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Pentaho 3.2 Data Integration
Beginner's Guide

Explore, transform, validate, and integrate your data with ease

María Carina Roldán

BIRMINGHAM - MUMBAI

Pentaho 3.2 Data Integration
Beginner's Guide

All rights reserved. No part of this book may be reproduced, stored in a retrieval system,
or transmitted in any form or by any means, without the prior written permission of the
publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the
information presented. However, the information contained in this book is sold without
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distributors will be held liable for any damages caused or alleged to be caused directly or
indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all the companies
and products mentioned in this book by the appropriate use of capitals. However, Packt
Publishing cannot guarantee the accuracy of this information.

First published: April 2010

Production Reference: 1050410

Published by Packt Publishing Ltd.
32 Lincoln Road
Olton
Birmingham, B27 6PA, UK.
ISBN 978-1-847199-54-6
www.packtpub.com

Cover Image by Parag Kadam (paragvkadam@gmail.com)

Credits
Author
María Carina Roldán
Reviewers
Jens Bleuel

Editorial Team Leader
Gagandeep Singh
Project Team Leader
Lata Basantani

Roland Bouman
Matt Casters
James Dixon
Will Gorman
Gretchen Moran
Acquisition Editor
Usha Iyer
Development Editor
Reshma Sundaresan
Technical Editors
Gaurav Datar

Project Coordinator
Poorvi Nair
Proofreader
Sandra Hopper
Indexer
Rekha Nair
Graphics
Geetanjali Sawant
Production Coordinator
Shantanu Zagade

Rukhsana Khambatta
Cover Work
Copy Editor
Sanchari Mukherjee

Shantanu Zagade

Foreword
If we look back at what has happened in the data integration market over the last 10
years we can see a lot of change. In the first half of that decade there was an explosion
in the number of data integration tools and in the second half there was a big wave of
consolidations. This consolidation wave put an ever growing amount of data integration
power in the hands of only a few large billion dollar companies. For any person, company
or project in need of data integration, this meant either paying large amounts of money or
doing hand-coding of their solution.
During that exact same period, we saw web servers, programming languages, operating
systems, and even relational databases turn into a commodity in the ICT market place. This
was driven among other things by the availability of open source software such as Apache,
GNU, Linux, MySQL, and many others. For the ICT market, this meant that more services
could be deployed at a lower cost. If you look closely at what has been going on in those last
10 years, you will notice that most companies increasingly deployed more ICT services to
end-users. These services get more and more connected over an ever growing network.
Pretty much anything ranging from tiny mobile devices to huge cloud-based infrastructure
is being deployed and all those can contain data that is valuable to an organization.
The job of any person that needs to integrate all this data is not easy. Complexity of
information services technology usually increases exponentially with the number of systems
involved. Because of this, integrating all these systems can be a daunting and scary task that
is never complete. Any piece of code lives in what can be described as a software ecosystem
that is always in a state of flux. Like in nature, certain ecosystems evolve extremely fast
where others change very slowly over time. However, like in nature all ICT systems change.
What is needed is another wave of commodification in the area of data integration and
business intelligence in general. This is where Pentaho comes in.
Pentaho tries to provide answers to these problems by making the integration software
available as open source, accessible, easy to use, and easy to maintain for users and
developers alike. Every release of our software we try to make things easier, better, and
faster. However, even if things can be done with nice user interfaces, there are still a huge
amount of possibilities and options to choose from.

As the founder of the project I've always liked the fact that Kettle users had a lot of choice.
Choice translates into creativity, and creativity often delivers good solutions that are
comfortable to the person implementing them. However, this choice can be daunting to any
beginning Kettle developer. With thousands of options to choose from, it can be very hard to
get started.
This is above all others the reason why I'm very happy to see this book come to life. It will
be a great and indispensable help for everyone that is taking steps into the wonderful world
of data integration with Kettle. As such, I hope you see this book as an open invitation to get
started with Kettle in the wonderful world of data integration.
Matt Casters
Chief Data Integration at Pentaho
Kettle founder

The Kettle Project
Whether there is a migration to do, an ETL process to run, or a need for massively loading
data into a database, you have several software tools, ranging from expensive and
sophisticated to free open source and friendly ones, which help you accomplish the task.
Ten years ago, the scenario was clearly different. By 2000, Matt Casters, a Belgian business
intelligent consultant, had been working for a while as a datawarehouse architect and
administrator. As such, he was one of quite a number of people who, no matter if the
company they worked for was big or small, had to deal with the difficulties that involve
bridging the gap between information technology and business needs. What made it even
worse at that time was that ETL tools were prohibitively expensive and everything had to
be crafted done. The last employer he worked for, didn't think that writing a new ETL tool
would be a good idea. This was one of the motivations for Matt to become an independent
contractor and to start his own company. That was in June 2001.
At the end of that year, he told his wife that he was going to write a new piece of software
for himself to do ETL tasks. It was going to take up some time left and right in the evenings
and weekends. Surprised, she asked how long it would take you to get it done. He replied
that it would probably take five years and that he perhaps would have something working
in three.
Working on that started in early 2003. Matt's main goals for writing the software included
learning about databases, ETL processes, and data warehousing. This would in turn improve
his chances on a job market that was pretty volatile. Ultimately, it would allow him to work
full time on the software.
Another important goal was to understand what the tool had to do. Matt wanted a scalable
and parallel tool, and wanted to isolate rows of data as much as possible.
The last but not least goal was to pick the right technology that would support the tool. The
first idea was to build it on top of KDE, the popular Unix desktop environment. Trolltech, the
people behind Qt, the core UI library of KDE, had released database plans to create drivers
for popular databases. However, the lack of decent drivers for those databases drove Matt
to change plans and use Java. He picked Java because he had some prior experience as he
had written a Japanese Chess (Shogi) database program when Java 1.0 was released. To
Sun's credit, this software still runs and is available at http://ibridge.be/shogi/.

After a year of development, the tool was capable of reading text files, reading from
databases, writing to databases and it was very flexible. The experience with Java was not
100% positive though. The code had grown unstructured, crashes occurred all too often, and
it was hard to get something going with the Java graphic library used at that moment, the
Abstract Window Toolkit (AWT); it looked bad and it was slow.
As for the library, Matt decided to start using the newly released Standard Widget Toolkit
(SWT), which helped solve part of the problem. As for the rest, Kettle was a complete mess.
It was time to ask for help. The help came in hands of Wim De Clercq, a senior enterprise
Java architect, co-owner of Ixor (www.ixor.be) and also friend of Matt. At various intervals
over the next few years, Wim involved himself in the project, giving advices to Matt about
good practices in Java programming. Listening to that advice meant performing massive
amounts of code changes. As a consequence, it was not unusual to spend weekends doing
nothing but refactoring code and fixing thousands of errors because of that. But, bit by bit,
things kept going in the right direction.
At that same time, Matt also showed the results to his peers, colleagues, and other senior
BI consultants to hear what they thought of Kettle. That was how he got in touch with the
Flemish Traffic Centre (www.verkeerscentrum.be/verkeersinfo/kaart) where billions
of rows of data had to be integrated from thousands of data sources all over Belgium. All of
a sudden, he was being paid to deploy and improve Kettle to handle that job. The diversity of
test cases at the traffic center helped to improve Kettle dramatically. That was somewhere in
2004 and Kettle was by its version 1.2.
While working at Flemish, Matt also posted messages on Javaforge (www.javaforge.com)
to let people know they could download a free copy of Kettle for their own use. He got a
few reactions. Despite some of them being remarkably negative, most were positive. The
most interesting response came from a nice guy called Jens Bleuel in Germany who asked if
it was possible to integrate third-party software into Kettle. In his specific case, he needed a
connector to link Kettle with the German SAP software (www.sap.com). Kettle didn't have a
plugin architecture, so Jens' question made Matt think about a plugin system, and that was
the main motivation for developing version 2.0.
For various reasons including the birth of Matt's son Sam and a lot of consultancy work,
it took around a year to release Kettle version 2.0. It was a fairly complete release with
advanced support for slowly changing dimensions and junk dimensions (Chapter 9 explains
those concepts), ability to connect to thirteen different databases, and the most important
fact being support for plugins. Matt contacted Jens to let him know the news and Jens was
really interested. It was a very memorable moment for Matt and Jens as it took them only a
few hours to get a new plugin going that read data from an SAP/R3 server. There was a lot
of excitement, and they agreed to start promoting the sales of Kettle from the Kettle.be
website and from Proratio (www.proratio.de), the company Jens worked for.

Those were days of improvements, requests, people interested in the project. However, it
became too much to handle. Doing development and sales all by themselves was no fun
after a while. As such, Matt thought about open sourcing Kettle early in 2005 and by late
summer he made his decision. Jens and Proratio didn't mind and the decision was final.
When they finally open sourced Kettle on December 2005, the response was massive. The
downloadable package put up on Javaforge got downloaded around 35000 times during first
week only. The news got spread all over the world pretty quickly.
What followed was a flood of messages, both private and on the forum. At its peak in March
2006, Matt got over 300 messages a day concerning Kettle.
In no time, he was answering questions like crazy, allowing people to join the development
team and working as a consultant at the same time. Added to this, the birth of his daughter
Hannelore in February 2006 was too much to deal with.
Fortunately, good times came. While Matt was trying to handle all that, a discussion was
taking place at the Pentaho forum (http://forums.pentaho.org/) concerning the ETL
tool that Pentaho should support. They had selected Enhydra Octopus, a Java-based ETL
software, but they didn't have a strong reliance on a specific tool.
While Jens was evaluating all sorts of open source BI packages, he came across that thread.
Matt replied immediately persuading people at Pentaho to consider including Kettle. And
he must be convincing because the answer came quickly and was positive. James Dixon,
Pentaho founder and CTO, opened Kettle the possibility to be the premier and only ETL
tool supported by Pentaho. Later on, Matt came in touch with one of the other Pentaho
founders, Richard Daley, who offered him a job. That allowed Matt to focus full-time on
Kettle. Four years later, he's still happily working for Pentaho as chief architect for data
integration, doing the best effort to deliver Kettle 4.0. Jens Bleuel, who collaborated with
Matt since the early versions, is now also part of the Pentaho team.

About the Author
María Carina was born in a small town in the Patagonia region in Argentina. She earned
her Bachelor degree in Computer Science at UNLP in La Plata and then moved to Buenos
Aires where she has lived since 1994 working in IT.
She has been working as a BI consultant for the last 10 years. At the beginning she worked
with Cognos suite. However, over the last three years, she has been dedicated, full time, to
developing Pentaho BI solutions both for local and several Latin-American companies, as well
as for a French automotive company in the last months.
She is also an active contributor to the Pentaho community.
At present, she lives in Buenos Aires, Argentina, with her husband Adrián and children
Camila and Nicolás.
Writing my first book in a foreign language and working on a full time job
at the same time, not to mention the upbringing of two small kids, was
definitely a big challenge. Now I can tell that it's not impossible.
I dedicate this book to my husband and kids; I'd like to thank them for all
their support and tolerance over the last year. I'd also like to thank my
colleagues and friends who gave me encouraging words throughout the
writing process.
Special thanks to the people at Packt; working with them has been
really pleasant.
I'd also like to thank the Pentaho community and developers for making
Kettle the incredible tool it is. Thanks to the technical reviewers who,
with their very critical eye, contributed to make this a book suited to
the audience.
Finally, I'd like to thank Matt Casters who, despite his busy schedule, was
willing to help me from the first moment he knew about this book.

About the Reviewers
Jens Bleuel is a Senior Consultant and Engineer at Pentaho. He is also working as a project
leader, trainer, and product specialist in the services and support department. Before he
joined Pentaho in mid 2007, he was software developer and project leader, and his main
business was Data Warehousing and the architecture along with designing and developing of
user friendly tools. He studied business economics, was on a grammar school for electronics,
and has been programming in a wide area of environments such as Assembler, C, Visual
Basic, Delphi, .Net, and these days mainly in Java. His customer focus is on the wholesale
market and consumer goods industries. Jens is 40 years old and lives with his wife and two
boys in Mainz, Germany (near the nice Rhine river). In his spare time, he practices Tai-Chi,
Qigong, and photography.

Roland Bouman has been working in the IT industry since 1998, mostly as a database and
web application developer. He has also worked for MySQL AB (later Sun Microsystems) as
certification developer and as curriculum developer.
Roland mainly focuses on open source web technology, databases, and Business Intelligence.
He's an active member of the MySQL and Pentaho communities and can often be found
speaking at worldwide conferences and events such as the MySQL user conference, the
O'Reilly Open Source conference (OSCON), and at Pentaho community events.
Roland is co-author of the MySQL 5.1 Cluster DBA Certification Study Guide (Vervante,
ISBN: 595352502) and Pentaho Solutions: Business Intelligence and Data Warehousing with
Pentaho and MySQL (Wiley, ISBN: 978-0-470-48432-6). He also writes on a regular basis for
the Dutch Database Magazine (DBM).
Roland is @rolandbouman on Twitter and maintains a blog at
http://rpbouman.blogspot.com/.

Matt Casters has been an independent senior BI consultant for almost two decades. In that
period he led, designed, and implemented numerous data warehouses and BI solutions for
large and small companies. In that capacity, he always had the need for ETL in some form
or another. Almost out of pure necessity, he has been busy writing the ETL tool called Kettle
(a.k.a. Pentaho Data Integration) for the past eight years. First, he developed the tool mostly
on his own. Since the end of 2005 when Kettle was declared an open source technology,
development took place with the help of a large community.
Since the Kettle project was acquired by Pentaho in early 2006, he has been Chief of Data
Integration at Pentaho as the lead architect, head of development, and spokesperson for the
Kettle community.
I would like to personally thank the complete community for their help
in making Kettle the success it is today. In particular, I would like to thank
Maria for taking the time to write this nice book as well as the many
articles on the Pentaho wiki (for example, the Kettle tutorials), and her
appreciated participation on the forum. Many thanks also go to my
employer Pentaho, for their large investment in open source BI in
general and Kettle in particular.

James Dixon is the Chief Geek and one of the co-founders of Pentaho Corporation—the
leading commercial open source Business Intelligence company. He has worked in the
business intelligence market since graduating in 1992 from Southampton University with a
degree in Computer Science. He has served as Software Engineer, Development Manager,
Engineering VP, and CTO at multiple business intelligence software companies. He regularly
uses Pentaho Data Integration for internal projects and was involved in the architectural
design of PDI V3.0.
He lives in Orlando, Florida, with his wife Tami and son Samuel.
I would like to thank my co-founders, my parents, and my wife Tami for all
their support and tolerance of my odd working hours.
I would like to thank my son Samuel for all the opportunities he gives me to
prove I'm not as clever as I think I am.

Will Gorman is an Engineering Team Lead at Pentaho. He works on a variety of Pentaho's
products, including Reporting, Analysis, Dashboards, Metadata, and the BI Server. Will
started his career at GE Research and earned his Masters degree in Computer Science at
Rensselaer Polytechnic Institute in Troy, New York. Will is the author of Pentaho Reporting
3.5 for Java Developers (ISBN: 3193), published by Packt Publishing.

Gretchen Moran is a graduate of University of Wisconsin – Stevens Point with a Bachelor's
degree in Computer Information Systems with a minor in Data Communications. Gretchen
began her career as a corporate data warehouse developer in the insurance industry and
joined Arbor Software/Hyperion Solutions in 1999 as a commercial developer for the
Hyperion Analyzer and Web Analytics team. Gretchen has been a key player with Pentaho
Corporation since its inception in 2004. As Community Leader and core developer, Gretchen
managed the explosive growth of Pentaho's open source community for her first 2 years
with the company. Gretchen has contributed to many of the Pentaho projects, including the
Pentaho BI Server, Pentaho Data Integration, Pentaho Metadata Editor, Pentaho Reporting,
Pentaho Charting, and others.
Thanks Doug, Anthony, Isabella and Baby Jack for giving me my favorite
challenges and crowning achievements—being a wife and mom.

Table of Contents
Preface
Chapter 1: Getting started with Pentaho Data Integration
Pentaho Data Integration and Pentaho BI Suite
Exploring the Pentaho Demo
Pentaho Data Integration
Using PDI in real world scenarios
Loading data warehouses or data marts
Integrating data
Data cleansing
Migrating information
Exporting data
Integrating PDI using Pentaho BI

Installing PDI
Time for action – installing PDI
Launching the PDI graphical designer: Spoon
Time for action – starting and customizing Spoon
Spoon
Setting preferences in the Options window
Storing transformations and jobs in a repository

Creating your first transformation
Time for action – creating a hello world transformation

1
7
7
9
9
11
11
12
12
13
13
13

14
14
15
15
18
18
19

20
20

Directing the Kettle engine with transformations
Exploring the Spoon interface
Running and previewing the transformation

25
26
27

Time for action – running and previewing the
hello_world transformation
Installing MySQL
Time for action – installing MySQL on Windows
Time for action – installing MySQL on Ubuntu
Summary

27
29
29
32
34

Table of Contents

Chapter 2: Getting Started with Transformations
Reading data from files
Time for action – reading results of football matches from files
Input files
Input steps

35
35
36
41
41

Reading several files at once
Time for action – reading all your files at a time using a single
Text file input step
Time for action – reading all your files at a time using a single
Text file input step and regular expressions
Regular expressions

42
42
43
44

Grids
Sending data to files
Time for action – sending the results of matches to a plain file
Output files
Output steps

46
47
47
49
50

Some data definitions

Rowset
Streams

50
51

The Select values step
Getting system information
Time for action – updating a file with news about examinations
Getting information by using Get System Info step
Data types
Date fields
Numeric fields

52
52
53
57
58
58
59

Running transformations from a terminal window
Time for action – running the examination transformation from
a terminal window
XML files
Time for action – getting data from an XML file with information
about countries
What is XML
PDI transformation files

60
60
62
62
67
68

Getting data from XML files

XPath
Configuring the Get data from XML step

68
69

Kettle variables

How and when you can use variables

Summary

[ ii ]

Table of Contents

Chapter 3: Basic data manipulation

Basic calculations
Time for action – reviewing examinations by using the Calculator step
Adding or modifying fields by using different PDI steps
The Calculator step
The Formula step

73
74
82
83
84

Time for action – reviewing examinations by using the Formula step
Calculations on groups of rows
Time for action – calculating World Cup statistics by grouping data
Group by step
Filtering
Time for action – counting frequent words by filtering
Filtering rows using the Filter rows step
Looking up data
Time for action – finding out which language people speak
The Stream lookup step
Summary

Chapter 4: Controlling the Flow of Data
Splitting streams
Time for action – browsing new PDI features by copying a dataset
Copying rows
Distributing rows
Time for action – assigning tasks by distributing
Splitting the stream based on conditions
Time for action – assigning tasks by filtering priorities with the Filter rows step
PDI steps for splitting the stream based on conditions
Time for action – assigning tasks by filtering priorities with the Switch/ Case step
Merging streams
Time for action – gathering progress and merging all together
PDI options for merging streams
Time for action – giving priority to Bouchard by using Append Stream
Summary

Chapter 5: Transforming Your Data with JavaScript Code and
the JavaScript Step
Doing simple tasks with the JavaScript step
Time for action – calculating scores with JavaScript
Using the JavaScript language in PDI
Inserting JavaScript code using the Modified Java Script Value step
Adding fields

84
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89
94
97
97
103
105
105
109
112

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113
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119
120
121
125
126
128
129
131
132
134
137
139

141
141
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147
148
150

[ iii ]

Table of Contents
Modifying fields
Turning on the compatibility switch

150
151

Testing your code
Time for action – testing the calculation of averages
Testing the script using the Test script button

151
152
153

Enriching the code
Time for action – calculating flexible scores by using variables
Using named parameters
Using the special Start, Main, and End scripts
Using transformation predefined constants
Reading and parsing unstructured files
Time for action – changing a list of house descriptions with JavaScript
Looking at previous rows
Avoiding coding by using purpose-built steps
Summary

Chapter 6: Transforming the Row Set

154
154
158
159
159
162
162
164
165
167

169

Converting rows to columns
Time for action – enhancing a films file by converting rows to columns
Converting row data to column data by using the Row denormalizer step
Aggregating data with a Row denormalizer step
Time for action – calculating total scores by performances by country
Using Row denormalizer for aggregating data
Normalizing data
Time for action – enhancing the matches file by normalizing the dataset
Modifying the dataset with a Row Normalizer step
Summarizing the PDI steps that operate on sets of rows
Generating a custom time dimension dataset by using Kettle variables
Time for action – creating the time dimension dataset
Getting variables
Time for action – getting variables for setting the default starting date
Using the Get Variables step

169
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173
176
177
178
180
180
182
184
186
187
191
192
193

Summary

194

Chapter 7: Validating Data and Handling Errors
Capturing errors
Time for action – capturing errors while calculating the age of a film
Using PDI error handling functionality
Aborting a transformation
Time for action – aborting when there are too many errors
Aborting a transformation using the Abort step

Fixing captured errors

195
195
196
200
201
202
203

203
[ iv ]

Table of Contents

Time for action – treating errors that may appear
Treating rows coming to the error stream

203
205

Avoiding unexpected errors by validating data
Time for action – validating genres with a Regex Evaluation step
Validating data
Time for action – checking films file with the Data Validator
Defining simple validation rules using the Data Validator

Cleansing data
Summary

206
206
208
209
211

213
215

Chapter 8: Working with Databases

217

Introducing the Steel Wheels sample database
Connecting to the Steel Wheels database
Time for action – creating a connection with the Steel Wheels database
Connecting with Relational Database Management Systems

Exploring the Steel Wheels database
Time for action – exploring the sample database
A brief word about SQL
Exploring any configured database with the PDI Database explorer

Querying a database
Time for action – getting data about shipped orders
Getting data from the database with the Table input step
Using the SELECT statement for generating a new dataset
Making flexible queries by using parameters

217
219
219
222

223
224
225
228

229
229
231
232
234

Time for action – getting orders in a range of dates by using parameters
Making flexible queries by using Kettle variables

Time for action – getting orders in a range of dates by using variables
Sending data to a database
Time for action – loading a table with a list of manufacturers
Inserting new data into a database table with the Table output step
Inserting or updating data by using other PDI steps
Time for action – inserting new products or updating existent ones
Time for action – testing the update of existing products
Inserting or updating data with the Insert/Update step

Eliminating data from a database
Time for action – deleting data about discontinued items
Deleting records of a database table with the Delete step
Summary

Chapter 9: Performing Advanced Operations with Databases
Preparing the environment
Time for action – populating the Jigsaw database
Exploring the Jigsaw database model
[]

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236

237
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245
246
246
249
251

256
256
259
260

261
261
261
264

Table of Contents

Looking up data in a database
266
Doing simple lookups
266
Time for action – using a Database lookup step to create a list of products to buy 266
Looking up values in a database with the Database lookup step

Doing complex lookups
Time for action – using a Database join step to create a list of
suggested products to buy
Joining data from the database to the stream data by using a Database join step

Introducing dimensional modeling
Loading dimensions with data
Time for action – loading a region dimension with a
Combination lookup/update step
Time for action – testing the transformation that loads the region dimension
Describing data with dimensions
Loading Type I SCD with a Combination lookup/update step

Keeping a history of changes
Time for action – keeping a history of product changes with the
Dimension lookup/update step
Time for action – testing the transformation that keeps a history
of product changes
Keeping an entire history of data with a Type II slowly changing dimension

Loading Type II SCDs with the Dimension lookup/update step
Summary

Chapter 10: Creating Basic Task Flows

268

270
270
272

275
276
276
279
281
282

286
286
288
289

291
296

297

Introducing PDI jobs
Time for action – creating a simple hello world job
Executing processes with PDI jobs
Using Spoon to design and run jobs

297
298
305
306

Using the transformation job entry
Receiving arguments and parameters in a job
Time for action – customizing the hello world file with
arguments and parameters
Using named parameters in jobs
Running jobs from a terminal window
Time for action – executing the hello world job from a terminal window
Using named parameters and command-line arguments in transformations
Time for action – calling the hello world transformation with
fixed arguments and parameters
Deciding between the use of a command-line argument and a named parameter
Running job entries under conditions

[ vi ]

307
309
309
312
312
313
314
315
317
318

Table of Contents

Time for action – sending a sales report and warning the
administrator if something is wrong
Changing the flow of execution on the basis of conditions
Creating and using a file results list
Summary

318
324
326
327

Chapter 11: Creating Advanced Transformations and Jobs

329

Enhancing your processes with the use of variables
Time for action – updating a file with news about examinations by setting
a variable with the name of the file
Setting variables inside a transformation
Enhancing the design of your processes
Time for action – generating files with top scores
Reusing part of your transformations
Time for action – calculating the top scores with a subtransformation
Creating and using subtransformations

329
330
335
337
337
341
341
345

Creating a job as a process flow
Time for action – splitting the generation of top scores by
copying and getting rows
Transferring data between transformations by using the copy /get rows mechanism

Nesting jobs
Time for action – generating the files with top scores by nesting jobs
Running a job inside another job with a job entry
Understanding the scope of variables

Iterating jobs and transformations
Time for action – generating custom files by executing a transformation
for every input row
Executing for each row
Summary

Chapter 12: Developing and Implementing a Simple Datamart
Exploring the sales datamart
Deciding the level of granularity
Loading the dimensions
Time for action – loading dimensions for the sales datamart
Extending the sales datamart model
Loading a fact table with aggregated data
Time for action – loading the sales fact table by looking up dimensions
Getting the information from the source with SQL queries
Translating the business keys into surrogate keys
Obtaining the surrogate key for a Type I SCD
Obtaining the surrogate key for a Type II SCD
Obtaining the surrogate key for the Junk dimension
Obtaining the surrogate key for the Time dimension
[ vii ]

348
348
352

354
354
355
356

357
358
361
366

367
367
370
370
371
376
378
378
384
388
388
389
391
391

Table of Contents

Getting facts and dimensions together
Time for action – loading the fact table using a range of dates obtained
from the command line
Time for action – loading the sales star
Getting rid of administrative tasks
Time for action – automating the loading of the sales datamart
Summary

Chapter 13: Taking it Further

394
394
396
399
399
403

405

PDI best practices
Getting the most out of PDI
Extending Kettle with plugins
Overcoming real world risks with some remote execution
Scaling out to overcome bigger risks
Integrating PDI and the Pentaho BI suite
PDI as a process action
PDI as a datasource
More about the Pentaho suite
PDI Enterprise Edition and Kettle Developer Support
Summary

Appendix A: Working with Repositories
Creating a repository
Time for action – creating a PDI repository
Creating repositories to store your transformations and jobs
Working with the repository storage system
Time for action – logging into a repository
Logging into a repository by using credentials
Defining repository user accounts

405
408
408
410
411
412
412
413
414
415
416

417
418
418
420
421
421
422
422

Creating transformations and jobs in repository folders
Creating database connections, partitions, servers, and clusters
Backing up and restoring a repository
Examining and modifying the contents of a repository with
the Repository explorer
Migrating from a file-based system to a repository-based system and
vice-versa
Summary

Appendix B: Pan and Kitchen: Launching Transformations and
Jobs from the Command Line
Running transformations and jobs stored in files
Running transformations and jobs from a repository
Specifying command line options
[ viii ]

423
424
424
424
426
427

429
429
430
431

Table of Contents

Checking the exit code
Providing options when running Pan and Kitchen
Log details
Named parameters
Arguments
Variables

Appendix C: Quick Reference: Steps and Job Entries
Transformation steps
Job entries

432
432
433
433
433
433

435
436
440

Appendix D: Spoon Shortcuts

443

General shortcuts
Designing transformations and jobs
Grids
Repositories

443
444
445
445

Appendix E: Introducing PDI 4 Features
Agile BI
Visual improvements for designing transformations and jobs
Experiencing the mouse-over assistance
Time for action – creating a hop with the mouse-over assistance

447
447
447
447
448

Using the mouse-over assistance toolbar

448

Experiencing the sniff-testing feature
Experiencing the job drill-down feature
Experiencing even more visual changes
Enterprise features
Summary

449
449
450
450
450

Appendix F: Pop Quiz Answers

451

Chapter 1
PDI data sources
PDI prerequisites
PDI basics
Chapter 2
formatting data
Chapter 3
concatenating strings
Chapter 4
data movement (copying and distributing)
splitting a stream
Chapter 5
finding the seven errors
[ ix ]

451
451
451
451
452
452
452
452
452
452
452
453
453

Table of Contents

Chapter 6
using Kettle variables inside transformations
Chapter 7
PDI error handling
Chapter 8
defining database connections
database datatypes versus PDI datatypes
Insert/Update step versus Table Output/Update steps
filtering the first 10 rows
Chapter 9
loading slowly changing dimensions
loading type III slowly changing dimensions
Chapter 10
defining PDI jobs
Chapter 11
using the Add sequence step
deciding the scope of variables
Chapter 12
modifying a star model and loading the star with PDI
Chapter 13
remote execution and clustering

Index

453
453
453
453
454
454
454
454
454
454
454
455
455
455
455
455
455
456
456
456
456

457

[]

Preface
Pentaho Data Integration (aka Kettle) is an engine along with a suite of tools responsible
for the processes of Extracting, Transforming, and Loading—better known as the ETL
processes. PDI not only serves as an ETL tool, but it's also used for other purposes such as
migrating data between applications or databases, exporting data from databases to flat
files, data cleansing, and much more. PDI has an intuitive, graphical, drag-and-drop design
environment, and its ETL capabilities are powerful. However, getting started with PDI can be
difficult or confusing. This book provides the guidance needed to overcome that difficulty,
covering the key features of PDI. Each chapter introduces new features, allowing you to
gradually get involved with the tool.
By the end of the book, you will have not only experimented with all kinds of examples, but
will also have built a basic but complete datamart with the help of PDI.

How to read this book

Although it is recommended that you read all the chapters, you don't need to. The book
allows you to tailor the PDI learning process according to your particular needs.
The first four chapters, along with Chapter 7 and Chapter 10, cover the core concepts. If
you don't know PDI and want to learn just the basics, reading those chapters would suffice.
Besides, if you need to work with databases, you could include Chapter 8 in the roadmap.
If you already know the basics, you can improve your PDI knowledge by reading chapters 5,
6, and 11.
Finally, if you already know PDI and want to learn how to use it to load or maintain a
datawarehouse or datamart, you will find all that you need in chapters 9 and 12.
While Chapter 13 is useful for anyone who is willing to take it further, all the appendices are
valuable resources for anyone who reads this book.

Preface

What this book covers

Chapter 1, Getting started with Pentaho Data Integration serves as the most basic
introduction to PDI, presenting the tool. The chapter includes instructions for installing PDI
and gives you the opportunity to play with the graphical designer (Spoon). The chapter also
includes instructions for installing a MySQL server.
Chapter 2, Getting Started with Transformations introduces one of the basic components
of PDI—transformations. Then, it focuses on the explanation of how to work with files. It
explains how to get data from simple input sources such as txt, csv, xml, and so on, do a
preview of the data, and send the data back to any of these common output formats. The
chapter also explains how to read command-line parameters and system information.
Chapter 3, Basic Data Manipulation explains the simplest and most commonly used ways of
transforming data, including performing calculations, adding constants, counting, filtering,
ordering, and looking for data.
Chapter 4—Controlling the Flow of Data explains different options that PDI offers to combine
or split flows of data.
Chapter 5, Transforming Your Data with JavaScript Code and the JavaScript Step explains how
JavaScript coding can help in the treatment of data. It shows why you need to code inside
PDI, and explains in detail how to do it.
Chapter 6, Transforming the Row Set explains the ability of PDI to deal with some
sophisticated problems, such as normalizing data from pivoted tables, in a simple fashion.
Chapter 7, Validating Data and Handling Errors explains the different options that PDI has to
validate data, and how to treat the errors that may appear.
Chapter 8, Working with Databases explains how to use PDI to work with databases. The
list of topics covered includes connecting to a database, previewing and getting data, and
inserting, updating, and deleting data. As database knowledge is not presumed, the chapter
also covers fundamental concepts of databases and the SQL language.
Chapter 9, Performing Advanced Operations with Databases explains how to perform
advanced operations with databases, including those specially designed to load
datawarehouses. A primer on datawarehouse concepts is also given in case you are not
familiar with the subject.
Chapter 10, Creating Basic Task Flow serves as an introduction to processes in PDI. Through
the creation of simple jobs, you will learn what jobs are and what they are used for.
Chapter 11, Creating Advanced Transformations and Jobs deals with advanced concepts that
will allow you to build complex PDI projects. The list of covered topics includes nesting jobs,
iterating on jobs and transformations, and creating subtransformations.
[]

Preface

Chapter 12, Developing and implementing a simple datamart presents a simple datamart
project, and guides you to build the datamart by using all the concepts learned throughout
the book.
Chapter 13, Taking it Further gives a list of best PDI practices and recommendations for
going beyond.
Appendix A, Working with repositories guides you step by step in the creation of a PDI
database repository and then gives instructions to work with it.
Appendix B, Pan and Kitchen: Launching Transformations and Jobs from the Command Line is
a quick reference for running transformations and jobs from the command line.
Appendix C, Quick Reference: Steps and Job Entries serves as a quick reference to steps and
job entries used throughout the book.
Appendix D, Spoon Shortcuts is an extensive list of Spoon shortcuts useful for saving time
when designing and running PDI jobs and transformations.
Appendix E, Introducing PDI 4 features quickly introduces you to the architectural and
functional features included in Kettle 4—the version that was under development while
writing this book.
Appendix F, Pop Quiz Answers, contains answers to pop quiz questions.

What you need for this book

PDI is a multiplatform tool. This means no matter what your operating system is, you will
be able to work with the tool. The only prerequisite is to have JVM 1.5 or a higher version
installed. It is also useful to have Excel or Calc along with a nice text editor.
Having an Internet connection while reading is extremely useful as well. Several links are
provided throughout the book that complement what is explained. Besides, there is the
PDI forum where you may search or post doubts if you are stuck with something.

Who this book is for

This book is for software developers, database administrators, IT students, and everyone
involved or interested in developing ETL solutions or, more generally, doing any kind of data
manipulation. If you have never used PDI before, this will be a perfect book to start with.
You will find this book to be a good starting point if you are a database administrator, a data
warehouse designer, an architect, or any person who is responsible for data warehouse
projects and need to load data into them.
[]

Preface

You don't need to have any prior data warehouse or database experience to read this book.
Fundamental database and data warehouse technical terms and concepts are explained in
an easy-to-understand language.

Conventions

In this book, you will find a number of styles of text that distinguish between different
kinds of information. Here are some examples of these styles, and an explanation of
their meaning.
Code words in text are shown as follows: "You read the examination.txt file, and did
some calculations to see how the students did."
New terms and important words are shown in bold. Words that you see on the screen, in
menus or dialog boxes for example, appear in our text like this: "Edit the Sort rows step by
double-clicking it, click the Get Fields button, and adjust the grid."
Warnings or important notes appear in a box like this.

Tips and tricks appear like this.

Reader feedback

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[]

Preface

Customer support

Now that you are the proud owner of a Packt book, we have a number of things to help you
to get the most from your purchase.
Downloading the example code for the book
Visit http://www.packtpub.com/files/code/9546_Code.zip to
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The downloadable files contain instructions on how to use them.

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[]

Getting Started with Pentaho
Data Integration
Pentaho Data Integration is an engine along with a suite of tools responsible
for the processes of extracting, transforming, and loading—best known as the
ETL processes. This book is meant to teach you how to use PDI.

In this chapter you will:


Learn what Pentaho Data Integration is



Install the software and start working with the PDI graphical designer



Install MySQL, a database engine that you will use when you start working
with databases

Pentaho Data Integration and Pentaho BI Suite
Before introducing PDI, let's talk about Pentaho BI Suite. The Pentaho Business Intelligence
Suite is a collection of software applications intended to create and deliver solutions for
decision making. The main functional areas covered by the suite are:


Analysis: The analysis engine serves multidimensional analysis. It's provided by the
Mondrian OLAP server and the JPivot library for navigation and exploring.

Getting Started with Pentaho Data Integration




Reporting: The reporting engine allows designing, creating, and distributing reports
in various known formats (HTML, PDF, and so on) from different kinds of sources.
The reports created in Pentaho are based mainly in the JFreeReport library, but it's
possible to integrate reports created with external reporting libraries such as Jasper
Reports or BIRT.
Data Mining: Data mining is running data through algorithms in order to understand
the business and do predictive analysis. Data mining is possible thanks to the
Weka Project.
Dashboards: Dashboards are used to monitor and analyze Key Performance
Indicators (KPIs). A set of tools incorporated to the BI Suite in the latest version
allows users to create interesting dashboards, including graphs, reports, analysis
views, and other Pentaho content, without much effort.

Data integration: Data integration is used to integrate scattered information
from different sources (applications, databases, files) and make the integrated
information available to the final user. Pentaho Data Integration—our main
concern—is the engine that provides this functionality.

All this functionality can be used standalone as well as integrated. In order to run analysis,
reports, and so on integrated as a suite, you have to use the Pentaho BI Platform. The
platform has a solution engine, and offers critical services such as authentication,
scheduling, security, and web services.
[]

Chapter 1

This set of software and services forms a complete BI Platform, which makes Pentaho Suite
the world's leading open source Business Intelligence Suite.

Exploring the Pentaho Demo
Despite being out of the scope of this book, it's worth to briefly introduce the Pentaho
Demo. The Pentaho BI Platform Demo is a preconfigured installation that lets you explore
several capabilities of the Pentaho platform. It includes sample reports, cubes, and
dashboards for Steel Wheels. Steel Wheels is a fictional store that sells all kind of scale
replicas of vehicles.
The demo can be downloaded from http://sourceforge.net/projects/pentaho/
files/. Under the Business Intelligence Server folder, look for the latest stable
version. The file you have to download is named biserver-ce-3.5.2.stable.zip for
Windows and biserver-ce-3.5.2.stable.tar.gz for other systems.
In the same folder you will find a file named biserver-getting_started-ce3.5.0.pdf. The file is a guide that introduces you the platform and gives you some
guidance on how to install and run it. The guide even includes a mini tutorial on building
a simple PDI input-output transformation.

You can find more about Pentaho BI Suite at www.pentaho.org.

Pentaho Data Integration
Most of the Pentaho engines, including the engines mentioned earlier, were created as
community projects and later adopted by Pentaho. The PDI engine is no exception—Pentaho
Data Integration is the new denomination for the business intelligence tool born as Kettle.
The name Kettle didn't come from the recursive acronym Kettle Extraction,
Transportation, Transformation, and Loading Environment it has now, but from
KDE Extraction, Transportation, Transformation and Loading Environment,
as the tool was planned to be written on top of KDE, as mentioned in the
introduction of the book.

In April 2006 the Kettle project was acquired by the Pentaho Corporation and Matt Casters,
Kettle's founder, also joined the Pentaho team as a Data Integration Architect.

[]

Getting Started with Pentaho Data Integration

When Pentaho announced the acquisition, James Dixon, the Chief Technology Officer, said:
We reviewed many alternatives for open source data integration, and Kettle clearly
had the best architecture, richest functionality, and most mature user interface.
The open architecture and superior technology of the Pentaho BI Platform
and Kettle allowed us to deliver integration in only a few days, and make that
integration available to the community.
By joining forces with Pentaho, Kettle benefited from a huge developer community, as well
as from a company that would support the future of the project.
From that moment the tool has grown constantly. Every few months a new release is
available, bringing to the users, improvements in performance and existing functionality,
new functionality, ease of use, and great changes in look and feel. The following is a timeline
of the major events related to PDI since its acquisition by Pentaho:




June 2006: PDI 2.3 is released. Numerous developers had joined the project and
there were bug fixes provided by people in various regions of the world. Among
other changes, the version included enhancements for large scale environments
and multilingual capabilities.
February 2007: Almost seven months after the last major revision, PDI 2.4 is
released including remote execution and clustering support (more on this in
Chapter 13), enhanced database support, and a single designer for the two
main elements you design in Kettle—jobs and transformations.
May 2007: PDI 2.5 is released including many new features, the main feature being
the advanced error handling.
November 2007: PDI 3.0 emerges totally redesigned. Its major library changed to
gain massive performance. The look and feel also changed completely.
October 2008: PDI 3.1 comes with an easier-to-use tool, along with a lot of new
functionalities as well.
April 2009: PDI 3.2 is released with a really large number of changes for a
minor version—new functionality, visualization improvements, performance
improvements, and a huge pile of bug fixes. The main change in this version was the
incorporation of dynamic clustering (see Chapter 13 for details).
In 2010 PDI 4.0 will be released, delivering mostly improvements with regard to
enterprise features such as version control.
Most users still refer to PDI as Kettle, its further name. Therefore, the names PDI,
Pentaho Data Integration, and Kettle will be used interchangeably throughout
the book.

[ 10 ]

Chapter 1

Using PDI in real world scenarios
Paying attention to its name, Pentaho Data Integration, you could think of PDI as a tool to
integrate data.
In you look at its original name, K.E.T.T.L.E., then you must conclude that it is a tool used
for ETL processes which, as you may know, are most frequently seen in data warehouse
environments.
In fact, PDI not only serves as a data integrator or an ETL tool, but is such a powerful tool
that it is common to see it used for those and for many other purposes. Here you have
some examples.

Loading datawarehouses or datamarts
The loading of a datawarehouse or a datamart involves many steps, and there are many
variants depending on business area or business rules. However, in every case, the process
involves the following steps:
Extracting information from one or different databases, text files, and other sources.
The extraction process may include the task of validating and discarding data that
doesn't match expected patterns or rules.
Transforming the obtained data to meet the business and technical needs required
on the target. Transformation implies tasks such as converting data types, doing
some calculations, filtering irrelevant data, and summarizing.
Loading the transformed data into the target database. Depending on the
requirements, the loading may overwrite the existing information, or may
add new information each time it is executed.

[ 11 ]

Getting Started with Pentaho Data Integration

Kettle comes ready to do every stage of this loading process. The following sample
screenshot shows a simple ETL designed with Kettle:

Integrating data
Imagine two similar companies that need to merge their databases in order to have a unified
view of the data, or a single company that has to combine information from a main ERP
application and a CRM application, though they're not connected. These are just two of
hundreds of examples where data integration is needed. Integrating data is not just a matter
of gathering and mixing data; some conversions, validation, and transport of data has to be
done. Kettle is meant to do all those tasks.

Data cleansing
Why do we need that data be correct and accurate? There are many reasons—for the
efficiency of business, to generate trusted conclusions in data mining or statistical studies,
to succeed when integrating data, and so on. Data cleansing is about ensuring that the
data is correct and precise. This can be ensured by verifying if the data meets certain rules,
discarding or correcting those that don't follow the expected pattern, setting default values
for missing data, eliminating information that is duplicated, normalizing data to conform
minimum and maximum values, and so on—tasks that Kettle makes possible, thanks to its
vast set of transformation and validation capabilities.
[ 12 ]

Chapter 1

Migrating information
Think of a company of any size that uses a commercial ERP application. One day the owners
realize that the licences are consuming an important share of its budget and so they decide
to migrate to an open source ERP. The company will no longer have to pay licences, but if
they want to do the change, they will have to migrate the information. Obviously it is not an
option to start from scratch, or type the information by hand. Kettle makes the migration
possible, thanks to its ability to interact with most kinds of sources and destinations such as
plain files, and commercial and free databases and spreadsheets.

Exporting data
Sometimes you are forced by government regulations to export certain data to be processed
by legacy systems. You can't just print and deliver some reports containing the required data.
The data has to have a rigid format, with columns that have to obey some rules (size, format,
content), different records for heading and tail, just to name some common demands. Kettle
has the power to take crude data from the source and generate these kinds of ad hoc reports.

Integrating PDI using Pentaho BI
The previous examples show typical uses of PDI as a standalone application. However, Kettle
may be used as part of a process inside the Pentaho BI Platform. There are many things
embedded in the Pentaho application that Kettle can do—preprocessing data for an on-line
report, sending mails in a schedule fashion, or generating spreadsheet reports.
You'll find more on this in Chapter 13. However, the use of PDI integrated
with the BI Suite is beyond the scope of this book.

Pop quiz – PDI data sources
Which of the following aren't valid sources in Kettle:
1. Spreadsheets
2. Free database engines
3. Commercial database engines
4. Flat files
5. None of the above

[ 13 ]

Getting Started with Pentaho Data Integration

Installing PDI
In order to work with PDI you need to install the software. It's a simple task; let's do it.

Time for action – installing PDI
These are the instructions to install Kettle, whatever your operating system.
The only prerequisite to install PDI is to have JRE 5.0 or higher installed. If you don't have it,
please download it from http://www.javasoft.com/ and install it before proceeding.
Once you have checked the prerequisite, follow these steps:

From http://community.pentaho.com/sourceforge/ follow the link to
Pentaho Data Integration (Kettle). Alternatively, go directly to the download page
http://sourceforge.net/projects/pentaho/files/Data Integration.

Choose the newest stable release. At this time, it is 3.2.0.

Download the file that matches your platform. The preceding screenshot should
help you.

Unzip the downloaded file in a folder of your choice
—C:/Kettle or /home/your_dir/kettle.

[ 14 ]

Chapter 1

If your system is Windows, you're done. Under UNIX-like environments, it's
recommended that you make the scripts executable. Assuming that you
chose Kettle as the installation folder, execute the following command:
cd Kettle
chmod +x *.sh

What just happened?
You have installed the tool in just a few minutes. Now you have all you need to start working.

Pop quiz – PDI prerequisites
Which of the following are mandatory to run PDI? You may choose more than one option.
1. Kettle
2. Pentaho BI platform
3. JRE
4. A database engine

Launching the PDI graphical designer: Spoon
Now that you've installed PDI, you must be eager to do some stuff with data. That will be
possible only inside a graphical environment. PDI has a desktop designer tool named Spoon.
Let's see how it feels to work with it.

Time for action – starting and customizing Spoon
I������������������������������������������������������������������������������������������������
n this tutorial you're going to launch the PDI graphical designer and get familiarized with its
main features.

Start Spoon.


If your system is Windows, type the following command:

Spoon.bat�


In other platforms such as Unix, Linux, and so on, type:
Spoon.sh



If you didn't make spoon.sh executable, you may type:
sh Spoon.sh

[ 15 ]

Getting Started with Pentaho Data Integration

As soon as Spoon starts, a dialog window appears asking for the repository
connection data. Click the No Repository button. The main window appears. You
will see a small window with the tip of the day. After reading it, close that window.

A welcome! window appears with some useful links for you to see.

Close the welcome window. You can open that window later from the main menu.

Click Options... from the Edit menu. A window appears where you can change
various general and visual characteristics. Uncheck the circled checkboxes:

Select the tab window Look Feel.

[ 16 ]

Chapter 1

Change the Grid size and Preferred Language settings as follows:

Click the OK button.

Restart Spoon in order to apply the changes. You should neither see the repository
dialog, nor the welcome window. You should see the following screen instead:

[ 17 ]

Getting Started with Pentaho Data Integration

What just happened?
You ran for the first time the graphical designer of PDI Spoon, and applied some
custom configuration.
From the Look Feel configuration window, you changed the size of the dotted grid that
appears in the canvas area while you are working. You also changed the preferred language.
In the Option tab window, you chose not to show either the repository dialog or the
welcome window at startup. These changes were applied as you restarted the tool, not
before.
The second time you launched the tool, the repository dialog didn't show up. When the
main window appeared, all the visible texts were shown in French, which was the selected
language, and instead of the welcome window, there was a blank screen.

Spoon
This tool that you're exploring in this section is the PDI's desktop design tool. With Spoon you
design, preview, and test all your work, that is, transformations and jobs. When you see PDI
screenshots, what you are really seeing are Spoon screenshots. The other PDI components
that you will meet in the following chapters are executed from terminal windows.

Setting preferences in the Options window
In the tutorial you changed some preferences in the Options window. There are several look
and feel characteristics you can change beyond those you changed. Feel free to experiment
with this setting.

Remember to restart Spoon in order to see the changes applied.

If you choose any language as preferred language other than English, you
should select a different language as alternative. If you do so, every name or
description not translated to your preferred language will be shown in the
alternative language.

Just for the curious people: Italian and French are the overall winners of the list of languages
to which the tool has been translated from English. Below them follow Korean, Argentinean
Spanish, Japanese, and Chinese.

[ 18 ]

Chapter 1

One of the settings you changed was the appearance of the welcome window at start up.
The welcome window has many useful links, all related with the tool: wiki pages, news,
forum access, and more. It's worth exploring them.
You don't have to change the settings again to see the welcome window.
You can open it from the menu Help | Show the Welcome Screen.

Storing transformations and jobs in a repository
The first time you launched Spoon, you chose No Repository. After that, you configured
Spoon to stop asking you for the Repository option. You must be curious about what the
repository is and why not to use it. Let's explain it.
As said, the results of working with PDI are Transformations and Jobs. In order to save the
Transformations and Jobs, PDI offers two methods:




Repository: When you use the repository method you save jobs and
transformations in a repository. A repository is a relational database specially
designed for this purpose.
Files: The files method consists of saving jobs and transformations as regular XML
files in the filesystem, with extension kjb and ktr respectively.

