type DOUBLE PRECISION indicating the latitude used to define the lower-left
corner of the bounding box.
llon
A value of type DOUBLE PRECISION indicating the longitude used to define the lowerleft corner of the bounding box.
ulat
A value of type DOUBLE PRECISION indicating the latitude used to define the upper-right
corner of the bounding box.
rlon
A value of type DOUBLE PRECISION indicating the longitude used in defining the upperright corner of the bounding box.
Example
The following example determines that the point (14,30) is not contained in the bounding box
defined by (23.0,45) and (13,37):
=> SELECT BB_WITHIN(14,30,23.0,45,13,37);
BB_WITHIN
----------f
(1 row)
The following example determines that the point (14,30) is contained in the bounding box defined by
(13.0,45) and (23,37).
=> SELECT BB_WITHIN(14,30,13.0,45,23,37);
BB_WITHIN
----------t
(1 row)
BEARING
Returns the approximate bearing from a starting point to an ending point, in degrees. It assumes a
flat earth and is useful only for short distances. The return value has the type DOUBLE
PRECISION.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
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Behavior Type
Immutable
Syntax
BEARING (lat1,lon1,lat2,lon2)
Parameters
lat1
A value of type DOUBLE PRECISION indicating latitude of the starting point.
lon1
A value of type DOUBLE PRECISION indicating longitude of the starting point.
lat2
A value of type DOUBLE PRECISION indicating latitude of the ending point.
lon2
A value of type DOUBLE PRECISION indicating longitude of the ending point.
Example
The following examples calculate the bearing, in degrees, from point (45,13) to (33,3) and from point
(33,3) to (45,13):
=> SELECT BEARING(45,13,33,3);
BEARING
-------------------140.194428907735
(1 row)
=> SELECT BEARING(33,3,45,13);
BEARING
-----------------39.8055710922652
(1 row)
CHORD_TO_ARC
Converts a chord (the straight line between two points) in meters to a geodesic arc length, also in
meters. The return value has the type DOUBLE PRECISION.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
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Syntax
CHORD_TO_ARC (chord)
Parameters
chord
A value of type DOUBLE PRECISION indicating chord length (in meters)
Example
The following examples convert the length of a chord to the length of its geodesic arc:
=> SELECT CHORD_TO_ARC(120);
CHORD_TO_ARC
-----------------120.000000001774
(1 row)
=> SELECT CHORD_TO_ARC(12000);
CHORD_TO_ARC
-----------------12000.0017738474
(1 row)
=> SELECT CHORD_TO_ARC(1200000);
CHORD_TO_ARC
-----------------1201780.96402514
(1 row)
DWITHIN
Determines whether a point (lat,lon) is within a circle of radius d kilometers centered at a given point
(lat0,lon0). The return value has the type BOOLEAN.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
DWITHIN (lat,lon,lat0,lon0,d)
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Parameters
lat
A value of type DOUBLE PRECISION indicating a given latitude.
lon
A value of type DOUBLE PRECISION indicating a given longitude.
lat0
A value of type DOUBLE PRECISION indicating the latitude of the center point of a circle.
lon0
A value of type DOUBLE PRECISION indicating the longitude of the center point of a
circle.
d
A value of type DOUBLE PRECISION indicating the radius of the circle (in kilometers).
Example
The following examples determine that the point (13.6,43.5) is within 3880–3890 kilometers of the
radius of a circle centered at (48.5,45.5):
=> SELECT DWITHIN(13.6,43.5,48.5,45.5,3880);
DWITHIN
--------f
(1 row)
=> SELECT DWITHIN(13.6,43.5,48.5,45.5,3890);
DWITHIN
--------t
(1 row)
ECEF_CHORD
Calculates the distance in meters between two ECEF coordinates. The return value has the type
DOUBLE PRECISION.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
ECEF_CHORD (lat1,lon1,h1,lat2,lon2,h2)
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Parameters
lat
A value of type DOUBLE PRECISION indicating the latitude of one end point of the line.
lon1
A value of type DOUBLE PRECISION indicating the longitude of one end point of the line.
h1
A value of type DOUBLE PRECISION indicating the height above sea level (in meters) of
one end point of the line.
lat2
A value of type DOUBLE PRECISION indicating the latitude of one end point of the line.
lon2
A value of type DOUBLE PRECISION indicating the longitude of one end point of the line.
h2
A value of type DOUBLE PRECISION indicating the height of one end point of the line.
Example
The following example calculates the distance in meters between the ECEF coordinates (12,10.0,14) and (12,-10,17):
=> SELECT ECEF_chord (-12,10.0,14,12,-10,17);
ECEF_chord
-----------------3411479.93992789
(1 row)
ECEF_x
Converts a given latitude, longitude, and height into the ECEF x coordinate in meters. The return
value has the type DOUBLE PRECISION.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
ECEF_x (lat,lon,h)
Parameters
lat
A value of type DOUBLE PRECISION indicating latitude.
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lon
A value of type DOUBLE PRECISION indicating longitude.
h
A value of type DOUBLE PRECISION indicating height.
Example
The following example calculates the ECEF x coordinate in meters for the point (-12,13.2,0):
=> SELECT ECEF_x(-12,13.2,0);
ECEF_x
-----------------6074803.56179976
(1 row)
ECEF_y
Converts a given latitude, longitude, and height into the ECEF y coordinate in meters. The return
value has the type DOUBLE PRECISION.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
ECEF_y (lat,lon,h)
Parameters
lat
A value of type DOUBLE PRECISION indicating latitude.
lon
A value of type DOUBLE PRECISION indicating longitude.
h
A value of type DOUBLE PRECISION indicating height.
Example
The following example calculates the ECEF y coordinate in meters for the point (12.0,-14.2,12):
=> SELECT ECEF_y(12.0,-14.2,12);
ECEF_y
-------------------
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-1530638.12327962
(1 row)
ECEF_z
Converts a given latitude, longitude, and height into the ECEF z coordinate in meters. The return
value has the type DOUBLE PRECISION.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
ECEF_Z (lat,lon,h)
Parameters
lat
A value of type DOUBLE PRECISION indicating latitude.
lon
A value of type DOUBLE PRECISION indicating longitude.
h
A value of type DOUBLE PRECISION indicating height.
Example
The following example calculates the ECEF z coordinate in meters for the point (12.0,-14.2,12):
=> SELECT ECEF_z(12.0,-14.2,12);
ECEF_z
-----------------1317405.02616989
(1 row)
ISLEFT
Determines whether a given point is anywhere to the left of a directed line that goes though two
specified points. The return value has the type FLOAT and has the following possible values:
l
> 0: The point is to the left of the line.
l
= 0: The point is on the line.
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l
< 0: The point is to the right of the line.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
ISLEFT (x0,y0,x1,y1,x2,y2)
Parameters
x0
A value of type DOUBLE PRECISION indicating the latitude of the first point through which
the directed line passes.
y0
A value of type DOUBLE PRECISION indicating the longitude of the the first point through
which the directed line passes.
x1
A value of type DOUBLE PRECISION indicating the latitude of the second point through
which the directed line passes.
y1
A value of type DOUBLE PRECISION indicating the longitude of the the second point
through which the directed line passes.
x2
A value of type DOUBLE PRECISION indicating the latitude of the point whose position you
are evaluating.
y2
A value of type DOUBLE PRECISION indicating the longitude of a whose position you are
evaluating.
Example
The following example determines that (0,0) is to the left of the line that passes through (1,1) and
(2,3):
=> SELECT ISLEFT(1,1,2,3,0,0);
ISLEFT
-------1
(1 row)
KM2MILES
Converts a value from kilometers to miles. The return value is of type DOUBLE PRECISION.
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This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
KM2MILES (km)
Parameters
km
A value of type DOUBLE PRECISION indicating the number of kilometers you want to
convert.
Example
The following example converts 1.0 kilometers to miles:
=> SELECT KM2MILES(1.0);
KM2MILES
------------------0.621371192237334
(1 row)
LAT_WITHIN
Determines whether a certain latitude (lat) is within d kilometers of another latitude point (lat0),
independent of longitude. The return value has the type BOOLEAN.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
LAT_WITHIN (lat,lat0,d)
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Parameters
lat
A value of type DOUBLE PRECISION indicating a given latitude.
lat0
A value of type DOUBLE PRECISION indicating the latitude of the point to which you are
comparing the first latitude.
d
A value of type DOUBLE PRECISION indicating the number of kilometers that
determines the range you are evaluating.
Example
The following examples determine that latitude 12 is between 220 and 230 kilometers of latitude
14.0:
=> SELECT LAT_WITHIN(12,14.0,220);
LAT_WITHIN
-----------f
(1 row)
=> SELECT LAT_WITHIN(12,14.0,230);
LAT_WITHIN
-----------t
(1 row)
LL_WITHIN
Determines whether a point (lat, lon) is within a bounding box whose sides are 2d kilometers long,
centered at a given point (lat0, lon0). The return value has the type BOOLEAN.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
LL_WITHIN (lat,lon,lat0,lon0,d);
Parameters
lat
A value of type DOUBLE PRECISION indicating a given latitude.
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lon
A value of type DOUBLE PRECISION indicating a given longitude.
lat0
A value of type DOUBLE PRECISION indicating the latitude of the center point of the
bounding box.
lon0
A value of type DOUBLE PRECISION indicating the longitude of the center point of the
bounding box.
d
A value of type DOUBLE PRECISION indicating the length of half the side of the box.
Example
The following examples determine that the point (16,15) is within a bounding box centered at (12,13)
whose sides are between 880 and 890 kilometers long:
=> SELECT LL_WITHIN(16,15,12,13.0,440);
LL_WITHIN
----------f
(1 row)
=> SELECT LL_WITHIN(16,15,12,13.0,445);
LL_WITHIN
----------t
(1 row)
LLD_WITHIN
Determines whether a point (lat,lon) is within a circle of radius d kilometers centered at a given point
(lat0,lon0). LLD_WITHIN is a faster, but less accurate version of DWITHIN. The return value has
the type BOOLEAN.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
LLD_WITHIN (lat,lon,lat0,lon0,d)
Parameters
lat
A value of type DOUBLE PRECISION indicating a given latitude.
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lon
A value of type DOUBLE PRECISION indicating a given longitude.
lat0
A value of type DOUBLE PRECISION indicating the latitude of the center point of a circle.
lon0
A value of type DOUBLE PRECISION indicating the longitude of the center point of a
circle.
d
A value of type DOUBLE PRECISION indicating the radius of the circle (in kilometers).
Example
The following examples determine that the point (13.6,43.5) is within a circle centered at (48.5,45.5)
whose radius is between 3800 and 3900 kilometers long:
=> SELECT LLD_WITHIN(13.6,43.5,48.5,45.5,3800);
LLD_WITHIN
-----------f
(1 row)
=> SELECT LLD_WITHIN(13.6,43.5,48.5,45.5,3900);
LLD_WITHIN
-----------t
(1 row)
LON_WITHIN
Determines whether a longitude (lon) is within d kilometers of a given point (lat0, lon0). The return
value has the type BOOLEAN.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
LON_WITHIN (lon,lat0,lon0,d)
Parameters
lon
A value of type DOUBLE PRECISION indicating a given longitude.
lat0
A value of type DOUBLE PRECISION indicating the latitude of the point to which you
want to compare the lon value.
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lon0
A value of type DOUBLE PRECISION indicating the longitude of the point to which you
want to compare the lon value.
d
A value of type DOUBLE PRECISION indicating the distance, in kilometers, that defines
your range.
Example
The following examples determine that the longitude 15 is between 1600 and 1700 kilometers from
the point (16,0):
=> SELECT LON_WITHIN(15,16,0,1600);
LON_WITHIN
-----------f
(1 row)
=> SELECT LON_WITHIN(15,16,0,1700);
LON_WITHIN
-----------t
(1 row)
MILES2KM
Converts a value from miles to kilometers. The return value is of type DOUBLE PRECISION.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
MILES2KM (miles)
Parameters
miles
A value of type DOUBLE PRECISION indicating the number of miles you want to
convert.
Example
The following example converts 1.0 miles to kilometers:
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=> SELECT MILES2KM(1.0);
MILES2KM
---------1.609344
(1 row)
RADIUS_LON
Returns the radius of the circle of longitude in kilometers at a given latitude. The return value has
the type DOUBLE PRECISION.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
RADIUS_LON (lat)
Parameters
lat
A value of type DOUBLE PRECISION indicating latitude at which you want to measure the
radius.
Example
The following example calculates the circle of longitude in kilometers at a latitude of 45:
=> SELECT RADIUS_LON(45);
RADIUS_LON
--------------------4517.59087884893
(1 row)
RADIUS_M
Returns the earth's radius of curvature in kilometers along the meridian at the given latitude. The
return value has the type DOUBLE PRECISION.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
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Behavior Type
Immutable
Syntax
RADIUS_M (lat)
Parameters
lat
A value of type DOUBLE PRECISION indicating latitude at which you want to measure the
radius of curvature.
Example
The following example calculates the earth's radius of curvature in kilometers along the meridian at
latitude –90 (the South Pole):
=> SELECT RADIUS_M(-90);
RADIUS_M
----------------6399.5936257585
(1 row)
RADIUS_N
Returns the earth's radius of curvature in kilometer normal to the meridian at a given latitude. The
return value has the type DOUBLE PRECISION.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
RADIUS_N (lat)
Parameters
lat
A value of type DOUBLE PRECISION indicating latitude at which you want to measure the
radius of curvature.
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Example
The following example calculates the earth's radius of curvature in kilometers normal to the
meridian at latitude –90 (the South Pole):
=> SELECT RADIUS_N(-90);
RADIUS_N
-----------------6399.59362575849
(1 row)
RADIUS_R
Returns the WGS-84 radius of the earth (to the center of mass) in kilometers at a given latitude. The
return value has the type DOUBLE PRECISION.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
RADIUS_R (lat)
Parameters
lat
A value of type DOUBLE PRECISION indicating latitude at which you want to measure the
earth's radius.
Example
The following example calculates the WGS-84 radius of the earth in kilometers at latitude –90 (the
South Pole):
=> SELECT RADIUS_R(-90);
RADIUS_R
-----------------6356.75231424518
(1 row)
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RADIUS_Ra
Returns the earth's average radius of curvature in kilometers at a given latitude. This function is the
geometric mean of RADIUS_M and RADIUS_N. (RADIUS_Rv is a faster approximation of this
function.)
The return value has the type DOUBLE PRECISION.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
RADIUS_Ra (lat)
Parameters
lat
A value of type DOUBLE PRECISION indicating latitude at which you want to measure the
radius of curvature.
Example
The following example calculates the earth's average radius of curvature in kilometers at latitude –
90 (the South Pole):
=> SELECT RADIUS_Ra(-90);
RADIUS_Ra
-----------------6399.59362575849
(1 row)
RADIUS_Rc
Returns the earth's radius of curvature in kilometers at a given bearing measured clockwise from
north. The return value has the type DOUBLE PRECISION.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
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Syntax
RADIUS_Rc (lat, bearing)
Parameters
lat
A value of type DOUBLE PRECISION indicating latitude at which you want to
measure the radius of curvature.
bearing
A value of type DOUBLE PRECISION indicating a given bearing.
Example
The following example measures the earth's radius of curvature in kilometers at latitude 45, with a
bearing of 45 measured clockwise from north:
=> SELECT RADIUS_Rc(45,45);
RADIUS_Rc
-----------------6378.09200754445
(1 row)
RADIUS_Rv
Returns the earth's average radius of curvature in kilometers at a given latitude. This value is the
geometric mean of RADIUS_M and RADIUS_N. This function is a fast approximation of RADIUS_
Ra. The return value has the type DOUBLE PRECISION.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
RADIUS_Rv (lat)
Parameters
lat
A value of type DOUBLE PRECISION indicating latitude at which you want to measure the
radius of curvature.
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Example
The following example calculates the earth's average radius of curvature in kilometers at latitude –
90 (the South Pole):
=> SELECT RADIUS_Rv(-90);
RADIUS_Rv
-----------------6399.59362575849
(1 row)
RADIUS_SI
Returns the International System of Units (SI) radius based on the nautical mile. (A nautical mile is
a unit of length about one minute of arc of latitude measured along any meridian, or about one
minute of arc of longitude measured at the equator.) The return value has the type NUMERIC.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
RADIUS_SI ()
Example
The following example calculates the SI radius based on the nautical mile:
=> SELECT RADIUS_SI();
RADIUS_SI
--------------------6366.70701949370750
(1 row)
RAYCROSSING
Determines whether a ray traveling to the right from point (x2,y2), in the direction of increasing x,
intersects a directed line segment that starts at point (x0,y0) and ends at point (x1,y1).
This function returns:
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l
0 if the ray does not intersect the directed line segment.
l
1 if the ray intersects the line and y1 is above y0.
l
–1 if the ray intersects the line and y1 is below or equal to y0.
The return value has the type DOUBLE PRECISION.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
RAYCROSSING (x0,y0,x1,y1,x2,y2)
Parameters
x0
A value of type DOUBLE PRECISION indicating the latitude of the starting point of the line
segment.
y0
A value of type DOUBLE PRECISION indicating the longitude of the starting point of the
line segment
x1
A value of type DOUBLE PRECISION indicating the latitude of the ending point of the line
segment.
y1
A value of type DOUBLE PRECISION indicating the longitude of the the ending point of the
line segment.
x2
A value of type DOUBLE PRECISION indicating the latitude of the point from which the ray
starts.
y2 A value of type DOUBLE PRECISION indicating the longitude of the point from which the
ray starts.
Example
The following example checks if a line traveling to the right from the point (0,0) intersects the line
from (1,1) to (2,3):
=> SELECT RAYCROSSING(1,1,2,3,0,0);
RAYCROSSING
-------------
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0
(1 row)
The following example checks if a line traveling to the right from the point (0,2) intersects the line
from (1,1) to (2,3):
=> SELECT RAYCROSSING(1,1,2,3,0,2);
RAYCROSSING
------------1
(1 row)
The following example checks if a line traveling to the right from the point (0,2) intersects the line
from (1,3) to (2,1):
=> SELECT RAYCROSSING(1,3,2,1,0,2);
RAYCROSSING
-------------1
(1 row)
WGS84_a
Returns the length, in kilometers, of the earth's semi-major axis. The return value is of type
NUMERIC.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
WGS84_a ()
Example
=> SELECT WGS84_a();
wgs84_a
------------6378.137000
(1 row)
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WGS84_b
Returns the WGS-84 semi-minor axis length value in kilometers. The return value is of type
NUMERIC.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
WGS84_b ()
Example
=> SELECT WGS84_b();
WGS84_b
--------------------6356.75231424517950
(1 row)
WGS84_e2
Returns the WGS-84 eccentricity squared value. The return value is of type NUMERIC.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
WGS84_e2 ()
Example
=> SELECT WGS84_e2();
WGS84_e2
----------------------.00669437999014131700
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(1 row)
WGS84_f
Returns the WGS-84 flattening value. The return value is of type NUMERIC.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
WGS84_f ()
Example
=> SELECT WGS84_f();
WGS84_f
----------------------.00335281066474748072
(1 row)
WGS84_if
Returns the WGS-84 inverse flattening value. The return value is of type NUMERIC.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
WGS84_if ()
Example
=> SELECT WGS84_if();
WGS84_if
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--------------298.257223563
(1 row)
WGS84_r1
Returns the International Union of Geodesy and Geophysics (IUGG) mean radius of the earth, in
kilometers. The return value is of type NUMERIC.
This function is available only if you install the HP Vertica Geospatial Package. See Geospatial
Package SQL Functions for information on installing the package.
Behavior Type
Immutable
Syntax
WGS84_r1 ()
Example
=> SELECT WGS84_r1();
WGS84_r1
--------------------6371.00877141505983
(1 row)
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IP Conversion Functions
IP functions perform conversion, calculation, and manipulation operations on IP, network, and
subnet addresses.
INET_ATON
Returns an integer that represents the value of the address in host byte order, given the dotted-quad
representation of a network address as a string.
Behavior Type
Immutable
Syntax
INET_ATON ( expression )
Parameters
expression
(VARCHAR) is the string to convert.
Notes
The following syntax converts an IPv4 address represented as the string A to an integer I. INET_
ATON trims any spaces from the right of A, calls the Linux function inet_pton, and converts the result
from network byte order to host byte order using ntohl.
INET_ATON(VARCHAR A) -> INT8 I
If A is NULL, too long, or inet_pton returns an error, the result is NULL.
Examples
The generated number is always in host byte order. In the following example, the number is
calculated as 209×256^3 + 207×256^2 + 224×256 + 40.
> SELECT INET_ATON('209.207.224.40');
inet_aton
-----------3520061480
(1 row)
> SELECT INET_ATON('1.2.3.4');
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inet_aton
----------16909060
(1 row)
> SELECT TO_HEX(INET_ATON('1.2.3.4'));
to_hex
--------1020304
(1 row)
See Also
l
INET_NTOA
INET_NTOA
Returns the dotted-quad representation of the address as a VARCHAR, given a network address as
an integer in network byte order.
Behavior Type
Immutable
Syntax
INET_NTOA ( expression )
Parameters
expression
(INTEGER) is the network address to convert.
Notes
The following syntax converts an IPv4 address represented as integer I to a string A.
INET_NTOA converts I from host byte order to network byte order using htonl, and calls the Linux
function inet_ntop.
INET_NTOA(INT8 I) -> VARCHAR A
If I is NULL, greater than 2^32 or negative, the result is NULL.
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Examples
> SELECT INET_NTOA(16909060);
inet_ntoa
----------1.2.3.4
(1 row)
> SELECT INET_NTOA(03021962);
inet_ntoa
------------0.46.28.138
(1 row)
See Also
l
INET_ATON
V6_ATON
Converts an IPv6 address represented as a character string to a binary string.
Behavior Type
Immutable
Syntax
V6_ATON ( expression )
Parameters
expression
(VARCHAR) is the string to convert.
Notes
The following syntax converts an IPv6 address represented as the character string A to a binary
string B.
V6_ATON trims any spaces from the right of A and calls the Linux function inet_pton.
V6_ATON(VARCHAR A) -> VARBINARY(16) B
If A has no colons it is prepended with '::ffff:'. If A is NULL, too long, or if inet_pton returns an error,
the result is NULL.
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Examples
SELECT V6_ATON('2001:DB8::8:800:200C:417A');
v6_aton
-----------------------------------------------------\001\015\270\000\000\000\000\000\010\010\000 \014Az
(1 row)
SELECT V6_ATON('1.2.3.4');
v6_aton
-----------------------------------------------------------------\000\000\000\000\000\000\000\000\000\000\377\377\001\002\003\004
(1 row)
SELECT TO_HEX(V6_ATON('2001:DB8::8:800:200C:417A'));
to_hex
---------------------------------20010db80000000000080800200c417a
(1 row)
SELECT V6_ATON('::1.2.3.4');
v6_aton
-----------------------------------------------------------------\000\000\000\000\000\000\000\000\000\000\000\000\001\002\003\004
(1 row)
See Also
l
V6_NTOA
V6_NTOA
Converts an IPv6 address represented as varbinary to a character string.
Behavior Type
Immutable
Syntax
V6_NTOA ( expression )
Parameters
expression
(VARBINARY) is the binary string to convert.
Notes
The following syntax converts an IPv6 address represented as VARBINARY B to a string A.
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V6_NTOA right-pads B to 16 bytes with zeros, if necessary, and calls the Linux function inet_ntop.
V6_NTOA(VARBINARY B) -> VARCHAR A
If B is NULL or longer than 16 bytes, the result is NULL.
HP Vertica automatically converts the form '::ffff:1.2.3.4' to '1.2.3.4'.
Examples
> SELECT V6_NTOA(' \001\015\270\000\000\000\000\000\010\010\000 \014Az');
v6_ntoa
--------------------------2001:db8::8:800:200c:417a
(1 row)
> SELECT V6_NTOA(V6_ATON('1.2.3.4'));
v6_ntoa
--------1.2.3.4
(1 row)
> SELECT V6_NTOA(V6_ATON('::1.2.3.4'));
v6_ntoa
----------::1.2.3.4
(1 row)
See Also
l
V6_ATON
V6_SUBNETA
Calculates a subnet address in CIDR (Classless Inter-Domain Routing) format from a binary or
alphanumeric IPv6 address.
Behavior Type
Immutable
Syntax
V6_SUBNETA ( expression1, expression2 )
Parameters
expression1
(VARBINARY or VARCHAR) is the string to calculate.
expression2
(INTEGER) is the size of the subnet.
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Notes
The following syntax calculates a subnet address in CIDR format from a binary or varchar IPv6
address.
V6_SUBNETA masks a binary IPv6 address B so that the N leftmost bits form a subnet address,
while the remaining rightmost bits are cleared. It then converts to an alphanumeric IPv6 address,
appending a slash and N.
V6_SUBNETA(BINARY B, INT8 N) -> VARCHAR C
The following syntax calculates a subnet address in CIDR format from an alphanumeric IPv6
address.
V6_SUBNETA(VARCHAR A, INT8 N) -> V6_SUBNETA(V6_ATON(A), N) -> VARCHAR C
Examples
> SELECT V6_SUBNETA(V6_ATON('2001:db8::8:800:200c:417a'), 28);
v6_subneta
--------------2001:db0::/28
(1 row)
See Also
l
V6_SUBNETN
V6_SUBNETN
Calculates a subnet address in CIDR (Classless Inter-Domain Routing) format from a varbinary or
alphanumeric IPv6 address.
Behavior Type
Immutable
Syntax
V6_SUBNETN ( expression1, expression2 )
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Parameters
expression1
(VARBINARY or VARCHAR) is the string to calculate.
Notes:
l
V6_SUBNETN(, ) returns VARBINARY.
OR
l
expression2
V6_SUBNETN(, ) returns VARBINARY, after using V6_ATON
to convert the string to .
(INTEGER) is the size of the subnet.
Notes
The following syntax masks a BINARY IPv6 address B so that the N left-most bits of S form a
subnet address, while the remaining right-most bits are cleared.
V6_SUBNETN right-pads B to 16 bytes with zeros, if necessary and masks B, preserving its N-bit
subnet prefix.
V6_SUBNETN(VARBINARY B, INT8 N) -> VARBINARY(16) S
If B is NULL or longer than 16 bytes, or if N is not between 0 and 128 inclusive, the result is NULL.
S = [B]/N in Classless Inter-Domain Routing notation (CIDR notation).
The following syntax masks an alphanumeric IPv6 address A so that the N leftmost bits form a
subnet address, while the remaining rightmost bits are cleared.
V6_SUBNETN(VARCHAR A, INT8 N) -> V6_SUBNETN(V6_ATON(A), N) -> VARBINARY(16) S
Example
This example returns VARBINARY, after using V6_ATON to convert the VARCHAR string to VARBINARY:
> SELECT V6_SUBNETN(V6_ATON('2001:db8::8:800:200c:417a'), 28);
v6_subnetn
--------------------------------------------------------------\001\015\260\000\000\000\000\000\000\000\000\000\000\000\000
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See Also
l
V6_ATON
l
V6_SUBNETA
V6_TYPE
Characterizes a binary or alphanumeric IPv6 address B as an integer type.
Behavior Type
Immutable
Syntax
V6_TYPE ( expression )
Parameters
expression
(VARBINARY or VARCHAR) is the type to convert.
Notes
V6_TYPE(VARBINARY B) returns INT8 T.
V6_TYPE(VARCHAR A) -> V6_TYPE(V6_ATON(A)) -> INT8 T
The IPv6 types are defined in the Network Working Group's IP Version 6 Addressing Architecture
memo.
GLOBAL = 0
LINKLOCAL = 1
LOOPBACK = 2
UNSPECIFIED = 3
MULTICAST = 4
Global unicast addresses
Link-Local unicast (and Private-Use) addresses
Loopback
Unspecified
Multicast
IPv4-mapped and IPv4-compatible IPv6 addresses are also interpreted, as specified in IPv4 Global
Unicast Address Assignments.
l
For IPv4, Private-Use is grouped with Link-Local.
l
If B is VARBINARY, it is right-padded to 16 bytes with zeros, if necessary.
l
If B is NULL or longer than 16 bytes, the result is NULL.
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Details
IPv4 (either kind):
0.0.0.0/8
127.0.0.0/8
169.254.0.0/16
172.16.0.0/12
192.168.0.0/16
224.0.0.0/4
others
UNSPECIFIED
LOOPBACK
LINKLOCAL
LINKLOCAL
LINKLOCAL
MULTICAST
GLOBAL
10.0.0.0/8
::0/128
fe80::/10
ff00::/8
others
UNSPECIFIED
LINKLOCAL
MULTICAST
GLOBAL
::1/128
LINKLOCAL
IPv6:
LOOPBACK
Examples
> SELECT V6_TYPE(V6_ATON('192.168.2.10'));
v6_type
--------1
(1 row)
> SELECT V6_TYPE(V6_ATON('2001:db8::8:800:200c:417a'));
v6_type
--------0
(1 row)
See Also
l
INET_ATON
l
IP Version 6 Addressing Architecture
l
IPv4 Global Unicast Address Assignments
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Mathematical Functions
Some of these functions are provided in multiple forms with different argument types. Except where
noted, any given form of a function returns the same data type as its argument. The functions
working with DOUBLE PRECISION data could vary in accuracy and behavior in boundary cases
depending on the host system.
See Also
l
Template Pattern Modifiers for Date/Time Formatting
ABS
Returns the absolute value of the argument. The return value has the same data type as the
argument..
Behavior Type
Immutable
Syntax
ABS ( expression )
Parameters
expression
Is a value of type INTEGER or DOUBLE PRECISION
Examples
SELECT ABS(-28.7);
abs
-----28.7
(1 row)
ACOS
Returns a DOUBLE PRECISION value representing the trigonometric inverse cosine of the
argument.
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Behavior Type
Immutable
Syntax
ACOS ( expression )
Parameters
expression
Is a value of type DOUBLE PRECISION
Example
SELECT ACOS (1);
acos
-----0
(1 row)
ASIN
Returns a DOUBLE PRECISION value representing the trigonometric inverse sine of the
argument.
Behavior Type
Immutable
Syntax
ASIN ( expression )
Parameters
expression
Is a value of type DOUBLE PRECISION
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Example
SELECT ASIN(1);
asin
----------------1.5707963267949
(1 row)
ATAN
Returns a DOUBLE PRECISION value representing the trigonometric inverse tangent of the
argument.
Behavior Type
Immutable
Syntax
ATAN ( expression )
Parameters
expression
Is a value of type DOUBLE PRECISION
Example
SELECT ATAN(1);
atan
------------------0.785398163397448
(1 row)
ATAN2
Returns a DOUBLE PRECISION value representing the trigonometric inverse tangent of the
arithmetic dividend of the arguments.
Behavior Type
Immutable
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Syntax
ATAN2 ( quotient, divisor )
Parameters
quotient
Is an expression of type DOUBLE PRECISION representing the quotient
divisor
Is an expression of type DOUBLE PRECISION representing the divisor
Example
SELECT ATAN2(2,1);
ATAN2
-----------------1.10714871779409
(1 row)
CBRT
Returns the cube root of the argument. The return value has the type DOUBLE PRECISION.
Behavior Type
Immutable
Syntax
CBRT ( expression )
Parameters
expression
Value of type DOUBLE PRECISION
Examples
SELECT CBRT(27.0);
cbrt
-----3
(1 row)
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CEILING (CEIL)
Rounds the returned value up to the next whole number. Any expression that contains even a slight
decimal is rounded up.
Behavior Type
Immutable
Syntax
CEILING ( expression )CEIL ( expression )
Parameters
expression
Is a value of type INTEGER or DOUBLE PRECISION
Notes
CEILING is the opposite of FLOOR, which rounds the returned value down:
=> SELECT CEIL(48.01) AS ceiling, FLOOR(48.01) AS floor;
ceiling | floor
---------+------49 |
48
(1 row)
Examples
=> SELECT CEIL(-42.8);
CEIL
------42
(1 row)
SELECT CEIL(48.01);
CEIL
-----49
(1 row)
COS
Returns a DOUBLE PRECISION value representing the trigonometric cosine of the argument.
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Behavior Type
Immutable
Syntax
COS ( expression )
Parameters
expression
Is a value of type DOUBLE PRECISION
Example
SELECT COS(-1);
cos
-----------------0.54030230586814
(1 row)
COT
Returns a DOUBLE PRECISION value representing the trigonometric cotangent of the argument.
Behavior Type
Immutable
Syntax
COT ( expression )
Parameters
expression
Is a value of type DOUBLE PRECISION
Example
SELECT COT(1);
cot
------------------0.642092615934331
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(1 row)
DEGREES
Converts an expression from RADIANS to fractional degrees, or from degrees, minutes, and
seconds to fractional degrees. The return value has the type DOUBLE PRECISION.
Behavior Type
Immutable
Syntax 1
DEGREES (radians)
Syntax 2
DEGREES (degrees, minutes, seconds)
Parameters
radians
A unit of angular measure, 2π radians is equal to a full rotation.
degrees
A unit of angular measure, equal to 1/360 of a full rotation.
minutes
A unit of angular measurement, representing 1/60 of a degree.
seconds
A unit of angular measurement, representing 1/60 of a minute.
Examples
SELECT DEGREES(0.5);
DEGREES
-----------------28.6478897565412
(1 row)
SELECT DEGREES(1,2,3);
DEGREES
-----------------1.03416666666667
(1 row)
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DISTANCE
Returns the distance (in kilometers) between two points. You specify the latitude and longitude of
both the starting point and the ending point. You can also specify the radius of curvature for greater
accuracy when using an ellipsoidal model.
Behavior Type
Immutable
Syntax
DISTANCE ( lat0, lon0, lat1, lon1, radius_of_curvature )
Parameters
lat0
Specifies the latitude of the starting point.
lon0
Specifies the longitude of the starting point.
lat1
Specifies the latitude of the ending point
lon1
Specifies the longitude of the ending point.
radius_of_curvature
Specifies the radius of the curvature of the earth at the midpoint between
the starting and ending points. This parameter allows for greater accuracy
when using an ellipsoidal earth model. If you do not specify this parameter,
it defaults to the WGS-84 average r1 radius, about 6371.009 km.
Example
This example finds the distance in kilometers for 1 degree of longitude at latitude 45 degrees,
assuming earth is spherical.
SELECT DISTANCE(45,0, 45,1);
DISTANCE
---------------------78.6262959272162
(1 row)
DISTANCEV
Returns the distance (in kilometers) between two points using the Vincenty formula. Because the
Vincenty formula includes the parameters of the WGS-84 ellipsoid model, you need not specify a
radius of curvature. You specify the latitude and longitude of both the starting point and the ending
point. This function is more accurate, but will be slower, than the DISTANCE function.
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Behavior Type
Immutable
Syntax
DISTANCEV (lat0, lon0, lat1, lon1);
Parameters
lat0
Specifies the latitude of the starting point.
lon0 Specifies the longitude of the starting point.
lat1
Specifies the latitude of the ending point.
lon1 Specifies the longitude of the ending point.
Example
This example finds the distance in kilometers for 1 degree of longitude at latitude 45 degrees,
assuming earth is ellipsoidal.
SELECT DISTANCEV(45,0, 45,1);
distanceV
-----------------78.8463347095916
(1 row)
EXP
Returns the exponential function, e to the power of a number. The return value has the same data
type as the argument.
Behavior Type
Immutable
Syntax
EXP ( exponent )
Parameters
exponent
Is an expression of type INTEGER or DOUBLE PRECISION
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Example
SELECT EXP(1.0);
exp
-----------------2.71828182845905
(1 row)
FLOOR
Rounds the returned value down to the next whole number. For example, each of these functions
evaluates to 5:
floor(5.01)
floor(5.5)
floor(5.99)
Behavior Type
Immutable
Syntax
FLOOR ( expression )
Parameters
expression
Is an expression of type INTEGER or DOUBLE PRECISION.
Notes
FLOOR is the opposite of CEILING, which rounds the returned value up:
=> SELECT FLOOR(48.01) AS floor, CEIL(48.01) AS ceiling;
floor | ceiling
-------+--------48 |
49
(1 row)
Examples
=> SELECT FLOOR((TIMESTAMP '2005-01-17 10:00' - TIMESTAMP '2005-01-01') / INTERVAL '7');
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FLOOR
------2
(1 row)
=> SELECT FLOOR(-42.8);
FLOOR
-------43
(1 row)
=> SELECT FLOOR(42.8);
FLOOR
------42
(1 row)
Although the following example looks like an INTEGER, the number on the left is 2^49 as an
INTEGER, but the number on the right is a FLOAT:
=> SELECT 1<<49, FLOOR(1 << 49);
?column?
|
floor
-----------------+----------------562949953421312 | 562949953421312
(1 row)
Compare the above example to:
=> SELECT 1<<50, FLOOR(1 << 50);
?column?
|
floor
------------------+---------------------1125899906842624 | 1.12589990684262e+15
(1 row)
HASH
Calculates a hash value over its arguments, producing a value in the range 0 <= x < 263 (two to the
sixty-third power or 2^63).
Behavior Type
Immutable
Syntax
HASH ( expression [ ,... ] )
Parameters
expression
Is an expression of any data type. For the purpose of hash segmentation, each
expression is a column reference .
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Notes
l
The HASH() function is used to provide projection segmentation over a set of nodes in a cluster
and takes up to 32 arguments, usually column names, and selects a specific node for each row
based on the values of the columns for that row. HASH (Col1, Col2).
l
If your data is fairly regular and you want more even distribution than you get with HASH,
consider using MODULARHASH() for project segmentation.
Examples
SELECT HASH(product_price, product_cost) FROM product_dimension
WHERE product_price = '11';
hash
--------------------4157497907121511878
1799398249227328285
3250220637492749639
(3 rows)
See Also
l
MODULARHASH
LN
Returns the natural logarithm of the argument. The return data type is the same as the argument.
Behavior Type
Immutable
Syntax
LN ( expression )
Parameters
expression
Is an expression of type INTEGER or DOUBLE PRECISION
Example
SELECT LN(2);
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ln
------------------0.693147180559945
(1 row)
LOG
Returns the logarithm to the specified base of the argument. The return data type is the same as the
argument.
Behavior Type
Immutable
Syntax
LOG ( [ base, ] expression )
Parameters
base
Specifies the base (default is base 10)
expression
Is an expression of type INTEGER or DOUBLE PRECISION
Examples
SELECT LOG(2.0, 64);
log
----6
(1 row)
SELECT LOG(100);
log
----2
(1 row)
MOD
Returns the remainder of a division operation. MOD is also called modulo.
Behavior Type
Immutable
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Syntax
MOD( expression1, expression2 )
Parameters
expression1
Specifies the dividend (INTEGER, NUMERIC, or FLOAT)
expression2
Specifies the divisor (type same as dividend)
Notes
When computing mod(N,M), the following rules apply:
l
If either N or M is the null value, then the result is the null value.
l
If M is zero, then an exception condition is raised: data exception — division by zero.
l
Otherwise, the result is the unique exact numeric value R with scale 0 (zero) such that all of the
following are true:
n
R has the same sign as N.
n
The absolute value of R is less than the absolute value of M.
n
N = M * K + R for some exact numeric value K with scale 0 (zero).
Examples
SELECT MOD(9,4);
mod
----1
(1 row)
SELECT MOD(10,3);
mod
----1
(1 row)
SELECT MOD(-10,3);
mod
-----1
(1 row)
SELECT MOD(-10,-3);
mod
-----
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-1
(1 row)
SELECT MOD(10,-3);
mod
----1
(1 row)
MOD(, 0) gives an error:
=> SELECT MOD(6.2,0);
ERROR: numeric division by zero
MODULARHASH
Calculates a hash value over its arguments for the purpose of projection segmentation. In all other
uses, returns 0.
If you can hash segment your data using a column with a regular pattern, such as a sequential
unique identifier, MODULARHASH distributes the data more evenly than HASH, which distributes
data using a normal statistical distribution.
Behavior Type
Immutable
Syntax
MODULARHASH ( expression [ ,... ] )
Parameters
expression
Is a column reference of any data type.
Notes
The MODULARHASH() function takes up to 32 arguments, usually column names, and selects a
specific node for each row based on the values of the columns for that row.
Example
CREATE PROJECTION fact_ts_2 (f_price, f_cid, f_tid, f_cost, f_date)
AS (SELECT price, cid, tid, cost, dwdate
FROM fact)
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SEGMENTED BY MODULARHASH(dwdate)
ALL NODES OFFSET 2;
See Also
l
HASH
PI
Returns the constant pi (Π), the ratio of any circle's circumference to its diameter in Euclidean
geometry The return type is DOUBLE PRECISION.
Behavior Type
Immutable
Syntax
PI()
Examples
SELECT PI();
pi
-----------------3.14159265358979
(1 row)
POWER (or POW)
Returns a DOUBLE PRECISION value representing one number raised to the power of another
number. You can use either POWER or POW as the function name.
Behavior Type
Immutable
Syntax
POWER ( expression1, expression2 )
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Parameters
expression1
Is an expression of type DOUBLE PRECISION that represents the base
expression2
Is an expression of type DOUBLE PRECISION that represents the exponent
Example
SELECT POWER(9.0, 3.0);
power
------729
(1 row)
RADIANS
Returns a DOUBLE PRECISION value representing an angle expressed in radians. You can
express the input angle in DEGREES, and optionally include minutes and seconds.
Behavior Type
Immutable
Syntax
RADIANS (degrees [, minutes, seconds])
Parameters
degrees
A unit of angular measurement, representing 1/360 of a full rotation.
minutes
A unit of angular measurement, representing 1/60 of a degree.
seconds
A unit of angular measurement, representing 1/60 of a minute.
Examples
SELECT RADIANS(45);
RADIANS
------------------0.785398163397448
(1 row)
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SELECT RADIANS (1,2,3);
RADIANS
------------------0.018049613347708
(1 row)
RANDOM
Returns a uniformly-distributed random number x, where 0 <= x < 1.
Behavior Type
Volatile
Syntax
RANDOM()
Parameters
RANDOM has no arguments. Its result is a FLOAT8 data type (also called DOUBLE PRECISION).
Notes
Typical pseudo-random generators accept a seed, which is set to generate a reproducible pseudorandom sequence. HP Vertica, however, distributes SQL processing over a cluster of nodes, where
each node generates its own independent random sequence.
Results depending on RANDOM are not reproducible because the work might be divided differently
across nodes. Therefore, HP Vertica automatically generates truly random seeds for each node
each time a request is executed and does not provide a mechanism for forcing a specific seed.
Examples
In the following example, the result is a float, which is >= 0 and < 1.0:
SELECT RANDOM();
random
------------------0.211625560652465
(1 row)
RANDOMINT
Returns an INT8 value, and accepts a positive integer (n). RANDOMINT(n) returns one of the n
integers from 0 through n – 1.
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Behavior Type
Volatile
Syntax
RANDOMINT ( n )
Example
In the following example, the result is an INT8, which is >= 0 and < n, randomly chosen from the set
{0,1,2,3,4}.
SELECT RANDOMINT(5);
RANDOMINT
---------3
(1 row)
Following are other examples of using this function:
dbt=> select randomint (-2);
ERROR 4163: Non-positive value supplied to randomint: -2 dbt=> select randomint(0);
dbt=> select randomint(1);
0
dbt=> select randomint(21);
randomint
----------15
(1 row)
dbt=> select randomint(233333333333321);
randomint
---------------21875589868430
(1 row)
ROUND
Rounds a value to a specified number of decimal places, retaining the original scale and precision.
Fractions greater than or equal to .5 are rounded up. Fractions less than .5 are rounded down
(truncated).
Behavior Type
Immutable
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Syntax
ROUND ( expression [ , decimal-places ] )
Parameters
expression
Is an expression of type NUMERIC.
decimal-places
If positive, specifies the number of decimal places to display to the right of the
decimal point; if negative, specifies the number of decimal places to display to
the left of the decimal point.
Notes
NUMERIC ROUND() returns NUMERIC, retaining the original scale and precision:
=> SELECT ROUND(3.5);
ROUND
------4.0
(1 row)
The internal floating-point representation used to compute the ROUND function causes the fraction
to be evaluated as 3.5, which is rounded up.
Examples
SELECT ROUND(2.0, 1.0 ) FROM dual;
round
------2
(1 row)
SELECT ROUND(12.345, 2.0 );
round
------12.35
(1 row)
SELECT ROUND(3.444444444444444);
ROUND
------------------3.000000000000000
(1 row)
SELECT ROUND(3.14159, 3);
ROUND
---------
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3.14200
(1 row)
SELECT ROUND(1234567, -3);
round
--------1235000
(1 row)
SELECT ROUND(3.4999, -1);
ROUND
------.0000
(1 row)
SELECT employee_last_name, ROUND(annual_salary,4) FROM employee_dimension;
employee_last_name | ROUND
--------------------+-------Li
|
1880
Rodriguez
|
1704
Goldberg
|
2282
Meyer
|
1628
Pavlov
|
3168
McNulty
|
1516
Dobisz
|
3006
Pavlov
|
2142
Goldberg
|
2268
Pavlov
|
1918
Robinson
|
2366
...
SIGN
Returns a DOUBLE PRECISION value of -1, 0, or 1 representing the arithmetic sign of the
argument.
Behavior Type
Immutable
Syntax
SIGN ( expression )
Parameters
expression
Is an expression of type DOUBLE PRECISION
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Examples
SELECT SIGN(-8.4);
sign
------1
(1 row)
SIN
Returns a DOUBLE PRECISION value representing the trigonometric sine of the argument.
Behavior Type
Immutable
Syntax
SIN ( expression )
Parameters
expression
Is an expression of type DOUBLE PRECISION
Example
SELECT SIN(30 * 2 * 3.14159 / 360);
sin
------------------0.499999616987256
(1 row)
SQRT
Returns a DOUBLE PRECISION value representing the arithmetic square root of the argument.
Behavior Type
Immutable
Syntax
SQRT ( expression )
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Parameters
expression
Is an expression of type DOUBLE PRECISION
Examples
SELECT SQRT(2);
sqrt
----------------1.4142135623731
(1 row)
TAN
Returns a DOUBLE PRECISION value representing the trigonometric tangent of the argument.
Behavior Type
Immutable
Syntax
TAN ( expression )
Parameters
expression
Is an expression of type DOUBLE PRECISION
Example
SELECT TAN(30);
tan
-------------------6.40533119664628
(1 row)
TRUNC
Returns a value representing the argument fully truncated (toward zero) or truncated to a specific
number of decimal places, retaining the original scale and precision.
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Behavior Type
Immutable
Syntax
TRUNC ( expression [ , places ]
Parameters
expression
Is an expression of type INTEGER or DOUBLE PRECISION that represents the
number to truncate
places
Is an expression of type INTEGER that specifies the number of decimal places to
return
Notes
NUMERIC TRUNC() returns NUMERIC, retaining the original scale and precision:
=> SELECT TRUNC(3.5);
TRUNC
------3.0
(1 row)
Examples
=> SELECT TRUNC(42.8);
TRUNC
------42.0
(1 row)
=> SELECT TRUNC(42.4382, 2);
TRUNC
--------42.4300
(1 row)
WIDTH_BUCKET
Constructs equiwidth histograms, in which the histogram range is divided into intervals (buckets)
of identical sizes. In addition, values below the low bucket return 0, and values above the high
bucket return bucket_count +1. Returns an integer value.
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Behavior Type
Immutable
Syntax
WIDTH_BUCKET ( expression, hist_min, hist_max, bucket_count )
Parameters
expression
The expression for which the histogram is created. This expression must evaluate
to a numeric or datetime value or to a value that can be implicitly converted to a
numeric or datetime value. If expression evaluates to null, then the expression
returns null.
hist_min
An expression that resolves to the low boundary of bucket 1. Must also evaluate
to numeric or datetime values and cannot evaluate to null.
hist_max
An expression that resolves to the high boundary of bucket bucket_count. Must
also evaluate to a numeric or datetime value and cannot evaluate to null.
bucket_count
An expression that resolves to a constant, indicating the number of buckets. This
expression always evaluates to a positive INTEGER.
Notes
l
WIDTH_BUCKET divides a data set into buckets of equal width. For example, Age = 0–20, 20–
40, 40–60, 60–80. This is known as an equiwidth histogram.
l
When using WIDTH_BUCKET pay attention to the minimum and maximum boundary values.
Each bucket contains values equal to or greater than the base value of that bucket, so that age
ranges of 0–20, 20–40, and so on, are actually 0–19.99 and 20–39.999.
l
WIDTH_BUCKET accepts the following data types: (FLOAT and/or INT), (TIMESTAMP and/or
DATE and/or TIMESTAMPTZ), or (INTERVAL and/or TIME).
Examples
The following example returns five possible values and has three buckets: 0 [Up to 100), 1 [100–
300), 2 [300–500), 3 [500–700), and 4 [700 and up):
SELECT product_description, product_cost, WIDTH_BUCKET(product_cost, 100, 700, 3);
The following example creates a nine-bucket histogram on the annual_income column for
customers in Connecticut who are female doctors. The results return the bucket number to an
“Income” column, divided into eleven buckets, including an underflow and an overflow. Note that if
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customers had an annual incomes greater than the maximum value, they would be assigned to an
overflow bucket, 10:
SELECT customer_name, annual_income, WIDTH_BUCKET (annual_income, 100000, 1000000, 9) AS
"Income"
FROM public.customer_dimension WHERE customer_state='CT'
AND title='Dr.' AND customer_gender='Female' AND household_id < '1000'
ORDER BY "Income";
In the following result set, the reason there is a bucket 0 is because buckets are numbered from 1 to
bucket_count. Anything less than the given value of hist_min goes in bucket 0, and anything
greater than the given value of hist_max goes in the bucket bucket_count+1. In this example,
bucket 9 is empty, and there is no overflow. The value 12,283 is less than 100,000, so it goes into
the underflow bucket.
customer_name
| annual_income | Income
--------------------+---------------+-------Joanna A. Nguyen
|
12283 |
0
Amy I. Nguyen
|
109806 |
1
Juanita L. Taylor |
219002 |
2
Carla E. Brown
|
240872 |
2
Kim U. Overstreet |
284011 |
2
Tiffany N. Reyes
|
323213 |
3
Rebecca V. Martin |
324493 |
3
Betty . Roy
|
476055 |
4
Midori B. Young
|
462587 |
4
Martha T. Brown
|
687810 |
6
Julie D. Miller
|
616509 |
6
Julie Y. Nielson
|
894910 |
8
Sarah B. Weaver
|
896260 |
8
Jessica C. Nielson |
861066 |
8
(14 rows)
See Also
l
NTILE [Analytic]
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NULL-handling Functions
NULL-handling functions take arguments of any type, and their return type is based on their
argument types.
COALESCE
Returns the value of the first non-null expression in the list. If all expressions evaluate to null, then
the COALESCE function returns null.
Behavior Type
Immutable
Syntax
COALESCE ( expression1, expression2 );
COALESCE ( expression1, expression2, ...
expression-n );
Parameters
l
COALESCE (expression1, expression2) is equivalent to the following CASE expression:
CASE WHEN expression1 IS NOT NULL THEN expression1 ELSE
expression2 END;
l
COALESCE (expression1, expression2, ... expression-n), for n >= 3, is equivalent to the
following CASE expression:
CASE WHEN expression1 IS NOT NULL THEN expression1ELSE
COALESCE (expression2, . . . , expression-n) END;
Notes
COALESCE is an ANSI standard function (SQL-92).
Example
SELECT product_description,
COALESCE(lowest_competitor_price,
highest_competitor_price,
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average_competitor_price) AS price
FROM product_dimension;
product_description
| price
------------------------------------+------Brand #54109 kidney beans
|
264
Brand #53364 veal
|
139
Brand #50720 ice cream sandwiches |
127
Brand #48820 coffee cake
|
174
Brand #48151 halibut
|
353
Brand #47165 canned olives
|
250
Brand #39509 lamb
|
306
Brand #36228 tuna
|
245
Brand #34156 blueberry muffins
|
183
Brand #31207 clams
|
163
(10 rows)
See Also
l
CASE Expressions
l
ISNULL
IFNULL
Returns the value of the first non-null expression in the list.
IFNULL is an alias of NVL.
Behavior Type
Immutable
Syntax
IFNULL ( expression1 , expression2 );
Parameters
l
If expression1 is null, then IFNULL returns expression2.
l
If expression1 is not null, then IFNULL returns expression1.
Notes
l
COALESCE is the more standard, more general function.
l
IFNULL is equivalent to ISNULL.
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l
IFNULL is equivalent to COALESCE except that IFNULL is called with only two arguments.
l
ISNULL(a,b) is different from x IS NULL.
l
The arguments can have any data type supported by HP Vertica.
l
Implementation is equivalent to the CASE expression. For example:
CASE WHEN expression1 IS NULL THEN expression2
ELSE expression1 END;
l
The following statement returns the value 140:
SELECT IFNULL(NULL, 140) FROM employee_dimension;
l
The following statement returns the value 60:
SELECT IFNULL(60, 90) FROM employee_dimension;
Examples
=> SELECT IFNULL (SCORE, 0.0) FROM TESTING;
IFNULL
-------100.0
87.0
.0
.0
.0
(5 rows)
See Also
l
CASE Expressions
l
COALESCE
l
NVL
l
ISNULL
ISNULL
Returns the value of the first non-null expression in the list.
ISNULL is an alias of NVL.
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Behavior Type
Immutable
Syntax
ISNULL ( expression1 , expression2 );
Parameters
l
If expression1 is null, then ISNULL returns expression2.
l
If expression1 is not null, then ISNULL returns expression1.
Notes
l
COALESCE is the more standard, more general function.
l
ISNULL is equivalent to COALESCE except that ISNULL is called with only two arguments.
l
ISNULL(a,b) is different from x IS NULL.
l
The arguments can have any data type supported by HP Vertica.
l
Implementation is equivalent to the CASE expression. For example:
CASE WHEN expression1 IS NULL THEN expression2
ELSE expression1 END;
l
The following statement returns the value 140:
SELECT ISNULL(NULL, 140) FROM employee_dimension;
l
The following statement returns the value 60:
SELECT ISNULL(60, 90) FROM employee_dimension;
Examples
SELECT product_description, product_price,
ISNULL(product_cost, 0.0) AS cost
FROM product_dimension;
product_description
| product_price | cost
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--------------------------------+---------------+-----Brand #59957 wheat bread
|
405 | 207
Brand #59052 blueberry muffins |
211 | 140
Brand #59004 english muffins
|
399 | 240
Brand #53222 wheat bread
|
323 |
94
Brand #52951 croissants
|
367 | 121
Brand #50658 croissants
|
100 |
94
Brand #49398 white bread
|
318 |
25
Brand #46099 wheat bread
|
242 |
3
Brand #45283 wheat bread
|
111 | 105
Brand #43503 jelly donuts
|
259 |
19
(10 rows)
See Also
l
CASE Expressions
l
COALESCE
l
NVL
NULLIF
Compares two expressions. If the expressions are not equal, the function returns the first
expression (expression1). If the expressions are equal, the function returns null.
Behavior Type
Immutable
Syntax
NULLIF( expression1, expression2 )
Parameters
expression1
Is a value of any data type.
expression2
Must have the same data type as expr1 or a type that can be implicitly cast to
match expression1. The result has the same type as expression1.
Examples
The following series of statements illustrates one simple use of the NULLIF function.
Creates a single-column table t and insert some values:
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CREATE TABLE t (x TIMESTAMPTZ);
INSERT INTO t VALUES('2009-09-04 09:14:00-04');
INSERT INTO t VALUES('2010-09-04 09:14:00-04');
Issue a select statement:
SELECT x, NULLIF(x, '2009-09-04 09:14:00 EDT') FROM t;
x
|
nullif
------------------------+-----------------------2009-09-04 09:14:00-04 |
2010-09-04 09:14:00-04 | 2010-09-04 09:14:00-04
SELECT NULLIF(1, 2);
NULLIF
-------1
(1 row)
SELECT NULLIF(1, 1);
NULLIF
-------(1 row)
SELECT NULLIF(20.45, 50.80);
NULLIF
-------20.45
(1 row)
NULLIFZERO
Evaluates to NULL if the value in the column is 0.
Syntax
NULLIFZERO(expression)
Parameters
expression
(INTEGER, DOUBLE PRECISION, INTERVAL, or NUMERIC) Is the string to
evaluate for 0 values.
Example
The TESTING table below shows the test scores for 5 students. Note that test scores are missing
for S. Robinson and K. Johnson (NULL values appear in the Score column.)
=> SELECT * FROM TESTING;
Name
| Score
-------------+------J. Doe
|
100
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R. Smith
L. White
S. Robinson
K. Johnson
(5 rows)
|
|
|
|
87
0
The SELECT statement below specifies that HP Vertica should return any 0 values in the Score
column as Null. In the results, you can see that HP Vertica returns L. White's 0 score as Null.
=> SELECT Name, NULLIFZERO(Score) FROM TESTING;
Name
| NULLIFZERO
-------------+-----------J. Doe
|
100
R. Smith
|
87
L. White
|
S. Robinson |
K. Johnson |
(5 rows)
NVL
Returns the value of the first non-null expression in the list.
Behavior Type
Immutable
Syntax
NVL ( expression1 , expression2 );
Parameters
l
If expression1 is null, then NVL returns expression2.
l
If expression1 is not null, then NVL returns expression1.
Notes
l
COALESCE is the more standard, more general function.
l
NVL is equivalent to COALESCE except that NVL is called with only two arguments.
l
The arguments can have any data type supported by HP Vertica.
l
Implementation is equivalent to the CASE expression:
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CASE WHEN expression1 IS NULL THEN expression2
ELSE expression1 END;
Examples
expression1 is not null, so NVL returns expression1:
SELECT NVL('fast', 'database');
nvl
-----fast
(1 row)
expression1 is null, so NVL returns expression2:
SELECT NVL(null, 'database');
nvl
---------database
(1 row)
expression2 is null, so NVL returns expression1:
SELECT NVL('fast', null);
nvl
-----fast
(1 row)
In the following example, expression1 (title) contains nulls, so NVL returns expression2 and
substitutes 'Withheld' for the unknown values:
SELECT customer_name, NVL(title, 'Withheld') as title
FROM customer_dimension
ORDER BY title;
customer_name
| title
------------------------+------Alexander I. Lang
| Dr.
Steve S. Harris
| Dr.
Daniel R. King
| Dr.
Luigi I. Sanchez
| Dr.
Duncan U. Carcetti
| Dr.
Meghan K. Li
| Dr.
Laura B. Perkins
| Dr.
Samantha V. Robinson
| Dr.
Joseph P. Wilson
| Mr.
Kevin R. Miller
| Mr.
Lauren D. Nguyen
| Mrs.
Emily E. Goldberg
| Mrs.
Darlene K. Harris
| Ms.
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Meghan J. Farmer
Bettercare
Ameristar
Initech
(17 rows)
|
|
|
|
Ms.
Withheld
Withheld
Withheld
See Also
l
CASE Expressions
l
COALESCE
l
ISNULL
l
NVL2
NVL2
Takes three arguments. If the first argument is not NULL, it returns the second argument, otherwise
it returns the third argument. The data types of the second and third arguments are implicitly cast to
a common type if they don't agree, similar to COALESCE.
Behavior Type
Immutable
Syntax
NVL2 ( expression1 , expression2 , expression3 );
Parameters
l
If expression1 is not null, then NVL2 returns expression2.
l
If expression1 is null, then NVL2 returns expression3.
Notes
Arguments two and three can have any data type supported by HP Vertica.
Implementation is equivalent to the CASE expression:
CASE WHEN expression1 IS NOT NULL THEN expression2 ELSE expression3 END;
Examples
In this example, expression1 is not null, so NVL2 returns expression2:
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SELECT NVL2('very', 'fast', 'database');
nvl2
-----fast
(1 row)
In this example, expression1 is null, so NVL2 returns expression3:
SELECT NVL2(null, 'fast', 'database');
nvl2
---------database
(1 row)
In the following example, expression1 (title) contains nulls, so NVL2 returns expression3
('Withheld') and also substitutes the non-null values with the expression 'Known':
SELECT customer_name, NVL2(title, 'Known', 'Withheld')
as title
FROM customer_dimension
ORDER BY title;
customer_name
| title
------------------------+------Alexander I. Lang
| Known
Steve S. Harris
| Known
Daniel R. King
| Known
Luigi I. Sanchez
| Known
Duncan U. Carcetti
| Known
Meghan K. Li
| Known
Laura B. Perkins
| Known
Samantha V. Robinson
| Known
Joseph P. Wilson
| Known
Kevin R. Miller
| Known
Lauren D. Nguyen
| Known
Emily E. Goldberg
| Known
Darlene K. Harris
| Known
Meghan J. Farmer
| Known
Bettercare
| Withheld
Ameristar
| Withheld
Initech
| Withheld
(17 rows)
See Also
l
CASE Expressions
l
COALESCE
l
COALESCE
ZEROIFNULL
Evaluates to 0 if the column is NULL.
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Syntax
ZEROIFNULL(expression)
Parameters
expression
(INTEGER, DOUBLE PRECISION, INTERVAL, or NUMERIC) Is the string to
evaluate for NULL values.
Example
The TESTING table below shows the test scores for 5 students. Note that L. White's score is 0,
and that scores are missing for S. Robinson and K. Johnson.
=> SELECT * FROM TESTING;
Name
| Score
-------------+------J. Doe
|
100
R. Smith
|
87
L. White
|
0
S. Robinson |
K. Johnson |
(5 rows)
The next SELECT statement specifies that HP Vertica should return any Null values in the Score
column as 0s. In the results, you can see that HP Vertica returns a 0 score for S. Robinson and K.
Johnson.
=> SELECT Name, ZEROIFNULL (Score) FROM TESTING;
Name
| ZEROIFNULL
-------------+-----------J. Doe
|
100
R. Smith
|
87
L. White
|
0
S. Robinson |
0
K. Johnson |
0
(5 rows)
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Pattern Matching Functions
Used with the MATCH Clause, the HP Vertica pattern matching functions return additional data
about the patterns found/output. For example, you can use these functions to return values
representing the name of the event or pattern that matched the input row, the sequential number of
the match, or a partition-wide unique identifier for the instance of the pattern that matched.