The following diagram summarizes this:
exclusive
REPOSITORY

FILE SYSTEM
Transformations

Transfor
mations

.ktr

Jobs

.kjb

Design, Preview, Run

Design, Preview, Run
SPOON
Kettle Engine

[ 19 ]

KETTLE

Getting Started with Pentaho Data Integration

You cannot mix the two methods (files and repository) in the same project. Therefore, you
must choose the method when you start the tool.
Why did we choose not to work with repository, or in other words, to work with files? This is
mainly for the following two reasons:



Working with files is more natural and practical for most users.
Working with repository requires minimum database knowledge and that you also
have access to a database engine from your computer. Having both preconditions
would allow you to learn working with both methods. However, it's probable that
you haven't.

Throughout this book, we will use the file method. For details of working with repositories,
please refer to Appendix A.

Creating your first transformation
Until now, you've seen the very basic elements of Spoon. For sure, you must be waiting to do
some interesting task beyond looking around. It's time to create your first transformation.

Time for action – creating a hello world transformation
How about starting by saying Hello to the World? Not original but enough for a very first
practical exercise. Here is how you do it:

Create a folder named pdi_labs under the folder of your choice.

Open Spoon.

From the main menu select File | New Transformation.

At the left-hand side of the screen, you'll see a tree of Steps. Expand the Input
branch by double-clicking it.

Left-click the Generate Rows icon.

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Chapter 1

Without releasing the button, drag-and-drop the selected icon to the main canvas.
The screen will look like this:

Double-click the Generate Rows step that you just put in the canvas and fill the text
boxes and grid as follows:

From the Steps tree, double-click the Flow step.

Click the Dummy icon and drag-and-drop it to the main canvas.

[ 21 ]

Getting Started with Pentaho Data Integration

10.

Click the Generate Rows step and holding the Shift key down, drag the cursor
towards the Dummy step. Release the button. The screen should look like this:

11.

Right-click somewhere on the canvas to bring up a contextual menu.

12.

Select New note. A note editor appears.

13.

Type some description such as Hello World! and click OK.

14.

From the main menu, select Transformation | Configuration. A window appears
to specify transformation properties. Fill the Transformation name with a simple
name as hello_world. Fill the Description field with a short description such as
My first transformation. Finally provide a more clear explanation in the Extended
description text box and click OK.

15.

From the main menu, select File | Save.

16.

Save the transformation in the folder pdi_labs with the name hello_world.

17.

Select the Dummy step by left-clicking it.

18.

Click on the Preview button in the menu above the main canvas.

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Chapter 1

19.

A debug window appears. Click the Quick Launch button.

20.

The following window appears to preview the data generated by the transformation:

21.

Close the preview window and click the Run button.

22.

A window appears. Click Launch.

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Getting Started with Pentaho Data Integration

23.

The execution results are shown in the bottom of the screen. The Logging tab
should look as follows:

What just happened?
You've just created your first transformation.
First, you created a new transformation. From the tree on the left, you dragged two steps
and drop them into the canvas. Finally, you linked them with a hop.
With the Generate Rows step, you created 10 rows of data with the message Hello World!.
The Dummy step simply served as a destination of those rows.
After creating the transformation, you did a preview. The preview allowed you to see the
content of the created data, this is, the 10 rows with the message Hello World!

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Chapter 1

Finally, you ran the transformation. You could see the results of the execution at the bottom
of the windows. There is a tab named Step Metrics with information about what happens
with each steps in the transformation. There is also a Logging tab showing a complete detail
of what happened.

Directing the Kettle engine with transformations
As shown in the following diagram, transformation is an entity made of steps linked by hops.
These steps and hops build paths through which data flows. The data enters or is created in a
step, the step applies some kind of transformation to it, and finally the data leaves that step.
Therefore, it's said that a transformation is data-flow oriented.

Steps

Input
Step1

Step2

Transformation

...

StepN

Output

Hops

A transformation itself is not a program nor an executable file. It is just plain XML. The
transformation contains metadata that tells the Kettle engine what to do.
A step is the minimal unit inside a transformation. A big set of steps is available. These steps
are grouped in categories such as the input and flow categories that you saw in the example.
Each step is conceived to accomplish a specific function, going from reading a parameter to
normalizing a dataset. Each step has a configuration window. These windows vary according
to the functionality of the steps and the category to which they belong. What all steps have
in common are the name and description:
Step property

Description

Name

A representative name inside the transformation.

Description

A brief explanation that allows you to clarify the purpose of the step.
It's not mandatory but it is useful.

A hop is a graphical representation of data flowing between two steps—an origin and a
destination. The data that flows through that hop constitutes the output data of the origin
step and the input data of the destination step.

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Getting Started with Pentaho Data Integration

Exploring the Spoon interface
As you just saw, the Spoon is the tool using which you create, preview, and run
transformations. The following screenshot shows you the basic work areas:

The words canvas and work area will be used interchangeably throughout
the book.

Viewing the transformation structure
If you click the View icon in the upper left corner of the screen, the tree will change to show
the structure of the transformation currently being edited.

[ 26 ]

Download from Wow! eBook

Chapter 1

Running and previewing the transformation
The Preview functionality allows you to see a sample of the data produced for selected steps.
In the previous example, you previewed the output of the Dummy Step. The Run option
effectively runs the whole transformation.
Whether you preview or run a transformation, you'll get an execution results window
showing what happened. Let's explain it through an example.

Time for action – running and previewing the hello_world
transformation
Let's do some testing and explore the results:

Open the hello_world transformation.

Edit the Generate Rows step, and change the limit from 10 to 1000 so that it
generates 1,000 rows.

Select the Logging tab window at the bottom of the screen.

Click on Run.

In the Log level drop-down list, select RowLevel detail.

Click on Launch.

You can see how the logging window shows every task in a very detailed way.

Edit the Generate Rows step, and change the limit to 10,000 so that it generates
10,000 rows.

Select the Step Metrics.

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Getting Started with Pentaho Data Integration

10.

Run the transformation.

11.

You can see how the numbers change as the rows travel through the steps.

What just happened?
You did some tests with the hello_world transformation and saw the results in the
Execution Results window.

Previewing the results in the Execution Results window
The Execution Results window shows you what is happening while you preview or run
a transformation.
The Logging tab shows the execution of your transformation, step by step. By default, the
level of the logging detail is Basic but you can change it to see different levels of detail—from
a minimal logging (level Minimal) to a very detailed one (level RowLevel).
The Step Metrics tab shows, for each step of the transformation, the executed operations
and several status and information columns. You may be interested in the following columns:
Column

Description

Read

Contains the number of rows coming from previous steps

Written

Contains the number of rows leaving from this step toward the next

Input

Number of rows read from a file or table

Output

Number of rows written to a file or table

Errors

Errors in the execution. If there are errors, the whole row becomes red

Active

Tells the current status of the execution

In the example, you can see that the Generate Rows step writes rows, which then are read
by the Dummy step. The Dummy step also writes the same rows, but in this case those
go nowhere.

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Chapter 1

Pop quiz – PDI basics
For each of the following, decide if the sentence is true or false:
1. There are several graphical tools in PDI, but Spoon is the most used.
2. You can choose to save Transformations either in files or in a database.
3. To run a Transformation, an executable file has to be generated from Spoon.
4. The grid size option in the Look and Feel windows allows you to resize the work area.
5. To create a transformation, you have to provide external data.

Installing MySQL
Before skipping to the next chapter, let's devote some minutes to the installation of MySQL.
In Chapter 8 you will begin working with databases from PDI. In order to do that, you will
need access to some database engine. As MySQL is the world's most popular open source
database, it was the database engine chosen for the database-related tutorials in the book.
In this section you will learn to install the MySQL database engine both in Windows and
Ubuntu, the most popular distribution of Linux these days. As the procedures for installing
the software are different, a separate explanation is given for each system.

Time for action – installing MySQL on Windows
In order to install MySQL on your Windows system, please follow these instructions:

Open an internet browser and type http://dev.mysql.com/downloads/mysql/.

Select the Microsoft Windows platform and download the mysql-essential package
that matches your system: 32-bit or 64-bit.

Double-click the downloaded file. A wizard will guide you through the process.

When asked about the setup type, select Typical.

Several screens follow. When the wizard is complete you'll have the option to
configure the server. Check Configure the MySQL Server now and click Finish.

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Getting Started with Pentaho Data Integration

A new wizard will be launched that lets you configure the server.

When asked about the configuration type, select Standard Configuration.

When prompted, set the Windows options as shown in the next screenshot:

When prompted for the security options, provide a password for the root user.
You'll have to retype the password.
Provide a password that you can remember. You'll need it
later to connect to the MySQL server.

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Chapter 1

10.

In the next window click on Execute to proceed with the configuration. When the
configuration is done, you'll see this:

11.

Click on Finish. After installing MySQL it is recommended that you install the GUI
tools for administering and querying the database.

12.

Open an Internet browser and type
http://dev.mysql.com/downloads/gui-tools/.

13.

Look for the Windows downloads and download the Windows (x86) package.

14.

Double-click the downloaded file. A wizard will guide you through the process.

15.

When asked about the setup type, select Complete.

16.

Several screens follow. Just follow the wizard instructions.

17.

When the wizard ends, you'll have the GUI tools added to the MySQL menu.

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Getting Started with Pentaho Data Integration

What just happened?
You downloaded and installed MySQL on your Windows system. You also installed MySQL
GUI tools, a software package that includes an administrator and a query browser utility and
that will make your life easier when working with the database.

Time for action – installing MySQL on Ubuntu
This tutorial shows you the procedure to install MySQL on Ubuntu.
In order to follow the tutorial you need to be connected to
the Internet.

Please follow these instructions:

Check that you have access to the Internet.

Open the Synaptic package manager from System | Administration | Synaptic
Package Manager.

Under Quick search type mysql-server and click on the Search button.

Among the results, locate mysql-server-5.1, click in the tiny square to the left,
and select Mark for Installation.

You'll be prompted for confirmation. Click on Mark.

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Chapter 1

Now search for a package named mysql-admin.

When found, mark it for installation in the same way.

Click on Apply on the main toolbar.

A window shows up asking for confirmation. Click on Mark again. What follows is
the download process followed by the installation process.

10.

At a particular moment a window appears asking you for a password for the root
user—the administrator of the database. Enter a password of your choice. You'll
have to enter it twice.
Think of a password that you can remember. You'll need it
later to connect to the MySQL server.

11.

When the process ends, you will see the changes applied.

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Getting Started with Pentaho Data Integration

12.

Under Applications a new menu will also be added to access the GUI tools.

What just happened?
You installed MySQL server and GUI Tools in your Ubuntu system.
The previous directions are for standard installations. For custom installations,
instructions related to other operating systems, or for troubleshooting, please
check the MySQL documentation at—http://dev.mysql.com/doc/
refman/5.1/en/installing.html.

Summary
In this first chapter, you were introduced to Pentaho Data Integration. Specifically, you learned
what Pentaho Data Integration is and you installed the tool. You were also introduced to
Spoon, the graphical designer of PDI, and you created your first transformation.
As an additional exercise, you installed a MySQL server and the MySQL GUI tools. You will
need this software when you start working with databases in Chapter 8.
Now that you've learned the basics, you're ready to begin creating your own transformations
to explore real data. That is the topic of the next chapter.

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Getting Started with Transformations
In the previous chapter you used the graphical designer Spoon to create
your first transformation: Hello world. Now you will start creating your own
transformations to explore data from the real world. Data is everywhere; in
particular you will find data in files. Product lists, logs, survey results, and
statistical information are just a sample of the different kinds of information
usually stored in files. In this chapter you will create transformations to get
data from files, and also to send data back to files. This in turn will allow you to
learn the basic PDI terminology related to data.

Reading data from files
Despite being the most primitive format used to store data, files are broadly used and they
exist in several flavors as fixed width, comma-separated values, spreadsheet, or even free
format files. PDI has the ability to read data from all types of files; in this first tutorial let's
see how to use PDI to get data from text files.

Getting Started with Transformations

You don't have one, but many files, all with the same structure. You now want to unify all the
information in one single file. Let's begin by reading the files.

Create the folder named pdi_files. Inside it, create the input and
output subfolders.

By using any text editor, type the file shown and save it under the name
group1.txt in the folder named input, which you just created. You can also
download the file from Packt's official website.

Start Spoon.

From the main menu select File | New Transformation.

Expand the Input branch of the steps tree.

Drag the Text file input icon to the canvas.

Double-click the text input file icon and give a name to the step.

Click the Browse... button and search the file group1.txt.

Select the file. The textbox File or directory will be temporarily populated with the full
path of the file—for example, C:\pdi_files\input\group1.txt.

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Chapter 2

10.

Click the Add button. The full text will be moved from the File or directory textbox to the
grid. The configuration window should look as follows:

11.

Select the Content tab and fill it like this:

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Getting Started with Transformations

12.

Select the Fields tab. Click the Get Fields button. The screen should look like this:

13.

In the small window that proposes you a number of sample lines, click OK.

14.

Close the scan results window.

15.

Change the second row. Under the Type column select Date, and under the Format
column, type dd/MMM.

16.

The result value is text, not a number, so change the fourth row too. Under the Type
column select String.

17.

Click the Preview rows button, and then the OK button.

18.

The previewed data should look like the following:

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Chapter 2

19.

Expand the Transform branch of the steps tree.

20.

Drag the Select values icon to the canvas.

21.

Create a hop from the Text file input step to the Select values step.
Remember that you do it by selecting the first step, then dragging
toward the second while holding down the Shift key.

22.

Double-click the Select values step icon and give a name to the step.

23.

Select the Remove tab.

24.

Click the Get fields to remove button.

25.

Delete every row except the first and the last one by left-clicking them and
pressing Delete.

26.

The tab window looks like this:

27.

Click OK.

28.

From the Flow branch of the steps tree, drag the Dummy icon to the canvas.

29.

Create a hop from the Select values step to the Dummy step. Your transformation

should look like the following:

[ 39 ]

Getting Started with Transformations

30.

Configure the transformation by pressing Ctrl+T and giving a name and a description to
the transformation.

31.

Save the transformation by pressing Ctrl+S.

32.

Select the Dummy step.

33.

Click the Preview button located on the transformation toolbar:

34.

Click the Quick Launch button.

35.

The following window appears, showing the final data:

What just happened?
You read your plain file with results of football matches into a transformation.
By using a Text file input step, you told Kettle the full path to your file, along with the
characteristics of the file so that Kettle was able to read the data correctly—you specified
that the file had a header, had three rows at the end that should be ignored, and specified
the name and type of the columns.
After reading the file, you used a Select values step to remove columns you didn't need— the
first and the last column.

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Chapter 2

With those two simple steps, you were able to preview the data in your file from inside
the transformation.
Another thing you may have noticed is the use of shortcuts instead of the menu options—for
example, to save the transformation.
Many of the menu options can be accessed more quickly by using shortcuts. The
available shortcuts for the menu options are mentioned as part of the name of
the operation—for example, Run F9.

For a full shortcut reference please check Appendix D.

Input files
Files are one of the most used input sources. PDI can take data from several types of files,
with very few limitations.
When you have a file to work with, the first thing you have to do is to specify where the file
is, how it looks, and what kinds of values it contains. That is exactly what you did in the first
tutorial of this chapter.
With the information you provide, Kettle can create the dataset to work within the
current transformation.

Input steps
There are several steps that allow you to take a file as the input data. All those steps such as
Text file input, Fixed file input, Excel Input, and so on are under the Input step category.
Despite the obvious differences that exist between these types of files, the ways to configure
the steps have much in common. The following are the main properties you have to specify
for an input step:




Name of the step: It is mandatory and must be different for every step in
the transformation.
Name and location of the file: These must be specified of course. At the moment
you create the transformation, it's not mandatory that the file exists. However, if it
does, you will find it easier to configure this step.
Content type: This data includes delimiter character, type of encoding, whether a
header is present, and so on. The list depends on the kind of file chosen. In every
case, Kettle propose default values, so you don't have to enter too much data.

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Getting Started with Transformations




Fields: Kettle has the facility to get the definitions automatically by clicking the Get
Fields button. However, Kettle doesn't always guess the data types, size, or format
as expected. So, after getting the fields you may change what you consider more
appropriate, as you did in the tutorial.
Filtering: Some steps allow you to filter the data—skip blank rows, read only the first
n rows, and so on.

After configuring an input step, you can preview the data just as you did, by Clicking
the Preview Rows button. This is useful to discover if there is something wrong in the
configuration. In that case, you can make the adjustments and preview again, until your
data looks fine.

Reading several files at once
Until now you used an input step to read one file. But you have several files, all with the very
same structure. That will not be a problem because with Kettle it is possible to read more
than a file at a time.

Time for action – reading all your files at a time using a single
Text file input step
To read all your files follow the next steps:

Open the transformation, double-click the input step, and add the other files in the
same way you added the first.

After Clicking the Preview rows button, you will see this:

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Chapter 2

What just happened?
You read several files at once. By putting in the grid the names of all the input files, you could
get the content of every specified file one after the other.

Time for action – reading all your files at a time using a single
Text file input step and regular expressions
You could do the same thing you did above by using a different notation.
Follow these instructions:

Open the transformation and edit the configuration windows of the input step.

Delete the lines with the names of the files.

In the first row of the grid, type C:\pdi_files\input\ under the File/Directory
column, and group[1-4]\.txt under the Wildcard (Reg.Exp.) column.

Click the Show filename(s)... button. You'll see the list of files that match
the expression.

Close the tiny window and click Preview rows to confirm that the rows shown
belong to the four files that match the expression you typed.

[ 43 ]

Getting Started with Transformations

What just happened?
In this particular case, all filenames follow a pattern—group1.txt, group2.txt, and so
on. In order to specify the names of the files, you used a regular expression. In the column
File/Directory you put the static part of the names, while in the Wildcard (Reg.Exp.) column
you put the regular expression with the pattern that a file must follow to be considered:
the text group followed by a number between 1 and 4, and then .txt. Then, all files that
matched the expression were considered as input files.

Regular expressions
There are many places inside Kettle where you may or have to provide a regular expression.
A regular expression is much more than specifying the known wildcards ? and *.
Here you have some examples of regular expressions you may use to specify filenames:
The following regular
expression ...
.*\.txt

Matches ...

Examples

Any txt file

thisisaValidExample.
txt
test2009-12.txt

test(19|20)\d\dAny txt file beginning with test
(0[1-9]|1[012])\.txt followed by a date using the format
yyyy-mm
(?i)test.+\.txt
Any txt file beginning with test,
upper or lower case

test2009-01.txt
TeSTcaseinsensitive.
tXt

Please note that the * wildcard doesn't work the same as it does on
the command line. If you want to match any character, the * has to be
preceded by a dot.

Here are some useful links in case you want to know more about regular expressions:


Regular Expression Quick Start:
http://www.regular-expressions.info/quickstart.html



The Java Regular Expression Tutorial:
http://java.sun.com/docs/books/tutorial/essential/regex/



Java Regular Expression Pattern Syntax: http://java.sun.com/javase/6/
docs/api/java/util/regex/Pattern.html

[ 44 ]

Chapter 2

Troubleshooting reading files
Despite the simplicity of reading files with PDI, obstacles and errors appear. Many times
the solution is simple but difficult to find if you are new to PDI. Here you have a list of
common problems and possible solutions for you to take into account while reading and
previewing a file:
Problem

Diagnostic

Possible solutions

You get the message
Sorry, no rows found to
be previewed.

This happens when the input file
doesn't exist or is empty.

Check the name of the input files.
Verify the syntax used, check that
you didn't put spaces or any strange
character as part of the name.

It also may happen if you
specified the input files with
regular expressions and there
is no file that matches the
expression.

If you used regular expressions, check
the syntax.
Also verify that you put the filename
in the grid. If you just put it in the File
or directory textbox, Kettle will not
read it.

When you preview the
data you see a grid with
blank lines

The file contains empty lines, or
you forgot to get the fields.

You see the whole line
under the first defined
field.

You didn't set the proper
Check and fix the separator in the
separator and Kettle couldn't split Content tab.
the different fields.

You see strange
characters.

You left the default content but
Check and fix the Format and Encoding
your file has a different format or in the Content tab.
encoding.
If you are not sure of the format, you
can specify mixed.

You don't see all the
You are previewing just a sample
lines you have in the file (100 lines by default).

Check the content of the file.
Also check that you got the fields in the
Fields tab.

When you preview, you see just a
sample. This is not a problem.

If you raise the previewed number of
rows and still have few lines, check the
Header, Footer and Limit options in
Another problem may be that you the Content tab.
set the wrong number of header
or footer lines.
Or you put a limit to the number
of rows to get.

[ 45 ]

Getting Started with Transformations

Problem

Diagnostic

Instead of rows of
data, you get a window
headed ERROR with an
extract of the log

Different errors may happen, but You could try to understand the log
the most common has to do with and fix the definition accordingly. For
problems in the definition of the example if you see:
fields.
Couldn't parse field [Integer] with
value [Italy].

Possible solutions

The error is that PDI found the text
Italy in a field that you defined as
Integer.
If you made a mistake, you could fix
it. On the other hand, if the file has
errors, you could read all fields as
String and you will not get the error
again. In chapter 7 you will learn how
to overcome these situations.

Grids
Grids are tables used in many Spoon places to enter or display information. You already saw
grids in several configuration windows—Text file input, Text file output, and Select values.
Many grids contain field information. Examples of these grids are the Field tab window in the
Text Input and Output steps, or the main configuration window of the Select Values step. In
these cases, the grids are usually accompanied by a Get Fields button. The Get Fields button
is a facility to avoid typing. When you press that button, Kettle fills the grid with all the
available fields.
For example, when reading a file, the Get Fields button fills the grid with the columns of the
incoming file. When using a Select Values step or a File output step, the Get Fields button
fills the grid with all the fields entering from a previous step.
Every time you see a Get Fields button, consider it as a shortcut to avoid typing.
Kettle will bring the fields available to the grid; you will only have to check the
information brought and make minimal changes.

There are many places in Spoon where the grid serves also to edit other kinds of information.
One example of that is the grid where you specify the list of files in a Text File Input step. No
matter what kind of grid you are editing, there is always a contextual menu, which you may
access by right-clicking on a row. That menu offers editing options to copy, paste, or move
rows of the grid.
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Chapter 2

When the number of rows in the grid is big, use shortcuts! Most of the editing
options of a grid have shortcuts that make the editing work easier and quicker.

You'll find a full list of shortcuts for editing grids in Appendix E.

Have a go hero – explore your own files
Try to read your own text files from Kettle. You must have several files with different kinds of
data, different separators, and with or without header or footer. You can also search for files
over the Internet; there are plenty of files there to download and play with. After configuring
the input step, do a preview. If the data is not shown properly, fix the configuration and
preview again until you are sure that the data is read as expected. If you have trouble
reading the files, please refer to the Troubleshooting reading files section seen earlier for
diagnosis and possible ways to solve the problems.

Sending data to files
Now you know how to bring data into Kettle. You didn't bring the data just to preview it; you
probably want to do some transformation on the data, to finally send it to a final destination
such as another plain file. Let's learn how to do this last task.

Time for action – sending the results of matches to a plain file
In the previous tutorial, you read all your "results of matches" files. Now you want to send
the data coming from all files to a single output file.

Create a new transformation.

Drag a Text file input step to the canvas and configure it just as you did in the
previous tutorial.

Drag a Select values step to the canvas and create a hop from the Text file input
step to the Select values step.

Double-click the Select values step.

Click the Get fields to select button.

[ 47 ]

Getting Started with Transformations

Modify the fields as follows:

Expand the Output branch of the steps tree.

Drag the Text file output icon to the canvas.

Create a hop from the Select values step to the Text file output step.

10.

Double-click the Text file output step and give it a name.

11.

In the file name type: C:/pdi_files/output/wcup_first_round.

Note that the path contains forward slashes. If your system is Windows,
you may use back or forward slashes. PDI will recognize both notations.

12.

In the Content tab, leave the default values.

13.

Select the Fields tab and configure it as follows:

[ 48 ]

Chapter 2

14.

Click OK.

15.

Give a name and description to the transformation.

16.

Save the transformation.

17.

Click Run and then Launch.

18.

Once the transformation is finished, check the file generated. It should have been
created as C:/pdi_files/output/wcup_first_round.txt and should look
like this:
Match Date;Home Team;Away Team;Result
02/06;Italy;France;2-1
02/06;Argentina;Hungary;2-1
06/06;Italy;Hungary;3-1
06/06;Argentina;France;2-1
10/06;France;Hungary;3-1
10/06;Italy;Argentina;1-0
01/06;Germany FR;Poland;0-0
02/06;Tunisia;Mexico;3-1
06/06;Germany FR;Mexico;6-0
…

What just happened?
You gathered information from several files and sent all the data to a single file. Before
sending the data out, you used a Select Value step to select the data you wanted for the file
and to rename the fields so that the header of the destination file looks clearer.

Output files
We saw that PDI could take data from several types of files. The same applies to output data.
The data you have in a transformation can be sent to different types of files. All you have to
do is redirect the flow of data towards an Output step.

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Getting Started with Transformations

Output steps
There are several steps that allow you to send the data to a file. All those steps are under the
Output step category: Text file output and Excel Output are examples of them.
For an Output step, just like you do for an Input step, you also have to define:




Name of the step: It is mandatory and must be different for every step in
the transformation.
Name and location of the file: These must be specified. If you specify an existing
file, the file will be replaced by a new one (unless you check the Append checkbox
present in some of the output steps).
Content type: This data includes delimiter character, type of encoding, whether to
put a header, and so on. The list depends on the kind of file chosen. If you check
Header, the header will be built with the names of the fields.
If you don't like the names of the fields as header names in your file,
you may use a Select values step just to rename those fields.



Fields: Here you specify the list of fields that has to be sent to the file, and provide
some format instructions. Just like in the input steps, you may use the Get Fields
button to fill the grid. In this case, the grid is going to be filled based on the data
that arrives from the previous step. You are not forced to send every piece of data
coming to the output step, nor to send the fields in the same order.

Some data definitions
From the Kettle's point of view, data can be anything ready to be processed by software (for
example files or data in databases). Whichever the subject or origin of the data, whichever
its format, Kettle transformations can get the data for further processing and delivering.

Rowset
Transformations deals with datasets, that is, data presented in a tabular form, where:




Each column represents a field. A field has a name and a data type. The data type
can be any of the common data types—number (float), string, date, Boolean, integer,
or big number.
Each row corresponds to a given member of the dataset. All rows in a dataset have
the same structure, that is, all rows have the same fields, in the same order. A field
in a row may be null, but it has to be present.

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Chapter 2


The dataset is called rowset. The following is an example of rowset. It is the rowset
generated in the World Cup tutorial:

Streams
Once the data is read, it travels from step to step, through the hops that link those steps.
Nothing happens in the hops except data flowing. The real manipulation of data, as well as
the modification of a stream by adding or removing columns, occurs in the steps.
Right-click on the Select values step of the transformation you created. In the contextual
menu select Show output fields. You'll see this:

This window shows the metadata of the data that leaves this step, this is, name, type, and
other properties of each field leaving this step towards the following step.
In the same way, if you select Show input fields, you will see the metadata of the data that
left the previous step.

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Getting Started with Transformations

The Select values step
The Select values step allows you to select, rename, and delete fields, or change the
metadata of a field. The step has three tabs:




Select & Alter: This tab is also used to rename the fields or reorder them. This is
how we used it in the last exercise.
Remove: This tab is useful to discard undesirable fields. We used it in the matches
exercise to drop the first and last fields. Alternatively, we could use the Select &
Alter tab, and specify the fields that you want to keep. Both are equivalent for
that purpose.
Meta-data: This tab is used when you want to change the definition of a field such
as telling Kettle to interpret a string field as a date. We will see examples of this later
in this book.
You may use only one of the Select Values step tabs at a
time. Kettle will not restrain you from filling more than one
tab, but that could lead to unexpected behavior.

Have a go hero – extending your transformations by writing output files
Suppose you read your own files in the previous section, modify your transformations by
writing some or all the data back into files, however, changing the format, headers, number
or order of fields, and so on this time around. The objective is to get some experience to see
what happens. After some tests, you will feel confident with input and output files, and be
ready to move forward.

Getting system information
Until now, you have learned how to read data from known files, and send data back to files.
What if you don't know beforehand the name of the file to process? There are several ways
to handle this with Kettle. Let's learn the simplest.

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Time for action – updating a file with news about examinations
Imagine you are responsible to collect the results of an annual examination that is being
taken in a language school. The examination evaluates writing, reading, speaking, and
listening skills. Every professor gives the exam to the students, the students take the
examination, the professors grade the examinations in the scale 0-100 for each skill, and
write the results in a text file, like the following:
student_code;name;writing;reading;speaking;listening
80711-85;William Miller;81;83;80;90
20362-34;Jennifer Martin;87;76;70;80
75283-17;Margaret Wilson;99;94;90;80
83714-28;Helen Thomas;89;97;80;80
61666-55;Maria Thomas;88;77;70;80

All the files follow that pattern.
When a professor has the file ready, he/she sends it to you, and you have to integrate the
results in a global list. Let's do it with Kettle.

Before starting, be sure to have a file ready to read. Type it or download the sample files
from the Packt's official website.

Create the file where the news will be appended. Type this:
--------------------------------------------------------Annual Language Examinations
Testing writing, reading, speaking and listening skills
--------------------------------------------------------student_code;name;writing;reading;speaking;listening;file_
processed;process_date

Save the file as C:/pdi_files/output/examination.txt.

Create a new transformation.

Expand the Input branch of the steps tree.

Drag the Get System Info and Text file input icons to the canvas.

Expand the Output branch of the steps tree, and drag a Text file output step to
the canvas.

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Getting Started with Transformations

Link the steps as follows:

Double-click the first Get System Info step icon and give it a name.

Fill the grid as follows:

10.

Click OK.

11.

Double-click the Text file Input step icon and configure it like here:

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Chapter 2

12.

Select the Content tab.

13.

Check the Include filename in output? checkbox and type file_processed in the
Filename fieldname textbox.

14.

Check the Add filenames to result checkbox.

15.

Select the Fields tab and Click the Get Fields button to fill the grid.

16.

Click OK.

17.

Double-click the second Get System Info step icon and give it a name.

18.

Add a field named process_date, and from the list of choices select system
date (fixed).

19.

Double-click the Text file output step icon and give it a name.

20.

Type C:/pdi_files/output/examination as the filename.

21.

In the Fields tab, press the Get Fields button to fill the grid.

22.

Change the format of the Date row to yy/MM/dd.

23.

Give a name and description to the transformation and save it.

24.

Press F9 to run the transformation.

25.

Fill in the argument grid, writing the full path of the file created.

26.

Click Launch.

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Getting Started with Transformations

27.

The output file should look like this:
--------------------------------------------------------Annual Language Examinations
Testing writing, reading, speaking and listening skills
--------------------------------------------------------student_code;name;writing;reading;speaking;listening;file_
processed;process_date
80711-85;William Miller;81;83;80;90;C:\exams\exam1.txt;28-05-2009
20362-34;Jennifer Martin;87;76;70;80;C:\exams\exam1.txt;28-05-2009
75283-17;Margaret Wilson;99;94;90;80;C:\exams\exam1.txt;28-05-2009
83714-28;Helen Thomas;89;97;80;80;C:\exams\exam1.txt;28-05-2009
61666-55;Maria Thomas;88;77;70;80;C:\exams\exam1.txt;28-05-2009

28.
29.
30.
31.

Run the transformation again.
This time fill the argument grid with the name of a second file.
Click Launch.
Verify that the data from this second file was appended to the previous data in the
output file.

What just happened?
You read a file whose name is known at runtime, and fed a destination file by appending the
contents of the input file.
The first Get System Info step tells Kettle to take the first command line argument, and
assume that it is the name of the file to read.
In the Text File Input step, you didn't specify the name of the file, but told Kettle to take as
the name of the file, the field coming from the previous step, which is the read argument.
With the second Get System Info step you just took from the system, the date, which you
used later to enrich the data sent to the destination file.
The destination file is appended with new data every time you run the transformation.
Beyond the basic required data (student code and grades), the name of the processed file
and the date on which the data is being appended are added as part of the data.
When you don't specify the name and location of a file (like in this example), or
when the real file is not available at design time, you won't be able to use the
Get Fields button, nor preview to see if the step is well configured. The trick is
to configure the step by using a real file identical to the expected one. After the
step is configured, change the name and location of the file as needed.

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Getting information by using Get System Info step
The Get System Info step allows you to get different information from the system. In this
exercise, you took the system date and an argument. If you look to the available list, you
will see more than just these two options.
Here we used the step in two different ways:


As a resource to take the name of the file from the command line



To add a field to the dataset

The use of this step will be clearer with a picture.

In this example, the Text File Input doesn't know the name or the location of the file. It takes
it from the previous step, which is a Get System Info Step. As the Get System Info serves as
a supplier of information, the hop that leaves the step changes its look and feel to show
the situation.

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Getting Started with Transformations

The second time the Get System Info is used, its function is simply to add a field to the
incoming dataset.

Data types
Every field must have a data type. The data type can be any of the common data
types—number (float), string, date, Boolean, integer, or big number. Strings are simple,
just text for which you may specify a length. Date and numeric fields have more variants,
and are worthy of while a separate explanation.

Date fields
Date is one the main data types available in Kettle. In the matches tutorial, you have an
example of date field—the match date field. Its values were 2/Jun, 6/Jun, 10/Jun. Take a
look at how you defined that field in the Text file input step. You defined the field as a date
field with format dd/MMM. What does it mean? To Kettle it means that it has to interpret the
field as a date, where the first two positions represent the day, then there is a slash, and
finally there is the month in letters (that's the meaning of the three last positions).
Generally speaking, when a date field is created, like the text input field of the example, you
have to define the format of the data so that Kettle can recognize in the field the different
components of the date. There are several formats that may be defined for a date, all of
them combinations of letters that represents date or time components. Here are the most
basic ones:
Letters
y
M
d
H
m
s

Meaning
Year
Month
Day
Hour (0-23)
Minutes
Seconds

Now let's see the other end of the same transformation—the output step. Here you set
another format for the same field: dd/MM. According the table, this means the date has to
have two positions for the day, then a slash, and then two positions for the month. Here, the
format specification represents the mask you want to apply when the date is shown. Instead
of 2/Jun, 6/Jun, 10/Jun, in the output file, you expect to see 02/06, 06/06, 10/06.
In the examination tutorial, you also have a Date field—the process date. When you created
it, you didn't specify a format because you took the system date which, by definition, is a
date and Kettle knows it. But when writing this date to the output file, again you defined a
format, in this case it was yyyy/MM/dd.

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In general, when you are writing a date, the format attribute is used of format the data
before sending it to the destination. In case you don't specify a format, Kettle sets a
default format.
As said earlier, there are more combinations to define the format to a date field.
For a complete reference, check the Sun Java API documentation located at
http://java.sun.com/javase/6/docs/api/java/text/SimpleDateFormat.html.

Numeric fields
Numeric fields are present in almost all Kettle transformations. In the Examination example,
you encountered numeric fields for the first time. The input file had four numeric fields.
As the numbers were all integer, you didn't set a specific format. When you have more
elaborate fields such as numbers with separators, dollar signs, and so on, you should specify
a format to tell Kettle how to interpret the number. If you don't, Kettle will do its best to
interpret the number, but this could lead to unexpected results.
At the other extreme of the flow, when writing to the output file text, you may specify the
format in which you want the number to be shown.
There are several formats you may apply to a numeric field. The format is basically a
combination of predefined symbols, each with a special meaning. The following are
the most used symbols:
Symbol

Meaning

Digit Leading zeros are not shown

Digit If the digit is not present, zero is displayed in its place

Decimal separator

Minus sign

Field has to be multiplied by 100 and shown as a percentage

These symbols are not used alone. In order to specify the format of your numbers, you
have to combine them. Suppose that you have a numeric field whose value is 99.55; the
following table shows you the same value after applying different formats to it:
Format
#
0
#.#
#.##
#.000
000.000

Result
100
100
99.6
99.55
99.550
099.550
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Getting Started with Transformations

If you don't specify a format for your numbers, you may still provide a Length and
Precision. Length is the total number of significant figures, while precision is the number
of floating-point digits.
If you neither specify format nor length or precision, Kettle behaves as follow. While reading,
it does its best to interpret the incoming number, and when writing, it sends the data as it
comes without applying any format.
For a complete reference on number formats, you can check the Sun Java API
documentation available at http://java.sun.com/javase/6/docs/api/java/text/
DecimalFormat.html.

Running transformations from a terminal window
In the examination exercise, you specified that the name of the input file will be taken
from the first command-line argument. That means when executing the transformation,
the filename has to be supplied as an argument. Until now, you only ran transformations
from inside Spoon. In the last exercise, you provided the argument by typing it in a dialog
window. Now it is time to learn how to run transformations with or without arguments from
a terminal window.

Time for action – running the examination transformation from
a terminal window
Before executing the transformation from a terminal window, make sure that you have a new
examination file to process, let's say exam3.txt. Then follow these instructions:

Open a terminal window and go to the directory where Kettle is installed.


On Windows systems type:
C:\pdi-ce>pan.bat /file:c:\pdi_labs\examinations.ktr c:\
pdi_files\input\exam3.txt



On Unix, Linux, and other Unix-based systems type:
/home/yourself/pdi-ce/pan.sh /file:/home/yourself/pdi_labs/
examinations.ktr c:/pdi_files/input/exam3.txt



If your transformation is in another folder, modify the command
accordingly.

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Chapter 2

You will see how the transformation runs, showing you the log in the terminal.

Check the output file. The contents of exam3.txt should be at the end of the file.

What just happened?
You executed a transformation with Pan, the program that runs transformations from
terminal windows. As part of the command, you specified the name of the transformation
file and provided the name of the file to process, which was the only argument expected by
the transformation. As a result, you got the same as if you had run the transformation from
Spoon—a small file appended to the global file.
When you are designing transformations, you run them with Spoon; you don't use Pan. Pan
is mainly used as part of batch processes, for example processes that run every night in a
scheduled fashion.
Appendix B tells you all the details about using Pan.

Have a go hero – using different date formats
Change the main transformation of the last tutorial so that the process_date is saved with
a full format, that is, including day of week (Monday, Tuesday, and so on), month in letters
(January, February, and so on), and time.

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Getting Started with Transformations

Go for a hero – formatting 99.55
Create a transformation to see for yourself the different formats for the number 99.55. Test
the formats shown in the Numeric fields section and try some other options as well.
To test this, you will need a dataset with a single row and a single field—the
number. You can generate it with a Generate rows step.

Pop quiz–formatting data
Suppose that you read a file where the first column is a numeric identifier: 1, 2, 3, and so on.
You read the field as a Number. Now you want to send the data back to a file. Despite being
a number, this field is regular text to you because it is a code. How do you define the field in
the Text output step (you may choose more than one option):
a. As a Number. In the format, you put #.
b. As a String. In the format, you put #.
c.

As a String. You leave the format blank.

XML files
Even if you're not a system developer, you must have heard about XML files. XML files
or documents are not only used to store data, but also to exchange data between
heterogeneous systems over the Internet. PDI has many features that enable you to
manipulate XML files. In this section you will learn to get data from those files.

Time for action – getting data from an XML file with information
about countries
In this tutorial you will build an Excel file with basic information about countries. The source
will be an XML file that you can download from the Packt website.

If you work under Windows, open the kettle.properties file located in the
C:/Documents and Settings/yourself/.kettle folder and add the
following line:
LABSOUTPUT=c:/pdi_files/output

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Chapter 2

On the other hand, if you work under Linux (or similar), open the kettle.
properties file located in the /home/yourself/.kettle folder and add the
following line:
LABSOUTPUT=/home/yourself/pdi_files/output

Make sure that the directory specified in kettle.properties exists.

Save the file.

Restart Spoon.

Create a new transformation.

Give a name to the transformation and save it in the same directory you have all the
other transformations.

From the Packt website, download the resources folder containing a file named
countries.xml. Save the folder in your working directory. For example, if your
transformations are in pdi_labs, the file will be in pdi_labs/resources/.
The last two steps are important. Don't skip them! If you do,
some of the following steps will fail.

Take a look at the file. You can edit it with any text editor, or you can double-click it to
see it within an explorer. In any case, you will see information about countries. This is
just the extract for a single country:

...

Argentina

Buenos Aires

Spanish

96.8

Italian

1.7

[ 63 ]

Getting Started with Transformations

Indian Languages

0.3

...

From the Input steps, drag a Get data from XML step to the canvas.

10.

Open the configuration window for this step by double-clicking it.

11.

In the File or directory textbox, press Ctrl+Space. A drop-down list appears as shown in
the next screenshot:

12.

Select Internal.Transformation.Filename.Directory. The textbox gets filled
with this text.

13.

Complete the text so that you can read ${Internal.Transformation.Filename.

14.

Click on the Add button. The full path is moved to the grid.

15.

Select the Content tab and click Get XPath nodes.

16.

In the list that appears, select /world/country/language.

Directory}/resources/countries.xml.

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Chapter 2

17.

Select the Fields tab and fill the grid as follows:

18.

Click Preview rows, and you should see something like this:

19.

Click OK.

20.

From the Output steps, drag an Excel Output step to the canvas.

21.

Create a hop from the Get data from XML step to the Excel Output step.

22.

Open the configuration window for this step by double-clicking it.

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Getting Started with Transformations

23.

In the Filename textbox press Ctrl+Space.

24.

From the drop-down list, select ${LABSOUTPUT}.

25.

By the side of that text type /countries_info. The complete text should be
${LABSOUTPUT}/countries_info.

26.

Select the Fields tab and click the Get Fields button to fill the grid.

27.

Click OK. This is your final transformation.

28.

Save the transformation.

29.

Run the transformation.

30.

Check that the countries_info.xls file has been created in the output directory
and contains the information you previewed in the input step.

What just happened?
You got information about countries from an XML file and saved it in a more readable
format—an Excel spreadsheet—for the common people.
To get the information, you used a Get data from XML step. As the source file was
taken from a folder relative to the folder where you stored the transformation, you set
the directory to ${Internal.Transformation.Filename.Directory}. When
the transformation ran, Kettle replaced ${Internal.Transformation.Filename.
Directory} with the real path of the transformation: c:/pdi_labs/.
In the same way, you didn't put a fixed value for the path of the final Excel file. As directory,
you used ${LABSOUTPUT}. When the transformation ran, Kettle replaced ${LABSOUTPUT}
with the value you wrote in the kettle.properties file. The output file was then saved in
that folder: c:/pdi_files/output.

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Chapter 2

What is XML
XML stands for EXtensible Markup Language. It is basically a language designed to describe
data. XML files or documents contain information wrapped in tags. Look at this piece of XML
taken from the countries file:

...

Argentina
Buenos Aires

Spanish
96.8

Italian
1.7

Indian Languages
0.3

...

The first line in the document is the XML declaration. It defines the XML version of the
document, and should always be present.
Below the declaration is the body of the document. The body is a set of nested elements.
An element is a logical piece enclosed by a start-tag and a matching end-tag—for example,
.
Within the start-tag of an element, you may have attributes. An attribute is a markup
construct consisting of a name/value pair—for example, isofficial="F".
These are the most basic terminology related to XML files. If you want to know more about
XML, you can visit http://www.w3schools.com/xml/.

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Getting Started with Transformations

PDI transformation files
Despite the .ktr extension, PDI transformations are just XML files. As such, you are able to
explore them inside and recognize different XML elements. Look the following sample text:

hello_world
My first transformation

This transformation generates 10 rows
with the message Hello World.

...

This is an extract from the hello_world.ktr file. Here you can see the root element
named transformation, and some inner elements such as info and name.
Note that if you copy a step by selecting it in the Spoon canvas and pressing Ctrl+C , and then
pass it to a text editor, you can see its XML definition. If you copy it back to the canvas, a new
identical step will be added to your transformation.

Getting data from XML files
In order to get data from an XML file, you have to use the Get Data From XML input step.
To tell PDI which information to get from the file, it is required that you use a particular
notation named XPath.

XPath
XPath is a set of rules used for getting information from an XML document. In XPath, XML
documents are treated as trees of nodes. There are several types of nodes; elements,
attributes, and texts are some of them. As an example, world, country, and isofficial
are some of the nodes in the sample file.
Among the nodes there are relationships. A node has a parent, zero or more children,
siblings, ancestors, and descendants depending on where the other nodes are in
the hierarchy.
In the sample countries file, country is the the parent of the elements name, capital, and
language. These three elements are children of country.
To select a node in an XML document, you have to use a path expression relative to a
current node.

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Chapter 2

The following table has some examples of path expressions that you may use to specify
fields. The examples assume that the current node is language.
Path expression
node_name

Description
Selects all child nodes of the
node named node_name.

Selects the current node

Selects the parent of the
current node

Selects an attribute

Sample expression
percentage
This expression selects all child nodes of
the node percentage. It looks for the node
percentage inside the current node language.
language
../capital
This expression selects all child nodes of the
node capital. It doesn't look in the current
node (language), but inside its parent, which
is country.
@isofficial
This expression gets the attribute isofficial
in the current node language.

Note that the expressions name and ../name are not the same. The
first selects the name of the language, while the second selects the
name of the country.

For more information on XPath, follow this link: http://www.w3schools.com/XPath/.

Configuring the Get data from XML step
In order to specify the name and location of an XML file, you have to fill the File tab just as
you do in any file input step. What is different here is how you get the data.
The first thing you have to do is select the path that will identify the current node. You do
it by filling the Loop XPath textbox in the Content tab. You can type it by hand, or you can
select it from the list of available paths by Clicking the Get XPath nodes button.
Once you have selected a path, PDI will generate one row of data for every found path.
In the tutorial you selected /world/country/language. Then PDI generates one row for
each /world/country/language element in the file.
After selecting the loop XPath, you have to specify the fields to get. In order to do that,
you have to fill the grid in the Fields tab by using XPath notation as explained in the
preceding section.

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Getting Started with Transformations

Note that if you click the Get fields button, PDI will fill the grid with the child nodes of the
current node. If you want to get some other node, you have to type its XPath by hand.
Also note the notation for the attributes. To get an attribute, you can use the @ notation as
explained, or you can simply type the name of the attribute without @ and select Attribute
under the Element column, as you did in the tutorial.

Kettle variables
In the last tutorial, you used the string ${Internal.Transformation.Filename.
Directory} to identify the folder where the current transformation was saved. You also
used the string ${LABSOUTPUT} to define the destination folder of the output file.
Both strings, ${Internal.Transformation.Filename.Directory} and
${LABSOUTPUT}, are Kettle variables, that is, keywords linked to a value. You use the
name of a variable, and when the transformation runs, the name of the variable is
replaced by its value.
The first of these two variables is an environment variable, and it is not the only available.
Other known environment variables are ${user.home}, ${java.io.tmpdir}, and
${java.home}. All these variables are ready to use any time you need.
The second variable is a variable you defined in the kettle.properties file. In this file
you may define as many variables as you want. The only thing you have to keep in mind is
that those variables will be available inside Spoon after you restart it.
These two kinds of variables—environment variables and variables defined in the
kettle.properties file—are the most primitive kinds of variables found in PDI.
All of these variables are string variables and their scope is the Java virtual machine.

How and when you can use variables
Any time you see a red dollar sign by the side of a textbox, you may use a variable. Inside the
textbox you can mix variable names with static text, as you did in the tutorial when you put
the name of the destination as ${LABSOUTPUT}/countries_info.
To see all the available variables, you have to position the cursor in the textbox, press
Ctrl+Space, and a full list is displayed for you to select the variable of your choice. If you put
the mouse cursor over any of the variables for a second, the actual value of the variable will
be shown.
If you know the name of the variable, you don't need to select it from the list. You may type
its name, by using either of these notations—${} or %%%%.

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Have a go hero – exploring XML files
Now you can explore by yourself. On the Packt website there are some sample XML files.
Download them and try this:
•

Read the customer.xml file and create a list of customers.

•

Read the tomcat-users.xml file and get the users and their passwords.

•

Read the areachart.xml and get the color palette, that is, the list of colors used.
The customer file is included in the Pentaho Report Designer software package.
The others come with the Pentaho BI package. This software has many XML files
for you to use. If you are interested you can download the software from
http://sourceforge.net/projects/pentaho/files/.

Have a go hero – enhancing the output countries file
Modify the transformation in the tutorial so that the Excel output uses a template. The
template will be an Excel file with the header and format already applied, and will be located
in a folder inside the pdi_labs folder.
Templates are configured in the Content tab of the Excel configuration window.
In order to set the name for the template, use internal variables.

Have a go hero – documenting your work
As explained, transformations are nothing different than XML files. Now you'll create a new
transformation that will take as input the transformations you've created so far, and will
create a simple Excel spreadsheet with the name and description of all your transformations.
If you keep this sheet updated by running the transformation on a regular basis, it will be
easier to find a particular transformation you created in the past.
To get data from the transformations files, use the Get data from XML step.
As wildcard, use .*\.ktr. Doing so, you'll get all the files.
On the other hand, as Loop XPath, use /transformation/info.

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Getting Started with Transformations

Summary
In this chapter you learned how to get data from files and put data back into files.
Specifically, you learned how to:


Get data from plain files and also from XML files



Put data into text files and Excel files



Get information from the operating system such as command-line arguments and
system date

We also discussed the following:




The main PDI terminology related to data, for example datasets, data types,
and streams
The Select values step, a commonly used step for selecting, reordering, removing
and changing data



How and when to use Kettle variables



How to run transformations from a terminal with the Pan command

Now that you know how to get data into a transformation, you are ready to start
manipulating data. This is going to happen in the next chapter.

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Basic Data Manipulation
In the previous chapter, you learned how to get data into PDI. Now you're ready to
begin transforming that data. This chapter explains the simplest and most used ways
of transforming data. We will cover the following:


Executing basic operations



Filtering and sorting of data



Looking up data outside the main stream of data

By the end of this chapter, you will be able to do simple but meaningful transformations on
different types of data.

Basic calculations
You already know how to create a transformation and read data from an external source.
Now, taking that data as a starting point, you will begin to do basic calculations.

Basic Data Manipulation

Time for action – reviewing examinations by using the
Calculator step
Can you recollect the exercise about examinations you did in the previous chapter? You
created an incremental file with examination results. The final file looked like the following:
--------------------------------------------------------Annual Language Examinations
Testing writing, reading, speaking and listening skills
--------------------------------------------------------student_code;name;writing;reading;speaking;listening;file_
processed;process_date
80711-85;William Miller; 81;83;80;90;C:\pdi_files\input\first_turn.
txt;28-05-2009
20362-34;Jennifer Martin; 87;76;70;80;C:\pdi_files\input\first_turn.
txt;28-05-2009
75283-17;Margaret Wilson; 99;94;90;80;C:\pdi_files\input\first_turn.
txt;28-05-2009
83714-28;Helen Thomas; 89;97;80;80;C:\pdi_files\input\first_turn.
txt;28-05-2009
61666-55;Maria Thomas; 88;77;70;80;C:\pdi_files\input\first_turn.
txt;28-05-2009
...

Now you want to convert all grades in the scale 0-100 to a new scale from 0 to 5. Also, you
want to take the average grade to see how the students did.

Create a new transformation, give it a name and description, and save it.

By using a Text file input step, read the examination.txt file. Give the name and
location of the file, check the Content tab to see that everything matches your file, and
fill the Fields tab as here:

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Chapter 3

Do a preview just to confirm that the step is well configured.
Notice that you have several lines as header. Because the
names of the fields are not in the first row, you won't be able
to use the Get Fields button successfully. You will have to write
the fields manually, or you can avoid it by doing the following:
Configure the step with a copy of the file that doesn't have the
extra heading, just the heading row with the names of the fields.
Then, restore the name of your file in the File tab, adjust the
number of headings in the Content tab, and your step is ready.

Use the Select values step to remove the fields you will not use—file_processed
and process_date.

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Basic Data Manipulation

Drag another Select values step to the canvas. Select the Meta-data tab and change the
meta-data of the numeric fields like here:

Near the upper-left corner of the screen, above the step tree, there is a textbox for
searching. Type calc in the textbox. While you type, a filter is applied to show you only
the steps that contain, in their name or description, the text you typed. You should be
seeing this:

Among the steps you see, select the Calculator step and drag it to the canvas.
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Chapter 3

To remove the filter, clear the typed text.

Create a hop from the Text file input step to the Calculator step.

10.

Edit the Calculator step and fill the grid as follows:

11.

To fill the Calculation column, simply select the operation from the list provided. Be sure
to fill every column in the grid like shown in the screenshot.
You don't have to feel like you are doing data entry instead
of learning PDI. You can avoid typing by copying and pasting
similar rows, and then fixing the values properly. Appendix D
has a list of shortcuts you can use when editing grids like these.

12.

Leave the Calculator step selected and click the Preview this transformation button
followed by the Quick Launch button. You should see something similar to the
following screenshot:

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Basic Data Manipulation

The numbers may vary according to the contents of your file.

13.

Edit the calculator again and change the content of the Remove column like here:

14.

From the Transform category of steps, add a Sort rows step and create a hop from the
Calculator step to this new step.

15.

Edit the Sort rows step by double-clicking it, click the Get Fields button, and adjust the
grid as follows:

16.

Click OK.
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Chapter 3

17.

Drag a third Select values step, create a hop from the Sort rows step to this new step,
and use it to keep only the fields by which you ordered the data:

18.

From the Flow category of steps, add a Dummy step and create a hop from the last
Select values step to this.

19.

Select the Dummy step and do a preview.

20.

The final preview looks like the following screenshot:

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Basic Data Manipulation

If you get an error or a different result, review the explanation and make
sure that you followed the instructions correctly. Do a preview on each
step to discover in which one you have the problem. If you realize that
the problem is in any of the steps that read the input files, please refer
to the Troubleshooting reading files section in Chapter 2.

What just happened?
You read the examination.txt file, and did some calculations to see how the students did.
You did the calculations by using the Calculator step.
First of all, you removed the fields you didn't need from the stream of data.
After that, you did the following calculations:
By dividing by 20, you converted all grades from the scale 0-100 to the scale 0-5.
Then, you calculated the average of the grades for the four skills—writing, reading, listening,
and speaking. You created two auxiliary fields, aux1 and aux2, to calculate partial sums. After
that, you created the field total with the sum of aux1 and aux2, another auxiliary field with
the number 4, and finally the avg as the division of the total by the field four.
In order to obtain the new grades, as well as the average with two decimal positions, you
need the result of the operation to be of a numeric type with precision 2. Therefore, you
had to change the metadata, by adding a Select values step before the Calculator. With the
Select values you changed the type of the numeric fields from integer to number, that is,
float numbers. If you didn't, the quotients would have been rounded to integer numbers.
You can try and see for yourself!
The first time you edited the calculator, you set the field Remove to N for every row in the
calculator grid. By doing this, you could preview every field created in the calculator, even
the auxiliary ones such as the fields twenty, aux1, and aux2. You then changed the field to
Y so that the auxiliary fields didn't pass to the next step.
After doing the calculations, you sorted the data by using a Sort rows step. You specified the
order by avg descending, then by student_code ascending.

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Sorting data
For small datasets, the sorting algorithm runs mainly using the JVM memory.
When the number of rows exceeds 5,000, it works differently. Every five
thousand rows, the process sorts them and writes them to a temporary file.
When there are no more rows, it does a merge sort on all those files and gives
you back the sorted dataset. You can conclude that for huge datasets a lot
of reading and writing operations are done on your disk, which slows down
the whole transformation. Fortunately, you can change the number of rows
in memory (5,000 by default) by setting a new value in the Sort size (rows in
memory) textbox. The bigger this number, the faster the sorting process.
Note that a sort size that works in your system may not work in a machine with
a different configuration. To avoid that risk, you can use a different approach.
In the Sort rows configuration window, you can set a Free memory threshold
(in %) value. The process begins to use temporary files when the percentage
of available memory drops below the indicated threshold. The lower the
percentage, the faster the process.

As it's not possible to know the exact amount of free memory, it's not
recommended to set a very small free memory threshold. You definitely
shouldn't use that option in complex transformations or when there is more
than one sort going on, as you could still run out of memory.

The two final steps were added to keep only the fields of interest, and to preview the result
of the transformation. You can change the Dummy step for any of the output steps you
already know.
You've used the Dummy step several times but still nothing has been said
about it. Mainly it was because it does nothing! However, you can use it as a
placeholder for testing purposes as in the last exercise.

Note that in this tutorial you used the Select values step in three different ways:


To remove fields by using the Remove tab.



To change the meta-data of some fields by using the Meta-data tab.



To select and rename fields by using the Select tab.
Remember that the Select values step's tabs are exclusive! You can't use more
than one in the same step!

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Basic Data Manipulation

Besides calculation, in this tutorial you did something you hadn't before—searching the
step tree.
When you don't remember where a step is in the steps tree, or when you just
want to find if there is a step that does some kind of operation, you could simply
type the search criterion in the textbox above the steps tree. PDI does a search
and filters all the steps that have that text as part of their name or description.

Adding or modifying fields by using different PDI steps
In this tutorial you used the Calculator step to create new fields and add them to
your dataset. The Calculator is one the many steps that PDI has to create new fields by
combining existent ones. Usually you will find these steps under the Transform category
of the steps tree. The following table describes some of them (the examples refer to the
examination file):
Step

Description

Example

Split Fields

Split a single field into two
or more. You have to give
the character that acts as
separator.

Split the name into two fields: Name and
Last Name. The separator would be a space
character.

Add constants

Add one or more constants
to the input rows

Add two constants: four and twenty. Then
you could use them in the Calculator step
without defining the auxiliary fields.

Replace in string

Replace all occurrences of
a text in a string field with
another text

Replace the – in the student code by a /.
For example: 108418-95 would become
108418/95.

Number range

Create a new field based on Create a new field called exam_range with
ranges of values. Applies to two ranges: Range A with the students with
a numeric field.
average grade below 3.5, and Range B with
students with average grade greater or equal
to 3.5.

Value Mapper

Creates a correspondence
between the values of
a field and a new set of
values.

Suppose you calculated the average grade as
an integer number ranging from 0 to 5. You can
map the average to A, B, C, D, like this:
Old value: 5; New value: A
Old value: 3, 4; New value: B
Old value: 1, 2; New value: C
Old value: 0; New value: D

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Chapter 3

Step

Description

Example

User Defined Java
Expression

Creates a new field by
using a Java expression that
involves one or more fields.
This step may eventually
replace any of the above
but it's only recommended
for those familiar with Java.

Create a flag (a Boolean field) that tells if a
student passed. A student passes if his/her
average grade is above 4.5.
The expression to use could be:
(((writing+reading+speaking+
listening)/4)>4.5)?true:false

Any of these steps when added to your transformation, are executed for every row in the
stream. It takes the row, identifies the fields needed to do its tasks, calculates the new
field(s), and adds it to the dataset.
For details on a particular step, don't hesitate to visit the Wiki page for steps:
http://wiki.pentaho.com/display/EAI/Pentaho+Data+Integration+v3.2.+St
eps

The Calculator step
The Calculator step you used in the tutorial, allows you to do simple calculations not only
on numeric fields, but also on data and text. The Calculator step is not the only means to do
calculations, but it is the simplest. It allows you to do simple calculations in a quick fashion.
The step has a grid where you can add all the fields you want to. Every row represents
an operation that involves from one up to three operands (depending on the selected
operation). When you select an operation, the description of the operation itself tells you
which argument it needs. For example:




If you select Set constant field to value A, you have to provide a constant value
under the column name A.
If you select A/B, the operation needs two arguments, and you have to provide
them by indicating the fields to use in the columns named A and B respectively.

The result of every operation becomes a new field in your dataset, unless you set the
Remove column to Y. The name of the new field is the one you type under the New
field column.
For each and every row of the data set, the operations defined in the Calculator are
calculated in the order in which they appear. Therefore, you may create auxiliary fields and
then use them in rows of the Calculator grid that are below them. That is what you did in
the tutorial when you defined the auxiliary fields aux1 and aux2 and then used them in the
field total.

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Basic Data Manipulation

Just like every grid in Kettle, you have a contextual menu (and its corresponding shortcuts)
that lets you manipulate the rows by deleting, moving, copying and pasting, and so on.

The Formula step
The Formula step is another step you can use for doing calculations. Let's give it a try by
using it in the examination tutorial.

Time for action – reviewing examinations by using the
Formula step
In this tutorial you will redo the previous exercise, but this time you will do the calculations
with the Formula step.

Open the transformation you just finished.

Delete from the transformation the Calculator step, and put in its place a Formula
step. You will find it under the Scripting category of steps.

Add a field named writing.

When you click the cell under the Formula column, a window appears to edit the
formula for the new field.

In the upper area of the window, type [writing]/20. You will notice that the
sentence is red if it is incomplete or the syntax is incorrect. In that case, the error is
shown below the editing area, like in the following example:

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Chapter 3

As soon as the formula is complete and correct, the red color disappears.

Click OK.

The formula you typed will be displayed in the cell you clicked.

Set Number as the type for the new field, and type writing in the Replace value
column.

10.

Add three more fields to the grid in the same way you added this field so that the
grid looks like the following:

11.

Click OK.

12.

Add a second Formula step.

13.

Add a field named avg and click the Formula cell to edit it.

14.

Expand the Mathematical category of functions to the leftside of the window, and click
the AVERAGE function.

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Basic Data Manipulation

15.

16.

The explanation of the selected function appears to guide you.

In the editing area, type average([writing];[reading];[speaking];
[listening]).

17.

Click OK.

18.

Set the Value type to Number.

19.

Click OK.

20.

Create a hop from this step to the Sort rows step.

21.

Edit the last Select values step.

22.

Click Get fields to select.

23.

A question appears to ask you what to do. Click Clear and add all.

24.

The grid is reloaded with the modified fields.

25.

Click on the Dummy step and do a preview.

26.

There should be no difference with what you had in the Calculator version of
the tutorial:

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Chapter 3

What just happened?
You read the examination.txt file, and did some calculations using the Formula step to
see how the students did.
It may happen that the preview window shows you less decimal positions than
expected. This is a preview issue. One of the ways you have to see the numbers
with more decimals is to send the numbers to an output file with a proper
format and see the numbers in the file.

As you saw, you have quite a lot of functions available for building formulas and expressions.
To reference a field you have to use square brackets, like in [writing]. You may reference
only the current fields of the row. You have no way to access previous rows of the grid as
you have in the Calculator step and so you needed two Formula steps to replace a single
Calculator. But you saved auxiliary fields because the Formula allows you to type complex
formulas in a single field without using partial calculations.
When the calculations are not simple, that is, they require resolving a complex
formula or involve many operands, then you might prefer the Formula step over
the Calculator.

The Formula step uses the library Libformula. The syntax used in LibFormula is based
on the OpenFormula standard. For more information on OpenFormula, you may visit
http://wiki.oasis-open.org/office/About_OpenFormula.

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Basic Data Manipulation

Have a go hero – listing students and their examinations results
Let's play a little with the examination file. Suppose you decide that only those students
whose average grade was above 3.9 will pass the examination; the others will not. List the
students ordered by average (desc.), last name (asc.), and name (asc.). The output list should
have the following fields:


Student code



Name



Last Name



Passed (yes/no)



average grade

Pop quiz – concatenating strings
Suppose that you want to create a new field as the student_code plus the name of the
student separated by a space, as for example 867432-94 Linda Rodriguez. Which of the
following are possible solutions for your problem:
a. Use a Calculator, using the calculation a+b+c, where a is student_code, b is a
space, and c is the name field.
b. Use a Formula, using as formula [student_code]+" "+[name]
c.

Use a Formula, using as formula [student_code]&" "&[name]

You may choose more than one option.

Calculations on groups of rows
You just learned to do simple operations for every row of a dataset. Now you are ready to
go beyond. Suppose you have a list of daily temperatures of a given country over a year. You
may want to know the overall average temperature, the average temperature by region,
or the coldest day of the year. When you work with data, these types of calculations are a
common requirement. In this section you will learn to address those requirements with PDI.

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Chapter 3

Time for action – calculating World Cup statistics by
grouping data
Let's forget the examinations for a while, and retake the World Cup tutorial from the
previous chapter. The file you obtained from that tutorial was a list of results of football
matches. These are sample rows of the final file:
Match Date;Home Team;Away Team;Result
02/06;Italy;France;2-1
02/06;Argentina;Hungary;2-1
06/06;Italy;Hungary;3-1
06/06;Argentina;France;2-1
10/06;France;Hungary;3-1
10/06;Italy;Argentina;1-0
...

Now you want to take that information to obtain some statistics such as the maximum
number of goals per match in a given day. To do it, follow these instructions:

Create a new transformation, give it a name and description, and save it.

By using a Text file input step, read the wcup_first_round.txt file you generated
in Chapter 2. Give the name and location of the file, check the Content tab to see that
everything matches your file, and fill the Fields tab.

Do a preview just to confirm that the step is well configured.

From the Transform category of step, select a Split Fields step, drag it to the work area,
and create a hop from the Text file input to this step.

Double-click the Split Fields steps and fill the grid like done in the following screenshot:

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Basic Data Manipulation

Add a Calculator step to the transformation and create a hop from the Split Fields step
to this step and edit the step to create the following new fields:

Add a Sort rows step to the transformation, create a hop from the Calculator step to this
step, and sort the fields by Match_Date.

Expand the Statistics category of steps, and drag a Group by step to the canvas. Create a
hop from the Sort rows step to this new step.

Edit the Group by step and fill the configuration window as shown next:

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Chapter 3

10.

When you click the OK button, a window appears to warn you that this step
needs the input to be sorted on the specified keys—the Range field in this case.
Click I understand, and don't worry because you already sorted the data in the
previous step.

11.

Add a final Dummy step.

12.

Select the Dummy and the Group by steps, left-click one and holding down the Shift
key, left-click the other.

13.

Click the Preview this transformation button. You will see the the following:

14.

Click Quick Launch. The following window appears:

15.
16.

Double-click the Sort rows step. A window appears with the data coming out of the Sort
rows step.

Double-click the Dummy step. A window appears with the data coming out of the
Dummy step.

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Basic Data Manipulation

17.

If you rearrange the preview windows, you can see both preview windows at a time, and
understand better what happened with the numbers. The following would be the data
shown in the windows:

What just happened?
You opened a file with results from several matches and got some statistics from it.
In the file, there was a column with the match result in the format n-m, with n being the
goals of the home team and m being the goals of the away team. With the Split Fields step,
you split this field in two—one with each of these two numbers.
With the Calculator you did two things:


You created a new field with the total number of goals for each match.



You created a description for the match.

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Chapter 3

Note that in order to create a description, you used the + operator to
concatenate string rather than add numbers.

After that, you ordered the data by match date with a Sort rows step.
In the preview window of the Sort rows step, you could see all the calculated fields: home
team goals, away team goals, match goals, and description.
Finally, you did some statistical calculations:


First, you grouped the rows by match date. You did this by typing Match_Date in the
upper grid of the Group by step.



Then, for every match date, you calculated some statistics. You did the calculations by
adding rows in the lower grid of the step, one for every statistic you needed.

Let's see how it works. Because the Group by step was preceded by a Sort rows step, the
rows came to the step already ordered. When the rows arrive to the Group by step, Kettle
creates groups based on the field(s) indicated in the upper grid—the Match_Date field in this
case. The following drawing shows this idea:

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Basic Data Manipulation

Then, for every group, the fields that you put in the lower grid are calculated. Let's see, for
example, the group for the match date 03/06. For the rows in this group, Kettle calculated
the following:


Matches: The number of matches played on 03/06. There were 4.



Sum of goals: The total number of goals converted on 03/06. There were 3+2+3+4=12.



Maximum: The maximum number of goals converted in a single match played on 03/06.
The maximum among 3, 2, 3, and 4 was 4.



Teams: The descriptions of the teams which played on 03/06, separated by ; : AustriaSpain; Sweden-Brazil; Netherlands-Iran; Peru-Scotland.

The same calculations were made for every group. You can verify the details by looking in the
preview window.
Look at the Step Metrics tab in the Execution Results area of the screen:

Note that 24 rows entered the Group by step and only 7 came out of that step towards the
Dummy step. That is because after the grouping, you no longer have the detail of matches.
The output of the Group by step is your new data now—one row for every group created.

Group by step
The Group by step allows you to create groups of rows and calculate new fields over
those groups.
In order to define the groups, you have to specify which field(s) are the keys. For every
combination of values for those fields, Kettle builds a new group.
In the tutorial you grouped by a single field Match_date. Then for every value of
Match_date, Kettle created a different group.

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Chapter 3

The Group by step operates on consecutive rows. Suppose that the rows are already sorted
by date, but those with date 10/06 are above the rest. The step traverses the dataset and
each time the value for any of the grouping field changes, it creates a new group. If you
see it this way, you will notice that the step will work even if the data is not sorted by the
grouping field.
As you probably don't know how the data is ordered, it is safer and
recommended that you sort the data by using a Sort rows step just
before using a Group by step.

Once you have defined the groups, you are free to specify new fields to be calculated
for every group. Every new field is defined as an aggregate function over some of the
existent fields.
Let's review some of the fields you created in the tutorial:




The Matches field is the result of applying the Number of values function over
the field Match_date.
The Sum of goals field is the result of applying the Sum function over the
field goals.
The Maximum field is the result of applying the Maximum function over the
field goals.

Finally, you have the option to calculate aggregate functions over the whole dataset. You do
this by leaving the upper grid blank. Following the same example, you could calculate the
total number of matches and the average number of goals for all those matches. This is how
you do it:

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Basic Data Manipulation

The following is what you get:

In any case, as a result of the Group by step, you will no longer have the detailed rows,
unless you check the Include all rows? checkbox.

Have a go hero – calculating statistics for the examinations
Here you have one more task related with the examinations file. Create a new
transformation, read the file, and calculate:


The number of students who passed



The number of students who failed



The average writing, reading, speaking, and listening grade obtained by students
who passed
The average writing, reading, speaking, and listening grade obtained by students
who failed



The minimum and maximum average grade among students who passed



The minimum and maximum average grade among students who failed
Use the Number range step to define the range of the average
grade; then use a Group by step to calculate the statistics.

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Chapter 3

Have a go hero – listing the languages spoken by country
Read the file with countries' information you used in Chapter 2. Build a file where each row
has two columns—the name of a country and the list of spoken languages in that country.

As aggregate, use the option Concatenate strings separated by.

Filtering
Until now you learned how to accomplish several kinds of calculations that enriched the set
of data. There is still another kind of operation that is frequently used, and does not have to
do with enriching the data but with discarding data. It is filtering unwanted data. Now you
will learn how to discard rows under given conditions.

Time for action – counting frequent words by filtering
Let's suppose, you have some plain text files, and you want to know what is said in them. You
don't want to read them, so you decide to count the times that words appear in the text, and
see the most frequent ones to get an idea of what the files are about.
Before starting, you'll need at least one text file to play with. The text file used in
this tutorial is named smcng10.txt and is available for you to download from
the Packt website.

Let's work:

Create a new transformation.

By using a Text file input step, read your file. The trick here is to put as a separator
a sign you are not expecting in the file, for example |. By doing so, the entire line
would be recognized as a single field. Configure the Fields tab by defining a single
string field named line.

From the Transform category of step, drag to the canvas a Split field to rows step,
and create a hop from Text file input step to this new step.

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Configure the step like this:

With this last step selected, do a preview. Your preview window should look like this:

Close the preview window.

Expand the Flow category of steps, and drag a Filter rows step to the work area.

Create a hop from the last step to the Filter rows step.

Edit the Filter rows step by double-clicking it.

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10.

Click the textbox to the left of the = sign. The list of fields appears.
Select word.

11.

Click the = sign. A list of operations appears. Select IS NOT NULL.

12.

The window looks like the following:

13.

Click OK.

14.

From the Transform category of steps drag a Sort rows step to the canvas, and
create a hop from the Filter rows step to this new step.

15.

Sort the rows by word.

16.

From the Statistics category, drag a Group by step, and create a hop from the Sort
rows step to this step.

17.

Configure the grids in the Group by configuration window like shown:

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18.

Add a Calculator step, create a hop from the last step to this, and calculate the new
field len_word representing the length of the words. For that, use the calculator
function Return the length of a string A and select word from the
drop-down menu for Field A.

19.

Expand the Flow category and drag another Filter rows step to the canvas.

20.

Create a hop from the Calculator step to this step and edit it.

21.

Click and select counter.

22.

Click the = sign, and select >.

23.

Click . A small window appears.

24.

In the Value textbox of the little window, enter 2.

25.

Click OK.

26.

Position the mouse cursor over the icon in the upper-right corner of the window.
When the text Add condition shows up, click on the icon.

27.

A new blank condition is shown below the one you created.

28.

Click on null = [] and create the condition len_word>3, in the same way you
created the condition counter>2.

29.

Click OK.

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30.

The final condition looks like this:

31.

Add one more Filter rows step to the transformation and create a hop from the last
step to this new step.

32.

On the left side of the condition, select word.

33.

As comparator select IN LIST.

34.

At the end of the condition, inside the textbox value, type the following:
a;an;and;the;that;this;there;these.

35.

Click the upper-left square above the condition and the word NOT will appear.

36.

The condition looks like the following:

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37.

Add a Sort rows step, create a hop from the previous step to this step, and sort the
rows in the descending order of counter.

38.

Add a Dummy step at the end of the transformation, create a hop from the last step
to the Dummy step.

39.

With the Dummy step selected, preview the transformation. The following is what
you should see now:

What just happened?
You read a regular plain file and arranged the words that appear in the file in some
particular fashion.
The first thing you did was to read the plain file and split the lines so that every word became
a new row in the dataset. Consider, for example, the following line:
subsidence; comparison with the Portillo chain.

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The splitting of this line resulted in the following rows being generated:

Thus, a new field named word became the basis for your transformation.
First of all, you discarded rows with null words. You did it by using a filter with the condition
word IS NOT NULL. Then, you counted the words by using the Group by step you learned
in the previous tutorial. Once you counted the words, you discarded those rows where the
word was too short (length less than 4) or too common (comparing to a list you typed).
Once you applied all those filters, you sorted the rows in the descending order of
the number of times the word appeared in the file so that you could see the most
frequent words.
Scrolling down a little the preview window to skip some prepositions, pronouns, and other
very common words that have nothing to do with a specific subject, you found words such
as shells, strata, formation, South, elevation, porphyritic, Valley, tertiary, calcareous, plain,
North, rocks, and so on. If you had to guess, you would say that this was a book or article
about geology, and you would be right. The text taken for this exercise was Geological
Observations on South America by Charles Darwin.

Filtering rows using the Filter rows step
The Filter rows step allows you to filter rows based on conditions and comparisons.
The step checks the condition for every row. Then it applies a filter letting pass only the rows
for which the condition is true. The other rows are lost.
In the counting words exercise, you used the Filter rows step several times so you already
have an idea of how it works. Let's review it.

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In the Filter rows setting window you have to enter a condition. The following table
summarizes the different kinds of conditions you may enter:
Condition

Description

Example
word IS NOT NULL

A single field followed by IS NULL or
IS NOT NULL

Checks whether the value of a
field in the stream is null

A field, a comparator, and a constant

Compares a field in the stream
against a constant value.

counter > 2

Two fields separated by a comparator

Compares two fields in the
stream

line CONTAINS
word

You can combine conditions as shown here:
counter > 2
AND
len_word>3

You can also create subconditions such as:
(
counter > 2
AND
len_word>3
)
OR
(word in list geology; sun)

In this last example, the condition lets the word geology pass even if it appears only once. It
also lets the word sun pass, despite its length.
When editing conditions, you always have a contextual menu which allows you to add and
delete sub-conditions, change the order of existent conditions, and more.
Maybe you wonder what the Send 'true' data to step: and Send 'false' data to step: textboxes
are for. Be patient, you will learn how to use them in Chapter 4.

Have a go hero – playing with filters
Now it is your turn to try filtering rows. Modify the counting_words transformation in the
following way:


Alter the Filter rows steps. By using a Formula step create a flag (a Boolean field)
that evaluates the different conditions (counter>2, and so on). Then use only one
Filter rows step that filters the rows for which the flag is true. Test it and verify that
the results are the same as before the change.
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In the Formula editing window, use the options under the Logic category.
Then in the Filter rows step, you can type true or Y as the value against which
you compare the flag.


Add a sub-condition to avoid excluding some words, just like the one in the example:
(word in list geology; sun). Change the list of words and test the filter to see
that the results are as expected.

Have a go hero – counting words and discarding those that are
commonly used
If you take a look at the results in the tutorial, you may notice that some words appear more
than once in the final list because of special signs such as . , ) or ", or because of lower
or upper case letters. For example, look how many times the word rock appears: rock (99
occurrences) - rock,(51 occurrences) – rock. (11 occurrences) – rock." (1 occurrence)
- rock: (6 occurrences) - rock; - (2 occurrences). You can fix this and make the word rock
appear only once: Before grouping the words, remove all extra signs and convert all words to
lower case or upper case, so they are grouped as expected.
Try one or more of the following steps: Formula, Calculator, Replace in string.

Looking up data
Until now, you have been working with a single stream of data. When you did calculations or
created conditions to compare fields, you only involved fields of your stream. Usually, this is
not enough, and you need data from other sources. In this section you will learn to look up
data outside your stream.

Time for action – finding out which language people speak
An International Musical Contest will take place and 24 countries will participate, each
presenting a duet. Your task is to hire interpreters so the contestants can communicate in
their native language. In order to do that, you need to find out the language they speak:

Create a new transformation.

By using a Get Data From XML step, read the countries.xml file that contains
information about countries that you used in Chapter 2.

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To avoid configuring the step again, you can open the transformation
that reads this file, copy the Get data from XML step, and paste it here.

Drag a Filter rows step to the canvas.

Create a hop from the Get data from XML step to the Filter rows step.

Edit the Filter rows step and create the condition- isofficial= T.

Click the Filter rows step and do a preview. The list of previewed rows will show the
countries along with the official languages:

Now let's create the main flow of data:

From the book website download the list of contestants. It looks like this:
ID;Country;Duet
1;Russia;Mikhail Davydova
;;Anastasia Davydova
2;Spain;Carmen Rodriguez
;;Francisco Delgado
3;Japan;Natsuki Harada
;;Emiko Suzuki
4;China;Lin Jiang
;;Wei Chiu
5;United States;Chelsea Thompson
;;Cassandra Sullivan
6;Canada;Mackenzie Martin
;;Nathan Gauthier
7;Italy;Giovanni Lombardi
;;Federica Lombardi

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In the same transformation, drag a Text file Input step to the canvas and read the
downloaded file.
The ID and country have values only in the first of the two
lines for each country. In order to repeat the values in the
second line use the flag Repeat in the Fields tab. Set it to Y.

Expand the Lookup category of steps.

10.

Drag a Stream lookup step to the canvas.

11.

Create a hop from the Text file input you just created, to the Stream lookup step.

12.

Create another hop from the Filter rows step to the Stream lookup step.

13.

Edit the Stream lookup step by double-clicking it.

14.

In the Lookup step drop-down list, select Filter official languages, the step that brings
the list of languages.

15.

Fill the grids in the configuration window as follows:

Note that Country Name is a field coming from the text file stream, while the country
field comes from the countries stream.

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16.

Click OK.

17.

The hop that goes from the Filter rows step to the Stream lookup step changes its look
and feel, to show that this is the stream where the Stream lookup is going to look:

18.

After the Stream lookup, add a Filter rows step.

19.

In the Filter rows step, type the condition language-IS NOT NULL.

20.

By using a Select values step, rename the fields Duet, Country Name and
language to Name, Country, and Language.

21.

Drag a Text file output step to the canvas and create the file
people_and_languages.txt with the selected fields.

22.

Save the transformation.

23.

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What just happened?
First of all, you read a file with information about countries and the languages spoken in
those countries.
Then you read a list of people along with the country they come from. For every row in this
list, you told Kettle to look for the country (Country Name field) in the countries stream
(country field), and to give you back a language and the percentage of people that speaks
that language (language and percentage fields). Let's explain it with a sample row: The
row for Francisco Delgado from Spain. When this row gets to the Stream lookup step,
Kettle looks in the list of countries for a row with the country Spain. It finds it. Then, it
returns the value of the columns language and percentage: Spanish and 74.4.
Now take another sample row—the row with the country Russia. When the row gets to the
Stream lookup step, Kettle looks for it in the list of countries, but it doesn't find it. So what
you get as language is a null string.
Whether the country is found or not, two new fields are added to your stream—language
and percentage.
After the Stream lookup step, you discarded the rows where language is null, that is, those
whose country wasn't found in the list of countries.
With the successful rows you generated an output file.

The Stream lookup step
The Stream lookup step allows you to look up data in a secondary stream.
You tell Kettle which of the incoming streams is the stream used to look up, by selecting the
right choice in the Lookup step list.
The upper grid in the configuration window allows you to specify the names of the fields that
are used to look up.

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In the left column, Field, you indicate the field of your main stream. You can fill this
column by using the Get Fields button, and deleting all the fields you don't want to use
for the search.
In the right column, Lookup Field, you indicate the field of the secondary stream.
When a row of data comes to the step, a lookup is made to see if there is a row in the
secondary stream for which, every pair (Field, LookupField) in the grid has the value of
Field equal to the value of LookupField. If there is one, the look up will be successful.

In the lower grid, you specify the names of the secondary stream fields that you want back
as a result of the look up. You can fill this column by using the Get lookup fields button, and
deleting all the fields you don't want to retrieve.
After the lookup, new fields are added to your dataset—one for every row of this grid.
For the rows for which the look up is successful, the values for the new fields will be taken
from the lookup stream.
For the others, the fields will remain null, unless you set a default value.

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When you use a Stream lookup, all lookup data is loaded into memory. Then the stream
lookup is made using a hash table algorithm. Even if you don't know how this algorithm
works, it is important that you know the implications of using this step:




First, if the data where you look is huge, you take the risk of running out
of memory.
Second, only one row is returned per key. If the key you are looking for is present
more than once in the lookup stream, only one will be returned—for example, in the
tutorial where there are more than one official languages spoken in a country, you
get just one. Sometimes you don't care, but on some occasions this is not acceptable
and you have to try some other methods. You'll learn other ways to do this later in
the book.

Have a go hero – counting words more precisely
The tutorial where you counted the words in a file worked pretty well, but you may have
noticed that it has some details you can fix or enhance.
You discarded a very small list of words, but there are much more that are quite usual
in English—prepositions, pronouns, auxiliary verbs, and many more. So here is the challenge:
Get a list of commonly used words and save it in a file. Instead of excluding words from a
small list as you did with a Filter rows step, exclude the words that are in your common
words file.
Use a Stream lookup step.
Test the transformation with the same file, and also with other files, and verify
that you get better results with all these changes.

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Summary
This chapter covered the simplest and most common ways of transforming data. Specifically,
it covered how to:


Use different transformation steps to calculate new fields



Use the Calculator and the Formula steps



Filter and sort data



Calculate statistics on groups of rows



Look up data

After learning basic manipulation of data, you may now create more complex
transformations, where the streams begin to split and merge. That is the core
subject of the next chapter.

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Controlling the Flow of Data
In the previous chapter, you learned the basics of transforming data. Basically
you read data from some file, did some transformation to the data, and sent
the data back to a different output. This is the simplest scenario. Think of
a different situation. Suppose you collect results from a survey. You receive
several files with the data and those files have different formats. You have to
merge those files somehow and generate a unified view of the information.
You also want to put aside the rows of data whose content is irrelevant. Finally,
based on the rows that interest you, you want to create another file with some
statistics. This kind of requirement is very common. In this chapter you will
learn how to implement it with PDI.

Splitting streams
Until now, you have been working with simple, straight flows of data. When you deal with
real problems, those simple flows are not enough. Many times, the rows of your dataset
have to take different paths. This situation is handled very easily, and you will learn how to
do it in this section.

Controlling the Flow of Data

Time for action – browsing new PDI features by copying
a dataset
Before starting, let's introduce the Pentaho BI Platform Tracking site. At the tracking site you
can see the current Pentaho roadmap and browse their issue tracking system. The PDI page
for that site is http://jira.pentaho.com/browse/PDI.
In this exercise, you will export the list of proposed new features for PDI from the site, and
generate detailed and summarized files from that information.

Access the main Pentaho tracking site page: http://jira.pentaho.com.

In the main menu, click on FIND ISSUES.

On the left side, select the following filters:



Project: Pentaho Data Integration {Kettle}



Issue Type: New Feature



Status: Open

At the bottom of the filter list, click View >>. A list of found issues will appear.

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Chapter 4

Above the list, select Current field to export the list to an Excel file.

Save the file to the folder of your choice.
The Excel file exported from the JIRA site is a Microsoft Excel 972003 Worksheet. PDI doesn't recognize this version of worksheets.
So, before proceeding, open the file with Excel or Calc and convert
it to Excel 97/2000/XP.

Create a transformation.

Read the file by using an Excel Input step. After selecting the file, click on the Sheets
tab, and fill it as shown in the next screenshot so that it skips the header rows and
the first column:

Click the Fields tab and fill the grid by clicking the Get fields from header
row... button.

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10.

Click the Preview rows just to be sure that you are reading the file properly. You
should see all the contents of the Excel file except the three heading lines.

11.

Click OK.

12.

Add a Filter rows step to drop the rows where the Summary field is null.

13.

After the Filter rows step, add a Value Mapper step and fill it like here:

14.

After the Value Mapper step, add a Sort rows step and order the rows by
priority_order (asc.), Summary (asc.).

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15.

After that add an Excel Output step, and configure it to send the priority_order
and Summary fields to an Excel file named new_features.xls.

16.

Drag a Group by step to the canvas.

17.

Create a new hop from the Sort rows step to the Group by step.

18.

A warning window appears asking you to decide whether you wish to Copy or
Distribute.

19.

Click Copy to send the rows toward both output steps.

20.

The hops leaving the Sort rows step change to show you the decision you made. So
far you have this:

21.

Configure the Group by steps like shown:

22.

Add a new Excel Output step to the canvas and create a hop from the Group by step
to this new step.

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Controlling the Flow of Data

23.

Configure the Excel Output step to send the Priority and Quantity fields to an
Excel file named new_features_summarized.xls.

24.

Save the transformation and run it.

25.

Verify that both files, new_features.xls and new_features_summarized.xls,
have been created.

26.

The first file should look like this:

27.

And the second file should look like this:

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What just happened?
After exporting an Excel file with the PDI new features from the JIRA site, you read the file and
created two Excel files—one with a list of the issues and the other with a summary of the list.
The first steps of the transformation are well known—read a file, filter null rows, map a field,
and sort.
Note that the mapping creates a new field to give an order to the Priority
field so that the more severe issues are first in the list, while the minor priorities
remain at the end of the list.

You linked the Sort rows step to two different steps. This caused PDI to ask you what to
do with the rows leaving the step. By answering Copy, you told PDI to create a copy of
the dataset. After that, two identical copies left the Sort rows step, each to a different
destination step.
From the moment you copied the dataset, those copies became independent, each following
its way. The first copy was sent to a detailed Excel file. The other copy was used to create a
summary of the fields, which then was sent to another Excel file.

Copying rows
At any place in a transformation, you may decide to split the main stream into two or more
streams. When you do so, you have to decide what to do with the data that leaves the last
step—copy or distribute.
To copy means that the whole dataset is copied to each of the destination steps. Once the
rows are sent to those steps, each follows its own way.
When you copy, the hops that leave the step from which you are copying change visually to
indicate the copy action.

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In the tutorial, you created two copies of the main dataset. You could have created more
than two, like in this example:

When you split the stream into two or more streams, you can do whatever you want with
each one as if they had never been the same. The transformations you apply to any of those
output streams will not modify the data in the others.
You shouldn't assume a particular order in the execution of the output
streams of a step. All the output streams receive the rows in synch and
you don't have control over the order in which they are executed.

Have a go hero – recalculating statistics
Do you remember the exercise from Chapter 3 where you calculated some statistics? You
created two transformations. One was to generate a file with students that failed. The other
was to create a file with some statistics such as average grade, number of students who
failed, and so.
Now you can do all that work in a single transformation, reading the file once.

Distributing rows
As said, when you split a stream, you can copy or distribute the rows. You already saw that
copy is about creating copies of the whole dataset and sending each of them to each output
stream. To distribute means the rows of the dataset are distributed among the destination
steps. Let's see how it works through a modified exercise.

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Time for action – assigning tasks by distributing
Let's suppose you want to distribute the issues among three programmers so that each of
them implements a subset of the new features.

Select Transformation | Copy transformation to clipboard in the main menu.
Close the transformation and select Transformation | Paste transformation from
clipboard. A new transformation is created identical to the one you copied. Change
the description and save the transformation under a different name.

Now delete all the steps after the Sort rows step.

Change the filter step to keep only the unassigned issues: Assignee field equal to
the string Unassigned. The condition looks like the next screenshot:

From the Transform category of steps, drag an Add sequence step to the canvas and
create a hop from the Sort rows step to this new step.

Double-click the Add sequence step and replace the content of the Name of value
textbox with nr.

Drag three Excel Output steps to the canvas.

Link the Add sequence step to one of these steps.

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Controlling the Flow of Data

Configure the Excel Output step to send the fields nr, Priority, and Summary to an Excel
file named f_costa.xls (the name of one of the programmers). The Fields tab should look
like this:

Create a hop from the Add sequence step to the second Excel Output step. When
asked to decide between Copy and Distribute, select Distribute.

Configure the step like before, but name the file as b_bouchard.xls
(the second programmer).

10.

Create a hop from the Add sequence step to the last Excel Output step.

11.

Configure this last step like before, but name the file as a_mercier.xls
(the last programmer).

12.

The transformation should look like the following:

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Chapter 4

13.

Run the transformation and look at the execution tab window to see
what happened:

14.

To see which rows belong to which of the created files, open any of them. It should
look like this:

What just happened?
You distributed the issues among three programmers.
In the execution window, you could see that 84 rows leave the Add sequence step, and 28
arrive to each of the Excel Output steps, that is, a third of the number of rows to each of
them. You verified that when you explored the Excel files.
In the transformation, you added an Add sequence step that did nothing more than adding
a sequential number to the rows. This sequence helps you recognize that one out of every
three rows were distributed to every file.
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Controlling the Flow of Data

Here you saw a practical example for the distributing option. When you distribute, the
destination steps receive the rows in turn. For example, if you have three target steps, the
first row goes to the first target step, the second row goes to the second step, the third row
goes to the third step, the fourth row now goes to the first step, and so on.
As you could see, when distributing, the hop leaving the step from which you distribute is
plain; it doesn't change its look and feel.
Despite this example showing clearly how the Distribute… method works, this is not how
you will regularly use this option. The Distribute… option is mainly used for performance
reasons. Throughout this book you will always use the Copy… option. To avoid being asked
for the action to take every time you create more that one hop leaving a step, you can set
the Copy… option as default; you do this by opening the PDI options window (Edit|Options
… from the main menu) and unchecking the option Show "copy or distribute" dialog?.
Remember that to see the change applied, you will have to restart Spoon.
Once you have changed this option, the default method is copying rows. If you want to
distribute rows, you can change the action by right-clicking the step from which you want
to copy or distribute, selecting Data Movement... in the contextual menu that appears, and
then selecting the desired option.

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Chapter 4

Pop quiz – data movement (copying and distributing)
Look at the following transformations:

If you do a preview on the Steps named Preview, which of the following is true:
a. The number of rows you see in (a) is greater or equal than the number of rows you
see in (b)
b. The number of rows you see in (b) is greater or equal than the number of rows you
see in (a)
c.

The dataset you see in (a) is exactly the same as you see in (b) no matter what data
you have in the Excel file.

You can create a transformation and test each option to check the results for yourself. To
be sure you understand correctly where and when the rows take one or other way, you can
preview every step in the transformation, not just the last one.

Splitting the stream based on conditions
In the previous section you learned to split the main stream of data into two or more
streams. The whole dataset was copied or distributed among the destination steps. Now
you will learn how to put conditions so that the rows take one way or another depending
on the conditions.

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Controlling the Flow of Data

Time for action – assigning tasks by filtering priorities with the
Filter rows step
Following with the JIRA subject, let's do a more realistic distribution of tasks among
programmers. Let's assign the serious task to our most experienced programmer,
and the remaining tasks to others.

Create a new transformation.

Read the JIRA file and filter the unassigned tasks, just as you did in the
previous tutorial.

Add a Filter rows step and two Excel Output steps to the canvas, and link them to
the other steps as follows:

Configure one of the Excel Output steps to send the fields, Priority and Summary,
to an Excel file named b_bouchard.xls (the name of the senior programmer).

Configure the other Excel Output step to send the fields Priority and Summary to
an Excel file named new_features_to_develop.xls.

6.
7.
8.

Double-click the Filter row step to edit it.

From the other drop-down list, Send 'false' data to step, select the step that creates
the Excel new_features_to_develop.xls Excel file.

10.

Click OK.

Enter the condition Priority = Critical OR Priority = Severe.
From the first drop-down list, Send 'true' data to step, select the step that creates
the b_bouchard.xls Excel file.

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11.

The hops leaving the Filter rows step change to show which way a row will take,
depending on the result of the condition.

12.
13.
14.

Save the transformation.
Run the transformation, and verify that the two Excel files were created.
The files should look like this:

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Controlling the Flow of Data

What just happened?
You sent the list of PDI new features to two Excel files—one file with the critical issues and
the other file with the rest of the issues.
In the Filter row step, you put a condition to evaluate if the priority of a task was severe
or critical. For every row coming to the filter, the condition was evaluated. The rows that
had a severe or critical priority were sent toward the Excel Output step that creates the
b_bouchard.xls file. The rows with another priority were sent towards the other Excel
Output step, the one that creates the new_features_to_develop.xls file.

PDI steps for splitting the stream based on conditions
When you have to make a decision, and upon that decision split the stream in two, you can
use the Filter row step as you did in this last exercise. In this case, the Filter rows step acts as a
decision maker. It has a condition and two possible destinations. For every row coming to the

filter, the step evaluates the condition. Then if the result of the condition is true, it decides
to send the row toward the step selected in the first drop-down list of the configuration
window—Send 'true' data to step.
If the result of the condition is false, it sends the row toward the step selected in the second
drop-down list of the configuration window: Send 'false' data to step.
Sometimes you have to make nested decisions; consider the next figure for example:

In the transformation shown in the preceding diagram, the conditions are as simple as testing
if a field is equal to a value. In situations like this you have a simpler way for accomplishing
the same..

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Time for action – assigning tasks by filtering priorities with the
Switch/ Case step
Let's use a Switch/Case step to replace the nested Filter Rows steps shown in the
preceding diagram

Create a transformation like the following:

You will find the Switch/Case step in the Flow category of steps.
To save time, you can take the last transformation you created
as the starting point.

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Controlling the Flow of Data

Note that the hops arriving to the Excel Output steps look strange. They are dotted
orange lines. This look and feel shows you that the target steps are unreachable. In
this case, it means that you still have to configure the Switch/Case step. Double-click
it and fill it like here:

Save the transformation and run it

Open the Excel files generated to see that the transformation distributed the task among
the files based on the given conditions.

What just happened?
In this tutorial you learned to use the Switch/Case step. This step routes rows of data to one
or more target steps based on the value encountered in a given field.
In the Switch/Case step configuration window, you told Kettle where to send the row
depending on a condition. The condition to evaluate was the equality of the field set in Field
name to switch and the value indicated in the grid. In this case, the field name to switch
is Priority, and the values against which it will be compared are the different values for
priorities: Severe, Critical, and so on. Depending on the values of the Priority field, the rows
will be sent to any of the target steps. For example, the rows where Priority=Medium, will be
sent toward the target step New Features for Federica Costa.
Note that it is possible to specify the same target step more than once.
The Default target step represents the step where the rows that don't match any of the case
values are sent. In this example, the rows with a priority not present in the list will be sent to
the step New Features without priority.
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Chapter 4

Have a go hero – listing languages and countries
Open the transformation you created in the Finding out which language people speak
tutorial in Chapter 3. If you run the transformation and check the content of the output file,
you'll notice that there are missing languages. Modify the transformation so that it generates
two files—one with the rows where there is a language, that is, the rows for which the
lookup didn't fail, and another file with the list of countries not found in the countries.
xml file.

Pop quiz – splitting a stream
Continuing with the contestant exercise, suppose that the number of interpreters you will
hire depends on the number of people that speak each language:
Number of people that speaks the language

Number of interpreters

Less than 3

Between 3 and 6

More that 6

You want to create a file with the languages with a single interpreter, another file with the
languages with two interpreters, and a final file with the languages with three interpreters.
Which of the following would solve your situation when it comes to splitting the languages
into three output streams:
a. A Number range step followed by a Switch/Case step.
b. A Switch/Case step.
c.

Both
In order to figure out the answer, create a transformation and count the number
of people that speak each language. You'll have to use a Sort rows step followed
by a Group by step. After that, try to develop each alternative solution and see
what happens.

Merging streams
You've just seen how the rows of a dataset can take different paths. Here you will learn the
opposite—how data coming from different places is merged into a single stream.

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Controlling the Flow of Data

Time for action – gathering progress and merging all together
Suppose that you delivered the Excel files you generated in the Assigning tasks by filtering
priorities tutorial earlier in the chapter. You gave the b_bouchard.xls to Benjamin
Bouchard, the senior programmer. You also gave the other Excel file to a project leader who
is going to assign the tasks to different programmers. Now they are giving you back the
worksheets, with a new column indicating the progress of the development. In the case of
the shared file, there is also a column with the name of the programmer who is working on
every issue. Your task is now to unify those sheets.
Here is what the Excel files look like:

Create a new transformation.

Drag an Excel Input step to the canvas and read one of the files.

Add a Filter row step to keep only the rows where the progress is not null, that is,
the rows belonging to tasks whose development has been started.

After the filter, add a Sort rows step, and configure it to order the fields by
Progress, in descending order.
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Chapter 4

Add another Excel Input step, read the other file, and filter and sort the rows just
like you did before. Your transformation should look like this:

From the Transform category of steps, select the Add Constants step and drag it
onto the canvas.

Link the step to the stream that reads the B. Bouchard's file; edit the step and add a
new field named Programmer, with type string and value Benjamin Bouchard.