Pattern matching is particularly useful for clickstream analysis where you might want to identify
users' actions based on their Web browsing behavior (page clicks). A typical online clickstream
funnel is:
Company home page -> product home page -> search -> results -> purchase online
Using the above clickstream funnel, you can search for a match on the user's sequence of web
clicks and identify that the user:
l
Landed on the company home page.
l
Navigated to the product page.
l
Ran a search.
l
Clicked a link from the search results.
l
Made a purchase.
For examples that use this clickstream model, see Event Series Pattern Matching in the
Programmer's Guide.
See Also
MATCH Clause
l
l
EVENT_NAME
Returns a VARCHAR value representing the name of the event that matched the row.
Syntax
EVENT_NAME()
Notes
Pattern matching functions must be used in MATCH Clause syntax; for example, if you call
EVENT_NAME() on its own, HP Vertica returns the following error message:
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=> SELECT event_name();
ERROR: query with pattern matching function event_name must include a MATCH clause
Example
Note: This example uses the schema defined in Event Series Pattern Matching in the
Programmer's Guide. For a more detailed example, see that topic.
The following statement analyzes users' browsing history on website2.com and identifies patterns
where the user landed on website2.com from another Web site (Entry) and browsed to any number
of other pages (Onsite) before making a purchase (Purchase). The query also outputs the values for
EVENT_NAME(), which is the name of the event that matched the row.
SELECT uid,
sid,
ts,
refurl,
pageurl,
action,
event_name()
FROM clickstream_log
MATCH
(PARTITION BY uid, sid ORDER BY ts
DEFINE
Entry
AS RefURL NOT ILIKE '%website2.com%' AND PageURL ILIKE '%website2.com%',
Onsite
AS PageURL ILIKE
'%website2.com%' AND Action='V',
Purchase AS PageURL ILIKE
'%website2.com%' AND Action = 'P'
PATTERN
P AS (Entry Onsite* Purchase)
RESULTS ALL ROWS);
uid | sid |
ts
|
refurl
|
pageurl
| action | event_name
-----+-----+----------+----------------------+----------------------+--------+----------1 | 100 | 12:00:00 | website1.com
| website2.com/home
| V
| Entry
1 | 100 | 12:01:00 | website2.com/home
| website2.com/floby
| V
| Onsite
1 | 100 | 12:02:00 | website2.com/floby
| website2.com/shamwow | V
| Onsite
1 | 100 | 12:03:00 | website2.com/shamwow | website2.com/buy
| P
| Purchase
2 | 100 | 12:10:00 | website1.com
| website2.com/home
| V
| Entry
2 | 100 | 12:11:00 | website2.com/home
| website2.com/forks
| V
| Onsite
2 | 100 | 12:13:00 | website2.com/forks
| website2.com/buy
| P
| Purchase
(7 rows)
See Also
l
MATCH Clause
l
MATCH_ID
PATTERN_ID
l
l
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MATCH_ID
Returns a successful pattern match as an INTEGER value. The returned value is the ordinal
position of a match within a partition.
Syntax
MATCH_ID()
Notes
Pattern matching functions must be used in MATCH Clause syntax; for example, if you call
MATCH_ID() on its own, HP Vertica returns the following error message:
=> SELECT match_id();
ERROR: query with pattern matching function match_id must include a MATCH clause
Example
Note: This example uses the schema defined in Event Series Pattern Matching in the
Programmer's Guide. For a more detailed example, see that topic.
The following statement analyzes users' browsing history on a site called website2.com and
identifies patterns where the user reached website2.com from another Web site (Entry in the
MATCH clause) and browsed to any number of other pages (Onsite) before making a purchase
(Purchase). The query also outputs values for the MATCH_ID(), which represents a sequential
number of the match.
SELECT uid,
sid,
ts,
refurl,
pageurl,
action,
match_id()
FROM clickstream_log
MATCH
(PARTITION BY uid, sid ORDER BY ts
DEFINE
Entry
AS RefURL NOT ILIKE '%website2.com%' AND PageURL ILIKE '%website2.com%',
Onsite
AS PageURL ILIKE
'%website2.com%' AND Action='V',
Purchase AS PageURL ILIKE
'%website2.com%' AND Action = 'P'
PATTERN
P AS (Entry Onsite* Purchase)
RESULTS ALL ROWS);
uid | sid |
ts
|
refurl
|
pageurl
| action | match_id
-----+-----+----------+----------------------+----------------------+--------+----------
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2 | 100
2 | 100
2 | 100
1 | 100
1 | 100
1 | 100
1 | 100
(7 rows)
|
|
|
|
|
|
|
12:10:00
12:11:00
12:13:00
12:00:00
12:01:00
12:02:00
12:03:00
|
|
|
|
|
|
|
website1.com
website2.com/home
website2.com/forks
website1.com
website2.com/home
website2.com/floby
website2.com/shamwow
|
|
|
|
|
|
|
website2.com/home
website2.com/forks
website2.com/buy
website2.com/home
website2.com/floby
website2.com/shamwow
website2.com/buy
|
|
|
|
|
|
|
V
V
P
V
V
V
P
|
|
|
|
|
|
|
1
2
3
1
2
3
4
See Also
l
MATCH Clause
l
EVENT_NAME
PATTERN_ID
l
l
PATTERN_ID
Returns an integer value that is a partition-wide unique identifier for the instance of the pattern that
matched.
Syntax
PATTERN_ID()
Notes
Pattern matching functions must be used in MATCH Clause syntax; for example, if call
PATTERN_ID() on its own, HP Vertica returns the following error message:
=> SELECT pattern_id();
ERROR: query with pattern matching function pattern_id must include a MATCH clause
Example
Note: This example uses the schema defined in Event Series Pattern Matching in the
Programmer's Guide. For a more detailed example, see that topic.
The following statement analyzes users' browsing history on website2.com and identifies patterns
where the user landed on website2.com from another Web site (Entry) and browsed to any number
of other pages (Onsite) before making a purchase (Purchase). The query also outputs values for
PATTERN_ID(), which represents the partition-wide identifier for the instance of the pattern that
matched.
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SELECT uid,
sid,
ts,
refurl,
pageurl,
action,
pattern_id()
FROM clickstream_log
MATCH
(PARTITION BY uid, sid ORDER BY ts
DEFINE
Entry
AS RefURL NOT ILIKE '%website2.com%' AND PageURL ILIKE '%website2.com%',
Onsite
AS PageURL ILIKE
'%website2.com%' AND Action='V',
Purchase AS PageURL ILIKE
'%website2.com%' AND Action = 'P'
PATTERN
P AS (Entry Onsite* Purchase)
RESULTS ALL ROWS);
uid | sid |
ts
|
refurl
|
pageurl
| action | pattern_id
-----+-----+----------+----------------------+----------------------+--------+----------2 | 100 | 12:10:00 | website1.com
| website2.com/home
| V
|
1
2 | 100 | 12:11:00 | website2.com/home
| website2.com/forks
| V
|
1
2 | 100 | 12:13:00 | website2.com/forks
| website2.com/buy
| P
|
1
1 | 100 | 12:00:00 | website1.com
| website2.com/home
| V
|
1
1 | 100 | 12:01:00 | website2.com/home
| website2.com/floby
| V
|
1
1 | 100 | 12:02:00 | website2.com/floby
| website2.com/shamwow | V
|
1
1 | 100 | 12:03:00 | website2.com/shamwow | website2.com/buy
| P
|
1
(7 rows)
See Also
l
MATCH Clause
l
EVENT_NAME
MATCH_ID
l
l
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Regular Expression Functions
A regular expression lets you perform pattern matching on strings of characters. The regular
expression syntax allows you to very precisely define the pattern used to match strings, giving you
much greater control than the wildcard matching used in the LIKE predicate. HP Vertica's regular
expression functions let you perform tasks such as determining if a string value matches a pattern,
extracting a portion of a string that matches a pattern, or counting the number of times a string
matches a pattern.
HP Vertica uses the Perl Compatible Regular Expression library (PCRE) to evaluate regular
expressions. As its name implies, PCRE's regular expression syntax is compatible with the syntax
used by the Perl 5 programming language. You can read PCRE's documentation on its regular
expression syntax. However, you might find the Perl Regular Expressions Documentation to be a
better introduction, especially if you are unfamiliar with regular expressions.
Note: The regular expression functions only operate on valid UTF-8 strings. If you attempt to
use a regular expression function on a string that is not valid UTF-8, then the query fails with an
error. To prevent an error from occurring, you can use the ISUTF8 function as a clause in the
statement to ensure the strings you want to pass to the regular expression functions are
actually valid UTF-8 strings, or you can use the 'b' argument to treat the strings as binary
octets rather than UTF-8 encoded strings.
ISUTF8
Tests whether a string is a valid UTF-8 string. Returns true if the string conforms to UTF-8
standards, and false otherwise. This function is useful to test strings for UTF-8 compliance before
passing them to one of the regular expression functions, such as REGEXP_LIKE, which expect
UTF-8 characters by default.
ISUTF8 checks for invalid UTF8 byte sequences, according to UTF-8 rules:
l
invalid bytes
l
an unexpected continuation byte
l
a start byte not followed by enough continuation bytes
l
an Overload Encoding
The presence of an invalid UTF8 byte sequence results in a return value of false.
Syntax
ISUTF8( string );
Parameters
string
The string to test for UTF-8 compliance.
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Examples
=> SELECT ISUTF8(E'\xC2\xBF'); -- UTF-8 INVERTED QUESTION MARK ISUTF8
-------t
(1 row)
=> SELECT ISUTF8(E'\xC2\xC0'); -- UNDEFINED UTF-8 CHARACTER
ISUTF8
-------f
(1 row)
REGEXP_COUNT
Returns the number times a regular expression matches a string.
Syntax
REGEXP_COUNT( string, pattern [, position [, regexp_modifier ] ] )
Parameters
string
The string to be searched for matches.
pattern
The regular expression to search for within the string. The syntax of the regular
expression is compatible with the Perl 5 regular expression syntax. See the
Perl Regular Expressions Documentation for details.
position
The number of characters from the start of the string where the function should
start searching for matches. The default value, 1, means to start searching for
a match at the first (leftmost) character. Setting this parameter to a value
greater than 1 starts searching for a match to the pattern that many characters
into the string.
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regexp_modifier
A string containing one or more single-character flags that change how the
regular expression is matched against the string:
b
Treat strings as binary octets rather than UTF-8 characters.
c
Forces the match to be case sensitive (the default).
i
Forces the match to be case insensitive.
m
Treats the string being matched as multiple lines. With this modifier, the
start of line (^) and end of line ($) regular expression operators match line
breaks (\n) within the string. Ordinarily, these operators only match the
start and end of the string.
n
Allows the single character regular expression operator (.) to match a
newline (\n). Normally, the . operator will match any character except a
newline.
x
Allows you to document your regular expressions. It causes all
unescaped space characters and comments in the regular expression to
be ignored. Comments start with a hash character (#) and end with a
newline. All spaces in the regular expression that you want to be
matched in strings must be escaped with a backslash (\) character.
Notes
This function operates on UTF-8 strings using the default locale, even if the locale has been set to
something else.
If you are porting a regular expression query from an Oracle database, remember that Oracle
considers a zero-length string to be equivalent to NULL, while HP Vertica does not.
Examples
Count the number of occurrences of the substring "an" in the string "A man, a plan, a canal,
Panama."
=> SELECT REGEXP_COUNT('a man, a plan, a canal: Panama', 'an');
REGEXP_COUNT
-------------4
(1 row)
Find the number of occurrences of the substring "an" in the string "a man, a plan, a canal: Panama"
starting with the fifth character.
=> SELECT REGEXP_COUNT('a man, a plan, a canal: Panama', 'an',5);
REGEXP_COUNT
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-------------3
(1 row)
Find the number of occurrences of a substring containing a lower-case character followed by "an."
In the first example, the query does not have a modifier. In the second example, the "i" query
modifier is used to force the regular expression to ignore case.
=> SELECT REGEXP_COUNT('a man, a plan, a canal: Panama', '[a-z]an');
REGEXP_COUNT
-------------3
(1 row)
=> SELECT REGEXP_COUNT('a man, a plan, a canal: Panama', '[a-z]an', 1, 'i');
REGEXP_COUNT
-------------4
REGEXP_INSTR
Returns the starting or ending position in a string where a regular expression matches. This function
returns 0 if no match for the regular expression is found in the string.
Syntax
REGEXP_INSTR( string, pattern [, position [, occurrence ... [, return_position [, regexp_modif
ier ]
... [, captured_subexp ] ] ] ] )
Parameters
string
The string to search for the pattern.
pattern
The regular expression to search for within the string. The syntax of the regular
expression is compatible with the Perl 5 regular expression syntax. See the
Perl Regular Expressions Documentation for details.
position
The number of characters from the start of the string where the function should
start searching for matches. The default value, 1, means to start searching for
a match at the first (leftmost) character. Setting this parameter to a value
greater than 1 starts searching for a match to the pattern that many characters
into the string.
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occurrence
Controls which occurrence of a match between the string and the pattern is
returned. With the default value (1), the function returns the position of the first
substring that matches the pattern. You can use this parameter to find the
position of additional matches between the string and the pattern. For example,
set this parameter to 3 to find the position of the third substring that matched
the pattern.
return_position
Sets the position within the string that is returned. When set to the default
value (0), this function returns the position in the string of the first character of
the substring that matched the pattern. If you set this value to 1, the function
returns the position of the first character after the end of the matching
substring.
regexp_modifier
A string containing one or more single-character flags that change how the
regular expression is matched against the string:
captured_subexp
b
Treat strings as binary octets rather than UTF-8 characters.
c
Forces the match to be case sensitive (the default).
i
Forces the match to be case insensitive.
m
Treats the string being matched as multiple lines. With this modifier, the
start of line (^) and end of line ($) regular expression operators match line
breaks (\n) within the string. Ordinarily, these operators only match the
start and end of the string.
n
Allows the single character regular expression operator (.) to match a
newline (\n). Normally, the . operator will match any character except a
newline.
x
Allows you to document your regular expressions. It causes all
unescaped space characters and comments in the regular expression to
be ignored. Comments start with a hash character (#) and end with a
newline. All spaces in the regular expression that you want to be
matched in strings must be escaped with a backslash (\) character.
The captured subexpression whose position should be returned. If omitted or
set to 0, the function returns the position of the first character in the entire
string that matched the regular expression. If set to 1 through 9, the function
returns the subexpression captured by the corresponding set of parentheses in
the regular expression. For example, setting this value to 3 returns the
substring captured by the third set of parentheses in the regular expression.
Note: The subexpressions are numbered left to right, based on the
appearance of opening parenthesis, so nested regular expressions . For
example, in the regular expression \s*(\w+\s+(\w+)), subexpression 1
is the one that captures everything but any leading whitespaces.
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Notes
This function operates on UTF-8 strings using the default locale, even if the locale has been set to
something else.
If you are porting a regular expression query from an Oracle database, remember that Oracle
considers a zero-length string to be equivalent to NULL, while HP Vertica does not.
Examples
Find the first occurrence of a sequence of letters starting with the letter e and ending with the letter y
in the phrase "easy come, easy go."
=> SELECT REGEXP_INSTR('easy come, easy go','e\w*y'); REGEXP_INSTR
-------------1
(1 row)
Find the first occurrence of a sequence of letters starting with the letter e and ending with the letter y
starting at the second character in the string "easy come, easy go."
=> SELECT REGEXP_INSTR('easy come, easy go','e\w*y',2); REGEXP_INSTR
-------------12
(1 row)
Find the second sequence of letters starting with the letter e and ending with the letter y in the string
"easy come, easy go" starting at the first character.
=> SELECT REGEXP_INSTR('easy come, easy go','e\w*y',1,2); REGEXP_INSTR
-------------12
(1 row)
Find the position of the first character after the first whitespace in the string "easy come, easy go."
=> SELECT REGEXP_INSTR('easy come, easy go','\s',1,1,1); REGEXP_INSTR
-------------6
(1 row)
Find the position of the start of the third word in a string by capturing each word as a subexpression,
and returning the third subexpression's start position.
=> SELECT REGEXP_INSTR('one two three','(\w+)\s+(\w+)\s+(\w+)', 1,1,0,'',3); REGEXP_INSTR
-------------9
(1 row)
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REGEXP_LIKE
Returns true if the string matches the regular expression. This function is similar to the LIKEpredicate, except that it uses regular expressions rather than simple wildcard character matching.
Syntax
REGEXP_LIKE( string, pattern [, modifiers ] )
Parameters
string
The string to match against the regular expression.
pattern
A string containing the regular expression to match against the string. The syntax of
the regular expression is compatible with the Perl 5 regular expression syntax. See
the Perl Regular Expressions Documentation for details.
modifiers
A string containing one or more single-character flags that change how the regular
expression is matched against the string:
b
Treat strings as binary octets rather than UTF-8 characters.
c
Forces the match to be case sensitive (the default).
i
Forces the match to be case insensitive.
m
Treats the string being matched as multiple lines. With this modifier, the start of
line (^) and end of line ($) regular expression operators match line breaks (\n)
within the string. Ordinarily, these operators only match the start and end of the
string.
n
Allows the single character regular expression operator (.) to match a newline
(\n). Normally, the . operator will match any character except a newline.
x
Allows you to document your regular expressions. It causes all unescaped
space characters and comments in the regular expression to be ignored.
Comments start with a hash character (#) and end with a newline. All spaces in
the regular expression that you want to be matched in strings must be escaped
with a backslash (\) character.
Notes
This function operates on UTF-8 strings using the default locale, even if the locale has been set to
something else.
If you are porting a regular expression query from an Oracle database, remember that Oracle
considers a zero-length string to be equivalent to NULL, while HP Vertica does not.
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Examples
This example creates a table containing several strings to demonstrate regular expressions.
=> CREATE TABLE t (v VARCHAR);
CREATE TABLE
=> CREATE PROJECTION t1 AS SELECT * FROM t;
CREATE PROJECTION
=> COPY t FROM stdin;
Enter data to be copied followed by a newline.
End with a backslash and a period on a line by itself.
>> aaa
>> Aaa
>> abc
>> abc1
>> 123
>> \.
=> SELECT * FROM t;
v
------aaa
Aaa
abc
abc1
123
(5 rows)
Select all records in the table that contain the letter "a."
=> SELECT v FROM t WHERE REGEXP_LIKE(v,'a');
v
-----Aaa
aaa
abc
abc1
(4 rows)
Select all of the rows in the table that start with the letter "a."
=> SELECT v FROM t WHERE REGEXP_LIKE(v,'^a');
v
-----aaa
abc
abc1
(3 rows)
Select all rows that contain the substring "aa."
=> SELECT v FROM t WHERE REGEXP_LIKE(v,'aa');
v
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----Aaa
aaa
(2 rows)
Select all rows that contain a digit.
=> SELECT v FROM t WHERE REGEXP_LIKE(v,'\d');
v
-----123
abc1
(2 rows)
Select all rows that contain the substring "aaa."
=> SELECT v FROM t WHERE REGEXP_LIKE(v,'aaa');
v
----aaa
(1 row)
Select all rows that contain the substring "aaa" using case insensitive matching.
=> SELECT v FROM t WHERE REGEXP_LIKE(v,'aaa', 'i');
v
----Aaa
aaa
(2 rows)
Select rows that contain the substring "a b c."
=> SELECT v FROM t WHERE REGEXP_LIKE(v,'a b c');
v
--(0 rows)
Select rows that contain the substring "a b c" ignoring space within the regular expression.
=> SELECT v FROM t WHERE REGEXP_LIKE(v,'a b c','x');
v
-----abc
abc1
(2 rows)
Add multi-line rows to demonstrate using the "m" modifier.
=> COPY t FROM stdin RECORD TERMINATOR '!';
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Enter data to be copied followed by a newline.
End with a backslash and a period on a line by itself.
>> Record 1 line 1
>> Record 1 line 2
>> Record 1 line 3!
>> Record 2 line 1
>> Record 2 line 2
>> Record 2 line 3!
>> \.
Select rows that start with the substring "Record" and end with the substring "line 2."
=> SELECT v from t WHERE REGEXP_LIKE(v,'^Record.*line 2$'); v
--(0 rows)
Select rows that start with the substring "Record" and end with the substring "line 2," treating
multiple lines as separate strings.
=> SELECT v from t WHERE REGEXP_LIKE(v,'^Record.*line 2$','m');
v
-------------------------------------------------Record 2
Record 2
Record 2
Record 1
Record 1
Record 1
(2 rows)
line
line
line
line
line
line
1
2
3
1
2
3
REGEXP_REPLACE
Replace all occurrences of a substring that match a regular expression with another substring. It is
similar to the REPLACE function, except it uses a regular expression to select the substring to be
replaced.
Syntax
REGEXP_REPLACE( string, target [, replacement [, position [, occurrence ... [, regexp_modifier
s ] ] ] ] )
Parameters
string
The string whose to be searched and replaced.
target
The regular expression to search for within the string. The syntax of the regular
expression is compatible with the Perl 5 regular expression syntax. See the
Perl Regular Expressions Documentation for details.
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replacement
The string to replace matched substrings. If not supplied, the matched
substrings are deleted. This string can contain baccalaureates for substrings
captured by the regular expression. The first captured substring is inserted into
the replacement string using \1, the second \2, and so on.
position
The number of characters from the start of the string where the function should
start searching for matches. The default value, 1, means to start searching for
a match at the first (leftmost) character. Setting this parameter to a value
greater than 1 starts searching for a match to the pattern that many characters
into the string.
occurrence
Controls which occurrence of a match between the string and the pattern is
replaced. With the default value (0), the function replaces all matching
substrings with the replacement string. For any value above zero, the function
replaces just a single occurrence. For example, set this parameter to 3 to
replace the third substring that matched the pattern.
regexp_modifier
A string containing one or more single-character flags that change how the
regular expression is matched against the string:
b
Treat strings as binary octets rather than UTF-8 characters.
c
Forces the match to be case sensitive (the default).
i
Forces the match to be case insensitive.
m
Treats the string being matched as multiple lines. With this modifier, the
start of line (^) and end of line ($) regular expression operators match line
breaks (\n) within the string. Ordinarily, these operators only match the
start and end of the string.
n
Allows the single character regular expression operator (.) to match a
newline (\n). Normally, the . operator will match any character except a
newline.
x
Allows you to document your regular expressions. It causes all
unescaped space characters and comments in the regular expression to
be ignored. Comments start with a hash character (#) and end with a
newline. All spaces in the regular expression that you want to be
matched in strings must be escaped with a backslash (\) character.
Notes
This function operates on UTF-8 strings using the default locale, even if the locale has been set to
something else.
If you are porting a regular expression query from an Oracle database, remember that Oracle
considers a zero-length string to be equivalent to NULL, while HP Vertica does not.
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Another key difference between Oracle and HP Vertica is that HP Vertica can handle an unlimited
number of captured subexpressions where Oracle is limited to nine. In HP Vertica, you are able to
use \10 in the replacement pattern to access the substring captured by the tenth set of parentheses
in the regular expression. In Oracle, \10 is treated as the substring captured by the first set of
parentheses followed by a zero. To force the same behavior in HP Vertica, use the \g
backreference with the number of the captured subexpression enclosed in curly braces. For
example, \g{1}0 is the substring captured by the first set of parentheses followed by a zero. You
can also name your captured subexpressions, to make your regular expressions less ambiguous.
See the PCRE documentation for details.
Examples
Find groups of "word characters" (letters, numbers and underscore) ending with "thy" in the string
"healthy, wealthy, and wise" and replace them with nothing.
=> SELECT REGEXP_REPLACE('healthy, wealthy, and wise','\w+thy');
REGEXP_REPLACE
---------------, , and wise
(1 row)
Find groups of word characters ending with "thy" and replace with the string "something."
=> SELECT REGEXP_REPLACE('healthy, wealthy, and wise','\w+thy', 'something');
REGEXP_REPLACE
-------------------------------something, something, and wise
(1 row)
Find groups of word characters ending with "thy" and replace with the string "something" starting at
the third character in the string.
=> SELECT REGEXP_REPLACE('healthy, wealthy, and wise','\w+thy', 'something', 3);
REGEXP_REPLACE
---------------------------------hesomething, something, and wise
(1 row)
Replace the second group of word characters ending with "thy" with the string "something."
=> SELECT REGEXP_REPLACE('healthy, wealthy, and wise','\w+thy', 'something', 1, 2);
REGEXP_REPLACE
-----------------------------healthy, something, and wise
(1 row)
Find groups of word characters ending with "thy" capturing the letters before the "thy", and replace
with the captured letters plus the letters "ish."
=> SELECT REGEXP_REPLACE('healthy, wealthy, and wise','(\w+)thy', '\1ish');
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REGEXP_REPLACE
---------------------------healish, wealish, and wise
(1 row)
Create a table to demonstrate replacing strings in a query.
=> CREATE TABLE customers (name varchar(50), phone varchar(11));
CREATE TABLE
=> CREATE PROJECTION customers1 AS SELECT * FROM customers;
CREATE PROJECTION
=> COPY customers FROM stdin;
Enter data to be copied followed by a newline.
End with a backslash and a period on a line by itself.
>> Able, Adam|17815551234
>> Baker,Bob|18005551111
>> Chu,Cindy|16175559876
>> Dodd,Dinara|15083452121
>> \.
Query the customers, using REGEXP_REPLACE to format the phone numbers.
=> SELECT name, REGEXP_REPLACE(phone, '(\d)(\d{3})(\d{3})(\d{4})', '\1-(\2) \3-\4') as ph
one FROM customers;
name
|
phone
-------------+-----------------Able, Adam | 1-(781) 555-1234
Baker,Bob
| 1-(800) 555-1111
Chu,Cindy
| 1-(617) 555-9876
Dodd,Dinara | 1-(508) 345-2121
(4 rows)
REGEXP_SUBSTR
Returns the substring that matches a regular expression within a string. If no matches are found,
this function returns NULL. This is different than an empty string, which can be returned by this
function if the regular expression matches a zero-length string.
Syntax
REGEXP_SUBSTR( string, pattern [, position [,
d_subexp ] ] ] )
occurrence ... [, regexp_modifier ] [, capture
Parameters
string
The string to search for the pattern.
pattern
The regular expression to find the substring to be extracted. The syntax of the
regular expression is compatible with the Perl 5 regular expression syntax.
See the Perl Regular Expressions Documentation for details.
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position
The character in the string where the search for a match should start. The
default value, 1, starts the search at the beginning of the string. If you supply
a value larger than 1 for this parameter, the function will start searching that
many characters into the string.
occurrence
Controls which matching substring is returned by the function. When given
the default value (1), the function will return the first matching substring it
finds in the string. By setting this value to a number greater than 1, this
function will return subsequent matching substrings. For example, setting this
parameter to 3 will return the third substring that matches the regular
expression within the string.
regexp_modifier
A string containing one or more single-character flags that change how the
regular expression is matched against the string:
captured_subexp
b
Treat strings as binary octets rather than UTF-8 characters.
c
Forces the match to be case sensitive (the default).
i
Forces the match to be case insensitive.
m
Treats the string being matched as multiple lines. With this modifier, the
start of line (^) and end of line ($) regular expression operators match
line breaks (\n) within the string. Ordinarily, these operators only match
the start and end of the string.
n
Allows the single character regular expression operator (.) to match a
newline (\n). Normally, the . operator will match any character except a
newline.
x
Allows you to document your regular expressions. It causes all
unescaped space characters and comments in the regular expression to
be ignored. Comments start with a hash character (#) and end with a
newline. All spaces in the regular expression that you want to be
matched in strings must be escaped with a backslash (\) character.
The captured subexpression whose contents should be returned. If omitted or
set to 0, the function returns the entire string that matched the regular
expression. If set to 1 through 9, the function returns the subexpression
captured by the corresponding set of parentheses in the regular expression.
For example, setting this value to 3 returns the substring captured by the third
set of parentheses in the regular expression.
Note: The subexpressions are numbered left to right, based on the
appearance of opening parenthesis, so nested regular expressions . For
example, in the regular expression \s*(\w+\s+(\w+)), subexpression 1
is the one that captures everything but any leading whitespaces.
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Notes
This function operates on UTF-8 strings using the default locale, even if the locale has been set to
something else.
If you are porting a regular expression query from an Oracle database, remember that Oracle
considers a zero-length string to be equivalent to NULL, while HP Vertica does not.
Examples
Select the first substring of letters that end with "thy."
=> SELECT REGEXP_SUBSTR('healthy, wealthy, and wise','\w+thy');
REGEXP_SUBSTR
--------------healthy
(1 row)
Select the first substring of letters that ends with "thy" starting at the second character in the string.
=> SELECT REGEXP_SUBSTR('healthy, wealthy, and wise','\w+thy',2);
REGEXP_SUBSTR
--------------ealthy
(1 row)
Select the second substring of letters that ends with "thy."
=> SELECT REGEXP_SUBSTR('healthy, wealthy, and wise','\w+thy',1,2);
REGEXP_SUBSTR
--------------wealthy
(1 row)
Return the contents of the third captured subexpression, which captures the third word in the string.
=> SELECT REGEXP_SUBSTR('one two three', '(\w+)\s+(\w+)\s+(\w+)', 1, 1, '', 3);
REGEXP_SUBSTR
--------------three
(1 row)
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Sequence Functions
The sequence functions provide simple, multiuser-safe methods for obtaining successive
sequence values from sequence objects.
NEXTVAL
Returns the next value in a sequence. Calling NEXTVAL after creating a sequence initializes the
sequence with its default value, incrementing a positive value for ascending sequences, and
decrementing a negative value for descending sequences. Thereafter, calling NEXTVAL
increments the sequence value. NEXTVAL is used in INSERT, COPY, and SELECT statements
to create unique values.
Behavior Type
Volatile
Syntax
[[db-name.]schema.]sequence_name.NEXTVALNEXTVAL('[[db-name.]schema.]sequence_name')
Parameters
[[db-name.]schema.]
[Optional] Specifies the schema name. Using a schema identifies objects
that are not unique within the current search path (see Setting Schema
Search Paths).
You can optionally precede a schema with a database name, but you must
be connected to the database you specify. You cannot make changes to
objects in other databases.
The ability to specify different database objects (from database and
schemas to tables and columns) lets you qualify database objects as
explicitly as required. For example, use a table and column
(mytable.column1), a schema, table, and column
(myschema.mytable.column1), and, as full qualification, a database,
schema, table, and column (mydb.myschema.mytable.column1).
sequence_name
Identifies the sequence for which to determine the next value.
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Permissions
l
SELECT privilege on sequence
l
USAGE privilege on sequence schema
Examples
The following example creates an ascending sequence called my_seq, starting at 101:
CREATE SEQUENCE my_seq START 101;
The following command generates the first number in the sequence:
SELECT NEXTVAL('my_seq');
nextval
--------101
(1 row)
The following command generates the next number in the sequence:
SELECT NEXTVAL('my_seq');
nextval
--------102
(1 row)
The following command illustrates how NEXTVAL is evaluated on a per-row basis, so in this
example, both calls to NEXTVAL yield the same result:
SELECT NEXTVAL('my_seq'), NEXTVAL('my_seq');
nextval | nextval
---------+--------103 |
103
(1 row)
The following example illustrates how the NEXTVAL is always evaluated first (and here, increments
the my_seq sequence from its previous value), even when CURRVAL precedes NEXTVAL:
SELECT CURRVAL('my_seq'), NEXTVAL('my_seq');
currval | nextval
---------+--------104 |
104
(1 row)
The following example shows how to use NEXTVAL in a table SELECT statement. Notice that the
nextval column is incremented by 1 again:
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SELECT NEXTVAL('my_seq'), product_description FROM product_dimension LIMIT 10;
nextval |
product_description
---------+-----------------------------105 | Brand #2 bagels
106 | Brand #1 butter
107 | Brand #6 chicken noodle soup
108 | Brand #5 golf clubs
109 | Brand #4 brandy
110 | Brand #3 lamb
111 | Brand #11 vanilla ice cream
112 | Brand #10 ground beef
113 | Brand #9 camera case
114 | Brand #8 halibut
(10 rows)
See Also
l
ALTER SEQUENCE
l
CREATE SEQUENCE
l
CURRVAL
l
DROP SEQUENCE
l
Using Named Sequences
l
Sequence Privileges
CURRVAL
For a sequence generator, returns the LAST value across all nodes returned by a previous
invocation of NEXTVAL in the same session. If there were no calls to NEXTVAL after the
sequence was created, an error is returned.
Behavior Type
Volatile
Syntax
[[db-name.]schema.]sequence_name.CURRVALCURRVAL('[[db-name.]schema.]sequence_name')
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Parameters
[[db-name.]schema.]
[Optional] Specifies the schema name. Using a schema identifies objects
that are not unique within the current search path (see Setting Schema
Search Paths).
You can optionally precede a schema with a database name, but you must
be connected to the database you specify. You cannot make changes to
objects in other databases.
The ability to specify different database objects (from database and
schemas to tables and columns) lets you qualify database objects as
explicitly as required. For example, use a table and column
(mytable.column1), a schema, table, and column
(myschema.mytable.column1), and, as full qualification, a database,
schema, table, and column (mydb.myschema.mytable.column1).
sequence_name
Identifies the sequence for which to return the current value.
Permissions
l
SELECT privilege on sequence
l
USAGE privilege on sequence schema
Examples
The following example creates an ascending sequence called sequential, starting at 101:
CREATE SEQUENCE seq2 START 101;
You cannot call CURRVAL until after you have initiated the sequence with NEXTVAL or the
system returns an error:
SELECT CURRVAL('seq2');
ERROR: Sequence seq2 has not been accessed in the session
Use the NEXTVAL function to generate the first number for this sequence:
SELECT NEXTVAL('seq2');
nextval
--------101
(1 row)
Now you can use CURRVAL to return the current number from this sequence:
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SELECT CURRVAL('seq2');
currval
--------101
(1 row)
The following command shows how to use CURRVAL in a SELECT statement:
CREATE TABLE customer3 (
lname VARCHAR(25),
fname VARCHAR(25),
membership_card INTEGER,
ID INTEGER
);
INSERT INTO customer3 VALUES ('Brown' ,'Sabra', 072753, CURRVAL('my_seq'));
SELECT CURRVAL('seq2'), lname FROM customer3;
CURRVAL | lname
---------+------101 | Brown
(1 row)
The following example illustrates how the NEXTVAL is always evaluated first (and here,
increments the my_seq sequence from its previous value), even when CURRVAL precedes
NEXTVAL:
SELECT CURRVAL('my_seq'), NEXTVAL('my_seq');
currval | nextval
---------+--------102 |
102
(1 row)
See Also
l
ALTER SEQUENCE
l
CREATE SEQUENCE
l
DROP SEQUENCE
NEXTVAL
l
l
l
LAST_INSERT_ID
Returns the last value of a column whose value is automatically incremented through the AUTO_
INCREMENT or IDENTITY Column-Constraint. If multiple sessions concurrently load the same
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table, the returned value is the last value generated for an AUTO_INCREMENT column by an
insert in that session.
Behavior Type
Volatile
Syntax
LAST_INSERT_ID()
Privileges
l
Table owner
l
USAGE privileges on schema
Notes
l
This function works only with AUTO_INCREMENT and IDENTITY columns. See columnconstraints for the CREATE TABLE statement.
l
LAST_INSERT_ID does not work with sequence generators created through the CREATE
SEQUENCE statement.
Examples
Create a sample table called customer4.
=> CREATE TABLE customer4(
ID IDENTITY(2,2),
lname VARCHAR(25),
fname VARCHAR(25),
membership_card INTEGER
);
=> INSERT INTO customer4(lname, fname, membership_card)
VALUES ('Gupta', 'Saleem', 475987);
Notice that the IDENTITY column has a seed of 2, which specifies the value for the first row loaded
into the table, and an increment of 2, which specifies the value that is added to the IDENTITY value
of the previous row.
Query the table you just created:
=> SELECT * FROM customer4;
ID | lname | fname | membership_card
----+-------+--------+----------------2 | Gupta | Saleem |
475987
(1 row)
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Insert some additional values:
=> INSERT INTO customer4(lname, fname, membership_card)
VALUES ('Lee', 'Chen', 598742);
Call the LAST_INSERT_ID function:
=> SELECT LAST_INSERT_ID();
LAST_INSERT_ID
---------------4
(1 row)
Query the table again:
=> SELECT * FROM customer4;
ID | lname | fname | membership_card
----+-------+--------+----------------2 | Gupta | Saleem |
475987
4 | Lee
| Chen
|
598742
(2 rows)
Add another row:
=> INSERT INTO customer4(lname, fname, membership_card)
VALUES ('Davis', 'Bill', 469543);
Call the LAST_INSERT_ID function:
=> SELECT LAST_INSERT_ID();
LAST_INSERT_ID
---------------6
(1 row)
Query the table again:
=> SELECT * FROM customer4; ID | lname | fname
----+-------+--------+----------------2 | Gupta | Saleem |
475987
4 | Lee
| Chen
|
598742
6 | Davis | Bill
|
469543
(3 rows)
| membership_card
See Also
l
ALTER SEQUENCE
l
CREATE SEQUENCE
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l
DROP SEQUENCE
l
SEQUENCES
l
Using Named Sequences
l
Sequence Privileges
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String Functions
String functions perform conversion, extraction, or manipulation operations on strings, or return
information about strings.
This section describes functions and operators for examining and manipulating string values.
Strings in this context include values of the types CHAR, VARCHAR, BINARY, and
VARBINARY.
Unless otherwise noted, all of the functions listed in this section work on all four data types. As
opposed to some other SQL implementations, HP Vertica keeps CHAR strings unpadded
internally, padding them only on final output. So converting a CHAR(3) 'ab' to VARCHAR(5)
results in a VARCHAR of length 2, not one with length 3 including a trailing space.
Some of the functions described here also work on data of non-string types by converting that data
to a string representation first. Some functions work only on character strings, while others work
only on binary strings. Many work for both. BINARY and VARBINARY functions ignore multibyte
UTF-8 character boundaries.
Non-binary character string functions handle normalized multibyte UTF-8 characters, as specified
by the Unicode Consortium. Unless otherwise specified, those character string functions for which
it matters can optionally specify whether VARCHAR arguments should be interpreted as octet
(byte) sequences, or as (locale-aware) sequences of UTF-8 characters. This is accomplished by
adding "USING OCTETS" or "USING CHARACTERS" (default) as a parameter to the function.
Some character string functions are stable because in general UTF-8 case-conversion, searching
and sorting can be locale dependent. Thus, LOWER is stable, while LOWERB is immutable. The
USING OCTETS clause converts these functions into their "B" forms, so they become immutable.
If the locale is set to collation=binary, which is the default, all string functions—except CHAR_
LENGTH/CHARACTER_LENGTH, LENGTH, SUBSTR, and OVERLAY—are converted to their
"B" forms and so are immutable.
BINARY implicitly converts to VARBINARY, so functions that take VARBINARY arguments work
with BINARY.
ASCII
Converts the first octet of a VARCHAR to an INTEGER.
Behavior Type
Immutable
Syntax
ASCII ( expression )
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Parameters
expression
(VARCHAR) is the string to convert.
Notes
l
ASCII is the opposite of the CHR function.
l
ASCII operates on UTF-8 characters, not only on single-byte ASCII characters. It continues to
get the same results for the ASCII subset of UTF-8.
Examples
Expression
Result
SELECT ASCII('A');
65
SELECT ASCII('ab');
97
SELECT ASCII(null);
SELECT ASCII('');
BIT_LENGTH
Returns the length of the string expression in bits (bytes * 8) as an INTEGER.
Behavior Type
Immutable
Syntax
BIT_LENGTH ( expression )
Parameters
expression
(CHAR or VARCHAR or BINARY or VARBINARY) is the string to convert.
Notes
BIT_LENGTH applies to the contents of VARCHAR and VARBINARY fields.
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Examples
Expression
Result
SELECT BIT_LENGTH('abc'::varbinary);
24
SELECT BIT_LENGTH('abc'::binary);
8
SELECT BIT_LENGTH(''::varbinary);
0
SELECT BIT_LENGTH(''::binary);
8
SELECT BIT_LENGTH(null::varbinary);
SELECT BIT_LENGTH(null::binary);
SELECT BIT_LENGTH(VARCHAR 'abc');
24
SELECT BIT_LENGTH(CHAR 'abc');
24
SELECT BIT_LENGTH(CHAR(6) 'abc');
48
SELECT BIT_LENGTH(VARCHAR(6) 'abc');
24
SELECT BIT_LENGTH(BINARY(6) 'abc');
48
SELECT BIT_LENGTH(BINARY 'abc');
24
SELECT BIT_LENGTH(VARBINARY 'abc');
24
SELECT BIT_LENGTH(VARBINARY(6) 'abc');
24
See Also
l
CHARACTER_LENGTH
l
LENGTH
l
OCTET_LENGTH
BITCOUNT
Returns the number of one-bits (sometimes referred to as set-bits) in the given VARBINARY value.
This is also referred to as the population count.
Behavior Type
Immutable
Syntax
BITCOUNT ( expression )
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Parameters
expression
(BINARY or VARBINARY) is the string to return.
Examples
SELECT BITCOUNT(HEX_TO_BINARY('0x10'));
bitcount
---------1
(1 row)
SELECT BITCOUNT(HEX_TO_BINARY('0xF0'));
bitcount
---------4
(1 row)
SELECT BITCOUNT(HEX_TO_BINARY('0xAB'))
bitcount
---------5
(1 row)
BITSTRING_TO_BINARY
Translates the given VARCHAR bitstring representation into a VARBINARY value.
Behavior Type
Immutable
Syntax
BITSTRING_TO_BINARY ( expression )
Parameters
expression
(VARCHAR) is the string to return.
Notes
VARBINARY BITSTRING_TO_BINARY(VARCHAR) converts data from character type (in
bitstring format) to binary type. This function is the inverse of TO_BITSTRING.
BITSTRING_TO_BINARY(TO_BITSTRING(x)) = x
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TO_BITSTRING(BITSTRING_TO_BINARY(x)) = x
Examples
If there are an odd number of characters in the hex value, the first character is treated as the low
nibble of the first (furthest to the left) byte.
SELECT BITSTRING_TO_BINARY('0110000101100010');
bitstring_to_binary
--------------------ab
(1 row)
If an invalid bitstring is supplied, the system returns an error:
SELECT BITSTRING_TO_BINARY('010102010');
ERROR: invalid bitstring "010102010"
BTRIM
Removes the longest string consisting only of specified characters from the start and end of a
string.
Behavior Type
Immutable
Syntax
BTRIM ( expression [ , characters-to-remove ] )
Parameters
expression
(CHAR or VARCHAR) is the string to modify
characters-to-remove
(CHAR or VARCHAR) specifies the characters to remove. The default is
the space character.
Example
SELECT BTRIM('xyxtrimyyx', 'xy');
btrim
------trim
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(1 row)
See Also
l
LTRIM
l
RTRIM
l
TRIM
CHARACTER_LENGTH
The CHARACTER_LENGTH() function:
l
Returns the string length in UTF-8 characters for CHAR and VARCHAR columns
l
Returns the string length in bytes (octets) for BINARY and VARBINARY columns
l
Strips the padding from CHAR expressions but not from VARCHAR expressions
l
Is identical to LENGTH() for CHAR and VARCHAR. For binary types, CHARACTER_LENGTH
() is identical to OCTET_LENGTH().
Behavior Type
Immutable if USING OCTETS, stable otherwise.
Syntax
[ CHAR_LENGTH | CHARACTER_LENGTH ] ( expression , ... [ USING { CHARACTERS | OCTETS } ] )
Parameters
expression
(CHAR or VARCHAR) is the string to measure
USING CHARACTERS | OCTETS
Determines whether the character length is expressed in characters
(the default) or octets.
Examples
SELECT CHAR_LENGTH('1234
char_length
------------4
(1 row)
'::CHAR(10), USING OCTETS);
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SELECT CHAR_LENGTH('1234
char_length
------------6
(1 row)
'::VARCHAR(10));
SELECT CHAR_LENGTH(NULL::CHAR(10)) IS NULL;
?column?
---------t
(1 row)
See Also
l
BIT_LENGTH
CHR
Converts the first octet of an INTEGER to a VARCHAR.
Behavior Type
Immutable
Syntax
CHR ( expression )
Parameters
expression
(INTEGER) is the string to convert and is masked to a single octet.
Notes
l
CHR is the opposite of the ASCII function.
l
CHR operates on UTF-8 characters, not only on single-byte ASCII characters. It continues to
get the same results for the ASCII subset of UTF-8.
Examples
Expression
Result
SELECT CHR(65);
A
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SELECT CHR(65+32);
a
SELECT CHR(null);
CONCAT
Used to concatenate two or more VARBINARY strings. The return value is of type VARBINARY.
Syntax
CONCAT ('a','b')
Behavior Type
Immutable
Parameters
a
First VARBINARY string.
b
Second VARBINARY string.
Example
=> SELECT CONCAT ('A','B'); CONCAT
-------AB
(1 row)
DECODE
Compares expression to each search value one by one. If expression is equal to a search, the
function returns the corresponding result. If no match is found, the function returns default. If default
is omitted, the function returns null.
Behavior Type
Immutable
Syntax
DECODE ( expression, search, result [ , search, result ]...[, default ] );
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Parameters
expression
The value to compare.
search
The value compared against expression.
result
The value returned, if expression is equal to search.
default
Optional. If no matches are found, DECODE returns default. If default is omitted,
then DECODE returns NULL (if no matches are found).
Notes
DECODE is similar to the IF-THEN-ELSE and CASE expression:
CASE expressionWHEN search THEN result
[WHEN search THEN result]
[ELSE default];
The arguments can have any data type supported by HP Vertica. The result types of individual
results are promoted to the least common type that can be used to represent all of them. This leads
to a character string type, an exact numeric type, an approximate numeric type, or a DATETIME
type, where all the various result arguments must be of the same type grouping.
Example
The following example converts numeric values in the weight column from the product_dimension
table to descriptive values in the output.
SELECT
2,
50,
71,
99,
product_description, DECODE(weight,
'Light',
'Medium',
'Heavy',
'Call for help',
'N/A')
FROM product_dimension
WHERE category_description = 'Food'
AND department_description = 'Canned Goods'
AND sku_number BETWEEN 'SKU-#49750' AND 'SKU-#49999'
LIMIT 15;
product_description
|
case
-----------------------------------+--------------Brand #499 canned corn
| N/A
Brand #49900 fruit cocktail
| Medium
Brand #49837 canned tomatoes
| Heavy
Brand #49782 canned peaches
| N/A
Brand #49805 chicken noodle soup | N/A
Brand #49944 canned chicken broth | N/A
Brand #49819 canned chili
| N/A
Brand #49848 baked beans
| N/A
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Brand #49989 minestrone soup
Brand #49778 canned peaches
Brand #49770 canned peaches
Brand #4977 fruit cocktail
Brand #49933 canned olives
Brand #49750 canned olives
Brand #49777 canned tomatoes
(15 rows)
|
|
|
|
|
|
|
N/A
N/A
N/A
N/A
N/A
Call for help
N/A
GREATEST
Returns the largest value in a list of expressions.
Behavior Type
Stable
Syntax
GREATEST ( expression1, expression2, ... expression-n );
Parameters
expression1, expression2, and expression-n are the expressions to be evaluated.
Notes
l
Works for all data types, and implicitly casts similar types. See Examples.
l
A NULL value in any one of the expressions returns NULL.
l
Depends on the collation setting of the locale.
Examples
This example returns 9 as the greatest in the list of expressions:
SELECT GREATEST(7, 5, 9);
greatest
---------9
(1 row)
Note that putting quotes around the integer expressions returns the same result as the first
example:
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SELECT GREATEST('7', '5', '9');
greatest
---------9
(1 row)
The next example returns FLOAT 1.5 as the greatest because the integer is implicitly cast to float:
SELECT GREATEST(1, 1.5);
greatest
---------1.5
(1 row)
The following example returns 'vertica' as the greatest:
SELECT GREATEST('vertica', 'analytic', 'database');
greatest
---------vertica
(1 row)
Notice this next command returns NULL:
SELECT GREATEST('vertica', 'analytic', 'database', null);
greatest
---------(1 row)
And one more:
SELECT GREATEST('sit', 'site', 'sight');
greatest
---------site
(1 row)
See Also
l
LEAST
GREATESTB
Returns its greatest argument, using binary ordering, not UTF-8 character ordering.
Behavior Type
Immutable
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Syntax
GREATESTB ( expression1, expression2, ... expression-n );
Parameters
expression1, expression2, and expression-n are the expressions to be evaluated.
Notes
l
Works for all data types, and implicitly casts similar types. See Examples.
l
A NULL value in any one of the expressions returns NULL.
l
Depends on the collation setting of the locale.
Examples
The following command selects straße as the greatest in the series of inputs:
SELECT GREATESTB('straße', 'strasse');
GREATESTB
----------straße
(1 row)
This example returns 9 as the greatest in the list of expressions:
SELECT GREATESTB(7, 5, 9);
GREATESTB
----------9
(1 row)
Note that putting quotes around the integer expressions returns the same result as the first
example:
GREATESTB
----------9
(1 row)
The next example returns FLOAT 1.5 as the greatest because the integer is implicitly cast to float:
SELECT GREATESTB(1, 1.5);
GREATESTB
----------1.5
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(1 row)
The following example returns vertica as the greatest:
SELECT GREATESTB('vertica', 'analytic', 'database');
GREATESTB
----------vertica
(1 row)
Notice this next command returns NULL:
SELECT GREATESTB('vertica', 'analytic', 'database', null);
GREATESTB
----------(1 row)
And one more:
SELECT GREATESTB('sit', 'site', 'sight');
GREATESTB
----------site
(1 row)
See Also
l
LEASTB
HEX_TO_BINARY
Translates the given VARCHAR hexadecimal representation into a VARBINARY value.
Behavior Type
Immutable
Syntax
HEX_TO_BINARY ( [ 0x ] expression )
Parameters
expression
(BINARY or VARBINARY) String to translate.
0x
Optional prefix.
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Notes
VARBINARY HEX_TO_BINARY(VARCHAR) converts data from character type in hexadecimal
format to binary type. This function is the inverse of TO_HEX.
HEX_TO_BINARY(TO_HEX(x)) = x)
TO_HEX(HEX_TO_BINARY(x)) = x)
If there are an odd number of characters in the hexadecimal value, the first character is treated as
the low nibble of the first (furthest to the left) byte.
Examples
If the given string begins with "0x" the prefix is ignored. For example:
=> SELECT HEX_TO_BINARY('0x6162') AS hex1, HEX_TO_BINARY('6162') AS hex2;
hex1 | hex2
------+-----ab
| ab
(1 row)
If an invalid hex value is given, HP Vertica returns an “invalid binary representation" error; for
example:
=> SELECT HEX_TO_BINARY('0xffgf');
ERROR: invalid hex string "0xffgf"
See Also
l
TO_HEX
HEX_TO_INTEGER
Translates the given VARCHAR hexadecimal representation into an INTEGER value.
HP Vertica completes this conversion as follows:
l
Adds the 0x prefix if it is not specified in the input
l
Casts the VARCHAR string to a NUMERIC
l
Casts the NUMERIC to an INTEGER
Behavior Type
Immutable
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Syntax
HEX_TO_INTEGER ( [ 0x ] expression )
Parameters
expression
VARCHAR is the string to translate.
0x
Is the optional prefix.
Examples
You can enter the string with or without the Ox prefix. For example:
=> SELECT HEX_TO_INTEGER ('0aedc')
AS hex1,HEX_TO_INTEGER ('aedc') AS hex2;
hex1 | hex2
-------+------44764 | 44764
(1 row)
If you pass the function an invalid hex value, HP Vertica returns an invalid input syntax error;
for example:
=> SELECT HEX_TO_INTEGER ('0xffgf');
ERROR 3691: Invalid input syntax for numeric: "0xffgf"
See Also
l
TO_HEX
l
HEX_TO_BINARY
INET_ATON
Returns an integer that represents the value of the address in host byte order, given the dotted-quad
representation of a network address as a string.
Behavior Type
Immutable
Syntax
INET_ATON ( expression )
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Parameters
expression
(VARCHAR) is the string to convert.
Notes
The following syntax converts an IPv4 address represented as the string A to an integer I. INET_
ATON trims any spaces from the right of A, calls the Linux function inet_pton, and converts the result
from network byte order to host byte order using ntohl.
INET_ATON(VARCHAR A) -> INT8 I
If A is NULL, too long, or inet_pton returns an error, the result is NULL.
Examples
The generated number is always in host byte order. In the following example, the number is
calculated as 209×256^3 + 207×256^2 + 224×256 + 40.
> SELECT INET_ATON('209.207.224.40');
inet_aton
-----------3520061480
(1 row)
> SELECT INET_ATON('1.2.3.4');
inet_aton
----------16909060
(1 row)
> SELECT TO_HEX(INET_ATON('1.2.3.4'));
to_hex
--------1020304
(1 row)
See Also
l
INET_NTOA
INET_NTOA
Returns the dotted-quad representation of the address as a VARCHAR, given a network address as
an integer in network byte order.
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Behavior Type
Immutable
Syntax
INET_NTOA ( expression )
Parameters
expression
(INTEGER) is the network address to convert.
Notes
The following syntax converts an IPv4 address represented as integer I to a string A.
INET_NTOA converts I from host byte order to network byte order using htonl, and calls the Linux
function inet_ntop.
INET_NTOA(INT8 I) -> VARCHAR A
If I is NULL, greater than 2^32 or negative, the result is NULL.
Examples
> SELECT INET_NTOA(16909060);
inet_ntoa
----------1.2.3.4
(1 row)
> SELECT INET_NTOA(03021962);
inet_ntoa
------------0.46.28.138
(1 row)
See Also
l
INET_ATON
INITCAP
Starting in Release 5.1, this function treats the string argument as a UTF-8 encoded string, rather
than depending on the collation setting of the locale (for example, collation=binary) to identify the
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encoding. Prior to Release 5.1, the behavior type of this function was stable.
Capitalizes first letter of each alphanumeric word and puts the rest in lowercase.
Behavior Type
Immutable
Syntax
INITCAP ( expression )
Parameters
expression
(VARCHAR) is the string to format.
Notes
l
Depends on collation setting of the locale.
l
INITCAP is restricted to 32750 octet inputs, since it is possible for the UTF-8 representation of
result to double in size.
Examples
Expression
Result
SELECT INITCAP('high speed database');
High Speed Database
SELECT INITCAP('LINUX TUTORIAL');
Linux Tutorial
SELECT INITCAP('abc DEF 123aVC 124Btd,lAsT');
Abc Def 123Avc 124Btd,Last
SELECT INITCAP('');
SELECT INITCAP(null);
INITCAPB
Capitalizes first letter of each alphanumeric word and puts the rest in lowercase. Multibyte
characters are not converted and are skipped.
Behavior Type
Immutable
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Syntax
INITCAPB ( expression )
Parameters
expression
(VARCHAR) is the string to format.
Notes
Depends on collation setting of the locale.
Examples
Expression
Result
SELECT INITCAPB('étudiant');
éTudiant
SELECT INITCAPB('high speed database');
High Speed Database
SELECT INITCAPB('LINUX TUTORIAL');
Linux Tutorial
SELECT INITCAPB('abc DEF 123aVC 124Btd,lAsT');
Abc Def 123Avc 124Btd,Last
SELECT INITCAPB('');
SELECT INITCAPB(null);
INSERT
Inserts a character string into a specified location in another character string.
Syntax
INSERT( 'string1', n, m, 'string2');
Behavior Type
Immutable
Parameters
string1
(VARCHAR) Is the string in which to insert the new string.
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n
A character of type INTEGER that represents the starting point for the insertion within
string1. You specify the number of characters from the first character in string1 as the
starting point for the insertion. For example, to insert characters before "c", in the string
"abcdef," enter 3.
m
A character of type INTEGER that represents the the number of characters in string1 (if
any) that should be replaced by the insertion. For example,if you want the insertion to
replace the letters "cd" in the string "abcdef, " enter 2.
string2
(VARCHAR) Is the string to be inserted.
Example
The following example changes the string Warehouse to Storehouse using the INSERT function:
=> SELECT INSERT ('Warehouse',1,3,'Stor');
INSERT
-----------Storehouse
(1 row)
INSTR
Starting in Release 5.1, this function treats the string argument as a UTF-8 encoded string, rather
than depending on the collation setting of the locale (for example, collation=binary) to identify the
encoding. Prior to Release 5.1, the behavior type of this function was stable.
Searches string for substring and returns an integer indicating the position of the character in string
that is the first character of this occurrence. The return value is based on the character position of
the identified character.
Behavior Type
Immutable
Syntax
INSTR ( string , substring [, position [, occurrence ] ] )
Parameters
string
(CHAR or VARCHAR, or BINARY or VARBINARY) Text expression to search.
substring
(CHAR or VARCHAR, or BINARY or VARBINARY) String to search for.
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position
Nonzero integer indicating the character of string where HP Vertica begins the
search. If position is negative, then HP Vertica counts backward from the end of
string and then searches backward from the resulting position. The first character
of string occupies the default position 1, and position cannot be 0.
occurrence
Integer indicating which occurrence of string HP Vertica searches. The value of
occurrence must be positive (greater than 0), and the default is 1.
Notes
Both position and occurrence must be of types that can resolve to an integer. The default values of
both parameters are 1, meaning HP Vertica begins searching at the first character of string for the
first occurrence of substring. The return value is relative to the beginning of string, regardless of the
value of position, and is expressed in characters.
If the search is unsuccessful (that is, if substring does not appear occurrence times after the
position character of string, the return value is 0.
Examples
The first example searches forward in string ‘abc’ for substring ‘b’. The search returns the position in
‘abc’ where ‘b’ occurs, or position 2. Because no position parameters are given, the default search
starts at ‘a’, position 1.
SELECT INSTR('abc', 'b');
INSTR
------2
(1 row)
The following three examples use character position to search backward to find the position of a
substring.
Note: Although it might seem intuitive that the function returns a negative integer, the position
of n occurrence is read left to right in the sting, even though the search happens in reverse
(from the end—or right side—of the string).
In the first example, the function counts backward one character from the end of the string, starting
with character ‘c’. The function then searches backward for the first occurrence of ‘a’, which it finds
it in the first position in the search string.
SELECT INSTR('abc', 'a', -1);
INSTR
------1
(1 row)
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In the second example, the function counts backward one byte from the end of the string, starting
with character ‘c’. The function then searches backward for the first occurrence of ‘a’, which it finds
it in the first position in the search string.
SELECT INSTR(VARBINARY 'abc', VARBINARY 'a', -1);
INSTR
------1
(1 row)
In the third example, the function counts backward one character from the end of the string, starting
with character ‘b’, and searches backward for substring ‘bc’, which it finds in the second position of
the search string.
SELECT INSTR('abcb', 'bc', -1);
INSTR
------2
(1 row)
In the fourth example, the function counts backward one character from the end of the string,
starting with character ‘b’, and searches backward for substring ‘bcef’, which it does not find. The
result is 0.
SELECT INSTR('abcb', 'bcef', -1);
INSTR
------0
(1 row)
In the fifth example, the function counts backward one byte from the end of the string, starting with
character ‘b’, and searches backward for substring ‘bcef’, which it does not find. The result is 0.
SELECT INSTR(VARBINARY 'abcb', VARBINARY 'bcef', -1);
INSTR
------0
(1 row)
Multibyte characters are treated as a single character:
dbadmin=> SELECT INSTR('aébc', 'b');
INSTR
------3
(1 row)
Use INSTRB to treat multibyte characters as binary:
dbadmin=> SELECT INSTRB('aébc', 'b');
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INSTRB
-------4
(1 row)
INSTRB
Searches string for substring and returns an integer indicating the octet position within string that is
the first occurrence. The return value is based on the octet position of the identified byte.
Behavior Type
Immutable
Syntax
INSTRB ( string , substring [, position [, occurrence ] ] )
Parameters
string
Is the text expression to search.
substring
Is the string to search for.
position
Is a nonzero integer indicating the character of string where HP Vertica begins the
search. If position is negative, then HP Vertica counts backward from the end of
string and then searches backward from the resulting position. The first byte of
string occupies the default position 1, and position cannot be 0.
occurrence
Is an integer indicating which occurrence of string HP Vertica searches. The value
of occurrence must be positive (greater than 0), and the default is 1.
Notes
Both position and occurrence must be of types that can resolve to an integer. The default values of
both parameters are 1, meaning HP Vertica begins searching at the first byte of string for the first
occurrence of substring. The return value is relative to the beginning of string, regardless of the
value of position, and is expressed in octets.
If the search is unsuccessful (that is, if substring does not appear occurrence times after the
position character of string, then the return value is 0.
Example
SELECT INSTRB('straße', 'ß');
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INSTRB
-------5
(1 row)
See Also
l
INSTR
ISUTF8
Tests whether a string is a valid UTF-8 string. Returns true if the string conforms to UTF-8
standards, and false otherwise. This function is useful to test strings for UTF-8 compliance before
passing them to one of the regular expression functions, such as REGEXP_LIKE, which expect
UTF-8 characters by default.
ISUTF8 checks for invalid UTF8 byte sequences, according to UTF-8 rules:
l
invalid bytes
l
an unexpected continuation byte
l
a start byte not followed by enough continuation bytes
l
an Overload Encoding
The presence of an invalid UTF8 byte sequence results in a return value of false.
Syntax
ISUTF8( string );
Parameters
string
The string to test for UTF-8 compliance.
Examples
=> SELECT ISUTF8(E'\xC2\xBF'); -- UTF-8 INVERTED QUESTION MARK ISUTF8
-------t
(1 row)
=> SELECT ISUTF8(E'\xC2\xC0'); -- UNDEFINED UTF-8 CHARACTER
ISUTF8
-------f
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(1 row)
LEAST
Returns the smallest value in a list of expressions.
Behavior Type
Stable
Syntax
LEAST ( expression1, expression2, ... expression-n );
Parameters
expression1, expression2, and expression-n are the expressions to be evaluated.
Notes
l
Works for all data types, and implicitly casts similar types. See Examples below.
l
A NULL value in any one of the expressions returns NULL.