After this step, add a Select values step and reorder the fields so that they remain
in a specific order Priority, Summary, Programmer, Progress—to resemble the
other stream.

Now, from the Transform category add an Add sequence step, name the new field
ID, and link the step with the Select values step.

10.

Create a hop from the Sort rows step of the other stream to the Add sequence step.
Your transformation should look like the one shown next:

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Controlling the Flow of Data

11.

Select the Add sequence step and do a preview. You will see this:

What just happened?
You read two similar Excel files and merged them into one single dataset.
First of all, you read, filtered, and sorted the files as usual. Then you altered the stream
belonging to B. Bouchard, so it looked similar to the other. You added the field Programmer,
and reordered the fields.
After that, you used an Add sequence step to create a single dataset containing the rows of
both streams, with the rows numbered.

PDI options for merging streams
You can create a union of two or more streams anywhere in your transformation. To create a
union of two or more data streams, you can use any step. The step unifies the data, takes the
incoming streams in any order, and then it completes its task in the same way as if the data
came from a single stream.
In the example, you used an Add sequence step as the step to join two streams. The step
gathered the rows from the two streams, and then proceeded to numerate the rows with
the sequence name ID.

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This is only one example of how you can mix streams together. As said, any step can be used
to unify two streams. Whichever the step, the most important thing you have to have in
mind is that you cannot mix rows that have a different layout. The rows have to have the
same lengths, the same data types, and the same fields in the same order.
Fortunately, there is a trap detector that provides warnings at design time if a step is
receiving mixed layouts.
You can try this out. Delete the Select values step. Create a hop from the Add constants step
to the Add sequence step. A warning message appears as shown next:

In this case, the third field of the first stream, Programmer (String), does not have the
same name or the same type as the third field of the second stream, Progress (Number).
Note that PDI warns you but it doesn't prevent you from mixing row layouts
when creating the transformation.
If you want Kettle to prevent you from running transformations with mixed row
layouts, you can check the option Enable safe mode in the window that shows
up when you dispatch the transformation. Have in mind that doing this will
cause a performance drop.

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Controlling the Flow of Data

When you use an arbitrary step to unify, the rows remain in the same order as they
were in their original stream, but the streams are joined in any order. Take a look at the
example's preview. The rows of the Bouchard's stream as well as the rows of the other
stream remained sorted within its original group. However, whether the Bouchard's stream
appeared before or after the rows of the other stream was just a matter of chance. You
didn't decide the order of the streams; PDI decided it for you. If you care about the order in
which the union is made, there are some steps that can help you. Here are the options
you have:
If you want to ...

You can do this ...

Append two or more streams, and
don't care about the order

Use any step. The selected step will take all the incoming
streams in any order, and then will proceed with its specific
task.

Append two streams in a given order

Use the Append streams step from the Flow category. It
helps to decide which stream goes first.

Merge two streams ordered by one or
more fields

Use a Sorted Merge step from the Joins category. This
step allows you to decide on which field(s) to order the
incoming rows before sending them to the destination
step(s). The input streams must be sorted on that field(s).

Merge two streams keeping the newest Use a Merge Rows (diff) step from the Joins category.
when there are duplicates
You tell PDI the key fields, that is, the fields that say that
a row is the same in both streams. You also give PDI the
fields to compare when the row is found in both streams.
PDI tries to match rows of both streams, based on the key
fields. Then it creates a field that will act as a flag, and fills
it as follows:
If a row was only found in the first stream, the

flag is set to deleted.


If a row was only found in the second stream, the
flag is set to new.



If the row was found in both streams, and the
fields to compare are the same, the flag is set to
identical.



If the row was found in both streams, and at least
one of the fields to compare is different, the flag
is set to changed.

Let's try one of these options.

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Time for action – giving priority to Bouchard by using
Append Stream
Suppose you want the Bouchard's row before the other rows. You can modify the
transformation as follows:

From the Flow category of steps, drag an Append Streams step to the canvas.
Rearrange the steps and hops so the transformation looks like this:

Edit the Append streams step and select as the Head hop the one belonging to the
Bouchard's rows, and as the Tail hop the other. Doing this, you indicate toPDI how it
has to order the streams.

Do a preview on the Add sequence step. You should see this:

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Controlling the Flow of Data

What just happened?
You changed the transformation to give priority to Bouchard's issues.
You made it by using the Append Streams step. By telling that the head hop was the one coming
from the Bouchard's file, you got the expected order—first the rows with the tasks assigned

to Bouchard, sorted by progress descending, and then the rows with the tasks assigned to
other programmers, also sorted by progress descending.
Whether you use arbitrary steps or some of the special steps mentioned
here to merge streams, don't forget to verify the layouts of the streams
you are merging. Pay attention to the warnings of the trap detector and
avoid mixing row layouts.

Have a go hero – sorting and merging all tasks
Modify the previous exercise so that the final output is sorted by priority. Try two possible
solutions:


Sort the input streams on their own and then use a Sorted Merge step.



Merge the stream with a Dummy step and then sort.

Which one do you think would give the best performance?
Refer to the Sort rows step issues in Chapter 3.

In which circumstances would you use the other option?

Have a go hero – trying to find missing countries
As you saw in the countries exercises, there are missing countries in the countries.xml
file. In fact, the countries are there, but with different names. For example, Russia in the
contestant file is Russian Federation in the XML file. Modify the transformation that
looks for the language. Split the stream in two—one for the rows where a language was
found and the other for the rows where no language was found. For this last stream, use a
Value Mapper step to rename the countries you identified as wrong, that is, rename Russia
as Russian Federation. Then look again for a language now with the new name. Finally,
merge the two streams and create the output file with the result.

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Chapter 4

Summary
In this chapter, you learned different options that PDI offers to combine or split flows of data.
The chapter covered the following:


Copying and distributing rows



Splitting streams based on conditions



Merging independent streams in different ways

With the concepts you learned in the initial chapters, the range of tasks you are able to
perform is already broad. In the next chapter, you will learn how to insert JavaScript code in
your transformations not only as an alternative to perform some of those tasks, but also as
a way to accomplish other tasks that are complicated or even unthinkable to carry out with
regular PDI steps.

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Transforming Your Data
with JavaScript Code and the
JavaScript Step
Whichever transformation you need to do on your data, you have a big chance
of finding that PDI steps are able to do the job. Despite that, it may happen that
there are not proper steps that serve your requirements, or that an apparently
minor transformation consumes a lot of steps linked in a very confusing
arrangement difficult to test or understand. Putting colorful icons here and
there is funny and practical, but there are some situations like the ones
described above where you inevitably will have to code. This chapter explains
how to do it with JavaScript and the special JavaScript step.

In this chapter you will learn how to:



Insert and test JavaScript code in your transformations
Distinguish situations where coding is the best option, from those where there are
better alternatives

Doing simple tasks with the JavaScript step
One of the traditional steps inside PDI is the JavaScript step that allows you to code inside
PDI. In this section you will learn how to use it for doing simple tasks.

Transforming Your Data with JavaScript Code and the JavaScript Step

Time for action – calculating scores with JavaScript
The International Musical Contest mentioned in Chapter 4 has already taken place. Each duet
performed twice. The first time technical skills were evaluated, while in the second, the focus
was on artistic performance.
Each performance was assessed by a panel of five judges who awarded a mark out of a
possible 10.
The following is the detailed list of scores:

Note that the fields don't fit in the screen, so the lines are wrapped and dotted lines are
added for you to distinguish each line.
Now you have to calculate, for each evaluated skill, the overall score as well as an
average score.

Download the sample file from the Packt website.

Create a transformation and drag a Fixed file input step to the canvas to read
the file.

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Chapter 5

Fill the configuration window as follows:

Press the Get Fields button. A window appears to help you define the columns.

Click between the fields to add markers that define the limits. The window will look
like this:

Click on Next >. A new window appears for you to configure the fields.

Click on the first field at the left of the window and change the name to Performance.
Verify that the type is set to String.

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Transforming Your Data with JavaScript Code and the JavaScript Step

To the right, you will see a preview of the data for the field.

Select each field to the left of the window, change the names, and adjust the types. Set
ID, Country, Duet, and Skill fields as String, and fields from Judge 1 to Judge
5 as Integer.

10.

Go back and forth between these two windows as many times as you need until you are
done with the definitions of the fields.

11.

Click on Finish.

12.

The grid at the bottom is now filled.

13.

Set the column Trim type to both for every field.

14.

The window should look like the following:

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Chapter 5

15.

Click on Preview the transformation. You should see this:

16.

From the Scripting category of steps, select a Modified JavaScript Value step and drag it
to the canvas.

17.

Link the step to the Fixed file input step, and double-click it to configure it.

18.

Most of the configuration window is blank, which is the editing area. Type the following
text in it:
var totalScore;
var wAverage;
totalScore = Judge1 + Judge2 + Judge3 + Judge4 + Judge5;
wAverage = 0.35 * Judge1 + 0.35 * Judge2
+ 0.10 * Judge3 + 0.10 * Judge4 + 0.10 * Judge5;

19.

Click on the Get variables button.

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Transforming Your Data with JavaScript Code and the JavaScript Step

20.

The grid under the editing area gets filled with the two variables defined in the code.
The window looks like this:

21.

Click on OK.

22.

Keep the JavaScript step selected and do a preview.

23.

This is how the final data looks like:

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Chapter 5

What just happened?
You read the detailed list of scores and added two fields with the overall score and an
average score for each evaluated skill.
In order to read the file, you used a step you hadn't used before—the Fixed file input step.
You configured the step with the help of a wizard. You could have also filled the field grid
manually if you wanted to.
After reading the file, you used a JavaScript step to create new fields. The code you typed
was pure JavaScript code. In this case, you typed a simple code to calculate the total score
and a weighted average combining the fields from Judge 1 to Judge 5.
Note that the average was defined by giving more weight, that is, more importance, to the
scores coming from Judge 1 and Judge 2.
For example, consider the first line of the file. This is how the new fields were calculated:
totalScore = Judge1 + Judge2 + Judge3 + Judge4 + Judge5 = 8+8+9+8+9
= 42
wAverage = 0.35*Judge1 + 0.35*Judge2+ 0.10*Judge3 + 0.10*Judge4 +
0.10*Judge5 = 0.35*8 + 0.35*8+ 0.10*8 + 0.10*8 + 0.10*8 = 8.2

In order to add these new fields to your dataset, you brought them to the grid at the bottom
of the window.
Note that this is not the only way to do calculations in PDI. All you did with the JavaScript
step can also be done with other steps.

Using the JavaScript language in PDI
JavaScript is a scripting language primarily used in website development. However, inside PDI
you use just the core language; you neither run a web browser nor do you care about HTML.
There are many available JavaScript engines. PDI uses the Rhino engine, from Mozilla. Rhino
is an open source implementation of the core JavaScript language; it doesn't contain objects
or methods related to manipulation of web pages. If you are interested in knowing more
about Rhino, you can visit https://developer.mozilla.org/en/Rhino_Overview.
The core language is not too different from other languages you might know. It has basic
statements, block statements (statements enclosed by curly brackets), conditional statements
(if..else and switch case), and loop statements ( for, do..while, and while). If you
are interested in the language itself, you can access a good JavaScript guide following this link:
https://developer.mozilla.org/En/Core_JavaScript_1.5_Guide.

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Transforming Your Data with JavaScript Code and the JavaScript Step

Besides the basics, an interesting feature included in the PDI implementation is E4X, a
programming language extension that allows you to manipulate XML objects inside JavaScript.
You can find an E4X tutorial as well as a reference manual at https://developer.
mozilla.org/En/E4X/Processing_XML_with_E4X.

Finally, there is a complete tutorial and reference at http://www.w3schools.com/
js/. Despite being quite oriented to web development, which is not your concern, it is clear,
complete, and has plenty of examples.

Inserting JavaScript code using the Modified Java Script
Value step
The Modified Java Script Value step (JavaScript step in short) allows you to insert JavaScript
code inside your transformation. The code you type here is executed once per row coming to
the step.
Let's explore its dialog window.

Most of the window is occupied by the editing area. It's there that you write JavaScript code
using the standard syntax of the language and the functions and fields from the tree
to the left of the window.
The Transform Functions branch of the tree contains a rich list of functions, ready to use.
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Chapter 5

The functions are grouped by category.


String, Numeric, Date, and Logic categories contain usual JavaScript functions.

This is not a full list of JavaScript functions. You are allowed to
use JavaScript functions even if they are not in this list.



The Special category contains a mix of utility functions. Most of them are not
JavaScript functions but Kettle functions. You will use some of them later in
this chapter.
Finally, the File category, as its name suggests, contains a list of functions that
do simple verifications or actions related to files and folders—for example,
fileExist() or createFolder().

To add a function to your script, simply double-click on it, and drag it to the location in your
script where you wish to use it, or just type it.

If you are not sure about how to use a particular function or what a
function does, just right-click on the function and select Sample. A new
script window appears with a description of the function and sample code
showing how to use it.

The Input fields branch contains the list of the fields coming from previous steps. To see and
use the value of a field for the current row, you need to double-click on it or drag it to the
code area. You can also type it by hand as you did in the tutorial.
When you use one of these fields in the code, it is treated as a JavaScript variable. As such,
the name of the field has to follow the conventions for a variable name—for example, it
cannot contain dots, nor can it start with non-character symbols.
As Kettle is quite permissive with names, you can have fields in your stream whose names
are not valid to be used inside JavaScript code.
If you intend to use a field with a name that doesn't follow the name rules,
rename it just before the JavaScript step with a Select values step. If you use
that field without renaming it, you will not be warned when coding, but you'll
get an error or unexpected results when you execute the transformation.

The Output fields is a list of the fields that will leave the step.

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Adding fields
At the bottom of the window, there is a grid where you put the fields you created in the
code. This is how you add a new field:
1. Define the field as a variable in the code—for example, var totalScore.
2. Fill the grid manually or by clicking the Get variables button. A new row will be filled
for every variable you defined in the code.
That was exactly what you did for the new fields, totalScore and wAverage.
In the JavaScript code you can create and use all variables you need without declaring them.
However, if you intend to add a variable as a field in your stream, the declaration with the
var sentence is mandatory.
The variables you define in the JavaScript code are not Kettle variables.
JavaScript variables are local to the step, and have nothing to do with the
Kettle variables you know.

Modifying fields
Instead of adding a field, you may want to change the value and eventually the data type of
an existent field. You can do that but not directly in the code.
Imagine that you wanted to change the field Skill, converting it to uppercase. To
accomplish this, double-click the JavaScript step and add the following two lines:
var uSkill;
uSkill = upper(Skill);

Add the new field to the grid at the bottom:

By renaming uSkill to Skill and setting the Replace value 'Fieldname' or 'Rename to' to
Y, the uSkill field is renamed to Skill and replaces the old Skill field.
Don't use the setValue() function to change existent fields. It may
cause problems and remains just for compatibility reasons.

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Chapter 5

Turning on the compatibility switch
In the JavaScript window, you might have seen the Compatibility mode checkbox. This
checkbox, unchecked by default, causes JavaScript to work like it did in version 2.5 of the
JavaScript engine. With that version, you could modify the values and their types directly in
the code, which allows mixing data types, thus causing many problems.
Old JavaScript programs run in compatibility mode. However, when creating new code,
you should make use of the new engine; that is, you should leave the compatibility mode
turned off.
Do not check the compatibility switch. Leaving it unchecked, you will have a
cleaner, faster, and safer code.

Have a go hero – adding and modifying fields to the contest data
Take the contest file as source and do the following:




Add a field named average. For the first performance, calculate the average as
a weighted average, just like you did in the tutorial. For the second performance,
calculate the field as a regular average, that is, the sum of the five scores divided
by five.
Modify the Performance field. Replace Duet 1st Performance and Duet 2nd
Performance by 1st and 2nd.

There is no single way to code this, but here you have a list of functions or sentences you can
use: if..then...else, indexOf(), substr()

Testing your code
After you type a script, you may want to test it. You can do it from inside the JavaScript
configuration window. Let's try it:

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Transforming Your Data with JavaScript Code and the JavaScript Step

Time for action – testing the calculation of averages
Let's test the code you've just created.

Double-click the JavaScript step.

Click on the Test script button.

A window appears to create a set of rows for testing. Fill it like here:

Click on Preview the transformation. A window appears showing five identical rows
with the provided sample values. Close the preview window.

Click on OK to test the code.
A window appears with the result that will appear when we execute the script with
the test data.

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Chapter 5

What just happened?
You tested the code of the JavaScript step.
You clicked on the Test script button, and created a dataset that served as the basis for
testing the script. You previewed the test dataset.
After that, you did the test itself. A window appeared showing you how the created dataset
looks like after the execution of the script—the totalScore and wAverage fields were
added, and the skill field was converted to uppercase.

Testing the script using the Test script button
The Test script button allows you to check that the script does what it is intended to do.
It actually generates a transformation in the back with two steps—a Generate Rows step
sending data to a copy of the JavaScript step. Just after clicking on the button, you are
allowed to fill the Generates Rows window with the test dataset.
The first thing that the test function does is to verify that the code is properly written; that is,
that there are no syntax errors in the code. Try deleting the last parenthesis in the code and
click on the Test script button. When you click OK to see the result of the execution, instead
of a dataset you will see an error window.
If the script is syntactically correct, what follows is the preview of the JavaScript for the
transformation in the back, that is, the JavaScript code applied to the test dataset.
If you don't see any error and the previewed data shows the expected results, you are
done. If not, you can check the code, fix it, and test it again until you see that the step
works properly.

Have a go hero – testing the new calculation of the average
Open the transformation of the previous Hero section, and test:


The weighted average code



The regular code
To test one or the other, simply change the test data. Don't
touch your code!

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Transforming Your Data with JavaScript Code and the JavaScript Step

Enriching the code
In the previous section, you learned how to insert code in your transformation by using
a JavaScript step. In this section, you will see how to use variables from outside to give
flexibility to your code. You also will learn how to take control of the rows from inside the
JavaScript step.

Time for action – calculating flexible scores by using variables
Suppose that by the time you are creating the transformation, the weights for calculating
the weighted average are unknown. You can modify the transformation by using parameters.
Let's do it:

Open the transformation of the previous section and save it with a new name.

Press Ctrl+T to open the Transformation properties dialog window.

Select the Parameters tab and fill it like here:

Replace the JavaScript step by a new one and double-click it.

Expand the Transform Scripts branch of the tree at the left of the window.

Right-click the script named Script 1, select Rename, and type main as the
new name.

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Chapter 5

Position the mouse cursor over the editing window and right-click to bring up the
following contextual menu:

Select Add new to add the script, which will execute before your main code.

A new script window appears. The script is added to the list of scripts under
Transform Scripts.

10.

Bring up the contextual menu again, but this time clicking on the title of the new script.

Select Set Start Script.

11.

Right-click the script in the tree list, and rename the new script as Start.

12.

In the editing area of the new script, type the following code to bring the
transformation parameters to the JavaScript code:
w1
w2
w3
w4
w5

=
=
=
=
=

str2num(getVariable('WEIGHT1',0));
str2num(getVariable('WEIGHT2',0));
str2num(getVariable('WEIGHT3',0));
str2num(getVariable('WEIGHT4',0));
str2num(getVariable('WEIGHT5',0));

writeToLog('Getting weights...');

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Transforming Your Data with JavaScript Code and the JavaScript Step

13.

Select the main script by clicking on its title and type the following code:
var wAverage;
wAverage = w1 * Judge1 + w2 * Judge2
+ w3 * Judge3 + w4 * Judge4 + w5 * Judge5;
writeToLog('row:' + getProcessCount('r') + ' wAverage:' +
num2str(wAverage));
if (wAverage >=7)
trans_Status = CONTINUE_TRANSFORMATION;
else
trans_Status = SKIP_TRANSFORMATION;

14.

Click Get variables to add the wAverage variable to the grid.

15.

Close the JavaScript window.

16.

With the JavaScript step selected, click on the Preview this transformation button.

17.

When the preview window appears, click on Configure.

18.

In the window that shows up, modify the parameters as follows:

19.

Click Launch.

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Chapter 5

20.

The preview window shows this data:

21.

The log window shows this:
...
2009/07/23
2009/07/23
2009/07/23
2009/07/23
2009/07/23
2009/07/23
...

14:46:54
14:46:54
14:46:54
14:46:54
14:46:54
14:46:54

wAverage
wAverage
wAverage
wAverage
wAverage
wAverage

with
with
with
with
with
with

Param..0
Param..0
Param..0
Param..0
Param..0
Param..0

Getting weights...
row:1 wAverage:8
row:2 wAverage:8
row:3 wAverage:7.5
row:4 wAverage:8
row:5 wAverage:7.5

What just happened?
You modified the code of the JavaScript step to use parameters.
First, you created four parameters for the transformation, containing the weights for
the calculation.
Then in the JavaScript step, you created a Start script to read the variables. That script
executed once, before the main script. Note that you didn't declare the variables. You could
have done it, but it's not mandatory unless you intend to add them as output fields.
In the main script, the script that is executed for every row, you typed the code to calculate
the average by using those variables instead of fixed numbers.
After the calculation of the average, you kept only the rows for which the average was
greater or equal to 7. You did it by setting the value of trans_Status to CONTINUE_
TRANSFORMATION for the rows you wanted to keep, and to SKIP_TRANSFORMATION
for the rows you wanted to discard.

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In the preview window, you could see that the average was calculated as a weighted
average of the scores you provided, and that only the rows with an average greater or
equal to 7 were kept.

Using named parameters
The parameters that you put in the transformation dialog window are called named
parameters. They can be used through the transformation as regular variables, as if you
had created them before—for example, in the kettle.properties file.
From the point of view of the transformation, the main difference between
variables defined in the kettle.properties file and named parameters is
that the named parameters have a default value that can be changed at the time
you run the transformation.

In this case, the default values for the variables defined as named parameters WEIGHT1 to
WEIGHT5 were 0.35, 0.35, 0.10, 0.10, and 0.10—the same that you had used in previous
exercises. But when you executed, you changed the default and used 0.50, 0.50, 0, 0, and 0
instead. This caused the formula for calculating the weighted average to work as an average
of the first two scores. Take, for example, the numbers for the first row of the file. Consider
the following code line:
wAverage = w1 * Judge1 + w2 * Judge2 + w3 * Judge3 + w4 * Judge4 + w5
* Judge5;

It was calculated as:
wAverage = 0.50 * 8 + 0.50 * 8 + 0 * 9 + 0 * 8 + 0 * 9;

giving a weighted average equal to 8.
Note that the named parameters are ready to use through the transformation as regular
variables. You can see and use them at any place where the icon with the dollar sign
is present.
If you want to use a named parameter or any other Kettle variable such as LABSINPUT or
java.io.tmpdir inside the JavaScript code, you have to use the getVariable() function
as you did in the Start script.
When you run the transformation from the command line, you also have the possibility to
specify values for named parameters. For details about this, check Appendix B.

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Chapter 5

Using the special Start, Main, and End scripts
The JavaScript step allows you to create multiple scripts. The Transformation Script list
displays a list with all scripts of the step.
In the tutorial, you added a special script named Start and used it to read the variables.
The Start Script is a script that executes only once, before the execution of the main script
you already know.
The Main script, the script that is created by default, executes for every row. As this script
is executed after the start script, all variables defined in the main script there are accessible
here. As an example of this, in the tutorial you used the start script to set values for the
variables w1 through w5. Then in the main script you used those variables.
It is also possible to have an End Script that executes at the end of the execution of the step,
that is, after the main script has been executed for all rows.
When you create a Start or an End script, don't forget to give it a name so
that you can recognize it. If you don't, you may get confused because nothing in
the step shows you the type of the scripts.

Beyond main, start, and end scripts, you can use extra scripts to avoid overloading the main
script with code. The code in the extra scripts will be available after the execution of the
special function LoadScriptFromTab().
Note that in the exercises, you wrote some text to the log by using the writeToLog()
function. That had the only purpose of showing you that the start script executed at the
beginning and the main script executed for every row. You can see this sequence in the
execution log.

Using transformation predefined constants
In the tree to the left-hand side of the JavaScript window, under Transformation Constants,
you have a list of predefined constants. You can use those constants to change the value of
the predefined variable, trans_Status, such as:
trans_Status = SKIP_TRANSFORMATION

Here is how it works:
Value of the trans_Status variable
SKIP_TRANSFORMATION
CONTINUE_TRANSFORMATION
ERROR_TRANSFORMATION

Effect on the current row
The current row is removed from the dataset
The current row is retained
The current row causes abortion of the transformation

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Transforming Your Data with JavaScript Code and the JavaScript Step

In other words, you can use that constant to control what will happen to the rows. In the
exercise you put:
if (wAverage >=7)
trans_Status = CONTINUE_TRANSFORMATION;
else
trans_Status = SKIP_TRANSFORMATION;

This means a row where the average is greater than or equal to 7 will continue its way to the
following steps. On the contrary, a row with a lower average will be discarded.

Pop quiz – finding the 7 errors
Look at the following screenshot:

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Chapter 5

Does it look good? Well, it is not. There are seven errors in it. Can you find them?

Have a go hero – keeping the top 10 performances
Modify the last tutorial. By using a JavaScript step, keep the top 10 performances, that is,
the 10 performances with the best average.
Sort the data using a regular Sort rows step. Give the
getProcessCount() function a try.

Have a go hero – calculating scores with Java code
If you are a Java programmer, or just curious, you will like to know that you can access
Java libraries from inside the JavaScript step. On the book site there is a jar file named
pdi_chapter_5.jar. The jar file contains a class with two methods—w_average()
and r_average(), for calculating a weighted average and a regular average.
Here is what you have to do:
1. Download the file from Packt's site, copy it to the libext folder inside the PDI
installation folder, and restart Spoon.
2. Replace the JavaScript calculation of the averages by a call to one of these
methods. You'll have to specify the complete name of the class. Consider the
next line for example:
wAverage = Packages.Averages.w_average(Judge1, Judge2, Judge3,
Judge4, Judge5);

3. Preview the transformation and verify that it works properly.
The Java file is available as well. You can change it by adding new methods and trying them
from PDI.
Likewise, you can try using any Java objects, as long as they are in PDI's classpath. Don't
forget to type the complete name as in the following examples:
java.lang.Character.isDigit(c);
var my_date = new java.util.Date();
var val = Math.floor(Math.random()*100);

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Transforming Your Data with JavaScript Code and the JavaScript Step

Reading and parsing unstructured files
It is marvelous to have input files where the information is well formed; that is, the number
of columns and the type of its data is precise, all rows follow the same pattern, and so on.
However, it is common to find input files where the information has little or no structure, or
the structure doesn't follow the matrix (n rows by m columns) you expect. In this section you
will learn how to deal with such files.

Time for action – changing a list of house descriptions with
JavaScript
You won the lottery and decided to invest the money in a new house. You asked a real-estate
agency for a list of candidate houses for you and it gave you this:
...
Property Code: MCX-011
Status: Active
5 bedrooms
5 baths
Style: Contemporary
Basement
Laundry room
Fireplace
2 car garage
Central air conditioning
More Features: Attic, Clothes dryer, Clothes washer, Dishwasher
Property Code: MCX-012
4 bedrooms
3 baths
Fireplace
Attached parking
More Features: Alarm System, Eat in Kitchen, Powder Room
Property Code: MCX-013
3 bedrooms
...

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Chapter 5

You want to compare the properties before visiting them, but you're finding it hard to do so
because the file doesn't have a precise structure. Fortunately, you have the JavaScript step,
which will help you to give the file some structure.

Create a new transformation.

Get the sample file from Packt site and read it with a Text file input step. Uncheck
the Header checkbox and create a single field named text.

Do a preview. You should see the content of the file under a single column named
text. Add a JavaScript step after the input step and double-click it to edit it.

In the editing area, type the following JavaScript code to create a field with the code
of the property:
var prop_code;
posCod = indexOf(text,'Property Code:');
if (posCod>=0)
prop_code = trim(substr(text,posCod+15));

Click Get variables to add the prop_code variable to the grid under the code.

Click OK.

With the JavaScript step selected, do a preview. You should see this:

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Transforming Your Data with JavaScript Code and the JavaScript Step

What just happened?
You read a file where each house was described in several rows. You added to every row
the code of the house to which that row belonged. In order to obtain the property code,
you identified the lines with a code, and then you cut the Property Code: text with the
substr function and discarded the leading spaces with trim.

Looking at previous rows
The code you wrote may seem a little strange at the beginning, but it is not. It
creates a variable named prod_code, which will be used to create a new field to
identify the properties. When the JavaScript code detects a property header row such
as Property Code: MCX-002, it sets the variable prop_code to the code it finds
in that line—MCX – 002 in this case.
Until a new header row appears, the prop_code variable keeps that value. Thus all the
rows following a row like the one shown above will have the same value for the variable
prop_code.
The variable is then used to create a new field, which will contain for every row, the code for
the house to which it belongs.
This is an example of when you can keep values from previous rows to be used in the
current row.
Note that here you use JavaScript to see and use values from previous rows, but
you can't modify them! JavaScript always works on the current row.

Have a go hero – enhancing the houses file
Modify the exercise from the tutorial by doing the following:
1. After keeping the property code, discard the rows that headed each property
description.
2. Create two new fields named feature and description. Fill the feature field
with the feature described in the row (Exterior construction) and the description
field with the description of that feature (Brick). If you think that is not worth
keeping some features (Living Room), you may discard some rows. Discard also the
original field text. Here you have a sample house description showing a possible
output after the changes:

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Chapter 5
prop_code; Feature; Description
MCX-023;bedrooms;4
MCX-023;baths;4
MCX-023;Style;Colonial
MCX-023;family room;yes
MCX-023;basement;yes
MCX-023;fireplace;yes
MCX-023;Heating features;Hot Water Heater
MCX-023;Central air conditioning present;yes
MCX-023;Exterior construction;Stucco
MCX-023;Waterview;yes
MCX-023;More Features;Attic, Living/Dining Room, Eat-In-Kitchen

Have a go hero – fill gaps in the contest file
Take a look at the contest file. Each performance occupies two rows, one showing each
evaluated skill. The name of the country appeared only in the first row.
Open the first version of the contest transformation and modify it to fill the column Country
where it is blank.

Avoiding coding by using purpose-built steps
You saw through the exercises how powerful the JavaScript step is for helping you in your
transformations. In older versions of PDI, coding JavaScript was the only means you had for
doing specific tasks. In the latest releases of PDI, actual steps appeared that eliminate the
need for coding in many cases. Here you have some examples of that:




Formula: You saw it in Chapter 3. Before the appearance of this step,
there were a lot of functions such as the text functions that you could only
solve with JavaScript.
Analytic Query: This step offers a way to retrieve information from rows before or
after the current.
Split field to rows: The step is used to create several rows from a single string value.
You used this step in Chapter 3 to create a new row for each word found in a file.

Analytic Query and Split fields to row are examples of where not only the need for coding
was eliminated, they also eliminated the need for accessing internal objects and functions
such as Clone() or putRow() that you probably saw in old sample code or when browsing
the PDI forum. The use of those objects and functions can lead to odd behavior and data
corruption, and so their use is strongly discouraged.

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Transforming Your Data with JavaScript Code and the JavaScript Step

Despite the appearance of new steps, you still have the choice to do the tasks with code.
In fact, quite a lot of tasks you do with regular PDI steps may also be done with JavaScript,
by using the JavaScript step. This is a temptation to programmers who end up with
transformations having plenty of JavaScript steps.
Whenever there is a step that does what you want to do, you should
prefer that step to coding.

Why should you prefer to use a specific step rather than code? Here are some reasons:




To code takes more time to develop. You don't have to waste your time coding if
there are steps that can solve your problem.
Code is hard to maintain. If you have to modify or fix a transformation, it will be
much easier to tackle the change if the transformation is a bunch of colorful steps
with meaningful names than if the transformation consists of just a couple of
JavaScript icons.
A bunch of icons is self documented. A JavaScript step is like Pandora's box. Until
you open it, you don't know exactly what it does, or whether it contains just a line of
code or thousands.
JavaScript is inherently slow. Faster alternatives for simple expressions are the User
Defined Java Expression and Calculator steps. They are typically more than twice
as fast. The next PDI release will feature a User Defined Java Class step. One of the
purposes of this step, intended to be used by Java developers, is to overcome the
drawbacks of JavaScript.

On the contrary, there are situations where you may prefer or have to use JavaScript. Let's
enumerate some of them:


To handle unstructured input data



For accessing Java libraries



When you need to use a function provided by the JavaScript language that is not
provided by any of the regular PDI steps
When the JavaScript code saves a lot of regular PDI steps (as well as screen space),
and you think it is not worth showing the details of what those steps do

In the end, it is up to you to choose one or the other option. The following exercise will help
you a little in the recognition of pros and cons.

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Chapter 5

Have a go hero – creating alternative solutions
Redo the following Hero exercises you did in this chapter:





Adding and modifying fields to the contest data
Keeping the top 10 performances
Enhancing the houses file
Filling gaps in the contest file

Do these exercises without using JavaScript when possible. In each case, compare both
versions, having in mind the following:







Time to develop
Maintenance
Documentation
Capability to handle unstructured data
Number of steps required
Performance

Decide which option you would choose if you had to decide.
To keep the 10 first performances, use an Add Sequence step.
To fill the gaps, use an Analytic Query step.

Summary
In this chapter, you learned to code JavaScript into PDI. Specifically, you learned:





What the JavaScript step is and how to use it
How to modify fields and add new fields to your dataset from inside your
JavaScript step
How to deal with unstructured input data

You also considered the pros and cons of coding JavaScript inside your transformations, as
well as alternative ways to do things, avoiding writing code when possible.
As a bonus, you learned the concept of named parameters.
If you feel confident with all you've learned until now, you are certainly ready to move on to
the next chapter, where you will learn in a simple fashion how to solve some sophisticated
problems such as normalizing data from pivot tables.
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Transforming the Row Set
So far, you have been working with simple datasets, that is, datasets where
the each row represented a different entity (for example a student) and each
column represented a different attribute for that entity (for example student
name). There are occasions when your dataset doesn’t resemble such a simple
format, and working with it as is, may be complicate or even impossible. In
other occasions your data simply does not have the structure you like or the
structure you need.

Whichever your situation, you have to transform the dataset in an appropriate format and
the solution is not always about changing or adding fields, or about filtering or adding rows.
Sometimes it has to do with twisting the whole dataset. In this chapter you will learn how to:


Convert rows to columns



Convert columns to rows



Operate on sets of rows

You will also be introduced to a core subject in data warehousing: Time dimensions.

Converting rows to columns
In most datasets each row belongs to a different element such as a different match or
a different student. However, there are datasets where a single row doesn't completely
describe one element. Take, for example, the real-estate file from Chapter 5. Every
house was described through several rows. A single row gave incomplete information
about the house. The ideal situation would be one in which all the attributes for the
house were in a single row. With PDI you can convert the data into this alternative
format. You will learn how to do it in this section.

Transforming the Row Set

Time for action – enhancing a films file by converting
rows to columns
In this tutorial we will work with a file that contains list of all French movies ever made. Each
movie is described through several rows. This is how it looks like:
...
Caché
Year: 2005
Director:Michael Haneke
Cast: Daniel Auteuil, Juliette Binoche, Maurice Bénichou
Jean de Florette
Year: 1986
Genre: Historical drama
Director: Claude Berri
Produced by: Pierre Grunstein
Cast: Yves Montand, Gérard Depardieu, Daniel Auteuil
Le Ballon rouge
Year: 1956
Genre: Fantasy | Comedy | Drama
...

In order to process the information of the file, it would be better if the rows belonging to
each movie were merged into a single row. Let's work on that.

Download the file from the Packt website.

Create a transformation and read the file with a Text file input step.

In the Content tab of the Text file input step put : as separator. Also uncheck the
Header and the No empty rows options.

In the Fields tab enter two string fields—feature and description. Do a preview of
the input file to see if it is well configured. You should see two columns—feature with
the texts to the left of the semicolons, and description with the text to the right of
the semicolons.

Add a JavaScript step and type the following code that will create the film field:
var film;
if (getProcessCount('r') == 1) film = '';
if (feature == null)
film = '';
else if (film == '')
film = feature;

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Chapter 6

Click on the Get variables button to add to the dataset the field film.

Add a Filter rows step with the condition description IS NOT NULL.

With the Filter rows step selected, do a preview. This is what you should see:

After the filter step, add a Row denormalizer step. You can find it under the
Transform category.

10.

Double-click the step and fill it like here:

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Transforming the Row Set

11.

From the Utility category select an If field value is null step.

12.

Double-click it , check the Select fields option, and fill the Fields grid as follows:

13.

With this last step selected, do a preview. You will see this:

What just happened?
You read a file with a selection of films in which each film was described through
several rows.
First of all, you created a new field with the name of the film by using a small piece of
JavaScript code. If you look at the code, you will note that the empty rows are key for
calculating the new field. They are used in order to distinguish between one film and the
next and that is the reason for unchecking the No empty rows option. When the code
executes for an empty row, it sets the film to an empty value. Then, when it executes for the
first line of a film (film == '' in the code), it sets the new value for the film field. When
the code executes for other lines, it does nothing but the film already has the right value.
After that, you used a Row denormalizer step to translate the description of films from rows
to columns, so the final dataset had a single row by film.
Finally, you used a new step to replace some null fields with the text n/a.

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Converting row data to column data by using the Row
denormalizer step
The Row denormaliser step converts the incoming dataset into a new dataset by moving
information from rows to columns according to the values of a key field.
To understand how the Row denormaliser works, let's do a sketch of the desired
final dataset:
FILM YEAR GENRE DIRECTOR ACTORS

1 film
by row

Here, a film is described by using a single row. On the contrary, in your input file the
description for every film was spread over several rows.
To tell PDI how to combine a group of rows into a single one, there are three things you have
to think about:




Among the input fields there must be a key field. Depending on the value of that key
field, you decide how the new fields will be filled. In your example, the key field is
feature. Depending on the value of the column feature, you will send the value
of the field description to some of the new fields: Year, Genres, Director,
or Actors.
You have to decide which field or fields make up the groups of rows. In our example,
that field is film. All rows with the same value for the field film make up a
different group.
Decide the rules that have to be applied in order to fill the new target fields. All rules
follow this pattern:




If the value for the key field is equal to A, then put the value of the
field B into the new field C.
A sample rule could be: If the value for the field feature
(our key field) is equal to Directed by, put the value of the
field description into the new field Director.

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Transforming the Row Set

Once you are clear about these three things, all you have to do is fill the Row denormaliser
configuration window to tell PDI how to do this task.

1. Fill the key field textbox with the name of the key field. In the example, the field
is feature.
2. Fill the upper grid with the fields that make up the grouping. In this case, it is film.
The dataset must be sorted on the grouping fields. If not, you will
get unexpected results.

3. Finally, fill the lower grid. This grid contains the rules for the new fields. Fill it
following this example:
To add this rule ...
If the value for the key field is equal to A, put the
value of the field B into the new field C.

Fill a row like this ...
Key value: A
Value fieldname: B
Target fieldname: C

This is how you fill the row for the sample rule:
If the value for the field feature (our key field) is Key value: Directed by
equal to 'Directed by,' put the value of the field Value fieldname: description
description into the new field Director.
Target fieldname: Director

For every rule you must fill a different row in the target fields' grid.
Let's see how the Row denormalizer works for the following sample rows:

PDI creates an output row for the film Manon Des Sources. Then it processes every row
looking for values to fill the new fields.

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Chapter 6

Let's take the first row. The value for the key field feature is Directed by. PDI searches
in the target fields' grid to see if there is an entry where the Key value is Directed by; it
finds it.

Then it puts the value of the field description as the content for the target field
Director. The output row is now like this:

Now take the second row. The value for the key field feature is 'Produced by.'
PDI searches in the target fields' grid to see if there is an entry where the Key value is
Produced by. It cannot find it, and the information for this row is lost.

The following screenshot shows the rule applied to the third sample row. It also shows how
the final output row looks like:

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Transforming the Row Set

Note that the presence of rows is not mandatory for every key value entered in the target
fields' grid. If an entry in the grid is not used, the target field is created anyway but it
remains empty.
In this sample film, the year was not present. Then the field Year remained empty.

Have a go hero – houses revisited
Take the output file for the Hero exercise to enhance the houses file from the previous
chapter. You can also download the sample file from the Packt site. Create a transformation
that reads that file and generates the following output:

Aggregating data with a Row denormalizer step
In the previous section, you learned how to use the Row denormalizer step to combine
several rows into one. The Row denormalizer step can also be used to take as input a dataset
and generate as output a new dataset with aggregated or consolidated data. Let's see it with
an example.

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Chapter 6

Time for action – calculating total scores by performances
by country
Let's work now with the contest file from Chapter 5. You will need the output file for the
Hero exercise. Fill gaps in the contest file from that chapter. If you don't have it, you can
download it from the Packt website.
In this tutorial, we will calculate the total score for each performance by country.

Create a new transformation.

Read the file with a Text file input step and do a preview to see that the step is well
configured. You should see this:

With a Select values step, keep only the following columns: Country, Performance,
and totalScore.

With a Sort Rows step sort the data by Country ascendant.

After the Sort Rows step, put a Row denormalizer step.

Double-click this last step to configure it.

As the key field put Performance, and as group fields put Country.

Fill the target fields' grid like shown:

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Transforming the Row Set

Close the window.

10.

With the Row denormalizer step selected, do a preview. You will see this:

What just happened?
You read the contest file, grouped the data by country, and then created a new column
for every performance. As values for those new fields you put the sum of the scores by
performance and by country.

Using Row denormalizer for aggregating data
The purpose for which you used the Row denormaliser step in this tutorial was different
from the purpose in the previous tutorial. In this case, you put the countries in rows, the
performances in columns, and in the cells you put sums. The final dataset was kind of a cross
tab like those you create with the DataPilot tool in Open Office, or the Pivot in Excel. The
big difference is that here the final dataset is not interactive because, in essence, PDI is not.
Another difference is that here you have to know the names or elements for the columns
in advance.
Let's explain how the Row denormalizer step works in these cases. Basically, the way it
works is quite the same as before:
The step groups the rows by the grouping fields and creates a new output row for
each group.
The novelty here is the aggregation of values. When more than one row in the group
matches the value for the key field, PDI calculates the new output field as the result of
applying an aggregate function to all the values. The aggregate functions available are the
same you already saw when you learned the Group by step—sum, minimum, first value,
and so on. Take a look at the following sample rows:
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Chapter 6

The first two rows had 1st as the value for the key field Performance. According to the rule
of the Row denormaliser step, the values for the field totalScore of these two rows go to
the new target field score_1st_performance. As the rule applies for two rows, the values
for those rows have to be added, as Sum was the selected aggregation function.
So, the output data for this sample group is this:

The value for the new field score_1st_performance is 77 and is the sum of 38 and 39,
the values of the field totalScore for the input rows where Performance was "1st."
Please note the difference between the Row denormaliser and the Group
by step for aggregating. With the Row denormaliser step, you generate
another new field for each interesting key value. Using the Group by step
for the tutorial, you couldn't have created the two columns shown in the
preceding screenshot—score_1st_performance and score_2nd_
performance.

Have a go hero – calculating scores by skill by continent
Create a new transformation. Read the contest file and generate the following output:

To get the continent for each country, download the countries.txt file from the Packt
website and get the information with a Stream lookup step.

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Transforming the Row Set

Normalizing data
Some datasets are nice to see but complicate to process further. Take a look at the matches
file we saw in Chapter 3:
Match Date;Home Team;Away Team;Result
02/06;Italy;France;2-1
02/06;Argentina;Hungary;2-1
06/06;Italy;Hungary;3-1
06/06;Argentina;France;2-1
10/06;France;Hungary;3-1
10/06;Italy;Argentina;1-0
...

Imagine you want to answer these questions:
1. How many teams played?
2. Which team converted most goals?
3. Which team won all matches it played?
The dataset is not prepared to answer those questions, at least in an easy way. If you want
to answer those questions in a simple way, you will first have to normalize the data, that is,
convert it to a suitable format before proceeding. Let's work on it.

Time for action – enhancing the matches file by normalizing
the dataset
Now you will convert the matches file you generated in Chapter 2 to a format suitable for
answering the proposed questions.

Search on your disk for the file you created in Chapter 2, or download it from the
Packt website.

Create a new transformation and read the file by using a Text file input step.

With a Split Fields step, split the Result field in two: home_t_goals and
away_t_goals. (Do you remember having done this in chapter 3?)

From the Transform category of steps, drag a Row Normalizer step to the canvas.

Create a hop from the last step to this new one.

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Chapter 6

Double-click the Row Normalizer step to edit it and fill the window as follows:

With the Row Normalizer selected, do a preview. You should see this:

What just happened?
You read the matches file and converted the dataset to a new one where both the home
team and the away team appeared under a new column named team, together with another
new column named goals holding the goals converted by each team. With this new format,
it is really easy now to answer the questions proposed at the beginning of the section.

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Modifying the dataset with a Row Normalizer step
The Row Normalizer step modifies your dataset, so it becomes more suitable for processing.
Usually this involves transforming columns into rows.
To understand how it works, let's take as example the file from the tutorial. Here is a sketch
of what we want to have at the end:

1st

tch

tc
Ma

MATCH DATE

TEAM

GOALS

02/06

Italy

Home Team

02/06

France

Away Team

02/06

Argentina

Home Team

02/06

Hungary

Away Team

...

Goals

Away Team

What we have now is this:
Match Date Home Team

Goals

1st Match

02/06

Italy

France

2nd Match

02/06

Argentina

Hungary

...

Now it is just a matter of creating a correspondence between the old columns and the
new ones.

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Just follow these steps and you have the work done:
Step

Example

Identify the new desired fields. Give them a name. team, goals.
Look at the old fields and identify which ones you
want to translate to the new fields.

Home_Team, home_t_goals, Away_Team,
away_t_goals.

From that list, identify the columns you want to
keep together in the same row, creating a sort
of classification of the fields. Give each group a
name. Also, give a name to the classification.

You want to keep together the fields Home_
Team and home_t_goals. So, you create a
group with those fields, and name it home.
Likewise, you create a group named away with
the fields Away_Team and away_t_goals.
Name the classification as class.

Define a correspondence between the fields
identified above, and the new fields.

The old field Home_Team goes to the new
field team.
The old field home_t_goals goes to the new
field goals.
The old field Away_Team goes to the new
field team.
The old field away_t_goals goes to the new
field goals.

Transcript all these definitions to the Row Normalizer configuration window as shown below:

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In the fields grid, insert one row for each of the fields you want to normalize.
Once you normalize, you have a new dataset where the fields for the groups you defined
were converted to rows.
The number of rows in the new dataset is equal to the number of groups defined by the
number of rows in the old dataset. In the tutorial, the final number is 24 rows x 2
groups = 48 rows.
Note that the fields not mentioned in the configuration of the Row Normalizer (Match_Date
field in the example) are kept without changes. They are simply duplicated for each new row.
In the tutorial, every group was made by two fields: Home_Team and home_t_goals for the
first group, and Away_Team and away_t_goals for the second. When you normalize, a group
may have just one field, two fields (as in this example), or more than two fields.

Summarizing the PDI steps that operate on sets of rows
The Row Normaliser and Row denormalizer steps you learned in this chapter are some of
the PDI steps which, rather than treating single rows, operate on sets of rows. The following
table gives you an overview of the main PDI steps that fall into this particular group of steps:
Step

Purpose

Group by

Builds aggregates such as Sum, Maximum, and so on, on groups of rows.

Univariate
Statistics

Computes some simple statistics. It complements the Group by. It has less
capabilities than that step but provides more aggregate functions such as
median and percentiles.

Split Fields

Splits a single field into more than one. Actually it doesn't operate on a set of
rows, but it's common to use it combined with some of the steps in this table.
For example: You could use a Group by step to concatenate a field, followed by
a Split Fields step that splits that concatenated field into several columns.

Row Normaliser

Transforms columns into rows making the dataset more suitable for processing.

Row denormaliser Moves information from rows to columns according to the values of a key field.
Row flattener

Flattens consecutive rows. You could achieve the same by using a Group by to
concatenate the field to flatten, followed by a Split Field step.

Sort rows

Sorts rows based on field values. Alternatively, it can keep only unique rows.

Split field to rows

Splits a single string field and creates a new row for each split term.

Unique rows

Removes double consecutive rows and leaves only unique occurrences.

For examples on using these steps or for getting more information about them, please refer
to Appendix C, Quick reference: Steps and Job Entries.

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Have a go hero – verifying the benefits of normalization
Extend the transformation and answer the questions proposed at the beginning of
the section:


How many teams played?



Which team converted most goals?



Which team won all matches it played?
For answering the third question, you'll have to modify the Row
Normalizer step as well.

If you are not convinced that the normalizer process makes the work easier, you can try to
answer the questions without normalizing. That effort will definitively convince you!

Have a go hero – normalizing the Films file
Consider the output of the first Time for action section in this chapter. Generate the
following output:

You have two options here:




To modify the tutorial by sending the output to a new file. Then to use that new file
to do this exercise.
To extend the stream in the original transformation by adding new steps after the
Row Denormalizer step.
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After doing the exercise, think about this: Does it make sense to denormalize and then
normalize again? What is the difference between the original file and the output of this
exercise? Could you have done the same without denormalizing and normalizing?

Have a go hero – calculating scores by judge
Take the contest file and generate the following output, where the columns represent the
minimum, maximum, and average score given by every judge:

This exercise may appear difficult at first, but here's a clue: After reading the file, use a Group
by step to calculate all the values you need for your final output. Leave the group field empty
so that the step groups all rows in the dataset.

Generating a custom time dimension dataset by using
Kettle variables
Dimensions are sets of attributes useful for describing a business. A list of products along
with their shape, color, or size is a typical example of dimension. The time dimension is a
special dimension used for describing a business in terms of when things happened. Just
think of a time dimension as a list of dates along with attributes describing those dates. For
example, given the date 05/08/2009, you know that it is a day of August, it belongs to the
third quarter and it is Wednesday. These are some of the attributes for that date.
In the following tutorial you will create a transformation that generates the dataset for a
time dimension. The dataset for a time dimension has one row for every date in a given
range of dates and one column for each attribute of the date.

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Time for action – creating the time dimension dataset
In this tutorial we will create a simple dataset for a time dimension.
First we will create a stream with the days of the week:

Create a new transformation.

Press Ctrl+T to access the Transformation settings window.

Select the Parameters tab and fill it like shown in the next screenshot:

Expand the Job category of steps.

Drag a Get Variables step to the canvas, double-click the step, and fill the window
like here:

After the Get Variables step, add a Split Fields step and use it to split the field
week_days into seven String fields named sun, mon, tue, wed, thu, fri, and
sat. As Delimiter, set a comma (,).

Add one more Split Fields step and use it to split the field week_days_short into
seven String fields named sun_sh, mon_sh, tue_sh, wed_sh, thu_sh, fri_sh,
and sat_sh. As Delimiter, set a comma (,).
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Transforming the Row Set

After this last step, add a Row Normalizer step.

Double-click the Row Normalizer step and fill it as follows:

10.

Keep the Row Normalizer step selected and do a preview. You will see this:

Now let's build the main stream:

Drag a Generate Rows step, an Add sequence step, a Calculator step, and a Filter
rows step to the canvas.
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Link them so you get this:

Double-click the Generate Rows step and use it to generate 45000 lines. Add a single
Date field named first_day. As Format select yyyyMMdd and as Value write
19000101.

Double-click the Add sequence step. In the Name of value textbox, type days.

Double-click the Calculator step and fill the window as shown next:

Double-click the Filter rows step and add the filter date <= 31/12/2020. When you
enter the date 31/12/2020, make sure to set the Type to Date and the Conversion
format to dd/MM/yyyy. After the Filter rows step add a Stream lookup step.

Create two hops—one from the Filter rows step to the Stream lookup step and the
other from the Row Normalizer step to the Stream lookup step.

Double-click the Stream lookup step. In the upper grid add a row, setting week_day
under the Field column and w_day under the LookupField column. Use the lower grid
to retrieve the String fields week_desc and week_short_desc. Finally, after the
Stream lookup step, add a Select values step.
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Transforming the Row Set

Use the Select values step to remove the unused fields first_day and days. Create a
hop from the Stream lookup step to this step.

10.

With the Select values step selected, click the preview button.

11.

When the preview window appears click on Configure.

12.

Fill the column value in the Parameters grid of the transformation execution window
as follows:

13.

Click the Launch button. You will see this:

What just happened?
You generated data for a time dimension with dates ranging from 01/01/1900 through
31/12/2020. Time dimensions are meant to answer questions related with time such as: Do
I sell more on Mondays or on Fridays? Am I selling more this quarter than the same quarter
last year? The list of attributes you need to include in your time dimension depends on the
kind of question you want to answer. Typical fields in a time dimension include: year, month
(a number between 1 and 12), description of month, day of month, week day, and quarter.
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In the tutorial you created a few attributes, but you could have added much more. Among
the attributes included you had the week day. The week descriptions were taken from
named parameters, which allowed you to set the language of the week descriptions at the
time you ran the transformation. In the tutorial you specified Portuguese descriptions. If
you had left the parameters grid empty, the transformation would have used the English
descriptions that you put as default.
Let's explain how you build the stream with the number and descriptions for the days of
the week. First, you created a dataset by getting the variables with the descriptions for the
days of the week. After creating the dataset, you split the descriptions and by using the Row
Normalize step, you converted that row into a list of rows, one for every day of the week.
In other words, you created a single row with all the descriptions for the days of the week.
Then you normalized it to create the list of days.
This method used for creating the list of days of a week is very useful when
you have to create a very small dataset. It avoids the creation of external
files to hold that data.

The transformation you created was inspired by the sample transformation
General - Populate date dimension.ktr found in the samples/transformations
folder inside the PDI installation folder. You can take a look at that transformation. It builds
the dataset in a slightly different way, also by using Row Normalizer steps.

Getting variables
To create the secondary stream of the tutorial, you used a Get Variables step. The Get
Variables step allows you to get the value of one or more variables. In this tutorial you
read two variables that had been defined as named parameters.
When put as the first step of a stream like in this case, this step creates a dataset with one
single row and as many fields as read variables.
The following is the dataset created by the Get Variables step in the time dimension tutorial:

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When put in the middle of a stream, this step adds to the incoming dataset, as many fields as
the number of variables it reads. Let's see how it works.

Time for action – getting variables for setting the default
starting date
Let's modify the transformation so that the starting date depends on a parameter.

Press Ctrl+T to open the transformation settings window.

Add a parameter named START_DATE with default value 01/12/1999.

Add a Get variables step between the Calculator step and the Filter rows step .

Edit the Get variables step and a new field named start_date. Under Variable write
${START_DATE}. As Type select Date, and under Format select or type dd/MM/yyyy.

Modify the filter step so the condition is now: date>=start_date and
date<=31/12/2020.

Modify the Select values step to remove the start_date field.

With the Select values step selected do a preview. You will see this:

What just happened?
You added a starting date as a named parameter. Then you read that variable into a new field
and used it to keep only the dates that are greater or equal to its value.

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Using the Get Variables step
As you just saw, the Get Variables step allows you to get the value of one or more variables. In
the main tutorial you saw how to use the step at the beginning of a stream. Now you saw how
to use it in the middle. The following is the dataset after the Get Variables step for this

last exercise:

With the Get Variables step, you can read any Kettle variable—variables defined in the
kettle.properties file, internal variables as for example ${user.dir}, named parameters
as in this tutorial, or variables defined in another transformation (you haven't yet learned about
these variables but you will soon).
As you know, the type of Kettle variables is String by default. However, at the time you get a
variable, you can change its metadata. As an example of that, in this last exercise you converted
${START_DATE} to a Date by using the mask dd/MM/yyyy.
Note that you specified the variables as ${name of the variable}. You could have used
%%name of the variable%% also. The full specification of the name of a variable allows you
to mix variables with plain text.
Suppose that instead of a date you create a parameter named YEAR with default value 1950.
In the Get variables step you may specify 01/01/${YEAR} as the value.

When you execute the transformation, this text will be expanded to 01/01/1950 or to
01/01/ plus the year you enter if you overwrite the default value.

Note that the purpose of using the Get Variable step is to have the values of
variables as fields in the dataset. Otherwise, you don't need to use this step
for using a variable. You just use it wherever you see a dollar sign icon.

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Have a go hero – enhancing the time dimension
Modify the time dimension generation by doing the following:


Add the following fields to the dataset, taking as model the generation of weeks:
Name of month, Short name of month, and Quarter.



Add two more parameters: start_year and end_year. Modify the transformation
so that it generates dates only between those years. In other words, you have
to discard dates out of that range. You may assume that the parameters will be
between 1900 and 2020.

Pop quiz – using Kettle variables inside transformations
There are some Kettle predefined variables that hold information about the logged in user:
user.country, user.language, etc. The following tasks involve the use of some of those
variables. Which of the tasks can be accomplished without using a Get Variables step or a
JavaScript step (Remember from the previous chapter that you can also get the value for a
Kettle variable with a Javascript step):
a. Create a file named hello_.txt, where is the name of the
logged user.
b. Create a file named hello.txt that contains a single line with the text Hello,
!, being is the name of the logged user.
c. Write to the log (by using the Write to log step) a greeting message like Hello,
user!. The message has to be written in a different language depending on the
language of the logged user.
d. All of the above
e. None of the above

Summary
In this chapter, you learned to transform your dataset by applying two magical steps: Row
Normalizer and Row denormalizer. These two steps aren't the kind of steps you use every
day such as a Filter Rows or a Select values step. But when you need to do the kind of task
they achieve, you are really grateful that these steps exist. They do a complex task in a quite
simple way. You also learned what a time dimension is and how to create a dataset for a
time dimension.
So far, you've been learning to transform data. In the next chapter, you will set that kind of
learning aside for a while. The chapter will be devoted to an essential subject when it comes
to working in productive environments and dealing with real data—data validation and
error handling.
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Validating Data and Handling Errors
So far, you have been working alone in front of your own computer. In
the "Time for action" exercises, the step-by-step instructions along with the
error-free sample data helped you create and run transformations free of errors.
During the "Have a go hero" exercises, you likely encountered numerous errors,
but tips and troubleshooting notes were there to help you get rid of them.

This is quite different from real scenarios, mainly for two reasons:




Real data has errors—a fact that can't be avoided. If you fail to heed it, the
transformations that run with your sample data will probably crash when
running with real data.
In most cases, who runs your final work is decided by an automated process and is
not user defined. Therefore, if a transformation crashes, there will be nobody to fix
the problem.

In this chapter you will learn about the options that PDI offers to treat errors and validate
data so that your transformations are well prepared to be run in a productive environment.

Capturing errors
Suppose that you are running or previewing a transformation from Spoon. As you already
know, if an error occurs it is shown in the Logging window inside the Execution Results
pane. As a consequence, you can look at the error, try to fix it, and run the transformation
again. This is far from what happens in real life. As said, transformations in real scenarios
are supposed to be automated. Therefore, it is not acceptable to have a transformation that
crashes without someone who notices it and reacts to that situation. On the contrary, it's
your duty to do everything you can to trap errors that may happen, avoiding unexpected
crashes when possible. In this section you will learn how to do that.

Validating Data and Handling Errors

Time for action – capturing errors while calculating the age
of a film
In this tutorial you will use the output of the denormalizing process from the previous
chapter. You will calculate the age of the films and classify them according to their age.

Get the file with the films. You can take the transformation that denormalized the
data and generate the file with a Text file output step, or you can take a sample file
from the Packt website.

Create a new transformation and read the file with a Text file input step.

Do a preview of the data. You will see the following:

After the Text file input step, add a Get System Info step.

Edit the step, add a new field named today, and choose Today 00:00:00 as
its value.

Add a JavaScript step.

Edit the step and type the following piece of code:
var diff;
film_date = str2date('01/01/' + Year, 'dd/MM/yyyy');
diff = dateDiff(film_date,today,”y”);

Click on Get variables to add diff as a new field.

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Chapter 7

Add a Number range step, edit it, and fill its window as follows:

10.

With a Sort rows step, sort the data by diff.

11.

Finally, add a Group by step and double-click to edit it.

12.

As group field put age_of_film. In the Aggregates grid create a field named
number_of_films to hold the number of films with that age. Put film as the
Subject and select Number of values (N) as the Type.

13.

Add a Dummy step at the end and do a preview. You will be surprised by an error
like this:

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Validating Data and Handling Errors

14.

Look at the logging window. It looks like this:

15.

Now drag Write to log step to the canvas from the Utility category.

16.

Create a hop from the JavaScript step to this new step.

17.

Select the JavaScript step, right-click it to bring up a contextual menu, and select
Define error handling....

18.

The error handling settings window appears. Fill it like shown:

19.

Click on OK.

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20.

Save the transformation and do a new preview on the Dummy step. You will
see this:

21.

The logging window will show you this:
... - Bad rows.0 ... - Bad rows.0 - ------------> Linenr 1------------------------... - Bad rows.0 - null
... - Bad rows.0 ... - Bad rows.0 - Javascript error:
... - Bad rows.0 - Could not apply the given format dd/MM/yyyy
on the string for 01/01/null : Format.parseObject(String) failed
(script#4)
... - Bad rows.0 ... - Bad rows.0 - --> 4:0
... - Bad rows.0 - SCR-001
... - Bad rows.0 ... - Bad rows.0 - ====================
... - Bad rows.0 ... - Bad rows.0 - ------------> Linenr 2------------------------... - Bad rows.0 - null
... - Bad rows.0 ... - Bad rows.0 - Javascript error:
... - Bad rows.0 - Could not apply the given format dd/MM/yyyy
on the string for 01/01/null : Format.parseObject(String) failed
(script#4)
... - Bad rows.0 ... - Bad rows.0 - --> 4:0
...

The date was cut from the log for clarity of the log messages.
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Validating Data and Handling Errors

22.

Now do a preview on the Write to log step. This is what you see:

What just happened?
You created a transformation to read a list of films and group them according to their age,
that is, how old the movie is. You were surprised by an unexpected error caused by the rows
in which the year was undefined. Then you implemented error handling to capture that error
and to avoid the abortion of the transformation. With the treatment of the error, you split
the stream in two:




The rows that caused the error went to a new stream that wrote to the log
information about the error
The rows that passed the JavaScript step without problem went through the
main path

Using PDI error handling functionality
With the error handling functionality, you can capture errors that otherwise would cause
the transformation to halt. Instead of aborting, the rows that cause the errors are sent to a
different stream for further treatment.
You don't need to implement error handling in every step, but in those where it's more
likely to have errors when running the transformation. A typical situation where you should
consider handling errors is in a JavaScript step. A code that works perfectly when designing
might fail while executing against real data, where the most common errors are related to
data type conversions or indexes out of range. Another common use of error handling is
when working with databases (you will see more on this later in the book).
To configure the error handling, you have to right-click the step and select Define
Error handling.

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Note that not all steps support error handling. The Define Error handling option
is available only when clicking on steps that support it.

After opening the settings window, you have to fill it just as you did in the tutorial. You have
to specify the target step for the bad rows along with the name of the extra fields being
added, as part of the treatment of errors:
Field

Description

Nr of errors fieldname

Name for the field that will have the number of errors

Error fields fieldname

Name for the field that will have the name of the field(s) that
caused the errors

Error codes fieldname

Name for the field that will have the error code

Error descriptions fieldname

Name for the field that will have the error description

The first two are trivial. The last two deserve an explanation. The values for the error code
and description fields are the same as those you see in the Logging tab when you don't trap
the error. In the tutorial there was a JavaScript error with code SCR-001 and description
JavaScript error: Could not apply the given format.... You saw this
code as well as its description in the Logging tab when you didn't trap the error and the
transformation crashed, and in the preview you made at the end of the error stream. This
particular error was a JavaScript one, but the kind of error you get depends always on the
kind of step where it occurs.
You are not forced to fill all the textboxes in the error setting window. Only the fields for
which you provide a name will be added to the dataset. By doing a preview on the target
step, you can see the extra fields that were added.

Aborting a transformation
You can handle the errors by detecting and sending bad rows to an extra stream. But when
the errors are too many or when the errors are severe, the best option is to cancel the whole
transformation. Let's see how to force the abortion of a transformation in such a situation.

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Validating Data and Handling Errors

Time for action – aborting when there are too many errors
1.

Open the transformation from the previous tutorial and save it under a
different name.

From the Flow category, drag an Abort step to the canvas.

Create a hop from the Write to log step to the Abort step.

Double-click the Abort step. Enter 5 as Abort threshold. As Abort message, type
Too many errors calculating age of film!.

Click OK.

Select the Dummy step and do a preview. As a result, a warning window shows up
informing you that there were no rows to display. In the Step Metrics tab, the Abort
after 5 errors line becomes red to show you that there was an error:

The log looks like this:
... - Bad rows.0 ... - Bad rows.0 - ====================
... - Abort after 5 errors.0 - Row nr 6 causing abort :
[Trois couleurs - Blanc], [null], [Comedy | Drama],
[Krzysztof Kieslowski], [Zbigniew Zamachowski, Julie Delpy],
[2009/08/18 00:00:00.000], [
... - Abort after 5 errors.0 - Javascript error:
... - Abort after 5 errors.0 - Could not apply the given
format dd/MM/yyyy on the string for 01/01/null : Format.
parseObject(String) failed (script#4)
... - Abort after 5 errors.0 ... - Abort after 5 errors.0 - --> 4:0], [SCR-001]
... - Abort after 5 errors.0 - Too many errors calculating age of
film!
... - Abort after 5 errors.0 - Finished processing (I=0, O=0, R=6,
W=6, U=0, E=1)
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...
... - Spoon - The transformation has finished!!
... - error_handling_with_abort - ERROR (version 3.2.0-GA, build
10572 from 2009-05-12 08.45.26 by buildguy) : Errors detected!

What just happened?
You forced the abortion of a transformation after five erroneous rows.

Aborting a transformation using the Abort step
Through the use of the Abort step, you force the abortion of a transformation. Its main use is
in error handling.
You can use the Abort step to force the abortion as soon as a row arrives to it, or after a
certain number of rows as you did in the tutorial. To decide between one and the other
option, you use the Abort threshold option. If threshold is 0, the Abort step will abort
after the first row arrives. If threshold is N, the Abort step will cause the abortion of the
transformation when the row number N+1 arrives at it.
Beyond the error handling situation, you may use the Abort step in any unexpected
situation. Examples of that could be when you expect parameters and they are not present
or when an incoming file is empty when it shouldn't be. In situations like these, you can force
an abnormal ending of the execution just by adding an Abort step after the step that detects
the anomaly.

Fixing captured errors
In the Time for action—capturing errors while calculating the age of a film section of this
chapter, you sent the bad rows to the log. However, when you capture errors, you can send
the bad rows toward any step as long as the step knows how to treat those rows. Let's see
an example of that.

Time for action – treating errors that may appear
1.

Open the transformation from the tutorial and save it under a different name.

From the Transform category, drag the Add constants step to the canvas.

Create a hop from the Write to log step to the Add constants step.

Add an Integer constant named diff with value 999, and a String constant
named age_of_film with value unknown.

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After the Add constants step, add a Select values step and use it to remove the
fields err_code and err_desc.

Create a hop from the Select values step to the Sort rows step. Your transformation
should look like this:

Note that you are merging two streams. Those
streams must have the same metadata. If you get a
trap detector warning, please verify that you executed
these instructions exactly as explained.

Select the Dummy step and do a preview. You will see this:

What just happened?
You modified the transformation so that you didn't end up discarding the erroneous rows. In
the error stream (the stream after the red dotted line), you fixed the rows by putting default
values for the new fields. After that you returned the rows to the main stream.
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Treating rows coming to the error stream
If the errors are not severe enough to discard the rows, if you can somehow guess
what data was supposed to be there instead of the error, or if you have default values for
erroneous data, you can do your best to fix the errors and send the rows back to the
main stream.
What you did instead of discarding the rows with no year information was to fix the rows
and send them back to the main stream. The Group by step grouped them under a separate
category named unknown.
There are no rules for what to do with bad rows where you handle errors. You always have
the option to discard the bad rows or try to fix them. Sometimes you can fix only a few and
discard the rest of them. It always depends on your particular data or business rules.

Pop quiz – PDI error handling
What does the PDI error-handling functionality do:
a. Avoids the happening of unexpected errors
b. Captures errors that happen and discards erroneous rows so you can continue
working with valid data
c. Captures errors that happen and sends erroneous rows to a new stream, letting you
decide what to do with them

Have a go hero – capturing errors while seeing who wins
On the Packt website you will find a modified football match file named
wcup_modified.txt. This modified file has some intentional errors.
Download the file and do the following:
1. Create a transformation, read the file with a Text file input step. Set all fields
as string.
2. Add a JavaScript step and type the following code in it:
var result_desc;
result_split = Result.split('-');
home_g = str2num(result_split[0]);
away_g = str2num(result_split[1]);
if (home_g > away_g)
result_desc = Home_Team + ' wins';
else if (home_g < away_g)
result_desc = Away_Team + ' wins';
else result_desc = 'Nobody wins';
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Validating Data and Handling Errors

3. In the grid below the code, add the string variable result_desc.
4. Do a preview on the JavaScript step and see what happens.
5. Now try any of the following two solutions:




Handle the errors and discard the rows that cause those errors.
Abort if there are more than 10 errors.
Handle the errors and fix the transformation by setting a default
result description for the rows that cause the errors.

Avoiding unexpected errors by validating data
To avoid unexpected errors that happen or just to meet your requirements is a common
practice to validate your data before processing it. Let's do some validations.

Time for action – validating genres with a Regex Evaluation step
In this tutorial you will read the modified films file and validate the genres field.

Create a new transformation.

Read the modified films file just as you did in the previous tutorial.

In the Content tab, check the Rownum in output? option and fill the Rownum
fieldname with the text rownum.

Do a preview. You should see this:

After the Text file input step, add a Regex Evaluation step. You will find it under the
Scripting category of steps.

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Chapter 7

Under the Step settings box, select Genres as the Field to evaluate, and type
genres_ok as the Result Fieldname.

In the Regular expression textbox type [A-Za-z\s\-]*(\|[A-Za-z\s\-]*)* .

Add the Filter rows step, an Add constants step, and two Text file output steps and
link them as shown next:

Edit the Add constants step.

10.

Add a String constant named err_code with value GEN_INV and a String constant
named err_desc with value Invalid list of genres.

11.

Configure the Text file output step after the Add constant step to create the
${LABSOUTPUT}/films_err.txt file, with the fields rownum, err_code, and
err_desc.

12.

Configure the other Text file output step to create the ${LABSOUTPUT}/films_
ok.txt file, with the fields film, Year, Genres, Director, and Actors.

13.

Double-click the Filter rows step and add the condition genres_ok = Y, Y being a
Boolean value. Send true data to the stream that generates the films_ok.txt
file. Send false data to the other stream.

14.

Run the transformation.

15.

Check the generated files. The films_err.txt file looks like the following:
rownum;err_code;err_desc
12;GEN_INV;Invalid list of
18;GEN_INV;Invalid list of
20;GEN_INV;Invalid list of
21;GEN_INV;Invalid list of
22;GEN_INV;Invalid list of
33;GEN_INV;Invalid list of
34;GEN_INV;Invalid list of
...

genres
genres
genres
genres
genres
genres
genres

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Validating Data and Handling Errors

The films_ok.txt file looks like this:
film;Year;Genres;Director;Actors
Persepolis;2007;Animation | Comedy | Drama | History;Vincent
Paronnaud, Marjane Satrapi;Chiara Mastroianni, Catherine Deneuve,
Danielle Darrieux
Trois couleurs - Rouge;1994;Drama;Krzysztof Kieslowski;Irène
Jacob, Jean-Louis Trintignant, Frédérique Feder, Jean-Pierre
Lorit, Samuel Le Bihan
Les Misérables;1933;Drama | History;Raymond Bernard;
...

What just happened?
You read the films file and checked that the Genres field was a list of strings separated by |.
You created two files:



One file with the valid rows.
Another file with the rows with an invalid Genres field. Note that the rownum field
you added when you read the file is used here for identifying the wrong lines.

In order to check the validity of the Genres field, you used a regular expression. The
expression you typed accepts any combination of characters, spaces, or hyphens separated
by a pipe. The * symbol allows empty genres as well. For a detailed explanation of regular
expressions, please refer to Chapter 2.

Validating data
As said, you would validate data mainly for two reasons:


To prevent the transformation from aborting because of unexpected errors



To check that your data meets some pre-existing requirements

For example, consider some of the sample data from previous chapters:





In the match file, the results field had to be a string formed by two numbers
separated by a In the real estate file, the flag for Fireplace had to be Yes or No
In the contest file the name of the country had to be a valid country, not a
random string

If your data doesn't meet these requirements, it is possible that you don't have errors but
you will still be working with invalid data.

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Chapter 7

In the last tutorial you just validated one of the fields. If you want to validate more than one
field, you have a specific step that simplifies that work: The Data Validator.

Time for action – checking films file with the Data Validator
Let's validate not only the Genres field, but also the Year field.

Open the last transformation and save it under a new name.

Delete all steps except the Text file input and Text file output steps.

In the Fields tab of the Text file input step, change the Type of the Year from
Integer to String.

From the Validation category add a Data Validator step. Also add a Select values
step. Link all steps as follows:

Double-click the Data Validator step.

Check the Report all errors, not only the first option found on at the top of the
window. This will enable the Output one row, concatenate errors with separator
option. Check this option too, and fill the textbox to the right with a slash /. Click on
New validation and type genres as the name of the validation rule.

Click on OK.

Click on genres. The right half of the window is filled with checkboxes and textboxes
where you will define the rule.

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Validating Data and Handling Errors

Fill the header of the rule definition as follows:

10.

In the Regular expression expected to match textbox, type
[A-Za-z\s\-]*(\|[A-Za-z\s\-]*)*

11.

Click on New validation and type year as the name of the validation rule.

12.

Click on OK.

13.

Click on year and fill the header of the rule definition as follows:

14.

In the data block, select the Only numeric data expected checkbox option.

15.

Click on OK.

16.

Right-click the Data Validator step and select Define error handling....

17.

Fill the error handling settings window as follows: As Target step, select the step that
generates the file with invalid rows. Check the Enable the error handling? checkbox.
Type err_desc as Error description field name, err_field as Error fields, and
err_code as Error codes. Click on OK.

18.

Use the Select values step to change the metadata for the Year from String
to Integer.

19.

Save the transformation and run it.
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Chapter 7

20.

Check the generated files. The films_err.txt file now has more detail, as you
validated two fields.
rownum;err_code;err_desc
9;YEAR_NULL;Year invalid or absent
12;GEN_INV;Invalid list of genres
18;GEN_INV;Invalid list of genres
20;GEN_INV;Invalid list of genres
21;GEN_INV;Invalid list of genres
22;GEN_INV;Invalid list of genres
33;GEN_INV;Invalid list of genres
34;GEN_INV;Invalid list of genres
47;YEAR_NULL/GEN_INV;Year invalid or absent/Invalid list of genres
48;YEAR_NULL/GEN_INV;Year invalid or absent/Invalid list of genres
49;YEAR_NULL;Year invalid or absent
...

21.

The films_ok.txt file should have less rows instead, as the films with year invalid
or absent are no longer sent to this file.

What just happened?
You used the Data Validator step to validate both the genres list and the year. You created
a file with the good rows, and another file with the information to show you which errors
were found.

Defining simple validation rules using the Data Validator
The Data Validator step, or DV for short, allows you to define simple validation rules to
describe the expected characteristics for the incoming fields. The good thing about the DV
step is that it concentrates several validations into a single step, and obviously it supports
error handling.
For every validation rule, you have to specify these fields:
Field

Description

Name of the field to validate

Name of the incoming field whose value will be
validated with this rule

Error code

The error code to pass to error handling. If
omitted, a default is set

Error description

The error description to pass to error handling. If
omitted, a default is set

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Validating Data and Handling Errors

The error code and error description are useful to identify which field was erroneous when
you have more than one validation rule in a single DV step.
It is possible for more than one field to cause a row pass to error handling. In that case,
you can generate one output row per error or a single row with all error descriptions
concatenated. In the tutorial you chose this last option.
In the settings window, once you select a validation rule, you have two blocks of settings—
the Data block where you define the expected data for a field and the Type block where you
validate if a field matches a given type or not.
In the Data block you set the actual validation rule for a field.
The following table summarizes the kinds of validations you may apply in this block:
Validation

Data block options

Allowing (only) null values

Null allowed? / Only null values allowed?

Making sure that the length of the selected
field is between a range of values

Max string length / Min string length.

Making sure that the value of the selected
field is between a range of values

Maximum value / Minimum value

Making sure that the selected field matches
a pattern

You may use one or both at the same time.
You may use one or both at the same time.





Making sure that the selected field doesn't
match a pattern





Making sure that the selected field is one of
the values in a given list




Only numeric data expected
Expected start string
Expected end string
Regular expression expected to match
Not allowed start string
Not allowed end string
Regular expression not allowed
to match
Allowed values (when you have a
fixed list)
Read allowed values from another step?
(when the list comes from
another stream)

In the tutorial, you used just a couple from this long list of options. For the validation of the
genres, you used a regular expression that the field had to match. For the year, you checked
that the field wasn't null and that it contained only numeric data.
Let's briefly explain what you did to validate the year. You read the year as a String. Then
with the DV you checked that it contained only numeric data. If the data was valid, you
changed the metadata to Integer after the row left the DV step.

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Chapter 7

Why didn't you simply validate whether the year was an Integer? This is because the type
validation just checks that the year is an integer field, rather than checking if it can be
converted into an integer. In this case, the year is of type String because you read it
as a String in the Text file input step.
What would happen if you read the year as an Integer? The invalid fields would cause
an error in the Text file input step, and the row would never arrive to the DV step to
be validated.
The type block allows you to validate the type of an incoming field. This just
checks the real data type, rather than checking if the field can be converted
into a given data type.

Have a go hero – validating the football matches file
From the Packt website, download the valid_countries.txt file. Modify the
transformation from the previous "Hero" section doing the following things.
After reading the file, apply the following validation rules:
Field
Match_Date

Validation rule

Home_Team

Belongs to the list of countries in the valid_countries.txt file.

Away_Team

Belongs to the list of countries in the valid_countries.txt file.

Result

n-n where n is a number.

dd/mm, where dd/mm is a valid date.

Also validate that Home_Team is different from Away_Team.
Use a Data Validator step when possible.
Send the bad rows to a file of invalid data and the good rows to the JavaScript step.
Test your transformation and check that every validation rule is applied as expected.

Cleansing data
While validation means mainly rejecting data, data cleansing detects and tries to fix not only
invalid data, but also data considered illegal or inaccurate in a specific domain.
For example, consider a field representing a year. A year containing non-numeric symbols
should always be considered invalid and then rejected.

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Validating Data and Handling Errors

Now look at the films example. In this specific case, the year might not be important to you.
If you find a non-numeric value, you could just replace it by a null year meaning unknown
and keep the data.
On the contrary, the simple rule that looks for numeric values is not enough. A year equal to
1084 should also be considered invalid as it is impossible to have a film made at that time.
However, as it is a common error to type 0 instead of 9, you may assume that there was a
human mistake and you could replace the 0 in 1084 by a 9 automatically.
Doing data cleansing actually involves trying to detect and deal with these kinds of
situations, knowing in advance the rules that apply.
Data cleansing, also known as data cleaning or data scrubbing, may be done manually
or automatically depending on the complexity of the cleansing. With PDI you can use the
automated option. For the validating part of the process, you can use any of the steps or
mechanisms explained above. While for the cleaning part you can use any PDI step that suits,
there are some steps that are particularly useful.
Step
If field value is null
Null if...
Number range
Value Mapper
Stream lookup
Database lookup
Unique rows

Purpose
If a field is null, it changes its value to a constant. It can be applied to all fields
of the same data type, or to particular fields.
Sets a field value to null if it is equal to a constant value.
Creates ranges based on a numeric field. An example of use is converting
floating numbers to a discrete scale such as 0, 0.25, 0.50, and so on.
Maps values of a field from one value to another. For example, you can use
this step to convert yes/no, true/false, or 0/1 values to Y/N.
Looks up values coming from another stream. In data cleansing, you can use
it to set a default value if your field is not in a given list.
Same as Stream lookup but looking in a database table.
Removes double consecutive rows and leaves only unique occurrences.

For examples that use these steps or for getting more information about them, please refer
to Appendix C, Job Entries and Steps Reference.

Have a go hero – cleansing films data
From the Packt's website, download the fix_genres.txt file. The file has the
following lines:
erroneous;fixed
commedy;comedy
sci-fi; science fiction
science-fiction; science fiction
musical;music
historical;history
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Chapter 7

Create a new transformation and do the following:
Read the modified films file that you have used throughout the chapter. Validate that the
genre is a list of strings separated by |. Send the bad rows to a file of bad rows. So far,
this is the same as you did in the last two tutorials. Now clean the genres in the lists.
For every genre:
1. Check that it is not null. If it is null, discard it.
2.

Split composed genres in two. For example, Historical drama becomes
historical and drama.

3. Standardize the descriptions:


Remove trailing spaces.



Change the descriptions to lower case.

4. Check that it is not misspelled. For doing that, use the miss_genres.txt file. If
the genre is in the list, replace the text by the correct description.
5. After all this cleaning add a Dummy step and preview the results.
To validate each genre, you can split the Genres field into rows. After the
cleansing, you can recover the original lines by grouping the rows, using as
aggregate Concatenate strings separated by | to concatenate
the validated genres.

Summary
In this chapter, you learned two essential subjects when it comes to the running of
transformations by nontechnical users, in productive environments, with real data—
validating data and handling errors.
In the next chapter, we go back to the development, this time with a subject that most of
you must be waiting for since Chapter 1—working with databases.

[ 215 ]

Working with Databases
Database systems are the main mechanism used by most organizations to store
and administer organizational data. Online sales, bank-related operations,
customer service history, and credit card transactions are some examples of
data stored in databases.

This is the first of two chapters fully dedicated to working with databases. This chapter
provides an overview of the main database concepts. It also covers the following topics:


Connecting to databases



Previewing and getting data from a database



Inserting, updating, and deleting data from a database

Introducing the Steel Wheels sample database
As you were told in the first chapter, there is a Pentaho Demo that includes data for a
fictional store named Steel Wheels and you can download it from the Internet. This data is
stored in a database that is going to be the starting point for you to learn how to work with
databases in PDI. Before beginning to work on databases, let's briefly introduce the Steel
Wheels database along with some database definitions.

Working with Databases

A relational database is a collection of items stored in tables. Typically, all items stored in a
table belong to a particular type of data. The following table lists some of the tables in the
Steel Wheels database:
Table

Content

CUSTOMERS
EMPLOYEES
PRODUCTS
OFFICES
ORDERS
ORDERDETAILS

Steel Wheels' customers

Steel Wheels' employees
Products sold by Steel Wheels
Steel Wheels' offices
Information about sales orders
Details about the sales orders

The items stored in the tables represent an entity or a concept in the real world. As an
example, the CUSTOMERS table stores items representing customers. The ORDERS table
stores items that represent sales orders in the real world.
In technical terms, a table is uniquely identified by a name such as CUSTOMERS, and contains
columns and rows of data.
You can think of a table as a PDI dataset. You have fields (the columns of the table) and rows
(the records of the table).
The columns, just like the fields in a PDI dataset, have a metadata describing their name, type,
and length. The records hold the data for those columns; each record represents a different
instance of the items in the table. As an example, the table CUSTOMERS describes the
customers with the columns CUSTOMERNUMBER, CUSTOMERNAME, CONTACTLASTNAME and so
forth. Each record of the table CUSTOMERS belongs to a different Steel Wheels' customer.
A table usually has a primary key. A primary key or PK is a combination of one or more
columns that uniquely identify each record of the table. In the sample table, CUSTOMERS,
the primary key is made up of a single column—CUSTOMERNUMBER. This means there cannot
be two customers with the same customer number.
Tables in a relational database are usually related to one another. For example, the
CUSTOMERS and ORDERS tables are related to convey the fact that real-world customers
have placed one or more real-world orders. In the database, the ORDERS table has a column
named CUSTOMERNUMBER with the number of the customer who placed the order. As said,
CUSTOMERNUMBER is the column that uniquely identifies a customer in the CUSTOMERS
table. Thus, there is a relationship between both tables. This kind of relationship between
columns in two tables is called foreign key or FK.

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Chapter 8

Connecting to the Steel Wheels database
The first thing you have to do in order to work with a database is tell PDI how to access the
database. Let's learn how to do it.

Time for action – creating a connection with the Steel Wheels
database
In this first database tutorial, you will download the sample database and create a
connection for accessing it from PDI.
The Pentaho BI demo includes the sample data. So, if you have already
downloaded the demo as explained in Chapter 1, just skip the first
three steps. If the Pentaho BI demo is running on your machine, the
database server is running as well. In that case, skip the first four steps.

Go to the Pentaho Download site: http://sourceforge.net/projects/
pentaho/files/.

Under the Business Intelligence Server | 1.7.1-stable, look
������������������������
for the file named
pentaho_sample_data-1.7.1.zip and download it.

Unzip the downloaded file.

Run start_hypersonic.bat under Windows or start_hypersonic.sh under
Unix-based operating systems. If you download the sample data, you will find
these scripts in the folder named pentaho-data. If you download the Pentaho BI
server instead, you will find them in the folder named data. The following screen is
displayed when the database server starts:

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Working with Databases

Don't close this window. It would cause the database
server to stop.

5.
6.
7.

Click on the View option that appears in the upper-left corner of the screen.

Fill the Database Connection dialog window as follows:

Open Spoon and create a new transformation.
Right-click the Database connections option and click on New.

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Chapter 8

Click on the Test button. The following window shows up:

If you get an error message instead of the Message window shown in the
previous screenshot, please recheck the data you entered in the connection
window. Also verify that the database is running, that is, the terminal window
is still opened and doesn't show an error message. If you see an error, or if
you don't see the terminal, please start the database server again as explained
at the beginning of the tutorial.

10. Click on OK to close the test window.
11. Click on OK again to close the database definition window. A new database
connection is added to the tree.

12. Right-click on the database connection and click on Share. The connection

is available in all transformations you create from now onwards. The shared
connections are shown in bold letters.

13. Save the transformation.

What just happened?
You created and tested a connection to the Pentaho Sample database. Finally, you shared the
connection so that it could be reused in other transformations.
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Working with Databases

Connecting with Relational Database Management Systems
Even if you've never worked with databases, you must have heard terms such as MySQL, Oracle,
DB2, or MS SQL server. These are just some of many Relational Database Management
Systems (RDBMS) on the market. An RDBMS is a software that lets you create and administer
relational databases.

In the tutorial you worked with HyperSQL DataBase (HSQLDB), just another RDBMS formerly
known as Hypersonic DB. HSQLDB has a small, fast database engine written in Java. HSQLDB
is currently being used in many open source software projects such as OpenOffice.org 3.1
as well as in commercial projects and products such as Mathematica. You can get more
information about HSQLDB at http://hsqldb.org/.
PDI has the ability to connect with both commercial RDBMSes such as Oracle or MS SQL
server and free RDBMSes such as MySQL. In order to get connected to a particular database,
you have to define a connection to it.
A database connection describes all parameters needed to connect PDI to a database.
To create a connection, you must give the connection a name and fill at least the
general settings:
Setting

Description

Steel Wheels sample
HSQLDB

Connection
type

Type of database system: HSQLDB, Oracle, MySQL,
Firebird, and so on.

Method of
access

Native (JDBC), ODBC, JNDI, or OCI. The available options
depend on the type of DB.

Host name

Name or IP address for the host where the database is. localhost

Database
name

Identifies the database to which you want to connect.

Port number

PDI sets as default the most usual port number for the 9001
selected type of database. You can change it of course.

User Name /
Password

Name of the user and password to connect to the
database.

Native (JDBC)

sampledata

pentaho_admin /
password

If you don't find your database engine in the list, you will still be able to connect
to it by specifying as connection type, the Generic database option. In that case,
you have to provide a connection URL and the driver class name.

After creating a connection, you can click the Test button to check that the connection has
been defined correctly and that you can reach them from PDI.
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Chapter 8

The database connections will be available just in the transformation where you defined
them, unless you share it for reuse as you did in the tutorial. Normally, you share
connections because you know that you will use them later in many transformations.
The information about shared connections is stored in a file named shared.xml, located in the
same folder as the kettle.properties file.

When you have shared connections and you save the transformation, the connection
information is saved in the transformation itself.
If there is more than one shared connection, all of them will be saved
along with the transformation, even if the transformation doesn't use
them all. To avoid this, go to the editing options and check the Only
save used connections to XML? option. This option limits the XML
content of a transformation to just the used connections.

Pop quiz – defining database connections
Which options do you have to connect to the same database in several transformations:
a. Define the connection in each transformation that needs it
b. Define a connection once and share it
c.

Either of the above options

d. Neither of the above options

Have a go hero – connecting to your own databases
You must have access to a database, whether local or in the network to which you are logged
in. Get the connection information for the database. From PDI create a connection to the
database and test it to verify that you can access it from PDI.

Exploring the Steel Wheels database
In the previous section, you learned about what RDBMSs are and how to connect to an
RDBMS from PDI. Before beginning to work with the data in a database, it would be useful to
get familiarized with that database. In this section, you will learn to explore databases with
the PDI Database explorer.

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Working with Databases

Time for action – exploring the sample database
Let's explore the sample database:

Open the transformation you just created.

Right-click the connection in the Database connections list and select Explore in the
contextual menu. The Database explorer on connection window opens.

Expand the Tables node of the tree and select CUSTOMERS. This is how the
explorer looks:

Click on the Open SQL for [CUSTOMERS] option.

The following SQL editor window appears:

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Chapter 8

Modify the text in the window so that you have the following:
SELECT
CUSTOMERNUMBER
, CUSTOMERNAME
, CITY
, COUNTRY
FROM CUSTOMERS

Click on Execute. You will see the following result:

Close the preview window (the window that tells the result of the execution) and
the SQL editor window.

Click on OK to close the database explorer window.

What just happened?
You explored the Pentaho sample database with the PDI Database explorer.

A brief word about SQL
Before explaining the details of the database explorer, it's worth giving an introduction to
SQL—a central topic in relational database terminology.
SQL, that is, Structured Query Language is the language that lets you access and manipulate
databases in a RDBMS.
SQL can be divided into two parts—DDL and DML.
[ 225 ]

Working with Databases

The DDL, that is, Data Definition Language is the branch of the language that basically allows
creating or deleting databases and tables.
The following is an example of DDL. It is the DDL statement that creates the
CUSTOMERS table.
CREATE TABLE CUSTOMERS
(
CUSTOMERNUMBER INTEGER
, CUSTOMERNAME VARCHAR(50)
, CONTACTLASTNAME VARCHAR(50)
, CONTACTFIRSTNAME VARCHAR(50)
, PHONE VARCHAR(50)
, ADDRESSLINE1 VARCHAR(50)
, ADDRESSLINE2 VARCHAR(50)
, CITY VARCHAR(50)
, STATE VARCHAR(50)
, POSTALCODE VARCHAR(15)
, COUNTRY VARCHAR(50)
, SALESREPEMPLOYEENUMBER INTEGER
, CREDITLIMIT BIGINT
)

;

This DDL statement tells the database to create the table CUSTOMERS with the columns
CUSTOMERNUMBER of the type INTEGER, the column CUSTOMERNAME of the type VARCHAR
with length 50, and so on.
Note that INTEGER, VARCHAR, and BIGINT are HSQLDB types of data, not PDI ones. The
DML, that is, Data Manipulation Language allows you to retrieve data from a database. It
also lets you insert, update, or delete data from the database.
The statement you typed in the SQL editor is an example of DML:
SELECT
CUSTOMERNUMBER
, CUSTOMERNAME
, CITY
, COUNTRY
FROM CUSTOMERS

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Chapter 8

This statement is asking the database to retrieve all the rows for the CUSTOMERS table,
showing only CUSTOMERNUMBER, CUSTOMERNAME, CITY, and COUNTRY columns. After you
clicked Execute, PDI queried the database and showed you a window with the data you had
asked for.
If you were to leave the following statement:
SELECT * FROM CUSTOMERS

the window would have showed you all columns for the CUSTOMERS table.
SELECT is the statement that allows you to retrieve data from one or more tables. It is the

most commonly used DML statement and you're going to use it a lot when working with
databases in PDI. You will learn more about the SELECT statement in the next section of
this chapter.
Other important DML statements are:


INSERT: This allows you to insert rows in a table



UPDATE : This allows you to update the values in rows of a table



DELETE: This statement is used to remove rows from a table

It is important to understand the meaning of these basic statements, but you are not
forced to learn them as PDI offers you ways to insert, update, and delete without typing
any SQL statement.
Although SQL is a standard, each database engine has its own version of the SQL language.
However, all database engines support the main commands.
When you type SQL statements in PDI, try to keep the code within the
standard. Your transformations will then be reusable in case you have
to change the database engine.

If you are interested in learning more about SQL, there are a lot of tutorials on the Internet.
The following are a few useful links with tutorials and SQL references:


http://www.sqlcourse.com/



http://www.w3schools.com/SQl/



http://sqlzoo.net/

Until now, you have used only HSQLDB. In the tutorials to come, you will also work with the
MySQL database engine. So, you may be interested in specific documentation for MySQL,
which you can find at http://dev.mysql.com/doc/. You can find even more information
in books; there are plenty of books available about both SQL language and MySQL databases.
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Exploring any configured database with the PDI Database explorer
The database explorer allows you to explore any configured database. When you open the
database explorer, the first thing you see is a tree with the different objects of the database.
As soon as you select a database table, all buttons to the right side become available for you
to explore that table. The following are the functions offered by the buttons at the right side
of the database explorer:
Option

Meaning

Preview first 100 rows of ...

Return the first 100 rows of the selected table, or all the rows
if the table has less that 100. This option shows all columns of
the table.

Preview first...rows of ...

The same as the previous option, but here you decide the
number of rows to show.

Number of rows of ...

Tells you the total number of records in the table.

Show layout of ...

Shows you the metadata for the columns of the table.

Generate DDL

Shows you the DDL statement that creates the selected table.

Generate DDL for other
connection

It lets you select another existent connection. Then it shows
you the DDL just like the previous option. The difference is that
the DDL is written with the syntax of the database engine of the
selected connection.

Open SQL for ...

Lets you edit a SELECT statement to query the table. Here you
decide which columns and rows to retrieve.

Truncate table

Deletes all rows from the selected table.
In the tutorial you opened the Database explorer from the contextual
menu in the Database connections tree. You can also open it by clicking
the Explore option in the database definition window.

Have a go hero – exploring the sample data in depth
In the tutorial you just tried the Open SQL button. Feel free to try other buttons to
explore not only the CUSTOMERS table but also the rest of the tables found in the Steel
Wheels database.

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Have a go hero – exploring your own databases
In the previous section, there was a Hero exercise that asked you to connect to your own
databases. If you have done that, then use a database connection defined by you and
explore the database. See if you can recognize the different objects of the database. Run
some previews to verify that everything looks as expected.

Querying a database
So far you have just connected to a database. You haven't yet worked with the data. Now is
the time to do that.

Time for action – getting data about shipped orders
Let's continue working with the sample data.

Create a new transformation.

Select the Design view.

Expand the input category of steps and drag a Table Input step to the canvas.

Double-click the step.

Click on the Get SQL select statement... button. The database explorer
window appears.

Expand the tables list and select ORDERS.

Click on OK.

PDI asks if you want to include the field names in the SQL. Answer Yes.

The SQL box gets filled with a SELECT SQL statement.
SELECT
ORDERNUMBER
, ORDERDATE
, REQUIREDDATE
, SHIPPEDDATE
, STATUS
, COMMENTS
, CUSTOMERNUMBER
FROM ORDERS

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10. At the end of the SQL statement, add the following clause:
WHERE STATUS = 'Shipped'

11. Click Preview and then OK. The following window appears:

12. Close the window and click OK to close the step configuration window.
13. After the Table input step add a Calculator step, a Number Range step, a Sort step,
and a Select values step and link them as follows:

14. With the Calculator step, add an Integer field to calculate the difference between
the shipped date and the required date. Use the calculation Date A – Date B
(in days) and name the field diff_days. Use the Number ranges step to classify
the delays in delivery.

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15. Use the Sort rows step to sort the rows by the diff_days field.
16. Use the Select values step to select the delivery, ORDERNUMBER, REQUIREDDATE,
and SHIPPEDDATE fields.

17. With the Select values step selected, do a preview. The following is how the final
data will look:

What just happened?
From the sample database, you got information about shipped orders. After you read
the data from the database, you classified the orders based on the time it took to do
the shipment.

Getting data from the database with the Table input step
The Table input step is the main step to get data from a database. In order to use it, you have
to specify the connection with the database. In the tutorial you didn't explicitly specify one
because there was just one connection and PDI put it as the default value.

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The connection was available because you shared it before. If you hadn't, you should have
created here again.
The output of a Table Input step is a regular dataset. Each column of the SQL query leads
to a new field and the rows generated by the execution of the query become the rows of
the dataset.
As the data types of the databases are not exactly the same as the PDI data types, when
getting data from a table, PDI implicitly converts the metadata of the new fields.
For example, consider the ORDERS table. Open the Database Explorer and look at the DDL
definition for the table. Then right-click the Table input step and select Show output fields to
see the metadata of the created dataset. The following table shows you how the metadata
was translated:
Table columns

Database data type

PDI metadata

ORDERNUMBER,
CUSTOMERNUMBER

INTEGER

Integer(9)

ORDERDATE, REQUIREDDATE,
SHIPPEDDATE
STATUS

TIMESTAMP

Date

VARCHAR(15)

String(15)

COMMENTS

TEXT

String(214748364)

Once the data comes out of the Table input step and the metadata is adjusted, PDI forgets
that it comes from a database. It treats it just as regular data, no matter if it came from a
database or any other data source.