Examples
This example returns 5 as the least:
SELECT LEAST(7, 5, 9);
least
------5
(1 row)
Putting quotes around the integer expressions returns the same result as the first example:
SELECT LEAST('7', '5', '9');
least
------5
(1 row)
In the above example, the values are being compared as strings, so '10' would be less than '2'.
The next example returns 1.5, as INTEGER 2 is implicitly cast to FLOAT:
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SELECT LEAST(2, 1.5);
least
------1.5
(1 row)
The following example returns 'analytic' as the least:
SELECT LEAST('vertica', 'analytic', 'database');
least
---------analytic
(1 row)
Notice this next command returns NULL:
SELECT LEAST('vertica', 'analytic', 'database', null);
least
------(1 row)
And one more:
SELECT LEAST('sit', 'site', 'sight');
least
------sight
(1 row)
See Also
l
GREATEST
LEASTB
Returns the function's least argument, using binary ordering, not UTF-8 character ordering.
Behavior Type
Immutable
Syntax
LEASTB ( expression1, expression2, ... expression-n );
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Parameters
expression1, expression2, and expression-n are the expressions to be evaluated.
Notes
l
Works for all data types, and implicitly casts similar types. See Examples below.
l
A NULL value in any one of the expressions returns NULL.
Examples
The following command selects strasse as the least in the series of inputs:
SELECT LEASTB('straße', 'strasse');
LEASTB
--------strasse
(1 row)
This example returns 5 as the least:
SELECT LEASTB(7, 5, 9);
LEASTB
-------5
(1 row)
Putting quotes around the integer expressions returns the same result as the first example:
SELECT LEASTB('7', '5', '9');
LEASTB
-------5
(1 row)
In the above example, the values are being compared as strings, so '10' would be less than '2'.
The next example returns 1.5, as INTEGER 2 is implicitly cast to FLOAT:
SELECT LEASTB(2, 1.5);
LEASTB
-------1.5
(1 row)
The following example returns 'analytic' as the least in the series of inputs:
SELECT LEASTB('vertica', 'analytic', 'database');
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LEASTB
---------analytic
(1 row)
Notice this next command returns NULL:
SELECT LEASTB('vertica', 'analytic', 'database', null);
LEASTB
-------(1 row)
See Also
l
GREATESTB
LEFT
Returns the specified characters from the left side of a string.
Behavior Type
Immutable
Syntax
LEFT ( string , length )
Parameters
string
(CHAR or VARCHAR) is the string to return.
length
Is an INTEGER value that specifies the count of characters to return.
Examples
SELECT LEFT('vertica', 3);
left
-----ver
(1 row)
SELECT LEFT('straße', 5);
LEFT
-------
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straß
(1 row)
See Also
l
SUBSTR
LENGTH
The LENGTH() function:
l
Returns the string length in UTF-8 characters for CHAR and VARCHAR columns
l
Returns the string length in bytes (octets) for BINARY and VARBINARY columns
l
Strips the padding from CHAR expressions but not from VARCHAR expressions
l
Is is identical to CHARACTER_LENGTH for CHAR and VARCHAR. For binary types,
LENGTH() is identical to OCTET_LENGTH.
Behavior Type
Immutable
Syntax
LENGTH ( expression )
Parameters
expression
(CHAR or VARCHAR or BINARY or VARBINARY) String to measure
Examples
Expression
Result
SELECT LENGTH('1234
'::CHAR(10));
4
SELECT LENGTH('1234
'::VARCHAR(10));
6
SELECT LENGTH('1234
'::BINARY(10));
10
SELECT LENGTH('1234
'::VARBINARY(10));
6
SELECT LENGTH(NULL::CHAR(10)) IS NULL;
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See Also
l
BIT_LENGTH
LOWER
Starting in Release 5.1, this function treats the string argument as a UTF-8 encoded string, rather
than depending on the collation setting of the locale (for example, collation=binary) to identify the
encoding. Prior to Release 5.1, the behavior type of this function was stable.
Returns a VARCHAR value containing the argument converted to lowercase letters.
Behavior Type
Immutable
Syntax
LOWER ( expression )
Parameters
expression
(CHAR or VARCHAR) String to convert
Notes
LOWER is restricted to 32750 octet inputs, since it is possible for the UTF-8 representation of
result to double in size.
Examples
SELECT LOWER('AbCdEfG');
lower
---------abcdefg
(1 row)
SELECT LOWER('The Cat In The Hat');
lower
-------------------the cat in the hat
(1 row)
dbadmin=> SELECT LOWER('ÉTUDIANT');
LOWER
----------
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étudiant
(1 row)
LOWERB
Returns a character string with each ASCII character converted to lowercase. Multibyte characters
are not converted and are skipped.
Behavior Type
Immutable
Syntax
LOWERB ( expression )
Parameters
expression
(CHAR or VARCHAR) is the string to convert
Examples
In the following example, the multibyte UTF-8 character É is not converted to lowercase:
SELECT LOWERB('ÉTUDIANT');
LOWERB
---------Étudiant
(1 row)
SELECT LOWER('ÉTUDIANT');
LOWER
---------étudiant
(1 row)
SELECT LOWERB('AbCdEfG'); LOWERB
--------abcdefg
(1 row)
SELECT LOWERB('The Vertica Database');
LOWERB
---------------------the vertica database
(1 row)
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LPAD
Returns a VARCHAR value representing a string of a specific length filled on the left with specific
characters.
Behavior Type
Immutable
Syntax
LPAD ( expression , length [ , fill ] )
Parameters
expression
(CHAR OR VARCHAR) specifies the string to fill
length
(INTEGER) specifies the number of characters to return
fill
(CHAR OR VARCHAR) specifies the repeating string of characters with which to
fill the output string. The default is the space character.
Examples
SELECT LPAD('database', 15, 'xzy');
lpad
----------------xzyxzyxdatabase
(1 row)
If the string is already longer than the specified length it is truncated on the right:
SELECT LPAD('establishment', 10, 'abc');
lpad
-----------establishm
(1 row)
LTRIM
Returns a VARCHAR value representing a string with leading blanks removed from the left side
(beginning).
Behavior Type
Immutable
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Syntax
LTRIM ( expression [ , characters ] )
Parameters
expression
(CHAR or VARCHAR) is the string to trim
characters
(CHAR or VARCHAR) specifies the characters to remove from the left side of
expression. The default is the space character.
Examples
SELECT LTRIM('zzzyyyyyyxxxxxxxxtrim', 'xyz');
LTRIM
------trim
(1 row)
See Also
l
BTRIM
l
RTRIM
l
TRIM
MD5
Calculates the MD5 hash of string, returning the result as a VARCHAR string in hexadecimal.
Behavior Type
Immutable
Syntax
MD5 ( string )
Parameters
string
Is the argument string.
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Examples
=> SELECT MD5('123');
md5
---------------------------------202cb962ac59075b964b07152d234b70
(1 row)
=> SELECT MD5('Vertica'::bytea);
md5
---------------------------------fc45b815747d8236f9f6fdb9c2c3f676
(1 row)
OCTET_LENGTH
Takes one argument as an input and returns the string length in octets for all string types.
Behavior Type
Immutable
Syntax
OCTET_LENGTH ( expression )
Parameters
expression
(CHAR or VARCHAR or BINARY or VARBINARY) is the string to measure.
Notes
l
If the data type of expression is a CHAR, VARCHAR or VARBINARY, the result is the same as
the actual length of expression in octets. For CHAR, the length does not include any trailing
spaces.
l
If the data type of expression is BINARY, the result is the same as the fixed-length of
expression.
l
If the value of expression is NULL, the result is NULL.
Examples
Expression
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SELECT OCTET_LENGTH(CHAR(10) '1234
');
4
SELECT OCTET_LENGTH(CHAR(10) '1234');
4
SELECT OCTET_LENGTH(CHAR(10) '
6
1234');
SELECT OCTET_LENGTH(VARCHAR(10) '1234
');
6
SELECT OCTET_LENGTH(VARCHAR(10) '1234 ');
5
SELECT OCTET_LENGTH(VARCHAR(10) '1234');
4
SELECT OCTET_LENGTH(VARCHAR(10) '
7
1234');
SELECT OCTET_LENGTH('abc'::VARBINARY);
3
SELECT OCTET_LENGTH(VARBINARY 'abc');
3
SELECT OCTET_LENGTH(VARBINARY 'abc
5
');
SELECT OCTET_LENGTH(BINARY(6) 'abc');
6
SELECT OCTET_LENGTH(VARBINARY '');
0
SELECT OCTET_LENGTH(''::BINARY);
1
SELECT OCTET_LENGTH(null::VARBINARY);
SELECT OCTET_LENGTH(null::BINARY);
See Also
l
BIT_LENGTH
l
CHARACTER_LENGTH
l
LENGTH
OVERLAY
Returns a VARCHAR value representing a string having had a substring replaced by another string.
Behavior Type
Immutable if using OCTETS, Stable otherwise
Syntax
OVERLAY ( expression1 PLACING expression2 FROM position
... [ FOR extent ]
... [ USING { CHARACTERS | OCTETS } ] )
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Parameters
expression1
(CHAR or VARCHAR) is the string to process
expression2
(CHAR or VARCHAR) is the substring to overlay
position
(INTEGER) is the character or octet position (counting from one) at
which to begin the overlay
extent
(INTEGER) specifies the number of characters or octets to replace
with the overlay
USING CHARACTERS | OCTETS
Determines whether OVERLAY uses characters (the default) or
octets
Examples
SELECT OVERLAY('123456789' PLACING 'xxx' FROM 2);
OVERLAY
----------1xxx56789
(1 row)
SELECT OVERLAY('123456789' PLACING 'XXX' FROM 2 USING OCTETS);
OVERLAY
----------1XXX56789
(1 row)
SELECT OVERLAY('123456789' PLACING 'xxx' FROM 2 FOR 4);
OVERLAY
---------1xxx6789
(1 row)
SELECT OVERLAY('123456789' PLACING 'xxx' FROM 2 FOR 5);
OVERLAY
--------1xxx789
(1 row)
SELECT OVERLAY('123456789' PLACING 'xxx' FROM 2 FOR 6);
OVERLAY
--------1xxx89
(1 row)
OVERLAYB
Returns an octet value representing a string having had a substring replaced by another string.
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Behavior Type
Immutable
Syntax
OVERLAYB ( expression1, expression2, position [ , extent ] )
Parameters
expression1
(CHAR or VARCHAR) is the string to process
expression2
(CHAR or VARCHAR) is the substring to overlay
position
(INTEGER) is the octet position (counting from one) at which to begin the overlay
extent
(INTEGER) specifies the number of octets to replace with the overlay
Notes
The OVERLAYB function treats the multibyte character string as a string of octets (bytes) and use
octet numbers as incoming and outgoing position specifiers and lengths. The strings themselves
are type VARCHAR, but they treated as if each byte was a separate character.
Examples
SELECT OVERLAYB('123456789', 'ééé', 2);
OVERLAYB
---------1ééé89
(1 row)
SELECT OVERLAYB('123456789', 'ßßß', 2);
OVERLAYB
---------1ßßß89
(1 row)
SELECT OVERLAYB('123456789', 'xxx', 2);
OVERLAYB
----------1xxx56789
(1 row)
SELECT OVERLAYB('123456789', 'xxx', 2, 4);
OVERLAYB
---------1xxx6789
(1 row)
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SELECT OVERLAYB('123456789', 'xxx', 2, 5);
OVERLAYB
--------1xxx789
(1 row)
SELECT OVERLAYB('123456789', 'xxx', 2, 6);
OVERLAYB
--------1xxx89
(1 row)
POSITION
Starting in Release 5.1, this function treats the string argument as a UTF-8 encoded string, rather
than depending on the collation setting of the locale (for example, collation=binary) to identify the
encoding. Prior to Release 5.1, the behavior type of this function was stable.
Returns an INTEGER value representing the character location of a specified substring with a
string (counting from one).
Behavior Type
Immutable
Syntax 1
POSITION ( substring IN string [ USING { CHARACTERS | OCTETS } ] )
Parameters
substring
(CHAR or VARCHAR) is the substring to locate
string
(CHAR or VARCHAR) is the string in which to locate the substring
USING CHARACTERS | OCTETS
Determines whether the position is reported by using characters (the
default) or octets.
Syntax 2
POSITION ( substring IN string )
Parameters
substring
(VARBINARY) is the substring to locate
string
(VARBINARY) is the string in which to locate the substring
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Notes
l
When the string and substring are CHAR or VARCHAR, the return value is based on either the
character or octet position of the substring.
l
When the string and substring are VARBINARY, the return value is always based on the octet
position of the substring.
l
The string and substring must be consistent. Do not mix VARBINARY with CHAR or
VARCHAR.
Examples
SELECT POSITION('é' IN 'étudiant' USING CHARACTERS);
position
---------1
(1 row)
SELECT POSITION('ß' IN 'straße' USING OCTETS);
position
---------5
(1 row)
SELECT POSITION('c' IN 'abcd' USING CHARACTERS);
position
---------3
(1 row)
SELECT POSITION(VARBINARY '456' IN VARBINARY '123456789');
position
---------4
(1 row)
SELECT POSITION('n' in 'León') as 'default',
POSITIONB('León', 'n') as 'POSITIONB',
POSITION('n' in 'León' USING CHARACTERS) as 'pos_chars',
POSITION('n' in 'León' USING OCTETS) as 'pos_oct',INSTR('León','n'),
INSTRB('León','n'),REGEXP_INSTR('León','n');
-[ RECORD 1 ]+-default
| 4
POSITIONB
| 5
pos_chars
| 4
pos_oct
| 5
INSTR
| 4
INSTRB
| 5
REGEXP_INSTR | 4
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POSITIONB
Returns an INTEGER value representing the octet location of a specified substring with a string
(counting from one).
Behavior Type
Immutable
Syntax
POSITIONB ( string, substring )
Parameters
string
(CHAR or VARCHAR) is the string in which to locate the substring
substring
(CHAR or VARCHAR) is the substring to locate
Examples
SELECT POSITIONB('straße', 'ße');
POSITIONB
----------5
(1 row)
SELECT POSITIONB('étudiant', 'é');
position
---------1
(1 row)
QUOTE_IDENT
Returns the given string, suitably quoted, to be used as an identifier in a SQL statement string.
Quotes are added only if necessary; that is, if the string contains non-identifier characters, is a SQL
keyword, such as '1time', 'Next week' and 'Select'. Embedded double quotes are doubled.
Behavior Type
Immutable
Syntax
QUOTE_IDENT( string )
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Parameters
string
Is the argument string.
Notes
l
SQL identifiers, such as table and column names, are stored as created, and references to them
are resolved using case-insensitive compares. Thus, you do not need to double-quote mixedcase identifiers.
l
HP Vertica quotes all currently-reserved keywords, even those not currently being used.
Examples
Quoted identifiers are case-insensitive, and HP Vertica does not supply the quotes:
SELECT QUOTE_IDENT('VErtIcA');
QUOTE_IDENT
------------VErtIcA
(1 row)
SELECT QUOTE_IDENT('Vertica database');
QUOTE_IDENT
-------------------"Vertica database"
(1 row)
Embedded double quotes are doubled:
SELECT QUOTE_IDENT('Vertica "!" database');
QUOTE_IDENT
------------------------"Vertica ""!"" database"
(1 row)
The following example uses the SQL keyword, SELECT; results are double quoted:
SELECT QUOTE_IDENT('select');
QUOTE_IDENT
------------"select"
(1 row)
QUOTE_LITERAL
Returns the given string, suitably quoted, to be used as a string literal in a SQL statement string.
Embedded single quotes and backslashes are doubled.
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Behavior Type
Immutable
Syntax
QUOTE_LITERAL ( string )
Parameters
string
Is the argument string.
Notes
HP Vertica recognizes two consecutive single quotes within a string literal as one single quote
character. For example, 'You''re here!'. This is the SQL standard representation and is
preferred over the form, 'You\'re here!', as backslashes are not parsed as before.
Examples
SELECT QUOTE_LITERAL('You''re here!');
QUOTE_LITERAL
----------------'You''re here!'
(1 row)
SELECT QUOTE_LITERAL('You\'re here!');
WARNING: nonstandard use of \' in a string literal at character 22
HINT: Use '' to write quotes in strings, or use the escape string syntax (E'\'').
See Also
l
Character String Literals
REPEAT
Returns a VARCHAR or VARBINARY value that repeats the given value COUNT times, given a
value and a count this function.
If the return value is truncated the given value might not be repeated count times, and the last
occurrence of the given value might be truncated.
Behavior Type
Immutable
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Syntax
REPEAT ( string , repetitions )
Parameters
string
(CHAR or VARCHAR or BINARY or VARBINARY) is the string to repeat
repetitions
(INTEGER) is the number of times to repeat the string
Notes
If the repetitions field depends on the contents of a column (is not a constant), then the repeat
operator maximum length is 65000 bytes. You can add a cast of the repeat to cast the result down
to a size big enough for your purposes (reflects the actual maximum size) so you can do other
things with the result.
REPEAT () and || check for result strings longer than 65000. REPEAT () silently truncates to 65000
octets, and || reports an error (including the octet length).
Examples
The following example repeats vmart three times:
SELECT REPEAT ('vmart', 3);
repeat
----------------vmartvmartvmart
(1 row)
If you run the following example, you get an error message:
SELECT '123456' || REPEAT('a', colx);
ERROR: Operator || may give a 65006-byte Varchar result; the limit is 65000 bytes.
If you know that colx can never be greater than 3, the solution is to add a cast (::VARCHAR(3)):
SELECT '123456' || REPEAT('a', colx)::VARCHAR(3);
If colx is greater than 3, the repeat is truncated to exactly three instalnce of a.
REPLACE
Replaces all occurrences of characters in a string with another set of characters.
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Behavior Type
Immutable
Syntax
REPLACE ( string , target , replacement )
Parameters
string
(CHAR OR VARCHAR) is the string to which to perform the replacement
target
(CHAR OR VARCHAR) is the string to replace
replacement
(CHAR OR VARCHAR) is the string with which to replace the target
Examples
SELECT REPLACE('Documentation%20Library', '%20', ' ');
replace
----------------------Documentation Library
(1 row)
SELECT REPLACE('This & That', '&', 'and');
replace
--------------This and That
(1 row)
SELECT REPLACE('straße', 'ß', 'ss');
REPLACE
--------strasse
(1 row)
RIGHT
Returns the specified characters from the right side of a string.
Behavior Type
Immutable
Syntax
RIGHT ( string , length )
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Parameters
string
(CHAR or VARCHAR) is the string to return.
length
Is an INTEGER value that specifies the count of characters to return.
Examples
The following command returns the last three characters of the string 'vertica':
SELECT RIGHT('vertica', 3);|
right
------ica
(1 row)
The following command returns the last two characters of the string 'straße':
SELECT RIGHT('straße', 2);
RIGHT
------ße
(1 row)
See Also
l
SUBSTR
RPAD
Returns a VARCHAR value representing a string of a specific length filled on the right with specific
characters.
Behavior Type
Immutable
Syntax
RPAD ( expression , length [ , fill ] )
Parameters
expression
(CHAR OR VARCHAR) specifies the string to fill
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length
(INTEGER) specifies the number of characters to return
fill
(CHAR OR VARCHAR) specifies the repeating string of characters with which to
fill the output string. The default is the space character.
Examples
SELECT RPAD('database', 15, 'xzy');
rpad
----------------databasexzyxzyx
(1 row)
If the string is already longer than the specified length it is truncated on the right:
SELECT RPAD('database', 6, 'xzy');
rpad
-------databa
(1 row)
RTRIM
Returns a VARCHAR value representing a string with trailing blanks removed from the right side
(end).
Behavior Type
Immutable
Syntax
RTRIM ( expression [ , characters ] )
Parameters
expression
(CHAR or VARCHAR) is the string to trim
characters
(CHAR or VARCHAR) specifies the characters to remove from the right side of
expression. The default is the space character.
Examples
SELECT RTRIM('trimzzzyyyyyyxxxxxxxx', 'xyz');
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ltrim
------trim
(1 row)
See Also
l
BTRIM
l
LTRIM
l
TRIM
SPACE
Inserts blank spaces into a specified location within a character string.
Behavior Type
Immutable
Syntax
SELECT INSERT( 'string1', || SPACE (n) || 'string2');
Parameters
string1
(VARCHAR) Is the string after which to insert the space.
n
A character of type INTEGER that represents the number of spaces to insert.
string2
( VARCHAR) Is the remainder of the string that appears after the inserted spaces
Example
The following example inserts 10 spaces between the strings 'x' and 'y':
SELECT 'x' || SPACE(10) || 'y';
?column?
-------------x
y
(1 row)
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SPLIT_PART
Starting in Release 5.1, this function treats the string argument as a UTF-8 encoded string, rather
than depending on the collation setting of the locale (for example, collation=binary) to identify the
encoding. Prior to Release 5.1, the behavior type of this function was stable.
Splits string on the delimiter and returns the location of the beginning of the given field (counting
from one).
Behavior Type
Immutable
Syntax
SPLIT_PART ( string , delimiter , field )
Parameters
string
Is the argument string.
delimiter
Is the given delimiter.
field
(INTEGER) is the number of the part to return.
Notes
Use this with the character form of the subfield.
Examples
The specified integer of 2 returns the second string, or def.
SELECT SPLIT_PART('abc~@~def~@~ghi', '~@~', 2);
SPLIT_PART
-----------def
(1 row)
In the next example, specify 3, which returns the third string, or 789.
SELECT SPLIT_PART('123~|~456~|~789', '~|~', 3);
SPLIT_PART
-----------789
(1 row)
The tildes are for readability only. Omitting them returns the same results:
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SELECT SPLIT_PART('123|456|789', '|', 3);
SPLIT_PART
-----------789
(1 row)
See what happens if you specify an integer that exceeds the number of strings: No results.
SELECT SPLIT_PART('123|456|789', '|', 4);
SPLIT_PART
-----------(1 row)
The previous result is not null, it is an empty string.
SELECT SPLIT_PART('123|456|789', '|', 4) IS NULL;
?column?
---------f
(1 row)
If SPLIT_PART had returned NULL, LENGTH would have returned null.
SELECT LENGTH (SPLIT_PART('123|456|789', '|', 4));
LENGTH
-------0
(1 row)
SPLIT_PARTB
Splits string on the delimiter and returns the location of the beginning of the given field (counting
from one). The VARCHAR arguments are treated as octets rather than UTF-8 characters.
Behavior Type
Immutable
Syntax
SPLIT_PARTB ( string , delimiter , field )
Parameters
string
(VARCHAR) Is the argument string.
delimiter
(VARCHAR) Is the given delimiter.
field
(INTEGER) is the number of the part to return.
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Notes
Use this function with the character form of the subfield.
Examples
The specified integer of 3 returns the third string, or soupçon.
SELECT SPLIT_PARTB('straße~@~café~@~soupçon', '~@~', 3);
SPLIT_PARTB
------------soupçon
(1 row)
The tildes are for readability only. Omitting them returns the same results:
SELECT SPLIT_PARTB('straße @ café @ soupçon', '@', 3);
SPLIT_PARTB
------------soupçon
(1 row)
See what happens if you specify an integer that exceeds the number of strings: No results.
SELECT SPLIT_PARTB('straße @ café @ soupçon', '@', 4);
SPLIT_PARTB
------------(1 row)
The above result is not null, it is an empty string.
SELECT SPLIT_PARTB('straße @ café @ soupçon', '@', 4) IS NULL;
?column?
---------f
(1 row)
STRPOS
Starting in Release 5.1, this function treats the string argument as a UTF-8 encoded string, rather
than depending on the collation setting of the locale (for example, collation=binary) to identify the
encoding. Prior to Release 5.1, the behavior type of this function was stable.
Returns an INTEGER value representing the character location of a specified substring within a
string (counting from one).
Behavior Type
Immutable
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Syntax
STRPOS ( string , substring )
Parameters
string
(CHAR or VARCHAR) is the string in which to locate the substring
substring
(CHAR or VARCHAR) is the substring to locate
Notes
STRPOS is identical to POSITION except for the order of the arguments.
Examples
SELECT STRPOS('abcd','c');
strpos
-------3
(1 row)
STRPOSB
Returns an INTEGER value representing the location of a specified substring within a string,
counting from one, where each octet in the string is counted (as opposed to characters).
Behavior Type
Immutable
Syntax
STRPOSB ( string , substring )
Parameters
string
(CHAR or VARCHAR) is the string in which to locate the substring
substring
(CHAR or VARCHAR) is the substring to locate
Notes
STRPOSB is identical to POSITIONB except for the order of the arguments.
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Examples
=> SELECT STRPOSB('straße', 'e');
STRPOSB
--------7
(1 row)
=> SELECT STRPOSB('étudiant', 'tud');
STRPOSB
--------3
(1 row)
SUBSTR
Returns VARCHAR or VARBINARY value representing a substring of a specified string.
Behavior Type
Immutable
Syntax
SUBSTR ( string , position [ , extent ] )
Parameters
string
(CHAR/VARCHAR or BINARY/VARBINARY) is the string from which to extract a
substring.
position
(INTEGER or DOUBLE PRECISION) is the starting position of the substring
(counting from one by characters).
extent
(INTEGER or DOUBLE PRECISION) is the length of the substring to extract (in
characters). The default is the end of the string.
Notes
SUBSTR truncates DOUBLE PRECISION input values.
Examples
=> SELECT SUBSTR('abc'::binary(3),1);
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substr
-------abc
(1 row)
=> SELECT SUBSTR('123456789', 3, 2);
substr
-------34
(1 row)
=> SELECT SUBSTR('123456789', 3);
substr
--------3456789
(1 row)
=> SELECT SUBSTR(TO_BITSTRING(HEX_TO_BINARY('0x10')), 2, 2);
substr
-------00
(1 row)
=> SELECT SUBSTR(TO_HEX(10010), 2, 2);
substr
-------71
(1 row)
SUBSTRB
Returns an octet value representing the substring of a specified string.
Behavior Type
Immutable
Syntax
SUBSTRB ( string , position [ , extent ] )
Parameters
string
(CHAR/VARCHAR) is the string from which to extract a substring.
position
(INTEGER or DOUBLE PRECISION) is the starting position of the substring
(counting from one in octets).
extent
(INTEGER or DOUBLE PRECISION) is the length of the substring to extract (in
octets). The default is the end of the string
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Notes
l
This function treats the multibyte character string as a string of octets (bytes) and uses octet
numbers as incoming and outgoing position specifiers and lengths. The strings themselves are
type VARCHAR, but they treated as if each octet were a separate character.
l
SUBSTRB truncates DOUBLE PRECISION input values.
Examples
=> SELECT SUBSTRB('soupçon', 5);
SUBSTRB
--------çon
(1 row)
=> SELECT SUBSTRB('soupçon', 5, 2);
SUBSTRB
--------ç
(1 row)
HP Vertica returns the following error message if you use BINARY/VARBINARY:
=>SELECT SUBSTRB('abc'::binary(3),1);
ERROR: function substrb(binary, int) does not exist, or permission is denied for substrb(
binary, int)
HINT: No function matches the given name and argument types. You may need to add explicit
type casts.
SUBSTRING
Returns a value representing a substring of the specified string at the given position, given a value,
a position, and an optional length.
Behavior Type
Immutable if USING OCTETS, stable otherwise.
Syntax
SUBSTRING ( string , position [ , length ]
... [USING {CHARACTERS | OCTETS } ] )
SUBSTRING ( string FROM position [ FOR length ]
... [USING { CHARACTERS | OCTETS } ] )
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Parameters
string
(CHAR/VARCHAR or BINARY/VARBINARY) is the string from
which to extract a substring
position
(INTEGER or DOUBLE PRECISION) is the starting position of the
substring (counting from one by either characters or octets). (The
default is characters.) If position is greater than the length of the
given value, an empty value is returned.
length
(INTEGER or DOUBLE PRECISION) is the length of the substring
to extract in either characters or octets. (The default is characters.)
The default is the end of the string.If a length is given the result is at
most that many bytes. The maximum length is the length of the
given value less the given position. If no length is given or if the
given length is greater than the maximum length then the length is
set to the maximum length.
USING CHARACTERS | OCTETS
Determines whether the value is expressed in characters (the
default) or octets.
Notes
l
SUBSTRING truncates DOUBLE PRECISION input values.
l
Neither length nor position can be negative, and the position cannot be zero because it is one
based. If these forms are violated, the system returns an error:
SELECT SUBSTRING('ab'::binary(2), -1, 2);
ERROR: negative or zero substring start position not allowed
Examples
=> SELECT SUBSTRING('abc'::binary(3),1);
SUBSTRING
----------abc
(1 row)
=> SELECT SUBSTRING('soupçon', 5, 2 USING CHARACTERS);
SUBSTRING
----------ço
(1 row)
=> SELECT SUBSTRING('soupçon', 5, 2 USING OCTETS);
SUBSTRB
--------ç
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(1 row)
TO_BITSTRING
Returns a VARCHAR that represents the given VARBINARY value in bitstring format.
Behavior Type
Immutable
Syntax
TO_BITSTRING ( expression )
Parameters
expression
(VARCHAR) is the string to return.
Notes
VARCHAR TO_BITSTRING(VARBINARY) converts data from binary type to character type
(where the character representation is the bitstring format). This function is the inverse of
BITSTRING_TO_BINARY:
TO_BITSTRING(BITSTRING_TO_BINARY(x)) = x)
BITSTRING_TO_BINARY(TO_BITSTRING(x)) = x)
Examples
SELECT TO_BITSTRING('ab'::BINARY(2));
to_bitstring
-----------------0110000101100010
(1 row)
SELECT TO_BITSTRING(HEX_TO_BINARY('0x10'));
to_bitstring
-------------00010000
(1 row)
SELECT TO_BITSTRING(HEX_TO_BINARY('0xF0'));
to_bitstring
--------------
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11110000
(1 row)
See Also
l
BITCOUNT
l
BITSTRING_TO_BINARY
TO_HEX
Returns a VARCHAR or VARBINARY representing the hexadecimal equivalent of a number.
Behavior Type
Immutable
Syntax
TO_HEX ( number )
Parameters
number
(INTEGER) is the number to convert to hexadecimal
Notes
VARCHAR TO_HEX(INTEGER) and VARCHAR TO_HEX(VARBINARY) are similar. The
function converts data from binary type to character type (where the character representation is in
hexadecimal format). This function is the inverse of HEX_TO_BINARY.
TO_HEX(HEX_TO_BINARY(x)) = x);
HEX_TO_BINARY(TO_HEX(x)) = x);
Examples
SELECT TO_HEX(123456789);
TO_HEX
--------75bcd15
(1 row)
For VARBINARY inputs, the returned value is not preceded by "0x". For example:
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SELECT TO_HEX('ab'::binary(2));
TO_HEX
-------6162
(1 row)
TRANSLATE
Replaces individual characters in string_to_replace with other characters.
Behavior Type
Immutable
Syntax
TRANSLATE ( string_to_replace , from_string , to_string );
Parameters
string_to_replace
String to be translated.
from_string
Contains characters that should be replaced in string_to_replace.
to_string
Any character in string_to_replace that matches a character in from_string is
replaced by the corresponding character in to_string.
Example
SELECT TRANSLATE('straße', 'ß', 'ss');
TRANSLATE
----------strase
(1 row)
TRIM
Combines the BTRIM, LTRIM, and RTRIM functions into a single function.
Behavior Type
Immutable
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Syntax
TRIM ( [ [ LEADING | TRAILING | BOTH ] characters FROM ] expression )
Parameters
LEADING
Removes the specified characters from the left side of the string
TRAILING
Removes the specified characters from the right side of the string
BOTH
Removes the specified characters from both sides of the string (default)
characters
(CHAR or VARCHAR) specifies the characters to remove from expression. The
default is the space character.
expression
(CHAR or VARCHAR) is the string to trim
Examples
SELECT '-' || TRIM(LEADING 'x' FROM 'xxdatabasexx') || '-';
?column?
--------------databasexx(1 row)
SELECT '-' || TRIM(TRAILING 'x' FROM 'xxdatabasexx') || '-';
?column?
--------------xxdatabase(1 row)
SELECT '-' || TRIM(BOTH 'x' FROM 'xxdatabasexx') || '-';
?column?
------------database(1 row)
SELECT '-' || TRIM('x' FROM 'xxdatabasexx') || '-';
?column?
------------database(1 row)
SELECT '-' || TRIM(LEADING FROM '
?column?
--------------database (1 row)
SELECT '-' || TRIM('
?column?
------------database(1 row)
database
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') || '-';
') || '-';
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See Also
l
BTRIM
l
LTRIM
l
RTRIM
UPPER
Starting in Release 5.1, this function treats the string argument as a UTF-8 encoded string, rather
than depending on the collation setting of the locale (for example, collation=binary) to identify the
encoding. Prior to Release 5.1, the behavior type of this function was stable.
Returns a VARCHAR value containing the argument converted to uppercase letters.
Behavior Type
Immutable
Syntax
UPPER ( expression )
Parameters
expression
(CHAR or VARCHAR) is the string to convert
Notes
UPPER is restricted to 32750 octet inputs, since it is possible for the UTF-8 representation of result
to double in size.
Examples
=> SELECT UPPER('AbCdEfG');
UPPER
---------ABCDEFG
(1 row)
=> SELECT UPPER('étudiant');
UPPER
---------ÉTUDIANT
(1 row)
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UPPERB
Returns a character string with each ASCII character converted to uppercase. Multibyte characters
are not converted and are skipped.
Behavior Type
Immutable
Syntax
UPPERB ( expression )
Parameters
expression
(CHAR or VARCHAR) is the string to convert
Examples
In the following example, the multibyte UTF-8 character é is not converted to uppercase:
=> SELECT UPPERB('étudiant');
UPPERB
---------éTUDIANT
(1 row)
=> SELECT UPPERB('AbCdEfG');
UPPERB
--------ABCDEFG
(1 row)
=> SELECT UPPERB('The Vertica Database');
UPPERB
---------------------THE VERTICA DATABASE
(1 row)
V6_ATON
Converts an IPv6 address represented as a character string to a binary string.
Behavior Type
Immutable
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Syntax
V6_ATON ( expression )
Parameters
expression
(VARCHAR) is the string to convert.
Notes
The following syntax converts an IPv6 address represented as the character string A to a binary
string B.
V6_ATON trims any spaces from the right of A and calls the Linux function inet_pton.
V6_ATON(VARCHAR A) -> VARBINARY(16) B
If A has no colons it is prepended with '::ffff:'. If A is NULL, too long, or if inet_pton returns an error,
the result is NULL.
Examples
SELECT V6_ATON('2001:DB8::8:800:200C:417A');
v6_aton
-----------------------------------------------------\001\015\270\000\000\000\000\000\010\010\000 \014Az
(1 row)
SELECT V6_ATON('1.2.3.4');
v6_aton
-----------------------------------------------------------------\000\000\000\000\000\000\000\000\000\000\377\377\001\002\003\004
(1 row)
SELECT TO_HEX(V6_ATON('2001:DB8::8:800:200C:417A'));
to_hex
---------------------------------20010db80000000000080800200c417a
(1 row)
SELECT V6_ATON('::1.2.3.4');
v6_aton
-----------------------------------------------------------------\000\000\000\000\000\000\000\000\000\000\000\000\001\002\003\004
(1 row)
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See Also
l
V6_NTOA
V6_NTOA
Converts an IPv6 address represented as varbinary to a character string.
Behavior Type
Immutable
Syntax
V6_NTOA ( expression )
Parameters
expression
(VARBINARY) is the binary string to convert.
Notes
The following syntax converts an IPv6 address represented as VARBINARY B to a string A.
V6_NTOA right-pads B to 16 bytes with zeros, if necessary, and calls the Linux function inet_ntop.
V6_NTOA(VARBINARY B) -> VARCHAR A
If B is NULL or longer than 16 bytes, the result is NULL.
HP Vertica automatically converts the form '::ffff:1.2.3.4' to '1.2.3.4'.
Examples
> SELECT V6_NTOA(' \001\015\270\000\000\000\000\000\010\010\000 \014Az');
v6_ntoa
--------------------------2001:db8::8:800:200c:417a
(1 row)
> SELECT V6_NTOA(V6_ATON('1.2.3.4'));
v6_ntoa
--------1.2.3.4
(1 row)
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> SELECT V6_NTOA(V6_ATON('::1.2.3.4'));
v6_ntoa
----------::1.2.3.4
(1 row)
See Also
l
V6_ATON
V6_SUBNETA
Calculates a subnet address in CIDR (Classless Inter-Domain Routing) format from a binary or
alphanumeric IPv6 address.
Behavior Type
Immutable
Syntax
V6_SUBNETA ( expression1, expression2 )
Parameters
expression1
(VARBINARY or VARCHAR) is the string to calculate.
expression2
(INTEGER) is the size of the subnet.
Notes
The following syntax calculates a subnet address in CIDR format from a binary or varchar IPv6
address.
V6_SUBNETA masks a binary IPv6 address B so that the N leftmost bits form a subnet address,
while the remaining rightmost bits are cleared. It then converts to an alphanumeric IPv6 address,
appending a slash and N.
V6_SUBNETA(BINARY B, INT8 N) -> VARCHAR C
The following syntax calculates a subnet address in CIDR format from an alphanumeric IPv6
address.
V6_SUBNETA(VARCHAR A, INT8 N) -> V6_SUBNETA(V6_ATON(A), N) -> VARCHAR C
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Examples
> SELECT V6_SUBNETA(V6_ATON('2001:db8::8:800:200c:417a'), 28);
v6_subneta
--------------2001:db0::/28
(1 row)
See Also
l
V6_SUBNETN
V6_SUBNETN
Calculates a subnet address in CIDR (Classless Inter-Domain Routing) format from a varbinary or
alphanumeric IPv6 address.
Behavior Type
Immutable
Syntax
V6_SUBNETN ( expression1, expression2 )
Parameters
expression1
(VARBINARY or VARCHAR) is the string to calculate.
Notes:
l
V6_SUBNETN(, ) returns VARBINARY.
OR
l
expression2
V6_SUBNETN(, ) returns VARBINARY, after using V6_ATON
to convert the string to .
(INTEGER) is the size of the subnet.
Notes
The following syntax masks a BINARY IPv6 address B so that the N left-most bits of S form a
subnet address, while the remaining right-most bits are cleared.
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V6_SUBNETN right-pads B to 16 bytes with zeros, if necessary and masks B, preserving its N-bit
subnet prefix.
V6_SUBNETN(VARBINARY B, INT8 N) -> VARBINARY(16) S
If B is NULL or longer than 16 bytes, or if N is not between 0 and 128 inclusive, the result is NULL.
S = [B]/N in Classless Inter-Domain Routing notation (CIDR notation).
The following syntax masks an alphanumeric IPv6 address A so that the N leftmost bits form a
subnet address, while the remaining rightmost bits are cleared.
V6_SUBNETN(VARCHAR A, INT8 N) -> V6_SUBNETN(V6_ATON(A), N) -> VARBINARY(16) S
Example
This example returns VARBINARY, after using V6_ATON to convert the VARCHAR string to VARBINARY:
> SELECT V6_SUBNETN(V6_ATON('2001:db8::8:800:200c:417a'), 28);
v6_subnetn
--------------------------------------------------------------\001\015\260\000\000\000\000\000\000\000\000\000\000\000\000
See Also
l
V6_ATON
l
V6_SUBNETA
V6_TYPE
Characterizes a binary or alphanumeric IPv6 address B as an integer type.
Behavior Type
Immutable
Syntax
V6_TYPE ( expression )
Parameters
expression
(VARBINARY or VARCHAR) is the type to convert.
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Notes
V6_TYPE(VARBINARY B) returns INT8 T.
V6_TYPE(VARCHAR A) -> V6_TYPE(V6_ATON(A)) -> INT8 T
The IPv6 types are defined in the Network Working Group's IP Version 6 Addressing Architecture
memo.
GLOBAL = 0
LINKLOCAL = 1
LOOPBACK = 2
UNSPECIFIED = 3
MULTICAST = 4
Global unicast addresses
Link-Local unicast (and Private-Use) addresses
Loopback
Unspecified
Multicast
IPv4-mapped and IPv4-compatible IPv6 addresses are also interpreted, as specified in IPv4 Global
Unicast Address Assignments.
l
For IPv4, Private-Use is grouped with Link-Local.
l
If B is VARBINARY, it is right-padded to 16 bytes with zeros, if necessary.
l
If B is NULL or longer than 16 bytes, the result is NULL.
Details
IPv4 (either kind):
0.0.0.0/8
127.0.0.0/8
169.254.0.0/16
172.16.0.0/12
192.168.0.0/16
224.0.0.0/4
others
UNSPECIFIED
LOOPBACK
LINKLOCAL
LINKLOCAL
LINKLOCAL
MULTICAST
GLOBAL
10.0.0.0/8
::0/128
fe80::/10
ff00::/8
others
UNSPECIFIED
LINKLOCAL
MULTICAST
GLOBAL
::1/128
LINKLOCAL
IPv6:
LOOPBACK
Examples
> SELECT V6_TYPE(V6_ATON('192.168.2.10'));
v6_type
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--------1
(1 row)
> SELECT V6_TYPE(V6_ATON('2001:db8::8:800:200c:417a'));
v6_type
--------0
(1 row)
See Also
l
INET_ATON
l
IP Version 6 Addressing Architecture
l
IPv4 Global Unicast Address Assignments
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System Information Functions
These functions provide system information regarding user sessions. A superuser has unrestricted
access to all system information, but users can view only information about their own, current
sessions.
CURRENT_DATABASE
Returns a VARCHAR value containing the name of the database to which you are connected.
Behavior Type
Immutable
Syntax
CURRENT_DATABASE()
Notes
l
The parentheses following the CURRENT_DATABASE function are optional.
l
This function is equivalent to DBNAME.
Examples
SELECT CURRENT_DATABASE();
CURRENT_DATABASE
-----------------VMart
(1 row)
The following command returns the same results without the parentheses:
SELECT CURRENT_DATABASE;
CURRENT_DATABASE
-----------------VMart
(1 row)
CURRENT_SCHEMA
Returns the name of the current schema.
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Behavior Type
Stable
Syntax
CURRENT_SCHEMA()
Privileges
None
Notes
The CURRENT_SCHEMA function does not require parentheses.
Examples
The following command returns the name of the current schema:
=> SELECT CURRENT_SCHEMA();
current_schema
---------------public
(1 row)
The following command returns the same results without the parentheses:
=> SELECT CURRENT_SCHEMA;
current_schema
---------------public
(1 row)
The following command shows the current schema, listed after the current user, in the search path:
=> SHOW SEARCH_PATH;
name
|
setting
-------------+--------------------------------------------------search_path | "$user", public, v_catalog, v_monitor, v_internal
(1 row)
See Also
l
SET SEARCH_PATH
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CURRENT_USER
Returns a VARCHAR containing the name of the user who initiated the current database
connection.
Behavior Type
Stable
Syntax
CURRENT_USER()
Notes
l
The CURRENT_USER function does not require parentheses.
l
This function is useful for permission checking.
l
CURRENT_USER is equivalent to SESSION_USER, USER, and USERNAME.
Examples
SELECT CURRENT_USER();
CURRENT_USER
-------------dbadmin
(1 row)
The following command returns the same results without the parentheses:
SELECT CURRENT_USER;
CURRENT_USER
-------------dbadmin
(1 row)
DBNAME (function)
Returns a VARCHAR value containing the name of the database to which you are connected.
DBNAME is equivalent to CURRENT_DATABASE.
Behavior Type
Immutable
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Syntax
DBNAME()
Examples
SELECT DBNAME();
dbname
-----------------VMart
(1 row)
HAS_TABLE_PRIVILEGE
Indicates whether a user can access a table in a particular way. The function returns a true (t) or
false (f) value.
A superuser can check all other user's table privileges.
Users without superuser privileges can use HAS_TABLE_PRIVILEGE to check:
l
Any tables they own.
l
Tables in a schema to which they have been granted USAGE privileges, and at least one other
table privilege, as described in GRANT (Table).
Behavior Type
Stable
Syntax
HAS_TABLE_PRIVILEGE ( [ user, ] [[db-name.]schema-name.]table , privilege )
Parameters
user
Specifies the name or OID of a database user. The default is the
CURRENT_USER.
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[[db-name.]schema.]
[Optional] Specifies the schema name. Using a schema identifies objects
that are not unique within the current search path (see Setting Schema
Search Paths).
You can optionally precede a schema with a database name, but you must
be connected to the database you specify. You cannot make changes to
objects in other databases.
The ability to specify different database objects (from database and
schemas to tables and columns) lets you qualify database objects as
explicitly as required. For example, use a table and column
(mytable.column1), a schema, table, and column
(myschema.mytable.column1), and, as full qualification, a database,
schema, table, and column (mydb.myschema.mytable.column1).
table
privilege
Specifies the name or OID of a table in the logical schema. If necessary,
specify the database and schema, as noted above.
l
SELECT Allows the user to SELECT from any column of the specified
table.
l
INSERT Allows the user to INSERT records into the specified table and
to use the COPY command to load the table.
l
UPDATE Allows the user to UPDATE records in the specified table.
l
DELETE Allows the user to delete a row from the specified table.
l
REFERENCES Allows the user to create a foreign key constraint
(privileges required on both the referencing and referenced tables).
Examples
SELECT HAS_TABLE_PRIVILEGE('store.store_dimension', 'SELECT');
HAS_TABLE_PRIVILEGE
--------------------t
(1 row)
SELECT HAS_TABLE_PRIVILEGE('release', 'store.store_dimension', 'INSERT');
HAS_TABLE_PRIVILEGE
--------------------t
(1 row)
SELECT HAS_TABLE_PRIVILEGE('store.store_dimension', 'UPDATE');
HAS_TABLE_PRIVILEGE
--------------------t
(1 row)
SELECT HAS_TABLE_PRIVILEGE('store.store_dimension', 'REFERENCES');
HAS_TABLE_PRIVILEGE
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--------------------t
(1 row)
SELECT HAS_TABLE_PRIVILEGE(45035996273711159, 45035996273711160, 'select');
HAS_TABLE_PRIVILEGE
--------------------t
(1 row)
SESSION_USER
Returns a VARCHAR containing the name of the user who initiated the current database session.
Behavior Type
Stable
Syntax
SESSION_USER()
Notes
l
The SESSION_USER function does not require parentheses.
l
SESSION_USER is equivalent to CURRENT_USER, USER, and USERNAME.
Examples
SELECT SESSION_USER();
session_user
-------------dbadmin
(1 row)
The following command returns the same results without the parentheses:
SELECT SESSION_USER;
session_user
-------------dbadmin
(1 row)
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USER
Returns a VARCHAR containing the name of the user who initiated the current database
connection.
Behavior Type
Stable
Syntax
USER()
Notes
l
The USER function does not require parentheses.
l
USER is equivalent to CURRENT_USER, SESSION_USER, and USERNAME.
Examples
SELECT USER();
current_user
-------------dbadmin
(1 row)
The following command returns the same results without the parentheses:
SELECT USER;
current_user
-------------dbadmin
(1 row)
USERNAME
Returns a VARCHAR containing the name of the user who initiated the current database
connection.
Behavior Type
Stable
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Syntax
USERNAME()
Notes
l
This function is useful for permission checking.
l
USERNAME is equivalent to CURRENT_USER, SESSION_USER and USER.
Examples
SELECT USERNAME();
username
-------------dbadmin
(1 row)
VERSION
Returns a VARCHAR containing an HP Vertica node's version information.
Behavior Type
Stable
Syntax
VERSION()
Examples
SELECT VERSION();
VERSION
-------------------------------------------------Vertica Analytic Database v4.0.12-20100513010203
(1 row)
The parentheses are required. If you omit them, the system returns an error:
SELECT VERSION;
ERROR: column "version" does not exist
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Timeseries Functions
Timeseries aggregate functions evaluate the values of a given set of variables over time and group
those values into a window for analysis and aggregation.
One output row is produced per time slice—or per partition per time slice—if partition expressions
are present.
See Also
l
TIMESERIES Clause
l
CONDITIONAL_CHANGE_EVENT [Analytic]
CONDITIONAL_TRUE_EVENT [Analytic]
l
l
TS_FIRST_VALUE
Processes the data that belongs to each time slice. A time series aggregate (TSA) function, TS_
FIRST_VALUE returns the value at the start of the time slice, where an interpolation scheme is
applied if the timeslice is missing, in which case the value is determined by the values
corresponding to the previous (and next) timeslices based on the interpolation scheme of const
(linear). There is one value per time slice per partition.
Behavior Type
Immutable
Syntax
TS_FIRST_VALUE ( expression [ IGNORE NULLS ]
... [, { 'CONST' | 'LINEAR' }
] )
Parameters
expression
Argument expression on which to aggregate and interpolate.
expression is data type INTEGER or FLOAT.
IGNORE NULLS
The IGNORE NULLS behavior changes depending on a CONST or LINEAR
interpolation scheme. See When Time Series Data Contains Nulls in the
Programmer's Guide for details.
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'CONST' | 'LINEAR'
(Default CONST) Optionally specifies the interpolation value as either
constant or linear.
l
CONST—New value are interpolated based on previous input records.
l
LINEAR—Values are interpolated in a linear slope based on the specified
time slice.
Notes
l
The function returns one output row per time slice or one output row per partition per time slice if
partition expressions are specified.
l
Multiple time series aggregate functions can exists in the same query. They share the same
gap-filling policy as defined by the TIMESERIES Clause; however, each time series aggregate
function can specify its own interpolation policy. For example:
SELECT slice_time, symbol,
TS_FIRST_VALUE(bid, 'const') fv_c,
TS_FIRST_VALUE(bid, 'linear') fv_l,
TS_LAST_VALUE(bid, 'const') lv_c
FROM TickStore
TIMESERIES slice_time AS '3 seconds'
OVER(PARTITION BY symbol ORDER BY ts);
You must use an ORDER BY clause with a TIMESTAMP column.
l
Example
For detailed examples, see Gap Filling and Interpolation in the Programmer's Guide.
See Also
TIMESERIES Clause
l
TS_LAST_VALUE
l
l
TS_LAST_VALUE
Processes the data that belongs to each time slice. A time series aggregate (TSA) function, TS_
LAST_VALUE returns the value at the end of the time slice, where an interpolation scheme is
applied if the timeslice is missing, in which case the value is determined by the values
corresponding to the previous (and next) timeslices based on the interpolation scheme of const
(linear). There is one value per time slice per partition.
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Behavior Type
Immutable
Syntax
TS_LAST_VALUE ( expression [ IGNORE NULLS ]
... [, { 'CONST' | 'LINEAR' }
] )
Parameters
expression
Argument expression on which to aggregate and interpolate.
expression is data type INTEGER or FLOAT.
IGNORE NULLS
The IGNORE NULLS behavior changes depending on a CONST or LINEAR
interpolation scheme. See When Time Series Data Contains Nulls in the
Programmer's Guide for details.
'CONST' | 'LINEAR'
(Default CONST) Optionally specifies the interpolation value as either
constant or linear.
l
CONST—New value are interpolated based on previous input records.
l
LINEAR—Values are interpolated in a linear slope based on the specified
time slice.
Notes
l
The function returns one output row per time slice or one output row per partition per time slice if
partition expressions are specified.
l
Multiple time series aggregate functions can exists in the same query. They share the same
gap-filling policy as defined by the TIMESERIES Clause; however, each time series aggregate
function can specify its own interpolation policy. For example:
SELECT slice_time, symbol,
TS_FIRST_VALUE(bid, 'const') fv_c,
TS_FIRST_VALUE(bid, 'linear') fv_l,
TS_LAST_VALUE(bid, 'const') lv_c
FROM TickStore
TIMESERIES slice_time AS 3 seconds
OVER(PARTITION BY symbol ORDER BY ts);
l
You must use the ORDER BY clause with a TIMESTAMP column.
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Example
For detailed examples, see Gap Filling and Interpolation in the Programmer's Guide.
See Also
TIMESERIES Clause
l
TS_FIRST_VALUE
l
l
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URI Encode/Decode Functions
The functions in this section follow the RFC 3986 standard for percent-encoding a Universal
Resource Identifier (URI).
URI_PERCENT_DECODE
Decodes a percent-encoded Universal Resource Identifier (URI) according to the RFC 3986
standard.
Syntax
URI_PERCENT_DECODE (expression)
Behavior Type
Immutable
Parameters
expression
(VARCHAR) is the string to convert.
Examples
The following example invokes uri_percent_decode on the Websites column of the URI table and
returns a decoded URI:
=> SELECT URI_PERCENT_DECODE(Websites) from URI;
URI_PERCENT_DECODE
----------------------------------------------http://www.faqs.org/rfcs/rfc3986.html x xj%a%
(1 row)
The following example returns the original URI in the Websites column and its decoded version:
=> SELECT Websites, URI_PERCENT_DECODE (Websites) from URI;
Websites
|
URI_PERCENT_DECODE
---------------------------------------------------+-------------------------------------------http://www.faqs.org/rfcs/rfc3986.html+x%20x%6a%a% | http://www.faqs.org/rfcs/rfc3986.htm
l x xj%a%
(1 row)
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URI_PERCENT_ENCODE
Encodes a Universal Resource Identifier (URI) according to the RFC 3986 standard for percent
encoding. In addition, for compatibility with older encoders this function converts '+' to space;
space is converted to %20 by uri_percent_encode.
Syntax
URI_PERCENT_ENCODE (expression)
Behavior Type
Immutable
Parameters
expression
(VARCHAR) is the string to convert.
Examples
The following example shows how the uri_percent_encode function is invoked on a the Websites
column of the URI table and returns an encoded URI:
=> SELECT URI_PERCENT_ENCODE(Websites) from URI;
URI_PERCENT_ENCODE
-----------------------------------------http%3A%2F%2Fexample.com%2F%3F%3D11%2F15
(1 row)
The following example returns the original URI in the Websites column and it's encoded form:
=> SELECT Websites, URI_PERCENT_ENCODE(Websites) from URI;
Websites
URI_PERCENT_ENCODE
----------------------------+-----------------------------------------http://example.com/?=11/15 | http%3A%2F%2Fexample.com%2F%3F%3D11%2F15
(1 row)
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HP Vertica Meta-Functions
HP Vertica built-in (meta) functions access the internal state of HP Vertica and are used in
SELECT queries with the function name and an argument (where required). These functions are not
part of the SQL standard and take the following form:
SELECT ();
Note: The query cannot contain other clauses, such as FROM or WHERE.
The behavior type of HP Vertica meta-functions is immutable.
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Alphabetical List of HP Vertica Meta-Functions
This section contains the HP Vertica meta-functions, listed alphabetically. These functions are also
grouped into their appropriate category.
ADD_LOCATION
Adds a storage location to the cluster. Use this function to add a new location, optionally with a
location label. You can also add a location specifically for user access, and then grant one or more
users access to the location.
Syntax
ADD_LOCATION ( 'path'
[, 'node' , 'usage', 'location_label' ] )
Parameters
path
[Required] Specifies where the storage location is mounted. Path must be an
empty directory with write permissions for user, group, or all.
node
[Optional] Indicates the cluster node on which a storage location resides. If you
omit this parameter, the function adds the location to only the initiator node.
Specifying the node parameter as an empty string ('') adds a storage location
to all cluster nodes in a single transaction.
Note: If you specify a node, you must also add a usage parameter.
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usage
[Optional] Specifies what the storage location will be used for:
l
DATA: Stores only data files. Use this option for labeled storage locations.
l
TEMP: Stores only temporary files, created during loads or queries.
l
DATA,TEMP: Stores both types of files in the location.
l
USER: Allows non-dbadmin users access to the storage location for data
files (not temp files), once they are granted privileges. DO NOT create a
storage location for later use in a storage policy. Storage locations with
policies must be for DATA usage. Also, note that this keyword is orthogonal
to DATA and TEMP, and does not specify a particular usage, other than
being accessible to non-dbadmin users with assigned privileges. You cannot
alter a storage location to or from USER usage.
NOTE: You can use this parameter only in conjunction with the node option. If
you omit the usage parameter, the default is DATA,TEMP.
location_label
[Optional] Specifies a location label as a string, for example, SSD. Labeling a
storage location lets you use the location label to create storage policies and as
part of a multi-tenanted storage scheme.
Privileges
Must be a superuser.
Storage Location Subdirectories
You cannot create a storage location in a subdirectory of an existing location. For example, if you
create a storage location at one location, you cannot add a second storage location in a
subdirectory of the first:
dbt=> select add_location ('/myvertica/Test/KMM','','DATA','SSD');
add_location
-----------------------------------------/myvertica/Test/KMM added.
(1 row)
dbt=> select add_location ('/myvertica/Test/KMM/SSD','','DATA','SSD');
ERROR 5615: Location [/myvertica/Test/KMM/SSD] conflicts with existing location [/myvert
ica/Test/KMM] on node v_node0001
ERROR 5615: Location [/myvertica/Test/KMM/SSD] conflicts with existing location [/myvert
ica/Test/KMM] on node v_node0002
ERROR 5615: Location [/myvertica/Test/KMM/SSD] conflicts with existing location [/myvert
ica/Test/KMM] on node v_node0003
Example
This example adds a location that stores data and temporary files on the initiator node:
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=> SELECT ADD_LOCATION('/secondverticaStorageLocation/');
This example adds a location to store data on v_vmartdb_node0004:
=> SELECT ADD_LOCATION('/secondverticaStorageLocation/' , 'v_vmartdb_node0004' , 'DATA');
This example adds a new DATA storage location with a label, SSD. The label identifies the location
when you create storage policies. Specifying the node parameter as an empty string adds the
storage location to all cluster nodes in a single transaction:
VMART=> select add_location ('home/dbadmin/SSD/schemas', '', 'DATA', 'SSD');
add_location
--------------------------------home/dbadmin/SSD/schemas added.
(1 row)
See Also
l
l
ALTER_LOCATION_USE
l
DROP_LOCATION
l
RESTORE_LOCATION
l
RETIRE_LOCATION
l
GRANT (Storage Location)
l
REVOKE (Storage Location)
ADVANCE_EPOCH
Manually closes the current epoch and begins a new epoch.
Syntax
ADVANCE_EPOCH ( [ integer ] )
Parameters
integer
Specifies the number of epochs to advance.
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Privileges
Must be a superuser.
Notes
This function is primarily maintained for backward compatibility with earlier versions of HP Vertica.
Example
The following command increments the epoch number by 1:
=> SELECT ADVANCE_EPOCH(1);
See Also
l
ALTER PROJECTION RENAME
ALTER_LOCATION_USE
Alters the type of files that can be stored at the specified storage location.
Syntax
ALTER_LOCATION_USE ( 'path' , [ 'node' ] , 'usage' )
Parameters
path
Specifies where the storage location is mounted.
node
[Optional] The HP Vertica node with the storage location. Specifying the node parameter
as an empty string ('') alters the location across all cluster nodes in a single transaction.
If you omit this parameter, node defaults to the initiator.
usage
Is one of the following:
l
DATA: The storage location stores only data files. This is the supported use for both a
USER storage location, and a labeled storage location.
l
TEMP: The location stores only temporary files that are created during loads or
queries.
l
DATA,TEMP: The location can store both types of files.
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Privileges
Must be a superuser.
USER Storage Location Restrictions
You cannot change a storage location from a USER usage type if you created the location that way,
or to a USER type if you did not. You can change a USER storage location to specify DATA
(storing TEMP files is not supported). However, doing so does not affect the primary objective of a
USER storage location, to be accessible by non-dbadmin users with assigned privileges.
Monitoring Storage Locations
Disk storage information that the database uses on each node is available in the V_
MONITOR.DISK_STORAGE system table.
Example
The following example alters the storage location across all cluster nodes to store only data:
=> SELECT ALTER_LOCATION_USE ('/thirdVerticaStorageLocation/' , '' , 'DATA');
See Also
l
l
ADD_LOCATION
l
DROP_LOCATION
l
RESTORE_LOCATION
l
RETIRE_LOCATION
l
GRANT (Storage Location)
l
REVOKE (Storage Location)
ALTER_LOCATION_LABEL
Alters the location label. Use this function to add, change, or remove a location label. You change a
location label only if it is not currently in use as part of a storage policy.
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You can use this function to remove a location label. However, you cannot remove a location label if
the name being removed is used in a storage policy, and the location from which you are removing
the label is the last available storage for its associated objects.
Note: If you label an existing storage location that already contains data, and then include the
labeled location in one or more storage policies, existing data could be moved. If the ATM
determines data stored on a labeled location does not comply with a storage policy, the ATM
moves the data elsewhere.
Syntax
ALTER_LOCATION_LABEL ( 'path' , 'node' , 'location_label' )
Parameters
path
Specifies the path of the storage location.
node
The HP Vertica node for the storage location.
If you enter node as an empty string (''), the function performs a cluster-wide
label change to all nodes. Any node that is unavailable generates an error.
location_label
Specifies a storage label as a string, for instance SSD. You can change an
existing label assigned to a storage location, or add a new label. Specifying an
empty string ('') removes an existing label.
Privileges
Must be a superuser.
Example
The following example alters (or adds) the label SSD to the storage location at the given path on all
cluster nodes:
VMART=> select alter_location_label('/home/dbadmin/SSD/tables','', 'SSD');
alter_location_label
--------------------------------------/home/dbadmin/SSD/tables label changed.
(1 row)
See Also
l
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l
CLEAR_OBJECT_STORAGE_POLICY
l
SET_OBJECT_STORAGE_POLICY
ANALYZE_CONSTRAINTS
Analyzes and reports on constraint violations within the current schema search path, or external to
that path if you specify a database name (noted in the syntax statement and parameter table).
You can check for constraint violations by passing arguments to the function as follows:
l
An empty argument (' '), which returns violations on all tables within the current schema
l
One argument, referencing a table
l
Two arguments, referencing a table name and a column or list of columns
Syntax
ANALYZE_CONSTRAINTS [ ( '' )
... | ( '[[db-name.]schema.]table [.column_name]' )
... | ( '[[db-name.]schema.]table' , 'column' ) ]
Parameters
('')
Analyzes and reports on all tables within the current schema search path.
[[db-name.]schema.]
[Optional] Specifies the database name and optional schema name. Using
a database name identifies objects that are not unique within the current
search path (see Setting Search Paths). You must be connected to the
database you specify, and you cannot change objects in other databases.