Using the SELECT statement for generating a new dataset
The SQL area of a Table input step is where you write the SELECT statement that will
generate the new dataset. As said before, SELECT is the statement that you use to retrieve
data from one or more tables in your database.
The simplest SELECT statement is as follows:
SELECT
FROM

Here is the name of the table that will be queried to get the result set and
is the list of the desired columns of that table, separated by commas.
This is another simple SELECT statement:
SELECT ORDERNUMBER, ORDERDATE, STATUS
FROM ORDERS
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If you want to select all columns, you can just put a * as here:
SELECT *
FROM ORDERS

There are some optional clauses that you can add to a SELECT statement. The most
commonly used among the optional clauses are WHERE and ORDER BY. The WHERE clause
limits the list of retrieved records, while ORDER BY is used to retrieve the rows sorted by
one or more columns.
Another common clause is DISTINCT that can be used to return only different records.
Let's see some sample SELECT statements:
Sample statement

Output

SELECT ORDERNUMBER, ORDERDATE
FROM ORDERS
WHERE SHIPPEDDATE IS NULL

Returns the number and order date for the orders
that have not been shipped.

SELECT *
FROM EMPLOYEES
WHERE JOBTITLE = 'Sales Rep'
ORDER BY LASTNAME, FIRSTNAME

Returns all columns for the employees whose job
is sales representative, ordered by last name and
first name.

SELECT PRODUCTNAME
Returns the list of products whose product line
FROM PRODUCTS
contains cars—for example, Classic cars and
WHERE PRODUCTLINE LIKE '%Cars%' Vintage cars.
SELECT DISTINCT CUSTOMERNUMBER
FROM PAYMENTS
WHERE AMOUNT > 80000

Returns the list of customer numbers who have
made payments with checks above USD80,000.
The customers who have paid more than once
with a check above USD80,000 appear more than
once in the PAYMENTS table, but only once in this
result set.

You can try these statements in the database explorer to check that the result sets are
as explained.
When you add a Table input step, it comes with a default SELECT statement for you
to complete.
SELECT FROM
WHERE

If you need to query a single table, you can take advantage of the Get SQL select
statement... button that generates the full statement for you. After you get the statement,
you can modify it at your will by adding, say, WHERE or ORDER clauses just as you did in the
tutorial. If you need to write more complex queries, you will have to do it manually.
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You can write any SELECT query as long as it is a valid SQL statement for
the selected type of database. Remember that every database engine has
its own dialect of the language.

Whether simple or complex, you may need to pass some parameters to the query. You can
do it in a couple of ways. Let's explain this with two practical examples.

Making flexible queries by using parameters
One of the ways you have to make your queries more flexible is by passing it through some
parameters. In the following tutorial you will learn how to do it.

Time for action – getting orders in a range of dates by using
parameters
Now you will modify your transformation so that it shows orders in a range of dates.

Open the transformation from the previous tutorial and save it under a new name.

From the Input category, add a Get System Info step.

Double-click it and use the step to get the command line argument 1 and command
line argument 2 values. Name the fields as date_from and date_to respectively.
Create a hop from the Get System Info step to the Table input step.

Double-click the Table input step.

Modify the SELECT statement as follows:
SELECT
ORDERNUMBER
, ORDERDATE
, REQUIREDDATE
, SHIPPEDDATE
FROM ORDERS
WHERE STATUS = 'Shipped'
AND ORDERDATE BETWEEN ? AND ?

In the drop-down list to the right side of Insert data from step, select the
incoming step.

Click OK.

With the Select values step selected, click the Preview button.
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Click on Configure.

10. Fill the Arguments grid. To the right of the argument 01, type 2004-12-01. To the
right of the argument 02, type 2004-12-10.

11. Click OK. The following window appears:

What just happened?
You modified the transformation from the previous tutorial to get orders in a range of dates
coming from the command line.

Adding parameters to your queries
You can make your queries more flexible by adding parameters. Let's explain how you do it.
The first thing to do is obtain the fields that will be plugged as parameters. You can get them
from any source by using any number of steps, as long as you create a hop from the last step
toward the Table input step.
In the tutorial you just used a Get System Info step that read the parameters from the
command line.
Once you have the parameters for the query, you have to change the Table input step
configuration. In the Insert data from step option, you have to select the name of the step
that the parameters will come from. In the query, you have to put a question mark (?) for
each incoming parameter.
When you execute the transformation, the question marks are replaced, one by one, with
the data that comes to the Table input step.
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Let's see how it works in the tutorial. The following is the output of the Get System Info step:

In the SQL statement, you have two question marks. The first is replaced by the value of the
date_from field and the second is replaced by the value of the date_to field. Now the SQL
statement becomes:
SELECT
ORDERNUMBER
, ORDERDATE
, REQUIREDDATE
, SHIPPEDDATE
FROM ORDERS
WHERE STATUS = 'Shipped'
AND ORDERDATE BETWEEN '2004-12-01' AND '2004-12-10'

Here 2004-12-01 and 2004-12-10 are the values you entered as arguments for
the transformation.
The replacement of the markers respects the order of the incoming fields.
When you use question marks to parameterize a query, you can't forget
the following—the number of fields coming to a Table input step must be
exactly the same as the number of question marks found in the query.

Making flexible queries by using Kettle variables
Another way you have to make your queries flexible is by using Kettle variables. Let's explain
how you do it using an example.

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Time for action – getting orders in a range of dates by using
variables
In this tutorial you will do the same as you did in the previous tutorial, but another method
will be explained to you.

Open the main transformation we created in the Time for action–getting
data about shipped orders section and save it under a new name.

2.
3.

Double-click the Table input step.
Modify the SELECT statement as follows:
SELECT
ORDERNUMBER
, ORDERDATE
, REQUIREDDATE
, SHIPPEDDATE
FROM ORDERS
WHERE STATUS = 'Shipped'
AND ORDERDATE BETWEEN '${DATE_FROM}' AND '${DATE_TO}'

4.
5.
6.
7.
8.

Tick the Replace variables in script? checkbox.

Click OK. This following window appears:

Save the transformation.
With the Select values step selected, click the Preview button.
Click on Configure.
Fill the Variables grid in the settings dialog window—type 2004-12-01 to the right
of the DATE_FROM option and 2004-12-10 to the right of the DATE_TO option.

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What just happened?
You modified the transformation from the previous tutorial, so the range of dates is taken
from two variables—DATE_FROM and DATE_TO. The final result set was exactly the same you
got in the previous version of the transformation.

Using Kettle variables in your queries
As an alternative to the use of positional parameters, you can use Kettle variables. Instead
of getting the parameters from an incoming step, you check the option Replace variables in
script? and replace the question marks by names of variables. The final result is the same.
PDI replaces the names of the variables by their values. Only after that, it sends the SQL
statement to the database engine to be evaluated.
The advantage of using positional parameters over the variables is quite obvious—you don't
have to define the variables in advance.
On the contrary, Kettle variables have several advantages over the use of question marks:



You can use the same variable more than once in the same query.
You can use variables for any portion of the query, not just the values. For example,
you could have the following query:
SELECT ORDERNUMBER FROM ${ORDER_TABLE}

Then the result will vary upon the content of the variable ${ORDER_TABLE}. In the
case of this example, the variable could be ORDERS or ORDERDETAILS.


A query with variables is easier to understand and less error prone than a query
with positional parameters. When you use positional parameters, it's quite common
to get confused and make mistakes.
Note that in order to provide parameters to a statement in a
Table input step, it's perfectly possible to combine both methods:
positional parameters and Kettle variables.

Pop quiz – database datatypes versus PDI datatypes
After you read data from the database with a Table Input step, what happens to the data
types of that data:
a. They remain unchanged
b. PDI converts the database data types to internal data types
c.

It depends on how you defined the database connection
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Have a go hero – querying the sample data
Based on the sample data:




Create a transformation to list the offices of Steel Wheels located in USA. Modify the
transformation so that the country is entered by command line.
Create a transformation that lists the contact information of clients whose credit
limit is above USD100,000. Modify the transformation so that the threshold is
100000 by default, but can be modified when you run the transformation.
(Hint: Use named parameters.)
Create a transformation that generates two Excel files—one with a list of planes
and the other with a list of ships. Include the code, name, and description of
the products.

Sending data to a database
By now you know how to get data from a database. Now you will learn how to insert data
into it. For the next tutorials we will use a MySQL database, so before proceeding make sure
you have MySQL installed ad running.
If you haven't yet installed MySQL, please refer to Chapter 1. It has
basic instructions on installing MySQL, both on Windows and on
Linux operating systems.

Time for action – loading a table with a list of manufacturers
Suppose you love jigsaw puzzles and decided to open a store for selling them. You have
made all the arrangements and the only missing thing is the software. You have already
acquired a software to handle your business, but you still have one hard task to do—insert
data into the database, that is, load the database with the basic information about the
products you are about to sell.
As this is the first of several tutorials in which you will interact with that database, the first
thing you have to do is to create the database.
For MySQL-specific tasks such as the creation of a database, we will use
the MySQL Query Browser, included in the MySQL GUI Tools software. If
you don't have it or don't like it, you can accomplish the same tasks by
using the MySQL Command Line Client or any other GUI Tool.

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From the Packt website, download the script file js.sql.

Launch the MySQL Query Browser.

A dialog window appears asking you for the connection information. Enter
localhost as Server Host, and as Username and Password, enter the name
and password of the user you created when you installed the software .

Click on OK.

From the File menu, select Open Script....

Locate the downloaded file and open it.

Click on the Execute button or press Ctrl+Enter.

In the Schemata tab window, a new database, js, appears.

Right-click the name of the database and select Make Default Schema.

10.

In the Schemata tab window, expand the js tree and you will see the tables of
the database.

11.

Close the script window.

Now that the database has been created, let's load some data into it:

From the Packt website, download the manufacturers.xls file.

Open Spoon and create a new transformation.

Create a connection to the created database. Under Connection Type, select
MySQL. In the Settings frame, insert the same values you provided for the
connection in MySQL Query Browser—enter localhost as Host Name and
js (the database you just created) as Database Name, and as User Name
and Password, enter the name and password of the user you created when
you installed MySQL. For other settings in the window, leave the default
values. Test the connection to see if it has been properly created.
The main reason for a failed test is either erroneous data provided
in the setting window or the non-functioning of the server. If the
test fails, please read the error message to know exactly what the
error was and act accordingly.

Right-click the database connection and share it.

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Drag an Excel Input step to the canvas and use it to read the
manufacturers.xls file.

Click on Preview Rows to check that you are reading the file properly. You should
see the following:

From the Output category of steps, drag a Table Output step to the canvas.

Create a hop from the Excel Input step to the Table output step.

Double-click the Table output step and fill the main settings window as
follows—select js as Connection, as Target table, browse and select the
table manufacturers or type it. Check the Specify database fields option.
It is not mandatory but recommended in this particular exercise that
you also check the Truncate table option. Otherwise, the output
table will have duplicate records if you run the transformation more
than once.

10.

Select the Database fields tab.

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11.

Fill the grid as follows:

12.

Click OK.

13.

After the Table output step, add a Write to log step.

14.

Right-click the Table output step and select Define error handling....

15.

Fill the error handling settings window. As Target step, select the
Write to log step. Check the Enable the error handling? option. Enter
db_err_desc as Error descriptions fieldname, db_err_field as
Error fields fieldname, and db_err_cod as Error codes fieldname.

16.

Click OK. The following is your final transformation:

17.

Save the transformation and run it.

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18.

Take a look at the Steps Metrics tab window. You will see the following:

19.

Now look at the Logging tab window. The following is what you see:

20.

Switch to MySQL Query Browser.

21.

In the Schemata window, double-click the manufacturers table.

22.

The query entry box is filled with a basic SELECT statement for that table such as:
SELECT * FROM manufacturers m;

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23.

Click Execute. The following result set is shown:

What just happened?
In the first part of the tutorial, you created the Jigsaw Puzzle database.
In Spoon, you created a connection to the new database.
Finally, you created a transformation that read an Excel file with a list of puzzle
manufacturers and inserted that data into the manufacturers table. Note that not
all rows were inserted. The row that couldn't be inserted was reported in the log.
In the data for the tutorial, there was a description too long to be inserted in the table. That
was properly reported in the log because you implemented error handling. Doing that, you
avoided the abortion of the transformation due to errors like that. As you learned in the
previous chapter, when a row causes an error, it is up to you to decide what to do with that
row. In this case, the row was sent to the log and wasn't inserted. Other possible options
for you are:

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




Fixing the problem in the Excel file and rerunning the transformation
Validating the data and fixing it properly (for example, cutting the descriptions)
before the data arrives to the Table output step
Sending the full data for the erroneous rows to a file, fixing manually the data in the
file, and creating a transformation that inserts only this data

Inserting new data into a database table with the Table
output step
The Table output step is the main PDI step for inserting new data into a database table.
The use of this step is simple. You have to enter the name of the database connection
and the name of the table where you want to insert data. The names for the connection
and the table are mandatory, but as you can see, there are some extra settings for the
Table output step.
The database field tab lets you specify the mapping between the dataset stream fields and
the table fields.
In the tutorial the dataset had two fields—CODE and NAME. The table has two columns
named man_code and man_desc.
As the names are different, you have to explicitly indicate that the CODE field is to be written
in the table field named man_code, and that the NAME field is to be written in the table field
named man_desc.
The following are some important tips and warnings about the use of the Table output step:




If the names of the fields in the PDI stream are equal to the names of the columns
in the table, you don't have to specify the mapping. In that case, you have to leave
the Specify database fields checkbox unchecked and make sure that all the fields
coming to the Table output step exist in the table.
Before sending data to the Table output step, check your transformation against the
definition of the table. All the mandatory columns that don't have a default value
must have a corresponding field in the PDI stream coming to the Table output step.
Check the data types for the fields you are sending to the table. It is possible
that a PDI field type and the table column data type don’t match. In that case, fix
the problem before sending the data to the table. You can, for example, use the
Metadata tab of a Select values step to change the data type of the data.

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In the Table output step, you may have noted a button named SQL. This button generates
the DDL to create the output table. In the tutorial, the output table, manufacturers,
already existed. But if you want to create the table from scratch, this button allows you
to do it based on the database fields you provided in the step.

Inserting or updating data by using other PDI steps
The Table output step provides the simplest but not the only way to insert data into a
database table. In this section, you will learn some alternatives for feeding a table with PDI.

Time for action – inserting new products or updating
existent ones
So far, you created the Jigsaw Puzzles database and loaded a list of puzzles manufacturers.
It's time to start loading information about the products you will sell— puzzles.
Suppose, in order to show you what they are selling, the suppliers provide you with the lists
of products made by the manufacturers themselves. Fortunately, they don't give you the lists
in the form of papers, but they give you either plain files or spreadsheets. In this tutorial, you
will take the list of products offered by the manufacturer Classic DeLuxe and load it into the
puzzles table.

1.
2.
3.

From the Packt website, download the sample lists of products.
Open Spoon and create a new transformation.
Add a Text file input step and configure it to read the
productlist_LUX_200908.txt file.
Pay attention to the each field. It's the price of the product and must be configured
as a Number with format $0.00.
Preview the file. You should see the following:

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In the Selected Files grid, replace the text productlist_
LUX_200908.txt by ${PRODUCTLISTFILE}.

Click on OK.

After the Text file input step, add an Add constants step.

Use it to add a String constant named man_code with value LUX.

From the Output category of steps, drag an Insert/Update step to the
canvas. Create a hop from the Add constants step to this new step.

10.

Double-click the step. Select js as Connection. As Target table,
browse and select products. In the upper grid of the window, add the
conditions pro_code = prodcod and man_code = man_code. Click
the Edit mapping button. The mapping dialog window shows up.

11.

Under the Source fields list, click on prodcod, under the Target fields list click
on pro_code, and then click the Add button. Again, under the Source fields
list click on title, under the Target fields list click on pro_name, and then
finally click Add. Proceed with the mapping until you get the following:

12.

Click OK.

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13.

Fill the Update column for the price row with the value Y. Fill the rest of the
column with the value N. The following is how the final grid looks like:

14.

After the Insert/Update step, add a Write to log step.

15.

Right-click the Insert/Update step and select Define error handling....

16.

Fill the error handling settings window just as you did in the previous tutorial.

17.

Save the transformation and run it by pressing the F9 key.

18.

In the settings window, assign the PRODUCTLISTFILE variable
with the value productlist_LUX_200908.txt.

19.

Click on Launch.

20.

When the transformation ends, check the Step Metrics. You will see the following:

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21.

Switch to the SQL Query Browser application.

22.

Type the following in the query entry box:
SELECT * FROM products p;

23.

Click on Execute. The following result set is shown:

What just happened?
You populated the products table with data found in text files. For inserting the data, you
used the Insert/Update step.
As this was the first time you dealt with the products table, before you ran the
transformation, the table was empty. After running the transformation, you could
see how all products in the file were inserted in the table.

Time for action – testing the update of existing products
In the preceding tutorial, you used an Insert/Update step, but only inserted records. Let's try
the transformation again to see how the update option works.

If you closed the transformation, please open it.

Press F9 to launch the transformation again.
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As the value for the PRODUCTLISTFILE variable,
insert productlist_LUX_200909.txt.

Click Launch.

When the transformation ends, check the Step Metrics tab. You will see
the following:

Switch to the SQL Query Browser application and click Execute to run the query
again. This time you will see this:

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What just happened?
You reran the transformation that was created in the previous tutorial, this time using a
different input file. In this file there were new products and some products were removed
from the list, whereas some had their descriptions, categories, and prices modified.
When you ran the transformation for the second time, the new products were added to
the table. Also, the modified prices of the products were updated. In the Step Metrics tab
window, you can see the number of inserted records (Output column) and the number of
updated ones (Update column).
Note that as the supplier may give you updated lists of products with different
names of files, for the name of the file you used a variable. Doing so, you were
able to reuse the transformation for reading different files each time.

Inserting or updating data with the Insert/Update step
While the Table output step allows you to insert brand new data, the Insert/Update step
allows you to do both, insert and update data in a single step.
The rows coming to the Insert/Update step can be new data or can be data that already
exists in the table. Depending on the case, the Insert/Update step behaves differently. Let's
see each case in detail:
For each incoming row, the first thing the step does is use the lookup condition you put in
the upper grid to check if the row already exists in the table.
In the tutorial you wrote two conditions: pro_code = prodcod and man_code = man_
code. Doing so, you told the step to look for a row in the products table for which the table
column pro_code is equal to the field prodcod of your row, and the table column
man_code is equal to the field with the same name of your row.
If the lookup fails, that is, the row doesn't exist, the step inserts the row in the table by using
the mapping you put in the lower grid.
The first time you ran the tutorial transformation, the table was empty. There were no
rows against which to compare. In this case, all the lookups failed and, consequently, all
rows were inserted.

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This insert operation is exactly the same that you could have done with a Table output step.
That implies that here you also have to be careful about the following:




All the mandatory columns that don't have a default value must be present in the
Update Field grid, including the keys you used in the upper grid
The data types for the fields you are sending to the table must match the data type
for the columns of the table

If the lookup succeeds, the step updates the table replacing the old values with the new
ones. This update is made only for the fields where you put Y as the value for the Update
column in the lower grid.
If you don't want to perform any update operation, you can check the Don't perform any
updates option.
The second time you ran the tutorial, you had two types of products in the file—products
that already existed in the database and new products. For example, consider the following
row found in the second file:
CLTR1001|A Saint at Radley|Trains|500 pieces|$13.30|Peter Webster

PDI looks for a row in the table where the prod_code is equal to CLTR1001 and man_code
is equal to LUX (the field added with the Add constants step). It doesnt find it. Then it
inserts a new row with the data coming from the file.
Take another sample row:
CLBO1007|Henley Regatta & Playing|Boats|500 pieces each|$19.94|2
Puzzles in a Box

PDI looks for a row in the table where the prod_code is equal to CLBO1007 and man_code
equal to LUX. It finds the following:

There are two differences between the old and the new versions of the product. Both the
name and the price have changed.

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As you configured the Insert/Update step to update only the price column, the update
operation does so. The new record in the table after the execution of the transformation
is this:

Have a go hero – populating a films database
From the Packt website, download the films.sql script file. Run the script In MySQL. A
new database will be created to hold film data.
Browse the folder where you have the files for Chapter 7 and get the French films file.
You will use it to populate the following tables of the films database: GENRES, PEOPLE,
and FILMS.
Now follow these instructions:
1. Create a connection to the database.
2. In order to populate the GENRES table, you have to build a list of genres, no
duplicates! For the primary key, GEN_ID, you don't have a value in the file. Create
the key with an Add sequence step.
3. The table, PEOPLE, will have the names of both actors and directors. In order to
populate that table, you will have to create a single list of people, no duplicates here
either! To generate the primary key, use the same method as before.
4. Finally, populate the FILMS table with the whole list of films found in the file.
Don't forget to handle errors so that you can detect bad rows.

Have a go hero – creating the time dimension
Now you're going to finish what you started back in Chapter 6—the creation of a
time dimension.
From the Packt website, download the js_dw.sql script file. Run the script in MySQL.
A new database named js_dw will be created.
Now you are going to modify the time_dimension.ktr transformation to load the time
dataset into the lk_time table.
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The following are some tips:



Create a connection to the created database
Find a correspondence between each field in the dataset and each column in the
LK_TIME table



Use a Table output step to send the dataset to the table

After running the transformation, check if all rows were inserted as expected.
Pay attention to the main field in the time dimension—date.
In the transformation the date is a field whose type is Date.
However, in the table the type for the date field is CHAR(8). This column
is meant to hold the date as a String with the format YYYYMMDD—for
example 20090915.
As explained, the data types of the data you sent to the table have to match
the data types in the table. In this case, as the types don't match, you will
have to use a Select values step and change the metadata of the date field
from Date to String.

Have a go hero – populating the products table
This exercise has two parts. The first is intended to enrich the transformation you created
in the tutorial. The transformation processed the product list files supplied by the Classics
DeLuxe manufacturer. In the file, there was some extra information that you could put in the
table such as the number of pieces of a puzzle. However, the data didn't come ready to use.
Consider, for example, this text: 500 pieces each. In order to get the number of pieces, you
need to do some transformation. Modify the transformation so that you can enrich the data
in the products table.
The second part of the exercise has to do with populating the products table with products
from other manufacturers. Unfortunately, you can't expect that all manufacturers to share
the same structure for the list of products. Not only the structure changes, but also the
kind of information they give you can vary. On the Packt website, you have several sample
product files belonging to different manufactures. Explore them, analyze them to see if you
can identify the different data you need for the products table, and load all the products
into the database by using a different transformation for each manufacturer.

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The following are some tips:





Take as a model the transformation for the tutorial. You may reuse most of it.
You don't have to worry about the stock columns or the pro_type column
because they already have default values.
Use the comments in the file to identify potential values for the pro_packaging,
pro_shape and pro_style columns. Use the pro_packaging field for values
such as 2 puzzles in a box. Use the pro_shape field for values such as
Panoramic Puzzle or 3D Puzzle. Use the puzzle_type field for values
such as Glow in the Dark or Wooden Puzzle.
You can leave the pro_description empty or put in it whatever you feel that
fits—a fix string such as Best in market!, or the full comment found in the file,
or whatever your imagination says.

Pop quiz – Insert/Update step versus Table Output/Update steps
In the last tutorial you read a file and used an Insert/Update step to populate the products
table. Look at the following variant of the transformation:

Suppose you use this transformation instead of the original. Compared to the results you got
in the tutorial, after the execution of this version of the transformation, the products table
will have:
a. The same number of records
b. More records
c.

Less records

d. It depends on the contents of the file

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Pop quiz – filtering the first 10 rows
The following SELECT statement:
SELECT TOP 10 * FROM CUSTOMERS

gives you the first ten customers in the CUSTOMERS table of the sample database.
Suppose you want to get the first ten products in the PRODUCTS table of the Jigsaw Puzzles
database. Which of the following statements would do that:
a. SELECT TOP 10 * FROM product
b. SELECT * FROM product WHERE ROWNUM<11
c.

SELECT * FROM product LIMIT 10

d. Any of the above statements

Eliminating data from a database
Deleting information from a database is not the most common operation with databases, but
it is an important one. Now you will learn how to do it with PDI.

Time for action – deleting data about discontinued items
Suppose a manufacturer informs you about the categories of products that will no longer be
available. You don't want to have in your database products something that you will not sell.
Then you use PDI to delete them.

From the Packt website, download the LUX_discontinued.txt file.

Create a new transformation.

With a Text file input step, read the file.

Preview the file. You will see the following:

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After the Text file input step, add an Add constants step to add
a String constant named man_code with value LUX.

Expand the Output category of steps and drag a Delete step to the canvas.

Create a hop from the Add constants step to the Delete step.

Double-click the Delete step. Select js as Connection and, as Target table, browse
and select products. In the grid add the conditions man_code = man_code and
pro_theme LIKE category. After the Delete step, add a Write to log step.

Right-click the Delete step and define the error handling just like you did in each of
the previous tutorials in this chapter.

10.

Save the transformation.

11.

Before running the transformation, open the Database Explorer.

12.

Under the js connection, locate the products table and click
Open SQL for [products].

13.

In the simple SQL editor type:
SELECT pro_theme, pro_name FROM js.products p
ORDER BY pro_theme, pro_name;

14.

Click on Execute. You will see the following result set:

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15.

Close the preview data window and the results of the SQL window.

16.

Minimize the database explorer window.

17.

The database explorer is collapsed at the bottom of the Spoon window.

18.

Run the transformation.

19.

Look at the Step Metrics. The following is what you should see:

20.

Maximize the database explorer window.

21.

In the SQL editor window click Execute again. This time you will see this:

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What just happened?
You deleted from the products table all products belonging to the categories found in the
LUX_discontinued.txt file.
Note that to query the list of products, you used the PDI Database explorer. You could have
done the same by using MySQL Query Browser.

Deleting records of a database table with the Delete step
The Delete step allows you to delete records of a database table based on a given condition.
For each row coming to the step, PDI deletes the records that match the condition set in its
configuration window.
Let's see how it worked in the tutorial. The following is the dataset coming to the
Delete step:

For each of these two rows PDI performs a new delete operation.
For the first row, the records deleted from the products table are those where man_code is
equal to LUX and pro_theme is like FAMOUS LANDMARKS.
For the second row, the records deleted from the products table are those where
man_code is equal to LUX and pro_theme is like COUNTRYSIDE.
You can verify the performed operations by comparing the result sets you got in the
database explorer before and after running the transformation.
Just for your information, you could have done the same task with the following
DELETE statements:
DELETE FROM products
WHERE man_code = 'LUX' and pro_theme LIKE 'FAMOUS LANDMARKS'
DELETE FROM products
WHERE man_code = 'LUX' and pro_theme LIKE 'COUNTRYSIDE'

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In the Step Metrics result, you may notice that the updated column for the Delete step has
value 2. This number is the number of delete operations, not the number of deleted records,
which was actually a bigger number.

Have a go hero – deleting old orders
Create a transformation that asks for a date from the command line and deletes all orders
from the Steel Wheels database whose order dates are before the given date.

Summary
This chapter discussed how to use PDI to work with databases. Specifically, the chapter
covered the following:


Introduction to the Pentaho Sample Data Steel Wheels—the starting point for you
to learn basic database theory



Creating connections from PDI to different database engines



Exploring databases with the PDI Database explorer



Basics of SQL



Performing CRUD (Create, Read, Update, and Delete) operations on databases

In the next chapter you will continue working with databases. You will learn some advanced
concepts, including datawarehouse-specific operations.

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with Databases
In this chapter you will learn about advanced operations with databases. The first part of the
chapter includes:


Populating the Jigsaw puzzle database so that it is prepared for the rest of the
activities in the chapter



Doing simple lookups in a database



Doing complex lookups

The second part of the chapter is fully devoted to datawarehouse-related concepts. The list
of the topics that will be covered includes:


Introducing dimensional modeling



Loading dimensions

Preparing the environment
In order to learn the concepts of this chapter, a database with little or no data is useless.
Therefore, the first thing you'll do is populating your Jigsaw puzzle database.

Time for action – populating the Jigsaw database
To load data massively into your Jigsaw database, you must have the Jigsaw database
created and the MySQL server running. You already know how to do this. If not, please
refer to Chapter 1 for the installation of MySQL and Chapter 8 for the creation of the
Jigsaw database.

Performing Advanced Operations with Databases

This tutorial will overwrite all your data in the js database. If you don't

want to overwrite the data in your js database, you could simply create a new
database with a different name and run the js.sql script to create the tables
in your new database.

After checking that everything is in order, follow these instructions:

From Packt's website download the js_data.sql script file.

Launch the MySQL query browser.

From the File menu select Open Script....

Locate the downloaded file and open it.

At the beginning of the script file you will see this line:
USE js;

If you created a new database, replace the name js by the name of your new
database.

Click on the Execute button.

At the bottom of the screen, you'll see a progress message.

When the script execution ends, verify that the database has been populated.
Execute some SELECT statements such as:
SELECT * FROM cities

All tables must have records.
Having populated the database, let's prepare the Spoon environment:

Edit the kettle.properties file located in the PDI home directory. Add the
following variables: DB_HOST, DB_NAME, DB_USER, DB_PASS, and DB_PORT. As
values put the setting for your connection to the Jigsaw database. Use the following
lines as a guide:
DB_HOST=localhost
DB_NAME=js
DB_USER=root
DB_PASS=1234
DB_PORT=3306

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Add the following variables: DW_HOST, DW_NAME, DW_USER, DW_PASS, and
DW_PORT. As values, put the setting for your connection to the js_dw
database—the database you created in Chapter 8 to load the time dimension.
Here are some sample lines for you to use:
DW_HOST=localhost
DW_NAME=js_dw
DW_USER=root
DW_PASS=1234
DW_PORT=3306

Save the file.

Included in the downloaded material is a file named shared.xml. Copy it to your
PDI home directory (the same directory where the kettle.properties file is)
overwriting the existing file.
Before overwriting the file, please take a backup, as this will
delete any share connections you might have created.

Launch Spoon. If it was running, restart it so that it recognizes the changes in the
kettle.properties file.

Create a new transformation.
If you don't see the shared database connections js and
dw, please verify that you copied the shared.xml file to
the right folder.

Right-click the js database connection and select Edit. In the Settings frame, instead
of fixed values, you will see variables: ${DS_HOST} for Host Name, ${DS_NAME} for
Database Name, and so on.

Test the connection.

Repeat the steps for the js_dw shared connection: Right-click the database
connection and select Edit. In the Settings frame, you will see the variables you
defined in the kettle.properties file—${DW_HOST}, ${DW_NAME}, and so on.

Test the dw_js connection.

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If any of the database tests fail, please check that the connection
variables you put in the kettle.properties file are correct.
Also check that MySQL is running database.

What just happened?
In this tutorial you prepared the environment for working in the rest of the chapter.
You did two different things:


First, you ran a script that emptied all the js database tables and loaded data into them.



Then, you redefined the database connections to the databases js and js_dw.
Note that the names for the connection don't have to match the
names of the databases. This can benefit you in the following way: If
you created a database with a different name for the Jigsaw database
puzzle, your connection may still be named js, and all code you
download from the Packt website should work without touching
anything but the kettle.properties file.

You edited the kettle.properties file by adding variables with the database connection
values such as host name, database name, and so on. Then you edited the database
connections. There you saw that the database settings didn't have values but variable
names—the variables you had defined in the kettle.properties file. For shared
connections, PDI takes the database definition from the shared.xml file.
Note that you didn't save the transformation you created. That was
intentional. The only purpose for creating it was to be able to see the
shared connections.

Exploring the Jigsaw database model
The information in this section allows you to understand the organization of the data in the
Jigsaw database. In the first place, you have a DER. A DER or entity relationship diagram is
a graphical representation that allows you to see how the tables in a database are related to
each other. The following is the DER for the js database:

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The following table contains a brief explanation of what each table is for:
Table name
manufacturers

Content

products

It is about the products you sell such as puzzles and accessories. The
table has descriptive information and data about prices and stock. The
pro_type column has the type of product—puzzle, glue, and so on.
Several of the columns apply only to puzzles, such as shape or pieces.

buy_methods

It contains information about the list of methods for buying—for
example, in store, by telephone, and so on.

payment_methods

Information about list of methods of payment such as cash, check,
credit card, and so on.

countries

The list of countries.

cities

The list of cities.

customers

A list of customers. A customer has a number, a name, and an address.

invoices

The header of invoices including date, customer number, and total
amount. The invoices dates range from 2004 to 2010.

Information about manufacturers of the products.

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Looking up data in a database
You already know how to create, update, and delete data from a database. It's now time to
learn to look up data. Lookup is the act of searching for information in a database. You can
look up a column of a single table or you can do more complex lookups. Let's begin with the
simplest way of looking up.

Doing simple lookups
Sometimes you need to get information from a database table based on the data you have in
your main stream. Let's see how you can do it.

Time for action – using a Database lookup step to create a list
of products to buy
Suppose you have an online system for your customers to order products. On a daily basis,
the system creates a file with the orders information. Now you will check if you have stock
for the ordered products and make a list of the products you'll have to buy.

Create a new transformation.

From the Input category of steps, drag a Get data from XML step to the canvas.

Use it to read the orders.xml file. In the Content tab, fill the Loop XPath option
with the /orders/order string. In the Fields tab get the fields.

Do a preview. You will see the following:

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To keep this exercise simple, the file contains a single
product by order.

Add a Sort rows step and use it to sort the data by man_code, prod_code.

Add a Group by step and double-click it.

Use the upper grid for grouping by man_code and prod_code.

Use the lower grid for adding a field with the number of orders in each group. As
Name write quantity, as Subject ordernumber, and as Type write Number of
Values (N). Expand the Lookup category of steps.

Drag a Database lookup step to the canvas and create a hop from the Group by step
toward this step.

10.

Double-click the Database lookup step.

11.

As Connection, select js and in Lookup table, browse the database and select
products or just type its name.

12.

Fill the grids as follows:

If you don't see both grids, just resize the window. This is
one of the few configuration steps that lack the scrollbar to
the right side.
Also remember that with all grids in PDI, you always have
the option to populate the grids by using the Get Fields and
Get lookup fields buttons respectively.

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13.

Click on OK.

14.

Add a filter step to pass only the rows where pro_stock, and so on. The
Stream lookup step looks only for equal values. As all the products in the file existed in your
database, the step found a record for every row, adding to your stream two fields: the name
and the stock for the product. You can check it by doing a preview on the Database lookup
step. After the Database lookup setup, you used a Filter rows step to discard the rows where
the stock was lower than the required quantity of products. You can avoid adding this step
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by refining the lookup configuration. In the upper grid you could add the condition pro_
stock=?

In the Number of rows to return textbox, type 4.

10.

Fill the grid as shown:

11.

Click on OK. The transformation looks like this:

12.

With the last step selected, do a Preview.

13.

You should see this:

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14.

In the Step Metrics you should see this:

What just happened?
You took the list of orders and filtered those for which you ran out of products. For the
customers that ordered those products you built a list of four alternative puzzles to buy.
The selection of the puzzles was based on the theme. To filter the suggested puzzles, you
used the theme of the ordered product.
The second parameter in the Database join step, the ordered quantity, was used to offer only
alternatives for products for which there is a sufficient stock.

Joining data from the database to the stream data by using a Database
join step
With the Database join step, you can combine your incoming stream with data from your
database, based on given conditions. The conditions are put as parameters in the query you
write in the Database join step.
Note that this is not really a database join as the name suggests; it is a
join of data from the database to the stream data.

In the tutorial you used two parameters—the theme and the quantity ordered. With those
parameters, you queried the list of products with the same theme:
where pro_theme like ?

and for which you have stock:
and pro_stock>=?

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You set the parameters as question marks. This works like the question marks in a Table
input step you learned in the last chapter—the parameters are replaced positionally. The
difference is that here you define the list and the order of the parameters. You do it in the
small grid at the bottom of the settings window. This means you aren't forced to use all the
incoming fields as parameters, and that you also may change the order.
Just as you do in a Table input step, instead of using positional parameters, you can use
Kettle variables by using the ${} notation and checking the Replace variables checkbox.
You don't need to add the Select values step to discard fields and rename the
parameters. You did it just to have fewer fields in the final screenshot so that it
was easier to understand the output of the Database join step.

The step will give you back the manufacturer code, the product code, and the product name
for the matching records.
As you cannot do a preview here, you can write and try your query inside a
Table input step or in MySQL Query Browser. When you are done, just copy
and paste the query here.

So far, you did the same you could have done with Database lookup step—looking for a
record with a given condition, and adding new fields to the stream. However, there is a big
difference here—you put 4 as the Number of rows to return. This means for each incoming
row, the step will give you back up to four results. The following shows you this:

Note that if you had left the Number of rows to return empty, the step would
have returned all found rows.

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You may need to use a Database join step in several situations:


When, as the result of the lookup, there is more than one row for each incoming row.
This was the case in the tutorial.



When you have to look in a combination of tables. Look at the following SQL statement:
SELECT co.country_name
FROM customers cu
, cities
ci
, countries co
WHERE cu.city_id = ci.city_id
AND
ci.cou_id = co.cou_id
AND

cu.cus_id

= 1000

This statement returns the name of the country where the customer with id 1000
lives. If you want to look up the countries where a list of customers live, you can do
it with a sentence like this by using a Database join step.


When you want to look for an aggregate result. Look at this sample query:
SELECT

pro_theme
, count(*) quant
FROM
products
GROUP BY pro_theme
ORDER BY pro_theme

This statement returns the number of puzzles by theme. If you have a list of themes
and you want to find out how many puzzles you have for each theme, you can use a
query like this also by using a Database join step.
The last option in the list can also be developed without using the Database join step.
You could execute the SELECT statement with a Table Input step, and then look for the
calculated quantity by using a Stream lookup step.
As you can see, this is another situation where PDI offers more that one
way to do the same thing. Sometimes it is a matter of taste. In general, you
should test each option and choose the method which gives you the best
performance.

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Have a go hero – rebuilding the list of customers
Redo the Hero exercise preparing the delivery of the products, this time using a Database
join step. Try to discover which one is preferable from the point of view of performance. If
you don't see any difference, try with a bigger number of records in the main stream. You
will have to create your own dataset for this test.

Introducing dimensional modeling
So far you have dealt with the Jigsaw puzzles database, a database used for daily operational
work. In the real-world, a database like this is maintained by an On-Line Transaction
Processing (OLTP) system. The users of an OLTP system perform operational tasks—sell
products, process orders, control stock, and so on.
As a counterpart, a datawarehouse is a nonoperational database; it is a specialized database
designed for decision support purposes. Users of a datawarehouse analyze the data, and
they do it from different points of view.
The most used technique for delivering data to datawarehouse users is dimensional
modeling. This technique makes databases simple and understandable.
The primary table in a dimensional model is the fact table. A fact table stores numerical
measurements of the business such as quantity of products sold, amount represented by the
sold products, discounts, taxes, number of invoices, number of claims, and anything that can
be measured. These measurements are referred as facts.
A fact is useless without the dimension tables. Dimension tables contain the textual
descriptors of the business. Typical dimensions are product, time, customers, and regions.
The fact along with all the surrounding dimension tables make a star-like structure often
called a star schema.
Datawarehouse is a very broad concept. In this book we will deal with datamarts. While a
datawarehouse represents a global vision of an enterprise, a datamart holds the data from a
single business process .
Data stored in datawarehouses and datamarts usually comes from different sources, the
operational database being the main. The process that takes the information from the source,
transforms it in several ways, and finally loads the data into the datamart or datawarehouse is
the already mentioned ETL process. As said, PDI is a perfect tool for accomplishing that task.
In the rest of this chapter, you will learn how to load dimension tables with PDI. This will build
the basis for the final project of the book: Loading a full datamart.

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Through the tutorials you will learn more about this. However, the terminology introduced
here constitutes just a preamble to dimensional modeling. There is much more you
can learn. If you are really interested in the subject, you should start by reading The
Data Warehouse Toolkit (Second Edition) by Ralph Kimball and Margy Ross. The book is
undoubtedly the best guide to dimensional modeling.

Loading dimensions with data
A dimension is an entity that describes your business—customers and products are examples
of dimensions. A very special dimension is the time dimension that you already know. A
dimension table (no surprises here) is a table that contains information about a dimension.
In this section you will learn to load dimension tables, that is, fill dimension tables with data.

Time for action – loading a region dimension with a
Combination lookup/update step
In this tutorial you will load a dimension that stores geographical information.

Launch Spoon.

Create a new transformation.

Drag a Table input step to the canvas and double-click it.

As connection select js.

In the SQL area type the following query:
SELECT ci.city_id, city_name, country_name
FROM cities ci, countries co
WHERE ci.cou_id = co.cou_id

Click on OK.

Expand the Data Warehouse category of steps.

Select the Combination lookup/update step and drag it to the canvas.

Create a hop from the Table input step to this new step.

10.

Double-click the Combination lookup/update step.

11.

As Connection select dw.

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12.

As Target table browse and select lk_regions or simply type it.

13.

Enter id as Technical key field and lastupdate as Date of last update field.

14.

Click OK.

15.

After the Combination lookup/update step, add an Update step.

16.

Double-click the Update step.

17.

Select dw as Connection and lk_regions as Target table.

18.

Fill the upper grid adding the condition id = id. The id to the left is the table id,
while the id to the right is the stream id.

19.

Fill the lower grid: Add one row with the values city and city_name. Add a
second row with the values country and country_name. This will update the
table columns city and country with the values city_name and country_name
coming in the stream.

20.

Now create another stream: Add to the canvas a Generate Rows step, a Table
output step, and a Dummy step.

21.

Link the steps in the order you added them.

22.

Edit the Generate Rows step and set Limit to 1.

23.

Add four fields in this order: An Integer field named id with value 0, a String
field named city with value N/A, another String named country with value
N/A, and an Integer field named id_js with value 0. Double-click the Table
Output step.

24.

Select dw as Connection and lk_regions as Target table.

25.

Click on OK.

26.

In the Table output step, enable error handling and send the bad rows to the
Dummy step.

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27.

The transformation looks like this:

28.

Save the transformation and run it.

29.

The Step metrics looks like this:

30.

Explore the js_dw database and do a preview of the lk_regions table. You should
see this:

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Chapter 9

What just happened?
You loaded the region dimension with geographical information—cities and countries.
Note that you took information from the operational database
js and loaded a table in another database js_dw.

Before running the transformation, the dimension table lk_region was empty. When the
transformation ran, all cities were inserted in the dimension table.
Besides the records with cities from the cities table, you also inserted a special record
with values n/a for the descriptive fields. You did it in the second stream added to the
transformation.
Note that the dimension table lk_regions has a column named region that you didn't
update because you don't have data for that column. The column is filled with a default
value set in the DDL definition of the table.

Time for action – testing the transformation that loads the
region dimension
1.

In the previous tutorial you loaded a dimension that stores geographical
information. You ran it once, causing the insertion of one record for each city and a
special record with values n/a for the descriptive fields. Let's apply some changes in
the operational database, and run the transformation again to see what happens.

Launch MySQL Query Browser.

Type the following sentence to change the names of the countries to upper case:
UPDATE countries SET country_name = UCASE(country_name)

Execute it.

If the transformation created in the last tutorial is not open, open it again.

Run the transformation.

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The Step Metrics looks like this:

Explore the js_dw database again and do a preview of the lk_regions table. This
time you will see the following:

What just happened?
After changing the letter case for the names of the countries in the transactional database
js, you again ran the transformation that updates the Regions dimension. This time the
descriptions for the dimension table were updated.
As for the special record with values n/a for the descriptive fields, it had been created the
first time the transformation ran. This time, as the record already existed, the row passed by
to the Dummy step.

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Describing data with dimensions
A dimension table contains descriptions about a particular entity or category of your
business. Dimensions are one of the basic blocks of a datawarehouse or a datamart.
A dimension has the purpose of grouping, filtering, and describing data.
Think of a typical report you would like to have—sales grouped by region, by customer, by
method of payment ordered by date. The by word lets you identify potential dimensions—
regions, customers, method of payments, and date.
Best practices say that a dimension table must have its own technical key column different
to the business key column used in the operational database. This technical key is known
as a surrogate key. In the lk_region dimension table the surrogate key is the column
named id.
While in the operational database the key may be a string such as the manufacturer code in
the manufacturers table, surrogate keys are always integers. Another good practice is to have
a special record for unavailable data. In the case of the regions example, this implies that
besides one record for every city, you should have a record with key equal to zero, and n/a
or unknown or something that represents invalid data for all the descriptive attributes.
Along with the descriptive attributes that you save in a dimension, you usually keep the
business key so that you can match the data in the dimension table with the data in the
source database. The following screenshot depicts typical columns in a dimension table:

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In the tutorial, you took information from the cities and countries tables and used that
data to load the regions dimension. When there were changes in the transactional database,
the changes were translated to the dimension table overwriting the old values. A dimension
where changes may occur from time to time is called a Slowly Changing Dimension or SCD
for short. If, when you update an SCD dimension, you don't preserve historical values but the
old values, the dimension is called Type I slowly changing dimension (Type I SCD).

Loading Type I SCD with a Combination lookup/update step
In the tutorial, you loaded a Type I SCD by using a Combination lookup/update step. The
Combination lookup/update or Combination L/U for short, looks in the dimension table
for a record that matches the key fields you put in the upper grid in the settings window. If
the combination exists, the step returns the surrogate key of the found record. If it doesn't
exist, the step generates a new surrogate key and inserts a row with the key fields and the
generated surrogate key. In any case, the surrogate key is added to the output stream.
Be aware that in the Combination Lookup/update step the following options
do not refer to fields in the stream, but to columns in the table: Dimension
field, Technical key field, and Date of last update field. You should read
Dimension column, Technical key column, and Date of last update column.
Also note that the term Technical refers to the surrogate key.

Let's see how the Combination lookup/update step works with an example. Look at the
following screenshot:

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The record to the right of the Table input icon is a sample city among the cities that the Table
input step gets from the js database.
With the Combination L/U step, PDI looks for a record in the lk_region table in the dw
database, where id_js is equal to the field city_id in the incoming stream, which is 7001.
The first time you run the transformation, the dimension table is empty, so the lookup fails.
This causes PDI to generate a new surrogate key according to what you put in the Technical
key field area of the settings window.
You told PDI that the column that holds the surrogate key is the column named id. You also
told PDI that in order to generate the key, the value should be equal to the maximum key
found in the target table plus one. In this example, it generates a key equal to 7. You may
also use a sequence or an auto increment field if the database engine allows it. If that is not
the case, those options are disabled.
Then PDI generates the key and inserts the record you can see to the right of the
Combination L/U step in the draw. Note that the record contains only values for the key
fields and the technical key field.
The Combination L/U step put the returned technical key in the output stream. Then you
used that key for updating the descriptions for city and country with the use of an Update
step. After that step, the record is fully generated, as shown in the record to the right of
the Update icon.
As the Combination L/U only maintains the key information, if
you have non-key columns in the table you must update them
with an extra Update step.
Note that those values must have a default value or must
allow null values. If none of these conditions is true, the insert
operation will fail.

After converting to upper case, all the country names in the source database, you run the
transformation again.
This time the incoming record for the same city is this:

PDI looks for a record in the lk_region table, in the dw database, where id_js is equal
to 7001. It finds it. It is the record inserted the first time you ran the transformation, as
explained above.
Then, the Combination L/U simply returns the key field adding it to the output stream.
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Then you use the key that the step added to update the descriptions for city and country.
After the Update step, the old values for city and country name are overwritten by the
new ones:

Have a go hero – adding regions to the Region Dimension
Modify the transformation that loads the Region dimension to fill the region column. Get
the values from the regions.xls file you can find among the downloaded material for this
chapter. To add the region information to your stream, use a Stream lookup step.
While you are playing with dimensions, you may want to throw away all the
inserted data and start over again. For doing that, simply explore the database
and use the Truncate table option. You can do the same in MySQL Query
Explorer. For the lk_regions dimension, you could execute any of
the following:
DELETE FROM lk_regions or TRUNCATE TABLE lk_regions

Have a go hero – loading the manufacturers dimension
Create a transformation that loads the manufacturers dimension—lk_manufacturers.
Here you have the table definition and some guidance for loading:
Column
id

Description

name

Name of the manufacturer.

id_js

Business key. Here you have to store the manufacturer's code
(man_code field of the source table manufacturers).

lastupdate

Date of dimension update—system date.

Surrogate key.

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Have a go hero – loading a mini-dimension
A mini-dimension is a dimension where you store the frequently analyzed or frequently
changing attributes of a large dimension. Look at the products in the Jigsaw puzzles
database. There are several puzzle attributes you may be interested in, for example, when
you analyze the sales—number of puzzles in a single pack, number of pieces of the puzzles,
material of the product, and so on. Instead of creating a big dimension with all puzzle
attributes, you can create a mini-dimension that stores only a selection of attributes. There
would be one row in this mini-dimension for each unique combination of the selected
attributes encountered in the products table, not one row per puzzle.
In this exercise, you'll have to load a mini-dimension with puzzle attributes. Here you have
the definition of the table that will hold the mini-dimension data:
Column
id

Description

glowsInDark

Y/N

is3D

Y/N

wooden

Y/N

isPanoramic

Y/N

nrPuzzles

Number of puzzles in a single pack

nrPieces

Number of pieces of the puzzle

Surrogate key

Take as a starting point the following query:
SELECT DISTINCT pro_type
, pro_packaging
, pro_shape
, pro_style
FROM products
WHERE pro_type = 'PUZZLE'

Use the output stream for creating the fields you need for the dimension—for example,
for the field is3D, you'll have to check the value of the pro_shape field.
Once you have all the fields you need, insert the records in the dimension table by using a
Combination L/U step. In this mini-dimension, the key is made by all the fields of the table.
As a consequence, you don’t need an extra Update step.