Specifying different database objects lets you qualify database objects as
explicitly as required. For example, you can use a database and a schema
name (mydb.myschema).
table
Analyzes and reports on all constraints referring to the specified table.
column
Analyzes and reports on all constraints referring to the specified table that
contains the column.
Privileges
l
SELECT privilege on table
l
USAGE privilege on schema
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Notes
ANALYZE_CONSTRAINTS() performs a lock in the same way that SELECT * FROM t1 holds a
lock on table t1. See LOCKS for additional information.
Detecting Constraint Violations During a Load Process
HP Vertica checks for constraint violations when queries are run, not when data is loaded. To
detect constraint violations as part of the load process, use a COPY statement with the NO
COMMIT option. By loading data without committing it, you can run a post-load check of your data
using the ANALYZE_CONSTRAINTS function. If the function finds constraint violations, you can
roll back the load because you have not committed it.
If ANALYZE_CONSTRAINTS finds violations, such as when you insert a duplicate value into a
primary key, you can correct errors using the following functions. Effects last until the end of the
session only:
l
SELECT DISABLE_DUPLICATE_KEY_ERROR
l
SELECT REENABLE_DUPLICATE_KEY_ERROR
Return Values
ANALYZE_CONSTRAINTS returns results in a structured set (see table below) that lists the
schema name, table name, column name, constraint name, constraint type, and the column values
that caused the violation.
If the result set is empty, then no constraint violations exist; for example:
> SELECT ANALYZE_CONSTRAINTS ('public.product_dimension', 'product_key');
Schema Name | Table Name | Column Names | Constraint Name | Constraint Type | Column Valu
es
-------------+------------+--------------+-----------------+-----------------+-------------(0 rows)
The following result set shows a primary key violation, along with the value that caused the
violation ('10'):
=> SELECT ANALYZE_CONSTRAINTS ('');
Schema Name | Table Name | Column Names | Constraint Name | Constraint Type | Column Valu
es
-------------+------------+--------------+-----------------+-----------------+-------------store
t1
c1
pk_t1
PRIMARY
('10')
(1 row)
The result set columns are described in further detail in the following table:
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Column Name Data Type
Description
Schema Name
VARCHAR
The name of the schema.
Table Name
VARCHAR
The name of the table, if specified.
Column Names
VARCHAR
Names of columns containing constraints. Multiple columns are
in a comma-separated list:
store_key, store_key, date_key,
Constraint Name
VARCHAR
The given name of the primary key, foreign key, unique, or not
null constraint, if specified.
Constraint Type
VARCHAR
Identified by one of the following strings: 'PRIMARY KEY',
'FOREIGN KEY', 'UNIQUE', or 'NOT NULL'.
Column Values
VARCHAR
Value of the constraint column, in the same order in which
Column Names contains the value of that column in the violating
row.
When interpreted as SQL, the value of this column forms a list of
values of the same type as the columns in Column Names; for
example:
('1'), ('1', 'z')
Understanding Function Failures
If ANALYZE_CONSTRAINTS() fails, HP Vertica returns an error identifying the failure condition,
such as if there are insufficient resources for the database to perform constraint checks.
If you specify the wrong table, the system returns an error message:
> SELECT ANALYZE_CONSTRAINTS('abc');
ERROR 2069: 'abc' is not a table in the current search_path
If you run the function on a table that has no constraints declared (even if duplicates are present),
the system returns an error message:
> SELECT ANALYZE_CONSTRAINTS('source');
ERROR 4072: No constraints defined
If you run the function with incorrect syntax, the system returns an error message with a hint; for
example, if you run one of the following:
l
ANALYZE ALL CONSTRAINT;
l
ANALYZE CONSTRAINT abc;
The system returns an informative error with hint:
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ERROR: ANALYZE CONSTRAINT is not supported.
HINT: You may consider using analyze_constraints().
If you run ANALYZE_CONSTRAINTS from a non-default locale, the function returns an error with a
hint:
> \locale LENINFO 2567: Canonical locale: 'en'
Standard collation: 'LEN'
English
> SELECT ANALYZE_CONSTRAINTS('t1');
ERROR: ANALYZE_CONSTRAINTS is currently not supported in
non-default locales
HINT: Set the locale in this session to
en_US@collation=binary using the command
"\locale en_US@collation=binary"
Examples
Given the following inputs, HP Vertica returns one row, indicating one violation, because the same
primary key value (10) was inserted into table t1 twice:
CREATE TABLE t1(c1 INT);
ALTER TABLE t1 ADD CONSTRAINT pk_t1 PRIMARY KEY (c1);
CREATE PROJECTION t1_p (c1) AS SELECT *
FROM t1 UNSEGMENTED ALL NODES;
INSERT INTO t1 values (10);
INSERT INTO t1 values (10); --Duplicate primary key value
\x
Expanded display is on.
SELECT ANALYZE_CONSTRAINTS('t1');
-[ RECORD 1 ]---+-------Schema Name
| public
Table Name
| t1
Column Names
| c1
Constraint Name | pk_t1
Constraint Type | PRIMARY
Column Values
| ('10')
If the second INSERT statement above had contained any different value, the result would have
been 0 rows (no violations).
In the following example, create a table that contains three integer columns, one a unique key and
one a primary key:
CREATE TABLE table_1(
a INTEGER,
b_UK INTEGER UNIQUE,
c_PK INTEGER PRIMARY KEY
);
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Issue a command that refers to a nonexistent table and column:
SELECT ANALYZE_CONSTRAINTS('a_BB');
ERROR: 'a_BB' is not a table name in the current search path
Issue a command that refers to a nonexistent column:
SELECT ANALYZE_CONSTRAINTS('table_1','x');
ERROR 41614: Nonexistent columns: 'x '
Insert some values into table table_1 and commit the changes:
INSERT INTO table_1 values (1, 1, 1);
COMMIT;
Run ANALYZE_CONSTRAINTS on table table_1. No constraint violations are reported:
SELECT ANALYZE_CONSTRAINTS('table_1');
(No rows)
Insert duplicate unique and primary key values and run ANALYZE_CONSTRAINTS on table
table_1 again. The system shows two violations: one against the primary key and one against the
unique key:
INSERT INTO table_1 VALUES (1, 1, 1);
COMMIT;
SELECT ANALYZE_CONSTRAINTS('table_1');
-[ RECORD 1 ]---+---------Schema Name
| public
Table Name
| table_1
Column Names
| b_UK
Constraint Name | C_UNIQUE
Constraint Type | UNIQUE
Column Values
| ('1')
-[ RECORD 2 ]---+---------Schema Name
| public
Table Name
| table_1
Column Names
| c_PK
Constraint Name | C_PRIMARY
Constraint Type | PRIMARY
Column Values
| ('1')
The following command looks for constraint validations on only the unique key in the table table_1,
qualified with its schema name:
=> SELECT ANALYZE_CONSTRAINTS('public.table_1', 'b_UK');
-[ RECORD 1 ]---+--------Schema Name
| public
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Table Name
Column Names
Constraint Name
Constraint Type
Column Values
|
|
|
|
|
table_1
b_UK
C_UNIQUE
UNIQUE
('1')
(1 row)
The following example shows that you can specify the same column more than once; ANALYZE_
CONSTRAINTS, however, returns the violation only once:
SELECT ANALYZE_CONSTRAINTS('table_1', 'c_PK, C_PK');
-[ RECORD 1 ]---+---------Schema Name
| public
Table Name
| table_1
Column Names
| c_PK
Constraint Name | C_PRIMARY
Constraint Type | PRIMARY
Column Values
| ('1')
The following example creates a new table, table_2, and inserts a foreign key and different
(character) data types:
CREATE TABLE table_2 (
x VARCHAR(3),
y_PK VARCHAR(4),
z_FK INTEGER REFERENCES table_1(c_PK));
Alter the table to create a multicolumn unique key and multicolumn foreign key and create
superprojections:
ALTER TABLE table_2
ADD CONSTRAINT table_2_multiuk PRIMARY KEY (x, y_PK);
WARNING 2623: Column "x" definition changed to NOT NULL
WARNING 2623: Column "y_PK" definition changed to NOT NULL
The following command inserts a missing foreign key (0) into table dim_1 and commits the
changes:
INSERT INTO table_2 VALUES ('r1', 'Xpk1', 0);
COMMIT;
Checking for constraints on the table table_2 in the public schema detects a foreign key
violation:
=> SELECT ANALYZE_CONSTRAINTS('public.table_2');
-[ RECORD 1 ]---+---------Schema Name
| public
Table Name
| table_2
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Column Names
Constraint Name
Constraint Type
Column Values
|
|
|
|
z_FK
C_FOREIGN
FOREIGN
('0')
Now add a duplicate value into the unique key and commit the changes:
INSERT INTO table_2 VALUES ('r2', 'Xpk1', 1);
INSERT INTO table_2 VALUES ('r1', 'Xpk1', 1);
COMMIT;
Checking for constraint violations on table table_2 detects the duplicate unique key error:
SELECT ANALYZE_CONSTRAINTS('table_2');
-[ RECORD 1 ]---+---------------Schema Name
| public
Table Name
| table_2
Column Names
| z_FK
Constraint Name | C_FOREIGN
Constraint Type | FOREIGN
Column Values
| ('0')
-[ RECORD 2 ]---+---------------Schema Name
| public
Table Name
| table_2
Column Names
| x, y_PK
Constraint Name | table_2_multiuk
Constraint Type | PRIMARY
Column Values
| ('r1', 'Xpk1')
Create a table with multicolumn foreign key and create the superprojections:
CREATE TABLE table_3(
z_fk1 VARCHAR(3),
z_fk2 VARCHAR(4));
ALTER TABLE table_3
ADD CONSTRAINT table_3_multifk FOREIGN KEY (z_fk1, z_fk2)
REFERENCES table_2(x, y_PK);
Insert a foreign key that matches a foreign key in table table_2 and commit the changes:
INSERT INTO table_3 VALUES ('r1', 'Xpk1');
COMMIT;
Checking for constraints on table table_3 detects no violations:
SELECT ANALYZE_CONSTRAINTS('table_3');
(No rows)
Add a value that does not match and commit the change:
INSERT INTO table_3 VALUES ('r1', 'NONE');
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COMMIT;
Checking for constraints on table dim_2 detects a foreign key violation:
SELECT ANALYZE_CONSTRAINTS('table_3');
-[ RECORD 1 ]---+---------------Schema Name
| public
Table Name
| table_3
Column Names
| z_fk1, z_fk2
Constraint Name | table_3_multifk
Constraint Type | FOREIGN
Column Values
| ('r1', 'NONE')
Analyze all constraints on all tables:
SELECT ANALYZE_CONSTRAINTS('');
-[ RECORD 1 ]---+---------------Schema Name
| public
Table Name
| table_3
Column Names
| z_fk1, z_fk2
Constraint Name | table_3_multifk
Constraint Type | FOREIGN
Column Values
| ('r1', 'NONE')
-[ RECORD 2 ]---+---------------Schema Name
| public
Table Name
| table_2
Column Names
| x, y_PK
Constraint Name | table_2_multiuk
Constraint Type | PRIMARY
Column Values
| ('r1', 'Xpk1')
-[ RECORD 3 ]---+---------------Schema Name
| public
Table Name
| table_2
Column Names
| z_FK
Constraint Name | C_FOREIGN
Constraint Type | FOREIGN
Column Values
| ('0')
-[ RECORD 4 ]---+---------------Schema Name
| public
Table Name
| t1
Column Names
| c1
Constraint Name | pk_t1
Constraint Type | PRIMARY
Column Values
| ('10')
-[ RECORD 5 ]---+---------------Schema Name
| public
Table Name
| table_1
Column Names
| b_UK
Constraint Name | C_UNIQUE
Constraint Type | UNIQUE
Column Values
| ('1')
-[ RECORD 6 ]---+---------------Schema Name
| public
Table Name
| table_1
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Column Names
| c_PK
Constraint Name | C_PRIMARY
Constraint Type | PRIMARY
Column Values
| ('1')
-[ RECORD 7 ]---+---------------Schema Name
| public
Table Name
| target
Column Names
| a
Constraint Name | C_PRIMARY
Constraint Type | PRIMARY
Column Values
| ('1')
(5 rows)
To quickly clean up your database, issue the following command:
DROP TABLE table_1 CASCADE;
DROP TABLE table_2 CASCADE;
DROP TABLE table_3 CASCADE;
To learn how to remove violating rows, see the DISABLE_DUPLICATE_KEY_ERROR function.
ANALYZE_CORRELATIONS
Analyzes the specified tables for columns that are strongly correlated. In addition, ANALYZE_
CORRELATIONS also collects statistics.
For example, state name and country name columns are strongly correlated because the city name
usually, but perhaps not always, identifies the state name. The city of Conshohoken is uniquely
associated with Pennsylvania, whereas the city of Boston exists in Georgia, Indiana, Kentucky,
New York, Virginia, and Massachusetts. In this case, city name is strongly correlated with state
name.
For Database Designer to take advantage of these correlations, run Database Designer
programmatically. Use DESIGNER_SET_ANALYZE_CORRELATIONS_MODE to specify that
Database Designer should consider existing column correlations. Make sure to specify that
Database Designer not analyze statistics so that Database Designer does not override the existing
statistics.
Behavior Type
Immutable
Syntax
ANALYZE_CORRELATIONS (
'[database_name.][schema_name.]table_name',
[recalculate] )
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Parameters
database_name
Specifies the table(s) for which to analyze correlated columns, type VARCHAR.
schema_name
table_name
recalculate
Specifies to analyze the correlated columns, even if they have been analyzed
before, type BOOLEAN. Default: 'false'.
Permissions
l
To run ANALYZE_CORRELATIONS on a table, you must be a superuser, or a user w USAGE
privilege on the design schema.
Notes
l
Column correlation analysis typically needs to be done only once.
l
Currently, ANALYZE_CORRELATIONS can analyze only pairwise single-column correlations.
l
Projections do not change based on the analysis results. To implement the results of
ANALYZE_CORRELATIONS, you must run Database Designer.
Example
In the following example, ANALYZE_CORRELATIONS analyzes column correlations for all tables
in the public schema,even if they currently exist. The correlations that ANALYZE_
CORRELATIONS finds are saved so that Database Designer can use them the next time it runs on
the VMart database:
=> SELECT ANALYZE_CORRELATIONS (
'public.*',
'true');
ANALYZE_CORRELATIONS
---------------------0
(1 row)
See Also
l
DESIGNER_SET_ANALYZE_CORRELATIONS_MODE
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ANALYZE_HISTOGRAM
Collects and aggregates data samples and storage information from all nodes that store projections
associated with the specified table or column.
If the function returns successfully (0), HP Vertica writes the returned statistics to the catalog. The
query optimizer uses this collected data to recommend the best possible plan to execute a query.
Without analyzing table statistics, the query optimizer would assume uniform distribution of data
values and equal storage usage for all projections.
ANALYZE_HISTOGRAM is a DDL operation that auto-commits the current transaction, if any. The
ANALYZE_HISTOGRAM function reads a variable amount of disk contents to aggregate sample
data for statistical analysis. Use the function's percent float parameter to specify the total disk
space from which HP Vertica collects sample data. The ANALYZE_STATISTICS function returns
similar data, but uses a fixed disk space amount (10 percent). Analyzing more than 10 percent disk
space takes proportionally longer to process, but produces a higher level of sampling accuracy.
ANALYZE_HISTOGRAM is supported on local temporary tables, but not on global temporary
tables.
Syntax
ANALYZE_HISTOGRAM ('') ... | ( '[ [ db-name.]schema.]table [.column-name ]' [, percent ] )
Return Value
0 - For success. If an error occurs, refer to vertica.log for details.
Parameters
''
Empty string. Collects statistics for all tables.
[[db-name.]schema.]
[Optional] Specifies the schema name. Using a schema identifies objects
that are not unique within the current search path (see Setting Schema
Search Paths).
You can optionally precede a schema with a database name, but you must
be connected to the database you specify. You cannot make changes to
objects in other databases.
The ability to specify different database objects (from database and
schemas to tables and columns) lets you qualify database objects as
explicitly as required. For example, use a table and column
(mytable.column1), a schema, table, and column
(myschema.mytable.column1), and, as full qualification, a database,
schema, table, and column (mydb.myschema.mytable.column1).
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table
Specifies the name of the table and collects statistics for all projections of
that table. If you are using more than one schema, specify the schema that
contains the projection, as noted in the [[db-name.]schema.] entry.
[.column-name]
[Optional] Specifies the name of a single column, typically a predicate
column. Using this option with a table specification lets you collect
statistics for only that column.
Note: If you alter a table to add or drop a column, or add a new column
to a table and populate its contents with either default or other values,
HP Vertica recommends calling this function on the new table column
to get the most current statistics.
percent
[Optional] Specifies what percentage of data to read from disk (not the
amount of data to analyze). Specify a float from 1 – 100, such as 33.3. By
default, the function reads 10% of the table data from disk.
For more information, see Collecting Statistics in the Administrator's
Guide.
Privileges
l
Any INSERT/UPDATE/DELETE privilege on table
l
USAGE privilege on schema that contains the table
Use the HP Vertica statistics functions as follows:
Use this
function...
ANALYZE_
STATISTICS
To obtain...
A fixed-size statistical data sampling (10 percent per disk). This function returns
results quickly, but is less accurate than using ANALYZE_HISTOGRAM to get
a larger sampling of disk data.
ANALYZE_
A specified percentage of disk data sampling (from 1–100). If you analyze more
HISTOGRAM than 10 percent data per disk, this function is more accurate than ANALYZE_
STATISTICS, but requires proportionately longer to return statistics.
Analyzing Results
To retrieve hints about under-performing queries and the associated root causes, use the
ANALYZE_WORKLOAD function. This function runs the Workload Analyzer and returns tuning
recommendations, such as "run analyze_statistics on schema.table.column". You or your
database administrator should act upon the tuning recommendations.
You can also find database tuning recommendations on the Management Console.
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Canceling ANALYZE_HISTOGRAM
You can cancel this function mid-analysis by issuing CTRL-C in a vsql shell or by invoking the
INTERRUPT_STATEMENT() function.
Notes
By default, HP Vertica analyzes more than one column (subject to resource limits) in a single-query
execution plan to:
l
Reduce plan execution latency
l
Help speed up analysis of relatively small tables that have a large number of columns
Examples
In this example, the ANALYZE_STATISTICS() function reads 10 percent of the disk data. This is
the static default value for this function. The function returns 0 for success:
=> SELECT ANALYZE_STATISTICS('shipping_dimension.shipping_key');
ANALYZE_STATISTICS
-------------------0
(1 row)
This example uses ANALYZE_HISTOGRAM () without specifying a percentage value. Since this
function has a default value of 10 percent, it returns the identical data as the ANALYZE_
STATISTICS() function, and returns 0 for success:
=> SELECT ANALYZE_HISTOGRAM('shipping_dimension.shipping_key');
ANALYZE_HISTOGRAM
------------------0
(1 row)
This example uses ANALYZE_HISTOGRAM (), specifying its percent parameter as 100, indicating
it will read the entire disk to gather data. After the function performs a full column scan, it returns 0
for success:
=> SELECT ANALYZE_HISTOGRAM('shipping_dimension.shipping_key', 100);
ANALYZE_HISTOGRAM
------------------0
(1 row)
In this command, only 0.1% (1/1000) of the disk is read:
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=> SELECT ANALYZE_HISTOGRAM('shipping_dimension.shipping_key', 0.1);
ANALYZE_HISTOGRAM
------------------0
(1 row)
See Also
l
ANALYZE_STATISTICS
l
ANALYZE_WORKLOAD
l
DROP_STATISTICS
l
EXPORT_STATISTICS
l
IMPORT_STATISTICS
INTERRUPT_STATEMENT
l
l
ANALYZE_STATISTICS
Collects and aggregates data samples and storage information from all nodes that store projections
associated with the specified table or column.
If the function returns successfully (0), HP Vertica writes the returned statistics to the catalog. The
query optimizer uses this collected data to recommend the best possible plan to execute a query.
Without analyzing table statistics, the query optimizer would assume uniform distribution of data
values and equal storage usage for all projections.
ANALYZE_STATISTICS is a DDL operation that auto-commits the current transaction, if any. The
ANALYZE_STATISTICS function reads a fixed, 10 percent of disk contents to aggregate sample
data for statistical analysis. To obtain a larger (or smaller) data sampling, use the ANALYZE_
HISTOGRAM function, which lets you specify the percent of disk to read. Analyzing more that 10
percent disk space takes proportionally longer to process, but results in a higher level of sampling
accuracy. ANALYZE_STATISTICS is supported on local temporary tables, but not on global
temporary tables.
Syntax
ANALYZE_STATISTICS [ ('') ... | ( '[ [ db-name.]schema.]table [.column-name ]' ) ]
Return value
0 - For success.
If an error occurs, refer to vertica.log for details.
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Parameters
''
Empty string. Collects statistics for all tables.
[[db-name.]schema.]
[Optional] Specifies the schema name. Using a schema identifies objects
that are not unique within the current search path (see Setting Schema
Search Paths).
You can optionally precede a schema with a database name, but you must
be connected to the database you specify. You cannot make changes to
objects in other databases.
The ability to specify different database objects (from database and
schemas to tables and columns) lets you qualify database objects as
explicitly as required. For example, use a table and column
(mytable.column1), a schema, table, and column
(myschema.mytable.column1), and, as full qualification, a database,
schema, table, and column (mydb.myschema.mytable.column1).
table
Specifies the name of the table and collects statistics for all projections of
that table.
Note: If you are using more than one schema, specify the schema
that contains the projection, as noted as noted in the [[db-name.]
schema.] entry.
[.column-name]
[Optional] Specifies the name of a single column, typically a predicate
column. Using this option with a table specification lets you collect
statistics for only that column.
Note: If you alter a table to add or drop a column, or add a new column
to a table and populate its contents with either default or other values,
HP Vertica recommends calling this function on the new table column
to get the most current statistics.
Privileges
l
Any INSERT/UPDATE/DELETE privilege on table
l
USAGE privilege on schema that contains the table
Use the HP Vertica statistics functions as follows:
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Use this
function...
ANALYZE_
STATISTICS
To obtain...
A fixed-size statistical data sampling (10 percent per disk). This function returns
results quickly, but is less accurate than using ANALYZE_HISTOGRAM to get
a larger sampling of disk data.
ANALYZE_
A specified percentage of disk data sampling (from 1–100). If you analyze more
HISTOGRAM than 10 percent data per disk, this function is more accurate than ANALYZE_
STATISTICS, but requires proportionately longer to return statistics.
Analyzing results
To retrieve hints about under-performing queries and the associated root causes, use the
ANALYZE_WORKLOAD function. This function runs the Workload Analyzer and returns tuning
recommendations, such as "run analyze_statistics on schema.table.column". You or your
database administrator should act upon the tuning recommendations.
You can also find database tuning recommendations on the Management Console.
Canceling this function
You can cancel statistics analysis by issuing CTRL+C in a vsql shell or by invoking the
INTERRUPT_STATEMENT() function.
Notes
l
Always run ANALYZE_STATISTICS on a table or column rather than a projection.
l
By default, HP Vertica analyzes more than one column (subject to resource limits) in a singlequery execution plan to:
l
n
Reduce plan execution latency
n
Help speed up analysis of relatively small tables that have a large number of columns
Pre-join projection statistics are updated on any pre-joined tables.
Examples
Computes statistics on all projections in the VMart database and returns 0 (success):
=> SELECT ANALYZE_STATISTICS ('');
analyze_statistics
-------------------0
(1 row)
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Computes statistics on a single table (shipping_dimension) and returns 0 (success):
=> SELECT ANALYZE_STATISTICS ('shipping_dimension');
analyze_statistics
-------------------0
(1 row)
Computes statistics on a single column (shipping_key) across all projections for the shipping_
dimension table and returns 0 (success):
=> SELECT ANALYZE_STATISTICS('shipping_dimension.shipping_key');
analyze_statistics
-------------------0
(1 row)
For use cases, see Collecting Statistics in the Administrator's Guide
See Also
l
ANALYZE_HISTOGRAM
l
ANALYZE_WORKLOAD
l
DROP_STATISTICS
l
EXPORT_STATISTICS
l
IMPORT_STATISTICS
l
INTERRUPT_STATEMENT
ANALYZE_WORKLOAD
Runs the Workload Analyzer (WLA), a utility that analyzes system information held in system
tables.
The Workload Analyzer intelligently monitors the performance of SQL queries and workload history,
resources, and configurations to identify the root causes for poor query performance. Calling the
ANALYZE_WORKLOAD function returns tuning recommendations for all events within the scope
and time that you specify.
Tuning recommendations are based on a combination of statistics, system and data collector
events, and database-table-projection design. WLA's recommendations let database
administrators quickly and easily tune query performance without needing sophisticated skills.
See Understanding WLA Triggering Conditions in the Administrator's Guide for the most common
triggering conditions and recommendations.
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Syntax 1
ANALYZE_WORKLOAD ( 'scope' , 'since_time' );
Syntax 2
ANALYZE_WORKLOAD ( 'scope' , [ true ] );
Parameters
scope
Specifies which HP Vertica catalog objects to analyze.
Can be one of:
sinc
e_tim
e
l
An empty string ('') returns recommendations for all database objects
l
'table_name' returns all recommendations related to the specified table
l
'schema_name' returns recommendations on all database objects in the specified
schema
Limits the recommendations from all events that you specified in 'scope' since the
specified time in this argument, up to the current system status. If you omit the since_
time parameter, ANALYZE_WORKLOAD returns recommendations on events since the
last recorded time that you called this function.
Note: You must explicitly cast strings that you use for the since_time parameter to
TIMESTAMP or TIMESTAMPTZ. For example:
SELECT ANALYZE_WORKLOAD('T1', '2010-10-04 11:18:15'::TIMESTAMPTZ);SELECT ANALYZE_WO
RKLOAD('T1', TIMESTAMPTZ '2010-10-04 11:18:15');
true
[Optional] Tells HP Vertica to record this particular call of WORKLOAD_ANALYZER() in
the system. The default value is false (do not record). If recorded, subsequent calls to
ANALYZE_WORKLOAD analyze only the events that have occurred since this recorded
time, ignoring all prior events.
Return Value
Column
Data type
Description
observation_coun
t
INTEGER
Integer for the total number of events observed for this tuning
recommendation. For example, if you see a return value of 1,
WLA is making its first tuning recommendation for the event
in 'scope'.
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Column
Data type
Description
first_observatio
n_time
TIMESTAM
PTZ
Timestamp when the event first occurred. If this column
returns a null value, the tuning recommendation is from the
current status of the system instead of from any prior event.
last_observatio
n_time
TIMESTAM
PTZ
Timestamp when the event last occurred. If this column
returns a null value, the tuning recommendation is from the
current status of the system instead of from any prior event.
tuning_parameter
VARCHAR
Objects on which you should perform a tuning action. For
example, a return value of:
tuning_descripti
on
VARCHAR
HP Vertica Analytic Database (7.0.x)
l
public.t informs the DBA to run Database Designer on
table t in the public schema
l
bsmith notifies a DBA to set a password for user bsmith
Textual description of the tuning recommendation from the
Workload Analyzer to perform on the tuning_parameter
object. Examples of some of the returned values include, but
are not limited to:
l
Run database designer on table schema.table
l
Create replicated projection for table schema.table
l
Consider incremental design on query
l
Reset configuration parameter with SELECT set_config_
parameter('parameter', 'new_value')
l
Re-segment projection projection-name on highcardinality column(s)
l
Drop the projection projection-name
l
Alter a table's partition expression
l
Reorganize data in partitioned table
l
Decrease the MoveOutInterval configuration parameter
setting
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Column
Data type
Description
tuning_command
VARCHAR
Command string if tuning action is a SQL command. For
example, the following example statements recommend that
the DBA:
Update statistics on a particular schema's table.column:
SELECT ANALYZE_STATISTICS('public.table.column');
Resolve mismatched configuration parameter 'LockTimeout':
SELECT * FROM CONFIGURATION_PARAMETERSWHERE parameter_name
= 'LockTimeout';
Set the password for user bsmith:
ALTER USER (user) IDENTIFIED BY ('new_password');
tuning_cost
VARCHAR
Cost is based on the type of tuning recommendation and is
one of:
l
LOW—minimal impact on resources from running the
tuning command
l
MEDIUM—moderate impact on resources from running
the tuning command
l
HIGH—maximum impact on resources from running the
tuning command
Depending on the size of your database or table, consider
running high-cost operations after hours instead of during
peak load times.
ANALYZE_WORKLOAD() returns aggregated tuning recommendations, as described in the
TUNING_RECOMMENDATIONS table.
Privileges
Must be a superuser.
Examples
See Analyzing Workloads through an API in the Administrator's Guide for examples.
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See Also
TUNING_RECOMMENDATIONS
l
l
l
AUDIT
Estimates the raw data size of a database, a schema, a projection, or a table as it is counted in an
audit of the database size.
The AUDIT function estimates the size using the same data sampling method as the audit that HP
Vertica performs to determine if a database is compliant with the database size allowances in its
license. The results of this function are not considered when HP Vertica determines whether the
size of the database complies with the HP Vertica license's data allowance. See How HP Vertica
Calculates Database Size in the Administrator's Guide for details.
Note: This function can only audit the size of tables, projections, schemas, and databases
which the user has permission to access. If a non-superuser attempts to audit the entire
database, the audit will only estimate the size of the data that the user is allowed to read.
Syntax
AUDIT([name] [, granularity] [, error_tolerance [, confidence_level]])
Parameters
name
Specifies the schema, projection, or table to audit. Enter name as a string, in
single quotes (''). If the name string is empty (''), the entire database is
audited.
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granularity
Indicates the level at which the audit reports its results. The recognized levels
are:
l
'schema'
l
'table'
l
'projection'
By default, the granularity is the same level as name. For example, if name is a
schema, then the size of the entire schema is reported. If you instead specify
'table' as the granularity, AUDIT reports the size of each table in the
schema. The granularity must be finer than that of object: specifying
'schema' for an audit of a table has no effect.
The results of an audit with a granularity are reported in the V_
CATALOG.USER_AUDITS system table.
error_tolerance
Specifies the percentage margin of error allowed in the audit estimate. Enter
the tolerance value as a decimal number, between 0 and 100. The default
value is 5, for a 5% margin of error.
Note: The lower this value is, the more resources the audit uses since it will
perform more data sampling. Setting this value to 0 results in a full audit of the
database, which is very resource intensive, as all of the data in the database
is analyzed. Doing a full audit of the database significantly impacts
performance and is not recommended on a production database.
confidence_level
Specifies the statistical confidence level percentage of the estimate. Enter
the confidence value as a decimal number, between 0 and 100. The default
value is 99, indicating a confidence level of 99%.
Note: The higher the confidence value, the more resources the function
uses since it will perform more data sampling. Setting this value to 1
results in a full audit of the database, which is very resource intensive, as
all of the database is analyzed. Doing a full audit of the database
significantly impacts performance and is not recommended on a
production database.
Permissions
l
SELECT privilege on table
l
USAGE privilege on schema
Note: AUDIT() works only on the tables where the user calling the function has SELECT
permissions.
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Notes
Due to the iterative sampling used in the auditing process, making the error tolerance a small
fraction of a percent (0.00001, for example) can cause the AUDIT function to run for a longer period
than a full database audit.
Examples
To audit the entire database:
=> SELECT AUDIT('');
AUDIT
---------76376696
(1 row)
To audit the database with a 25% error tolerance:
=> SELECT AUDIT('',25);
AUDIT
---------75797126
(1 row)
To audit the database with a 25% level of tolerance and a 90% confidence level:
=> SELECT AUDIT('',25,90);
AUDIT
---------76402672
(1 row)
To audit just the online_sales schema in the VMart example database:
VMart=> SELECT AUDIT('online_sales');
AUDIT
---------35716504
(1 row)
To audit the online_sales schema and report the results by table:
=> SELECT AUDIT('online_sales','table');
AUDIT
-----------------------------------------------------------------See table sizes in v_catalog.user_audits for schema
online_sales
(1 row)
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=> \x
Expanded display is on.
=> SELECT * FROM user_audits WHERE object_schema =
'online_sales';
-[ RECORD 1 ]-------------------------+-----------------------------size_bytes
| 64960
user_id
| 45035996273704962
user_name
| dbadmin
object_id
| 45035996273717636
object_type
| TABLE
object_schema
| online_sales
object_name
| online_page_dimension
audit_start_timestamp
| 2011-04-05 09:24:48.224081-04
audit_end_timestamp
| 2011-04-05 09:24:48.337551-04
confidence_level_percent
| 99
error_tolerance_percent
| 5
used_sampling
| f
confidence_interval_lower_bound_bytes | 64960
confidence_interval_upper_bound_bytes | 64960
sample_count
| 0
cell_count
| 0
-[ RECORD 2 ]-------------------------+-----------------------------size_bytes
| 20197
user_id
| 45035996273704962
user_name
| dbadmin
object_id
| 45035996273717640
object_type
| TABLE
object_schema
| online_sales
object_name
| call_center_dimension
audit_start_timestamp
| 2011-04-05 09:24:48.340206-04
audit_end_timestamp
| 2011-04-05 09:24:48.365915-04
confidence_level_percent
| 99
error_tolerance_percent
| 5
used_sampling
| f
confidence_interval_lower_bound_bytes | 20197
confidence_interval_upper_bound_bytes | 20197
sample_count
| 0
cell_count
| 0
-[ RECORD 3 ]-------------------------+-----------------------------size_bytes
| 35614800
user_id
| 45035996273704962
user_name
| dbadmin
object_id
| 45035996273717644
object_type
| TABLE
object_schema
| online_sales
object_name
| online_sales_fact
audit_start_timestamp
| 2011-04-05 09:24:48.368575-04
audit_end_timestamp
| 2011-04-05 09:24:48.379307-04
confidence_level_percent
| 99
error_tolerance_percent
| 5
used_sampling
| t
confidence_interval_lower_bound_bytes | 34692956
confidence_interval_upper_bound_bytes | 36536644
sample_count
| 10000
cell_count
| 9000000
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AUDIT_FLEX
Estimates the ROS size of one or more flexible tables contained in a database, schema, or
projection. Use this function for flex tables only. Invoking audit_flex() with a columnar table
results in an error.
The audit_flex() function measures encoded, compressed data stored in ROS containers for the
__raw__ column of one or more flexible tables. The function does not audit other flex table columns
that are created as, or promoted to, real columns. Temporary flex tables are not included in the
audit.
Each time a user calls audit_flex(), HP Vertica stores the results in the V_CATALOG.USER_
AUDITS system table.
Syntax
AUDIT_FLEX (name)
Parameters
name
Specifies what database entity to audit. Enter the entity name as a string in single quotes
(''), as follows:
l
Empty string ('') — Return the size of the ROS containers for all flexible tables in the
database. You cannot enter the database name.
l
Schema name ('schema_name') — Return the size of all __raw__ columns of flexible
tables in schema_name.
l
A projection name ('proj_name') — Return the ROS size of a projection for a __raw__
column.
l
A flex table name ('flex_table_name') — Return the ROS size of a flex table's __
raw__ column.
Permissions
l
SELECT privilege on table
l
USAGE privilege on schema
Note: AUDIT_FLEX() works only on the flexible tables, projections, schemas, and databases
to which the user has permissions.
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Examples
To audit the flex tables in the database:
dbs=> select audit_flex('');
audit_flex
-----------8567679
(1 row)
To audit the flex tables in a specific schema, such as public:
dbs=> select audit_flex('public');
audit_flex
-----------8567679
(1 row)
To audit the flex tables in a specific projection, such as bakery_b0:
dbs=> select audit_flex('bakery_b0');
audit_flex
-----------8566723
(1 row)
To audit a flex table, such as bakery:
dbs=> select audit_flex('bakery');
audit_flex
-----------8566723
(1 row)
To report the results of all audits saved in the USER_AUDITS, the following shows part of an
extended display from the system table showing an audit run on a schema called test, and the
entire database, dbs:
dbs=> \x
Expanded display is on.
dbs=> select * from user_audits;
-[ RECORD 1 ]-------------------------+-----------------------------size_bytes
| 0
user_id
| 45035996273704962
user_name
| release
object_id
| 45035996273736664
object_type
| SCHEMA
object_schema
|
object_name
| test
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audit_start_timestamp
| 2014-02-04 14:52:15.126592-05
audit_end_timestamp
| 2014-02-04 14:52:15.139475-05
confidence_level_percent
| 99
error_tolerance_percent
| 5
used_sampling
| f
confidence_interval_lower_bound_bytes | 0
confidence_interval_upper_bound_bytes | 0
sample_count
| 0
cell_count
| 0
-[ RECORD 2 ]-------------------------+-----------------------------size_bytes
| 38051
user_id
| 45035996273704962
user_name
| release
object_id
| 45035996273704974
object_type
| DATABASE
object_schema
|
object_name
| dbs
audit_start_timestamp
| 2014-02-05 13:44:41.11926-05
audit_end_timestamp
| 2014-02-05 13:44:41.227035-05
confidence_level_percent
| 99
error_tolerance_percent
| 5
used_sampling
| f
confidence_interval_lower_bound_bytes | 38051
confidence_interval_upper_bound_bytes | 38051
sample_count
| 0
cell_count
| 0
-[ RECORD 3 ]-------------------------+-----------------------------.
.
.
AUDIT_LICENSE_SIZE
Triggers an immediate audit of the database size to determine if it is in compliance with the raw data
storage allowance included in your HP Vertica license. The audit is performed in the background, so
this function call returns immediately. To see the results of the audit when it is done, use the GET_
COMPLIANCE_STATUS function.
Syntax
AUDIT_LICENSE_SIZE()
Privileges
Must be a superuser.
Example
=> SELECT audit_license_size();
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audit_license_size
-------------------Service hurried
(1 row)
AUDIT_LICENSE_TERM
Triggers an immediate audit to determine if the HP Vertica license has expired. The audit happens
in the background, so this function returns immediately. To see the result of the audit, use the GET_
COMPLIANCE_STATUS function.
Syntax
AUDIT_LICENSE_TERM()
Privileges
Must be a superuser.
Example
=> SELECT AUDIT_LICENSE_TERM();
AUDIT_LICENSE_TERM
-------------------Service hurried
(1 row)
BUILD_FLEXTABLE_VIEW
Creates, or recreates, a view for a default or user-defined _keys table. If you do not specify a view_
name argument, the default name is the flex table name with a _view suffix. For example, if you
specify the table darkdata as the sole argument to this function, the default view is called
darkdata_view.
You cannot specify a custom view name with the same name as the default view flex_table_
view, unless you first do the following:
1. Drop the default-named view
2. Create your own view of the same name
Usage
build_flextable_view('flex_table' [ [,'view_name'] [,'user_keys_table'] ])
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Arguments
flex_table
The flex table name. By default, this function builds or
rebuilds a view for the input table with the current contents of
the associated flex_table_keys table.
view_name
[Optional] A custom view name. Use this option to build or
rebuild a new or existing view of your choice for the input
table with the current contents of the associated flex_
table_keys table, rather than the default view ( flex_
table_view).
user_keys_
table
[Optional] Specifies a keys table from which to create a view.
Use this option if you created a custom user_keys table for
keys of interest from the flex table map data, rather than the
default flex_table_keys table. The function builds a view
from the keys in user_keys table, rather than from the flex_
table_keys table.
Examples
Following are examples of calling build_flextable_view with 1, 2, or 3 arguments.
Creating a Default View
To create, or recreate, a default view:
1. Call the function with a single argument of a flex table, darkdata, in this example:
kdb=> select build_flextable_view('darkdata');
build_flextable_view
----------------------------------------------------The view public.darkdata_view is ready for querying
(1 row)
The function creates a view from the darkdata_keys table.
2. Query from the default view name (darkdata_view):
kdb=> select "user.id" from darkdata_view;
user.id
----------340857907
727774963
390498773
288187825
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164464905
125434448
601328899
352494946
(12 rows)
Creating a Custom Name View
To create, or recreate, a default view with a custom name:
1. Call the function with two arguments, a flex table, darkdata, and the name of the view to
create, dd_view, in this example:
kdb=> select build_flextable_view('darkdata', 'dd_view');
build_flextable_view
----------------------------------------------The view public.dd_view is ready for querying
(1 row)
2. Query from the custom view name (dd_view):
kdb=> select "user.lang" from dd_view;
user.lang
----------tr
en
es
en
en
it
es
en
(12 rows)
Creating a View From a Custom Keys Table
To create a view from a custom _keys table with build_flextable_view, the table must already
exist. The custom table must have the same schema and table definition as the default table
(darkdata_keys).
Following are a couple of ways to create a custom keys table:
1. Create a table with the all keys from the keys table:
kdb=> create table new_darkdata_keys as select * from darkdata_keys;
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CREATE TABLE
2. Alternatively, create a table based on the default keys table, but without content:
kdb=> create table new_darkdata_keys as select * from darkdata_keys LIMIT 0;
CREATE TABLE
kdb=> select * from new_darkdata_keys;
key_name | frequency | data_type_guess
----------+-----------+----------------(0 rows)
3. Given an existing table (or creating one with no data), insert one or more keys:
kdb=> create table dd_keys as select * from darkdata_keys limit 0;
CREATE TABLE
kdb=> insert into dd_keys (key_name) values ('user.lang');
OUTPUT
-------1
(1 row)
kdb=> insert into dd_keys (key_name) values ('user.name');
OUTPUT
-------1
(1 row)
kdb=> select * from dd_keys;
key_name | frequency | data_type_guess
-----------+-----------+----------------user.lang |
|
user.name |
|
(2 rows)
Continue once your custom keys table exists.
1. Call the function with all arguments, a flex table, the name of the view to create, and the
custom keys table:
kdb=> select build_flextable_view('darkdata', 'dd_view', 'new_darkdata_keys');
build_flextable_view
----------------------------------------------The view public.dd_view is ready for querying
(1 row)
2. Query the new view:
SELECT * from dd_view;
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See Also
l
COMPUTE_FLEXTABLE_KEYS
l
COMPUTE_FLEXTABLE_KEYS_AND_BUILD_VIEW
l
MATERIALIZE_FLEXTABLE_COLUMNS
l
RESTORE_FLEXTABLE_DEFAULT_KEYS_TABLE_AND_VIEW
CANCEL_REBALANCE_CLUSTER
Stops any rebalance task currently in progress.
Syntax
CANCEL_REBALANCE_CLUSTER()
Privileges
Must be a superuser.
Example
=> SELECT CANCEL_REBALANCE_CLUSTER();
CANCEL_REBALANCE_CLUSTER
-------------------------CANCELED
(1 row)
See Also
l
START_REBALANCE_CLUSTER
l
REBALANCE_CLUSTER
CANCEL_REFRESH
Cancels refresh-related internal operations initiated by START_REFRESH().
Syntax
CANCEL_REFRESH()
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Privileges
None
Notes
l
Refresh tasks run in a background thread in an internal session, so you cannot use
INTERRUPT_STATEMENT to cancel those statements. Instead, use CANCEL_REFRESH to
cancel statements that are run by refresh-related internal sessions.
l
Run CANCEL_REFRESH() on the same node on which START_REFRESH() was initiated.
l
CANCEL_REFRESH() cancels the refresh operation running on a node, waits for the
cancelation to complete, and returns SUCCESS.
l
Only one set of refresh operations runs on a node at any time.
Example
Cancel a refresh operation executing in the background.
t=> SELECT START_REFRESH();
START_REFRESH
---------------------------------------Starting refresh background process.
(1 row)
=> SELECT CANCEL_REFRESH();
CANCEL_REFRESH
---------------------------------------Stopping background refresh process.
(1 row)
See Also
l
INTERRUPT_STATEMENT
l
SESSIONS
l
START_REFRESH
l
PROJECTION_REFRESHES
CHANGE_CURRENT_STATEMENT_RUNTIME_PRIORITY
Changes the run-time priority of a query that is actively running.
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Syntax
CHANGE_CURRENT_STATEMENT_RUNTIME_PRIORITY(TRANSACTION_ID, 'value')
Parameters
TRANSACTION_ID
An identifier for the transaction within the session.
TRANSACTION_ID cannot be NULL.
You can find the transaction ID in the Sessions table.
'value'
The RUNTIMEPRIORITY value. Can be HIGH, MEDIUM, or LOW.
Privileges
No special privileges required. However, non-super users can change the run-time priority of their
own queries only. In addition, non-superusers can never raise the run-time priority of a query to a
level higher than that of the resource pool.
Example
=> SELECT CHANGE_CURRENT_STATEMENT_RUNTIME_PRIORITY(45035996273705748, 'low');
CHANGE_RUNTIME_PRIORITY
Changes the run-time priority of a query that is actively running. Note that, while this function is still
valid, you should instead use CHANGE_CURRENT_STATEMENT_RUNTIME_PRIORITY to change run-time
priority. CHANGE_RUNTIME_PRIORITY will be deprecated in a future release of Vertica.
Syntax
CHANGE_RUNTIME_PRIORITY(TRANSACTION_ID,STATEMENT_ID, 'value')
Parameters
TRANSACTION_ID
An identifier for the transaction within the session.
TRANSACTION_ID cannot be NULL.
You can find the transaction ID in the Sessions table.
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STATEMENT_ID
A unique numeric ID assigned by the HP Vertica catalog, which identifies the
currently executing statement.
You can find the statement ID in the Sessions table.
You can specify NULL to change the run-time priority of the currently running
query within the transaction.
'value'
The RUNTIMEPRIORITY value. Can be HIGH, MEDIUM, or LOW.
Privileges
No special privileges required. However, non-super users can change the run-time priority of their
own queries only. In addition, non-superusers can never raise the run-time priority of a query to a
level higher than that of the resource pool.
Example
=> SELECT CHANGE_RUNTIME_PRIORITY(45035996273705748, NULL, 'low');
CLEAR_CACHES
Clears the HP Vertica internal cache files.
Syntax
CLEAR_CACHES ( )
Privileges
Must be a superuser.
Notes
If you want to run benchmark tests for your queries, in addition to clearing the internal HP Vertica
cache files, clear the Linux file system cache. The kernel uses unallocated memory as a cache to
hold clean disk blocks. If you are running version 2.6.16 or later of Linux and you have root access,
you can clear the kernel filesystem cache as follows:
1. Make sure that all data is the cache is written to disk:
# sync
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2. Writing to the drop_caches file causes the kernel to drop clean caches, dentries, and inodes
from memory, causing that memory to become free, as follows:
n
To clear the page cache:
# echo 1 > /proc/sys/vm/drop_caches
n
To clear the dentries and inodes:
# echo 2 > /proc/sys/vm/drop_caches
n
To clear the page cache, dentries, and inodes:
# echo 3 > /proc/sys/vm/drop_caches
Example
The following example clears the HP Vertica internal cache files:
=> CLEAR_CACHES();
CLEAR_CACHES
-------------Cleared
(1 row)
CLEAR_DATA_COLLECTOR
Clears all memory and disk records on the Data Collector tables and functions and resets collection
statistics in the V_MONITOR.DATA_COLLECTOR system table. A superuser can clear Data
Collector data for all components or specify an individual component
After you clear the Data Collector log, the information is no longer available for querying.
Syntax
CLEAR_DATA_COLLECTOR( [ 'component' ] )
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Parameters
component
Clears memory and disk records for the specified component only. If you provide no
argument, the function clears all Data Collector memory and disk records for all
components.
For the current list of component names, query the V_MONITOR.DATA_
COLLECTOR system table.
Privileges
Must be a superuser.
Examples
The following command clears memory and disk records for the ResourceAcquisitions component:
=> SELECT clear_data_collector('ResourceAcquisitions');
clear_data_collector
---------------------CLEAR
(1 row)
The following command clears data collection for all components on all nodes:
=> SELECT clear_data_collector();
clear_data_collector
---------------------CLEAR
(1 row)
See Also
DATA_COLLECTOR
l
l
CLEAR_PROFILING
HP Vertica stores profiled data is in memory, so depending on how much data you collect, profiling
could be memory intensive. You can use this function to clear profiled data from memory.
Syntax
CLEAR_PROFILING( 'profiling-type' )
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Parameters
profiling-type
The type of profiling data you want to clear. Can be one of:
l
session—clears profiling for basic session parameters and lock time out
data
l
query—clears profiling for general information about queries that ran, such as
the query strings used and the duration of queries
l
ee—clears profiling for information about the execution run of each query
Example
The following statement clears profiled data for queries:
=> SELECT CLEAR_PROFILING('query');
See Also
l
DISABLE_PROFILING
l
ENABLE_PROFILING
l
Profiling Database Performance
CLEAR_PROJECTION_REFRESHES
Triggers HP Vertica to clear information about refresh operations for projections immediately.
Syntax
CLEAR_PROJECTION_REFRESHES()
Notes
Information about a refresh operation—whether successful or unsuccessful—is maintained in the
PROJECTION_REFRESHES system table until either the CLEAR_PROJECTION_
REFRESHES() function is executed or the storage quota for the table is exceeded. The
PROJECTION_REFRESHES.IS_EXECUTING column returns a boolean value that indicates whether the
refresh is currently running (t) or occurred in the past (f).
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Privileges
Must be a superuser.
Example
To immediately purge projection refresh history, use the CLEAR_PROJECTION_REFRESHES()
function:
=> SELECT CLEAR_PROJECTION_REFRESHES();
CLEAR_PROJECTION_REFRESHES
---------------------------CLEAR
(1 row)
Only the rows where the PROJECTION_REFRESHES.IS_EXECUTING column equals false are cleared.
See Also
l
PROJECTION_REFRESHES
l
REFRESH
START_REFRESH
l
l
CLEAR_RESOURCE_REJECTIONS
Clears the content of the RESOURCE_REJECTIONS and DISK_RESOURCE_REJECTIONS
system tables. Normally, these tables are only cleared during a node restart. This function lets you
clear the tables whenever you need. For example, you might want to clear the system tables after
you resolved a disk space issue that was causing disk resource rejections.
Syntax
CLEAR_RESOURCE_REJECTIONS();
Privileges
Must be a superuser.
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Example
The following command clears the content of the RESOURCE_REJECTIONS and DISK_
RESOURCE_REJECTIONS system tables:
=> SELECT clear_resource_rejections();
clear_resource_rejections
--------------------------OK
(1 row)
See Also
l
DISK_RESOURCE_REJECTIONS
l
RESOURCE_REJECTIONS
CLEAR_OBJECT_STORAGE_POLICY
Removes an existing storage policy. The specified object will no longer use a default storage
location. Any existing data stored currently at the labeled location in the object's storage policy is
moved to default storage during the next TM moveout operation.
Syntax
CLEAR_OBJECT_STORAGE_POLICY ( 'object_name' , [', key_min, key_max '])
Parameters
object_name
Specifies the database object with a storage policy to clear.
key_min, key_max
Specifies the table partition key value ranges stored at the labeled location.
These parameters are applicable only when object_name is a table.
Privileges
Must be a superuser.
Example
This example clears the storage policy for the object lineorder:
release=> select clear_object_storage_policy('lineorder');
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clear_object_storage_policy
----------------------------------Default storage policy cleared.
(1 row)
See Also
l
Clearing Storage Policies
l
ALTER_LOCATION_LABEL
l
SET_OBJECT_STORAGE_POLICY
CLOSE_SESSION
Interrupts the specified external session, rolls back the current transaction, if any, and closes the
socket.
Syntax
CLOSE_SESSION ( 'sessionid' )
Parameters
sessionid
A string that specifies the session to close. This identifier is unique within the cluster
at any point in time but can be reused when the session closes.
Privileges
None; however, a non-superuser can only close his or her own session.
Notes
l
Closing of the session is processed asynchronously. It could take some time for the session to
be closed. Check the SESSIONS table for the status.
l
Database shutdown is prevented if new sessions connect after the CLOSE_SESSION()
command is invoked (and before the database is actually shut down. See Controlling
Sessions below.
Messages
The following are the messages you could encounter:
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l
For a badly formatted sessionID
close_session | Session close command sent. Check SESSIONS for progress.Error: invalid
Session ID format
l
For an incorrect sessionID parameter
Error: Invalid session ID or statement key
Examples
User session opened. RECORD 2 shows the user session running COPY DIRECT statement.
=> SELECT * FROM sessions;
-[ RECORD 1 ]--------------+----------------------------------------------node_name
| v_vmartdb_node0001
user_name
| dbadmin
client_hostname
| 127.0.0.1:52110
client_pid
| 4554
login_timestamp
| 2011-01-03 14:05:40.252625-05
session_id
| stress04-4325:0x14
client_label
|
transaction_start
| 2011-01-03 14:05:44.325781
transaction_id
| 45035996273728326
transaction_description
| user dbadmin (SELECT * FROM sessions;)
statement_start
| 2011-01-03 15:36:13.896288
statement_id
| 10
last_statement_duration_us | 14978
current_statement
| select * from sessions;
ssl_state
| None
authentication_method
| Trust
-[ RECORD 2 ]--------------+----------------------------------------------node_name
| v_vmartdb_node0002
user_name
| dbadmin
client_hostname
| 127.0.0.1:57174
client_pid
| 30117
login_timestamp
| 2011-01-03 15:33:00.842021-05
session_id
| stress05-27944:0xc1a
client_label
|
transaction_start
| 2011-01-03 15:34:46.538102
transaction_id
| -1
transaction_description
| user dbadmin (COPY ClickStream_Fact FROM
'/data/clickstream/1g/ClickStream_Fact.tbl'
DELIMITER '|' NULL '\\n' DIRECT;)
statement_start
| 2011-01-03 15:34:46.538862
statement_id
|
last_statement_duration_us | 26250
current_statement
| COPY ClickStream_Fact FROM '/data/clickstream
/1g/ClickStream_Fact.tbl' DELIMITER '|' NULL
'\\n' DIRECT;
ssl_state
| None
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authentication_method
| Trust
Close user session stress05-27944:0xc1a
=> \xExpanded display is off.
=> SELECT CLOSE_SESSION('stress05-27944:0xc1a');
CLOSE_SESSION
-------------------------------------------------------------------Session close command sent. Check v_monitor.sessions for progress.
(1 row)
Query the sessions table again for current status, and you can see that the second session has
been closed:
=> SELECT * FROM SESSIONS;
-[ RECORD 1 ]--------------+-------------------------------------------node_name
| v_vmartdb_node0001
user_name
| dbadmin
client_hostname
| 127.0.0.1:52110
client_pid
| 4554
login_timestamp
| 2011-01-03 14:05:40.252625-05
session_id
| stress04-4325:0x14
client_label
|
transaction_start
| 2011-01-03 14:05:44.325781
transaction_id
| 45035996273728326
transaction_description
| user dbadmin (select * from SESSIONS;)
statement_start
| 2011-01-03 16:12:07.841298
statement_id
| 20
last_statement_duration_us | 2099
current_statement
| SELECT * FROM SESSIONS;
ssl_state
| None
authentication_method
| Trust
Controlling Sessions
The database administrator must be able to disallow new incoming connections in order to shut
down the database. On a busy system, database shutdown is prevented if new sessions connect
after the CLOSE_SESSION or CLOSE_ALL_SESSIONS() command is invoked—and before the
database actually shuts down.
One option is for the administrator to issue the SHUTDOWN('true') command, which forces the
database to shut down and disallow new connections. See SHUTDOWN in the SQL Reference
Manual.
Another option is to modify the MaxClientSessions parameter from its original value to 0, in order
to prevent new non-dbadmin users from connecting to the database.
1. Determine the original value for the MaxClientSessions parameter by querying the V_
MONITOR.CONFIGURATIONS_PARAMETERS system table:
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=> SELECT CURRENT_VALUE FROM CONFIGURATION_PARAMETERS WHERE parameter_name='MaxClient
Sessions';
CURRENT_VALUE
--------------50
(1 row)
2. Set the MaxClientSessions parameter to 0 to prevent new non-dbadmin connections:
=> SELECT SET_CONFIG_PARAMETER('MaxClientSessions', 0);
Note: The previous command allows up to five administrators to log in.
3. Issue the CLOSE_ALL_SESSIONS() command to remove existing sessions:
=> SELECT CLOSE_ALL_SESSIONS();
4. Query the SESSIONS table:
=> SELECT * FROM SESSIONS;
When the session no longer appears in the SESSIONS table, disconnect and run the Stop
Database command.
5. Restart the database.
6. Restore the MaxClientSessions parameter to its original value:
=> SELECT SET_CONFIG_PARAMETER('MaxClientSessions', 50);
See Also
l
CLOSE_ALL_SESSIONS
l
CONFIGURATION_PARAMETERS
l
SESSIONS
SHUTDOWN
l
l
l
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CLOSE_ALL_SESSIONS
Closes all external sessions except the one issuing the CLOSE_ALL_SESSIONS functions.
Syntax
CLOSE_ALL_SESSIONS()
Privileges
None; however, a non-superuser can only close his or her own session.
Notes
Closing of the sessions is processed asynchronously. It might take some time for the session to be
closed. Check the SESSIONS table for the status.
Database shutdown is prevented if new sessions connect after the CLOSE_SESSION or CLOSE_
ALL_SESSIONS() command is invoked (and before the database is actually shut down). See
Controlling Sessions below.
Message
close_all_sessions | Close all sessions command sent. Check SESSIONS for progress.
Examples
Two user sessions opened, each on a different node:
vmartdb=> SELECT * FROM sessions;
-[ RECORD 1 ]--------------+---------------------------------------------------node_name
| v_vmartdb_node0001
user_name
| dbadmin
client_hostname
| 127.0.0.1:52110
client_pid
| 4554
login_timestamp
| 2011-01-03 14:05:40.252625-05
session_id
| stress04-4325:0x14
client_label
|
transaction_start
| 2011-01-03 14:05:44.325781
transaction_id
| 45035996273728326
transaction_description
| user dbadmin (select * from sessions;)
statement_start
| 2011-01-03 15:36:13.896288
statement_id
| 10
last_statement_duration_us | 14978
current_statement
| select * from sessions;
ssl_state
| None
authentication_method
| Trust
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-[ RECORD 2 ]--------------+---------------------------------------------------node_name
| v_vmartdb_node0002
user_name
| dbadmin
client_hostname
| 127.0.0.1:57174
client_pid
| 30117
login_timestamp
| 2011-01-03 15:33:00.842021-05
session_id
| stress05-27944:0xc1a
client_label
|
transaction_start
| 2011-01-03 15:34:46.538102
transaction_id
| -1
transaction_description
| user dbadmin (COPY Mart_Fact FROM '/data/mart_Fact.tbl'
DELIMITER '|' NULL '\\n';)
statement_start
| 2011-01-03 15:34:46.538862
statement_id
|
last_statement_duration_us | 26250
current_statement
| COPY Mart_Fact FROM '/data/Mart_Fact.tbl' DELIMITER '|'
NULL '\\n';
ssl_state
| None
authentication_method
| Trust
-[ RECORD 3 ]--------------+---------------------------------------------------node_name
| v_vmartdb_node0003
user_name
| dbadmin
client_hostname
| 127.0.0.1:56367
client_pid
| 1191
login_timestamp
| 2011-01-03 15:31:44.939302-05
session_id
| stress06-25663:0xbec
client_label
|
transaction_start
| 2011-01-03 15:34:51.05939
transaction_id
| 54043195528458775
transaction_description
| user dbadmin (COPY Mart_Fact FROM '/data/Mart_Fact.tbl'
DELIMITER '|' NULL '\\n' DIRECT;)
statement_start
| 2011-01-03 15:35:46.436748
statement_id
|
last_statement_duration_us | 1591403
current_statement
| COPY Mart_Fact FROM '/data/Mart_Fact.tbl' DELIMITER '|'
NULL '\\n' DIRECT;
ssl_state
| None
authentication_method
| Trust
Close all sessions:
vmartdb=> \xExpanded display is off.
vmartdb=> SELECT CLOSE_ALL_SESSIONS();
CLOSE_ALL_SESSIONS
------------------------------------------------------------------------Close all sessions command sent. Check v_monitor.sessions for progress.
(1 row)
Session contents after issuing the CLOSE_ALL_SESSIONS() command:
=> SELECT * FROM SESSIONS;-[ RECORD 1 ]--------------+--------------------------------------node_name
| v_vmartdb_node0001
user_name
| dbadmin
client_hostname
| 127.0.0.1:52110
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client_pid
login_timestamp
session_id
client_label
transaction_start
transaction_id
transaction_description
statement_start
statement_id
last_statement_duration_us
current_statement
ssl_state
authentication_method
|
|
|
|
|
|
|
|
|
|
|
|
|
4554
2011-01-03 14:05:40.252625-05
stress04-4325:0x14
2011-01-03 14:05:44.325781
45035996273728326
user dbadmin (SELECT * FROM sessions;)
2011-01-03 16:19:56.720071
25
15605
SELECT * FROM SESSIONS;
None
Trust
Controlling Sessions
The database administrator must be able to disallow new incoming connections in order to shut
down the database. On a busy system, database shutdown is prevented if new sessions connect
after the CLOSE_SESSION or CLOSE_ALL_SESSIONS() command is invoked—and before the
database actually shuts down.
One option is for the administrator to issue the SHUTDOWN('true') command, which forces the
database to shut down and disallow new connections. See SHUTDOWN in the SQL Reference
Manual.
Another option is to modify the MaxClientSessions parameter from its original value to 0, in order
to prevent new non-dbadmin users from connecting to the database.
1. Determine the original value for the MaxClientSessions parameter by querying the V_
MONITOR.CONFIGURATIONS_PARAMETERS system table:
=> SELECT CURRENT_VALUE FROM CONFIGURATION_PARAMETERS WHERE parameter_name='MaxClient
Sessions';
CURRENT_VALUE
--------------50
(1 row)
2. Set the MaxClientSessions parameter to 0 to prevent new non-dbadmin connections:
=> SELECT SET_CONFIG_PARAMETER('MaxClientSessions', 0);
Note: The previous command allows up to five administrators to log in.
3. Issue the CLOSE_ALL_SESSIONS() command to remove existing sessions:
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=> SELECT CLOSE_ALL_SESSIONS();
4. Query the SESSIONS table:
=> SELECT * FROM SESSIONS;
When the session no longer appears in the SESSIONS table, disconnect and run the Stop
Database command.