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Keeping a history of changes
The Region dimension is a typical Type I SCD dimension. If some description changes, as
the country names did, it makes no sense to keep the old values. The new values simply
overwrite the old ones. This is not always the best choice. Sometimes you would like to
keep a history of the changes. Now you will learn to load a dimension that keeps a history.

Time for action – keeping a history of product changes with the
Dimension lookup/update step
Let's load a puzzles dimension along with the history of the changes in puzzle attributes:

Create a new transformation.

Drag a Table input step to the work area and double-click it.

Select js as Connection.

Type the following query in the SQL area:
SELECT pro_code
, man_code
, pro_name
, pro_theme
FROM products
WHERE pro_type LIKE 'PUZZLE'

Click on OK.

Add an Add constants step, and create a hop from the Table input, step toward it.

Use the step to add a Date field named changedate. As Format type dd/MM/
yyyy, and as Value, type 01/10/2009.

Expand the Data Warehouse category of steps.

Select the Dimension lookup/update step and drag it to the canvas.

10.

Create a hop from the Add constants step to this new step.

11.

Double-click the Dimension lookup/update step.

12.

As Connection select dw.

13.

As Target table type lk_puzzles.

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14.

Fill the Key fields as shown:

15.

Select id as Technical key field.

16.

In the frame Creation of technical key, leave the default to
Use table maximum + 1.

17.

As Version field, select version.

18.

As Stream Datefield, select changedate.

19.

As Date range start field, select start_date.

20.

As Table daterange end, select end_date.

21.

Select the Fields tab and fill it like this:

22.

Close the settings window.

23.

Save the transformation, and run it.

24.

Explore the js_dw database and do a preview of the lk_puzzles table.

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25.

You should see this:

What just happened?
You loaded the puzzle dimension with the name and theme of the puzzles you sell. The
dimension table has the usual columns for a dimension—technical id (field id), fields that
store the key fields in the table of the operational database (prod_code and man_code),
and columns for the puzzle attributes (name and theme). It also has some extra fields
specially designed to keep history.
When you ran the transformation, all records were inserted in the dimension table. Also a
special record was automatically inserted for unavailable data.
So far, there is nothing new except for a few extra columns with dates. In the next tutorial,
you will learn more about those columns.

Time for action – testing the transformation that keeps a history
of product changes
1.

In the previous tutorial you loaded a dimension with products by using a Dimension
lookup/update step. You ran the transformation once, causing the insertion of one
record for each product and a special record with values n/a for the descriptive fields.
Let's apply some changes in the operational database, and run the transformation again
to see how the Dimension lookup/update step keeps history.

In MySQL Query Browser, open the script update_jumbo_products.sql and
run it.

Switch to Spoon.

If the transformation created in the last tutorial is not open, open it again.
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Run the transformation. Explore the js_dw database again. Press Open SQL for
[lk_puzzles] and type the following sentence:
SELECT
FROM
WHERE
ORDER BY
,

*
lk_puzzles
id_js_man = 'JUM'
id_js_prod
version

You will see this:

What just happened?
After making some changes in the operational database, you ran the transformation for a
second time. The modifications you made caused the insertion of new records recreating the
history of the puzzle attributes.

Keeping an entire history of data with a Type II slowly changing dimension
Type II SCDs differ from Type I SCDs in that a Type II keeps the whole history of the data of
your dimension. Typical examples of attributes for which you would like to keep a history are
sales territories that change over time, categories of products that are reclassified from time
to time, and promotions that you apply to products and are valid in a given range of dates.
There are no rules that dictate whether or not you keep/retain the history in a
dimension. It's the final user who decides based on his requirements.

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In the puzzle dimension, you kept information about the changes for the name and theme
attributes. Let's see how the history is kept for this sample dimension.
Each puzzle is to be represented by one or more records, each with the information valid
during a certain period of time, as in the following example:
01
01
1900

01
10
1900

31
12
2199

VERSION : 1

VERSION : 2 (current)

Valid From : 01-01-1900

Valid From : 01-10-2009

To : 01-10-2009

To : 31-12-2199

Surrogate Key : 664

Surrogate Key : 1031

Business Key : JUM, JUMB0107

Fields :

Name : Cindrellas Grand Arrival

Theme : Castles

Theme : Disney

The history is kept in three extra fields in the dimension table—version, date_from,
and date_to.
The version field is an automatically incremented value that maintains a revision number of
the records for a particular puzzle.
The date range is used to indicate the period of applicability of the data.
In the tutorial you also had a current field, that acted as a flag to show if a record is the
record valid in the present day.
The sample puzzle, Cinderellas Grand Arrival, was classified in the category Castles until
October 1, 2009. After that date, the puzzle was reclassified as a Disney puzzle. This is the
second version of the puzzle, as indicated by the column version. It's also the current
version, as indicated by the column current.

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In general, if you have to implement a Type II SCD with PDI, your dimension table
must have the first three fields—version, date from, and date to. The current flag
is optional.

Loading Type II SCDs with the Dimension lookup/update step
Type II SCDs can be loaded by using the Dimension lookup/update step. The Dimension
lookup/update or Dimension L/U for short, looks in the dimension for a record that matches
the information you put in the Keys grid of the settings window.
If the lookup fails, it inserts a new record. If a record is found, the step inserts or updates
records depending on how you configured the step.
Let's explain how the Dimension L/U works with the following sample puzzle in the
js database:

The first time you run the transformation, the step looks in the dimension for a record where
id_js_prod is equal to JUMBO107and id_js_man is equal to JUM. Not only that, the
period from start_date to end_date of the found record must contain the value of the
stream datefield, which is 01/10/2009.
Because you never loaded this table before, the table was empty and so the lookup failed.
As a result, the step inserts the following record:

Note the values that the step put for the special fields:
The version for the new record is 1, the current flag is set to true, and the start_date and
end_date take as values the dates you put in the Min.year and Max.year: 01/01/1900
and 31/12/2199.

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After making some modifications to the operational database, you ran the transformation
again. Look at the following screenshot:

The puzzle information changed. As you see to the right of the Table input step, the puzzle is
now classified as a Disney puzzle.
This time the lookup succeeds. There is a record for which the keys match and the period
from start_date to end_date of the found record, 01/01/1900 to 31/12/2199,
obviously contains the value of the stream datefield, 01/10/2009.
Once found, the step compares the fields you put in the Fields tab—name and theme in the
dimension table against pro_name and pro_theme in the incoming stream.
As there is a difference in the theme field, the step inserts a new record, and modifies the
current—it changes the validity dates and sets the current flag to false. Now this puzzle has
two versions in the dimension table, as you see below the Dimension L/U icon in the drawing.
These update and insert operations are made for all records that changed.
For the records that didn't change, dimension records are found but as nothing changed,
nothing is inserted or updated.
Take a note about the stream date: The field you put here is key to the loading
process of the dimension, as its value is interpreted by PDI as the effective
date of the change. In the tutorial, you put a fixed date—01/10/2009. In
real situations you should use the effective or last changed date of the data if
that date is available. If it is not available, leave the field blank. PDI will use the
system date.
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In this example, you filled the column Type of SCD update with the option Insert for every
field. Doing so, you loaded a pure Type II SCD, that is, a dimension that keeps track of all
changes in all fields.
In the sample puzzles dimension, you kept a history of changes both in the theme and in
the name. For the sample puzzle, the theme was changed from Castles to Disney. If, after
some time, you query the sales and notice that the sales for that puzzle increased after the
change, then you may conclude that the customers are more interested in Disney puzzles
than in castle puzzles. The possibility of creating these kinds of reports is a good reason for
maintaining a Type II SCD.
On the other hand, if the name of the puzzle changes, you may not be so interested in
knowing what the name was before. Fortunately, you may change the configuration and
create a Hybrid SCD. Instead of selecting Insert for every field, you may select Update
or Punch through:




When there is a change in a field for which you chose Update, the new value
overwrites the old value in the last dimension record version, this being the usual
behavior in Type I SCDs.
When there is a change in a field for which you chose Punch through, the new
data overwrites the old value in all record versions.

Note that selecting Punch through for all the fields, the Dimension L/U step allows you
to load a Type I SCD dimension. When you build Type I SCD you are not interested in range
dates. Thus, you can leave the Stream datefield textbox empty. The current date is assumed
by default.
In practice both Type I, Type II, and Hybrid SCDs are used. The choice of the type of SCD
depends on the business needs.
Besides all those inserts and updates operations, the Dimension L/U automatically inserts in
the dimension a record for unavailable data.
In order to insert the special record with key equal to zero, all fields must have
default values or allow nulls. If none of these conditions are true, the automatic
insertion will fail.

In order to load a dimension with the Dimension L/U step, your table has to have columns
for the version, date from, and date to. The step automatically maintains those columns.
You simply have to put their names in the right textbox in the settings window.
Besides those fields, your dimension table may have a column for the current flag, and
another column for the date of last insert or update. To fill those optional columns, you
have to add them in the Fields tab as you did in the tutorial.
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Have a go hero – keeping a history just for the theme of a product
Modify the loading of the products dimension so that it only keeps a history of the theme. If
the name of the product changes, just overwrite the old values. Modify some data in the js
database and run your transformation to confirm that it works as expected.

Have a go hero – loading a Type II SCD dimension
As you saw in the Hero exercise to add regions to the Region Dimension, the countries were
grouped in three: Spain, Rest of Europe, Rest of the World.
As the sales rose in several countries of the world, you decided to regroup the countries in
more than three groups. However, you want to do it starting in 2008. For older sales you
prefer to keep seeing the sales grouped by the original categories.
This is what you will do: Use the table named lk_regions_2 to create a Type II Region
dimension. Here is a guide to follow:
Create a transformation that loads the dimension. You will take the stream date (the date
you use for loading the dimension) from the command line. If the command line argument is
empty, use the present day.
As the name for the sheet with the region definition, use a named parameter.
Stream date

If the command line argument is present, remember to change it to Date
before using it. You do that with a Select values step.
Note that you have to define the format of the entered data in advance.
Suppose that you want to enter as argument the date January 1, 2008. If
you chose the format dd-mm-yyyy, you'll have to enter the argument as
01-01-2008.
In case the command line argument is absent, you can get the default with
a Get System Info step. Note that the system date you add with this step is
already a Date field.

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Now just follow these steps:
1. Run the transformation by using the regions.xls file. Don't worry about the
command line argument. Check that the dimension was loaded as expected. There
has to be a single record for every city.
2. Run the transformation again. This time use the regions2008.xls file as source
for the region column. As command line, enter January 1st, 2008. Remember to type
the date in the expected format (check the preceding tip). Explore the dimension
table. There has to be two records for each country—one valid before 2008 and
one valid after that date.
3. Modify the sheet to create a new grouping for the American countries. Use your
imagination for this task! Run the transformation for the third time. This time use
the sheet you created and as date, type the present day (or leave the argument
blank). Explore the dimension table. Now each city for the countries you regrouped
has to have three versions, where the current is the version you created. The other
cities should continue to have two versions each, because nothing related to those
cities changed.

Pop quiz – loading slowly changing dimensions
Suppose you have DVDs with the French films in the catalog you've created so far. You
rent those DVDs and keep the rental information in the database. Now you will design a
dimensional model for that data.
1. You begin by designing a dimension to store the names of the films. How do you
create the Films dimension:
a.

As a Type I SCD

As a Type II SCD

You will decide when you have rented enough films so you make the
right decision.

2. In order to create that dimension, you could use:
a.

A Dimension L/U step

A Combination L/U step

Either of the above

Neither of the above

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Pop quiz – loading type III slowly changing dimensions
Type III SCD are dimensions that store the immediately preceding and current value for a
descriptive field of the dimension. Each entity is stored in a single record. The field for which
you want to keep the previous value has two columns assigned in the record: One for the
current value and the other for the old. Sometimes, it is possible to have a third column
holding the date of effective change.
Type III SCDs are appropriate when you don't want to keep all the history, but mainly when
you need to support two views of the attribute simultaneously—the previous and the
current. Suppose you have an Employees dimension. Among the attributes you have their
position. People are promoted from time to time and you want to keep these changes in the
dimension; however, you are not interested in knowing all the intermediate positions the
employees have been through. In this case, you may implement a Type III SCD.
The question is, how would you load a Type III SCD with PDI:
a. With a Dimension L/U step configuring it properly
b. By using a Database lookup step to get the previous value. Then with a Dimension
L/U step or a Combination L/U step to insert or update the records.
c.

You can't load Type III SCDs with PDI

It's worth saying that type III SCD are used rather infrequently and not always can be
automated. Sometimes they are used to represent human-applied changes and the
implementation has to be made manually.

Summary
In this chapter you learned to perform some advanced operations on databases.
First, you populated the Jigsaw database in order to have data for the activities in the
chapter. Then, you learned to do simple and complex searches in a database.
Then you were introduced to dimensional concepts and learned what dimensions are
and how to load them with PDI. You learned about Type I, Type II, Type III SCDs and
mini-dimensions. You still have to learn when and how to use those dimensions. You
will do so in Chapter 12.
The steps you learned in this and the preceding chapter are far from being the full list of
steps that PDI offers to work with databases. However, taking into account all you learned,
you are now ready to use PDI for implementing most of your database requirements. In the
next chapter, you will switch to a totally different yet core subject needed to work with
PDI—jobs.
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Creating Basic Task Flows
So far you have been working with data. You got data from a file, a sheet, or a
database, transformed it somehow, and sent it back to some file or table in a
database. You did it by using PDI transformations. A PDI transformation does
not run in isolation. Usually, it is embedded in a bigger process. Here are
some examples:





Download a file, clean it, load the information of the file in a database,
and fill an audit file with the result of the operation.
Generate a daily report and transfer the report to a shared repository.
Update a datawarehouse. If something goes wrong, notify the
administrator by e-mail.

All these examples are typical processes of which a transformation is only a piece. These
types of processes can be implemented by PDI Jobs. In this chapter, you will learn to build
basic jobs. These are the topics that will be covered:


Introduction to jobs



Executing tasks depending upon conditions

Introducing PDI jobs
A PDI job is analogous to a process. As with processes in real life, there are basic jobs and
there are jobs that do really complex tasks. Let's start by creating a job in the first group—a
hello world job.

Creating Basic Task Flows

Time for action – creating a simple hello world job
In this tutorial, you will create a very simple job so that you get an idea of what jobs
are about.
Although you will now learn how to create a job, for this tutorial you first have to create
a transformation.

1.
2.
3.
4.
5.
6.

Open Spoon.

7.
8.
9.
10.
11.

Double-click the step.

12.

Create a new transformation.
Drag a Generate rows step to the canvas and double-click it.
Add a String value named message, with the value Hello, World!.
Click on OK.
Add a Text file output step and create a hop from the Generate rows step to this
new step.
Type ${LABSOUTPUT}/chapter10/hello as filename.
In the Fields tab, add the only field in the stream—message.
Click on OK.
Inside the folder where you save your work, create a folder named
transformations.
Save the transformation with the name hello_world_file.ktr in the folder you
just created. The following is your final transformation:

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Now you are ready to create the main job.

13.

Select File | New | Job or press Ctrl+Alt+N. A new job is created.

14.

Press Ctrl+J. The Job properties window appears.

15.

Give a name and description to the job.

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16.

Save the job in the folder where you created the transformations folder, with
the name hello_world.kjb.

17.

To the left of the screen, there is a tree with job entries. Expand the General
category of job entries, select the START entry, and drag it to the work area.

18.

Expand the File management category, select the Create a folder entry, and drag it
to the canvas.

19.

Select both entries. With the mouse cursor over the second entry, right-click and
select New hop. A new hop is created.

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Just like in a transformation, you have several ways to create hops.
For more detail, please refer to the Time for action – creating a
Hello Word transformation section in Chapter 1 where hops were
introduced or to Appendix D, Spoon Shortcuts.

20.

Double-click the Create a folder...icon.

21.

In the textbox next to the Folder name option, type ${LABSOUTPUT}/chapter10
and click on OK. From the General category, drag a transformation job entry to
the canvas.

22.

Create a hop from the Create a folder entry to the transformation entry.

23.

Double-click the transformation job entry.

24.

Position the cursor in the Transformation filename textbox, press Ctrl+Space, and
select ${Internal.Job.Filename.Directory}.

This variable is the counterpart to the variable {Internal.
Transformation.Filename.Directory} you already know.
{Internal.Job.Filename.Directory} evaluates the
directory where the job resides.

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25.

Click on the icon to the right of the textbox. The following dialog window shows up:

26.

As you can see, the {Internal.Job.Filename.Directory} variable provides
a convenient starting place for looking up the transformation file. Select the
hello_world_file.ktr transformation and click OK.

27.

Now the Transformation filename has the full path to the transformation.
Replace the full job path back to ${Internal.Job.Filename.Directory}
so that the final text for the Transformation filename field is as shown in the
following screenshot:

28.

Click on OK.

29.

Press Ctrl+S to save the job.

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30.

Press F9 to run the job. The following window shows up:

Remember that in the initial chapters, you defined the
LABSOUTPUT variable in the kettle.properties file. You
should see its value in the Variables grid. If you removed the
variable from that file, provide a value here.

31. Click on Launch.
32.

At the bottom of the screen, you'll see the Execution results. The Job metrics screen
looks as follows:

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33.

Select the Logging tab. It looks like this:

34.

Explore the folder pointed to by your ${LABSOUTPUT} variable—for example, c:/
pdi_files/output. You should see a new folder named chapter10.

35.

Inside the chapter10 folder, you should see a file named hello.txt.

36.

Explore the file. It should have the following content:
Message
Hello, World!
Hello, World!
Hello, World!
Hello, World!

What just happened?
First of all, you created a transformation that generated a simple file with the message
Hello, World!. The file was configured to be created in a folder named chapter10.
After that, you created a PDI Job. The job was built to create a folder named chapter10
and then to execute the hello_world transformation.
When you ran the job, the chapter10 folder was created, and inside it, a file with the
Hello, World! message was generated.

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Executing processes with PDI jobs
A Job is a PDI entity designed for the execution of processes. In the tutorial, you ran a
simple process that created a folder and then generated a file in that folder. A more complex
example could be the one that truncates all the tables in a database and loads data in all the
tables from a set of text files. Other examples involve sending e-mails, transferring files, and
executing shell scripts.
The unit of execution inside a job is called a job entry. In Spoon you can see the entries
grouped into categories according to the purpose of the entries. In the tutorial, you used
job entries from two of those categories: General and File management.
Most of the job entries in the File management category have a self-explanatory name such
as Create a folder, and their use is quite intuitive. Feel free to experiment with them!
As to the General category, it contains many of the most used entries. Among them is the
START job entry that you used. A job must start with a START job entry.
Don't forget to start your sequence of job entries with a START. A job
can have any mix of job entries and hops, as long as they start with this
special kind of job entry.

A Hop is a graphical representation that links two job entries. The direction of the hop
defines the order of execution of the job entries it links. Besides, the execution of the
destination job entry does not begin until the job entry that precedes it has finished. Look,
for example, at the job in the tutorial. There is an entry that creates a folder, followed by an
entry that executes a transformation. First of all, the job creates the folder. Once the folder
has been created, the execution of the transformation begins. This allows the transformation
to assume that the folder exists. So, it safely creates a file in that folder.
A hop connects only two job entries. However, a job entry may be reached by more than one
hop. Also, more than one hop may leave a job entry.
A job, like a transformation, is neither a program nor an executable file. It is simply
plain XML. The job contains metadata that tells the Kettle engine which processes to
run and the order of execution of those processes. Therefore, it is said that a job is
flow-control oriented.

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Using Spoon to design and run jobs
As you just saw, with Spoon you not only create, preview, and run transformations, but you
also create and run jobs.
You are already familiar with this graphical tool, so you don't need too much explanation
about the basic work areas. So, let's do a brief review.

The following table describes the main differences you will notice while designing a job
compared to designing a transformation:
Area
Design tree
Job menu
Job metrics tab
(Execution results window)

Description
You don’t see a list of steps but a list of job entries (despite on top of
the list you see the word Steps).
You no longer see some options that only have sense while working
with datasets. One of them is the Preview button.
Instead of a Step Metrics, you have this tab. Here you can see metrics
for each job entry.

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If you click the View icon in the upper-left corner of the screen, the tree will change to show
the structure of the job currently being edited.

Using the transformation job entry
The ������������������������������������������������������������������������
transformation����������������������������������������������������������
job entry allows you to call a transformation from a job.
There are several situations where you may need to use a transformation job entry.
In the tutorial, you had a transformation that generated a file in a given folder. You called the
transformation from a job that created that folder in advance. In this case, the job and the
transformation performed complementary tasks.
Sometimes the job just keeps your work organized. Consider the transformations that loaded
the dimension tables for the js database. As you will usually run them together, you can
embed them into a single job as shown in this figure:

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Creating Basic Task Flows

The only task done by this job is to keep the transformations together.����������������������
Although the picture
implies the entries are run simultaneoulsy, that is not the case.
Job entries typically execute sequentially, this being one of the central
differences between jobs and transformations.

When you link two entries with a hop, you force an order of execution. On the contrary,
when you create a job as shown in this preceding figure, you needn't give an order and the
entries still run in sequence, one entry after another depending on the creation sequence.
Launching job entries in parallel
As the transformations that load dimensions are not dependent on each other,
as an option, you can ask the START entry to launch them simultaneously. For
doing that, right-click the START entry and select Launch next entries in parallel.
Once selected, the arrows to the next job entries will be shown in dashed lines.
This option is available in any entry, not just in the START entry.

The jobs explained earlier are just two examples of how and when you use a transformation
job entry. Note that many transformations perform their tasks by themselves. In that
case you are not forced to embed them into jobs. It makes no sense to have a job with
just a START entry, followed by a transformation job entry. You can still execute those
transformations alone, as you used to do until now.

Pop quiz – defining PDI jobs
1. A job is:
a. A big transformation that groups smaller transformations
b. An ordered group of task definitions
c.

An unordered group of task definitions

2. For each of the following sentences select True or False. A job allows you to:
a. Send e-mails
b. Compare folders
c.

Run transformations

d. Truncate database tables
e. Transfer files with FTP

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Have a go hero – loading the dimension tables
Create a job that loads the main dimension tables in the Jigsaw database—manufacturers,
products, and regions. Test the job.

Receiving arguments and parameters in a job
Jobs, as well as transformations, are more flexible when receiving parameters from outside.
You already learned to parameterize your transformations by using named parameters and
command-line arguments. Let's extend these concepts to jobs.

Time for action – customizing the hello world file with
arguments and parameters
Let's create a more flexible version of the job you did in the previous section.

Create a new transformation.

Press Ctrl+T to bring up the Transformation properties window.

Select the Parameters tab.

Add a named parameter HELLOFOLDER. Insert chapter10 as the default value.

Click on OK.

Drag a Get System Info step to the canvas .

Double-click the step.

Add a field named yourname. Select command line argument 1 as the Type.

Click on OK.

10.

Now add a Formula step located in the Scripting category of steps.

11.

Use the step to add a String field named message. As Formula, type "Hello, "
& [yourname] & "!".

12.

Finally, add a Text file output step.

13. Use the step to send the message data to a file. Enter ${LABSOUTPUT}/
${HELLOFOLDER}/hello as the name of the file.

14.

Save the transformation in the transformations folder you created in the
previous tutorial, under the name hello_world_param.ktr.
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Creating Basic Task Flows

15.

Open the hello_world.kjb job you created in the previous tutorial and save it
under a new job named hello_world_param.kjb.

16.

Press Ctrl+J to open the Job properties window.

17.

Select the Parameters tab.

18.

Add the same named parameter you added in the transformation.

19.

Click on OK.

20.

Double-click the Create a folder entry.

21.

Change the Folder name textbox content to ${LABSOUTPUT}/${HELLOFOLDER}.

22.

Double-click the Transformation entry.

23.

Change the transformation filename textbox to point to the new transformation:
${Internal.Job.Filename.Directory}/transformations/hello_world_
param.ktr.

24.

Click on OK.

25.

Save the job and run it.

26.

Fill the dialog window with a value for the named parameter and a value for the
command-line argument.

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27.

Click on Launch.

28.

When the execution finishes, check the output folder. The folder named
my_folder, which you initially specified as a named parameter, should be created.

29.

Inside that folder there should be a file named hello.txt. This time the content of
the file has been customized with the name you provided:
Hello, pdi student!

What just happened?
You created a transformation that generated a hello.txt file in a folder given as the
named parameter. The content of the file is a customized "Hello" message that gets the
name of the reader from the command line.
In the main job you also defined a named parameter, the same that you defined in the
transformation. The job needs the parameter to create the folder.
When you run the job, you provided both the command-line argument and the named
parameter in the job dialog window that shows up when you launch the execution. Then
a folder was created with the name you gave, and a file was generated with the name you
typed as argument.

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Using named parameters in jobs
You can use named parameters in jobs in the same way you do in transformations. You
define them in the Job properties window. You provide names and default values, and then
you use them just as regular variables. The places where you can use variables, just as in a
transformation, are identified with a dollar sign to the right of the textboxes. In the tutorial,
you used a named parameter in the Create a folder job entry. In this particular example,
you used the same named parameter both in the main job and in the transformation called
by the job. So, you defined the named parameter HELLOFOLDER in two places—in the Job
settings window and in the Transformation properties window.
If a named parameter is used only in the transformation, you
don't need to define it in the job that calls the transformation.

Have a go hero – backing up your work
Suppose you want to back up your output files regularly, that is, the files in your
${LABSOUTPUT} directory. Build a job that creates a ZIP file with all your output files. For
the name and location of the ZIP file, use two named parameters.
Use the Zip file job entry located in the File
management category.

Running jobs from a terminal window
In the main tutorial of this section, both the job and the transformation called by the job
used a named parameter. The transformation also required a command-line argument.
When you executed the job from Spoon, you provided both the parameter and the
argument in the job dialog window. You will now learn to launch the job and provide
that information from a terminal window.

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Time for action – executing the hello world job from a terminal
window
In order to run the job from a terminal window, follow these instructions:

Open a terminal window.

Go to the directory where Kettle is installed.


On Windows systems type:
C:\pdi-ce>kitchen /file:c:/pdi_labs/hello_world_param.kjb
Maria -param:"HELLOFOLDER=my_work" /norep



On Unix, Linux, and other Unix-like systems type:
/home/yourself/pdi-ce/kitchen.sh /file:/home/yourself/
pdi_labs/hello_world_param.kjb Maria -param:"HELLOFOLDER=
my_work" /norep

If your job is in another folder, modify the command accordingly. You may also
replace the name Maria with your name, of course. If your name has spaces,
enclose the whole argument within "".

You will see how the job runs, following the log in the terminal:

Go to the output folder—the folder pointed by your LABS_OUTPUT variable.

A folder named my_work should have been created.

Check the content of the folder. A file named hello.txt should be there. Edit the
file. You should see the following:
Hello,Maria!
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Creating Basic Task Flows

What just happened?
You ran the job with Kitchen, the program that executes jobs from the terminal window.
After the name of the command, kitchen.bat or kitchen.sh, depending on the
platform, you provided the following:


The full path to the job file: /file:c:/pdi_labs/hello_world_param.kjb



A command-line argument: Maria.



A named parameter, and a -param:"HELLOFOLDER=my_work"



The switch /norep to tell Kettle not to connect to a repository

After running the job, you could see that the folder had been created and a file with a
custom "Hello" message had been generated.
Here you used some of the options available when you run Kitchen. Appendix B tells you all
the details about using Kitchen for running jobs.

Have a go hero – experiencing Kitchen
Run the hello_world_param.kjb job from Kitchen, with and without providing
arguments and parameters. See what happens in each case.

Using named parameters and command-line arguments
in transformations
As you know, transformations accept both arguments from the command line and named
parameters. When you run a transformation from Spoon, you supply the values for
arguments and named parameters in the transformation dialog window that shows up
when you launch the execution. From a terminal window, you provide those values in the
Pan command line.
In this chapter you learned to run a transformation embedded in a job. Here, the methods
you have for supplying named parameters and arguments needed by the transformation
are quite similar. From Spoon you supply the values in the job dialog window that shows up
when you launch the job execution. From the terminal window you provide the values in the
Kitchen command line.
Whether you run a job from Spoon or from Kitchen, the named parameters
and arguments you provide are unique and shared by the main job and
all transformations called by that job. Each transformation, as well as the
main job, may or may not use them according to their needs.
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There is still another way in which you can pass parameters and arguments to a
transformation. Let's see it by example.

Time for action – calling the hello world transformation with
fixed arguments and parameters
This time you will call the parameterized transformation from a new job.

Open the hello_world.kjb job you created in the first section and save it as
hello_world_fixedvalues.kjb.

Double-click the Create a folder job entry.

Replace the chapter10 string by the string fixedfolder.

Double-click the transformation job entry.

Change the Transformation filename as ${Internal.Job.Filename.
Directory}/transformations/hello_world_param.ktr.

Fill the Argument tab as follows.

Click the Parameters tab and fill it as follows:

Click on OK.

Save the job.
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10.

Open a terminal window and go to the directory where Kettle is installed.


On Windows systems type:
C:\pdi-ce>kitchen /file:c:/pdi_labs/
hello_world_param.kjb /norep



On Unix, Linux, and other Unix-like systems type:
/home/yourself/pdi-ce/kitchen.sh /file:/home/yourself/
pdi_labs/hello_world_param.kjb /norep

11.

When the execution finishes, check the output folder. A folder named
fixedfolder has been created.

12.

In that folder, you can see a hello.txt with the following content:
Hello, reader!

What just happened?
You reused the transformation that expects an argument and a named parameter from the
command line. This time you created a job that called the transformation and set both the
parameter and the argument in the transformation job entry setting window.
Then you ran the job from a terminal window, without typing any arguments or parameters.
It didn't make any difference for the transformation. Whether you provide parameters and
arguments from the command line or you set constant values in a transformation job entry,
the transformation does its job—creating a file with a custom message in the folder with the
name given by the ${HELLOFOLDER}parameter.
Instead of running from the terminal window, you could have run the
job by pressing F9 and then clicking Launch, without typing anything
in either the parameter or the argument grid. The final result should
be exactly the same.

Have a go hero – saying hello again and again
Modify the hello_world_param.kjb job so that it generates three files in the default
${HELLOFOLDER}, each saying "hello" to a different person.
After the creation of the folder, use three transformation job entries.
Provide different arguments for each.

Run the job to see that it works as expected.

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Have a go hero – loading the time dimension from a job
In Chapter 6, you built a transformation that created the data for a time dimension. Then in
Chapter 8, you finished the transformation loading the data into a time dimension table.
The transformation had several named parameters, one of them being START_DATE.
Create a job that loads a time dimension with dates starting at 01/01/2000. In
technical jargon, create a job that calls your transformation and passes it a value for
the START_DATE parameter.

Deciding between the use of a command-line argument
and a named parameter
Both command-line arguments and named parameters are means for creating more flexible
jobs and transformations. The following table summarizes the differences and the reasons
for using one or the other. In the first column, the word argument refers to the external
value you will use in your job or transformation. That argument could be implemented
as a named parameter or as a command-line argument.
Situation

Solution using named
parameters
It is desirable to have a
Named parameters are
default for the argument perfect in this case. You
provide default values at the
time you define them.

Solution using arguments

The argument is
mandatory

To know if the user provided a value
for the command-line argument, you
just get the command-line argument
and compare it to a null value.
Let's suppose that you expect three
command line arguments. If you
have a value only for the third, you
still have to provide empty values for
the first and the second.
The command-line is simple as you
just list the values one after the
other. However, there is a risk—you
may unintentionally enter the values
unordered, which could lead to
unexpected results.

You need several
arguments but it is
probable that not all of
them are present.
You need several
arguments and it is
highly probable that all
of them are present.

You don't have means
to determine if the user
provided a value for the
named parameter.
If you don't have a value for a
named parameter, you are not
forced to enter it when you
run the job or transformation.
The command line would be
too long. It will help explain
clearly the purpose of each
parameter, but typing the
command line would be
tedious.

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Before using the command-line
argument, you have to evaluate if it
was provided in the command line. If
not, you have to set the default value
at that moment.

Creating Basic Task Flows

Situation

Solution using named
parameters

You want to use the
You can do it, but you must
argument in several places assure that the value will not be
overwritten in the middle of the
execution.
You need to use the value Named parameters are ready to
in a place where a variable be used as Kettle variables.
is needed

Solution using arguments
You can get the command-line
argument by using a Get System Info
step as many times as you need.
First, you need to set a variable with
the command-line argument value.
Usually this requires creating additional
transformations to be run before any
other job or transformation.

Depending on your particular situation, you would prefer one or the other solution. Note
that you can mix both as you did in the previous tutorials.

Have a go hero – analysing the use of arguments and named parameters
In the Time for action – customizing the hello world file with fixed arguments and parameters
section, you created a transformation that used an argument and a named parameter. Based
on this preceding table, try to understand why the folder was defined as named parameter
and the name of the person you want to say Hello to was defined as command-line
argument. Would you have applied the same approach?

Running job entries under conditions
A job may contain any number of entries. Not all of them execute always. Some of them
execute depending on the result of previous entries in the flow. Let's see it in practice.

Time for action – sending a sales report and warning the
administrator if something is wrong
Now you will build a sales report and send it by e-mail. In order to follow the tutorial, you
will need two simple prerequisites:




As the report will be based on the Jigsaw database you created in Chapter 8, you will
need the MySQL server running.
In order to send e-mails, you will need at least one valid Gmail account. Sign up for
an account. Alternatively, if you are familiar with you own SMTP configuration, you
could use it instead.

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Once you've checked these prerequisites, you are ready to start.

Create a new transformation.

Add a Get System Info step. Use it to add a field named today. As Type, select
Today 00:00:00.

Now add a Table input step.

Double-click the step.

As Connection, select js—the name of the connection to the jigsaw
puzzles database.
Note that if the connection is not shared, you will have to
define it.

In the SQL frame, type the following statement:
SELECT

pay_code
, COUNT(*) quantity
, SUM(inv_price) amount
FROM
invoices
WHERE
inv_date = ?
GROUP BY pay_code

In the drop-down list to the right of Insert data from step, select the name of the
Get System Info step.

Finally, add an Excel Output step.

Double-click the step.

10.

Enter type ${LABSOUTPUT}/sales_ as Filename.

11.

Check the Specify Date time format option. In the Date time format drop-down list,
select yyyyMMdd.

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Creating Basic Task Flows

12.

Make sure you don't uncheck the Add filenames to result option. Click on OK. Fill
the Fields tab as here:

13.

Save the transformation under the transformations folder you created in a
previous tutorial, with the name sales_report.ktr.

14.

Create a new job by pressing Ctrl+Alt+N.

15.

Add a START job entry.

16.

After the START entry, add a Transformation entry.

17.

Double-click the Transformation entry.

18.

Enter ${Internal.Job.Filename.Directory}/transformations/sales_
report.ktr as the transformation filename, either by hand or by browsing the
folder and selecting the file.

19.

Click on OK.

20.

Expand the Mail category of entries and drag a Mail entry to the canvas.

21.

Create a hop from the transformation entry to the Mail entry.

22.

Double-click the Mail entry.

23.

Fill the main tab Addresses with the destination and the sender e-mail addresses,
that is, provide values for the Destination address, Sender name, and Sender
address textboxes. If you have two accounts to play with, put one of them as
destination and the other as sender. If not, use the same e-mail twice.

24.

Select the Server tab and fill the SMTP Server frame as follows—enter smtp.gmail.
com as SMTP Server and 465 as Port.
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25.

Fill the Authentication frame. Check the Use authentication? checkbox. Fill the
Authentication user and Authentication password textboxes. For example, if your
account is pdi_account@gmail.com, then as user enter pdi_account and as
password provide your e-mail password.

26.

Check the Use secure authentication? option. In Secure connection type, leave the
default to SSL. Select the Email Message tab. In the Message Settings frame, check
the Only send comment in mail body? option.

27.

Fill the Message frame, providing a subject and a comment for the e-mail—enter
Sales report as Subject and Please check the attachment as Comment. Select the
Attached Files tab and check the Attach file(s) to message? option.

28.

In the Select file type list, select the type General.

29.

Click OK.

30.

Drag another Mail job entry to the canvas.

31.

Create a hop from the transformation entry to this new entry. This hop will appear
in red.

32.

Double-click the new entry.

33.

Fill the Destination and Sender frames with destination and sender e-mail
addresses. If you have another account to use as destination, use it here. Select the
Server tab and fill it exactly as you did in the other Mail entry.

34.

Select the Email Message tab. In the Subject textbox, type Error generating
sales report.

35.

Click on OK.

36.

Save the job and run it.

37.

Once the job finished, log into your account. You should have received a mail!
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Creating Basic Task Flows

38.

Open the e-mail. This is what you should see:

39.

Click on the Open as a Google spreadsheet option. You will see the following:

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40.

Simulate being an intruder and do something that makes your transformation
fail. You could, for example, stop MySQL or add some strange characters in the
SQL statement.

41.

Run the job again.

42.

Check the administrator e-mail—the mail you put as destination in the second Mail
job entry.

43.

The following is the e-mail you received this time:

What just happened?
You generated an Excel file with a crosstab report of sales on a particular day. If the file is
generated successfully, an e-mail is sent with the Excel file attached. If some error occurs,
an e-mail reporting the problem is sent to the administrator.

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Creating Basic Task Flows

If you skipped Chapter 8 and still know nothing about databases with PDI, don't
miss this exercise. Instead of the proposed sales report, create a transformation
that generates any Excel file. The contents of the sheet is not the key here. Just
make sure you leave the Add filenames to result option checked in the Excel
output configuration window. Then proceed as explained.

In this example you used Gmail accounts for sending e-mails from a PDI job. You
can use any mail server as long as you have access to the information required in
the Server tab.

Changing the flow of execution on the basis of conditions
The execution of any job entry either succeeds or fails.
In particular, the job entries under the category Conditions just evaluates something and
success or failure depends upon the result of the evaluation.
For example, the job entry File Exists succeeds if the file you put in its window exists.
Otherwise, it fails.
Whichever the job entry, you can use the result of its execution to decide which of the
entries following it execute and which don't.
In the tutorial, you included a transformation job entry. If the transformation runs without
problem, this entry succeeds. Then the execution follows the green hop to the first Mail
job entry.
If, while running the transformation, some error occurs, the transformation entry fails. Then
the execution follows the red path toward the e-mail to the administrator.
So, when you create a job, you not only arrange the entries and hops according to the
expected order of execution, you also specify under which condition each job entry runs.

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You can define the conditions in the hops. The following table lists the possibilities:
Color of the hop

What the color represents The interpretation

Black

Unconditional execution

Green

Execution upon success

Red

Execution upon failure

The destination entry executes no matter the
result of the previous entry.
The destination entry executes only if the
previous job entry is successful.
The destination entry executes only if the
previous job entry failed.

At any hop, you can define the condition under which the destination job entry will execute.
By default, the first hop that leaves an entry is created green, whereas the second hop is
created red. You can change the color, that is, the behavior of the hop. Just right-click on the
hop, select Evaluation, and then the condition.

One exception is the hop or hops that leave the START step. You cannot edit them. The
destination job entries execute unconditionally, that is, always.
Another exception is the special entry Dummy that does nothing, not even allowing you to
decide if the job entries after it run or not. They always run.

Have a go hero – refining the sales report
Here we will modify the job that sends the e-mail containing the sales report.
1. Modify the transformation so that the file is generated in the temporary folder
${java.io.tmpdir}. If there is no sale for today, don't generate the file. You do
this by checking the Do not create file at start option in the Excel output step.
2. Send the e-mail only if there were sales, that is, only if the file exists.
3. After sending the e-mail with the report attached, delete the file.

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Creating Basic Task Flows

Use these new job entries: File Exists from the Conditions category and
Delete file from the File management category.

Creating and using a file results list
In the tutorial you configured two Mail job entries. In the mail that follows the green hop,
you attached the Excel file generated by the transformation. However, you didn't explicitly
specify the name of the file to attach. How could PDI realize that you wanted to attach
that file? it could because of the Add filenames to result checkbox in the Excel output
configuration window. By checking that option, you added the name of the Excel file to a
special list named File result.
When PDI hits an e-mail entry where Attach file(s) to message? is checked, it attaches to the
e-mail all files in the File result list.
Most of the transformation steps that read or write files have this checkbox, and it is checked
by default. The following sample belongs to a Text file input step:

Each time you use one of these steps you are adding names of files to this list, unless you
uncheck the checkbox.

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There are also several job entries in the File management and the File transfer categories
that add one or more files to the File result list. Consider the following Copy Files…
entry screen:

As with the Mail entry, there are some other entries that use the File result list. One example
is Copy or Move result filenames. This entry copies or moves the files whose names are in
this special list named File result.

Have a go hero – sharing your work
Suppose you want to share your PDI work with a friend. Send to him/her some of your ktr
files by mail.
Use the Add filenames to result job entry located in the File management
category to build the File result list. Then send the e-mail with the files attached.

Summary
In this chapter, you learned the basics about PDI jobs—what a job is, what you can do with a
job, and how jobs are different from transformations. In particular, you learned to use a job
for running one or more transformations.
You also saw how to use named parameters in jobs, and how to supply parameters and
arguments to transformations when they are run from jobs.
In the next chapter, you will learn to create jobs that are a little more elaborative than the
jobs you created here, which will give you more power to implement all types of processes.

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Creating Advanced
Transformations and Jobs
Iterating over a list of items (files, people, codes, and so on), implementing
a process flow, and developing a reusable procedure are very common
requirements in real world projects. Implementing these kind of needs in PDI
is not intuitive, but it’s not complicate either. It’s just a matter of learning the
right techniques that we will see in this chapter. Among other things, you will
learn to implement process flows, nest jobs, and iterate the execution of jobs
and transformations.

Enhancing your processes with the use of variables
For the tutorials in this chapter, you will take as your starting point a Time for action tutorial
you did in Chapter 2 that involves updating a file with news about examinations. You are
responsible for collecting the results of an annual examination where writing, reading,
speaking, and listening skills are evaluated. The professors grade the examinations of their
students in the scale 0-100 for each skill, and generate text files with the information. Then
they send the files to you for integrating the results in a global list.
In the initial chapters, you were learning the basics of PDI. You were worried about how to
do simple stuff such as reading a file or doing simple calculations. In this chapter, you will go
beyond that and take care of the details such as making a decision if the filename expected
as a command line is not provided or if it doesn't exist.

Creating Advanced Transformations and Jobs

Time for action – updating a file with news about examinations
by setting a variable with the name of the file
The transformation in the Time for action from Chapter 2 that we just talked about reads a
file provided by a professor, simply by taking the name of the file from the command line,
and appends the file to the global one. Let's enhance that work.

Copy the examination files you used in Chapter 2 to the input files and folder
defined in your kettle.properties file. If you don't have them, download them
from the Packt website.

Open Spoon and create a new transformation.

Use a Get System Info step to get the first command-line argument. Name the field
as filename.

Add a Filter rows step and create a hop from the Get System Info step to this step.

From the Flow category drag an Abort step to the canvas, and from the Job category
of steps drag a Set Variables step.

From the Filter rows step, create two hops—one to the Abort step and the other
to the Set Variables step. Double-click the Abort step. As Abort message, put File
name is mandatory.

Double-click the Set Variables step and click on Get Fields. The window will be filled
as shown here:

Click on OK.

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Double-click the Filter rows step. Add the following filter: filename IS NOT
NULL. In the drop-down list to the right of Send 'true' data to step, select the Set
Variables step, whereas in the drop-down list to the right of Send 'false' data to
step, select the Abort step.

10.

The final transformation looks like this:

11.

Save the transformation in the transformations folder under the name
getting_filename.ktr.

12.

Open the transformation named examinations.ktr that was created in Chapter
2 or download it from the Packt website. Save it in the transformations folder
under the name examinations_2.ktr.

13.

Delete the Get System Info step.

14.

Double-click the Text file input step.

15.

In the Accept filenames from previous steps frame, uncheck the Accept filenames
from previous step option.

16.

Under File/Directory in the Selected files grid, type ${FILENAME}. Save the
transformation.

17.

Create a new job.

18.

From the General category, drag a START entry and a Transformation entry to the
canvas and link them.

19.

Save the job as examinations.kjb.

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20.

Double-click the Transformation entry. As Transformation filename, put the name
of the first transformation that you created: ${Internal.Job.Filename.
Directory}/transformations/getting_filename.ktr.

21.

Click on OK.
Remember that you can avoid typing that long variable name by
clicking Ctrl+Space and selecting the variable from the list.

22.

From the Conditions category, drag a File Exists entry to the canvas and create a hop
from the Transformation entry to this new one.

23.

Double-click the File Exists entry.

24.

Write ${FILENAME} in the File name textbox and click on OK.

25.

Add a new Transformation entry and create a hop from the File Exists entry to
this one.

26.

Double-click the entry and, as Transformation filename, put the name of the
second transformation you created:${Internal.Job.Filename.Directory}/
transformations/examinations_2.ktr.

27.

Add a Write To Log entry, and create a hop from the File Exists entry to this. The hop
should be red, to indicate when execution fails. If not, right-click the hop and change
the evaluation condition to Follow when result is false.

28.

Double-click the entry and fill all the textboxes as shown:

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29.

Add two entries—an abort and a success. Create hops to these new entries as
shown next:

30.

Save the job.

31.

Press F9 to run the job.

32.

Set the logging level to Minimal logging and click on Launch.

33.

The job fails. The following is what you should see in the Logging tab in the
Execution results window:

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34.

Press F9 again. This time set Basic logging as the logging level.

35.

In the arguments grid, write the name of a fictitious file—for example,
c:/pdi_files/input/nofile.txt.

36.

Click on Launch. This is what you see now in the Logging tab window:

37.

Press F9 for the third time. Now provide a real examination filename such as
c:/pdi_files/input/exam1.txt.

38.

Click on Launch. This time you see no errors. The examination file is appended to
the global file:

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What just happened?
You enhanced the transformation you created in Chapter 3 for appending an examination file
to a global examination file. This time you embedded the transformation in a job. The first
transformation checks that the argument is not null. In that case, it sets a variable with the
name provided. The main job verifies that the file exists. If everything is all right, then the
second transformation performs the main task—it appends the given file to the global file.
Note that you changed the logging levels just according to what you needed to see—the
highlighted lines in the earlier explanation.
You may choose any logging level you want depending on the details
of information you want to see.

Setting variables inside a transformation
So far, you had defined variables only in the kettle.properties file or inside Spoon while
you were designing a transformation. In this last exercise, you learned to define your own
variables at run time. You set a variable with the name of the file provided as a command-line
argument. You used that variable in the main job to check if the file existed. Then you used the
variable again in the main transformation. There you used it as the name of the file to read.

This example showed you the how to set a variable with the value of a command-line
argument. This is not always the case. The value you set in a variable can be originated in
different ways—it can be a value coming from a table in a database, a value defined with a
Generate rows step, a value calculated with a Formula or a Calculator step, and so on.
The variables you define with a Set variables step can be used in the same way and the same

places where you use any Kettle variable. Just take precautions to avoid using these variables
in the same transformation where you have set them.
The variables defined in a transformation are not available
for using until you leave that transformation.

Have a go hero – enhancing the examination tutorial even more
Modify the job in the tutorial to avoid processing the same file twice. If the file is
successfully appended to the global file, rename the original file by changing the
extension to processed—for example, after processing the exam1.txt file rename
it to exam1.processed.

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After verifying if the file exists, also check whether the .processed version exists. If it
exists, put a proper message in the log and abort. If someone accidently tries to process
a file that is already processed, it will be ignored.
Besides the variable with the filename, create a variable with the name
for the processed file. To build this name, simply manipulate the given
name with some PDI steps.

Have a go hero – enhancing the jigsaw database update process
In the Time for action – inserting new products or updating existent ones section in Chapter
8, you read a file with a list of products belonging to the manufacturer Classic DeLuxe.
The list was expected as a named parameter. Enhance that process. Create a job that first
validates the existence of the provided file. If the file doesn't exist, put the proper error
message in the log. If it exists, process the list. Then move the processed file to a folder
named processed.
You don't need to create a transformation to set a variable with the
name of the file. As it is expected as a named parameter, it is already
available as a variable.

Have a go hero – executing the proper jigsaw database update process
In the hero exercise in Chapter 8 that involves populating the products table, you created
different transformations for updating the products—one for each manufacturer. Now you
will put all that work together.
Create a job that accepts two arguments—the name of the file to process and the code of
the manufacturer to which the file belongs.
Create a transformation that validates that the code provided belongs to an existent
manufacturer. If the code exists, set a variable named TRANSFORMATION_FILE with the
name of the transformation that knows how to process the file for that manufacturer.
The transformation must also check that the name provided is not null. If it is not null, set a
variable named FILENAME with the name supplied.
Then, in the job, check that the file exists. If it exists and the manufacturer code is valid, run
the proper transformation. In order to do so, put ${TRANSFORMATION_FILE} as the name
of the transformation in the transformation job entry dialog window. Now test your job.