5. Restart the database.
6. Restore the MaxClientSessions parameter to its original value:
=> SELECT SET_CONFIG_PARAMETER('MaxClientSessions', 50);
See Also
l
CLOSE_SESSION
l
CONFIGURATION_PARAMETERS
l
SHUTDOWN
SESSIONS
l
l
l
COMPUTE_FLEXTABLE_KEYS
Computes the virtual columns (keys and values) from the map data of a flex table and repopulates
the associated _keys table. The keys table has the following columns:
l
key_name
l
frequency
l
data_type_guess
This function sorts the keys table by frequency and key_name.
Use this function to compute keys without creating an associated table view. To build a view as
well, use COMPUTE_FLEXTABLE_KEYS_AND_BUILD_VIEW.
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Usage
compute_flextable_keys('flex_table')
Arguments
flex_table The name of the flex table.
Examples
During execution, this function determines a data type for each virtual column, casting the values it
computes to VARCHAR, LONG VARCHAR, or LONG VARBINARY, depending on the length of the key, and
whether the key includes nested maps.
The following examples illustrate this function and the results of populating the _keys table, once
you've created a flex table (darkdata1) and loaded data:
kdb=> create flex table darkdata1();
CREATE TABLE
kdb=> copy darkdata1 from '/test/flextable/DATA/tweets_12.json' parser fjsonparser();
Rows Loaded
------------12
(1 row)
kdb=> select compute_flextable_keys('darkdata1');
compute_flextable_keys
-------------------------------------------------Please see public.darkdata1_keys for updated keys
(1 row)
kdb=> select * from darkdata1_keys;
key_name
| frequency |
data_type_guess
----------------------------------------------------------+-----------+--------------------contributors
|
8 | varchar(20)
coordinates
|
8 | varchar(20)
created_at
|
8 | varchar(60)
entities.hashtags
|
8 | long varbinary(18
6)
entities.urls
|
8 | long varbinary(3
2)
entities.user_mentions
|
8 | long varbinary(67
4)
.
.
.
retweeted_status.user.time_zone
|
1 | varchar(20)
retweeted_status.user.url
|
1 | varchar(68)
retweeted_status.user.utc_offset
|
1 | varchar(20)
retweeted_status.user.verified
|
1 | varchar(20)
(125 rows)
The flex keys table has these columns:
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Column
Description
key_name
The name of the virtual column (key).
frequency The number of times the virtual column occurs in the map.
data_
type_
guess
The data type for each virtual column, cast to VARCHAR, LONG VARCHAR or LONG
VARBINARY, depending on the length of the key, and whether the key includes one or
more nested maps.
In the _keys table output, the data_type_guess column values are also followed by
a value in parentheses, such as varchar(20). The value indicates the padded
width of the key column, as calculated by the longest field, multiplied by the
FlexTableDataTypeGuessMultiplier configuration parameter value. For more
information, see Setting Flex Table Parameters.
See Also
l
BUILD_FLEXTABLE_VIEW
l
COMPUTE_FLEXTABLE_KEYS_AND_BUILD_VIEW
l
MATERIALIZE_FLEXTABLE_COLUMNS
l
RESTORE_FLEXTABLE_DEFAULT_KEYS_TABLE_AND_VIEW
COMPUTE_FLEXTABLE_KEYS_AND_BUILD_VIEW
Combines the functionality of BUILD_FLEXTABLE_VIEW and COMPUTE_FLEXTABLE_KEYS
to compute virtual columns (keys) from the map data of a flex table , and construct a view. If you
don't need to perform both operations together, use one of the single-operation functions.
Usage
compute_flextable_keys_and_build_view('flex_table')
Arguments
flex_table The name of a flex table.
Examples
The following example calls the function for the darkdata flex table.
kdb=> select compute_flextable_keys_and_build_view('darkdata');
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compute_flextable_keys_and_build_view
----------------------------------------------------------------------Please see public.darkdata_keys for updated keys
The view public.darkdata_view is ready for querying
(1 row)
See Also
l
BUILD_FLEXTABLE_VIEW
l
COMPUTE_FLEXTABLE_KEYS
l
MATERIALIZE_FLEXTABLE_COLUMNS
l
RESTORE_FLEXTABLE_DEFAULT_KEYS_TABLE_AND_VIEW
CURRENT_SCHEMA
Returns the name of the current schema.
Behavior Type
Stable
Syntax
CURRENT_SCHEMA()
Privileges
None
Notes
The CURRENT_SCHEMA function does not require parentheses.
Examples
The following command returns the name of the current schema:
=> SELECT CURRENT_SCHEMA();
current_schema
---------------public
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(1 row)
The following command returns the same results without the parentheses:
=> SELECT CURRENT_SCHEMA;
current_schema
---------------public
(1 row)
The following command shows the current schema, listed after the current user, in the search path:
=> SHOW SEARCH_PATH;
name
|
setting
-------------+--------------------------------------------------search_path | "$user", public, v_catalog, v_monitor, v_internal
(1 row)
See Also
l
SET SEARCH_PATH
DATA_COLLECTOR_HELP
Returns online usage instructions about the Data Collector, the DATA_COLLECTOR system table, and
the Data Collector control functions.
Syntax
DATA_COLLECTOR_HELP()
Privileges
None
Returns
The DATA_COLLECTOR_HELP() function returns the following information:
=> SELECT DATA_COLLECTOR_HELP();
----------------------------------------------------------------------------Usage Data Collector
The data collector retains history of important system activities.
This data can be used as a reference of what actions have been taken
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by users, but it can also be used to locate performance bottlenecks,
or identify potential improvements to the Vertica configuration.
This data is queryable via Vertica system tables.
Acccess a list of data collector components, and some statistics, by running:
SELECT * FROM v_monitor.data_collector;
The amount of data retained by size and time can be controlled with several
functions.
To just set the size amount:
set_data_collector_policy(,
,
);
To set both the size and time amounts (the smaller one will dominate):
set_data_collector_policy(,
,
,
);
To set just the time amount:
set_data_collector_time_policy(,
);
To set the time amount for all tables:
set_data_collector_time_policy();
The current retention policy for a component can be queried with:
get_data_collector_policy();
Data on disk is kept in the "DataCollector" directory under the Vertica
\catalog path. This directory also contains instructions on how to load
the monitoring data into another Vertica database.
To move the data collector logs and instructions to other storage locations,
create labeled storage locations using add_location and then use:
set_data_collector_storage_location();
Additional commands can be used to configure the data collection logs.
The log can be cleared with:
clear_data_collector([]);
The log can be synchronized with the disk storage using:
flush_data_collector([]);
See Also
l
DATA_COLLECTOR
l
TUNING_RECOMMENDATIONS
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l
Analyzing Workloads
l
Retaining Monitoring Information
DISABLE_DUPLICATE_KEY_ERROR
Disables error messaging when HP Vertica finds duplicate PRIMARY KEY/UNIQUE KEY values
at run time. Queries execute as though no constraints are defined on the schema. Effects are
session scoped.
Caution: When called, DISABLE_DUPLICATE_KEY_ERROR() suppresses data integrity
checking and can lead to incorrect query results. Use this function only after you insert
duplicate primary keys into a dimension table in the presence of a pre-join projection. Then
correct the violations and turn integrity checking back on with REENABLE_DUPLICATE_
KEY_ERROR().
Syntax
DISABLE_DUPLICATE_KEY_ERROR();
Privileges
Must be a superuser.
Examples
The following series of commands create a table named dim and the corresponding projection:
CREATE TABLE dim (pk INTEGER PRIMARY KEY, x INTEGER);
CREATE PROJECTION dim_p (pk, x) AS SELECT * FROM dim ORDER BY x UNSEGMENTED ALL NODES;
The next two statements create a table named fact and the pre-join projection that joins fact to
dim.
CREATE TABLE fact(fk INTEGER REFERENCES dim(pk));
CREATE PROJECTION prejoin_p (fk, pk, x) AS SELECT * FROM fact, dim WHERE pk=fk ORDER BY x
;
The following statements load values into table dim. The last statement inserts a duplicate primary
key value of 1:
INSERT INTO dim values (1,1);INSERT INTO dim values (2,2);
INSERT INTO dim values (1,2); --Constraint violation
COMMIT;
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Table dim now contains duplicate primary key values, but you cannot delete the violating row
because of the presence of the pre-join projection. Any attempt to delete the record results in the
following error message:
ROLLBACK: Duplicate primary key detected in FK-PK join Hash-Join (x dim_p), value 1
In order to remove the constraint violation (pk=1), use the following sequence of commands, which
puts the database back into the state just before the duplicate primary key was added.
To remove the violation:
1. Save the original dim rows that match the duplicated primary key:
CREATE TEMP TABLE dim_temp(pk integer, x integer);
INSERT INTO dim_temp SELECT * FROM dim WHERE pk=1 AND x=1; -- original dim row
2. Temporarily disable error messaging on duplicate constraint values:
SELECT DISABLE_DUPLICATE_KEY_ERROR();
Caution: Remember that running the DISABLE_DUPLICATE_KEY_ERROR function
suppresses the enforcement of data integrity checking.
3. Remove the original row that contains duplicate values:
DELETE FROM dim WHERE pk=1;
4. Allow the database to resume data integrity checking:
SELECT REENABLE_DUPLICATE_KEY_ERROR();
5. Reinsert the original values back into the dimension table:
INSERT INTO dim SELECT * from dim_temp;COMMIT;
6. Validate your dimension and fact tables.
If you receive the following error message, it means that the duplicate records you want to
delete are not identical. That is, the records contain values that differ in at least one column
that is not a primary key; for example, (1,1) and (1,2).
ROLLBACK: Delete: could not find a data row to delete (data integrity violation?)
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The difference between this message and the rollback message in the previous example is that
a fact row contains a foreign key that matches the duplicated primary key, which has been
inserted. A row with values from the fact and dimension table is now in the pre-join projection.
In order for the DELETE statement (Step 3 in the following example) to complete successfully,
extra predicates are required to identify the original dimension table values (the values that are
in the pre-join).
This example is nearly identical to the previous example, except that an additional INSERT
statement joins the fact table to the dimension table by a primary key value of 1:
INSERT INTO dim values (1,1);INSERT INTO dim values (2,2);
INSERT INTO fact values (1);
-- New insert statement joins fact with dim on primar
y key value=1
INSERT INTO dim values (1,2); -- Duplicate primary key value=1
COMMIT;
To remove the violation:
1. Save the original dim and fact rows that match the duplicated primary key:
CREATE TEMP TABLE dim_temp(pk integer, x integer);CREATE TEMP TABLE fact_temp(fk inte
ger);
INSERT INTO dim_temp SELECT * FROM dim WHERE pk=1 AND x=1; -- original dim row
INSERT INTO fact_temp SELECT * FROM fact WHERE fk=1;
2. Temporarily suppresses the enforcement of data integrity checking:
SELECT DISABLE_DUPLICATE_KEY_ERROR();
3. Remove the duplicate primary keys. These steps also implicitly remove all fact rows with the
matching foreign key.
4. Remove the original row that contains duplicate values:
DELETE FROM dim WHERE pk=1 AND x=1;
Note: The extra predicate (x=1) specifies removal of the original (1,1) row, rather than the
newly inserted (1,2) values that caused the violation.
5. Remove all remaining rows:
DELETE FROM dim WHERE pk=1;
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6. Reenable integrity checking:
SELECT REENABLE_DUPLICATE_KEY_ERROR();
7. Reinsert the original values back into the fact and dimension table:
INSERT INTO dim SELECT * from dim_temp;
INSERT INTO fact SELECT * from fact_temp;
COMMIT;
8. Validate your dimension and fact tables.
See Also
l
ANALYZE_CONSTRAINTS
l
REENABLE_DUPLICATE_KEY_ERROR
DISABLE_ELASTIC_CLUSTER
Disables elastic cluster scaling, which prevents HP Vertica from bundling data into chunks that are
easily transportable to other nodes when performing cluster resizing. The main reason to disable
elastic clustering is if you find that the slightly unequal data distribution in your cluster caused by
grouping data into discrete blocks results in performance issues.
Syntax
DISABLE_ELASTIC_CLUSTER()
Privileges
Must be a superuser.
Example
=> SELECT DISABLE_ELASTIC_CLUSTER();
DISABLE_ELASTIC_CLUSTER
------------------------DISABLED
(1 row)
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See Also
l
ENABLE_ELASTIC_CLUSTER
DISABLE_LOCAL_SEGMENTS
Disable local data segmentation, which breaks projections segments on nodes into containers that
can be easily moved to other nodes. See Local Data Segmentation in the Administrator's Guide for
details.
Syntax
DISABLE_LOCAL_SEGMENTS()
Privileges
Must be a superuser.
Example
=> SELECT DISABLE_LOCAL_SEGMENTS();
DISABLE_LOCAL_SEGMENTS
-----------------------DISABLED
(1 row)
DISABLE_PROFILING
Disables profiling for the profiling type you specify.
Syntax
DISABLE_PROFILING( 'profiling-type' )
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Parameters
profiling-type
The type of profiling data you want to disable. Can be one of:
l
session—disables profiling for basic session parameters and lock time out
data
l
query—disables profiling for general information about queries that ran, such
as the query strings used and the duration of queries
l
ee—disables profiling for information about the execution run of each query
Example
The following statement disables profiling on query execution runs:
=> SELECT DISABLE_PROFILING('ee');
DISABLE_PROFILING
----------------------EE Profiling Disabled
(1 row)
See Also
l
CLEAR_PROFILING
l
ENABLE_PROFILING
l
Profiling Database Performance
DISPLAY_LICENSE
Returns the terms of your HP Vertica license. The information this function displays is:
l
The start and end dates for which the license is valid (or "Perpetual" if the license has no
expiration).
l
The number of days you are allowed to use HP Vertica after your license term expires (the grace
period)
l
The amount of data your database can store, if your license includes a data allowance.
Syntax
DISPLAY_LICENSE()
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Privileges
None
Examples
=> SELECT DISPLAY_LICENSE();
DISPLAY_LICENSE
---------------------------------------------------HP Vertica Systems, Inc.
1/1/2011
12/31/2011
30
50TB
(1 row)
DO_TM_TASK
Runs a Tuple Mover operation on one or more projections defined on the specified table.
Tip: You do not need to stop the Tuple Mover to run this function.
Syntax
DO_TM_TASK ( 'task' [ , '[[db-name.]schema.]table' |
'[[db-name.]schema.]projection' ] )
Parameters
task
Is one of the following tuple mover operations:
l
'moveout' — Moves out all projections on the specified table (if a
particular projection is not specified) from WOS to ROS.
l
'mergeout' — Consolidates ROS containers and purges deleted
records.
l
'analyze_row_count' — Automatically collects the number of rows
in a projection every 60 seconds and aggregates row counts calculated
during loads.
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[[db-name.]schema.]
[Optional] Specifies the schema name. Using a schema identifies objects
that are not unique within the current search path (see Setting Schema
Search Paths).
You can optionally precede a schema with a database name, but you must
be connected to the database you specify. You cannot make changes to
objects in other databases.
The ability to specify different database objects (from database and
schemas to tables and columns) lets you qualify database objects as
explicitly as required. For example, use a table and column
(mytable.column1), a schema, table, and column
(myschema.mytable.column1), and, as full qualification, a database,
schema, table, and column (mydb.myschema.mytable.column1).
table
Runs a tuple mover operation for all projections within the specified table.
When using more than one schema, specify the schema that contains the
table with the projections you want to affect, as noted above.
projection
If projection is not passed as an argument, all projections in the system are
used. If projection is specified, DO_TM_TASK looks for a projection of
that name and, if found, uses it; if a named projection is not found, the
function looks for a table with that name and, if found, moves out all
projections on that table.
Privileges
l
Any INSERT/UPDATE/DELETE privilege on table
l
USAGE privileges on schema
Notes
DO_TM_TASK() is useful for moving out all projections from a table or database without having to
name each projection individually.
Examples
The following example performs a moveout of all projections for table t1:
=> SELECT DO_TM_TASK('moveout', 't1');
The following example performs a moveout for projection t1_proj:
=> SELECT DO_TM_TASK('moveout', 't1_proj')
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See Also
l
COLUMN_STORAGE
l
DROP_PARTITION
l
DUMP_PARTITION_KEYS
l
DUMP_PROJECTION_PARTITION_KEYS
l
DUMP_TABLE_PARTITION_KEYS
PARTITION_PROJECTION
l
l
l
DROP_LICENSE
Drops a Flex Zone license key from the global catalog.
Syntax
DROP_LICENSE( 'license name' )
Parameters
license name
The name of the license to drop. The name can be found in the licenses table.
Privileges
Must be a superuser.
Notes
For more information about license keys, see Managing Licenses in the Administrator's Guide.
Examples
=> SELECT DROP_LICENSE('/tmp/vlicense.dat');
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DROP_LOCATION
Removes the specified storage location.
Syntax
DROP_LOCATION ( 'path' , 'node' )
Parameters
path
Specifies where the storage location to drop is mounted.
node
Is the HP Vertica node where the location is available.
Privileges
Must be a superuser.
Retiring or Dropping a Storage Location
Dropping a storage location is a permanent operation and cannot be undone. Therefore, HP
recommends that you retire a storage location before dropping it. Retiring a storage location lets you
verify that you do not need the storage before dropping it. Additionally, you can easily restore a
retired storage location if you determine it is still in use.
Storage Locations with Temp and Data Files
Dropping storage locations is limited to storage locations that contain only temp files.
If you use a storage location to store data and then alter it to store only temp files, the location can
still contain data files. HP Vertica does not let you drop a storage location containing data files. You
can manually merge out the data files from the storage location, and then wait for the ATM to
mergeout the data files automatically, or, you can drop partitions. Deleting data files does not work.
Example
The following example drops a storage location on node3 that was used to store temp files:
=> SELECT DROP_LOCATION('/secondHP VerticaStorageLocation/' , 'node3');
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See Also
l
l
l
ADD_LOCATION
l
ALTER_LOCATION_USE
l
RESTORE_LOCATION
l
RETIRE_LOCATION
l
GRANT (Storage Location)
l
REVOKE (Storage Location)
DROP_PARTITION
Forces the partition of projections (if needed) and then drops the specified partition.
Syntax
DROP_PARTITION ( table_name , partition_value [ , ignore_moveout_errors, reorganize_data ])
Parameters
table-name
Specifies the name of the table.
Note: The table_name argument cannot be used as a dimension table in
a pre-joined projection and cannot contain projections that are not up to
date (have not been refreshed).
partition_value
The key of the partition to drop. For example: DROP_PARTITION
('trade', 2006);
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ignore_moveout_errors
Optional Boolean, defaults to false.
l
true—Ignores any WOS moveout errors and forces the operation to
continue. Set this parameter to true only if there is no WOS data for
the partition.
l
false (or omit)—Displays any moveout errors and aborts the
operation on error.
Note: If you set this parameter to true and the WOS includes data for
the partition in WOS, partition data in WOS is not dropped.
reorganize_data
Optional Boolean, defaults to false.
l
true—Reorganizes the data if it is not organized, and then drops the
partition.
l
false—Does not attempt to reorganize the data before dropping the
partition. If this parameter is false and the function encounters a
ROS without partition keys, an error occurs.
Permissions
l
Table owner
l
USAGE privilege on schema that contains the table
Notes and Restrictions
The results of a DROP_PARTITION call go into effect immediately. If you drop a partition using
DROP_PARTITION and then try to add data to a partition with the same name, HP Vertica creates
a new partition.
If the operation cannot obtain an O Lock on the table(s), HP Vertica attempts to close any internal
Tuple Mover (TM) sessions running on the same table(s) so that the operation can proceed. Explicit
TM operations that are running in user sessions are not closed. If an explicit TM operation is running
on the table, then the operation cannot proceed until the explicit TM operation completes.
In general, if a ROS container has data that belongs to n+1 partitions and you want to drop a
specific partition, the DROP_PARTITION operation:
1. Forces the partition of data into two containers where
n
One container holds the data that belongs to the partition that is to be dropped.
n
Another container holds the remaining n partitions.
2. Drops the specified partition.
DROP_PARTITION forces a moveout if there is data in the WOS (WOS is not partition aware).
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DROP_PARTITION acquires an exclusive lock on the table to prevent DELETE | UPDATE |
INSERT | COPY statements from affecting the table, as well as any SELECT statements issued at
SERIALIZABLE isolation level.
You cannot perform a DROP_PARTITION operation on tables with projections that are not up to
date (have not been refreshed).
DROP_PARTITION fails if you do not set the optional third parameter to true and the function
encounters ROS's that do not have partition keys.
Examples
Using the example schema in Defining Partitions, the following command explicitly drops the 2009
partition key from table trade:
SELECT DROP_PARTITION('trade', 2009);
DROP_PARTITION
------------------Partition dropped
(1 row)
Here, the partition key is specified:
SELECT DROP_PARTITION('trade', EXTRACT('year' FROM '2009-01-01'::date));
DROP_PARTITION
------------------Partition dropped
(1 row)
The following example creates a table called dates and partitions the table by year:
CREATE TABLE dates (year INTEGER NOT NULL,
month VARCHAR(8) NOT NULL)
PARTITION BY year * 12 + month;
The following statement drops the partition using a constant for Oct 2010 (2010*12 + 10 = 24130):
SELECT DROP_PARTITION('dates', '24130');
DROP_PARTITION
------------------Partition dropped
(1 row)
Alternatively, the expression can be placed in line: SELECT DROP_PARTITION('dates', 2010*12
+ 10);
The following command first reorganizes the data if it is unpartitioned and then explicitly drops the
2009 partition key from table trade:
SELECT DROP_PARTITION('trade', 2009, false, true);
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DROP_PARTITION
------------------Partition dropped
(1 row)
See Also
l
Dropping Partitions
l
ADVANCE_EPOCH
l
ALTER PROJECTION RENAME
l
COLUMN_STORAGE
l
CREATE TABLE
l
DO_TM_TASK
l
DUMP_PARTITION_KEYS
l
DUMP_PROJECTION_PARTITION_KEYS
l
DUMP_TABLE_PARTITION_KEYS
l
MERGE_PARTITIONS
l
PARTITION_PROJECTION
l
PARTITION_TABLE
l
PROJECTIONS
DROP_STATISTICS
Removes statistics for the specified table and lets you optionally specify the category of statistics
to drop.
Syntax
DROP_STATISTICS { ('') | ('[[db-name.]schema-name.]table'
[, {'BASE' | 'HISTOGRAMS' | 'ALL'} ])};
Return Value
0 - If successful, DROP_STATISTICS always returns 0. If the command fails, DROP_
STATISTICS displays an error message. See vertica.log for message details.
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Parameters
''
Empty string. Drops statistics for all projections.
[[db-name.]schema.]
[Optional] Specifies the database name and optional schema name. Using
a database name identifies objects that are not unique within the current
search path (see Setting Search Paths). You must be connected to the
database you specify, and you cannot change objects in other databases.
Specifying different database objects lets you qualify database objects as
explicitly as required. For example, you can use a database and a schema
name (mydb.myschema).
table
Drops statistics for all projections within the specified table. When using
more than one schema, specify the schema that contains the table with
the projections you want to delete, as noted in the syntax.
CATEGORY
Specifies the category of statistics to drop for the named [db-name.]
schema-name.]table:
l
'BASE' (default) drops histograms and row counts (min/max column
values, histogram.
l
'HISTOGRAMS' drops only the histograms. Row counts statistics
remain.
l
'ALL' drops all statistics.
Privileges
l
INSERT/UPDATE/DELETE privilege on table
l
USAGE privilege on schema that contains the table
Notes
Once dropped, statistics can be time consuming to regenerate.
Examples
The following command analyzes all statistics on the VMart schema database:
=> SELECT ANALYZE_STATISTICS('');
ANALYZE_STATISTICS
-------------------0
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(1 row)
This command drops base statistics for table store_sales_fact in the store schema:
=> SELECT DROP_STATISTICS('store.store_sales_fact', 'BASE');
DROP_STATISTICS
----------------0
(1 row)
Note that this command works the same as the previous command:
=> SELECT DROP_STATISTICS('store.store_sales_fact');
DROP_STATISTICS
----------------0
(1 row)
This command also drops statistics for all table projections:
=> SELECT DROP_STATISTICS ('');
DROP_STATISTICS
----------------0
(1 row)
For use cases, see Collecting Statistics in the Administrator's Guide
See Also
l
ANALYZE_STATISTICS
l
EXPORT_STATISTICS
l
IMPORT_STATISTICS
DUMP_CATALOG
Returns an internal representation of the HP Vertica catalog. This function is used for diagnostic
purposes.
Syntax
DUMP_CATALOG()
Privileges
None; however, function dumps only the objects visible to the user.
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Notes
To obtain an internal representation of the HP Vertica catalog for diagnosis, run the query:
=> SELECT DUMP_CATALOG();
The output is written to the specified file:
\o /tmp/catalog.txtSELECT DUMP_CATALOG();
\o
DUMP_LOCKTABLE
Returns information about deadlocked clients and the resources they are waiting for.
Syntax
DUMP_LOCKTABLE()
Privileges
None
Notes
Use DUMP_LOCKTABLE if HP Vertica becomes unresponsive:
1. Open an additional vsql connection.
2. Execute the query:
=> SELECT DUMP_LOCKTABLE();
The output is written to vsql. See Monitoring the Log Files.
You can also see who is connected using the following command:
=> SELECT * FROM SESSIONS;
Close all sessions using the following command:
=>SELECT CLOSE_ALL_SESSIONS();
Close a single session using the following command:
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=> SELECT CLOSE_SESSION('session_id');
You get the session_id value from the V_MONITOR.SESSIONS system table.
See Also
l
CLOSE_ALL_SESSIONS
l
CLOSE_SESSION
l
LOCKS
l
SESSIONS
DUMP_PARTITION_KEYS
Dumps the partition keys of all projections in the system.
Syntax
DUMP_PARTITION_KEYS( )
Note: The ROS objects of partitioned tables without partition keys are ignored by the tuple
mover and are not merged during automatic tuple mover operations.
Privileges
None; however function dumps only the tables for which user has SELECT privileges.
Example
=> SELECT DUMP_PARTITION_KEYS( );
Partition keys on node v_vmart_node0001
Projection 'states_b0'
Storage [ROS container]
No of partition keys: 1
Partition keys: NH
Storage [ROS container]
No of partition keys: 1
Partition keys: MA
Projection 'states_b1'
Storage [ROS container]
No of partition keys: 1
Partition keys: VT
Storage [ROS container]
No of partition keys: 1
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Partition keys: ME
Storage [ROS container]
No of partition keys: 1
Partition keys: CT
See Also
l
DO_TM_TASK
l
DROP_PARTITION
l
DUMP_PROJECTION_PARTITION_KEYS
l
DUMP_TABLE_PARTITION_KEYS
l
PARTITION_PROJECTION
l
PARTITION_TABLE
l
PARTITIONS
l
Working with Table Partitions
DUMP_PROJECTION_PARTITION_KEYS
Dumps the partition keys of the specified projection.
Syntax
DUMP_PROJECTION_PARTITION_KEYS(
'projection_name' )
Parameters
projection_name
Specifies the name of the projection.
Privileges
l
SELECT privilege on table
l
USAGE privileges on schema
Example
The following example creates a simple table called states and partitions the data by state:
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=> CREATE TABLE states (year INTEGER NOT NULL,
state VARCHAR NOT NULL)
PARTITION BY state;
=> CREATE PROJECTION states_p (state, year)
AS SELECT * FROM states
ORDER BY state, year UNSEGMENTED ALL NODES;
Now dump the partition key of the specified projection:
=> SELECT DUMP_PROJECTION_PARTITION_KEYS( 'states_p_node0001' );
Partition keys on node helios_node0001
Projection 'states_p_node0001'
No of partition keys: 1
Partition keys on node helios_node0002
...
(1 row)
See Also
l
DO_TM_TASK
l
DROP_PARTITION
l
DUMP_PARTITION_KEYS
l
DUMP_TABLE_PARTITION_KEYS
l
PARTITION_PROJECTION
l
PARTITION_TABLE
l
PROJECTIONS
l
Working with Table Partitions
DUMP_TABLE_PARTITION_KEYS
Dumps the partition keys of all projections anchored on the specified table.
Syntax
DUMP_TABLE_PARTITION_KEYS ( 'table_name' )
Parameters
table_name
Specifies the name of the table.
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Privilege
l
SELECT privilege on table
l
USAGE privileges on schema
Examples
The following example creates a simple table called states and partitions the data by state:
=> CREATE TABLE states (year INTEGER NOT NULL,
state VARCHAR NOT NULL)
PARTITION BY state;
=> CREATE PROJECTION states_p (state, year) AS
SELECT * FROM states
ORDER BY state, year UNSEGMENTED ALL NODES;
Now dump the partition keys of all projections anchored on table states:
=> SELECT DUMP_TABLE_PARTITION_KEYS( 'states' );
Partition keys on helios_node0001
Projection 'states_p_node0004'
No of partition keys: 1
Projection 'states_p_node0003'
No of partition keys: 1
Projection 'states_p_node0002'
No of partition keys: 1
Projection 'states_p_node0001'
No of partition keys: 1
Partition keys on helios_node0002
...
(1 row)
See Also
l
DO_TM_TASK
l
DROP_PARTITION
l
DUMP_PROJECTION_PARTITION_KEYS
l
DUMP_TABLE_PARTITION_KEYS
l
PARTITION_PROJECTION
l
PARTITION_TABLE
l
Working with Table Partitions
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ENABLE_ELASTIC_CLUSTER
Enables elastic cluster scaling, which makes enlarging or reducing the size of your database
cluster more efficient by segmenting a node's data into chunks that can be easily moved to other
hosts.
Note: Databases created using HP Vertica Version 5.0 and later have elastic cluster enabled
by default. You need to use this function on databases created before version 5.0 in order for
them to use the elastic clustering feature.
Syntax
ENABLE_ELASTIC_CLUSTER()
Privileges
Must be a superuser.
Example
=> SELECT ENABLE_ELASTIC_CLUSTER();
ENABLE_ELASTIC_CLUSTER
-----------------------ENABLED
(1 row)
See Also
l
DISABLE_ELASTIC_CLUSTER
ENABLE_LOCAL_SEGMENTS
Enables local storage segmentation, which breaks projections segments on nodes into containers
that can be easily moved to other nodes. See Local Data Segmentation in the Administrator's Guide
for more information.
Syntax
ENABLE_LOCAL_SEGMENTS()
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Privileges
Must be a superuser.
Example
=> SELECT ENABLE_LOCAL_SEGMENTS();
ENABLE_LOCAL_SEGMENTS
----------------------ENABLED
(1 row)
ENABLE_PROFILING
Enables profiling for the profiling type you specify.
Note: HP Vertica stores profiled data is in memory, so depending on how much data you
collect, profiling could be memory intensive.
Syntax
ENABLE_PROFILING( 'profiling-type' )
Parameters
profiling-type
The type of profiling data you want to enable. Can be one of:
l
session—enables profiling for basic session parameters and lock time out
data
l
query—enables profiling for general information about queries that ran, such
as the query strings used and the duration of queries
l
ee—enables profiling for information about the execution run of each query
Example
The following statement enables profiling on query execution runs:
=> SELECT ENABLE_PROFILING('ee');
ENABLE_PROFILING
----------------------
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EE Profiling Enabled
(1 row)
See Also
l
CLEAR_PROFILING
l
DISABLE_PROFILING
l
Profiling Database Performance
EVALUATE_DELETE_PERFORMANCE
Evaluates projections for potential DELETE performance issues. If there are issues found, a
warning message is displayed. For steps you can take to resolve delete and update performance
issues, see Optimizing Deletes and Updates for Performance in the Administrator's Guide. This
function uses data sampling to determine whether there are any issues with a projection. Therefore,
it does not generate false-positives warnings, but it can miss some cases where there are
performance issues.
Note: Optimizing for delete performance is the same as optimizing for update performance.
So, you can use this function to help optimize a projection for updates as well as deletes.
Syntax
EVALUATE_DELETE_PERFORMANCE ( 'target' )
Parameters
target
The name of a projection or table. If you supply the name of a projection, only that
projection is evaluated for DELETE performance issues. If you supply the name of a
table, then all of the projections anchored to the table will be evaluated for issues.
If you do not provide a projection or table name, EVALUATE_DELETE_
PERFORMANCE examines all of the projections that you can access for DELETE
performance issues. Depending on the size you your database, this may take a long
time.
Privileges
None
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Notes
When evaluating multiple projections, EVALUATE_DELETE_PERFORMANCE reports up to ten
projections that have issues, and refers you to a table that contains the full list of issues it has
found.
Example
The following example demonstrates how you can use EVALUATE_DELETE_PERFORMANCE
to evaluate your projections for slow DELETE performance.
=> create table example (A int, B int,C int);
CREATE TABLE
=> create projection one_sort (A,B,C) as (select A,B,C from example) order by A;
CREATE PROJECTION
=> create projection two_sort (A,B,C) as (select A,B,C from example) order by A,B;
CREATE PROJECTION
=> select evaluate_delete_performance('one_sort');
evaluate_delete_performance
--------------------------------------------------No projection delete performance concerns found.
(1 row)
=> select evaluate_delete_performance('two_sort');
evaluate_delete_performance
--------------------------------------------------No projection delete performance concerns found.
(1 row)
The previous example showed that there was no structural issue with the projection that would
cause poor DELETE performance. However, the data contained within the projection can create
potential delete issues if the sorted columns do not uniquely identify a row or small number of rows.
In the following example, Perl is used to populate the table with data using a nested series of loops.
The inner loop populates column C, the middle loop populates column B, and the outer loop
populates column A. The result is column A contains only three distinct values (0, 1, and 2), while
column B slowly varies between 20 and 0 and column C changes in each row. EVALUATE_
DELETE_PERFORMANCE is run against the projections again to see if the data within the
projections causes any potential DELETE performance issues.
=> \! perl -e 'for ($i=0; $i<3; $i++) { for ($j=0; $j<21; $j++) { for ($k=0; $k<19; $k++)
{ printf "%d,%d,%d\n", $i,$j,$k;}}}' | /opt/vertica/bin/vsql -c "copy example from stdin
delimiter ',' direct;"
Password:
=> select * from example;
A | B | C
---+----+---0 | 20 | 18
0 | 20 | 17
0 | 20 | 16
0 | 20 | 15
0 | 20 | 14
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0 | 20 | 13
0 | 20 | 12
0 | 20 | 11
0 | 20 | 10
0 | 20 | 9
0 | 20 | 8
0 | 20 | 7
0 | 20 | 6
0 | 20 | 5
0 | 20 | 4
0 | 20 | 3
0 | 20 | 2
0 | 20 | 1
0 | 20 | 0
0 | 19 | 18
1157 rows omitted
2 | 1 | 0
2 | 0 | 18
2 | 0 | 17
2 | 0 | 16
2 | 0 | 15
2 | 0 | 14
2 | 0 | 13
2 | 0 | 12
2 | 0 | 11
2 | 0 | 10
2 | 0 | 9
2 | 0 | 8
2 | 0 | 7
2 | 0 | 6
2 | 0 | 5
2 | 0 | 4
2 | 0 | 3
2 | 0 | 2
2 | 0 | 1
2 | 0 | 0
=> SELECT COUNT (*) FROM example;
COUNT
------1197
(1 row)
=> SELECT COUNT (DISTINCT A) FROM example;
COUNT
------3
(1 row)
=> select evaluate_delete_performance('one_sort');
evaluate_delete_performance
--------------------------------------------------Projection exhibits delete performance concerns.
(1 row)
release=> select evaluate_delete_performance('two_sort');
evaluate_delete_performance
--------------------------------------------------No projection delete performance concerns found.
(1 row)
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The one_sort projection has potential delete issues since it only sorts on column A which has few
distinct values. This means that each value in the sort column corresponds to many rows in the
projection, which negatively impacts DELETE performance. Since the two_sort projection is sorted
on columns A and B, each combination of values in the two sort columns identifies just a few rows,
allowing deletes to be performed faster.
Not supplying a projection name results in all of the projections you can access being evaluated for
DELETE performance issues.
=> select evaluate_delete_performance();
evaluate_delete_performance
--------------------------------------------------------------------------The following projection exhibits delete performance concerns:
"public"."one_sort"
See v_catalog.projection_delete_concerns for more details.
(1 row)
EXPORT_CATALOG
Generates a SQL script that you can use to recreate a physical schema design in its current state
on a different cluster. This function always attempts to recreate projection statements with KSAFE
clauses, if they exist in the original definitions, or OFFSET clauses if they do not.
Syntax
EXPORT_CATALOG ( [ 'destination' ] , [ 'scope' ] )
Parameters
destination
Specifies the path and name of the SQL output file. An empty string (''), which is
the default, outputs the script to standard output. The function writes the script to
the catalog directory if no destination is specified.
If you specify a file that does not exist, the function creates one. If the file preexists, the function silently overwrites its contents.
scope
Determines what to export:
l
DESIGN—Exports schemas, tables, constraints, views, and projections to
which the user has access. This is the default value.
l
DESIGN_ALL—Exports all the design objects plus system objects created in
Database Designer (for example, design contexts and their tables). The objects
that are exported are those to which the user has access.
l
TABLES—Exports all tables, constraints, and projections for for which the user
has permissions. See also EXPORT_TABLES.
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Privileges
None. However:
l
EXPORT_CATALOG exports only the objects visible to the user .
l
Only a superuser can export output to a file.
Example
The following example exports the design to standard output:
=> SELECT EXPORT_CATALOG('','DESIGN');
See Also
EXPORT_OBJECTS
l
EXPORT_TABLES
l
l
EXPORT_OBJECTS
Generates a SQL script you can use to recreate catalog objects on a different cluster. The
generated script includes only the non-virtual objects to which the user has access. The function
exports catalog objects in order dependency for correct recreation. Running the generated SQL
script on another cluster then creates all referenced objects before their dependent objects.
The EXPORT_OBJECTS function always attempts to recreate projection statements with KSAFE
clauses, if they existed in the original definitions, or OFFSET clauses, if they did not.
None of the EXPORT_OBJECTS parameters accepts a NULL value as input. EXPORT_
OBJECTS returns an error if an explicitly-specified object does not exist, or the user does not have
access to the object.
Syntax
EXPORT_OBJECTS( [ 'destination' ] , [ 'scope' ] , [ 'ksafe' ] )
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Parameters
destination
Specifies the path and name of the SQL output file. The default empty string ('')
outputs the script to standard output. The function writes the script to the catalog
directory if no destination is specified.
If you specify a file that does not exist, the function creates one. If the file preexists, the function silently overwrites its contents.
scope
ksafe
Determines the scope of the catalog objects to export:
l
An empty string (' ')—exports all non-virtual objects to which the user has
access, including constraints. (Note that constraints are not objects that can be
passed as individual arguments.) An empty string is the default scope value if
you do not limit the export.
l
A comma-delimited list of catalog objects to export, which can include the
following:
n
' [dbname.]schema.object '—matches each named schema object. You can
optionally qualify the schema with a database prefix. A named schema
object can be a table, projection, view, sequence, or user-defined SQL
function.
n
' [dbname.]schema—matches the named schema, which you can optionally
qualify with a database prefix. For a schema, HP Vertica exports all nonvirtual objects that the user has access to within the schema. If a schema
and table have the same name, the schema takes precedence.
Determines if the statistics are regenerated before loading them into the design
context
Specifies whether to incorporate a MARK_DESIGN_KSAFE statement with the
correct K-safe value for the database:
l
true—adds the MARK_DESIGN_KSAFE statement to the end of the output
script. This is the default value.
l
false—omits the MARK_DESIGN_KSAFE statement from the script.
Privileges
None. However:
l
EXPORT_OBJECTS exports only the objects visible to the user .
l
Only a superuser can export output to a file.
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Example
The following example exports all the non-virtual objects to which the user has access to standard
output. The example uses false for the last parameter, indicating that the file will not include the
MARK_DESIGN_KSAFE statement at the end.
=> SELECT EXPORT_OBJECTS(' ',' ',false);
See Also
l
EXPORT_CATALOG
l
EXPORT_TABLES
l
Exporting Objects
EXPORT_STATISTICS
Generates an XML file that contains statistics for the database. You can optionally export statistics
on a single database object (table, projection, or table column).
Before you export statistics for the database, run ANALYZE_STATISTICS() to automatically
collect the most up to date statistics information.
Note: Use the second argument only if statistics in the database do not match the statistics of
data.
Syntax
EXPORT_STATISTICS [ ( 'destination' )
... | ( '[ [ db-name.]schema.]table [.column-name ]' ) ]
Parameters
destination
Specifies the path and name of the XML output file. An empty string
returns the script to the screen.
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[[db-name.]schema.]
[Optional] Specifies the schema name. Using a schema identifies objects
that are not unique within the current search path (see Setting Schema
Search Paths).
You can optionally precede a schema with a database name, but you must
be connected to the database you specify. You cannot make changes to
objects in other databases.
The ability to specify different database objects (from database and
schemas to tables and columns) lets you qualify database objects as
explicitly as required. For example, use a table and column
(mytable.column1), a schema, table, and column
(myschema.mytable.column1), and, as full qualification, a database,
schema, table, and column (mydb.myschema.mytable.column1).
table
Specifies the name of the table and exports statistics for all projections of
that table.
Note: If you are using more than one schema, specify the schema
that contains the projection, as noted as noted in the [[db-name.]
schema.] entry.
[.column-name]
[Optional] Specifies the name of a single column, typically a predicate
column. Using this option with a table specification lets you export
statistics for only that column.
Privileges
Must be a superuser.
Examples
The following command exports statistics on the VMart example database to a file:
vmart=> SELECT EXPORT_STATISTICS('/opt/vertica/examples/VMart_Schema/vmart_stats.xml');
EXPORT_STATISTICS
----------------------------------Statistics exported successfully
(1 row)
The next statement exports statistics on a single column (price) from a table called food:
=> SELECT EXPORT_STATISTICS('/opt/vertica/examples/VMart_Schema/price.xml', 'food.pric
e');
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See Also
l
ANALYZE_STATISTICS
l
DROP_STATISTICS
l
IMPORT_STATISTICS
l
Collecting Database Statistics
EXPORT_TABLES
Generates a SQL script that can be used to recreate a logical schema (schemas, tables,
constraints, and views) on a different cluster.
Syntax
EXPORT_TABLES ( [ 'destination' ] , [ 'scope' ] )
Parameters
destination
Specifies the path and name of the SQL output file. An empty string (''), which is
the default, outputs the script to standard output. The function writes the script to
the catalog directory if no destination is specified.
If you specify a file that does not exist, the function creates one. If the file preexists, the function silently overwrites its contents.
scope
Determines the tables to export. Specify the scope as follows:
l
An empty string (' ')—exports all non-virtual table objects to which the user has
access, including table schemas, sequences, and constraints. Exporting all
non-virtual objects is the default scope, and what the function exports if you do
not specify a scope.
l
A comma-delimited list of objects, which can include the following:
n
' [dbname.][schema.]object '—matches the named objects, which can be
schemas, tables, or views, in the schema. You can optionally qualify a
schema with a database prefix, and objects with a schema. You cannot pass
constraints as individual arguments.
n
' [dbname.]object '—matches a named object, which can be a schema, table,
or view. You can optionally qualify a schema with a database prefix, and an
object with its schema. For a schema, HP Vertica exports all non-virtual
objects to which the user has access within the schema. If a schema and
table both have the same name, the schema takes precedence.
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Privileges
None; however:
l
Function exports only the objects visible to the user
l
Only a superuser can export output to file
Example
The following example exports the store_orders_fact table of the store schema (in the current
database) to standard output:
=> SELECT EXPORT_TABLES(' ','store.store_orders_fact');
EXPORT_TABLES returns an error if:
l
You explicitly specify an object that does not exist
l
The current user does not have access to a specified object
See Also
EXPORT_CATALOG
l
EXPORT_OBJECTS
l
l
FLUSH_DATA_COLLECTOR
Waits until memory logs are moved to disk and then flushes the Data Collector, synchronizing the
log with the disk storage. A superuser can flush Data Collector information for an individual
component or for all components.
Syntax
FLUSH_DATA_COLLECTOR( [ 'component' ] )
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Parameters
component
Flushes the specified component. If you provide no argument, the function flushes
the Data Collector in full.
For the current list of component names, query the V_MONITOR.DATA_
COLLECTOR system table.
Privileges
Must be a superuser.
Examples
The following command flushes the Data Collector for the ResourceAcquisitions component:
=> SELECT flush_data_collector('ResourceAcquisitions');
flush_data_collector
---------------------FLUSH
(1 row)
The following command flushes data collection for all components:
=> SELECT flush_data_collector();
flush_data_collector
---------------------FLUSH
(1 row)
See Also
DATA_COLLECTOR
l
l
GET_AHM_EPOCH
Returns the number of the epoch in which the Ancient History Mark is located. Data deleted up to
and including the AHM epoch can be purged from physical storage.
Syntax
GET_AHM_EPOCH()
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Note: The AHM epoch is 0 (zero) by default (purge is disabled).
Privileges
None
Examples
SELECT GET_AHM_EPOCH();
GET_AHM_EPOCH
---------------------Current AHM epoch: 0
(1 row)
GET_AHM_TIME
Returns a TIMESTAMP value representing the Ancient History Mark. Data deleted up to and
including the AHM epoch can be purged from physical storage.
Syntax
GET_AHM_TIME()
Privileges
None
Examples
SELECT GET_AHM_TIME();
GET_AHM_TIME
------------------------------------------------Current AHM Time: 2010-05-13 12:48:10.532332-04
(1 row)
See Also
l
SET DATESTYLE
l
TIMESTAMP
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GET_AUDIT_TIME
Reports the time when the automatic audit of database size occurs. HP Vertica performs this audit
if your HP Vertica license includes a data size allowance. For details of this audit, see Managing
Your License Key in the Administrator's Guide. To change the time the audit runs, use the SET_
AUDIT_TIME function.
Syntax
GET_AUDIT_TIME()
Privileges
None
Example
=> SELECT get_audit_time();
get_audit_time
----------------------------------------------------The audit is scheduled to run at 11:59 PM each day.
(1 row)
GET_COMPLIANCE_STATUS
Displays whether your database is in compliance with your HP Vertica license agreement. This
information includes the results of HP Vertica's most recent audit of the database size (if your
license has a data allowance as part of its terms), and the license term (if your license has an end
date).
The information displayed by GET_COMPLIANCE_STATUS includes:
l
The estimated size of the database (see How HP Vertica Calculates Database Size in the
Administrator's Guide for an explanation of the size estimate).
l
The raw data size allowed by your HP Vertica license.
l
The percentage of your allowance that your database is currently using.
l
The date and time of the last audit.
l
Whether your database complies with the data allowance terms of your license agreement.
l
The end date of your license.
l
How many days remain until your license expires.
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Note: If your license does not have a data allowance or end date, some of the values may not
appear in the output for GET_COMPLIANCE_STATUS.
If the audit shows your license is not in compliance with your data allowance, you should either
delete data to bring the size of the database under the licensed amount, or upgrade your license. If
your license term has expired, you should contact HP immediately to renew your license. See
Managing Your License Key in the Administrator's Guide for further details.
Syntax
GET_COMPLIANCE_STATUS()
Privileges
None
Example
GET_COMPLIANCE_STATUS
--------------------------------------------------------------------------------Raw Data Size: 2.00GB +/- 0.003GB
License Size : 4.000GB
Utilization : 50%
Audit Time
: 2011-03-09 09:54:09.538704+00
Compliance Status : The database is in compliance with respect to raw data size.
License End Date: 04/06/2011
Days Remaining: 28.59
(1 row)
GET_CURRENT_EPOCH
The epoch into which data (COPY, INSERT, UPDATE, and DELETE operations) is currently being
written. The current epoch advances automatically every three minutes.
Returns the number of the current epoch.
Syntax
GET_CURRENT_EPOCH()
Privileges
None
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Examples
SELECT GET_CURRENT_EPOCH();
GET_CURRENT_EPOCH
------------------683
(1 row)
GET_DATA_COLLECTOR_POLICY
Retrieves a brief statement about the retention policy for the specified component.
Syntax
GET_DATA_COLLECTOR_POLICY( 'component' )
Parameters
component
Returns the retention policy for the specified component.
For a current list of component names, query the V_MONITOR.DATA_
COLLECTOR system table
Privileges
None
Example
The following query returns the history of all resource acquisitions by specifying the
ResourceAcquisitions component:
=> SELECT get_data_collector_policy('ResourceAcquisitions');
get_data_collector_policy
---------------------------------------------1000KB kept in memory, 10000KB kept on disk.
(1 row)
See Also
DATA_COLLECTOR
l
l
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GET_LAST_GOOD_EPOCH
A term used in manual recovery, LGE (Last Good Epoch) refers to the most recent epoch that can
be recovered.
Returns the number of the last good epoch.
Syntax
GET_LAST_GOOD_EPOCH()
Privileges
None
Examples
SELECT GET_LAST_GOOD_EPOCH();
GET_LAST_GOOD_EPOCH
--------------------682
(1 row)
GET_NUM_ACCEPTED_ROWS
Returns the number of rows loaded into the database for the last completed load for the current
session. GET_NUM_ACCEPTED_ROWS is a meta-function. Do not use it as a value in an
INSERT query.
The number of accepted rows is not available for a load that is currently in process. Check the
LOAD_STREAMS system table for its status.
Also, this meta-function supports only loads from STDIN or a single file on the initiator. You cannot
use GET_NUM_ACCEPTED_ROWS for multi-node loads.
Syntax
GET_NUM_ACCEPTED_ROWS();
Privileges
None
Note: The data regarding accepted rows from the last load during the current session does not
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persist, and is lost when you initiate a new load.
See Also
l
GET_NUM_REJECTED_ROWS
GET_NUM_REJECTED_ROWS
Returns the number of rows that were rejected during the last completed load for the current
session. GET_NUM_REJECTED_ROWS is a meta-function. Do not use it as a value in an
INSERT query.
Rejected row information is unavailable for a load that is currently running. The number of rejected
rows is not available for a load that is currently in process. Check the LOAD_STREAMS system
table for its status.
Also, this meta-function supports only loads from STDIN or a single file on the initiator. You cannot
use GET_NUM_REJECTED_ROWS for multi-node loads.
Syntax
GET_NUM_REJECTED_ROWS();
Privileges
None
Note: The data regarding rejected rows from the last load during the current session does not
persist, and is dropped when you initiate a new load.
See Also
l
GET_NUM_ACCEPTED_ROWS
GET_PROJECTION_STATUS
Returns information relevant to the status of a projection.
Syntax
GET_PROJECTION_STATUS ( '[[db-name.]schema-name.]projection' );
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Parameters
[[db-name.]schema.]
[Optional] Specifies the database name and optional schema name. Using
a database name identifies objects that are not unique within the current
search path (see Setting Search Paths). You must be connected to the
database you specify, and you cannot change objects in other databases.
Specifying different database objects lets you qualify database objects as
explicitly as required. For example, you can use a database and a schema
name (mydb.myschema).
projection
Is the name of the projection for which to display status. When using more
than one schema, specify the schema that contains the projection, as
noted above.
Privileges
None
Description
GET_PROJECTION_STATUS returns information relevant to the status of a projection:
l
The current K-safety status of the database
l
The number of nodes in the database
l
Whether the projection is segmented
l
The number and names of buddy projections
l
Whether the projection is safe
l
Whether the projection is up-to-date
l
Whether statistics have been computed for the projection
Notes
l
You can use GET_PROJECTION_STATUS to monitor the progress of a projection data
refresh. See ALTER PROJECTION.
l
To view a list of the nodes in a database, use the View Database Command in the
Administration Tools.
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Examples
=> SELECT GET_PROJECTION_STATUS('public.customer_dimension_site01');
GET_PROJECTION_STATUS
---------------------------------------------------------------------------------------------Current system K is 1.
# of Nodes: 4.
public.customer_dimension_site01 [Segmented: No] [Seg Cols: ] [K: 3] [public.customer_dim
ension_site04, public.customer_dimension_site03,
public.customer_dimension_site02]
[Safe: Yes] [UptoDate: Yes][Stats: Yes]
See Also
l
ALTER PROJECTION RENAME
l
GET_PROJECTIONS, GET_TABLE_PROJECTIONS
GET_PROJECTIONS, GET_TABLE_PROJECTIONS
Note: This function was formerly named GET_TABLE_PROJECTIONS(). HP Vertica still
supports the former function name.
Returns information relevant to the status of a table:
l
The current K-safety status of the database
l
The number of sites (nodes) in the database
l
The number of projections for which the specified table is the anchor table
l
For each projection:
n
The projection's buddy projections
n
Whether the projection is segmented
n
Whether the projection is safe
n
Whether the projection is up-to-date
Syntax
GET_PROJECTIONS ( '[[db-name.]schema-name.]table' )
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Parameters
[[db-name.]schema.]
[Optional] Specifies the database name and optional schema name. Using
a database name identifies objects that are not unique within the current
search path (see Setting Search Paths). You must be connected to the
database you specify, and you cannot change objects in other databases.
Specifying different database objects lets you qualify database objects as
explicitly as required. For example, you can use a database and a schema
name (mydb.myschema).
table
Is the name of the table for which to list projections. When using more than
one schema, specify the schema that contains the table.
Privileges
None
Notes
l
You can use GET_PROJECTIONS to monitor the progress of a projection data refresh. See
ALTER PROJECTION.
l
To view a list of the nodes in a database, use the View Database Command in the
Administration Tools.
Examples
The following example gets information about the store_dimension table in the VMart schema:
=> SELECT GET_PROJECTIONS('store.store_dimension');
-------------------------------------------------------------------------------------Current system K is 1.
# of Nodes: 4.
Table store.store_dimension has 4 projections.
Projection Name: [Segmented] [Seg Cols] [# of Buddies] [Buddy Projections] [Safe] [UptoDa
te]
---------------------------------------------------------store.store_dimension_node0004 [Segmented: No] [Seg Cols: ] [K: 3] [store.store_dimensio
n_node0003, store.store_dimension_node0002, store.store_dimension_node0001]
[Safe: Yes] [UptoDate: Yes][Stats: Yes]
store.store_dimension_node0003 [Segmented: No] [Seg Cols: ] [K: 3] [store.store_dimensio
n_node0004, store.store_dimension_node0002, store.store_dimension_node0001]
[Safe: Yes] [UptoDate: Yes][Stats: Yes]
store.store_dimension_node0002 [Segmented: No] [Seg Cols: ] [K: 3] [store.store_dimensio
n_node0004, store.store_dimension_node0003, store.store_dimension_node0001]
[Safe: Yes] [UptoDate: Yes][Stats: Yes]
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store.store_dimension_node0001 [Segmented: No] [Seg Cols: ] [K: 3] [store.store_dimensio
n_node0004, store.store_dimension_node0003, store.store_dimension_node0002]
[Safe: Yes] [UptoDate: Yes][Stats: Yes]
(1 row)
See Also
l
ALTER PROJECTION RENAME
l
GET_PROJECTION_STATUS
HAS_ROLE
Indicates, with a Boolean value, whether a role has been assigned to a user. This function is useful
for letting you check your own role membership.
Behavior Type
Stable
Syntax 1
HAS_ROLE( [ 'user_name' ,] 'role_name' );
Syntax 2
HAS_ROLE( 'role_name' );
Parameters
user_name
[Optional] The name of a user to look up. Currently, only a superuser can supply the
user_name argument.
role_name
The name of the role you want to verify has been granted.
Privileges
Users can check their own role membership by calling HAS_ROLE('role_name'), but only a
superuser can look up other users' memberships using the optional user_name parameter.
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Notes
You can query V_CATALOG system tables ROLES, GRANTS, and USERS to show any directlyassigned roles; however, these tables do not indicate whether a role is available to a user when
roles may be available through other roles (indirectly).
Examples
User Bob wants to see if he has been granted the commentor role:
=> SELECT HAS_ROLE('commentor');
Output t for true indicates that Bob has been assigned the commentor role:
HAS_ROLE
---------t
(1 row)
In the following function call, a superuser checks if the logadmin role has been granted to user Bob:
=> SELECT HAS_ROLE('Bob', 'logadmin');
HAS_ROLE
---------t
(1 row)
To view the names of all roles users can access, along with any roles that have been assigned to
those roles, query the V_CATALOG.ROLES system table. An asterisk in the output means role
granted WITH ADMIN OPTION.
=> SELECT * FROM roles;
role_id
|
name
| assigned_roles
-------------------+-----------------+---------------45035996273704964 | public
|
45035996273704966 | dbduser
|
45035996273704968 | dbadmin
| dbduser*
45035996273704972 | pseudosuperuser | dbadmin*
45035996273704974 | logreader
|
45035996273704976 | logwriter
|
45035996273704978 | logadmin
| logreader,
logwriter
(7 rows)
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See Also
l
GRANTS
l
ROLES
l
USERS
l
Managing Users and Privileges
l
Viewing a user's Role
IMPORT_STATISTICS
Imports statistics from the XML file generated by the EXPORT_STATISTICS command.
Syntax
IMPORT_STATISTICS ( 'destination' )
Parameters
destination
Specifies the path and name of the XML input file (which is the output of EXPORT_
STATISTICS function).
Privileges
Must be a superuser.
Notes
l
Imported statistics override existing statistics for all projections on the specified table.
l
For use cases, see Collecting Statistics in the Administrator's Guide
Example
Import the statistics for the VMart database that EXPORT_STATISTICS saved.
-> SELECT IMPORT_STATISTICS('/opt/vertica/examples/VMart_Schema/vmart_stats.xml');
IMPORT_STATISTICS
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---------------------------------------------------------------------------Importing statistics for projection date_dimension_super column date_key failure (stats d
id not contain row counts)
Importing statistics for projection date_dimension_super column date failure (stats did n
ot contain row counts)
Importing statistics for projection date_dimension_super column full_date_description fai
lure (stats did not contain row counts)
...
(1 row)
VMart=>
See Also
l
ANALYZE_STATISTICS
l
DROP_STATISTICS
l
EXPORT_STATISTICS
INTERRUPT_STATEMENT
Interrupts the specified statement (within an external session), rolls back the current transaction,
and writes a success or failure message to the log file.
Syntax
INTERRUPT_STATEMENT( 'session_id ', statement_id )
Parameters
session_id
Specifies the session to interrupt. This identifier is unique within the cluster at
any point in time.
statement_id
Specifies the statement to interrupt
Privileges
Must be a superuser.
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Notes
l
Only statements run by external sessions can be interrupted.
l
Sessions can be interrupted during statement execution.
l
If the statement_id is valid, the statement is interruptible. The command is successfully sent
and returns a success message. Otherwise the system returns an error.
Messages
The following list describes messages you might encounter:
Message
Meaning
Statement interrupt sent. Check SESSIONS for progress.
This message
indicates
success.
Session could not be successfully interrupted: session not found.
The session ID
argument to the
interrupt
command does
not match a
running session.
Session could not be successfully interrupted: statement not found.
The statement ID
does not match (or
no longer
matches) the ID of
a running
statement (if any).
No interruptible statement running
The statement is
DDL or otherwise
non-interruptible.
Internal (system) sessions cannot be interrupted.
The session is
internal, and only
statements run by
external sessions
can be interrupted.
Examples
Two user sessions are open. RECORD 1 shows user session running SELECT FROM SESSION, and
RECORD 2 shows user session running COPY DIRECT:
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=> SELECT * FROM SESSIONS;
-[ RECORD 1 ]--------------+---------------------------------------------------node_name
| v_vmartdb_node0001
user_name
| dbadmin
client_hostname
| 127.0.0.1:52110
client_pid
| 4554
login_timestamp
| 2011-01-03 14:05:40.252625-05
session_id
| stress04-4325:0x14
client_label
|
transaction_start
| 2011-01-03 14:05:44.325781
transaction_id
| 45035996273728326
transaction_description
| user dbadmin (select * from sessions;)
statement_start
| 2011-01-03 15:36:13.896288
statement_id
| 10
last_statement_duration_us | 14978
current_statement
| select * from sessions;
ssl_state
| None
authentication_method
| Trust
-[ RECORD 2 ]--------------+---------------------------------------------------node_name
| v_vmartdb_node0003
user_name
| dbadmin
client_hostname
| 127.0.0.1:56367
client_pid
| 1191
login_timestamp
| 2011-01-03 15:31:44.939302-05
session_id
| stress06-25663:0xbec
client_label
|
transaction_start
| 2011-01-03 15:34:51.05939
transaction_id
| 54043195528458775
transaction_description
| user dbadmin (COPY Mart_Fact FROM '/data/Mart_Fact.tbl'
DELIMITER '|' NULL '\\n' DIRECT;)
statement_start
| 2011-01-03 15:35:46.436748
statement_id
| 5
last_statement_duration_us | 1591403
current_statement
| COPY Mart_Fact FROM '/data/Mart_Fact.tbl' DELIMITER '|'
NULL '\\n' DIRECT;
ssl_state
| None
authentication_method
| Trust
Interrupt the COPY DIRECT statement running in stress06-25663:0xbec:
=> \xExpanded display is off.
=> SELECT INTERRUPT_STATEMENT('stress06-25663:0x1537', 5);
interrupt_statement
-----------------------------------------------------------------Statement interrupt sent. Check v_monitor.sessions for progress.
(1 row)
Verify that the interrupted statement is no longer active by looking at the current_statement column
in the SESSIONS system table. This column becomes blank when the statement has been
interrupted:
=> SELECT * FROM SESSIONS;
-[ RECORD 1 ]--------------+---------------------------------------------------node_name
| v_vmartdb_node0001
user_name
| dbadmin
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client_hostname
| 127.0.0.1:52110
client_pid
| 4554
login_timestamp
| 2011-01-03 14:05:40.252625-05
session_id
| stress04-4325:0x14
client_label
|
transaction_start
| 2011-01-03 14:05:44.325781
transaction_id
| 45035996273728326
transaction_description
| user dbadmin (select * from sessions;)
statement_start
| 2011-01-03 15:36:13.896288
statement_id
| 10
last_statement_duration_us | 14978
current_statement
| select * from sessions;
ssl_state
| None
authentication_method
| Trust
-[ RECORD 2 ]--------------+---------------------------------------------------node_name
| v_vmartdb_node0003
user_name
| dbadmin
client_hostname
| 127.0.0.1:56367
client_pid
| 1191
login_timestamp
| 2011-01-03 15:31:44.939302-05
session_id
| stress06-25663:0xbec
client_label
|
transaction_start
| 2011-01-03 15:34:51.05939
transaction_id
| 54043195528458775
transaction_description
| user dbadmin (COPY Mart_Fact FROM '/data/Mart_Fact.tbl'
DELIMITER '|' NULL '\\n' DIRECT;)
statement_start
| 2011-01-03 15:35:46.436748
statement_id
| 5
last_statement_duration_us | 1591403
current_statement
|
ssl_state
| None
authentication_method
| Trust
See Also
l
SESSIONS
l
Managing Sessions
l
Configuration Parameters
INSTALL_LICENSE
Installs the license key in the global catalog.
Syntax
INSTALL_LICENSE( 'filename' )
Parameters
filename
specifies the absolute pathname of a valid license file.
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Privileges
Must be a superuser.
Notes
For more information about license keys, see Managing Your License Key in the Administrator's
Guide.
Examples
=> SELECT INSTALL_LICENSE('/tmp/vlicense.dat');
LAST_INSERT_ID
Returns the last value of a column whose value is automatically incremented through the AUTO_
INCREMENT or IDENTITY Column-Constraint. If multiple sessions concurrently load the same
table, the returned value is the last value generated for an AUTO_INCREMENT column by an
insert in that session.
Behavior Type
Volatile
Syntax
LAST_INSERT_ID()
Privileges
l
Table owner
l
USAGE privileges on schema
Notes
l
This function works only with AUTO_INCREMENT and IDENTITY columns. See columnconstraints for the CREATE TABLE statement.
l
LAST_INSERT_ID does not work with sequence generators created through the CREATE
SEQUENCE statement.
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Examples
Create a sample table called customer4.
=> CREATE TABLE customer4(
ID IDENTITY(2,2),
lname VARCHAR(25),
fname VARCHAR(25),
membership_card INTEGER
);
=> INSERT INTO customer4(lname, fname, membership_card)
VALUES ('Gupta', 'Saleem', 475987);
Notice that the IDENTITY column has a seed of 2, which specifies the value for the first row loaded
into the table, and an increment of 2, which specifies the value that is added to the IDENTITY value
of the previous row.
Query the table you just created:
=> SELECT * FROM customer4;
ID | lname | fname | membership_card
----+-------+--------+----------------2 | Gupta | Saleem |
475987
(1 row)
Insert some additional values:
=> INSERT INTO customer4(lname, fname, membership_card)
VALUES ('Lee', 'Chen', 598742);
Call the LAST_INSERT_ID function:
=> SELECT LAST_INSERT_ID();
LAST_INSERT_ID
---------------4
(1 row)
Query the table again:
=> SELECT * FROM customer4;
ID | lname | fname | membership_card
----+-------+--------+----------------2 | Gupta | Saleem |
475987
4 | Lee
| Chen
|
598742
(2 rows)
Add another row:
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=> INSERT INTO customer4(lname, fname, membership_card)
VALUES ('Davis', 'Bill', 469543);
Call the LAST_INSERT_ID function:
=> SELECT LAST_INSERT_ID();
LAST_INSERT_ID
---------------6
(1 row)
Query the table again:
=> SELECT * FROM customer4; ID | lname | fname
----+-------+--------+----------------2 | Gupta | Saleem |
475987
4 | Lee
| Chen
|
598742
6 | Davis | Bill
|
469543
(3 rows)
| membership_card
See Also
l
ALTER SEQUENCE
l
CREATE SEQUENCE
l
DROP SEQUENCE
l
SEQUENCES
l
Using Named Sequences
l
Sequence Privileges
MAKE_AHM_NOW
Sets the Ancient History Mark (AHM) to the greatest allowable value, and lets you drop any
projections that existed before the issue occurred.
Caution: This function is intended for use by Administrators only.
Syntax
MAKE_AHM_NOW ( [ true ] )
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Parameters
true
[Optional] Allows AHM to advance when nodes are down. Note: If the AHM is advanced
after the last good epoch of the failed nodes, those nodes must recover all data from
scratch. Use with care.
Privileges
Must be a superuser.
Notes
l
l
The MAKE_AHM_NOW function performs the following operations:
n
Advances the epoch.
n
Performs a moveout operation on all projections.
n
Sets the AHM to LGE — at least to the current epoch at the time MAKE_AHM_NOW() was
issued.
All history is lost and you cannot perform historical queries prior to the current epoch.
Example
=> SELECT MAKE_AHM_NOW();
MAKE_AHM_NOW
-----------------------------AHM set (New AHM Epoch: 683)
(1 row)
The following command allows the AHM to advance, even though node 2 is down:
=> SELECT
WARNING:
WARNING:
WARNING:
MAKE_AHM_NOW(true);
Received no response from v_vmartdb_node0002 in get cluster LGE
Received no response from v_vmartdb_node0002 in get cluster LGE
Received no response from v_vmartdb_node0002 in set AHM
MAKE_AHM_NOW
-----------------------------AHM set (New AHM Epoch: 684)
(1 row)
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See Also
l
DROP PROJECTION
l
MARK_DESIGN_KSAFE
l
SET_AHM_EPOCH
l
SET_AHM_TIME
MARK_DESIGN_KSAFE
Enables or disables high availability in your environment, in case of a failure. Before enabling
recovery, MARK_DESIGN_KSAFE queries the catalog to determine whether a cluster's physical
schema design meets the following requirements:
l
Small, unsegmented tables are replicated on all nodes.
l
Large table superprojections are segmented with each segment on a different node.
l
Each large table projection has at least one buddy projection for K-safety=1 (or two buddy
projections for K-safety=2).
Buddy projections are also segmented across database nodes, but the distribution is modified
so that segments that contain the same data are distributed to different nodes. See High
Availability Through Projections in the Concepts Guide.
Note: Projections are considered to be buddies if they contain the same columns and have
the same segmentation. They can have different sort orders.
MARK_DESIGN_KSAFE does not change the physical schema in any way.
Syntax
MARK_DESIGN_KSAFE ( k )
Parameters
k
2 enables high availability if the schema design meets requirements for K-safety=2
1 enables high availability if the schema design meets requirements for K-safety=1
0 disables high availability
If you specify a k value of one (1) or two (2), HP Vertica returns one of the following messages.
Success:
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Marked design n-safe
Failure:
The schema does not meet requirements for K=n.
Fact table projection projection-name
has insufficient "buddy" projections.
n in the message is 1 or 2 and represents the k value.
Privileges
Must be a superuser.
Notes
l
The database's internal recovery state persists across database restarts but it is not checked at
startup time.
l
If a database has automatic recovery enabled, you must temporarily disable automatic recovery
before creating a new table.
l
When one node fails on a system marked K-safe=1, the remaining nodes are available for DML
operations.
Examples
=> SELECT MARK_DESIGN_KSAFE(1);
mark_design_ksafe
---------------------Marked design 1-safe
(1 row)
If the physical schema design is not K-Safe, messages indicate which projections do not have a
buddy:
=> SELECT MARK_DESIGN_KSAFE(1);
The given K value is not correct;
the schema is 0-safe
Projection pp1 has 0 buddies,
which is smaller that the given K of 1
Projection pp2 has 0 buddies,
which is smaller that the given K of 1
.
.
.
(1 row)
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See Also
l
SYSTEM
l
High Availability and Recovery
l
HP Vertica System Tables
l
Avoiding Resegmentation During Joins
l
Failure Recovery
MATERIALIZE_FLEXTABLE_COLUMNS
Materializes virtual columns that are listed as key_names in the flextable_keys table. You can
optionally indicate the number of columns to materialize, and use a keys table other than the
default. If you do not specify the number of columns, the function materializes up to 50 virtual
column key names. Calling this function requires that you first compute flex table keys using either
COMPUTE_FLEXTABLE_KEYS or COMPUTE_FLEXTABLE_KEYS_AND_BUILD_VIEW .
Note: Materializing any virtual column into a real column with this function affects data storage
limits. Each materialized column counts against the data storage limit of your HP Vertica
Enterprise Edition (EE) license. This increase is reflected when HP Vertica next performs a
license compliance audit. To manually check your EE license compliance, call the audit()
function, described in the SQL Reference Manual.
Usage
materialize_flextable_columns('flex_table' [, n-columns [, keys_table_name] ])
Arguments
flex_table
The name of the flex table with columns to materialize. Specifying only
the flex table name attempts to materialize up to 50 columns of key
names in the default flex_table_keys table, skipping any columns
already materialized. To materialize a specific number of columns, use
the optional parameter n_columns, described next.
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n-columns
[Optional ] The number of columns to materialize. The function
attempts to materialize the number of columns from the flex_table_
keys table, skipping any columns already materialized.
HP VERTICA tables support a total of 1600 columns, which is the
greatest value you can specify for n-columns. The function orders the
materialized results by frequency, descending, key_namewhen
materializing the first n columns.
keys_table_name
[Optional] The name of a flex_keys_table from which to materialize
columns. The function attempts to materialize the number of columns
(value of n-columns) from keys_table_name, skipping any columns
already materialized. The function orders the materialized results by
frequency, descending, key_namewhen materializing the first n
columns.
Examples
The following example loads a sample file of tweets (tweets_10000.json) into the flex table
twitter_r.
After loading data and computing keys for the sample flex table, the example calls materialize_
flextable_columns to materialize the first four columns:
dbt=> copy twitter_r from '/home/release/KData/tweets_10000.json' parser fjsonparser();
Rows Loaded
------------10000
(1 row)
dbt=> select compute_flextable_keys ('twitter_r');
compute_flextable_keys
--------------------------------------------------Please see public.twitter_r_keys for updated keys
(1 row)
dbt=> select materialize_flextable_columns('twitter_r', 4);
materialize_flextable_columns
------------------------------------------------------------------------------The following columns were added to the table public.twitter_r:
contributors
entities.hashtags
entities.urls
For more details, run the following query:
SELECT * FROM v_catalog.materialize_flextable_columns_results WHERE table_schema = 'publi
c' and table_name = 'twitter_r';
(1 row)
The last message in the example recommends querying the materialize_flextable_columns_
results system table for the results of materializing the columns. Following is an example of
running that query:
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dbt=> SELECT * FROM v_catalog.materialize_flextable_columns_results WHERE table_schema =
'public' and table_name = 'twitter_r';
table_id
| table_schema | table_name |
creation_time
|
key_name
| status |
message
-------------------+--------------+------------+------------------------------+-------------------+--------+-------------------------------------------------------45035996273733172 | public
| twitter_r | 2013-11-20 17:00:27.945484-05
| contributors
| ADDED | Added successfully
45035996273733172 | public
| twitter_r | 2013-11-20 17:00:27.94551-05
| entities.hashtags | ADDED | Added successfully
45035996273733172 | public
| entities.urls
| ADDED
| twitter_r | 2013-11-20 17:00:27.945519-05
| Added successfully
45035996273733172 | public
| twitter_r | 2013-11-20 17:00:27.945532-05
| created_at
| EXISTS | Column of same name already exists in table definition
(4 rows)
See the MATERIALIZE_FLEXTABLE_COLUMNS_RESULTS system table in the SQL Reference
Manual.
See Also
l
BUILD_FLEXTABLE_VIEW
l
COMPUTE_FLEXTABLE_KEYS
l
COMPUTE_FLEXTABLE_KEYS_AND_BUILD_VIEW
l
RESTORE_FLEXTABLE_DEFAULT_KEYS_TABLE_AND_VIEW
MEASURE_LOCATION_PERFORMANCE
Measures disk performance for the location specified.
Syntax
MEASURE_LOCATION_PERFORMANCE ( 'path'
, 'node' )
Parameters
path
Specifies where the storage location to measure is mounted.
node
Is the HP Vertica node where the location to be measured is available.
Privileges
Must be a superuser.
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Notes
l
To get a list of all node names on your cluster, query the V_MONITOR.DISK_STORAGE
system table:
=> SELECT node_name from DISK_STORAGE;
node_name
------------------v_vmartdb_node0004
v_vmartdb_node0004
v_vmartdb_node0005
v_vmartdb_node0005
v_vmartdb_node0006
v_vmartdb_node0006
(6 rows)
l
If you intend to create a tiered disk architecture in which projections, columns, and partitions are
stored on different disks based on predicted or measured access patterns, you need to measure
storage location performance for each location in which data is stored. You do not need to
measure storage location performance for temp data storage locations because temporary files
are stored based on available space.
l
The method of measuring storage location performance applies only to configured clusters. If
you want to measure a disk before configuring a cluster see Measuring Storage Performance.
l
Storage location performance equates to the amount of time it takes to read and write 1MB of
data from the disk. This time equates to:
IO time = Time to read/write 1MB + Time to seek = 1/Throughput + 1/Latency
Throughput is the average throughput of sequential reads/writes (units in MB per second)
Latency is for random reads only in seeks (units in seeks per second)
Note: The IO time of a faster storage location is less than a slower storage location.
Example
The following example measures the performance of a storage location on v_vmartdb_node0004:
=> SELECT MEASURE_LOCATION_PERFORMANCE('/secondVerticaStorageLocation/' , 'v_vmartdb_node
0004');
WARNING: measure_location_performance can take a long time. Please check logs for progre
ss
measure_location_performance
-------------------------------------------------Throughput : 122 MB/sec. Latency : 140 seeks/sec
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See Also
l
ADD_LOCATION
l
ALTER_LOCATION_USE
l
RESTORE_LOCATION
l
RETIRE_LOCATION
l
Measuring Storage Performance
MERGE_PARTITIONS
Merges ROS containers that have data belonging to partitions in a specified partition key range:
partitionKeyFromto partitionKeyTo.
Note: This function is deprecated in HP Vertica 7.0.
Syntax
MERGE_PARTITIONS ( table_name , partition_key_from , partition_key_to )
Parameters
table_name
Specifies the name of the table
partition_key_from
Specifies the start point of the partition
partition_key_to
Specifies the end point of the partition
Privileges
l
Table owner
l
USAGE privilege on schema that contains the table
Notes
l
You cannot run MERGE_PARTITIONS() on a table with data that is not reorganized. You must
reorganize the data first using ALTER_TABLE table REORGANIZE, or PARTITION_TABLE(table).
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l
The edge values are included in the range, and partition_key_from must be less than or equal
to partition_key_to.
l
Inclusion of partitions in the range is based on the application of less than (<)/greater than (>)
operators of the corresponding data type.
Note: No restrictions are placed on a partition key's data type.
l
If partition_key_from is the same as partition_key_to, all ROS containers of the partition
key are merged into one ROS.
Examples
=>
=>
=>
=>
=>
=>
=>
=>
SELECT
SELECT
SELECT
SELECT
SELECT
SELECT
SELECT
SELECT
MERGE_PARTITIONS('T1',
MERGE_PARTITIONS('T1',
MERGE_PARTITIONS('T1',
MERGE_PARTITIONS('T1',
MERGE_PARTITIONS('T1',
MERGE_PARTITIONS('T1',
MERGE_PARTITIONS('T1',
MERGE_PARTITIONS('T1',
'200', '400');
'800', '800');
'CA', 'MA');
'false', 'true');
'06/06/2008', '06/07/2008');
'02:01:10', '04:20:40');
'06/06/2008 02:01:10', '06/07/2008 02:01:10');
'8 hours', '1 day 4 hours 20 seconds');
MOVE_PARTITIONS_TO_TABLE
Moves partitions from a source table to a target table. The target table must have the same
projection column definitions, segmentation, and partition expressions as the source table. If the
target table does not exist, the function creates a new table based on the source definition. The
function requires both minimum and maximum range values, indicating what partition values to
move.
Syntax
MOVE_PARTITIONS_TO_TABLE ( '[[db-name.]schema.]source_table', 'min_range_value',
'max_range_value', '[[db-name.]schema.]target_table' )
Parameters
[[db-name.]schema.]source_table
The source table (optionally qualified), from which you want to
move partitions.
min_range_value
The minimum value in the partition to move.
max_range_value
The maximum value of the partition being moved.
target_table
The table to which the partitions are being moved.
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Privileges
l
Table owner
l
If target table is created as part of moving partitions, the new table has the same owner as the
target. If the target table exists, user must have own the target table, and have ability to call this
function.
Example
If you call MOVE_PARTITIONS_TO_TABLE and the destination table does not exist, the function will
create the table automatically:
VMART=> SELECT MOVE_PARTITIONS_TO_TABLE (
'prod_trades',
'200801',
'200801',
'partn_backup.trades_200801');
MOVE_PARTITIONS_TO_TABLE
--------------------------------------------------------------------------1 distinct partition values moved at epoch 15. Effective move epoch: 14.
(1 row)
See Also
l
DROP_PARTITION
l
DUMP_PARTITION_KEYS
l
DUMP_PROJECTION_PARTITION_KEYS
l
DUMP_TABLE_PARTITION_KEYS
l
PARTITION_PROJECTION
l
Moving Partitions
l
Creating a Table Like Another
PARTITION_PROJECTION
Forces a split of ROS containers of the specified projection.
Syntax
PARTITION_PROJECTION ( '[[db-name.]schema.]projection_name' )
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Parameters
[[db-name.]schema.]
[Optional] Specifies the database name and optional schema name. Using
a database name identifies objects that are not unique within the current
search path (see Setting Search Paths). You must be connected to the
database you specify, and you cannot change objects in other databases.
Specifying different database objects lets you qualify database objects as
explicitly as required. For example, you can use a database and a schema
name (mydb.myschema).
projection_name
Specifies the name of the projection.
Privileges
l
Table owner
l
USAGE privilege on schema
Notes
Partitioning expressions take immutable functions only, in order that the same information be
available across all nodes.
PARTITION_PROJECTION() is similar to PARTITION_TABLE(), except that PARTITION_
PROJECTION works only on the specified projection, instead of the table.
Users must have USAGE privilege on schema that contains the table.
PARTITION_PROJECTION() purges data while partitioning ROS containers if deletes were
applied before the AHM epoch.
Example
The following command forces a split of ROS containers on the states_p_node01 projection:
=> SELECT PARTITION_PROJECTION ('states_p_node01');
partition_projection
-----------------------Projection partitioned
(1 row)
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See Also
l
DO_TM_TASK
l
DROP_PARTITION
l
DUMP_PARTITION_KEYS
l
DUMP_PROJECTION_PARTITION_KEYS
l
DUMP_TABLE_PARTITION_KEYS
l
PARTITION_TABLE
l
Working with Table Partitions
PARTITION_TABLE
Forces the system to break up any ROS containers that contain multiple distinct values of the
partitioning expression. Only ROS containers with more than one distinct value participate in the
split.
Syntax
PARTITION_TABLE ( '[[db-name.]schema.]table_name' )
Parameters
[[db-name.]schema.]
[Optional] Specifies the database name and optional schema name. Using
a database name identifies objects that are not unique within the current
search path (see Setting Search Paths). You must be connected to the
database you specify, and you cannot change objects in other databases.
Specifying different database objects lets you qualify database objects as
explicitly as required. For example, you can use a database and a schema
name (mydb.myschema).
table_name
Specifies the name of the table.
Privileges
l
Table owner
l
USAGE privilege on schema
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Notes
PARTITION_TABLE is similar to PARTITION_PROJECTION, except that PARTITION_TABLE
works on the specified table.
Users must have USAGE privilege on schema that contains the table.
Partitioning functions take immutable functions only, in order that the same information be
available across all nodes.
Example
The following example creates a simple table called states and partitions data by state.
=> CREATE TABLE states (year INTEGER NOT NULL,
state VARCHAR NOT NULL)
PARTITION BY state;
=> CREATE PROJECTION states_p (state, year) AS
SELECT * FROM states
ORDER BY state, year UNSEGMENTED ALL NODES;
Now call the PARTITION_TABLE function to partition table states:
=> SELECT PARTITION_TABLE('states');
PARTITION_TABLE
------------------------------------------------------partition operation for projection 'states_p_node0004'
partition operation for projection 'states_p_node0003'
partition operation for projection 'states_p_node0002'
partition operation for projection 'states_p_node0001'
(1 row)
See Also
l
DO_TM_TASK
l
DROP_PARTITION
l
DUMP_PARTITION_KEYS
l
DUMP_PROJECTION_PARTITION_KEYS
l
DUMP_TABLE_PARTITION_KEYS
l
PARTITION_PROJECTION
l
Working with Table Partitions
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PURGE
Permanently removes deleted data from physical storage so that the disk space can be reused.
You can purge historical data up to and including the epoch in which the Ancient History Mark is
contained.
Purges all projections in the physical schema. PURGE does not delete temporary tables.
Syntax
PURGE()
Privileges
l
Table owner
l
USAGE privilege on schema
Notes
l
PURGE() was formerly named PURGE_ALL_PROJECTIONS. HP Vertica supports both
function calls.
Caution: PURGE could temporarily take up significant disk space while the data is being
purged.
See Also
l
MERGE_PARTITIONS
l
PARTITION_TABLE
l
PURGE_PROJECTION
l
PURGE_TABLE
l
STORAGE_CONTAINERS
l
Purging Deleted Data
PURGE_PARTITION
Purges a table partition of deleted rows. Similar to PURGE() and PURGE_PROJECTION(), this function
removes deleted data from physical storage so you can reuse the disk space. PURGE_PARTITION()
removes data from the AHM epoch and earlier only.
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Syntax
PURGE_PARTITION ( '[[db_name.]schema_name.]table_name', partition_key )
Parameters
[[db_name.]schema_name.]
[Optional] Specifies the database name and optional schema name.
Using a database name identifies objects that are not unique within
the current search path (see Setting Search Paths). You must be
connected to the database you specify, and you cannot change
objects in other databases.
Specifying different database objects lets you qualify database
objects as explicitly as required. For example, you can use a
database and a schema name (mydb.myschema).
table_name
The name of the partitioned table
partition_key
The key of the partition to be purged of deleted rows
Privileges
l
Table owner
l
USAGE privilege on schema
Example
The following example lists the count of deleted rows for each partition in a table, then calls PURGE_
PARTITION() to purge the deleted rows from the data.
=> SELECT partition_key,table_schema,projection_name,sum(deleted_row_count)
AS deleted_row_count FROM partitions
GROUP BY partition_key,table_schema,projection_name
ORDER BY partition_key;
partition_key | table_schema | projection_name | deleted_row_count
---------------+--------------+-----------------+------------------0
| public
| t_super
|
2
1
| public
| t_super
|
2
2
| public
| t_super
|
2
3
| public
| t_super
|
2
4
| public
| t_super
|
2
5
| public
| t_super
|
2
6
| public
| t_super
|
2
7
| public
| t_super
|
2
8
| public
| t_super
|
2
9
| public
| t_super
|
1
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(10 rows)
=> SELECT PURGE_PARTITION('t',5); -- Purge partition with key 5.
purge_partition
-----------------------------------------------------------------------Task: merge partitions
(Table: public.t) (Projection: public.t_super)
(1 row)
=> SELECT partition_key,table_schema,projection_name,sum(deleted_row_count)
AS deleted_row_count FROM partitions
GROUP BY partition_key,table_schema,projection_name
ORDER BY partition_key;
partition_key | table_schema | projection_name | deleted_row_count
---------------+--------------+-----------------+------------------0
| public
| t_super
|
2
1
| public
| t_super
|
2
2
| public
| t_super
|
2
3
| public
| t_super
|
2
4
| public
| t_super
|
2
5
| public
| t_super
|
0
6
| public
| t_super
|
2
7
| public
| t_super
|
2
8
| public
| t_super
|
2
9
| public
| t_super
|
1
(10 rows)
See Also
l
MERGE_PARTITIONS
l
PURGE
l
PURGE_PROJECTION
l
PURGE_TABLE
l
STORAGE_CONTAINERS
PURGE_PROJECTION
Permanently removes deleted data from physical storage so that the disk space can be reused.
You can purge historical data up to and including the epoch in which the Ancient History Mark is
contained.
Purges the specified projection.
Caution: PURGE_PROJECTION could temporarily take up significant disk space while
purging the data.
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Syntax
PURGE_PROJECTION ( '[[db-name.]schema.]projection_name' )
Parameters
[[db-name.]schema.]
[Optional] Specifies the database name and optional schema name. Using
a database name identifies objects that are not unique within the current
search path (see Setting Search Paths). You must be connected to the
database you specify, and you cannot change objects in other databases.
Specifying different database objects lets you qualify database objects as
explicitly as required. For example, you can use a database and a schema
name (mydb.myschema).
projection_name
Identifies the projection name. When using more than one schema,
specify the schema that contains the projection, as noted above.
Privileges
l
Table owner
l
USAGE privilege on schema
Notes
See PURGE for notes about the outcome of purge operations.
See Also
l
PURGE_TABLE
l
STORAGE_CONTAINERS
l
Purging Deleted Data
PURGE_TABLE
Note: This function was formerly named PURGE_TABLE_PROJECTIONS(). HP Vertica still
supports the former function name.
Permanently removes deleted data from physical storage so that the disk space can be reused.
You can purge historical data up to and including the epoch in which the Ancient History Mark is
contained.
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Purges all projections of the specified table. You cannot use this function to purge temporary tables.
Syntax
PURGE_TABLE ( '[[db-name.]schema.]table_name' )
Parameters
[[db-name.]schema.]
[Optional] Specifies the database name and optional schema name. Using
a database name identifies objects that are not unique within the current
search path (see Setting Search Paths). You must be connected to the
database you specify, and you cannot change objects in other databases.
Specifying different database objects lets you qualify database objects as
explicitly as required. For example, you can use a database and a schema
name (mydb.myschema).
table_name
Specifies the table to purge.
Privileges
l
Table owner
l
USAGE privilege on schema
Caution: PURGE_TABLE could temporarily take up significant disk space while the data is
being purged.
Example
The following example purges all projections for the store sales fact table located in the Vmart
schema:
=> SELECT PURGE_TABLE('store.store_sales_fact');
See Also
l
PURGE
l
PURGE_TABLE
l
STORAGE_CONTAINERS
l
Purging Deleted Data
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REALIGN_CONTROL_NODES
Chooses control nodes (spread hosts) from all cluster nodes and assigns the rest of the nodes in
the cluster to a control node. Calling this function respects existing fault groups, which you can
view by querying the V_CATALOG.CLUSTER_LAYOUT system table. This view also lets you see the
proposed new layout for nodes in the cluster.
Note: You use this function with other cluster management functions. For details, see Defining
and Realigning Control Nodes on an Existing Cluster in the Administrator's Guide.
Syntax
REALIGN_CONTROL_NODES()
Privileges
Must be a superuser.
Example
The following command chooses control nodes from all cluster nodes and assigns the rest of the
nodes in the cluster to a control node:
=> SELECT realign_control_nodes();
See Also
Cluster Management Functions
V_CATALOG.CLUSTER_LAYOUT
Large Cluster in the Administrator's Guide
REBALANCE_CLUSTER
Call this function to begin rebalancing data in the cluster synchronously.
A rebalance operation performs the following tasks:
l
Distributes data based on user-defined fault groups, if specified, or based on large cluster
automatic fault groups
l
Redistributes the database projections' data across all nodes
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l
Refreshes projections
l
Sets the Ancient History Mark
l
Drops projections that are no longer needed
When to rebalance the cluster
Rebalancing is useful (or necessary) after you:
l
Mark one or more nodes as ephemeral in preparation of removing them from the cluster
l
Add one or more nodes to the cluster so HP Vertica can populate the empty nodes with data
l
Remove one or more nodes from the cluster so HP Vertica can redistribute the data among the
remaining nodes
l
Change the scaling factor of an elastic cluster, which determines the number of storage
containers used to store a projection across the database
l
Set the control node size or realign control nodes on a large cluster layout
l
Specify more than 120 nodes in your initial HP Vertica cluster configuration
l
Add nodes to or remove nodes from a fault group
Because this function runs the rebalance task synchronously, it does not return until the data has
been rebalanced. Closing or dropping the session cancels the rebalance task.
Important: On large cluster arrangements, you typically use this function in a flow, described
Defining and Realigning Control Nodes in the Administrator's Guide. After you change the
number and distribution of control nodes (spread hosts), you must run REBALANCE_CLUSTER()
for fault tolerance to be realized.
Syntax
REBALANCE_CLUSTER()
Privileges
Must be a superuser.
Example
The following command rebalances data across the cluster.
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=> SELECT REBALANCE_CLUSTER();
REBALANCE_CLUSTER
------------------REBALANCED
(1 row)
See Also
START_REBALANCE_CLUSTER
CANCEL_REBALANCE_CLUSTER
Rebalancing Data Across Nodes in the Administrator's Guide
REENABLE_DUPLICATE_KEY_ERROR
Restores the default behavior of error reporting by reversing the effects of DISABLE_DUPLICATE_
KEY_ERROR. Effects are session scoped.
Syntax
REENABLE_DUPLICATE_KEY_ERROR();
Privileges
Must be a superuser.
Examples
For examples and usage, see DISABLE_DUPLICATE_KEY_ERROR.
See Also
l
ANALYZE_CONSTRAINTS
REFRESH
Performs a synchronous, optionally-targeted refresh of a specified table's projections.
Information about a refresh operation—whether successful or unsuccessful—is maintained in the
PROJECTION_REFRESHES system table until either the CLEAR_PROJECTION_
REFRESHES() function is executed or the storage quota for the table is exceeded. The
PROJECTION_REFRESHES.IS_EXECUTING column returns a boolean value that indicates whether the
refresh is currently running (t) or occurred in the past (f).
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Syntax
REFRESH ( '[[db-name.]schema.]table_name [ , ... ]' )
Parameters
[[db-name.]schema.]
[Optional] Specifies the database name and optional schema name. Using
a database name identifies objects that are not unique within the current
search path (see Setting Search Paths). You must be connected to the
database you specify, and you cannot change objects in other databases.
Specifying different database objects lets you qualify database objects as
explicitly as required. For example, you can use a database and a schema
name (mydb.myschema).
table_name
Specifies the name of a specific table containing the projections to be
refreshed. The REFRESH() function attempts to refresh all the tables
provided as arguments in parallel. Such calls will be part of the Database
Designer deployment (and deployment script).
When using more than one schema, specify the schema that contains the
table, as noted above.
Returns
Column Name Description
Projection Name
The name of the projection that is targeted for refresh.
Anchor Table
The name of the projection's associated anchor table.
Status
The status of the projection:
l
Queued—Indicates that a projection is queued for refresh.
l
Refreshing—Indicates that a refresh for a projection is in process.
l
Refreshed—Indicates that a refresh for a projection has successfully
completed.
l
Failed—Indicates that a refresh for a projection did not successfully
complete.
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Refresh Method
The method used to refresh the projection:
l
Buddy—Uses the contents of a buddy to refresh the projection. This
method maintains historical data. This enables the projection to be used for
historical queries.
l
Scratch—Refreshes the projection without using a buddy. This method
does not generate historical data. This means that the projection cannot
participate in historical queries from any point before the projection was
refreshed.
Error Count
The number of times a refresh failed for the projection.
Duration (sec)
The length of time that the projection refresh ran in seconds.
Privileges
REFRESH() works only if invoked on tables owned by the calling user.
Notes
l
Unlike START_REFRESH(), which runs in the background, REFRESH() runs in the foreground
of the caller's session.
l
The REFRESH() function refreshes only the projections in the specified table.
l
If you run REFRESH() without arguments, it refreshes all non up-to-date projections. If the
function returns a header string with no results, then no projections needed refreshing.
Examples
The following example refreshes the projections in tables t1 and t2:
=> SELECT REFRESH('t1, t2');
REFRESH
---------------------------------------------------------------------------------------Refresh completed with the following outcomes:
Projection Name: [Anchor Table] [Status] [Refresh Method] [Error Count] [Duration (sec)]
---------------------------------------------------------------------------------------"public"."t1_p": [t1] [refreshed] [scratch] [0] [0]"public"."t2_p": [t2] [refreshed] [scr
atch] [0] [0]
This next example shows that only the projection on table t was refreshed:
=> SELECT REFRESH('allow, public.deny, t');
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REFRESH
---------------------------------------------------------------------------------------Refresh completed with the following outcomes:
Projection Name: [Anchor Table] [Status] [Refresh Method] [Error Count] [Duration (sec)]
---------------------------------------------------------------------------------------"n/a"."n/a": [n/a] [failed: insufficient permissions on table "allow"] [] [1] [0]
"n/a"."n/a": [n/a] [failed: insufficient permissions on table "public.deny"] [] [1] [0]
"public"."t_p1": [t] [refreshed] [scratch] [0] [0]
See Also
l
CLEAR_PROJECTION_REFRESHES
l
PROJECTION_REFRESHES
l
START_REFRESH
l
Clearing PROJECTION_REFRESHES History
RELEASE_ALL_JVM_MEMORY
Forces all sessions to release the memory consumed by their Java Virtual Machines (JVM).
Syntax
RELEASE_ALL_JVM_MEMORY();
Permissions
Must be a superuser.
Example
The following example demonstrates viewing the JVM memory use in all open sessions, then
calling RELEASE_ALL_JVM_MEMORY() to release the memory:
=> select user_name,jvm_memory_kb FROM V_MONITOR.SESSIONS;
user_name | jvm_memory_kb
-----------+--------------dbadmin
|
79705
(1 row)
=> SELECT RELEASE_ALL_JVM_MEMORY();
RELEASE_ALL_JVM_MEMORY
-----------------------------------------------------------------------------
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Close all JVM sessions command sent. Check v_monitor.sessions for progress.
(1 row)
=> SELECT user_name,jvm_memory_kb FROM V_MONITOR.SESSIONS;
user_name | jvm_memory_kb
-----------+--------------dbadmin
|
0
(1 row)
See Also
l
RELEASE_JVM_MEMORY
RELEASE_JVM_MEMORY
Terminates a Java Virtual Machine (JVM), making available the memory the JVM was using.
Syntax
RELEASE_JVM_MEMORY();
Privileges
None.
Examples
User session opened. RECORD 2 shows the user session running COPY DIRECT statement.
=> SELECT RELEASE_JVM_MEMORY();
release_jvm_memory
----------------------------------------Java process killed and memory released
(1 row)
See Also
l
RELEASE_ALL_JVM_MEMORY
RELOAD_SPREAD
Calling this function with the required true argument updates cluster changes (such as new or realigned control nodes spread hosts or fault groups or new/dropped cluster nodes), to the catalog's
spread configuration file.
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Important: This function is often used in a multi-step process for large and elastic cluster
arrangements. Calling RELOAD_SPREAD(true) might require that you restart the database,
which you do using the Administration Tools. You must then rebalance the cluster for fault
tolerance to be realized. See Defining and Realigning Control Nodes in the Administrator's
Guide for more information.
Syntax
RELOAD_SPREAD(true)
Parameters
true
Updates cluster changes related to control
message responsibilities to the spread
configuration file.
Privileges
Must be a superuser.
Example
The following command updates the cluster with changes to control messaging:
=> SELECT reload_spread(true);
reload_spread
--------------reloaded
(1 row)
See Also
Cluster Management Functions
REBALANCE_CLUSTER
V_CATALOG.CLUSTER_LAYOUT
Large Cluster in the Administrator's Guide
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RESET_LOAD_BALANCE_POLICY
Resets the counter each host in the cluster maintains to track which host it will refer a client to
when the native connection load balancing scheme is set to ROUNDROBIN.
Syntax
RESET_LOAD_BALANCE_POLICY()
Notes
This function only has an effect if the current native connection load balancing scheme is
ROUNDROBIN.
Permissions
This function can be called only by a superuser .
Example
The following example demonstrates calling RESET_LOAD_BALANCE_POLICY:
=> SELECT RESET_LOAD_BALANCE_POLICY();
RESET_LOAD_BALANCE_POLICY
------------------------------------------------------------------------Successfully reset stateful client load balance policies: "roundrobin".
(1 row)
RESTORE_LOCATION
Restores a storage location that was previously retired with RETIRE_LOCATION.
Syntax
RESTORE_LOCATION ( 'path', 'node' )
Parameters
path
Specifies where the retired storage location is mounted.
node
Is the HP Vertica node where the retired location is available.
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Privileges
Must be a superuser.
Effects of Restoring a Previously Retired Location
After restoring a storage location, HP Vertica re-ranks all of the cluster storage locations and uses
the newly-restored location to process queries as determined by its rank.
Monitoring Storage Locations
Disk storage information that the database uses on each node is available in the V_
MONITOR.DISK_STORAGE system table.
Example
The following example restores the retired storage location on node3:
=> SELECT RESTORE_LOCATION ('/thirdHP VerticaStorageLocation/' , 'v_vmartdb_node0004');
See Also
l
Altering Storage Location Use
l
ADD_LOCATION
l
ALTER_LOCATION_USE
l
DROP_LOCATION
l
RETIRE_LOCATION
l
GRANT (Storage Location)
l
REVOKE (Storage Location)
RESTORE_FLEXTABLE_DEFAULT_KEYS_TABLE_AND_
VIEW
Restores the _keys table and the _view, linking them with their associated flex table if either is
dropped. This function notes whether it restores one or both.
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Usage
restore_flextable_default_keys_table_and_view('flex_table')
Arguments
flex_table The name of the flex table .
Examples
This example invokes the function with an existing flex table, restoring both the _keys table and _
view:
kdb=> select restore_flextable_default_keys_table_and_view('darkdata');
restore_flextable_default_keys_table_and_view
---------------------------------------------------------------------------------The keys table public.darkdata_keys was restored successfully.
The view public.darkdata_view was restored successfully.
(1 row)
This example shows the function restoring darkdata_view, but noting that darkdata_keys does
not need restoring:
kdb=> select restore_flextable_default_keys_table_and_view('darkdata');
restore_flextable_default_keys_table_and_view
-----------------------------------------------------------------------------------------------The keys table public.darkdata_keys already exists and is linked to darkdata.
The view public.darkdata_view was restored successfully.
(1 row)
The _keys table has no content after it is restored:
kdb=> select * from darkdata_keys;
key_name | frequency | data_type_guess
----------+-----------+----------------(0 rows)
See Also
l
BUILD_FLEXTABLE_VIEW
l
COMPUTE_FLEXTABLE_KEYS
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l
COMPUTE_FLEXTABLE_KEYS_AND_BUILD_VIEW
l
MATERIALIZE_FLEXTABLE_COLUMNS
RETIRE_LOCATION
Makes the specified storage location inactive.
Syntax
RETIRE_LOCATION ( 'path', 'node' )
Parameters
path
Specifies where the storage location to retire is mounted.
node
Is the HP Vertica node where the location is available.
Privileges
Must be a superuser.
Effects of Retiring a Storage Location
When you use this function, HP Vertica checks that the location is not the only storage for data and
temp files. At least one location must exist on each node to store data and temp files, though you
can store both sorts of files in either the same location, or separate locations.
Note: You cannot retire a location if it is used in a storage policy, and is the last available
storage for its associated objects.
When you retire a storage location:
l
No new data is stored at the retired location, unless you first restore it with the RESTORE_
LOCATION() function.
l
If the storage location being retired contains stored data, the data is not moved, so you cannot
drop the storage location. Instead, HP Vertica removes the stored data through one or more
mergeouts.
l
If the storage location being retired was used only for temp files, you can drop the location. See
Dropping Storage Locations in the Administrators Guide and the DROP_LOCATION() function.
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Monitoring Storage Locations
Disk storage information that the database uses on each node is available in the V_
MONITOR.DISK_STORAGE system table.
Example
The following example retires a storage location:
=> SELECT RETIRE_LOCATION ('/secondVerticaStorageLocation/' , 'v_vmartdb_node0004');
See Also
l
Retiring Storage Locations
l
ADD_LOCATION
l
ALTER_LOCATION_USE
l
DROP_LOCATION
l
RESTORE_LOCATION
l
GRANT (Storage Location)
l
REVOKE (Storage Location)
SET_AHM_EPOCH
Sets the Ancient History Mark (AHM) to the specified epoch. This function allows deleted data up
to and including the AHM epoch to be purged from physical storage.
SET_AHM_EPOCH is normally used for testing purposes. Consider SET_AHM_TIME instead,
which is easier to use.
Syntax
SET_AHM_EPOCH ( epoch, [ true ] )
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Parameters
epoch
true
Specifies one of the following:
l
The number of the epoch in which to set the AHM
l
Zero (0) (the default) disables PURGE
Optionally allows the AHM to advance when nodes are down.
Note: If the AHM is advanced after the last good epoch of the failed nodes, those
nodes must recover all data from scratch. Use with care.
Privileges
Must be a superuser.
Notes
If you use SET_AHM_EPOCH , the number of the specified epoch must be:
l
Greater than the current AHM epoch
l
Less than the current epoch
l
Less than or equal to the cluster last good epoch (the minimum of the last good epochs of the
individual nodes in the cluster)
l
Less than or equal to the cluster refresh epoch (the minimum of the refresh epochs of the
individual nodes in the cluster)
Use the SYSTEM table to see current values of various epochs related to the AHM; for example:
=> SELECT * from SYSTEM;
-[ RECORD 1 ]------------+--------------------------current_timestamp
| 2009-08-11 17:09:54.651413
current_epoch
| 1512
ahm_epoch
| 961
last_good_epoch
| 1510
refresh_epoch
| -1
designed_fault_tolerance | 1
node_count
| 4
node_down_count
| 0
current_fault_tolerance | 1
catalog_revision_number | 1590
wos_used_bytes
| 0
wos_row_count
| 0
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ros_used_bytes
ros_row_count
total_used_bytes
total_row_count
|
|
|
|
41490783
1298104
41490783
1298104
All nodes must be up. You cannot use SET_AHM_EPOCH when any node in the cluster is down,
except by using the optional true parameter.
When a node is down and you issue SELECT MAKE_AHM_NOW(), the following error is printed to the
vertica.log:
Some nodes were excluded from setAHM. If their LGE is before the AHM they will perform fu
ll recovery.
Examples
The following command sets the AHM to a specified epoch of 12:
=> SELECT SET_AHM_EPOCH(12);
The following command sets the AHM to a specified epoch of 2 and allows the AHM to advance
despite a failed node:
=> SELECT SET_AHM_EPOCH(2, true);
See Also
l
MAKE_AHM_NOW
l
SET_AHM_TIME
l
SYSTEM
SET_AHM_TIME
Sets the Ancient History Mark (AHM) to the epoch corresponding to the specified time on the
initiator node. This function allows historical data up to and including the AHM epoch to be purged
from physical storage.
Syntax
SET_AHM_TIME ( time , [ true ] )
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Parameters
time
Is a TIMESTAMP value that is automatically converted to the appropriate epoch number.
true
[Optional] Allows the AHM to advance when nodes are down.
Note: If the AHM is advanced after the last good epoch of the failed nodes, those nodes
must recover all data from scratch.
Privileges
Must be a superuser.
Notes
l
SET_AHM_TIME returns a TIMESTAMP WITH TIME ZONE value representing the end point of
the AHM epoch.
l
You cannot change the AHM when any node in the cluster is down, except by using the optional
true parameter.
l
When a node is down and you issue SELECT MAKE_AHM_NOW(), the following error is printed to
the vertica.log:
Some nodes were excluded from setAHM. If their LGE is before the AHM they will perform
full recovery.
Examples
Epochs depend on a configured epoch advancement interval. If an epoch includes a three-minute
range of time, the purge operation is accurate only to within minus three minutes of the specified
timestamp:
=> SELECT SET_AHM_TIME('2008-02-27 18:13');
set_ahm_time
-----------------------------------AHM set to '2008-02-27 18:11:50-05'
(1 row)
Note: The –05 part of the output string is a time zone value, an offset in hours from UTC
(Universal Coordinated Time, traditionally known as Greenwich Mean Time, or GMT).
In the previous example, the actual AHM epoch ends at 18:11:50, roughly one minute before the
specified timestamp. This is because SET_AHM_TIME selects the epoch that ends at or before
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the specified timestamp. It does not select the epoch that ends after the specified timestamp
because that would purge data deleted as much as three minutes after the AHM.
For example, using only hours and minutes, suppose that epoch 9000 runs from 08:50 to 11:50 and
epoch 9001 runs from 11:50 to 15:50. SET_AHM_TIME('11:51') chooses epoch 9000 because it
ends roughly one minute before the specified timestamp.
In the next example, if given an environment variable set as date =`date`; the following
command fails if a node is down:
=> SELECT SET_AHM_TIME('$date');
In order to force the AHM to advance, issue the following command instead:
=> SELECT SET_AHM_TIME('$date', true);
See Also
l
MAKE_AHM_NOW
l
SET_AHM_EPOCH
l
SET DATESTYLE
l
TIMESTAMP
SET_AUDIT_TIME
Sets the time that HP Vertica performs automatic database size audit to determine if the size of the
database is compliant with the raw data allowance in your HP Vertica license. Use this function if
the audits are currently scheduled to occur during your database's peak activity time. This is
normally not a concern, since the automatic audit has little impact on database performance.
Audits are scheduled by the preceding audit, so changing the audit time does not affect the next
scheduled audit. For example, if your next audit is scheduled to take place at 11:59PM and you use
SET_AUDIT_TIME to change the audit schedule 3AM, the previously scheduled 11:59PM audit
still runs. As that audit finishes, it schedules the next audit to occur at 3AM.
If you want to prevent the next scheduled audit from running at its scheduled time, you can change
the scheduled time using SET_AUDIT_TIME then manually trigger an audit to run immediately
using AUDIT_LICENSE_SIZE. As the manually-triggered audit finishes, it schedules the next audit
to occur at the time you set using SET_AUDIT_TIME (effectively overriding the previously
scheduled audit).
Syntax
SET_AUDIT_TIME(time)
time
A string containing the time in 'HH:MM AM/PM' format (for example, '1:00 AM') when the
audit should run daily.
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Privileges
Must be a superuser.
Example
=> SELECT SET_AUDIT_TIME('3:00 AM');
SET_AUDIT_TIME
----------------------------------------------------------------------The scheduled audit time will be set to 3:00 AM after the next audit.
(1 row)
SET_CONTROL_SET_SIZE
For existing database clusters, use this function to specify the number of cluster nodes on which
you want to deploy control messaging (spread). The SET_CONTROL_SET_SIZE() function works the
same as the install_vertica --large cluster option.
You can run SET_CONTROL_SET_SIZE()after the database cluster is already defined, but before you
call this function, the database must be up.
Note: You use this function with other cluster management functions. For details, see Defining
and Realigning Control Nodes on an Existing Cluster in the Administrator's Guide.
Syntax
SET_CONTROL_SET_SIZE(integer)
Parameters
integer
Specifies the number of cluster hosts from the
database cluster on which spread runs.
Privileges
Must be a superuser.
Note
To see if the current spread hosts and the control designations in the Catalog match, query the V_
CATALOG.LARGE_CLUSTER_CONFIGURATION_STATUS system table.
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Example
The following command tells HP Vertica that you want to run spread on two cluster nodes:
=> SELECT set_control_set_size(2);
SET_CONTROL_SET_SIZE
---------------------Control size set
(1 row)
See Also
Cluster Management Functions
V_CATALOG.CLUSTER_LAYOUT
Large Cluster in the Administrator's Guide
SET_DATA_COLLECTOR_POLICY
Sets a size restraint (memory and disk space in kilobytes) for the specified Data Collector table on
all nodes. If nodes are down, the failed nodes receive the setting when they rejoin the cluster.
You can use this function to set a size restraint only, or you can include the optional interval
argument to set disk capacity for both size and time in a single command.
If you specify interval, HP Vertica enforces the setting that is exceeded first (size or time).
Before you include a time restraint, be sure the disk size capacity is sufficiently large.
If you want to specify just a time restraint, or you want to turn off a time restraint you set using this
function, see SET_DATA_COLLECTOR_TIME_POLICY().
Syntax
SET_DATA_COLLECTOR_POLICY('component', 'memoryKB', 'diskKB' [,'interval']
)
Parameters
component
Configures the retention policy for the specified component.
memoryKB
Specifies the memory size to retain in kilobytes.
diskKB
Specifies the disk size in kilobytes.
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interval
[Default off] Takes an optional interval argument to specify how long to retain the
specified component on disk. To disable a time restraint, set interval to -1.
Note: Any negative input will turn off the time restraint
Privileges
Must be a superuser.
Notes
l
Before you change a retention policy, view its current setting by calling the GET_DATA_
COLLECTOR_POLICY() function.
l
If you don't know the name of a component, query the V_MONITOR.DATA_COLLECTOR system
table for a list; for example:
=> SELECT DISTINCT component, description FROM data_collector
ORDER BY 1 ASC;
Examples
The following command returns the retention policy for the ResourceAcquisitions component:
=> SELECT get_data_collector_policy('ResourceAcquisitions');
get_data_collector_policy
---------------------------------------------1000KB kept in memory, 10000KB kept on disk.
(1 row)
This command changes the memory and disk setting for ResourceAcquisitions from its current
setting of 1,000 KB memory and 10,000 KB disk space to 1500 KB and 25000 KB, respectively:
=> SELECT set_data_collector_policy('ResourceAcquisitions', '1500', '25000');
set_data_collector_policy
--------------------------SET
(1 row)
This command sets the RequestsIssued component to 1500 KB memory and 11000 KB on disk,
and includes a 3-minute time restraint:
=> SELECT set_data_collector_policy('RequestsIssued', '1500', '11000', '3 minutes'::inter
val);
set_data_collector_policy
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--------------------------SET
(1 row)
The following command disables the 3-minute retention policy for the RequestsIssued component:
=> SELECT set_data_collector_policy('RequestsIssued', '-1');
set_data_collector_policy
--------------------------SET
(1 row)
See Also
l
GET_DATA_COLLECTOR_POLICY
l
SET_DATA_COLLECTOR_TIME_POLICY()
l
DATA_COLLECTOR
l
Retaining Monitoring Information in the Administrator's Guide
SET_DATA_COLLECTOR_TIME_POLICY
Sets a time capacity for individual Data Collector tables on all nodes. If nodes are down, the failed
nodes receive the setting when they rejoin the cluster.
If you want to configure both time and size restraints at the same time, see SET_DATA_COLLECTOR_
POLICY().
Syntax
SET_DATA_COLLECTOR_TIME_POLICY( ['component',] 'interval' )
Parameters
component
[Optional] Configures the time retention policy for the specified component. If you
omit the component argument, HP Vertica sets the specified time capacity for all
Data Collector tables.
interval
Specifies the time restraint on disk using an INTERVAL type. To disable a time
restraint, set interval to -1.
Note: Any negative input turns off the time restraint
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Privileges
Must be a superuser.
Notes
l
Before you change a retention policy, view its current setting by calling the GET_DATA_
COLLECTOR_POLICY() function.
l
If you don't know the name of a component, query the V_MONITOR.DATA_COLLECTOR system
table for a list. For example:
=> SELECT DISTINCT component, description FROM data_collector
ORDER BY 1 ASC;
Setting time interval for system tables
You can also use the interval argument to query system tables the same way you query Data
Collector tables; for example:
set_data_collector_time_policy('', <'interval'>);
To illustrate, the following command in the left column is equivalent to running the series of
commands on the right:
Run one command
Instead of a series of commands
SELECT set_data_collector_time_policy
('v_monitor.query_requests',
'3 minutes'::interval);
SELECT set_data_collector_time_policy
('RequestsIssued',
'3 minutes'::interval);
SELECT set_data_collector_time_policy
('RequestsCompleted',
'3 minutes'::interval);
SELECT set_data_collector_time_policy
('Errors',
'3 minutes'::interval);
SELECT set_data_collector_time_policy
('ResourceAcquisitions',
'3 minutes'::interval);
The SET_DATA_COLLECTOR_TIME_POLICY() function updates the time capacity for all Data
Collector tables in the V_MONITOR.QUERY_REQUESTS view. The new setting overrides any previous
settings for every Data Collector table in that view.
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Examples
The following command configures the Backups component to be retained on disk for 1 day:
=> SELECT set_data_collector_time_policy('Backups', '1 day'::interval);
set_data_collector_time_policy
-------------------------------SET
(1 row)
This command disables the 1-day restraint for the Backups component:
=> SELECT set_data_collector_time_policy('Backups', '-1');
set_data_collector_time_policy
-------------------------------SET
(1 row)
This command sets a 30-minute time capacity for all Data Collector tables in a single command:
=> SELECT set_data_collector_time_policy('30 minutes'::interval);
set_data_collector_time_policy
-------------------------------SET
(1 row)
To view current retention policy settings for each Data Collector table, call the GET_DATA_
COLLECTION_POLICY() function. In the next example, the time restraint is included.
=> SELECT get_data_collector_policy('RequestsIssued');
get_data_collector_policy
----------------------------------------------------------------------------2000KB kept in memory, 50000KB kept on disk. 2 years 3 days 15:08 hours kept
on disk.
(1 row)
If the time policy setting is disabled, the output of GET_DATA_COLLECTION_POLICY() returns "Time
based retention disabled."
2000KB kept in memory, 50000KB kept on disk. Time based retention disabled.
See Also
l
GET_DATA_COLLECTOR_POLICY
l
SET_DATA_COLLECTOR_POLICY
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l
DATA_COLLECTOR
SET_LOAD_BALANCE_POLICY
Sets how native connection load balancing chooses a host to handle a client connection. See About
Native Connection Load Balancing in the Administrator's Guide for more information.
Syntax
SET_LOAD_BALANCE_POLICY('policy')
Parameters
policy
The name of the load balancing policy to use. Can be one of the following:
l
NONE: Disables native connection load balancing. This is the default setting.
l
ROUNDROBIN: Chooses the next host from a circular list of currently up hosts in
the database (i.e. node #1, node #2, node #3, etc. until it wraps back to node #1
again). Each host in the cluster maintains its own pointer to the next host in the
circular list, rather than there being a single cluster-wide state.
l
RANDOM: Chooses a host at random from the list of currently up hosts in the
cluster.
Notes
Even if the load balancing policy is set to something other than NONE on the server, clients must
indicate they want their connections to be load balanced by setting a connection property.
Permissions
Can only be used by a superuser .
Example
The following example demonstrates enabling native connection load balancing on the server by
setting the load balancing scheme to ROUNDROBIN:
=> SELECT SET_LOAD_BALANCE_POLICY('ROUNDROBIN');
SET_LOAD_BALANCE_POLICY
-------------------------------------------------------------------------------Successfully changed the client initiator load balancing policy to: roundrobin
(1 row)
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SET_LOCATION_PERFORMANCE
Sets disk performance for the location specified.
Syntax
SET_LOCATION_PERFORMANCE ( 'path' , 'node' , 'throughput' , 'average_latency' )
Parameters
path
Specifies where the storage location to set is mounted.
node
Is the HP Vertica node where the location to be set is available.
If this parameter is omitted, node defaults to the initiator.
throughput
Specifies the throughput for the location, which must be 1 or more.
average_latency
Specifies the average latency for the location. The average_latency must be 1
or more.
Privileges
Must be a superuser.
Notes
To obtain the throughput and average latency for the location, run the MEASURE_LOCATION_
PERFORMANCE() function before you attempt to set the location's performance.
Example
The following example sets the performance of a storage location on node2 to a throughput of 122
megabytes per second and a latency of 140 seeks per second.
=> SELECT SET_LOCATION_PERFORMANCE('/secondVerticaStorageLocation/','node2','122','140');
See Also
l
ADD_LOCATION
l
MEASURE_LOCATION_PERFORMANCE
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l
Measuring Storage Performance
l
Setting Storage Performance
SET_SCALING_FACTOR
Sets the scaling factor that determines the size of the storage containers used when rebalancing
the database and when using local data segmentation is enabled. See Cluster Scaling for details.
Syntax
SET_SCALING_FACTOR(factor)
Parameters
factor
An integer value between 1 and 32. HP Vertica uses this value to calculate the number
of storage containers each projection is broken into when rebalancing or when local data
segmentation is enabled.
Note: Setting the scaling factor value too high can cause nodes to create too many small
container files, greatly reducing efficiency and potentially causing a "Too many
ROS containers" error (also known as "ROS pushback"). The scaling factor should be set high
enough so that rebalance can transfer local segments to satisfy the skew threshold, but small
enough that the number of storage containers does not exceed ROS pushback. The number of
storage containers should be greater than or equal to the number of partitions multiplied by the
number local of segments (# storage containers >= # partitions * # local segments).
Privileges
Must be a superuser.
Example
=> SELECT SET_SCALING_FACTOR(12);
SET_SCALING_FACTOR
-------------------SET
(1 row)
SET_OBJECT_STORAGE_POLICY
Creates or changes an object storage policy by associating a database object with a labeled
storage location.
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Note: You cannot create a storage policy on a USER type storage location.
Syntax
SET_OBJECT_STORAGE_POLICY ( 'object_name', 'location_label' [, 'key_min, key_max'] [, 'enforc
e_storage_move' ] )
Parameters
object_name
Identifies the database object assigned to a labeled storage location.
The object_name can resolve to a database, schema, or table.
location_label
The label of the storage location with which object_name is being
associated.
key_min, key_max
Applicable only when object_name is a table, key_min and key_max
specify the table partition key value range to be stored at the location.
enforce_storage_move=
{true | false}
[Optional] Applicable only when setting a storage policy for an object
that has data stored at another labeled location. Specify this parameter
as true to move all existing storage data to the target location within this
function's transaction.
Privileges
Must be the object owner to set the storage policy, and have access to the storage location.
New Storage Policy
If an object does not have a storage policy, this function creates a new policy. The labeled location
is then used as the default storage location during TM operations, such as moveout and mergeout.
Existing Storage Policy
If the object already has an active storage policy, calling this function changes the default storage
for the object to the new labeled location. Any existing data stored on the previous storage location
is marked to move to the new location during the next TM moveout operations, unless you use the
enforce_storage_move option.
Forcing Existing Data Storage to a New Storage Location
You can optionally use this function to move existing data storage to a new location as part of
completing the current transaction, by specifying the last parameter as true.
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To move existing data as part of the next TM moveout, either omit the parameter, or specify its
value as false.
Note: Specifying the parameter as true performs a cluster-wide operation. If an error occurs
on any node, the function displays a warning message, skips the offending node, and
continues execution on the remaining nodes.
Example
This example sets a storage policy for the table states to use the storage labeled SSD as its default
location:
VMART=> select set_object_storage_policy ('states', 'SSD');
set_object_storage_policy
----------------------------------Default storage policy set.
(1 row)
See Also
l
ALTER_LOCATION_LABEL
l
CLEAR_OBJECT_STORAGE_POLICY
l
Creating Storage Policies
l
Moving Data Storage Locations
SHUTDOWN
Forces a database to shut down, even if there are users connected.
Syntax
SHUTDOWN ( [ 'false' | 'true' ] )
Parameters
false
[Default] Returns a message if users are connected. Has the same effect as supplying
no parameters.
true
Performs a moveout operation and forces the database to shut down, disallowing further
connections.
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Privileges
Must be a superuser.
Notes
l
Quotes around the true or false arguments are optional.
l
Issuing the shutdown command without arguments or with the default (false) argument returns a
message if users are connected, and the shutdown fails. If no users are connected, the
database performs a moveout operation and shuts down.
l
Issuing the SHUTDOWN('true') command forces the database to shut down whether users are
connected or not.
l
You can check the status of the shutdown operation in the vertica.log file:
2010-03-09 16:51:52.625 unknown:0x7fc6d6d2e700
[Init] Shutdown complete. Exiting.
l
As an alternative to SHUTDOWN(), you can also temporarily set MaxClientSessions to 0 and
then use CLOSE_ALL_SESSIONS(). New client connections cannot connect unless they
connect using the dbadmin account. See CLOSE_ALL_SESSIONS for details.
Examples
The following command attempts to shut down the database. Because users are connected, the
command fails:
=> SELECT SHUTDOWN('false');
NOTICE: Cannot shut down while users are connected
SHUTDOWN
----------------------------Shutdown: aborting shutdown
(1 row)
SHUTDOWN() and SHUTDOWN('false') perform the same operation:
=> SELECT SHUTDOWN();
NOTICE: Cannot shut down while users are connected
SHUTDOWN
----------------------------Shutdown: aborting shutdown
(1 row)
Using the 'true' parameter forces the database to shut down, even though clients might be
connected:
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=> SELECT SHUTDOWN('true');
SHUTDOWN
---------------------------Shutdown: moveout complete
(1 row)
See Also
l
SESSIONS
SLEEP
Waits a specified number of seconds before executing another statement or command.
Syntax
SLEEP( seconds )
Parameters
seconds
The wait time, specified in one or more seconds (0 or higher) expressed as a positive
integer. Single quotes are optional; for example, SLEEP(3) is the same as SLEEP
('3').
Notes
l
This function returns value 0 when successful; otherwise it returns an error message due to
syntax errors.
l
You cannot cancel a sleep operation.
l
Be cautious when using SLEEP() in an environment with shared resources, such as in
combination with transactions that take exclusive locks.
Example
The following command suspends execution for 100 seconds:
=> SELECT SLEEP(100);
sleep
------0
(1 row)
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START_REBALANCE_CLUSTER
A rebalance operation performs the following tasks:
l
Distributes data based on user-defined fault groups, if specified, or based on large cluster
automatic fault groups
l
Redistributes the database projections' data across all nodes
l
Refreshes projections
l
Sets the Ancient History Mark
l
Drops projections that are no longer needed
When to rebalance the cluster
Rebalancing is useful (or necessary) after you:
l
Mark one or more nodes as ephemeral in preparation of removing them from the cluster
l
Add one or more nodes to the cluster so HP Vertica can populate the empty nodes with data
l
Remove one or more nodes from the cluster so HP Vertica can redistribute the data among the
remaining nodes
l
Change the scaling factor of an elastic cluster, which determines the number of storage
containers used to store a projection across the database
l
Set the control node size or realign control nodes on a large cluster layout
l
Specify more than 120 nodes in your initial HP Vertica cluster configuration
l
Add nodes to or remove nodes from a fault group
Asynchronously starts a data rebalance task. Since this function starts the rebalance task in the
background, it returns immediately after the task has started. Since it is a background task,
rebalancing will continue even if the session that started it is closed. It even continues after a
cluster recovery if the database shuts down while it is in progress. The only way to stop the task is
by the CANCEL_REBALANCE_CLUSTER function.
Syntax
START_REBALANCE_CLUSTER()
Privileges
Must be a superuser.
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Example
=> SELECT START_REBALANCE_CLUSTER();
START_REBALANCE_CLUSTER
------------------------REBALANCING
(1 row)
See Also
l
Rebalancing Data Across Nodes
l
CANCEL_REBALANCE_CLUSTER
l
REBALANCE_CLUSTER
START_REFRESH
Transfers data to projections that are not able to participate in query execution due to missing or
out-of-date data.
Syntax
START_REFRESH()
Notes
l
When a design is deployed through the Database Designer, it is automatically refreshed. See
Deploying a Design in the Administrator's Guide.
l
All nodes must be up in order to start a refresh.
l
START_REFRESH() has no effect if a refresh is already running.
l
A refresh is run asynchronously.
l
Shutting down the database ends the refresh.
l
To view the progress of the refresh, see the PROJECTION_REFRESHES and
PROJECTIONS system tables.
l
If a projection is updated from scratch, the data stored in the projection represents the table
columns as of the epoch in which the refresh commits. As a result, the query optimizer might not
choose the new projection for AT EPOCH queries that request historical data at epochs older
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than the refresh epoch of the projection. Projections refreshed from buddies retain history and
can be used to answer historical queries.
Privileges
None
Example
The following command starts the refresh operation:
=> SELECT START_REFRESH();
start_refresh
---------------------------------------Starting refresh background process.
See Also
l
CLEAR_PROJECTION_REFRESHES
l
MARK_DESIGN_KSAFE
l
PROJECTION_REFRESHES
l
PROJECTIONS
l
Clearing PROJECTION_REFRESHES History
SYNCH_WITH_HCATALOG_SCHEMA
Copies the structure of a Hive database schema available through the HCatalog Connector to an
Vertica Analytic Database schema.
Syntax
SYNC_WITH_HCATALOG_SCHEMA( local_schema, hcatalog_schema, [drop_tables] )
Parameters
local_schema The existing Vertica Analytic Database schema to store the copied HCatalog
schema's metadata
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hcatalog_
schema
The HCatalog schema to copy
[drop_
tables]
Drop any tables in local_schema that do not correspond to a table in hcatalog_
schema
Notes
You should always create an empty schema for the local_schema parameter. Tables in the
hcatalog_schema overwrite any identically named table in local_schema, which can lead to data
loss.
Permissions
The user must have CREATE privileges on local_schema and USAGE permissions on hcatalog_
schema.
Example
The following example shows using SYNCH_WITH_HCATALOG_SCHEMA to copy the metadata
from an HCatalog schema named hcat to an Vertica Analytic Database schema named hcat_local:
=> CREATE SCHEMA hcat_local;
CREATE SCHEMA
=> SELECT sync_with_hcatalog_schema('hcat_local', 'hcat');
sync_with_hcatalog_schema
---------------------------------------Schema hcat_local synchronized with hcat
tables in hcat = 56
tables altered in hcat_local = 0
tables created in hcat_local = 56
stale tables in hcat_local = 0
table changes erred in hcat_local = 0
(1 row)
=> -- Use vsql's \d command to describe a table in the synced schema
=> \d hcat_local.messages
List of Fields by Tables
Schema
|
Table | Column |
Type
| Size | Default | Not Null | Primary K
ey | Foreign Key
-----------+----------+---------+----------------+-------+---------+----------+------------+------------hcat_local | messages | id
| int
|
8 |
| f
| f
|
hcat_local | messages | userid | varchar(65000) | 65000 |
| f
| f
|
hcat_local | messages | "time" | varchar(65000) | 65000 |
| f
| f
|
hcat_local | messages | message | varchar(65000) | 65000 |
| f
| f
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|
(4 rows)
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Catalog Management Functions
This section contains catalog management functions specific to HP Vertica.
DROP_LICENSE
Drops a Flex Zone license key from the global catalog.
Syntax
DROP_LICENSE( 'license name' )
Parameters
license name
The name of the license to drop. The name can be found in the licenses table.
Privileges
Must be a superuser.
Notes
For more information about license keys, see Managing Licenses in the Administrator's Guide.
Examples
=> SELECT DROP_LICENSE('/tmp/vlicense.dat');
DUMP_CATALOG
Returns an internal representation of the HP Vertica catalog. This function is used for diagnostic
purposes.
Syntax
DUMP_CATALOG()
Privileges
None; however, function dumps only the objects visible to the user.
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Notes
To obtain an internal representation of the HP Vertica catalog for diagnosis, run the query:
=> SELECT DUMP_CATALOG();
The output is written to the specified file:
\o /tmp/catalog.txtSELECT DUMP_CATALOG();
\o
EXPORT_CATALOG
Generates a SQL script that you can use to recreate a physical schema design in its current state
on a different cluster. This function always attempts to recreate projection statements with KSAFE
clauses, if they exist in the original definitions, or OFFSET clauses if they do not.
Syntax
EXPORT_CATALOG ( [ 'destination' ] , [ 'scope' ] )
Parameters
destination
Specifies the path and name of the SQL output file. An empty string (''), which is
the default, outputs the script to standard output. The function writes the script to
the catalog directory if no destination is specified.
If you specify a file that does not exist, the function creates one. If the file preexists, the function silently overwrites its contents.
scope
Determines what to export:
l
DESIGN—Exports schemas, tables, constraints, views, and projections to
which the user has access. This is the default value.
l
DESIGN_ALL—Exports all the design objects plus system objects created in
Database Designer (for example, design contexts and their tables). The objects
that are exported are those to which the user has access.
l
TABLES—Exports all tables, constraints, and projections for for which the user
has permissions. See also EXPORT_TABLES.
Privileges
None. However:
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l
EXPORT_CATALOG exports only the objects visible to the user .
l
Only a superuser can export output to a file.
Example
The following example exports the design to standard output:
=> SELECT EXPORT_CATALOG('','DESIGN');
See Also
EXPORT_OBJECTS
l
EXPORT_TABLES
l
l
EXPORT_OBJECTS
Generates a SQL script you can use to recreate catalog objects on a different cluster. The
generated script includes only the non-virtual objects to which the user has access. The function
exports catalog objects in order dependency for correct recreation. Running the generated SQL
script on another cluster then creates all referenced objects before their dependent objects.
The EXPORT_OBJECTS function always attempts to recreate projection statements with KSAFE
clauses, if they existed in the original definitions, or OFFSET clauses, if they did not.
None of the EXPORT_OBJECTS parameters accepts a NULL value as input. EXPORT_
OBJECTS returns an error if an explicitly-specified object does not exist, or the user does not have
access to the object.
Syntax
EXPORT_OBJECTS( [ 'destination'
] , [ 'scope'
] , [
'ksafe' ] )
Parameters
destination
Specifies the path and name of the SQL output file. The default empty string ('')
outputs the script to standard output. The function writes the script to the catalog
directory if no destination is specified.
If you specify a file that does not exist, the function creates one. If the file preexists, the function silently overwrites its contents.
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scope
ksafe
Determines the scope of the catalog objects to export:
l
An empty string (' ')—exports all non-virtual objects to which the user has
access, including constraints. (Note that constraints are not objects that can be
passed as individual arguments.) An empty string is the default scope value if
you do not limit the export.
l
A comma-delimited list of catalog objects to export, which can include the
following:
n
' [dbname.]schema.object '—matches each named schema object. You can
optionally qualify the schema with a database prefix. A named schema
object can be a table, projection, view, sequence, or user-defined SQL
function.
n
' [dbname.]schema—matches the named schema, which you can optionally
qualify with a database prefix. For a schema, HP Vertica exports all nonvirtual objects that the user has access to within the schema. If a schema
and table have the same name, the schema takes precedence.
Determines if the statistics are regenerated before loading them into the design
context
Specifies whether to incorporate a MARK_DESIGN_KSAFE statement with the
correct K-safe value for the database:
l
true—adds the MARK_DESIGN_KSAFE statement to the end of the output
script. This is the default value.
l
false—omits the MARK_DESIGN_KSAFE statement from the script.
Privileges
None. However:
l
EXPORT_OBJECTS exports only the objects visible to the user .
l
Only a superuser can export output to a file.
Example
The following example exports all the non-virtual objects to which the user has access to standard
output. The example uses false for the last parameter, indicating that the file will not include the
MARK_DESIGN_KSAFE statement at the end.
=> SELECT EXPORT_OBJECTS(' ',' ',false);
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See Also
l
EXPORT_CATALOG
l
EXPORT_TABLES
l
Exporting Objects
INSTALL_LICENSE
Installs the license key in the global catalog.
Syntax
INSTALL_LICENSE( 'filename' )
Parameters
filename
specifies the absolute pathname of a valid license file.
Privileges
Must be a superuser.
Notes
For more information about license keys, see Managing Your License Key in the Administrator's
Guide.
Examples
=> SELECT INSTALL_LICENSE('/tmp/vlicense.dat');
MARK_DESIGN_KSAFE
Enables or disables high availability in your environment, in case of a failure. Before enabling
recovery, MARK_DESIGN_KSAFE queries the catalog to determine whether a cluster's physical
schema design meets the following requirements:
l
Small, unsegmented tables are replicated on all nodes.
l
Large table superprojections are segmented with each segment on a different node.
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l
Each large table projection has at least one buddy projection for K-safety=1 (or two buddy
projections for K-safety=2).
Buddy projections are also segmented across database nodes, but the distribution is modified
so that segments that contain the same data are distributed to different nodes. See High
Availability Through Projections in the Concepts Guide.
Note: Projections are considered to be buddies if they contain the same columns and have
the same segmentation. They can have different sort orders.
MARK_DESIGN_KSAFE does not change the physical schema in any way.
Syntax
MARK_DESIGN_KSAFE ( k )
Parameters
k
2 enables high availability if the schema design meets requirements for K-safety=2
1 enables high availability if the schema design meets requirements for K-safety=1
0 disables high availability
If you specify a k value of one (1) or two (2), HP Vertica returns one of the following messages.
Success:
Marked design n-safe
Failure:
The schema does not meet requirements for K=n.
Fact table projection projection-name
has insufficient "buddy" projections.
n in the message is 1 or 2 and represents the k value.
Privileges
Must be a superuser.
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Notes
l
The database's internal recovery state persists across database restarts but it is not checked at
startup time.
l
If a database has automatic recovery enabled, you must temporarily disable automatic recovery
before creating a new table.
l
When one node fails on a system marked K-safe=1, the remaining nodes are available for DML
operations.
Examples
=> SELECT MARK_DESIGN_KSAFE(1);
mark_design_ksafe
---------------------Marked design 1-safe
(1 row)
If the physical schema design is not K-Safe, messages indicate which projections do not have a
buddy:
=> SELECT MARK_DESIGN_KSAFE(1);
The given K value is not correct;
the schema is 0-safe
Projection pp1 has 0 buddies,
which is smaller that the given K of 1
Projection pp2 has 0 buddies,
which is smaller that the given K of 1
.
.
.
(1 row)
See Also
l
SYSTEM
l
High Availability and Recovery
l
HP Vertica System Tables
l
Avoiding Resegmentation During Joins
l
Failure Recovery
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SYNCH_WITH_HCATALOG_SCHEMA
Copies the structure of a Hive database schema available through the HCatalog Connector to an
Vertica Analytic Database schema.
Syntax
SYNC_WITH_HCATALOG_SCHEMA( local_schema, hcatalog_schema, [drop_tables] )
Parameters
local_schema The existing Vertica Analytic Database schema to store the copied HCatalog
schema's metadata
hcatalog_
schema
The HCatalog schema to copy
[drop_
tables]
Drop any tables in local_schema that do not correspond to a table in hcatalog_
schema
Notes
You should always create an empty schema for the local_schema parameter. Tables in the
hcatalog_schema overwrite any identically named table in local_schema, which can lead to data
loss.
Permissions
The user must have CREATE privileges on local_schema and USAGE permissions on hcatalog_
schema.
Example
The following example shows using SYNCH_WITH_HCATALOG_SCHEMA to copy the metadata
from an HCatalog schema named hcat to an Vertica Analytic Database schema named hcat_local:
=> CREATE SCHEMA hcat_local;
CREATE SCHEMA
=> SELECT sync_with_hcatalog_schema('hcat_local', 'hcat');
sync_with_hcatalog_schema
---------------------------------------Schema hcat_local synchronized with hcat
tables in hcat = 56
tables altered in hcat_local = 0
tables created in hcat_local = 56
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stale tables in hcat_local = 0
table changes erred in hcat_local = 0
(1 row)
=> -- Use vsql's \d command to describe a table in the synced schema
=> \d hcat_local.messages
List of Fields by Tables
Schema
|
Table | Column |
Type
| Size | Default | Not Null | Primary K
ey | Foreign Key
-----------+----------+---------+----------------+-------+---------+----------+------------+------------hcat_local | messages | id
| int
|
8 |
| f
| f
|
hcat_local | messages | userid | varchar(65000) | 65000 |
| f
| f
|
hcat_local | messages | "time" | varchar(65000) | 65000 |
| f
| f
|
hcat_local | messages | message | varchar(65000) | 65000 |
| f
| f
|
(4 rows)
Client Connection Management Functions
This section contains client connection management functions specific to HP Vertica.
SET_LOAD_BALANCE_POLICY
Sets how native connection load balancing chooses a host to handle a client connection. See About
Native Connection Load Balancing in the Administrator's Guide for more information.
Syntax
SET_LOAD_BALANCE_POLICY('policy')
Parameters
policy
The name of the load balancing policy to use. Can be one of the following:
l
NONE: Disables native connection load balancing. This is the default setting.
l
ROUNDROBIN: Chooses the next host from a circular list of currently up hosts in
the database (i.e. node #1, node #2, node #3, etc. until it wraps back to node #1
again). Each host in the cluster maintains its own pointer to the next host in the
circular list, rather than there being a single cluster-wide state.
l
RANDOM: Chooses a host at random from the list of currently up hosts in the
cluster.
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Notes
Even if the load balancing policy is set to something other than NONE on the server, clients must
indicate they want their connections to be load balanced by setting a connection property.
Permissions
Can only be used by a superuser .
Example
The following example demonstrates enabling native connection load balancing on the server by
setting the load balancing scheme to ROUNDROBIN:
=> SELECT SET_LOAD_BALANCE_POLICY('ROUNDROBIN');
SET_LOAD_BALANCE_POLICY
-------------------------------------------------------------------------------Successfully changed the client initiator load balancing policy to: roundrobin
(1 row)
RESET_LOAD_BALANCE_POLICY
Resets the counter each host in the cluster maintains to track which host it will refer a client to
when the native connection load balancing scheme is set to ROUNDROBIN.
Syntax
RESET_LOAD_BALANCE_POLICY()
Notes
This function only has an effect if the current native connection load balancing scheme is
ROUNDROBIN.
Permissions
This function can be called only by a superuser .
Example
The following example demonstrates calling RESET_LOAD_BALANCE_POLICY:
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=> SELECT RESET_LOAD_BALANCE_POLICY();
RESET_LOAD_BALANCE_POLICY
------------------------------------------------------------------------Successfully reset stateful client load balance policies: "roundrobin".
(1 row)
Cluster Management Functions
This section contains functions that manage spread deployment on large, distributed database
clusters.
SET_CONTROL_SET_SIZE
For existing database clusters, use this function to specify the number of cluster nodes on which
you want to deploy control messaging (spread). The SET_CONTROL_SET_SIZE() function works the
same as the install_vertica --large cluster option.
You can run SET_CONTROL_SET_SIZE()after the database cluster is already defined, but before you
call this function, the database must be up.
Note: You use this function with other cluster management functions. For details, see Defining
and Realigning Control Nodes on an Existing Cluster in the Administrator's Guide.
Syntax
SET_CONTROL_SET_SIZE(integer)
Parameters
integer
Specifies the number of cluster hosts from the
database cluster on which spread runs.
Privileges
Must be a superuser.
Note
To see if the current spread hosts and the control designations in the Catalog match, query the V_
CATALOG.LARGE_CLUSTER_CONFIGURATION_STATUS system table.
Example
The following command tells HP Vertica that you want to run spread on two cluster nodes:
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=> SELECT set_control_set_size(2);
SET_CONTROL_SET_SIZE
---------------------Control size set
(1 row)
See Also
Cluster Management Functions
V_CATALOG.CLUSTER_LAYOUT
Large Cluster in the Administrator's Guide
REALIGN_CONTROL_NODES
Chooses control nodes (spread hosts) from all cluster nodes and assigns the rest of the nodes in
the cluster to a control node. Calling this function respects existing fault groups, which you can
view by querying the V_CATALOG.CLUSTER_LAYOUT system table. This view also lets you see the
proposed new layout for nodes in the cluster.
Note: You use this function with other cluster management functions. For details, see Defining
and Realigning Control Nodes on an Existing Cluster in the Administrator's Guide.
Syntax
REALIGN_CONTROL_NODES()
Privileges
Must be a superuser.
Example
The following command chooses control nodes from all cluster nodes and assigns the rest of the
nodes in the cluster to a control node:
=> SELECT realign_control_nodes();
See Also
Cluster Management Functions
V_CATALOG.CLUSTER_LAYOUT
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Large Cluster in the Administrator's Guide
RELOAD_SPREAD
Calling this function with the required true argument updates cluster changes (such as new or realigned control nodes spread hosts or fault groups or new/dropped cluster nodes), to the catalog's
spread configuration file.
Important: This function is often used in a multi-step process for large and elastic cluster
arrangements. Calling RELOAD_SPREAD(true) might require that you restart the database,
which you do using the Administration Tools. You must then rebalance the cluster for fault
tolerance to be realized. See Defining and Realigning Control Nodes in the Administrator's
Guide for more information.
Syntax
RELOAD_SPREAD(true)
Parameters
true
Updates cluster changes related to control
message responsibilities to the spread
configuration file.
Privileges
Must be a superuser.
Example
The following command updates the cluster with changes to control messaging:
=> SELECT reload_spread(true);
reload_spread
--------------reloaded
(1 row)
See Also
Cluster Management Functions
REBALANCE_CLUSTER
V_CATALOG.CLUSTER_LAYOUT
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Large Cluster in the Administrator's Guide
REBALANCE_CLUSTER
Call this function to begin rebalancing data in the cluster synchronously.
A rebalance operation performs the following tasks:
l
Distributes data based on user-defined fault groups, if specified, or based on large cluster
automatic fault groups
l
Redistributes the database projections' data across all nodes
l
Refreshes projections
l
Sets the Ancient History Mark
l
Drops projections that are no longer needed
When to rebalance the cluster
Rebalancing is useful (or necessary) after you:
l
Mark one or more nodes as ephemeral in preparation of removing them from the cluster
l
Add one or more nodes to the cluster so HP Vertica can populate the empty nodes with data
l
Remove one or more nodes from the cluster so HP Vertica can redistribute the data among the
remaining nodes
l
Change the scaling factor of an elastic cluster, which determines the number of storage
containers used to store a projection across the database
l
Set the control node size or realign control nodes on a large cluster layout
l
Specify more than 120 nodes in your initial HP Vertica cluster configuration
l
Add nodes to or remove nodes from a fault group
Because this function runs the rebalance task synchronously, it does not return until the data has
been rebalanced. Closing or dropping the session cancels the rebalance task.
Important: On large cluster arrangements, you typically use this function in a flow, described
Defining and Realigning Control Nodes in the Administrator's Guide. After you change the
number and distribution of control nodes (spread hosts), you must run REBALANCE_CLUSTER()
for fault tolerance to be realized.
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Syntax
REBALANCE_CLUSTER()
Privileges
Must be a superuser.
Example
The following command rebalances data across the cluster.
=> SELECT REBALANCE_CLUSTER();
REBALANCE_CLUSTER
------------------REBALANCED
(1 row)
See Also
START_REBALANCE_CLUSTER
CANCEL_REBALANCE_CLUSTER
Rebalancing Data Across Nodes in the Administrator's Guide
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Cluster Scaling Functions
This section contains functions that control how the cluster organizes data for rebalancing.
CANCEL_REBALANCE_CLUSTER
Stops any rebalance task currently in progress.
Syntax
CANCEL_REBALANCE_CLUSTER()
Privileges
Must be a superuser.
Example
=> SELECT CANCEL_REBALANCE_CLUSTER();
CANCEL_REBALANCE_CLUSTER
-------------------------CANCELED
(1 row)
See Also
l
START_REBALANCE_CLUSTER
l
REBALANCE_CLUSTER
DISABLE_ELASTIC_CLUSTER
Disables elastic cluster scaling, which prevents HP Vertica from bundling data into chunks that are
easily transportable to other nodes when performing cluster resizing. The main reason to disable
elastic clustering is if you find that the slightly unequal data distribution in your cluster caused by
grouping data into discrete blocks results in performance issues.
Syntax
DISABLE_ELASTIC_CLUSTER()
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Privileges
Must be a superuser.
Example
=> SELECT DISABLE_ELASTIC_CLUSTER();
DISABLE_ELASTIC_CLUSTER
------------------------DISABLED
(1 row)
See Also
l
ENABLE_ELASTIC_CLUSTER
DISABLE_LOCAL_SEGMENTS
Disable local data segmentation, which breaks projections segments on nodes into containers that
can be easily moved to other nodes. See Local Data Segmentation in the Administrator's Guide for
details.
Syntax
DISABLE_LOCAL_SEGMENTS()
Privileges
Must be a superuser.
Example
=> SELECT DISABLE_LOCAL_SEGMENTS();
DISABLE_LOCAL_SEGMENTS
-----------------------DISABLED
(1 row)
ENABLE_ELASTIC_CLUSTER
Enables elastic cluster scaling, which makes enlarging or reducing the size of your database
cluster more efficient by segmenting a node's data into chunks that can be easily moved to other
hosts.
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Note: Databases created using HP Vertica Version 5.0 and later have elastic cluster enabled
by default. You need to use this function on databases created before version 5.0 in order for
them to use the elastic clustering feature.
Syntax
ENABLE_ELASTIC_CLUSTER()
Privileges
Must be a superuser.
Example
=> SELECT ENABLE_ELASTIC_CLUSTER();
ENABLE_ELASTIC_CLUSTER
-----------------------ENABLED
(1 row)
See Also
l
DISABLE_ELASTIC_CLUSTER
ENABLE_LOCAL_SEGMENTS
Enables local storage segmentation, which breaks projections segments on nodes into containers
that can be easily moved to other nodes. See Local Data Segmentation in the Administrator's Guide
for more information.
Syntax
ENABLE_LOCAL_SEGMENTS()
Privileges
Must be a superuser.
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Example
=> SELECT ENABLE_LOCAL_SEGMENTS();
ENABLE_LOCAL_SEGMENTS
----------------------ENABLED
(1 row)
REBALANCE_CLUSTER
Call this function to begin rebalancing data in the cluster synchronously.
A rebalance operation performs the following tasks:
l
Distributes data based on user-defined fault groups, if specified, or based on large cluster
automatic fault groups
l
Redistributes the database projections' data across all nodes
l
Refreshes projections
l
Sets the Ancient History Mark
l
Drops projections that are no longer needed
When to rebalance the cluster
Rebalancing is useful (or necessary) after you:
l
Mark one or more nodes as ephemeral in preparation of removing them from the cluster
l
Add one or more nodes to the cluster so HP Vertica can populate the empty nodes with data
l
Remove one or more nodes from the cluster so HP Vertica can redistribute the data among the
remaining nodes
l
Change the scaling factor of an elastic cluster, which determines the number of storage
containers used to store a projection across the database
l
Set the control node size or realign control nodes on a large cluster layout
l
Specify more than 120 nodes in your initial HP Vertica cluster configuration
l
Add nodes to or remove nodes from a fault group
Because this function runs the rebalance task synchronously, it does not return until the data has
been rebalanced. Closing or dropping the session cancels the rebalance task.
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Important: On large cluster arrangements, you typically use this function in a flow, described
Defining and Realigning Control Nodes in the Administrator's Guide. After you change the
number and distribution of control nodes (spread hosts), you must run REBALANCE_CLUSTER()
for fault tolerance to be realized.
Syntax
REBALANCE_CLUSTER()
Privileges
Must be a superuser.
Example
The following command rebalances data across the cluster.
=> SELECT REBALANCE_CLUSTER();
REBALANCE_CLUSTER
------------------REBALANCED
(1 row)
See Also
START_REBALANCE_CLUSTER
CANCEL_REBALANCE_CLUSTER
Rebalancing Data Across Nodes in the Administrator's Guide
SET_SCALING_FACTOR
Sets the scaling factor that determines the size of the storage containers used when rebalancing
the database and when using local data segmentation is enabled. See Cluster Scaling for details.
Syntax
SET_SCALING_FACTOR(factor)
Parameters
factor
An integer value between 1 and 32. HP Vertica uses this value to calculate the number
of storage containers each projection is broken into when rebalancing or when local data
segmentation is enabled.
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Note: Setting the scaling factor value too high can cause nodes to create too many small
container files, greatly reducing efficiency and potentially causing a "Too many
ROS containers" error (also known as "ROS pushback"). The scaling factor should be set high
enough so that rebalance can transfer local segments to satisfy the skew threshold, but small
enough that the number of storage containers does not exceed ROS pushback. The number of
storage containers should be greater than or equal to the number of partitions multiplied by the
number local of segments (# storage containers >= # partitions * # local segments).
Privileges
Must be a superuser.
Example
=> SELECT SET_SCALING_FACTOR(12);
SET_SCALING_FACTOR
-------------------SET
(1 row)
START_REBALANCE_CLUSTER
A rebalance operation performs the following tasks:
l
Distributes data based on user-defined fault groups, if specified, or based on large cluster
automatic fault groups
l
Redistributes the database projections' data across all nodes
l
Refreshes projections
l
Sets the Ancient History Mark
l
Drops projections that are no longer needed
When to rebalance the cluster
Rebalancing is useful (or necessary) after you:
l
Mark one or more nodes as ephemeral in preparation of removing them from the cluster
l
Add one or more nodes to the cluster so HP Vertica can populate the empty nodes with data
l
Remove one or more nodes from the cluster so HP Vertica can redistribute the data among the
remaining nodes
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l
Change the scaling factor of an elastic cluster, which determines the number of storage
containers used to store a projection across the database
l
Set the control node size or realign control nodes on a large cluster layout
l
Specify more than 120 nodes in your initial HP Vertica cluster configuration
l
Add nodes to or remove nodes from a fault group
Asynchronously starts a data rebalance task. Since this function starts the rebalance task in the
background, it returns immediately after the task has started. Since it is a background task,
rebalancing will continue even if the session that started it is closed. It even continues after a
cluster recovery if the database shuts down while it is in progress. The only way to stop the task is
by the CANCEL_REBALANCE_CLUSTER function.
Syntax
START_REBALANCE_CLUSTER()
Privileges
Must be a superuser.
Example
=> SELECT START_REBALANCE_CLUSTER();
START_REBALANCE_CLUSTER
------------------------REBALANCING
(1 row)
See Also
l
Rebalancing Data Across Nodes
l
CANCEL_REBALANCE_CLUSTER
l
REBALANCE_CLUSTER
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Constraint Management Functions
This section contains constraint management functions specific to HP Vertica.
See also SQL system table V_CATALOG.TABLE_CONSTRAINTS
ANALYZE_CONSTRAINTS
Analyzes and reports on constraint violations within the current schema search path, or external to
that path if you specify a database name (noted in the syntax statement and parameter table).
You can check for constraint violations by passing arguments to the function as follows:
l
An empty argument (' '), which returns violations on all tables within the current schema
l
One argument, referencing a table
l
Two arguments, referencing a table name and a column or list of columns
Syntax
ANALYZE_CONSTRAINTS [ ( '' )
... | ( '[[db-name.]schema.]table [.column_name]' )
... | ( '[[db-name.]schema.]table' , 'column' ) ]
Parameters
('')
Analyzes and reports on all tables within the current schema search path.
[[db-name.]schema.]
[Optional] Specifies the database name and optional schema name. Using
a database name identifies objects that are not unique within the current
search path (see Setting Search Paths). You must be connected to the
database you specify, and you cannot change objects in other databases.
Specifying different database objects lets you qualify database objects as
explicitly as required. For example, you can use a database and a schema
name (mydb.myschema).
table
Analyzes and reports on all constraints referring to the specified table.
column
Analyzes and reports on all constraints referring to the specified table that
contains the column.
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Privileges
l
SELECT privilege on table
l
USAGE privilege on schema
Notes
ANALYZE_CONSTRAINTS() performs a lock in the same way that SELECT * FROM t1 holds a
lock on table t1. See LOCKS for additional information.
Detecting Constraint Violations During a Load Process
HP Vertica checks for constraint violations when queries are run, not when data is loaded. To
detect constraint violations as part of the load process, use a COPY statement with the NO
COMMIT option. By loading data without committing it, you can run a post-load check of your data
using the ANALYZE_CONSTRAINTS function. If the function finds constraint violations, you can
roll back the load because you have not committed it.
If ANALYZE_CONSTRAINTS finds violations, such as when you insert a duplicate value into a
primary key, you can correct errors using the following functions. Effects last until the end of the
session only:
l
SELECT DISABLE_DUPLICATE_KEY_ERROR
l
SELECT REENABLE_DUPLICATE_KEY_ERROR
Return Values
ANALYZE_CONSTRAINTS returns results in a structured set (see table below) that lists the
schema name, table name, column name, constraint name, constraint type, and the column values
that caused the violation.
If the result set is empty, then no constraint violations exist; for example:
> SELECT ANALYZE_CONSTRAINTS ('public.product_dimension', 'product_key');
Schema Name | Table Name | Column Names | Constraint Name | Constraint Type | Column Valu
es
-------------+------------+--------------+-----------------+-----------------+-------------(0 rows)
The following result set shows a primary key violation, along with the value that caused the
violation ('10'):
=> SELECT ANALYZE_CONSTRAINTS ('');
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Schema Name | Table Name | Column Names | Constraint Name | Constraint Type | Column Valu
es
-------------+------------+--------------+-----------------+-----------------+-------------store
t1
c1
pk_t1
PRIMARY
('10')
(1 row)
The result set columns are described in further detail in the following table:
Column Name Data Type
Description
Schema Name
VARCHAR
The name of the schema.
Table Name
VARCHAR
The name of the table, if specified.
Column Names
VARCHAR
Names of columns containing constraints. Multiple columns are
in a comma-separated list:
store_key, store_key, date_key,
Constraint Name
VARCHAR
The given name of the primary key, foreign key, unique, or not
null constraint, if specified.
Constraint Type
VARCHAR
Identified by one of the following strings: 'PRIMARY KEY',
'FOREIGN KEY', 'UNIQUE', or 'NOT NULL'.
Column Values
VARCHAR
Value of the constraint column, in the same order in which
Column Names contains the value of that column in the violating
row.
When interpreted as SQL, the value of this column forms a list of
values of the same type as the columns in Column Names; for
example:
('1'), ('1', 'z')
Understanding Function Failures
If ANALYZE_CONSTRAINTS() fails, HP Vertica returns an error identifying the failure condition,
such as if there are insufficient resources for the database to perform constraint checks.
If you specify the wrong table, the system returns an error message:
> SELECT ANALYZE_CONSTRAINTS('abc');
ERROR 2069: 'abc' is not a table in the current search_path
If you run the function on a table that has no constraints declared (even if duplicates are present),
the system returns an error message:
> SELECT ANALYZE_CONSTRAINTS('source');
ERROR 4072: No constraints defined
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If you run the function with incorrect syntax, the system returns an error message with a hint; for
example, if you run one of the following:
l
ANALYZE ALL CONSTRAINT;
l
ANALYZE CONSTRAINT abc;
The system returns an informative error with hint:
ERROR: ANALYZE CONSTRAINT is not supported.
HINT: You may consider using analyze_constraints().
If you run ANALYZE_CONSTRAINTS from a non-default locale, the function returns an error with a
hint:
> \locale LENINFO 2567: Canonical locale: 'en'
Standard collation: 'LEN'
English
> SELECT ANALYZE_CONSTRAINTS('t1');
ERROR: ANALYZE_CONSTRAINTS is currently not supported in
non-default locales
HINT: Set the locale in this session to
en_US@collation=binary using the command
"\locale en_US@collation=binary"
Examples
Given the following inputs, HP Vertica returns one row, indicating one violation, because the same
primary key value (10) was inserted into table t1 twice:
CREATE TABLE t1(c1 INT);
ALTER TABLE t1 ADD CONSTRAINT pk_t1 PRIMARY KEY (c1);
CREATE PROJECTION t1_p (c1) AS SELECT *
FROM t1 UNSEGMENTED ALL NODES;
INSERT INTO t1 values (10);
INSERT INTO t1 values (10); --Duplicate primary key value
\x
Expanded display is on.
SELECT ANALYZE_CONSTRAINTS('t1');
-[ RECORD 1 ]---+-------Schema Name
| public
Table Name
| t1
Column Names
| c1
Constraint Name | pk_t1
Constraint Type | PRIMARY
Column Values
| ('10')
If the second INSERT statement above had contained any different value, the result would have
been 0 rows (no violations).
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In the following example, create a table that contains three integer columns, one a unique key and
one a primary key:
CREATE TABLE table_1(
a INTEGER,
b_UK INTEGER UNIQUE,
c_PK INTEGER PRIMARY KEY
);
Issue a command that refers to a nonexistent table and column:
SELECT ANALYZE_CONSTRAINTS('a_BB');
ERROR: 'a_BB' is not a table name in the current search path
Issue a command that refers to a nonexistent column:
SELECT ANALYZE_CONSTRAINTS('table_1','x');
ERROR 41614: Nonexistent columns: 'x '
Insert some values into table table_1 and commit the changes:
INSERT INTO table_1 values (1, 1, 1);
COMMIT;
Run ANALYZE_CONSTRAINTS on table table_1. No constraint violations are reported:
SELECT ANALYZE_CONSTRAINTS('table_1');
(No rows)
Insert duplicate unique and primary key values and run ANALYZE_CONSTRAINTS on table
table_1 again. The system shows two violations: one against the primary key and one against the
unique key:
INSERT INTO table_1 VALUES (1, 1, 1);
COMMIT;
SELECT ANALYZE_CONSTRAINTS('table_1');
-[ RECORD 1 ]---+---------Schema Name
| public
Table Name
| table_1
Column Names
| b_UK
Constraint Name | C_UNIQUE
Constraint Type | UNIQUE
Column Values
| ('1')
-[ RECORD 2 ]---+---------Schema Name
| public
Table Name
| table_1
Column Names
| c_PK
Constraint Name | C_PRIMARY
Constraint Type | PRIMARY
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Column Values
| ('1')
The following command looks for constraint validations on only the unique key in the table table_1,
qualified with its schema name:
=> SELECT ANALYZE_CONSTRAINTS('public.table_1', 'b_UK');
-[ RECORD 1 ]---+--------Schema Name
| public
Table Name
| table_1
Column Names
| b_UK
Constraint Name | C_UNIQUE
Constraint Type | UNIQUE
Column Values
| ('1')
(1 row)
The following example shows that you can specify the same column more than once; ANALYZE_
CONSTRAINTS, however, returns the violation only once:
SELECT ANALYZE_CONSTRAINTS('table_1', 'c_PK, C_PK');
-[ RECORD 1 ]---+---------Schema Name
| public
Table Name
| table_1
Column Names
| c_PK
Constraint Name | C_PRIMARY
Constraint Type | PRIMARY
Column Values
| ('1')
The following example creates a new table, table_2, and inserts a foreign key and different
(character) data types:
CREATE TABLE table_2 (
x VARCHAR(3),
y_PK VARCHAR(4),
z_FK INTEGER REFERENCES table_1(c_PK));
Alter the table to create a multicolumn unique key and multicolumn foreign key and create
superprojections:
ALTER TABLE table_2
ADD CONSTRAINT table_2_multiuk PRIMARY KEY (x, y_PK);
WARNING 2623: Column "x" definition changed to NOT NULL
WARNING 2623: Column "y_PK" definition changed to NOT NULL
The following command inserts a missing foreign key (0) into table dim_1 and commits the
changes:
INSERT INTO table_2 VALUES ('r1', 'Xpk1', 0);
COMMIT;
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Checking for constraints on the table table_2 in the public schema detects a foreign key
violation:
=> SELECT ANALYZE_CONSTRAINTS('public.table_2');
-[ RECORD 1 ]---+---------Schema Name
| public
Table Name
| table_2
Column Names
| z_FK
Constraint Name | C_FOREIGN
Constraint Type | FOREIGN
Column Values
| ('0')
Now add a duplicate value into the unique key and commit the changes:
INSERT INTO table_2 VALUES ('r2', 'Xpk1', 1);
INSERT INTO table_2 VALUES ('r1', 'Xpk1', 1);
COMMIT;
Checking for constraint violations on table table_2 detects the duplicate unique key error:
SELECT ANALYZE_CONSTRAINTS('table_2');
-[ RECORD 1 ]---+---------------Schema Name
| public
Table Name
| table_2
Column Names
| z_FK
Constraint Name | C_FOREIGN
Constraint Type | FOREIGN
Column Values
| ('0')
-[ RECORD 2 ]---+---------------Schema Name
| public
Table Name
| table_2
Column Names
| x, y_PK
Constraint Name | table_2_multiuk
Constraint Type | PRIMARY
Column Values
| ('r1', 'Xpk1')
Create a table with multicolumn foreign key and create the superprojections:
CREATE TABLE table_3(
z_fk1 VARCHAR(3),
z_fk2 VARCHAR(4));
ALTER TABLE table_3
ADD CONSTRAINT table_3_multifk FOREIGN KEY (z_fk1, z_fk2)
REFERENCES table_2(x, y_PK);
Insert a foreign key that matches a foreign key in table table_2 and commit the changes:
INSERT INTO table_3 VALUES ('r1', 'Xpk1');
COMMIT;
Checking for constraints on table table_3 detects no violations:
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SELECT ANALYZE_CONSTRAINTS('table_3');
(No rows)
Add a value that does not match and commit the change:
INSERT INTO table_3 VALUES ('r1', 'NONE');
COMMIT;
Checking for constraints on table dim_2 detects a foreign key violation:
SELECT ANALYZE_CONSTRAINTS('table_3');
-[ RECORD 1 ]---+---------------Schema Name
| public
Table Name
| table_3
Column Names
| z_fk1, z_fk2
Constraint Name | table_3_multifk
Constraint Type | FOREIGN
Column Values
| ('r1', 'NONE')
Analyze all constraints on all tables:
SELECT ANALYZE_CONSTRAINTS('');
-[ RECORD 1 ]---+---------------Schema Name
| public
Table Name
| table_3
Column Names
| z_fk1, z_fk2
Constraint Name | table_3_multifk
Constraint Type | FOREIGN
Column Values
| ('r1', 'NONE')
-[ RECORD 2 ]---+---------------Schema Name
| public
Table Name
| table_2
Column Names
| x, y_PK
Constraint Name | table_2_multiuk
Constraint Type | PRIMARY
Column Values
| ('r1', 'Xpk1')
-[ RECORD 3 ]---+---------------Schema Name
| public
Table Name
| table_2
Column Names
| z_FK
Constraint Name | C_FOREIGN
Constraint Type | FOREIGN
Column Values
| ('0')
-[ RECORD 4 ]---+---------------Schema Name
| public
Table Name
| t1
Column Names
| c1
Constraint Name | pk_t1
Constraint Type | PRIMARY
Column Values
| ('10')
-[ RECORD 5 ]---+---------------Schema Name
| public
Table Name
| table_1
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Column Names
| b_UK
Constraint Name | C_UNIQUE
Constraint Type | UNIQUE
Column Values
| ('1')
-[ RECORD 6 ]---+---------------Schema Name
| public
Table Name
| table_1
Column Names
| c_PK
Constraint Name | C_PRIMARY
Constraint Type | PRIMARY
Column Values
| ('1')
-[ RECORD 7 ]---+---------------Schema Name
| public
Table Name
| target
Column Names
| a
Constraint Name | C_PRIMARY
Constraint Type | PRIMARY
Column Values
| ('1')
(5 rows)
To quickly clean up your database, issue the following command:
DROP TABLE table_1 CASCADE;
DROP TABLE table_2 CASCADE;
DROP TABLE table_3 CASCADE;
To learn how to remove violating rows, see the DISABLE_DUPLICATE_KEY_ERROR function.
ANALYZE_CORRELATIONS
Analyzes the specified tables for columns that are strongly correlated. In addition, ANALYZE_
CORRELATIONS also collects statistics.
For example, state name and country name columns are strongly correlated because the city name
usually, but perhaps not always, identifies the state name. The city of Conshohoken is uniquely
associated with Pennsylvania, whereas the city of Boston exists in Georgia, Indiana, Kentucky,
New York, Virginia, and Massachusetts. In this case, city name is strongly correlated with state
name.
For Database Designer to take advantage of these correlations, run Database Designer
programmatically. Use DESIGNER_SET_ANALYZE_CORRELATIONS_MODE to specify that
Database Designer should consider existing column correlations. Make sure to specify that
Database Designer not analyze statistics so that Database Designer does not override the existing
statistics.
Behavior Type
Immutable
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Syntax
ANALYZE_CORRELATIONS (
'[database_name.][schema_name.]table_name',
[recalculate] )
Parameters
database_name
Specifies the table(s) for which to analyze correlated columns, type VARCHAR.
schema_name
table_name
recalculate
Specifies to analyze the correlated columns, even if they have been analyzed
before, type BOOLEAN. Default: 'false'.
Permissions
l
To run ANALYZE_CORRELATIONS on a table, you must be a superuser, or a user w USAGE
privilege on the design schema.
Notes
l
Column correlation analysis typically needs to be done only once.
l
Currently, ANALYZE_CORRELATIONS can analyze only pairwise single-column correlations.
l
Projections do not change based on the analysis results. To implement the results of
ANALYZE_CORRELATIONS, you must run Database Designer.
Example
In the following example, ANALYZE_CORRELATIONS analyzes column correlations for all tables
in the public schema,even if they currently exist. The correlations that ANALYZE_
CORRELATIONS finds are saved so that Database Designer can use them the next time it runs on
the VMart database:
=> SELECT ANALYZE_CORRELATIONS (
'public.*',
'true');
ANALYZE_CORRELATIONS
---------------------0
(1 row)
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See Also
l
DESIGNER_SET_ANALYZE_CORRELATIONS_MODE
DISABLE_DUPLICATE_KEY_ERROR
Disables error messaging when HP Vertica finds duplicate PRIMARY KEY/UNIQUE KEY values
at run time. Queries execute as though no constraints are defined on the schema. Effects are
session scoped.
Caution: When called, DISABLE_DUPLICATE_KEY_ERROR() suppresses data integrity
checking and can lead to incorrect query results. Use this function only after you insert
duplicate primary keys into a dimension table in the presence of a pre-join projection. Then
correct the violations and turn integrity checking back on with REENABLE_DUPLICATE_
KEY_ERROR().
Syntax
DISABLE_DUPLICATE_KEY_ERROR();
Privileges
Must be a superuser.
Examples
The following series of commands create a table named dim and the corresponding projection:
CREATE TABLE dim (pk INTEGER PRIMARY KEY, x INTEGER);
CREATE PROJECTION dim_p (pk, x) AS SELECT * FROM dim ORDER BY x UNSEGMENTED ALL NODES;
The next two statements create a table named fact and the pre-join projection that joins fact to
dim.
CREATE TABLE fact(fk INTEGER REFERENCES dim(pk));
CREATE PROJECTION prejoin_p (fk, pk, x) AS SELECT * FROM fact, dim WHERE pk=fk ORDER BY x
;
The following statements load values into table dim. The last statement inserts a duplicate primary
key value of 1:
INSERT INTO dim values (1,1);INSERT INTO dim values (2,2);
INSERT INTO dim values (1,2); --Constraint violation
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COMMIT;
Table dim now contains duplicate primary key values, but you cannot delete the violating row
because of the presence of the pre-join projection. Any attempt to delete the record results in the
following error message:
ROLLBACK: Duplicate primary key detected in FK-PK join Hash-Join (x dim_p), value 1
In order to remove the constraint violation (pk=1), use the following sequence of commands, which
puts the database back into the state just before the duplicate primary key was added.
To remove the violation:
1. Save the original dim rows that match the duplicated primary key:
CREATE TEMP TABLE dim_temp(pk integer, x integer);
INSERT INTO dim_temp SELECT * FROM dim WHERE pk=1 AND x=1; -- original dim row
2. Temporarily disable error messaging on duplicate constraint values:
SELECT DISABLE_DUPLICATE_KEY_ERROR();
Caution: Remember that running the DISABLE_DUPLICATE_KEY_ERROR function
suppresses the enforcement of data integrity checking.
3. Remove the original row that contains duplicate values:
DELETE FROM dim WHERE pk=1;
4. Allow the database to resume data integrity checking:
SELECT REENABLE_DUPLICATE_KEY_ERROR();
5. Reinsert the original values back into the dimension table:
INSERT INTO dim SELECT * from dim_temp;COMMIT;
6. Validate your dimension and fact tables.
If you receive the following error message, it means that the duplicate records you want to
delete are not identical. That is, the records contain values that differ in at least one column
that is not a primary key; for example, (1,1) and (1,2).
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ROLLBACK: Delete: could not find a data row to delete (data integrity violation?)
The difference between this message and the rollback message in the previous example is that
a fact row contains a foreign key that matches the duplicated primary key, which has been
inserted. A row with values from the fact and dimension table is now in the pre-join projection.
In order for the DELETE statement (Step 3 in the following example) to complete successfully,
extra predicates are required to identify the original dimension table values (the values that are
in the pre-join).
This example is nearly identical to the previous example, except that an additional INSERT
statement joins the fact table to the dimension table by a primary key value of 1:
INSERT INTO dim values (1,1);INSERT INTO dim values (2,2);
INSERT INTO fact values (1);
-- New insert statement joins fact with dim on primar
y key value=1
INSERT INTO dim values (1,2); -- Duplicate primary key value=1
COMMIT;
To remove the violation:
1. Save the original dim and fact rows that match the duplicated primary key:
CREATE TEMP TABLE dim_temp(pk integer, x integer);CREATE TEMP TABLE fact_temp(fk inte
ger);
INSERT INTO dim_temp SELECT * FROM dim WHERE pk=1 AND x=1; -- original dim row
INSERT INTO fact_temp SELECT * FROM fact WHERE fk=1;
2. Temporarily suppresses the enforcement of data integrity checking:
SELECT DISABLE_DUPLICATE_KEY_ERROR();
3. Remove the duplicate primary keys. These steps also implicitly remove all fact rows with the
matching foreign key.
4. Remove the original row that contains duplicate values:
DELETE FROM dim WHERE pk=1 AND x=1;
Note: The extra predicate (x=1) specifies removal of the original (1,1) row, rather than the
newly inserted (1,2) values that caused the violation.
5. Remove all remaining rows:
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DELETE FROM dim WHERE pk=1;
6. Reenable integrity checking:
SELECT REENABLE_DUPLICATE_KEY_ERROR();
7. Reinsert the original values back into the fact and dimension table:
INSERT INTO dim SELECT * from dim_temp;
INSERT INTO fact SELECT * from fact_temp;
COMMIT;
8. Validate your dimension and fact tables.
See Also
l
ANALYZE_CONSTRAINTS
l
REENABLE_DUPLICATE_KEY_ERROR
LAST_INSERT_ID
Returns the last value of a column whose value is automatically incremented through the AUTO_
INCREMENT or IDENTITY Column-Constraint. If multiple sessions concurrently load the same
table, the returned value is the last value generated for an AUTO_INCREMENT column by an
insert in that session.
Behavior Type
Volatile
Syntax
LAST_INSERT_ID()
Privileges
l
Table owner
l
USAGE privileges on schema
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Notes
l
This function works only with AUTO_INCREMENT and IDENTITY columns. See columnconstraints for the CREATE TABLE statement.
l
LAST_INSERT_ID does not work with sequence generators created through the CREATE
SEQUENCE statement.
Examples
Create a sample table called customer4.
=> CREATE TABLE customer4(
ID IDENTITY(2,2),
lname VARCHAR(25),
fname VARCHAR(25),
membership_card INTEGER
);
=> INSERT INTO customer4(lname, fname, membership_card)
VALUES ('Gupta', 'Saleem', 475987);
Notice that the IDENTITY column has a seed of 2, which specifies the value for the first row loaded
into the table, and an increment of 2, which specifies the value that is added to the IDENTITY value
of the previous row.
Query the table you just created:
=> SELECT * FROM customer4;
ID | lname | fname | membership_card
----+-------+--------+----------------2 | Gupta | Saleem |
475987
(1 row)
Insert some additional values:
=> INSERT INTO customer4(lname, fname, membership_card)
VALUES ('Lee', 'Chen', 598742);
Call the LAST_INSERT_ID function:
=> SELECT LAST_INSERT_ID();
LAST_INSERT_ID
---------------4
(1 row)
Query the table again:
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=> SELECT * FROM customer4;
ID | lname | fname | membership_card
----+-------+--------+----------------2 | Gupta | Saleem |
475987
4 | Lee
| Chen
|
598742
(2 rows)
Add another row:
=> INSERT INTO customer4(lname, fname, membership_card)
VALUES ('Davis', 'Bill', 469543);
Call the LAST_INSERT_ID function:
=> SELECT LAST_INSERT_ID();
LAST_INSERT_ID
---------------6
(1 row)
Query the table again:
=> SELECT * FROM customer4; ID | lname | fname
----+-------+--------+----------------2 | Gupta | Saleem |
475987
4 | Lee
| Chen
|
598742
6 | Davis | Bill
|
469543
(3 rows)
| membership_card
See Also
l
ALTER SEQUENCE
l
CREATE SEQUENCE
l
DROP SEQUENCE
l
SEQUENCES
l
Using Named Sequences
l
Sequence Privileges
REENABLE_DUPLICATE_KEY_ERROR
Restores the default behavior of error reporting by reversing the effects of DISABLE_DUPLICATE_
KEY_ERROR. Effects are session scoped.
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Syntax
REENABLE_DUPLICATE_KEY_ERROR();
Privileges
Must be a superuser.
Examples
For examples and usage, see DISABLE_DUPLICATE_KEY_ERROR.
See Also
l
ANALYZE_CONSTRAINTS
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Data Collector Functions
The HP Vertica Data Collector is a utility that extends system table functionality by providing a
framework for recording events. It gathers and retains monitoring information about your database
cluster and makes that information available in system tables, requiring few configuration
parameter tweaks, and having negligible impact on performance.
Collected data is stored on disk in the DataCollector directory under the HP Vertica /catalog path.
You can use the information the Data Collector retains to query the past state of system tables and
extract aggregate information, as well as do the following:
l
See what actions users have taken
l
Locate performance bottlenecks
l
Identify potential improvements to HP Vertica configuration
Data Collector works in conjunction with an advisor tool called Workload Analyzer, which
intelligently monitors the performance of SQL queries and workloads and recommends tuning
actions based on observations of the actual workload history.
By default, Data Collector is on and retains information for all sessions. If performance issues
arise, a superuser can disable DC. See Data Collector Parameters and Enabling and Disabling
Data Collector in the Administrator's Guide.
This section describes the Data Collection control functions.
Related Topics
V_MONITOR.DATA_COLLECTOR
Retaining monitoring information and Analyzing Workloads in the Administrator's Guide
CLEAR_DATA_COLLECTOR
Clears all memory and disk records on the Data Collector tables and functions and resets collection
statistics in the V_MONITOR.DATA_COLLECTOR system table. A superuser can clear Data
Collector data for all components or specify an individual component
After you clear the Data Collector log, the information is no longer available for querying.
Syntax
CLEAR_DATA_COLLECTOR( [ 'component' ] )
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Parameters
component
Clears memory and disk records for the specified component only. If you provide no
argument, the function clears all Data Collector memory and disk records for all
components.
For the current list of component names, query the V_MONITOR.DATA_
COLLECTOR system table.
Privileges
Must be a superuser.
Examples
The following command clears memory and disk records for the ResourceAcquisitions component:
=> SELECT clear_data_collector('ResourceAcquisitions');
clear_data_collector
---------------------CLEAR
(1 row)
The following command clears data collection for all components on all nodes:
=> SELECT clear_data_collector();
clear_data_collector
---------------------CLEAR
(1 row)
See Also
DATA_COLLECTOR
l
l
DATA_COLLECTOR_HELP
Returns online usage instructions about the Data Collector, the DATA_COLLECTOR system table, and
the Data Collector control functions.
Syntax
DATA_COLLECTOR_HELP()
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Privileges
None
Returns
The DATA_COLLECTOR_HELP() function returns the following information:
=> SELECT DATA_COLLECTOR_HELP();
----------------------------------------------------------------------------Usage Data Collector
The data collector retains history of important system activities.
This data can be used as a reference of what actions have been taken
by users, but it can also be used to locate performance bottlenecks,
or identify potential improvements to the Vertica configuration.
This data is queryable via Vertica system tables.
Acccess a list of data collector components, and some statistics, by running:
SELECT * FROM v_monitor.data_collector;
The amount of data retained by size and time can be controlled with several
functions.
To just set the size amount:
set_data_collector_policy(,
,
);
To set both the size and time amounts (the smaller one will dominate):
set_data_collector_policy(,
,
,
);
To set just the time amount:
set_data_collector_time_policy(,
);
To set the time amount for all tables:
set_data_collector_time_policy();
The current retention policy for a component can be queried with:
get_data_collector_policy();
Data on disk is kept in the "DataCollector" directory under the Vertica
\catalog path. This directory also contains instructions on how to load
the monitoring data into another Vertica database.
To move the data collector logs and instructions to other storage locations,
create labeled storage locations using add_location and then use:
set_data_collector_storage_location();
Additional commands can be used to configure the data collection logs.
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The log can be cleared with:
clear_data_collector([]);
The log can be synchronized with the disk storage using:
flush_data_collector([]);
See Also
l
DATA_COLLECTOR
l
TUNING_RECOMMENDATIONS
l
Analyzing Workloads
l
Retaining Monitoring Information
FLUSH_DATA_COLLECTOR
Waits until memory logs are moved to disk and then flushes the Data Collector, synchronizing the
log with the disk storage. A superuser can flush Data Collector information for an individual
component or for all components.
Syntax
FLUSH_DATA_COLLECTOR( [ 'component' ] )
Parameters
component
Flushes the specified component. If you provide no argument, the function flushes
the Data Collector in full.
For the current list of component names, query the V_MONITOR.DATA_
COLLECTOR system table.
Privileges
Must be a superuser.
Examples
The following command flushes the Data Collector for the ResourceAcquisitions component:
=> SELECT flush_data_collector('ResourceAcquisitions');
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flush_data_collector
---------------------FLUSH
(1 row)
The following command flushes data collection for all components:
=> SELECT flush_data_collector();
flush_data_collector
---------------------FLUSH
(1 row)
See Also
DATA_COLLECTOR
l
l
GET_DATA_COLLECTOR_POLICY
Retrieves a brief statement about the retention policy for the specified component.
Syntax
GET_DATA_COLLECTOR_POLICY( 'component' )
Parameters
component
Returns the retention policy for the specified component.
For a current list of component names, query the V_MONITOR.DATA_
COLLECTOR system table
Privileges
None
Example
The following query returns the history of all resource acquisitions by specifying the
ResourceAcquisitions component:
=> SELECT get_data_collector_policy('ResourceAcquisitions');
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get_data_collector_policy
---------------------------------------------1000KB kept in memory, 10000KB kept on disk.
(1 row)
See Also
DATA_COLLECTOR
l
l
SET_DATA_COLLECTOR_POLICY
Sets a size restraint (memory and disk space in kilobytes) for the specified Data Collector table on
all nodes. If nodes are down, the failed nodes receive the setting when they rejoin the cluster.
You can use this function to set a size restraint only, or you can include the optional interval
argument to set disk capacity for both size and time in a single command.
If you specify interval, HP Vertica enforces the setting that is exceeded first (size or time).
Before you include a time restraint, be sure the disk size capacity is sufficiently large.
If you want to specify just a time restraint, or you want to turn off a time restraint you set using this
function, see SET_DATA_COLLECTOR_TIME_POLICY().
Syntax
SET_DATA_COLLECTOR_POLICY('component', 'memoryKB', 'diskKB' [,'interval']
)
Parameters
component
Configures the retention policy for the specified component.
memoryKB
Specifies the memory size to retain in kilobytes.
diskKB
Specifies the disk size in kilobytes.
interval
[Default off] Takes an optional interval argument to specify how long to retain the
specified component on disk. To disable a time restraint, set interval to -1.
Note: Any negative input will turn off the time restraint
Privileges
Must be a superuser.
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Notes
l
Before you change a retention policy, view its current setting by calling the GET_DATA_
COLLECTOR_POLICY() function.
l
If you don't know the name of a component, query the V_MONITOR.DATA_COLLECTOR system
table for a list; for example:
=> SELECT DISTINCT component, description FROM data_collector
ORDER BY 1 ASC;
Examples
The following command returns the retention policy for the ResourceAcquisitions component:
=> SELECT get_data_collector_policy('ResourceAcquisitions');
get_data_collector_policy
---------------------------------------------1000KB kept in memory, 10000KB kept on disk.
(1 row)
This command changes the memory and disk setting for ResourceAcquisitions from its current
setting of 1,000 KB memory and 10,000 KB disk space to 1500 KB and 25000 KB, respectively:
=> SELECT set_data_collector_policy('ResourceAcquisitions', '1500', '25000');
set_data_collector_policy
--------------------------SET
(1 row)
This command sets the RequestsIssued component to 1500 KB memory and 11000 KB on disk,
and includes a 3-minute time restraint:
=> SELECT set_data_collector_policy('RequestsIssued', '1500', '11000', '3 minutes'::inter
val);
set_data_collector_policy
--------------------------SET
(1 row)
The following command disables the 3-minute retention policy for the RequestsIssued component:
=> SELECT set_data_collector_policy('RequestsIssued', '-1');
set_data_collector_policy
--------------------------SET
(1 row)
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See Also
l
GET_DATA_COLLECTOR_POLICY
l
SET_DATA_COLLECTOR_TIME_POLICY()
l
DATA_COLLECTOR
l
Retaining Monitoring Information in the Administrator's Guide
SET_DATA_COLLECTOR_TIME_POLICY
Sets a time capacity for individual Data Collector tables on all nodes. If nodes are down, the failed
nodes receive the setting when they rejoin the cluster.
If you want to configure both time and size restraints at the same time, see SET_DATA_COLLECTOR_
POLICY().
Syntax
SET_DATA_COLLECTOR_TIME_POLICY( ['component',] 'interval' )
Parameters
component
[Optional] Configures the time retention policy for the specified component. If you
omit the component argument, HP Vertica sets the specified time capacity for all
Data Collector tables.
interval
Specifies the time restraint on disk using an INTERVAL type. To disable a time
restraint, set interval to -1.
Note: Any negative input turns off the time restraint
Privileges
Must be a superuser.
Notes
l
Before you change a retention policy, view its current setting by calling the GET_DATA_
COLLECTOR_POLICY() function.
l
If you don't know the name of a component, query the V_MONITOR.DATA_COLLECTOR system
table for a list. For example:
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=> SELECT DISTINCT component, description FROM data_collector
ORDER BY 1 ASC;
Setting time interval for system tables
You can also use the interval argument to query system tables the same way you query Data
Collector tables; for example:
set_data_collector_time_policy('', <'interval'>);
To illustrate, the following command in the left column is equivalent to running the series of
commands on the right:
Run one command
Instead of a series of commands
SELECT set_data_collector_time_policy
('v_monitor.query_requests',
'3 minutes'::interval);
SELECT set_data_collector_time_policy
('RequestsIssued',
'3 minutes'::interval);
SELECT set_data_collector_time_policy
('RequestsCompleted',
'3 minutes'::interval);
SELECT set_data_collector_time_policy
('Errors',
'3 minutes'::interval);
SELECT set_data_collector_time_policy
('ResourceAcquisitions',
'3 minutes'::interval);
The SET_DATA_COLLECTOR_TIME_POLICY() function updates the time capacity for all Data
Collector tables in the V_MONITOR.QUERY_REQUESTS view. The new setting overrides any previous
settings for every Data Collector table in that view.
Examples
The following command configures the Backups component to be retained on disk for 1 day:
=> SELECT set_data_collector_time_policy('Backups', '1 day'::interval);
set_data_collector_time_policy
-------------------------------SET
(1 row)
This command disables the 1-day restraint for the Backups component:
=> SELECT set_data_collector_time_policy('Backups', '-1');
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set_data_collector_time_policy
-------------------------------SET
(1 row)
This command sets a 30-minute time capacity for all Data Collector tables in a single command:
=> SELECT set_data_collector_time_policy('30 minutes'::interval);
set_data_collector_time_policy
-------------------------------SET
(1 row)
To view current retention policy settings for each Data Collector table, call the GET_DATA_
COLLECTION_POLICY() function. In the next example, the time restraint is included.
=> SELECT get_data_collector_policy('RequestsIssued');
get_data_collector_policy
----------------------------------------------------------------------------2000KB kept in memory, 50000KB kept on disk. 2 years 3 days 15:08 hours kept
on disk.
(1 row)
If the time policy setting is disabled, the output of GET_DATA_COLLECTION_POLICY() returns "Time
based retention disabled."
2000KB kept in memory, 50000KB kept on disk. Time based retention disabled.
See Also
l
GET_DATA_COLLECTOR_POLICY
l
SET_DATA_COLLECTOR_POLICY
l
DATA_COLLECTOR
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Database Designer Functions
The Database Designer functions allow you to access Database Designer functionality outside the
Administration Tools.
Permissions
To run the Database Designer functions, you must be one of the following:
l
Superuser
l
Have been granted the DBDUSER role and executed the SET ROLE DBDUSER command.
Once you have been granted the DBDUSER role, the role is in effect until it is revoked.
Important: When you grant the DBDUSER role, make sure to associate a resource pool
with that user to manage resources during Database Designer runs. Multiple users can run
Database Designer concurrently without interfering with each other or using up all the
cluster resources. When a user runs Database Designer, either using the Administration
Tools or programmatically, its execution is mostly contained by the user's resource pool,
but may spill over into some system resource pools for less-intensive tasks.
You can run Database Designer functions in vsql:
Setup Functions
This function directs Database Designer to create a new design:
l
DESIGNER_CREATE_DESIGN
Configuration Functions
The following functions allow you to specify properties of a particular design:
l
DESIGNER_DESIGN_PROJECTION_ENCODINGS
l
DESIGNER_SET_DESIGN_KSAFETY
l
DESIGNER_SET_OPTIMIZATION_OBJECTIVE
l
DESIGNER_SET_DESIGN_TYPE
l
DESIGNER_SET_PROPOSED_UNSEGMENTED_PROJECTIONS
l
DESIGNER_SET_ANALYZE_CORRELATIONS_MODE
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Input Functions
The following functions allow you to add tables and queries to your Database Designer design:
l
DESIGNER_ADD_DESIGN_QUERIES
l
DESIGNER_ADD_DESIGN_QUERIES_FROM RESULTS
l
DESIGNER_ADD_DESIGN_QUERY
l
DESIGNER_ADD_DESIGN_TABLES
Invocation Functions
These functions populate the Database Designer workspace and create design and deployment
scripts. You can also analyze statistics, deploy the design automatically, and drop the workspace
after the deployment:
l
DESIGNER_RUN_POPULATE_DESIGN_AND_DEPLOY
l
DESIGNER_WAIT_FOR_DESIGN
Output Functions
The following functions display information about projections and scripts that the Database
Designer created:
l
DESIGNER_OUTPUT_ALL_DESIGN_PROJECTIONS
l
DESIGNER_OUTPUT_DEPLOYMENT_SCRIPT
Cleanup Functions
The following functions cancel any running Database Designer operation or drop a Database
Designer design and all its contents:
l
DESIGNER_CANCEL_POPULATE_DESIGN
l
DESIGNER_DROP_DESIGN
l
DESIGNER_DROP_ALL_DESIGNS
DESIGNER_ADD_DESIGN_QUERIES
Reads and parses all queries in the specified file. Adds accepted queries to the design and sets the
weight of each accepted query to 1.
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Behavior Type
Immutable
Syntax
DESIGNER_ADD_DESIGN_QUERIES (
'design_name',
'file_name',
'informative_output' )
Parameters
design_name
Name of the design to which to add the queries, type VARCHAR.
file_name
Absolute path to the queries file, type VARCHAR.
informative_output
Specifies verbose output, type BOOLEAN. Default: 'false'. If 'true', the
output lists:
l
Number of accepted queries
l
Number of queries referencing non-design tables
l
Number of unsupported queries
l
Number of illegal queries
l
Maximum number of queries was reached, if applicable. Only valid if the
optimization objective is set to INCREMENTAL.
Permissions
l
To run DESIGNER_ADD_DESIGN_QUERIES, you must be a superuser, a user granted the
DBDUSER role, or the user who created the design.
l
You must have READ privilege on the storage location of the queries file.
l
You must have sufficient privileges to execute all queries in the queries file.
Notes
l
You must run DESIGNER_ADD_DESIGN_TABLES before you run DESIGNER_ADD_
DESIGN_QUERIES. If no tables have been added to the design, HP Vertica does not accept
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the design queries.
l
l
l
Database Designer rejects a query and returns an error if the query:
n
Contains illegal syntax.
n
References only external tables or system tables.
n
Is a DELETE or UPDATE query with one or more subqueries.
n
References a local temporary table or other non-design table.
n
Is an INSERT query that does not include a SELECT clause.
n
Is unoptimizable by the Database Designer.
If the third parameter is 'true', DESIGNER_ADD_DESIGN_QUERIES returns the following
information:
n
Number of accepted queries
n
Number of queries referencing non-design tables
n
Number of unsupported queries
n
Number of illegal queries
n
If the number of accepted queries exceeds 100, DESIGNER_ADD_DESIGN_QUERIES
reports that as well. If you are running an incremental design, any queries after 100 are
ignored.
DESIGNER_ADD_DESIGN_QUERIES:
n
Populates the V_MONITOR.DESIGN_QUERIES system table.
n
Creates the V_MONITOR.OUTPUT_EVENT_HISTORY system table.
Examples
The following example adds the queries file vmart_queries.sql to the VMART_DESIGN design.
This file contains nine queries. Since the third parameter is 'true', DESIGNER_ADD_DESIGN_
QUERIES returns the results of adding the queries:
=> SELECT DESIGNER_ADD_DESIGN_QUERIES (
'VMART_DESIGN',
'/tmp/examples/vmart_queries.sql',
'true'
);
...
DESIGNER_ADD_DESIGN_QUERIES
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---------------------------------------------------Number of accepted queries
=9
Number of queries referencing non-design tables =0
Number of unsupported queries
=0
Number of illegal queries
=0
(1 row)
See Also
l
DESIGNER_ADD_DESIGN_QUERIES_FROM_RESULTS
l
DESIGNER_ADD_DESIGN_QUERY
l
DESIGNER_ADD_DESIGN_TABLES
DESIGNER_ADD_DESIGN_QUERIES_FROM_RESULTS
Parses and executes a user query and retrieves only queries that contain the following columns:
l
QUERY_TEXT: Text of potential design queries
l
QUERY_WEIGHT: Corresponding query weight values for the queries in QUERY_TEXT, a value
greater than 0 but no greater than 1. If empty, Database Designer sets the weight of that query to
1.
DESIGNER_ADD_DESIGN_QUERIES_FROM_RESULTS parses all the retrieved queries and
adds all accepted queries to the design.
Behavior Type
Immutable
Syntax
DESIGNER_ADD_DESIGN_QUERIES_FROM_RESULTS ( 'design_name', 'user_query' )
Parameters
design_name
Name of the design to which to add the queries, type VARCHAR.
user_query
A valid SQL query whose results contain columns named QUERY_TEXT and QUERY_
WEIGHT, type VARCHAR.
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Permissions
l
To run DESIGNER_ADD_DESIGN_QUERIES_FROM_RESULTS, you must be a superuser
or a user granted the DBDUSER role who created the design.
l
You must have sufficient privileges to execute user_query and to execute each design query
retrieved from the results of user_query.
Notes
l
You must run DESIGNER_ADD_DESIGN_TABLES before you run DESIGNER_ADD_
DESIGN_QUERIES_FROM_RESULTS. If no tables have been added to the design, HP
Vertica does not accept the design queries.
l
An unlimited number of queries can be added to the design using DESIGNER_ADD_DESIGN_
QUERIES_FROM_RESULTS.
l
The rejects a query and returns an error if the query:
n
Contains illegal syntax.
n
References only external tables.
n
Is a DELETE or UPDATE query with one or more subqueries.
n
References a local temporary table or other non-design table.
n
Is an INSERT query that does not include a SELECT clause.
n
Is unoptimizable by the Database Designer.
Example
The following example retrieves the query_text field from the 10 most recent queries in the
QUERY_REQUESTS system table QUERY_REQUESTS and adds the accepted queries to the
VMART_DESIGN design. QUERY_REQUESTS does not contains any weight value for those
queries:
=> SELECT DESIGNER_ADD_DESIGN_QUERIES_FROM_RESULTS (
'VMART_DESIGN',
'SELECT request AS query_text FROM query_requests
ORDER BY start_timestamp DESC LIMIT 10;');
);
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See Also
l
DESIGNER_ADD_DESIGN_QUERIES
l
DESIGNER_ADD_DESIGN_TABLES
l
DESIGNER_SET_PROPOSE_UNSEGMENTED_PROJECTIONS
DESIGNER_ADD_DESIGN_QUERY
Reads and parses the specified query, and if accepted, adds it to the design.
Behavior Type
Immutable
Syntax
DESIGNER_ADD_DESIGN_QUERY (
'design_name',
'design_query',
query_weight )
Parameters
design_name
Name of the Database Designer design to which you want to add the query, type
VARCHAR.
design_query
Executable SQL query, type VARCHAR.
query_weight
Weight of query, any positive real number greater than 0 and no greater than 1.
Default: 1 You cannot add a weight of 0.
The weight of a query indicates its relative importance so that Database Designer
can prioritize the query when creating the design.
Permissions
l
To run DESIGNER_ADD_DESIGN_QUERY, you must be a superuser or a user granted the
DBDUSER role who created the design.
l
You must have sufficient privileges to execute the specified query.
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Notes
l
You must run DESIGNER_ADD_DESIGN_TABLES before you run DESIGNER_ADD_
DESIGN_QUERY. If no tables have been added to the design, HP Vertica does not accept the
design queries.
l
Database Designer rejects a query and returns an error if the query:
n
Contains illegal syntax.
n
References only external tables or system tables.
n
Is a DELETE or UPDATE query with one or more subqueries.
n
References a local temporary table or other non-design table.
n
Is an INSERT query that does not include a SELECT clause.
n
Is unoptimizable by the Database Designer.
Examples
The following example adds the specified query to the VMART_DESIGN design and assigns that
query a weight of 0.5:
=> SELECT DESIGNER_ADD_DESIGN_QUERY (
'VMART_DESIGN',
'SELECT customer_name, customer_type FROM vmart_design
ORDER BY customer_name ASC;',
0.5
);
See Also
l
DESIGNER_ADD_DESIGN_QUERIES
l
DESIGNER_ADD_DESIGN_QUERIES_FROM_RESULTS
l
DESIGNER_ADD_DESIGN_TABLES
DESIGNER_ADD_DESIGN_TABLES
Adds the specified tables to a design.
Behavior Type
Immutable
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Syntax
DESIGNER_ADD_DESIGN_TABLES (
'design_name',
'table_pattern',
[ 'analyze_statistics']
)
Parameters
design_name
Name of the Database Designer design, type VARCHAR.
table_pattern
Comma-delimited list of tables, type VARCHAR. The table_pattern must
be one of the following:
analyze_statistics
l
'*' indicates to add all user tables in the current database.
l
'.*' indicates to add all tables in the specified schema.
l
'.' indicates to add the specified table in the specified
schema.
l
'' indicates the specified table in the current search path.
(Optional) BOOLEAN that specifies whether or not to analyze statistics for
the design tables when adding them to the design. Default is 'false'.
Accurate statistics help Database Designer optimize compression and
query performance. Updating statistics takes time and resources. If 'true' ,
DESIGNER_ADD_DESIGN_TABLES executes the ANALYZE_
STATISTICS function. If ANALYZE_STATISTICS has previously run, set
this parameter to 'false'.
Permissions
To run DESIGNER_ADD_DESIGN_TABLES and add tables to your design, you must be a
superuser or a user granted the DBDUSER role who:
l
Created the design.
l
Has USAGE privilege on the design table schema.
l
Is the owner of the design table.
Notes
You must run DESIGNER_ADD_DESIGN_TABLES before you add design queries to the design.
If no tables have been added to the design, HP Vertica does not accept the design queries.
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Examples
The following example adds all the VMart tables to the VMART_DESIGN design, and analyzes
statistics for those tables:
=> SELECT DESIGNER_ADD_DESIGN_TABLES(
'VMART_DESIGN',
'online_sales.*',
'true'
);
DESIGNER_ADD_DESIGN_TABLES
---------------------------9
(1 row)
=> SELECT DESIGNER_ADD_DESIGN_TABLES(
'VMART_DESIGN',
'public.*',
'true'
);
DESIGNER_ADD_DESIGN_TABLES
---------------------------9
(1 row)
=> SELECT DESIGNER_ADD_DESIGN_TABLES(
'VMART_DESIGN',
'store.*',
'true'
);
DESIGNER_ADD_DESIGN_TABLES
---------------------------3
(1 row)
See Also
l
DESIGNER_ADD_DESIGN_QUERIES
l
DESIGNER_ADD_DESIGN_QUERIES_FROM_RESULTS
l
DESIGNER_ADD_DESIGN_QUERY
DESIGNER_CANCEL_POPULATE_DESIGN
Cancels the population or deployment operation for the specified design if it is currently running.
Behavior Type
Immutable
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Syntax
DESIGNER_CANCEL_POPULATE_DESIGN ( 'design_name' )
Parameters
design_name
Name of a currently running design that you want to cancel, type VARCHAR.
Permissions
To run DESIGNER_CANCEL_POPULATE_DESIGN on a design, you must be a superuser, or a
user granted the DBDUSER role who created the design.
Notes
l
When you cancel a deployment, the Database Designer cancels the projection refresh
operation. It does not roll back any projections that it has already deployed and refreshed.
l
Use DESIGNER_DROP_DESIGN to clean up any leftover files from the design.
Examples
The following example cancels a currently running design for VMART_DESIGN and then drops the
design:
=> SELECT DESIGNER_CANCEL_POPULATE_DESIGN ('VMART_DESIGN');=> SELECT DESIGNER_DROP_DESIGN
('VMART_DESIGN');
See Also
l
DESIGNER_DROP_ALL_DESIGNS
l
DESIGNER_DROP_DESIGN
DESIGNER_CREATE_DESIGN
Creates a design with the specified name.
Note: Be sure to back up the existing design using the EXPORT_CATALOG function before
running the Database Designer functions on an existing schema. You must explicitly back up
the existing design when using Database Designer programmatically.
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Behavior Type
Immutable
Syntax
DESIGNER_CREATE_DESIGN ( 'design_name' )
Parameters
design_name
Name of the design you want to create, type VARCHAR. A Database Designer
design name can only contain alphanumeric characters and underscore (_)
characters.
Permissions
To run DESIGNER_CREATE_DESIGN, you must be a superuser, or a user granted the
DBDUSER role.
Notes
l
Two users may not have designs with the same name at the same time.
l
DESIGNER_CREATE_DESIGN creates the following system tables in the V_MONITOR
schema:
n
DESIGNS
n
DESIGN_TABLES
n
DEPLOYMENT_PROJECTIONS
n
DEPLOYMENT_PROJECTION_STATEMENTS
n
DESIGN_QUERIES
n
OUTPUT_DEPLOYMENT_STATUS
n
OUTPUT_EVENT_HISTORY
Examples
The following example creates a design named VMART_DESIGN:
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=> SELECT DESIGNER_CREATE_DESIGN('VMART_DESIGN');
DESIGNER_CREATE_DESIGN
-----------------------0
(1 row)
See Also
l
DESIGNER_DROP_DESIGN
l
DESIGNER_DROP_ALL_DESIGNS
DESIGNER_DESIGN_PROJECTION_ENCODINGS
Analyze encoding in the specified projections, create a script to implement encoding
recommendations, and deploy the recommendations.
Behavior Type
Immutable
Syntax
DESIGNER_DESIGN_PROJECTION_ENCODINGS (
'projection_list',
'projection_ddl_script_file,
'deploy'
)
Parameters
projection_list
List of projections for which encoding analysis should be
performed:
l
''—All projections in the design
l
'.*'—All projections in the specified schema
l
'.'—The named projection in the
specified schema
l
''—The named projection in the PUBLIC schema. If
you omit the schema name, DESIGNER_DESIGN_
PROJECTION_ENCODINGS uses the PUBLIC schema.
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projection_ddl_script_file
deploy
Script to deploy the encoding changes, type VARCHAR. Can be
one of the following
l
Full path to script file
l
''—Output results to STDOUT in a foreground process
Specifies to deploy the encoding changes or not. Default: 'false'.
Privileges
l
To run DESIGNER_DESIGN_PROJECTION_ENCODINGS on a design, you must
n
Be the OWNER of the projections for which you want to perform encoding analysis.
n
Have USAGE privilege on the schema that corresponds to all the specified projections.
Examples
The following example requests that Database Designer analyze the encoding of the projections of
the online_sales schema in the VMart example database, save the SQL statements in the script file
encodings.sql, but do not deploy the changes:
=> SELECT DESIGNER_DESIGN_PROJECTION_ENCODINGS (
'online_sales.*',
'encodings.sql',
'false'
);
DESIGNER_DESIGN_PROJECTION_ENCODINGS
-------------------------------------(1 row)
DESIGNER_DROP_ALL_DESIGNS
Removes all Database Designer-related schemas associated with the current user.
Use DESIGNER_DROP_ALL_DESIGNS to remove database objects after one or more Database
Designer session completes.
Behavior Type
Immutable
Syntax
DESIGNER_DROP_ALL_DESIGNS()
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Parameters
None.
Privileges
If the superuser runs DESIGNER_DROP_ALL_DESIGNS, all designs are dropped.
If the DBDUSER runs DESIGNER_DROP_ALL_DESIGNS, the function only drops the designs
that the DBDUSER created.
Example
The following example removes all schema and their contents associated with the current user.
DESIGNER_DROP_ALL_DESIGNS returns the number of designs dropped:
=> SELECT DESIGNER_DROP_ALL_DESIGNS();
DESIGNER_DROP_ALL_DESIGNS
--------------------------2
(1 row)
See Also
l
DESIGNER_CANCEL_POPULATE_DESIGN
l
DESIGNER_DROP_DESIGN
DESIGNER_DROP_DESIGN
Removes the schema associated with the specified design and all its contents. Use DESIGNER_
DROP_DESIGN after a Database Designer design or deployment completes successfully or
terminates unexpectedly.
Behavior Type
Immutable
Syntax
DESIGNER_DROP_DESIGN ( 'design_name' )
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Parameters
design_name
Name of the design you want to drop, type VARCHAR. To drop all designs you
have created, use DESIGNER_DROP_ALL_DESIGNS.
Permissions
You must be a superuser or a user granted the DBDUSER role who created the design, to run
DESIGNER_DROP_DESIGN.
Notes
If a Database Designer session terminates unexpectedly, you cannot recreate that design with the
same name unless you run DESIGNER_DROP_DESIGN to clean up any leftover files.
Example
The following example deletes the Database Designer design VMART_DESIGN, and all its
contents:
=> SELECT DESIGNER_DROP_DESIGN ('VMART_DESIGN');
See Also
l
DESIGNER_CANCEL_POPULATE_DESIGN
l
DESIGNER_DROP_ALL_DESIGNS
DESIGNER_OUTPUT_ALL_DESIGN_PROJECTIONS
Displays the DDL statements that define the design projections to STDOUT.
Behavior Type
Immutable
Syntax
DESIGNER_OUTPUT_ALL_DESIGN_PROJECTIONS ( 'design_name' )
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Parameters
design_name
Name of the design for which to display the DDL statements that create the design
projections, type VARCHAR.
Permissions
You must be a superuser, or a user assigned the DBDUSER role, to run DESIGNER_OUTPUT_
ALL_DESIGN_PROJECTIONS.
Examples
The following example returns the design projection DDL statements for vmart_design:
=> SELECT DESIGNER_OUTPUT_ALL_DESIGN_PROJECTIONS('vmart_design');
CREATE PROJECTION customer_dimension_DBD_1_rep_VMART_DESIGN /*createtype(D)*/
(
customer_key ENCODING DELTAVAL,
customer_type ENCODING AUTO,
customer_name ENCODING AUTO,
customer_gender ENCODING REL,
title ENCODING AUTO,
household_id ENCODING DELTAVAL,
customer_address ENCODING AUTO,
customer_city ENCODING AUTO,
customer_state ENCODING AUTO,
customer_region ENCODING AUTO,
marital_status ENCODING AUTO,
customer_age ENCODING DELTAVAL,
number_of_children ENCODING BLOCKDICT_COMP,
annual_income ENCODING DELTARANGE_COMP,
occupation ENCODING AUTO,
largest_bill_amount ENCODING DELTAVAL,
store_membership_card ENCODING BLOCKDICT_COMP,
customer_since ENCODING DELTAVAL,
deal_stage ENCODING AUTO,
deal_size ENCODING DELTARANGE_COMP,
last_deal_update ENCODING DELTARANGE_COMP
)
AS
SELECT customer_key,
customer_type,
customer_name,
customer_gender,
title,
household_id,
customer_address,
customer_city,
customer_state,
customer_region,
marital_status,
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customer_age,
number_of_children,
annual_income,
occupation,
largest_bill_amount,
store_membership_card,
customer_since,
deal_stage,
deal_size,
last_deal_update
FROM public.customer_dimension
ORDER BY customer_gender,
annual_income
UNSEGMENTED ALL NODES;
CREATE PROJECTION product_dimension_DBD_2_rep_VMART_DESIGN /*+createtype(D)*/
(
...
See Also
l
DESIGNER_OUTPUT_DEPLOYMENT_SCRIPT
DESIGNER_OUTPUT_DEPLOYMENT_SCRIPT
Displays the deployment script for the specified design to STDOUT.
The deployment script includes the CREATE PROJECTION commands that are also in the design
script that you can display using DESIGNER_OUTPUT_ALL_DESIGN_PROJECTIONS.
If you have already deployed the design, this function does not return the script.
Behavior Type
Immutable
Syntax
DESIGNER_OUTPUT_DEPLOYMENT_SCRIPT ( 'design_name' )
Parameters
design_name
Name of the design for which you want to display the deployment script, type
VARCHAR.
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Permissions
To run DESIGNER_OUTPUT_DEPLOYMENT_SCRIPT, you must be a superuser or a user
granted the DBDUSER role who created the design.
Examples
The following example displays the deployment script for VMART_DESIGN at STDOUT:
=> SELECT DESIGNER_OUTPUT_DEPLOYMENT_SCRIPT('VMART_DESIGN');
CREATE PROJECTION customer_dimension_DBD_1_rep_VMART_DESIGN /*createtype(D)*/
...
CREATE PROJECTION product_dimension_DBD_2_rep_VMART_DESIGN /*+createtype(D)*/
...
select refresh('public.customer_dimension,
public.product_dimension,
public.promotion.dimension,
public.date_dimension');
select make_ahm_now();
DROP PROJECTION public.customer_dimension_super CASCADE;
DROP PROJECTION public.product_dimension_super CASCADE;
...
See Also
l
DESIGNER_OUTPUT_ALL_DESIGN_PROJECTIONS
DESIGNER_RESET_DESIGN
Clears the specified design but preserves the design's configuration such as parameters, tables,
and queries. Running this function allows you to make additional changes to the design with or
without deployment.
DESIGNER_RESET_DESIGN discards all the run-specific information of the previous Database
Designer build or deployment of the specified design but keeps its configuration. You can make
changes to the design as needed, for example, by changing parameters or adding additional tables
and/or queries, before rerunning the design.
Behavior Type
Immutable
Syntax
DESIGNER_RESET_DESIGN ( 'design_name' )
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Parameters
design_name
Name of the design you want to reset, type VARCHAR.
Permissions
You must be a superuser or a user granted the DBDUSER role who created the design, to run
DESIGNER_RESET_DESIGN.
Example
The following example resets the Database Designer design VMART_DESIGN:
=> SELECT DESIGNER_RESET_DESIGN ('VMART_DESIGN');
DESIGNER_RUN_POPULATE_DESIGN_AND_DEPLOY
Populates the design and creates the design and deployment scripts. If specified, DESIGNER_
RUN_POPULATE_DESIGN_AND_DEPLOY also analyzes statistics, deploys the design, and
drops the workspace after the deployment.
Note: Make sure to back up the existing design using the EXPORT_CATALOG function
before running the Database Designer functions on an existing schema. You must explicitly
back up the existing design when using Database Designer programmatically.
Behavior Type
Immutable
Syntax
DESIGNER_RUN_POPULATE_DESIGN_AND_DEPLOY (
'design_name',
'output_design_file',
'output_deployment_file',
[ 'analyze_statistics', ]
[ 'deploy', ]
[ 'drop_design_workspace', ]
[ 'continue_after_error', ]
)
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Parameters
design_name
Name of the design that you want to populate and deploy, type
VARCHAR.
output_design_file
Absolute path for saving the file that contains the DDL statements that
create the design projections, type VARCHAR.
output_deployment_file
Absolute path for saving the file that contains the deployment script,
type VARCHAR.
analyze_statistics
(Optional) BOOLEAN that specifies whether or not to collect or refresh
statistics for the tables before populating the design. Default is 'false'.
Accurate statistics help Database Designer optimize compression and
query performance. Updating statistics takes time and resources. If
'true', executes ANALYZE_STATISTICS. If ANALYZE_STATISTICS
has run recently, set this parameter to 'false'.
deploy
(Optional) BOOLEAN that specifies whether or not to deploy the
Database Designer design using the deployment script created by this
function. Default: 'true'.
drop_design_workspace
(Optional) BOOLEAN that specifies whether or not to drop the design
workspace after the design has been deployed. Default: 'true'.
continue_after_error
(Optional) BOOLEAN that specifies whether DESIGNER_RUN_
POPULATE_DESIGN_AND_DEPLOY should continue running if an
error occurs. Default: 'false'.
Permissions
To run DESIGNER_RUN_POPULATE_DESIGN_AND_DEPLOY, you must
l
Be a superuser, or a user granted the DBDUSER role who created the design.
l
Have WRITE privilege on the storage locations of the design and deployment scripts. If you do
not have permission, DESIGNER_RUN_POPULATE_DESIGN_AND_DEPLOY creates and
deploys the design (if the deploy parameter is 'true'), but it cannot save the design and
deployment scripts.
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Notes
l
l
Prior to calling DESIGNER_RUN_POPULATE_DESIGN_AND_DEPLOY, you must:
n
Create a design, a logical schema with tables.
n
Associates tables with the design.
n
Load queries to the design.
n
Set the design properties (K-safety level, mode, and policy)
DESIGNER_RUN_POPULATE_DESIGN_AND_DEPLOY does not create a backup copy of
the current design before deploying the new design.
Examples
The following example creates projections for and deploys the VMART_DESIGN design, and
analyzes statistics about the design tables. If an error occurs during execution, DESIGNER_RUN_
POPULATE_DESIGN_AND_DEPLOY terminates.
=> SELECT DESIGNER_RUN_POPULATE_DESIGN_AND_DEPLOY (
'VMART_DESIGN',
'/tmp/examples/vmart_design_files/vmart_design_DDL',
'/tmp/examples/vmart_design_files/vmart_design_deployment_scripts',
'true',
'false',
'false',
);
DESIGNER_SET_ANALYZE_CORRELATIONS_MODE
Specifies how Database Designer should handle column correlations in a design. Depending on
what mode you set, Database Designer analyzes or reanalyzes existing column correlations and
considers them when creating a database design..
Behavior Type
Immutable
Syntax
DESIGNER_SET_ANALYZE_CORRELATIONS_MODE (
'design_name',
analyze_correlations_mode )
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Parameters
design_name
Name of the design for which to specify how
Database Designer handles correlated columns,
type VARCHAR.
analyze_correlations_mode
Specifies the mode for analyzing correlations,
type INTEGER. Default: 0.
l
0—(Default) When creating a design, ignore
any column correlations in the specified
tables.
l
1—Consider the existing correlations in the
tables when creating the design. If you set the
mode to 1, and there are no existing
correlations, Database Designer does not
consider correlations.
l
2—Analyze column correlations on tables
where the correlation analysis was not
previously performed. When creating the
design, consider all column correlations (new
and existing).
l
3—Analyze all column correlations in the
tables and consider them when creating the
design. Even if correlations exist for a table,
reanalyze the table for correlations.
Permissions
l
To run DESIGNER_SET_ANALYZE_CORRELATIONS_MODE on a design, you must be a superuser, or
a user assigned the DBDUSER role with USAGE privilege on the design schema.
Notes
l
Analyzing column correlations typically needs to be done only once.
l
HP Vertica recommends that you analyze correlations when the row count is at least
DBDCorrelationSampleRowCount, which defaults to 4000.
l
To enable correlation analysis, you need to run DESIGNER_SET_ANALYZE_CORRELATIONS_
MODE for each design.
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Setting the correlation analysis mode does not have any affect on whether or not Database
Designer analyzes statistics when creating a design.
Example
The following example specifies that Database Designer analyze all correlated columns and
consider them when creating a design:
=> SELECT DESIGNER_SET_ANALYZE_CORRELATIONS_MODE (
'VMART_DESIGN',
'3);
DESIGNER_SET_ANALYZE_CORRELATIONS_MODE
---------------------------------------3
(1 row)
See Also
l
ANALYZE_CORRELATIONS
DESIGNER_SET_DESIGN_KSAFETY
Sets the K-safety value for a comprehensive design and stores the K-safety value in the DESIGNS
table.
Behavior Type
Immutable
Syntax
DESIGNER_SET_DESIGN_KSAFETY (
'design_name'
[, ksafety_level ]
)
Parameters
design_name
Name of the design for which you want to set the K-safety value, type
VARCHAR.
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ksafety_level
Value of K-safety that you want for the specified design, type INTEGER. The
value must be a valid K-safety value for your cluster. For example, if you have a
three- or four-node cluster, you cannot set the K-safety to 2.
If you do not set the design K-safety using this function, the defaults are:
l
Number of nodes = 1 or 2, K-safety = 0.
l
Number of nodes => 3, K-safety = 1.
If you are a DBDUSER, the system K-safety value is unchanged after creating
your design.
If you are a DBADMIN user and you set the design K-safety to a value:
l
Lower than the system K-safety, the system K-safety changes to the lower
value after Database Designer deploys the design.
l
Higher than the system K-safety, Database Designer creates the correct
number of buddy projections for the specified value. If you create the design
on all tables in the database, or all the tables in your database have the right
number of buddy projections, Database Designer changes the system Ksafety to that value if it’s valid on your cluster.
If you are not creating a design on all tables in the database and some of the
tables do not have enough buddy projections, Database Designer gives a
warning that the system K-safety cannot change and recommends which
tables need buddy projections in order to raise the K -safety value.
Permissions
To run DESIGNER_SET_DESIGN_KSAFETY, you must be a superuser, or a user granted the
DBDUSER role who created the design.
Notes
l
You cannot change the K-safety value of an incremental design. Incremental designs assume
the K-safety value of the database.
Examples
The following examples set the K-safety level for the VMART_DESIGN design:
=> SELECT DESIGNER_SET_DESIGN_KSAFETY('VMART_DESIGN', 1);
=> SELECT DESIGNER_SET_DESIGN_KSAFETY('VMART_DESIGN', 2);
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See Also
l
DESIGNER_SET_OPTIMIZATION_OBJECTIVE
l
DESIGNER_SET_DESIGN_TYPE
l
DESIGNER_SET_PROPOSE_UNSEGMENTED_PROJECTIONS
DESIGNER_SET_DESIGN_TYPE
Specifies whether Database Designer should create an initial or replacement design (the
COMPREHENSIVE option) or make incremental changes to the existing design using the design
queries you loaded into the design (the INCREMENTAL option). DESIGNER_SET_DESIGN_TYPE
stores the design mode in the DESIGNS table.
If you do not specify a design mode, Database Designer creates a comprehensive design.
Behavior Type
Immutable
Syntax
DESIGNER_SET_DESIGN_TYPE ( 'design_name', 'mode_name' )
Parameters
design_name
Name of the design for which you want to specify the design mode, type
VARCHAR.
mode_name
Name of the mode that Database Designer should use when designing the
database, type VARCHAR. Valid values are:
l
'COMPREHENSIVE'
l
'INCREMENTAL'
Permissions
You must be a superuser, or a user assigned the DBDUSER role, to run DESIGNER_SET_
DESIGN_TYPE.
Notes
Incremental designs always inherit the K-safety value of the database.
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Examples
The following examples show the two design mode options for the VMART_DESIGN design:
=> SELECT DESIGNER_SET_DESIGN_TYPE(
'VMART_DESIGN',
'COMPREHENSIVE');
DESIGNER_SET_DESIGN_TYPE
-------------------------0
(1 row)
=> SELECT DESIGNER_SET_DESIGN_TYPE(
'VMART_DESIGN',
'INCREMENTAL');
DESIGNER_SET_DESIGN_TYPE
-------------------------0
(1 row)
See Also
l
DESIGNER_SET_DESIGN_KSAFETY
l
DESIGNER_SET_OPTIMIZATION_OBJECTIVE
l
DESIGNER_SET_PROPOSE_UNSEGMENTED_PROJECTIONS
DESIGNER_SET_OPTIMIZATION_OBJECTIVE
Designates what optimization objective Database Designer should use when optimizing your
database design:
l
QUERY—Optimize for query performance, so that the queries run faster. This can result in a larger
database storage footprint because additional projections might be created.
l
LOAD—Optimize for load performance, so that the size of the database is minimized. This can
result in slower query performance.
l
BALANCED—Balance the design between query performance and database size.
DESIGNER_SET_OPTIMIZATION_OBJECTIVE stores the optimization objective in the
DESIGNS table.
Behavior Type
Immutable
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Syntax
DESIGNER_SET_OPTIMIZATION_OBJECTIVE ( 'design_name', 'policy_name' )
Parameters
design_name
Name of the design for which you want to specify the optimization policy, type
VARCHAR.
policy_name
Name of the optimization policy for Database Designer to use when designing the
database, type VARCHAR. Valid values are:
l
'QUERY'
l
'LOAD'
l
'BALANCED'
Permissions
To run DESIGNER_SET_OPTIMIZATION_OBJECTIVE, you must be a superuser, or a user
granted the DBDUSER role who created the design.
Notes
The optimization only applies to a comprehensive design; Database Designer ignores this value for
incremental designs.
Examples
The following examples show the three optimization objective options for the VMART_DESIGN
design:
=> SELECT DESIGNER_SET_OPTIMIZATION_OBJECTIVE(
'VMART_DESIGN', 'LOAD');
DESIGNER_SET_OPTIMIZATION_OBJECTIVE
----------------------------------0
(1 row)
=> SELECT DESIGNER_SET_OPTIMIZATION_OBJECTIVE(
'VMART_DESIGN', 'QUERY');
DESIGNER_SET_OPTIMIZATION_OBJECTIVE
-----------------------------------0
(1 row)
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=> SELECT DESIGNER_SET_OPTIMIZATION_OBJECTIVE(
'VMART_DESIGN', 'BALANCED');
DESIGNER_SET_OPTIMIZATION_OBJECTIVE
-----------------------------------0
(1 row)
See Also
l
DESIGNER_SET_DESIGN_KSAFETY
l
DESIGNER_SET_DESIGN_TYPE
l
DESIGNER_SET_PROPOSE_UNSEGMENTED_PROJECTIONS
DESIGNER_SET_PROPOSE_UNSEGMENTED_
PROJECTIONS
Specifies that Database Designer can propose unsegmented projections for all design tables and
stores the setting in the DESIGNS table.
Segmentation splits individual projections into chunks of data of similar size, called segments. One
segment is created for and stored on each node. Projection segmentation provides high availability
and recovery, and optimizes query execution. For more information about segmentation, see
Projection Segmentation.
Behavior Type
Immutable
Syntax
DESIGNER_SET_PROPOSE_UNSEGMENTED_PROJECTIONS ( 'design_name', unsegmented )
Parameters
design_name
Name of the design for which you want segmented projections, type VARCHAR.
unsegmented
If 'true', Database Designer can proposed unsegmented projections for design
tables. If 'false' (the default), Database Designer only proposes segmented
projections.
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Permissions
To run DESIGNER_SET_PROPOSE_UNSEGMENTED_PROJECTIONS, you must be a superuser, or a user
granted the DBDUSER role who created the design.
Notes
DESIGNER_SET_PROPOSE_UNSEGMENTED_PROJECTIONS has no affect on one-node clusters; all
projections will be unsegmented.
Example
The following example specifies that Database Designer propose a database design where all
projections are segmented:
=> SELECT DESIGNER_SET_PROPOSE_UNSEGMENTED_PROJECTIONS(
'VMART_DESIGN', 'false');
See Also
l
DESIGNER_SET_DESIGN_KSAFETY
l
DESIGNER_SET_DESIGN_TYPE
l
DESIGNER_SET_OPTIMIZATION_OBJECTIVE
DESIGNER_WAIT_FOR_DESIGN
Waits for a currently running design to complete. DESIGNER_WAIT_FOR_DESIGN waits for
operations that are populating and deploying the design.
Behavior Type
Immutable
Syntax
DESIGNER_WAIT_FOR_DESIGN ( 'design_name' )
Parameters
design_name
Name of the currently running design that you want to complete, type VARCHAR.
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Permissions
To run DESIGNER_WAIT_FOR_DESIGN on a currently running design, you must be a superuser,
or a user granted the DBDUSER role with USAGE privilege on the design schema.
Notes
If DESIGNER_WAIT_FOR_DESIGN is running in the foreground and still waiting, you can enter
Ctrl+C to stop DESIGNER_WAIT_FOR_DESIGN and return control to the user.
Examples
The following example requests to wait for the currently running design of VMART_DESIGN to
complete:
=> SELECT DESIGNER_WAIT_FOR_DESIGN ('VMART_DESIGN');
See Also
l
DESIGNER_CANCEL_POPULATE_DESIGN
l
DESIGNER_DROP_ALL_DESIGNS
l
DESIGNER_DROP_DESIGN
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Database Management Functions
This section contains the database management functions specific to HP Vertica.
CLEAR_RESOURCE_REJECTIONS
Clears the content of the RESOURCE_REJECTIONS and DISK_RESOURCE_REJECTIONS
system tables. Normally, these tables are only cleared during a node restart. This function lets you
clear the tables whenever you need. For example, you might want to clear the system tables after
you resolved a disk space issue that was causing disk resource rejections.
Syntax
CLEAR_RESOURCE_REJECTIONS();
Privileges
Must be a superuser.
Example
The following command clears the content of the RESOURCE_REJECTIONS and DISK_
RESOURCE_REJECTIONS system tables:
=> SELECT clear_resource_rejections();
clear_resource_rejections
--------------------------OK
(1 row)
See Also
l
DISK_RESOURCE_REJECTIONS
l
RESOURCE_REJECTIONS
DUMP_LOCKTABLE
Returns information about deadlocked clients and the resources they are waiting for.
Syntax
DUMP_LOCKTABLE()
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Privileges
None
Notes
Use DUMP_LOCKTABLE if HP Vertica becomes unresponsive:
1. Open an additional vsql connection.
2. Execute the query:
=> SELECT DUMP_LOCKTABLE();
The output is written to vsql. See Monitoring the Log Files.
You can also see who is connected using the following command:
=> SELECT * FROM SESSIONS;
Close all sessions using the following command:
=>SELECT CLOSE_ALL_SESSIONS();
Close a single session using the following command:
=> SELECT CLOSE_SESSION('session_id');
You get the session_id value from the V_MONITOR.SESSIONS system table.
See Also
l
CLOSE_ALL_SESSIONS
l
CLOSE_SESSION
l
LOCKS
l
SESSIONS
DUMP_PARTITION_KEYS
Dumps the partition keys of all projections in the system.
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Syntax
DUMP_PARTITION_KEYS( )
Note: The ROS objects of partitioned tables without partition keys are ignored by the tuple
mover and are not merged during automatic tuple mover operations.
Privileges
None; however function dumps only the tables for which user has SELECT privileges.
Example
=> SELECT DUMP_PARTITION_KEYS( );
Partition keys on node v_vmart_node0001
Projection 'states_b0'
Storage [ROS container]
No of partition keys: 1
Partition keys: NH
Storage [ROS container]
No of partition keys: 1
Partition keys: MA
Projection 'states_b1'
Storage [ROS container]
No of partition keys: 1
Partition keys: VT
Storage [ROS container]
No of partition keys: 1
Partition keys: ME
Storage [ROS container]
No of partition keys: 1
Partition keys: CT
See Also
l
DO_TM_TASK
l
DROP_PARTITION
l
DUMP_PROJECTION_PARTITION_KEYS
l
DUMP_TABLE_PARTITION_KEYS
l
PARTITION_PROJECTION
l
PARTITION_TABLE
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PARTITIONS
l
Working with Table Partitions
EXPORT_TABLES
Generates a SQL script that can be used to recreate a logical schema (schemas, tables,
constraints, and views) on a different cluster.
Syntax
EXPORT_TABLES ( [ 'destination' ] , [ 'scope' ] )
Parameters
destination
Specifies the path and name of the SQL output file. An empty string (''), which is
the default, outputs the script to standard output. The function writes the script to
the catalog directory if no destination is specified.
If you specify a file that does not exist, the function creates one. If the file preexists, the function silently overwrites its contents.
scope
Determines the tables to export. Specify the scope as follows:
l
An empty string (' ')—exports all non-virtual table objects to which the user has
access, including table schemas, sequences, and constraints. Exporting all
non-virtual objects is the default scope, and what the function exports if you do
not specify a scope.
l
A comma-delimited list of objects, which can include the following:
n
' [dbname.][schema.]object '—matches the named objects, which can be
schemas, tables, or views, in the schema. You can optionally qualify a
schema with a database prefix, and objects with a schema. You cannot pass
constraints as individual arguments.
n
' [dbname.]object '—matches a named object, which can be a schema, table,
or view. You can optionally qualify a schema with a database prefix, and an
object with its schema. For a schema, HP Vertica exports all non-virtual
objects to which the user has access within the schema. If a schema and
table both have the same name, the schema takes precedence.
Privileges
None; however:
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Function exports only the objects visible to the user
l
Only a superuser can export output to file
Example
The following example exports the store_orders_fact table of the store schema (in the current
database) to standard output:
=> SELECT EXPORT_TABLES(' ','store.store_orders_fact');
EXPORT_TABLES returns an error if:
l
You explicitly specify an object that does not exist
l
The current user does not have access to a specified object
See Also
EXPORT_CATALOG
l
EXPORT_OBJECTS
l
l
HAS_ROLE
Indicates, with a Boolean value, whether a role has been assigned to a user. This function is useful
for letting you check your own role membership.
Behavior Type
Stable
Syntax 1
HAS_ROLE( [ 'user_name' ,] 'role_name' );
Syntax 2
HAS_ROLE( 'role_name' );
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Parameters
user_name
[Optional] The name of a user to look up. Currently, only a superuser can supply the
user_name argument.
role_name
The name of the role you want to verify has been granted.
Privileges
Users can check their own role membership by calling HAS_ROLE('role_name'), but only a
superuser can look up other users' memberships using the optional user_name parameter.
Notes
You can query V_CATALOG system tables ROLES, GRANTS, and USERS to show any directlyassigned roles; however, these tables do not indicate whether a role is available to a user when
roles may be available through other roles (indirectly).
Examples
User Bob wants to see if he has been granted the commentor role:
=> SELECT HAS_ROLE('commentor');
Output t for true indicates that Bob has been assigned the commentor role:
HAS_ROLE
---------t
(1 row)
In the following function call, a superuser checks if the logadmin role has been granted to user Bob:
=> SELECT HAS_ROLE('Bob', 'logadmin');
HAS_ROLE
---------t
(1 row)
To view the names of all roles users can access, along with any roles that have been assigned to
those roles, query the V_CATALOG.ROLES system table. An asterisk in the output means role
granted WITH ADMIN OPTION.
=> SELECT * FROM roles;
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role_id
|
name
| assigned_roles
-------------------+-----------------+---------------45035996273704964 | public
|
45035996273704966 | dbduser
|
45035996273704968 | dbadmin
| dbduser*
45035996273704972 | pseudosuperuser | dbadmin*
45035996273704974 | logreader
|
45035996273704976 | logwriter
|
45035996273704978 | logadmin
| logreader,
logwriter
(7 rows)
See Also
l
GRANTS
l
ROLES
l
USERS
l
Managing Users and Privileges
l
Viewing a user's Role
SET_CONFIG_PARAMETER
Use SET_CONFIG_PARAMETER to specify the value of a configuration parameter.
Note: HP Vertica is designed to operate with minimal configuration changes. Use this function
sparingly and carefully follow any documented guidelines for that parameter.
If a node is down when you invoke this function, changes will occur on UP nodes only. You must reissue the function after down nodes recover in order for the changes to take effect on those nodes.
Alternatively, use the Administration Tools to copy the files. Redistributing Configuration Files to
Nodes.
Syntax
SET_CONFIG_PARAMETER( 'parameter', value )
Parameters
parameter
Specifies the name of the parameter value being set. See Configuration Parameters
in the Administrator's Guide for a list of supported parameters, their function, and
usage examples.
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value
Specifies the value of the supplied parameter argument. Syntax for this argument will
vary depending upon the parameter and its expected data type. For strings, you must
enclose the argument in single quotes; integer arguments can be unquoted.
You can also query the V_MONITOR.CONFIGURATION_PARAMETERS system table to get
information about configuration parameters currently in use by the system.
Examples
The following example sets the AnalyzeRowCountInterval parameter to 3600.
SELECT SET_CONFIG_PARAMETER ('AnalyzeRowCountInterval',3600);
The following statement returns all current configuration parameters and information about them,
including their current and default values:
=> SELECT * FROM CONFIGURATION_PARAMETERS;
SHUTDOWN
Forces a database to shut down, even if there are users connected.
Syntax
SHUTDOWN ( [ 'false' | 'true' ] )
Parameters
false
[Default] Returns a message if users are connected. Has the same effect as supplying
no parameters.
true
Performs a moveout operation and forces the database to shut down, disallowing further
connections.
Privileges
Must be a superuser.
Notes
l
Quotes around the true or false arguments are optional.
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Issuing the shutdown command without arguments or with the default (false) argument returns a
message if users are connected, and the shutdown fails. If no users are connected, the
database performs a moveout operation and shuts down.
l
Issuing the SHUTDOWN('true') command forces the database to shut down whether users are
connected or not.
l
You can check the status of the shutdown operation in the vertica.log file:
2010-03-09 16:51:52.625 unknown:0x7fc6d6d2e700
[Init] Shutdown complete. Exiting.
l
As an alternative to SHUTDOWN(), you can also temporarily set MaxClientSessions to 0 and
then use CLOSE_ALL_SESSIONS(). New client connections cannot connect unless they
connect using the dbadmin account. See CLOSE_ALL_SESSIONS for details.
Examples
The following command attempts to shut down the database. Because users are connected, the
command fails:
=> SELECT SHUTDOWN('false');
NOTICE: Cannot shut down while users are connected
SHUTDOWN
----------------------------Shutdown: aborting shutdown
(1 row)
SHUTDOWN() and SHUTDOWN('false') perform the same operation:
=> SELECT SHUTDOWN();
NOTICE: Cannot shut down while users are connected
SHUTDOWN
----------------------------Shutdown: aborting shutdown
(1 row)
Using the 'true' parameter forces the database to shut down, even though clients might be
connected:
=> SELECT SHUTDOWN('true');
SHUTDOWN
---------------------------Shutdown: moveout complete
(1 row)
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See Also
l
SESSIONS
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Epoch Management Functions
This section contains the epoch management functions specific to HP Vertica.
ADVANCE_EPOCH
Manually closes the current epoch and begins a new epoch.
Syntax
ADVANCE_EPOCH ( [ integer ] )
Parameters
integer
Specifies the number of epochs to advance.
Privileges
Must be a superuser.
Notes
This function is primarily maintained for backward compatibility with earlier versions of HP Vertica.
Example
The following command increments the epoch number by 1:
=> SELECT ADVANCE_EPOCH(1);
See Also
l
ALTER PROJECTION RENAME
GET_AHM_EPOCH
Returns the number of the epoch in which the Ancient History Mark is located. Data deleted up to
and including the AHM epoch can be purged from physical storage.
Syntax
GET_AHM_EPOCH()
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Note: The AHM epoch is 0 (zero) by default (purge is disabled).
Privileges
None
Examples
SELECT GET_AHM_EPOCH();
GET_AHM_EPOCH
---------------------Current AHM epoch: 0
(1 row)
GET_AHM_TIME
Returns a TIMESTAMP value representing the Ancient History Mark. Data deleted up to and
including the AHM epoch can be purged from physical storage.
Syntax
GET_AHM_TIME()
Privileges
None
Examples
SELECT GET_AHM_TIME();
GET_AHM_TIME
------------------------------------------------Current AHM Time: 2010-05-13 12:48:10.532332-04
(1 row)
See Also
l
SET DATESTYLE
l
TIMESTAMP
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GET_CURRENT_EPOCH
The epoch into which data (COPY, INSERT, UPDATE, and DELETE operations) is currently being
written. The current epoch advances automatically every three minutes.
Returns the number of the current epoch.
Syntax
GET_CURRENT_EPOCH()
Privileges
None
Examples
SELECT GET_CURRENT_EPOCH();
GET_CURRENT_EPOCH
------------------683
(1 row)
GET_LAST_GOOD_EPOCH
A term used in manual recovery, LGE (Last Good Epoch) refers to the most recent epoch that can
be recovered.
Returns the number of the last good epoch.
Syntax
GET_LAST_GOOD_EPOCH()
Privileges
None
Examples
SELECT GET_LAST_GOOD_EPOCH();
GET_LAST_GOOD_EPOCH
---------------------
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(1 row)
MAKE_AHM_NOW
Sets the Ancient History Mark (AHM) to the greatest allowable value, and lets you drop any
projections that existed before the issue occurred.
Caution: This function is intended for use by Administrators only.
Syntax
MAKE_AHM_NOW ( [ true ] )
Parameters
true
[Optional] Allows AHM to advance when nodes are down. Note: If the AHM is advanced
after the last good epoch of the failed nodes, those nodes must recover all data from
scratch. Use with care.
Privileges
Must be a superuser.
Notes
l
l
The MAKE_AHM_NOW function performs the following operations:
n
Advances the epoch.
n
Performs a moveout operation on all projections.
n
Sets the AHM to LGE — at least to the current epoch at the time MAKE_AHM_NOW() was
issued.
All history is lost and you cannot perform historical queries prior to the current epoch.
Example
=> SELECT MAKE_AHM_NOW();
MAKE_AHM_NOW
------------------------------
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AHM set (New AHM Epoch: 683)
(1 row)
The following command allows the AHM to advance, even though node 2 is down:
=> SELECT
WARNING:
WARNING:
WARNING:
MAKE_AHM_NOW(true);
Received no response from v_vmartdb_node0002 in get cluster LGE
Received no response from v_vmartdb_node0002 in get cluster LGE
Received no response from v_vmartdb_node0002 in set AHM
MAKE_AHM_NOW
-----------------------------AHM set (New AHM Epoch: 684)
(1 row)
See Also
l
DROP PROJECTION
l
MARK_DESIGN_KSAFE
l
SET_AHM_EPOCH
l
SET_AHM_TIME
SET_AHM_EPOCH
Sets the Ancient History Mark (AHM) to the specified epoch. This function allows deleted data up
to and including the AHM epoch to be purged from physical storage.
SET_AHM_EPOCH is normally used for testing purposes. Consider SET_AHM_TIME instead,
which is easier to use.
Syntax
SET_AHM_EPOCH ( epoch, [ true ] )
Parameters
epoch
Specifies one of the following:
l
The number of the epoch in which to set the AHM
l
Zero (0) (the default) disables PURGE
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true
Optionally allows the AHM to advance when nodes are down.
Note: If the AHM is advanced after the last good epoch of the failed nodes, those
nodes must recover all data from scratch. Use with care.
Privileges
Must be a superuser.
Notes
If you use SET_AHM_EPOCH , the number of the specified epoch must be:
l
Greater than the current AHM epoch
l
Less than the current epoch
l
Less than or equal to the cluster last good epoch (the minimum of the last good epochs of the
individual nodes in the cluster)
l
Less than or equal to the cluster refresh epoch (the minimum of the refresh epochs of the
individual nodes in the cluster)
Use the SYSTEM table to see current values of various epochs related to the AHM; for example:
=> SELECT * from SYSTEM;
-[ RECORD 1 ]------------+--------------------------current_timestamp
| 2009-08-11 17:09:54.651413
current_epoch
| 1512
ahm_epoch
| 961
last_good_epoch
| 1510
refresh_epoch
| -1
designed_fault_tolerance | 1
node_count
| 4
node_down_count
| 0
current_fault_tolerance | 1
catalog_revision_number | 1590
wos_used_bytes
| 0
wos_row_count
| 0
ros_used_bytes
| 41490783
ros_row_count
| 1298104
total_used_bytes
| 41490783
total_row_count
| 1298104
All nodes must be up. You cannot use SET_AHM_EPOCH when any node in the cluster is down,
except by using the optional true parameter.
When a node is down and you issue SELECT MAKE_AHM_NOW(), the following error is printed to the
vertica.log:
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Some nodes were excluded from setAHM. If their LGE is before the AHM they will perform fu
ll recovery.
Examples
The following command sets the AHM to a specified epoch of 12:
=> SELECT SET_AHM_EPOCH(12);
The following command sets the AHM to a specified epoch of 2 and allows the AHM to advance
despite a failed node:
=> SELECT SET_AHM_EPOCH(2, true);
See Also
l
MAKE_AHM_NOW
l
SET_AHM_TIME
l
SYSTEM
SET_AHM_TIME
Sets the Ancient History Mark (AHM) to the epoch corresponding to the specified time on the
initiator node. This function allows historical data up to and including the AHM epoch to be purged
from physical storage.
Syntax
SET_AHM_TIME ( time , [ true ] )
Parameters
time
Is a TIMESTAMP value that is automatically converted to the appropriate epoch number.
true
[Optional] Allows the AHM to advance when nodes are down.
Note: If the AHM is advanced after the last good epoch of the failed nodes, those nodes
must recover all data from scratch.
Privileges
Must be a superuser.
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Notes
l
SET_AHM_TIME returns a TIMESTAMP WITH TIME ZONE value representing the end point of
the AHM epoch.
l
You cannot change the AHM when any node in the cluster is down, except by using the optional
true parameter.
l
When a node is down and you issue SELECT MAKE_AHM_NOW(), the following error is printed to
the vertica.log:
Some nodes were excluded from setAHM. If their LGE is before the AHM they will perform
full recovery.
Examples
Epochs depend on a configured epoch advancement interval. If an epoch includes a three-minute
range of time, the purge operation is accurate only to within minus three minutes of the specified
timestamp:
=> SELECT SET_AHM_TIME('2008-02-27 18:13');
set_ahm_time
-----------------------------------AHM set to '2008-02-27 18:11:50-05'
(1 row)
Note: The –05 part of the output string is a time zone value, an offset in hours from UTC
(Universal Coordinated Time, traditionally known as Greenwich Mean Time, or GMT).
In the previous example, the actual AHM epoch ends at 18:11:50, roughly one minute before the
specified timestamp. This is because SET_AHM_TIME selects the epoch that ends at or before
the specified timestamp. It does not select the epoch that ends after the specified timestamp
because that would purge data deleted as much as three minutes after the AHM.
For example, using only hours and minutes, suppose that epoch 9000 runs from 08:50 to 11:50 and
epoch 9001 runs from 11:50 to 15:50. SET_AHM_TIME('11:51') chooses epoch 9000 because it
ends roughly one minute before the specified timestamp.
In the next example, if given an environment variable set as date =`date`; the following
command fails if a node is down:
=> SELECT SET_AHM_TIME('$date');
In order to force the AHM to advance, issue the following command instead:
=> SELECT SET_AHM_TIME('$date', true);
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See Also
l
MAKE_AHM_NOW
l
SET_AHM_EPOCH
l
SET DATESTYLE
l
TIMESTAMP
Flex Table Functions
This section contains helper functions for use in working with flex tables.
Note: While the functions are available to all users, they are applicable only to flex table, their
associated flex_table_keys table and flex_table_view views. By computing keys and
creating views from flex table data, the functions facilitate SELECT queries. One function
restores the original keys table and view that were made when you first created the flex table.
For more information, see the Flex Tables Guide.
COMPUTE_FLEXTABLE_KEYS
Computes the virtual columns (keys and values) from the map data of a flex table and repopulates
the associated _keys table. The keys table has the following columns:
l
key_name
l
frequency
l
data_type_guess
This function sorts the keys table by frequency and key_name.
Use this function to compute keys without creating an associated table view. To build a view as
well, use COMPUTE_FLEXTABLE_KEYS_AND_BUILD_VIEW.
Usage
compute_flextable_keys('flex_table')
Arguments
flex_table The name of the flex table.
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During execution, this function determines a data type for each virtual column, casting the values it
computes to VARCHAR, LONG VARCHAR, or LONG VARBINARY, depending on the length of the key, and
whether the key includes nested maps.
The following examples illustrate this function and the results of populating the _keys table, once
you've created a flex table (darkdata1) and loaded data:
kdb=> create flex table darkdata1();
CREATE TABLE
kdb=> copy darkdata1 from '/test/flextable/DATA/tweets_12.json' parser fjsonparser();
Rows Loaded
------------12
(1 row)
kdb=> select compute_flextable_keys('darkdata1');
compute_flextable_keys
-------------------------------------------------Please see public.darkdata1_keys for updated keys
(1 row)
kdb=> select * from darkdata1_keys;
key_name
| frequency |
data_type_guess
----------------------------------------------------------+-----------+--------------------contributors
|
8 | varchar(20)
coordinates
|
8 | varchar(20)
created_at
|
8 | varchar(60)
entities.hashtags
|
8 | long varbinary(18
6)
entities.urls
|
8 | long varbinary(3
2)
entities.user_mentions
|
8 | long varbinary(67
4)
.
.
.
retweeted_status.user.time_zone
|
1 | varchar(20)
retweeted_status.user.url
|
1 | varchar(68)
retweeted_status.user.utc_offset
|
1 | varchar(20)
retweeted_status.user.verified
|
1 | varchar(20)
(125 rows)
The flex keys table has these columns:
Column
Description
key_name
The name of the virtual column (key).
frequency The number of times the virtual column occurs in the map.
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Column
Description
data_
type_
guess
The data type for each virtual column, cast to VARCHAR, LONG VARCHAR or LONG
VARBINARY, depending on the length of the key, and whether the key includes one or
more nested maps.
In the _keys table output, the data_type_guess column values are also followed by
a value in parentheses, such as varchar(20). The value indicates the padded
width of the key column, as calculated by the longest field, multiplied by the
FlexTableDataTypeGuessMultiplier configuration parameter value. For more
information, see Setting Flex Table Parameters.
See Also
l
BUILD_FLEXTABLE_VIEW
l
COMPUTE_FLEXTABLE_KEYS_AND_BUILD_VIEW
l
MATERIALIZE_FLEXTABLE_COLUMNS
l
RESTORE_FLEXTABLE_DEFAULT_KEYS_TABLE_AND_VIEW
COMPUTE_FLEXTABLE_KEYS
Computes the virtual columns (keys and values) from the map data of a flex table and repopulates
the associated _keys table. The keys table has the following columns:
l
key_name
l
frequency
l
data_type_guess
This function sorts the keys table by frequency and key_name.
Use this function to compute keys without creating an associated table view. To build a view as
well, use COMPUTE_FLEXTABLE_KEYS_AND_BUILD_VIEW.
Usage
compute_flextable_keys('flex_table')
Arguments
flex_table The name of the flex table.
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During execution, this function determines a data type for each virtual column, casting the values it
computes to VARCHAR, LONG VARCHAR, or LONG VARBINARY, depending on the length of the key, and
whether the key includes nested maps.
The following examples illustrate this function and the results of populating the _keys table, once
you've created a flex table (darkdata1) and loaded data:
kdb=> create flex table darkdata1();
CREATE TABLE
kdb=> copy darkdata1 from '/test/flextable/DATA/tweets_12.json' parser fjsonparser();
Rows Loaded
------------12
(1 row)
kdb=> select compute_flextable_keys('darkdata1');
compute_flextable_keys
-------------------------------------------------Please see public.darkdata1_keys for updated keys
(1 row)
kdb=> select * from darkdata1_keys;
key_name
| frequency |
data_type_guess
----------------------------------------------------------+-----------+--------------------contributors
|
8 | varchar(20)
coordinates
|
8 | varchar(20)
created_at
|
8 | varchar(60)
entities.hashtags
|
8 | long varbinary(18
6)
entities.urls
|
8 | long varbinary(3
2)
entities.user_mentions
|
8 | long varbinary(67
4)
.
.
.
retweeted_status.user.time_zone
|
1 | varchar(20)
retweeted_status.user.url
|
1 | varchar(68)
retweeted_status.user.utc_offset
|
1 | varchar(20)
retweeted_status.user.verified
|
1 | varchar(20)
(125 rows)
The flex keys table has these columns:
Column
Description
key_name
The name of the virtual column (key).
frequency The number of times the virtual column occurs in the map.
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Column
Description
data_
type_
guess
The data type for each virtual column, cast to VARCHAR, LONG VARCHAR or LONG
VARBINARY, depending on the length of the key, and whether the key includes one or
more nested maps.
In the _keys table output, the data_type_guess column values are also followed by
a value in parentheses, such as varchar(20). The value indicates the padded
width of the key column, as calculated by the longest field, multiplied by the
FlexTableDataTypeGuessMultiplier configuration parameter value. For more
information, see Setting Flex Table Parameters.
See Also
l
BUILD_FLEXTABLE_VIEW
l
COMPUTE_FLEXTABLE_KEYS_AND_BUILD_VIEW
l
MATERIALIZE_FLEXTABLE_COLUMNS
l
RESTORE_FLEXTABLE_DEFAULT_KEYS_TABLE_AND_VIEW
COMPUTE_FLEXTABLE_KEYS_AND_BUILD_VIEW
Combines the functionality of BUILD_FLEXTABLE_VIEW and COMPUTE_FLEXTABLE_KEYS
to compute virtual columns (keys) from the map data of a flex table , and construct a view. If you
don't need to perform both operations together, use one of the single-operation functions.
Usage
compute_flextable_keys_and_build_view('flex_table')
Arguments
flex_table The name of a flex table.
Examples
The following example calls the function for the darkdata flex table.
kdb=> select compute_flextable_keys_and_build_view('darkdata');
compute_flextable_keys_and_build_view
----------------------------------------------------------------------Please see public.darkdata_keys for updated keys
The view public.darkdata_view is ready for querying
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(1 row)
See Also
l
BUILD_FLEXTABLE_VIEW
l
COMPUTE_FLEXTABLE_KEYS
l
MATERIALIZE_FLEXTABLE_COLUMNS
l
RESTORE_FLEXTABLE_DEFAULT_KEYS_TABLE_AND_VIEW
MATERIALIZE_FLEXTABLE_COLUMNS
Materializes virtual columns that are listed as key_names in the flextable_keys table. You can
optionally indicate the number of columns to materialize, and use a keys table other than the
default. If you do not specify the number of columns, the function materializes up to 50 virtual
column key names. Calling this function requires that you first compute flex table keys using either
COMPUTE_FLEXTABLE_KEYS or COMPUTE_FLEXTABLE_KEYS_AND_BUILD_VIEW .
Note: Materializing any virtual column into a real column with this function affects data storage
limits. Each materialized column counts