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Enhancing the design of your processes
When your jobs or transformations begin to grow, you may find them a little disorganized or
jumbled up. It's now time to do some rework. Let's see an example of this.

Time for action – generating files with top scores
In this tutorial, you will read the examination global file and generate four files—one for each
particular skill. The files will contain the top 10 scores for each skill. The scores will not be
the original, but converted to a scale with values in the range 0-5.
As you must be already quite confident with PDI, some explanations in this
section will not have the full details. On the contrary, the general explanation
will be focused on the structure of the jobs and transformations.

Create a new transformation and save it in the transformations folder under the
name top_scores.ktr.

Use a Text file input step to read the global examination file generated in the
previous tutorial.

After the Text file input step, add the following steps and link them in the
same order:


A Select values step to remove the unused fields—
file_processed and process_date.






A Split Fields to split the name of the students in two—name and
last name.
A Formula step to convert name and last name to uppercase.
With the same Formula step, change the scale of the scores.
Replace each skill field writing, reading, speaking, and
listening with the same value divided by 20—for example,
[writing]/20. You have already done this in Chapter 3.

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Do a preview on completion of the final step to check that you are doing well. You
should see this:

After the last Formula step, add and link in this order the following steps:




A Sort rows step to order the rows in descending order by the
writing field.
A JavaScript step to filter the first 10 rows. Remember that you
learned to do this in the chapter devoted to JavaScript. You do it by
typing the following piece of code:
trans_Status = CONTINUE_TRANSFORMATION;
if (getProcessCount('r')>10) trans_Status =
SKIP_TRANSFORMATION;



An Add sequence step to add a field named seq_w. Leave the
defaults so that the field contains the values 1, 2, 3 …
A Select values step to rename the field seq_w as position and
the field writing as score. Specify this change in the Select &
Alter tab, and check the option Include unspecified fields, ordered.
A Text file output step to generate a file named writing_top10.
txt at the location specified by the ${LABSOUTPUT} variable. In the
Fields tab, put the following fields— position, student_code,
student_name, student_lastname, and score.

Save the transformation, as you've added a lot of steps and don't want to lose
your work.

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Repeat step number 5, but this time sort by the reading field, rename the sequence
seq_r as position and the field reading as score, and send the data to the
reading_top10.txt file.
To save time, you can copy all those steps, paste them, and do
the proper adjustments.

Repeat the same procedure for the speaking field and the listening field.

This is how the transformation looks like:

10.

Save the transformation.

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11.

Run the transformation. Four files should have been generated. All the files should look
similar. Let's check the writing_top10.txt file (the names and values may vary
depending on the examination files that you have appended to the global file):

What just happened?
You read the big file with examination results and generated four files with information
about the top scores—one file for each skill.
Beyond having used the Add sequences step for the first time, there was nothing new.
However, there are several improvements you can do to this transformation. The next
tutorials are meant to teach you some tricks.

Pop quiz – using the Add Sequence step
In the previous tutorial, you used different names for the sequences and then you renamed
all of them to position. Which of the following options gives you the same results you got
in the tutorial?
a. Using position as the name of the sequence in all Add sequence steps
b. Joining the four streams with a single Add sequence step and then splitting
the stream back into four streams by using the Distribute method you learned
in Chapter 4
c.

Joining the four streams with a single Add sequence step and then splitting the
stream back into four streams by using a Switch case step that distributes the
rows properly

d. All of them
e. None of them
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Reusing part of your transformations
As you noticed, the sequence of steps used to get the ranks are almost identical for the four
skills. You could have avoided copying and pasting or doing the same work several times by
moving those steps to a subtransformation. Let's do it.

Time for action – calculating the top scores with a
subtransformation
Let's modify the transformation that calculates the top scores to avoid unnecessary
duplication of steps:

Under the transformation folder, create a new folder named
subtransformations.

Create a new transformation and save it in that new folder with the name
scores.ktr.

Expand the Mapping category of steps. Select a Mapping input specification step
and drag it to the work area.

Double-click the step and fill it like this:

Add a Sort rows step and use it to sort the score field in descending order.

Add a JavaScript step and type the following code to filter the top 10 rows:
trans_Status = CONTINUE_TRANSFORMATION;
if (getProcessCount('r')>10) trans_Status = SKIP_TRANSFORMATION;

Add an Add sequence step to add a sequence field named seq.
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Finally, add a Mapping output specification step. You will find it in the Mapping
category of steps. Your transformation looks like this:

Save the transformation.

10.

Open the transformation top_scores.ktr and save it as top_scores_with_
subtransformations.ktr.

11.

Modify the writing stream. Delete all steps except the Text file output step—the
Sort rows, JavaScript, Add sequence, and the Select rows steps.

12.

Drag a Mapping (sub-transformation) step to the canvas and put it in the place
where all the deleted steps were. You should have this:

13.

Double-click the Mapping step.

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14.

In the Mapping transformation frame, select the option named Use a file for
the mapping transformation. In the textbox below it, type ${Internal.
Transformation.Filename.Directory}/subtransformations/scores.
ktr. Select the Input tab, check the Is this the main data path? option, and fill the

grid as shown:

15.

Select the Output tab and fill the grid as shown:

16.

Click on OK.

17.

Repeat the steps 11 to 16 for the other streams—reading, speaking, and listening.
The only difference is what you put in the Input tab of the Mapping steps—instead
of writing, you should put reading, speaking, and listening.
Note that you added four Mapping (subtransformation)
steps, but you only need one subtransformation file.

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18.

The final transformation looks as follows:

19.

Save the transformation.

20.

Press F9 to run the transformation.

21.

Select Minimal logging and click on Launch. The Logging window looks like
the following:

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22.

The output files should have been generated and should look exactly the same as
before. This time let's check the reading_top10.txt file (the names and values
may vary depending on the examination files that you appended to the global file):

What just happened?
You took the bunch of steps that calculate the top scores and moved it to a
subtransformation. Then, in the main transformation, you simply called the
subtransformation four times, each time using a different field.
It's worth saying that the Text file output step could also have been moved to the
subtransformation. However, instead of simplifying the work, it would have complicated it.
This is because the names of the files are different in each case and, in order to build that
name, it would have been necessary to add some extra logic.

Creating and using subtransformations
Subtransformations are, as the named suggests, transformations inside transformations.
The PDI proper name for a subtransformation is mapping. However, as the
word mapping is also used with other meanings in PDI, we will use the old,
more intuitive name subtransformation.

In the tutorial, you created a subtransformation to isolate a task that you needed
to apply four times. This is a common reason for creating a subtransformation—to
isolate a functionality that is likely to be needed more than once. Then you called the
subtransformations by using a single step.

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Let's see how subtransformations work. A subtransformation is like a regular transformation,
but it has input and output steps, connecting it to the transformations that use it.
The Mapping input specification step defines the entry point to the subtransformation.
You specify here just the fields needed by the subtransformation. The Mapping output
specification step simply defines where the flow ends.
The presence of Mapping input specification and Mapping output
specification steps is the only fact that makes a subtransformation
different from a regular transformation.

In the sample subtransformation you created in the tutorial, you defined a single field named
score. You sorted the rows by that field, filtered the top 10 rows, and added a sequence to
identify the rank—a number from 1 to 10.
You call or execute a subtransformation by using a Mapping (sub-transformation) step. In
order to execute the subtransformation successfully, you have to establish a relationship
between your fields and the fields defined in the subtransformation.
Let's first see how to define the relationship between your data and the input specification.
For the sample subtransformation, you have to define which of your fields is to be used as
the input field score defined in the input specification. You can do it in an Input tab in the
Mapping step dialog window. In the first Mapping step, you told the subtransformation to
use the field writing as its score field.
If you look at the output fields coming out of the Mapping step, you will no longer see the
writing field but a field named score. It is the same field writing that was renamed as
score. If you don't want your fields to be renamed, simply check the Ask these values to

be renamed back on output? option found in the Input tab. That will cause the field to be
renamed back to its original name—writing in this example.
Let's now see how to define the relationship between your data and the output specification.
If the subtransformation creates new fields, you may want to add them to your main
dataset. To add to your dataset, a field created in the subtransformation, you use an Output
tab of the Mapping step dialog window. In the tutorial, you were interested in adding the
sequence. So, you configured the Output tab, telling the subtransformation to retrieve the
field named seq in the subtransformation but renamed as position. This causes a new
field named position to be added to your stream.
If you want the subtransformation to simply transform the incoming stream without adding
new fields, or if you are not interested in the fields added in the subtransformation, you
don't have to create an Output tab.

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The following screenshot summarizes what was explained just now. The upper and lower grids
show the datasets before and after the streams have flown through the subtransformation.

The subtransformation in the tutorial allowed you to reuse a bunch of steps that were
present in several places, avoiding doing the same task several times. Another common
situation where you may use subtransformations is the one where you have a transformation
with too many steps. If you can identify a subset of steps that accomplish a specific purpose,
you may move those steps to a subtransformation. Doing so, your transformation will
become cleaner and easier to understand.

Have a go hero – refining the subtransformation
Modify the subtransformation in the following way:
Add a new field named below_first. The field should have the difference between the
score in the current row and the maximum score. For example, if the maximum score is 5
and the current score is 4.85, the value for the field should be 0.15.
Modify the main transformation by adding the new field to all output files.
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Have a go hero – counting words more precisely (second version)
Combine the following Hero exercises from Chapter 3:


Counting words, discarding those that are commonly used



Counting words more precisely

Create a subtransformation that receives a String value and cleans it. Remove extra signs that
may appear as part of the string such as . , ) or ". Then convert the string to lower case.
Also create a flag that tells whether the string is a valid word. Remember that the word is
valid if its length is at least 3 and if it is not in a given list of common words.
Retrieve the modified word and the flag.
Modify the main transformation by using the subtransformation. After the
subtransformation step, filter the words by looking at the flag.

Creating a job as a process flow
With the implementation of a subtransformation, you simplify much of the transformation.
But you still have some reworking to do. In the main transformation, you basically do two
things. First you read the source data from a file and prepare it for further processing. And
then, after the preparation of the data, you generate the files with the top scores. To have a
clearer vision of these two tasks, you can split the transformation in two, creating a job as a
process flow. Let's see how to do that.

Time for action – splitting the generation of top scores by
copying and getting rows
Now you will split your transformation into two smaller transformation so that each meets a
specific task. Here are the instructions.

Open the transformation in the previous tutorial. Select all steps related to
the preparation of data, that is, all steps from the Text file input step upto the
Formula step.

Copy the steps and paste them in a new transformation.

Expand the Job category of steps.

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Select a Copy rows to result step, drag it to the canvas, and create a hop from the
last step to this new one. Your transformation looks like this:

Save the transformation in the transformations folder with the name
top_scores_flow_preparing.ktr.

Go back to the original transformation and select the rest of the steps, that is, the
Mapping and the Text file output steps.

Copy the steps and paste them in a new transformation.

From the Job category of steps select a Get rows from result step, drag it to
the canvas, and create a hop from this step to each of the Mapping steps. Your
transformation looks like this:

Save the transformation in the transformations folder with the name top_
scores_flow_processing.ktr.

10.

In the top_scores_flow_preparing transformation , right-click the step Copy
rows to result and select Show output fields.
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Creating Advanced Transformations and Jobs

11.

The grid with the output dataset shows up.

12.

Select all rows. Press Ctrl+C to copy the rows.

13.

In the top_scores_flow_processing transformation, double-click the step Get
rows from result.

14.

Press Ctrl+V to paste the values. You have the following result:

15.

Save the transformation.

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16.

Create a new Job.

17.

Add a START and two transformation entries to the canvas and link them one after
the other.

18.

Double-click the first transformation. Put ${Internal.Job.Filename.
Directory}/transformations/top_scores_flow_preparing.ktr as the

name of the transformation.

19.

Double-click the second transformation. Put ${Internal.Job.Filename.
Directory}/transformations/top_scores_flow_processing.ktr as

the name of the transformation.

20.

Your job looks like the following:

21.

Save the job. Press F9 to open the Job properties window and click on Launch.
Again, the four files should have been generated, with the very same information.

What just happened?
You split the main transformation in two—one for the preparation of data and the other for
the generation of the files. Then you embedded the transformations into a job that executed
them one after the other. By using the Copy rows to result step, you sent the flow of data
outside the transformation, and using Get rows from result step, you picked that data to
continue with the flow. The final result was the same as before the change.
Notice that you split the last version of the transformation—the
one with the subtransformations inside. You could have split the
original. The result would have been exactly the same.

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Transferring data between transformations by using the copy /get rows
mechanism
The copy/get rows mechanism allows you to transfer data between two transformations,
creating a process flow. The following drawing shows you how it works:
Transformation A

Transformation B

Copy
rows
step

Get
rows
step

Data being
transferred

The Copy rows to result step transfers your rows of data to the outside of the
transformation. You can then pick that data by using a Get rows from result step. In the
preceding image, Transformation A copies the rows and, Transformation B, which executes
right after Transformation A, gets the rows. If you create a single transformation with all
steps from Transformation A followed by all steps from Transformation B, you would get
the same result.

The copy of the dataset is made in memory. It's useful when you have
small datasets. For bigger datasets, you should prefer saving the data in a
temporary file or database table in the first transformation, and then create
the dataset from the file or table in the second transformation.
The Serialize to file /De-serialize from file steps are very useful for this, as the
data and the metadata are saved together.

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There is no limit to the number of transformations that can be chained using this
mechanism. Look at the following image:
Transformation A

.....

Copy
rows

Transformation B
Get
Rows

.....

Transformation N
Copy
rows

.....

Get
Rows

.....

As you can see, you may have a transformation that copies the rows, followed by another
that gets the rows and copies again, followed by a third transformation that gets the rows,
and so on.

Have a go hero – modifying the flow
Modify the last exercise in the following way:


Include just the students who had an average score above 70.

Note that you have to modify just the transformation that prepares
the information, without caring about what the second process
does with that data.



Generate just the top five scores for every skill.

Note that you have to modify just the transformation (or the
subtransformation) that processes the information, without

caring about how the list of students was built.


Create each file in a different transformation. The transformations execute one after
the other.

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This exercise requires that you modify the flow. Each
transformation gets the rows from the previous transformation,
then generates a file, and copies the rows to the result to be
used for the next transformation.

Nesting jobs
Suppose that every time you append a file with examination results, you want to generate
updated files with the top 10 scores. You can do it manually, running one job after the other,
or you can nest jobs.

Time for action – generating the files with top scores by
nesting jobs
Let's modify the job that updates the global examination file, so at the end it generates
updated top scores files:

Open the examinations job you created in the first tutorial of this chapter.

After the last transformation job entry, add a job entry as Job. You will find it under
the General category of entries.

Double-click the Job job entry.

Type ${Internal.Job.Filename.Directory}/top_scores_flow.kjb
as Job filename.

Click on OK.

Save the job.

Pick an examination that you have not yet appended to the global file—for example,
exam5.txt.

Press F9.

9.
10.

In the Arguments grid, type the full path of the chosen file: c:/pdi_files/input/
exam5.txt.

Click on Launch.

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Chapter 11

11.

In the Job metrics tab of the Execution results window, you will see the following:

12.

Also the chosen file should have been added to the global file, and updated files with
top scores should have been generated.

What just happened?
You modified the job that updates the global examination file by including the generation of
the files with top scores as part of the process. You did it by using a Job job entry whose task
is to run a job inside a job.
In the Job metrics, you could see a hierarchy showing the details of the nested job as a
sub-tree of that hierarchy.

Running a job inside another job with a job entry
The job entry, Job, allows you to run a job inside a job. Just like any job entry, this entry may
end successfully or fail. Upon that result, the main job decides which of the entries that
follows it will execute. None of the entries following the job entry starts until the nested job
ends its execution. There is no limit to the levels of nesting. You may call a job, which calls
a job, which again calls a job, and so on. Usually you will not need more than two or
three levels.

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As with a transformation job entry, you must specify the location and name of the job file.
If the job (or any transformation inside the nested job) uses arguments or has defined
named parameters, you have the possibility of providing fixed values just as you do in a
Transformation job entry—by filling the Arguments and Parameters tabs.

Understanding the scope of variables
By nesting jobs, you implicitly create a relationship between the jobs. Look at the
following diagram:

Here you can see how a job, and even a transformation, may have parents and grandparents.
The main job is called root job. This hierarchy is useful to understand the scope of variables.
When you define a variable, you have the option to set the scope, that is, define the places
where the variable is visible.

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The following table explains which jobs and transformations can access the variable
depending on the variable's scope.
Variable scope type
Valid in the parent job
Valid in the grand-parent job
Valid in the root job
Valid in the Java Virtual
Machine

Visibility of the variable
Can be seen by the job that called the transformation and any
transformation called by this job.
Can be seen by the job that called the transformation, the job that
called that job, and any transformation called by any of these jobs.
Can be seen by all jobs in the chain starting with the main job, and
any transformation called by any of these jobs.
Seen by all the jobs and transformations run from the same
Java Virtual Machine. For example, suppose that you define a
variable with scope in the Java Virtual Machine. If you run the
transformation from Spoon, then the variable will be available in all
jobs and transformations you run from Spoon as long as you don't
exit Spoon.

Pop quiz – deciding the scope of variables
In the first tutorial you created a transformation that set a variable with the name of a file.
For the scope, you left the default value: Valid in the root job. Which of the following scope
types could you have chosen getting the same results (you may select more than one):
a. Valid in the parent job
b. Valid in the grand-parent job
c.

Valid in the Java Virtual Machine
In general, if you have doubts about which scope type to use, you can use Valid
in the root job and you will be good. Simply ensure that you are not using the
same name of variable for different purposes.

Iterating jobs and transformations
It may happen that you develop a job or a transformation to be executed several times, once
for each different row of your data. Consider that you have to send a custom e-mail to a list
of customers. You would build a job that, for a given customer, get the relevant data such as
name or e-mail account and send the e-mail. You would then run the job manually several
times, once for each customer. Instead of doing that, PDI allows you to execute the job
automatically once for each customer in your list.

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The same applies to transformations. If you have to execute the same transformation several
times, once for each row of a set of data, you can do it by iterating the execution. The next
Time for action tutorial shows you how to do this.

Time for action – generating custom files by executing a
transformation for every input row
Suppose that 60 is the threshold below which a student must retake the examination. Let's
find out the list of students with a score below 60, that is, those who didn't succeed in the
writing examination. Then, let's create one file per student telling him/her about this.
First of all, let's create a transformation that generates the list of students who will take
the examination:

Create a new transformation.

Drag a Text file input, a Filter rows, and a Select values step to the canvas and link
them in that order.

Use the Text file input step to read the global examination file.

Use the Filter rows step to keep only those students with a writing score below 60.

With the Select values step, keep just the student_code and name values.

After this last step, add a Copy rows to result step.

Do a preview on this last step. You will see the following (the exact names and
values depend on the number of files you have appended to the global file):

Save the transformation in the transformations folder with the name
students_list.ktr.
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Now let's create a transformation that generates a single file. This transformation will be
executed for each student in the list shown in the preceding screenshot:

Create a new transformation.

Drag a Get rows from result step to the canvas.

Double-click the Get rows from result step and use it to define two String
fields—a field named student_code and another field named name.

Add a Formula step and create a hop from the Get rows from result step to this
new step.

Use the Formula step to create a new String field named text. As value, type:
"You'll have to take the examination again, " & [name] & ".".

After the Formula step, add a Delay row step. You will find it under the Utility
category of steps.

Finally, add a Text file output step, and double-click the step to configure it.

As filename type ${LABSOUTPUT}/hello. Check the option Include time
in filename?.

In the content tab, uncheck Header. As Field, select the field text.

10.

This is how your final transformation looks:

11.

Save the transformation in the transformations folder under the name
hello_each.ktr.
You can't test this transformation alone. If you want to test it, just
replace temporarily the Copy rows from result step with a Generate
rows step, generate a single row with fixed values for the fields, and
run the transformation.

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Let's create a job that puts everything together:

Create a job.

Drag a START, a Delete files, and two transformation entries to the canvas, and link
them one after the other as shown:

Save the job.

Double-click the Delete files step. Fill the Files/Folders: grid with a single
row—under File/Folder type ${LABSOUTPUT} and under Wilcard (RegExp) type
hello.*\.txt. This regular expression includes all .txt files whose name start
with the string "hello" in the ${LABSOUTPUT} folder.

Double-click the first transformation entry. As Transformation filename, put
${Internal.Job.Filename.Directory}/transformations/student_
list.ktr and click on OK.

Double-click the second transformation entry. As Transformation filename,
put ${Internal.Job.Filename.Directory}/transformations/
hello_each.ktr.

Check the option Execute for every input row? and click on OK.

Save the job and press F9 to run it.

When the execution finishes, explore the folder pointed by your ${LABSOUTPUT}
variable. You should see one file for each student in the list. The files are named
hello_.txt where is the time in your system at the
moment that the file was generated. The generated files look like the following:

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What just happened?
You built a list of students who had to retake the writing examination and, for each student,
you generated a file with a custom message.
First, you created a transformation that built the list of the students and copied the rows
outside the transformation by using the Copy rows to result step.
Then you created another transformation that gets a row from the result and generates a
file with a custom hello message.
Finally, you created the main job. First of all, the job deletes all files just in case you run
the job more than once. Then it calls the first transformation and then executes the
transformation that generates the file once for every copied row, that is, once for every
student. Each time the transformation gets the rows from the result, it gets a single row with
information about a single student and generates a file with the message for that student.
Before proceeding with the details about executing each row mechanism, let's briefly
explain the new step used here—the Delay row step that is used to deliberately slow down
a transformation. For each incoming row, the step waits for the amount of time indicated in
its setting window which, by default, is 1 second. After that time, the row is given to the
next step.
In this tutorial, the Delay row step is used to ensure that each time the transformation
executes, the name of the file is different. As part of the name for the file, you put the time
of your system including hours, minutes, and seconds. By waiting for a second, you can be
sure that in every execution of the transformation the name of the file will be different from
the name of the previous file.

Executing for each row
The execute for every input row? option you have in the transformation entry setting
window allows you to run the transformation once for every row copied in a previous
transformation by using the Copy rows to result step. PDI executes the transformation as
many times as the number of copied rows, one after the other. Each time the transformation
executes and gets the rows from the result, it actually gets a different row.
Note that in the transformation you don't limit the number of incoming
rows. You simply assume that you are receiving a single row. If you forget to
set the execute for every input row? option in the job, the transformation
will run but you will get unexpected results.

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This drawing shows you the mechanism for a dataset with three rows:

Get rows
step

t
1s

......

Copy
rows
step

Transformation B

2nd row

Transformation A

......

Get rows
step

......

Transformation B

Get rows
step

......

Transformation B

The transformation A in the example copies three rows. Then the transformation B is
executed three times—first for the first copied row, then for the second, and finally for
the third row.

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If you look at the log in the tutorial, you can see it working:

The transformation that builds the list of students copies four rows to the results. Then the
main job executes the second transformation four times—once for each of those students.

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The following sketch shows it clearly:

This mechanism of executing for every input row applies also to jobs. To execute a single
job several times, once for every copied row, you have to check the execute for every input
row? option that you have in the job entry settings window.

Have a go hero – processing several files at once
Modify the first tutorial about Updating a file with news about examinations. But this time
accept a folder as parameter. Then process all the text files in that folder, ordered by date of
the file. For each processed file, put a line in the log telling the name of the processed file.
You can use the following hint. Create a first transformation that, instead of validating the
parameter as a file, validates it as a folder. In order to do that, use the File exists step inside
the Lookup category of steps.
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If the folder exists, use a Get File Names step. That step allows you to retrieve the list of
filenames in a given folder, including the attributes for those files. To define which files to
get, use the options in the box Filenames from field. Sort the list by file date and copy the
names to the results.
In the second transformation, executed for every input row, get a row from the result, then
use a Text file input step accepting the name from the previous step, and proceed as usual.
As you may find it difficult to use steps you never used before, you
may download a working version for the first transformation. You'll
find it among the material for this chapter.

Have a go hero – building lists of products to buy
This exercise is related to the JS database.
Create a transformation to find out the manufacturers for the products that have been sold
best in the current month. Take the first three manufacturers in the list.
Create another transformation that, for every manufacturer in that list, builds a file with a list
of products out of stock.
Hint
The first transformation must copy the rows to the result. The second transformation must
execute for every input row. Start the transformation with a Get rows from result step, then
a Table Input step that receives as parameter a manufacturer's code. The SQL to use could
be something like:
SELECT *
FROM products
WHERE code_man LIKE '?' AND pro_stock2000



Create another transformation that loads the lk_puzzles dimensions. This is a
Type II SCD, and you have already loaded it in Chapter 9. If you have the transformation
that does it, half of your work is done.



Finally, modify the job in the tutorial by adding the execution of these new
transformations. Note that the lk_pieces dimension has to be loaded just once.

Loading a fact table with aggregated data
Now that you have data in your dimensions, you are ready to load the sales fact table. In this
section, you will learn how to do it.

Time for action – loading the sales fact table by looking up
dimensions
Let's load the sales fact table, ft_sales, with sales information for a given range of dates.
Before doing this exercise, be sure that you have already loaded the dimensions. You did it in
the previous tutorial.
Also check that the database engine is up and that both the js and the js_dw databases are
accessible from PDI. If everything is in order, you are ready to start:

Create a new transformation.

Drag a Table input step to the canvas.

Double-click the step. Select js as Connection—the connection to the
operational database.

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In the SQL frame type the following query:
SELECT i.inv_date
,d.man_code
,cu.city_id
,pr.pro_type
product_type
,b.buy_desc
,p.pay_desc
,sum(d.cant_prod) quantity
,sum(d.price)
amount
FROM
invoices
i
,invoices_detail d
,customers
cu
,buy_methods
b
,payment_methods p
,products
pr
WHERE i.invoice_number = d.invoice_number
AND
i.cus_id
= cu.cus_id
AND
i.buy_code = b.buy_code
AND
i.pay_code = p.pay_code
AND
d.pro_code = pr.pro_code
AND
d.man_code = pr.man_code
AND i.inv_date BETWEEN cast('${DATE_FROM}' as date)
AND cast('${DATE_TO}'
as date)
GROUP BY i.inv_date
,d.man_code
,cu.city_id
,pr.pro_type
,b.buy_desc
,p.pay_desc

Check the Replace variables in script? option and click OK.
Let's retrieve the surrogate key for the manufacturer:

From the Lookup category, drag a Database lookup step to the canvas.

Create a hop from the Table input step to this new step.

Double-click the Database lookup step.

Select dw as Connection—the connection to the datamart database.

10.

Click on Browse...and select the lk_manufacturers table.

11.

Fill the upper grid with the following condition: id_js = man_code.

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12.

Fill the lower grid—under Field type id, as New name type id_manufacturer, as
Default type 0, and as Type select Integer.

13.

Click on OK.
Now you will get the surrogate key for the region:

14.

From the Data Warehouse category drag a Dimension lookup/update step to
the canvas.

15.

Create a hop from the Database lookup step to this new step.

16.

Double-click the Dimension lookup/update step.

17.

As Connection select dw.

18.

Browse and select the lk_regions_2 table.

19.

Fill the Keys grid as shown next:

20.

Select id as Technical key field. In the new name textbox, type id_region.

21.

As Stream Datefield select inv_date.

22.

As Date range star field and Table daterange end select start_date and end_date
respectively.

23.

Select the Fields tab and fill it like here:

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Now it's time to generate the surrogate key for the junk dimension:

24.

From the Data Warehouse category drag a Combination lookup/update step to the
canvas.

25.

Create a hop from the Dimension lookup/update step to this new step.

26.

Double-click the Combination lookup/update step.

27.

Select dw as Connection.

28.

Browse and select the lk_junk_sales table.

29.

Fill the grid as shown:

30.

As Technical key field type id. In the Creation of technical key frame, leave the default
value Use table maximum + 1.

31.

Click OK.

32.

Add a Select values step and use it to rename the field id to id_junk_sales.
Finally, let's do some adjustments and send the data to the fact table:

33.

Add another Select values step to change the metadata of the inv_date field
as shown:

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34.

Add a Table output step and double-click it.

35.

Select dw as Connection.

36.

Browse and select the ft_sales table.

37.

Check the Specify database fields option, select the Database fields grid, and fill it as
shown:

Remember that you can avoid typing by using the
Get fields button.

38.

Click on OK. The following is your final transformation. Press Ctrl+S to save it.

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39.

Press F9 to run it.

40.

In the settings window, provide some values for the date range.

41.

Click on Launch.

42.

The fact table should have been loaded. To check it, open the database explorer and run
the following query:
SELECT * FROM ft_sales

You will get the following:

43.

To verify that only the sales between the provided dates were processed, run the
following query:
SELECT MIN(dt), MAX(dt) FROM ft_sales

44.

You will get the following:

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What just happened?
You loaded the sales fact table with the sales in a given range of dates.
First of all you got the information from the source database. You did it by typing an SQL
query in a Table input step. You already know how a Table input step works.
As said, a fact table has foreign keys to the primary key of the dimension tables. The query
you wrote gave you business keys. So, after getting the data from the source, you translated
the business keys into surrogate keys. You did it in different ways depending on the kind of
each related dimension.
Finally, you inserted the obtained data into the fact table ft_sales.

Getting the information from the source with SQL queries
You already know how to use a Table input step to get information from any database.
However, the query in the tutorial may have looked strange or long compared with the
queries you wrote in previous chapters. There is nothing mysterious in that query: It's simply
a matter of knowing what to put in it. Let's explain it in detail.
The first thing you have to do in order to load the fact table is to look at the grain.
As mentioned at the beginning of the chapter, the grain, or level of detail, of the fact is
implicitly expressed in terms of the dimension.
Looking at the model, you can see the following dimensions, along with their level of detail:
Dimension
Manufacturers
Regions
Time
Product Type
Payment method
Buy method

Level of detail (most atomic data)
manufacturer
city
day
product type
payment method
buy method

Does this have anything to do with loading the fact? Well, the answer is yes. This is
because the numbers you have to put as measures in the numeric fields must be aggregated
accordingly to the dimensions. These are the measurements—quantity representing the
number of products sold and Sales representing the amounts.
So, in order to feed the table, what you need to take from the source is the sum of
quantity and the sum of sales for every combination of manufacturer, day, city,
product type, payment method, and buy method.

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In SQL terms you do it with a query such as the one you wrote in the Table input step. The
query is not as complicated as it may seem at first. Let's dissect the query, beginning with
the FROM clause.
FROM

invoices
,invoices_detail
,customers
,buy_methods
,payment_methods
,products

i
d
cu
b
p
pr

These are the tables to take the information from. The word following the name of the
table is an alias for the table—for example, pr for the table products. The alias is used
to distinguish fields that have the same name but are in different tables.
The database engine takes all the records for all the listed tables, side by side, and creates
all the possible combination of records where each new record has all the fields for all
the tables.
WHERE i.invoice_number = d.invoice_number
AND
i.cus_id
= cu.cus_id
AND
i.buy_code = b.buy_code
AND
i.pay_code = p.pay_code
AND
d.pro_code = pr.pro_code
AND
d.man_code = pr.man_code

These conditions represent the join between tables. A join limits the number of
records you have when combining tables as explained above. For example, consider
the following condition:
i.cus_id = cu.cus_id

This condition implies that out of all the records, the engine keeps only those where
the customer ID in the table invoices is the same as that of the customer ID in the
table customers.
AND i.inv_date BETWEEN cast('${DATE_FROM}' as date)
AND cast('${DATE_TO}'
as date)

This query simply filters the sales in the given range. The cast function converts a string
to a date.

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Different engines have different ways to cast or convert fields from one data type
to another. If you are using an engine different from MySQL, you may have to
check your database documentation and fix this part of the query.
GROUP BY i.inv_date
,d.man_code
,cu.city_id
,pr.pro_type
,b.buy_desc
,p.pay_desc

By using the GROUP BY clause, you ask the SQL engine that for each different combination of
the listed fields, it should return just one record.
Finally, look at the fields following the SELECT clause:
SELECT i.inv_date
,d.man_code
,cu.city_id
,pr.pro_type
product_type
,b.buy_desc
,p.pay_desc
,sum(d.cant_prod) quantity
,sum(d.price) amount

These fields are the business keys you need—date of sale, manufacturer, city, and so
on—one for each dimension in the sales model. Note the word product_type after
the pro_type field. This is an alias for the field. Using an alia,s the field is renamed in
the output.
As you can see, with the exception of the highlighted fields, the fields you put after the
SELECT clause are exactly the same as you put in the GROUP BY clause. When you have a
GROUP BY clause in your sentence, after the SELECT clause you can put only those fields
that are listed in the GROUP BY clause or aggregated functions such as the following:
,sum(d.cant_prod) quantity
,sum(d.price)
amount

sum() is an aggregate function that gives you the sum of the column you put into brackets.
Therefore, these last two fields are the sum of the cant_prod field and the sum of the
price field for all the grouped records. These two fields give you the measures for your

fact table.

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To confirm that the GROUP BY works as explained, let's explore one example. Remove from
the query, the sum() functions, leaving just the fields, along with the GROUP BY clause.
Do a preview setting 2009-07-07 both as start_date and end_date. You will see
the following:

As you can see, in the same day, in the same city, you sold two products of the same type, made
for the same manufacturer, by using the same payment and buy method. In the fact table you will
not save two records, but will save a single record. Restore the original query and do a preview.
You will see the following:

Here you can see that the GROUP BY clause has grouped those two records into a single one. For
quantity and amount it summed the individual values.

Note that the GROUP BY clause, along with the aggregate functions, does the same as you
could have done by using a Sort rows step to sort by the listed fields, followed by a Group by
step to get the sum of the numeric fields.
Wherever the database can do the operations, for performance reasons it's
recommended that you allow the database engine do it.

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Translating the business keys into surrogate keys
You already have the transactional data for the fact table. But that data contains business
keys. Look at the fields definition for your fact table:
dt CHAR(8) NOT NULL,
id_manufacturer INT(10) NOT NULL,
id_region INT(4) NOT NULL,
id_junk_sales INT(10) NOT NULL,
product_type CHAR(10) NOT NULL,
quantity INT(6) DEFAULT 0 NOT NULL,
amount NUMERIC(8,2) DEFAULT 0 NOT NULL

id_manufacturer, id_region, and id_junk_sales are foreign keys to surrogate keys.

So, before inserting the data into the fact, for each business key you have to find the proper
surrogate key. Depending on the kind of dimensions referenced by the IDs in the fact table,
you get those IDs in a different way. Let's see in the following section, how you do it in
each case.

Obtaining the surrogate key for a Type I SCD
For getting the surrogate key in the case of a Type I SCD such as the Manufacturer one, you
used a Database lookup step. You are already familiar with this step, so understanding how
to use it is easy.

In the first grid you provided the business keys. The key to look up in the incoming stream is
man_code, whereas the key to look up in the dimension table is stored in the field id_js.
With the Database lookup step you returned the field named id, which is the field that
stores the surrogate key. You renamed it to id_manufacturer, as this is the name you
need for the fact table.
If the key is not found, you use 0 as default, that is, the record in the dimension reserved for
unknown values.

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The following screenshot shows you how it works:

Obtaining the surrogate key for a Type II SCD
In the case of a Type II SCD such as the Region dimension, you used the same step that was
used to load the table dimension—a Dimension L/U step. The difference is that here you
unchecked the Update the dimension? option. By doing that, the step behaves just as a
database lookup—you provide the keys to lookup and the step returns the fields you put
both in the Fields tab and in the Technical key field option. The difference with this step is
that here you have to provide time information. By using that time information, PDI finds
and returns, from the Type II SCD, the proper record in time:

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Here you give PDI the names for the columns that store the data ranges—start_date
and end_date. You also give it the name of the field stream to use in order to compare
the dates—in this case inv_date, that is, the date of the sale.
Look at the following screenshot to understand how the lookup works:

The step has to get the surrogate key for the city with ID 261. There are two records
for that city. The key is in finding the proper record, the record valid on 07/07/2009.
So, PDI compares the date provided against the start_date and end_date fields
and returns the surrogate key 582, for which the city is classified as belonging to the
Nordic Countries region.
If no record is found for the given keys on the given date, the step retrieves the ID 0, which is
used for the unknown data.

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Obtaining the surrogate key for the Junk dimension
The payment and buy methods are stored in a junk dimension. A junk dimension can be
loaded by using a Combination L/U step. You learned how to use this step in the Time for
action named Loading a region dimension with a Combination lookup/update step in
Chapter 9. As all the fields in a junk dimension are part of the primary key, you don't
need an extra Update step to load it.
In the tutorial, you loaded the dimension at the same time you loaded the fact. You know
from Chapter 9 that when you use a Combination L/U step, the step returns you the
generated key. So, the use of the step here for loading and getting the key at the same
time fits perfectly.
If the dimension had been loaded previously, instead of a Combination
L/U step you could have used a Database lookup step by putting the key
fields in the upper grid and the key in the lower grid of the Database lookup
configuration window.

Obtaining the surrogate key for the Time dimension
You already obtained the surrogate keys for Type I and Type II SCDs and for the Junk
dimension. Finally, there is a Time dimension. As for the key, you use the date in string format;
the method for getting the surrogate key is simply changing the metadata from date to string
by using the proper format. Once again, if you had used a regular surrogate key instead of the
date, for getting the surrogate key you would have to use a Database lookup step.
The following table summarizes the different possibilities:
Dimension type

Method for getting the surrogate key

Sample
dimension

Type I SCD

Database lookup step.

Manufacturer

Type II SCD

Dimension L/U step.

Regions

Junk and Mini

Combination L/U step if you load the dimension at the same
time as you load the fact (as in the tutorial).

Sales Junk
dimension

Degenerate

As you don't have a table nor key to translate, you just store the
data as a field in the fact. You don't have to worry about getting
surrogate keys.

Time

Change the metadata to the proper format if you use date as the Time
key (as in the tutorial).

Database lookup step if the dimension is already loaded.

Dimension L/U step if you use a normal surrogate key.

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Pop quiz – modifying a star model and loading the star with PDI
Suppose you want to do some modifications to your star model. What are the changes you'll
have to make in each case:
1. Instead of using a region dimension that keeps history of the changes (Type II SCD),
you want to use a classic region dimension (Type I).
a. As table for the region dimension:
i.

You reuse the table lk_regions_2.

ii.

You use a different table.

iii. Any of the above.
b. As field with the foreign key in the fact table:

You reuse the id_region field.

ii.

You create a new field.

For getting the surrogate key:
i.

You keep using the Dimension lookup/update step.

ii.

You replace the Dimension lookup/update step by another step.

iii. It depends on the how your dimension table looks.
2. You want to change the grain for the Time dimension; you are interested in
monthly information.
a. As table for the time dimension:
i.

You reuse the table lk_time.

ii.

You use a different table.

iii. Any of the above.
b. As field with the foreign key in the fact table:

You reuse the dt field.

ii.

You create a new field.

For getting the surrogate key:
i.

You keep using the Select values step and changing the metadata.

ii.

You use another method.

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3. You decided to create a new table for the product type dimension. The table will
have the following columns: id, product_type_description, and product_
type. As data you would have, for example: 1, puzzle, puzzle for the product
type puzzle, or 2, glue, accessory for the product type glue.
a. As field with the foreign key in the fact table:
i.

You reuse the product_type field.

ii.

You create a new field.

b. For getting the surrogate key:
i.

You use a Combination lookup/update step

ii.

You use a Dimension lookup/update step

iii. You use a Database lookup/update step

Have a go hero – loading a puzzles fact table
In the previous Hero exercise you were asked to load the dimensions for the puzzle star
model. Now you will load the fact table.
To load the fact table you'll need to build a query taking data from the source. Try to figure
out what the query looks like. Then you may try writing the query by yourself, or you may
cheat; this query will serve you as a starting point:
SELECT
i.inv_date
,d.man_code
,cu.city_id
,pr.pro_theme
,pr.pro_pieces
,pr.pro_packaging
,pr.pro_shape
,pr.pro_style
,SUM(d.cant_prod) quantity
FROM invoices
i
,invoices_detail d
,customers
cu
,products
pr
WHERE i.invoice_number = d.invoice_number
AND
i.cus_id
= cu.cus_id
AND
d.pro_code = pr.pro_code
AND
d.man_code = pr.man_code
AND pr.pro_type like 'PUZZLE'
AND i.inv_date BETWEEN cast('${DATE_FROM}' as date)
AND cast('${DATE_TO}' as date)
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Developing and Implementing a Simple Datamart
GROUP BY i.inv_date
,d.man_code
,cu.city_id
,pr.pro_theme
,pr.pro_pieces
,pr.pro_packaging
,pr.pro_shape
,pr.pro_style

After that, look for the surrogate keys for dimensions of Type I and II.
Here you have a mini-dimension. You may load it at the same time you load the fact as you
did in the tutorial with the Junk dimension. Also, make sure that you properly modify the
metadata for the time field.
Insert the data into the fact, and check whether the data was loaded as expected.

Getting facts and dimensions together
Loading the star involves both loading the dimensions and loading the fact. You already loaded
the dimensions and the fact separately. In the following two tutorials, you will put it all together:

Time for action – loading the fact table using a range of dates
obtained from the command line
Now you will get the range of dates from the command line and load the fact table using
that range:

1.
2.

Create a new transformation.

By using a couple of steps, check that the arguments are not null, have the proper
format (yyyy-mm-dd), and are valid dates.

4.
5.

If something is wrong with the arguments, abort.

Save the transformation in the same folder you saved the transformation that loads the
fact table.

Test the transformation by providing valid and invalid arguments to see that it works
as expected.

With a Get system info step, get the first two arguments from the command line and
name them date_from and date_to.

If the arguments are valid, use a Set variables step to set two variables named
DATE_FROM and DATE_TO.

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Chapter 12

Create a job and save it in the same folder you saved the job that loads the dimensions.

Drag to the canvas a START and two transformation job entries, and link them one after
the other.

10.

Use the first transformation entry to execute the transformation you just created.

11.

Use the second transformation entry to execute the transformation that loads the
fact table.

12.

This is how your job should look like:

13.

Save the job.

14.

Press F9 to run the job.

15.

Fill the job settings window as follows:

16.

Click on Launch.

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Developing and Implementing a Simple Datamart

17.

When the execution finishes, explore the database to check that the data for the given
dates was loaded in the fact table. You will see this:

What just happened?
You built a main job that loads the sales fact table. First, it reads from the command line the
range of dates to be used for loading the fact and validates it. If they are not valid, the process
aborts. If they are valid, the fact table is loaded for the dates in that range.

Time for action – loading the sales star
You already created a job for loading the dimensions and another job for loading the fact.
In this tutorial, you will put them together in a single main job:

Create a new job in the same folder in which you saved those jobs. Name this job
load_dm_sales.kjb.

Drag to the canvas a START and two job entries, and link them one after the other.

Use the first job entry to execute the job that loads the dimensions.

Use the second Job entry to execute the job you just created for loading the fact table.

Save the job. This is how it looks:

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Press F9 to run the job.

As arguments, provide a new range of dates: 2009-09-01, 2009-09-30. Then
press Launch.

The dimensions will be loaded first, followed by the loading of the fact table.

The Job metrics tab in the Execution results window shows you the whole
process running:

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Developing and Implementing a Simple Datamart

10.

Exploring the database, you'll see once again the data updated:

What just happened?
You built a main job that loads the sales datamart. First, it loads the dimensions. After that, it
loads the fact table by filtering sales in a range of dates coming from the command line.

Have a go hero – enhancing the loading process of the sales fact table
Facts tables are rarely updated. Usually you just insert new data. However, after loading a
fact, you may detect that there were errors in the source. Or it could also happen that some
data arrives late to the system. In order to take into account those situations, you should
have the possibility to reprocess data already processed. To avoid duplicates in the fact table,
do the following modification to the loading process:
After getting the start and end date and before loading the fact table, delete the records that
may have been inserted in a previous execution for the given range of dates.

Have a go hero – loading the puzzles sales star
Modify the main job so that it also loads the puzzle fact table.
Make sure that the job that loads the dimensions includes all the dimensions needed for both
fact tables. Also, pay attention that you don't read and validate the arguments twice.

Have a go hero – loading the facts once a month
Modify the whole solution so the loading of the fact tables is made once a month. Don't modify
the model! You still want to have daily information in the fact tables; what you want to do is
simply replace the daily updating process with a monthly process. Ask for a single parameter as
yyyymm and validate it. Replace the old parameters START_DATE and END_DATE with this new
one, wherever you use them.
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Chapter 12

Getting rid of administrative tasks
The solution you built during the chapter loads both dimensions and fact in a star model for
a given range of dates. Now suppose that you want to keep your datamart always updated.
Would you sit every day in front of your computer, and run the same job over and over
again? You probably would, but you know that it wouldn't be a good idea. There are better
ways to do this. Let's see how you can get rid of that task.

Time for action – automating the loading of the sales datamart
Suppose that every day you want to update your sales datamart by adding the information
about the sales for the day before. Let's do some modifications to the jobs and
transformations you did so that the job can run automatically.
In order to test the changes, you'll have to change the date for your system. Set the current
date as 2009-10-02.

Create a new transformation.

Drag to the canvas a Get system data step and fill it like here:

With a Select values step, change the metadata of both fields: As type put String
and as format, yyyy-MM-dd.

Add a Set variables step and use the two fields to create two variables named
START_DATE and END_DATE.

Save the transformation in the same folder you saved the transformation that loads
the fact.

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Developing and Implementing a Simple Datamart

Modify the job that loads the fact so that instead of executing, the transformation
that takes the range of dates from the command line executes this one. The job
looks like this:

Save it.

Now let's create the scripts for executing the job from the command line:

Create a folder named log in the folder of your choice.

Open a terminal window.

Create a new file with your favorite text editor.

If your system is not Windows, go to step 7.

Under Windows systems, type the following:
for /f "tokens=1-3 delims=/- " %%a in ('date /t') do set
XDate=%%c%%b%%a
for /f "tokens=1-2 delims=: " %%a in ('time /t') do set
XTime=%%a.%%b
set path_etl=C:\pdi_labs
set path_log=C:\logs
c:\
cd ..
cd pdi-ce
kitchen.bat /file:%path_etl%\load_dm_sales.kjb /level:Detailed >>
%path_log%\sales_"%Xdate% %XTime%".log

Save the file as dm_sales.bat in a folder of your choice. Skip the following
two steps.

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Chapter 12

Under Linux, Unix, and similar systems, type the following:
UNXETL=/pdi_labs
UNXLOG=/logs
cd /pdi-ce
kitchen.sh /file:$UNXETL/load_dm_sales.kjb /level:Detailed >>
$UNXLOG/sales_'date +%y%m%d-%H%M'.log

Save the file as dm_sales.sh in a folder of your choice.
Irrespective of your system, please replace the names of the
folders in the highlighted lines with the names of your own
folders, that is path_etl (the folder where your main job is),
path_log (the folder you just created), and pdi-ce (the
folder where PDI is installed).

Now let's test what you've done:

Execute the batch you created:
Under windows, type: dm_sales.bat
Under Unix-like systems, type: sh dm_sales.sh

When the prompt in the command window is available, it means that the batch
ended. Check the log folder. You'll find a new file with the extension log, named
sales followed by the date and hour, for example:sales_0210Fri 06.46.log.

Edit the log. You'll see the full log for the execution of the job. Within the lines, you'll
see these:
INFO
- Set
INFO
- Set

02-10 17:46:39,015 - Set Variables DATE_FROM and DATE_TO.0
variable DATE_FROM to value [2009-10-01]
02-10 17:46:39,015 - Set Variables DATE_FROM and DATE_TO.0
variable DATE_TO to value [2009-10-01]

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Developing and Implementing a Simple Datamart

Also check the fact table. The fact should have data for the sales made yesterday:

Don't forget to restore the date in your system!

What just happened?
You modified the job that loads the sales datamart so that it always loads the sales from a
day before. You also created a script that embedded the execution of the Kitchen command
and sent the result to a log. The name of the log is different for every day; this allows you
keep a history of logs.
To understand exactly the full Kitchen command line you put into the scripts, please refer to
Appendix B, Pan and Kitchen: Launching Transformations and Jobs from the Command Line.
Doing all this, you don't have to worry about providing dates for the process, nor running
Spoon, nor remembering the syntax of the Kitchen command. Not only that, if you use a
system utility such as a cron in Unix or the scheduler in Windows to schedule this script to
run every day after midnight, you are done. You got rid of all the administrative tasks!

Have a go hero – Creating a back up of your work automatically
Choose a folder where you use to save your work (it could be for example the pdi_labs folder)
Create a job that zips your work under the name backup_yyyymmdd.zip where yyyymmdd
represents the system date. Test the job.

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Then create a .bat or .sh file that executes your job sending the log to a file. Test the script.
Finally, schedule the script to be executed weekly.

Have a go hero – enhancing the automate process by sending an e-mail if
an error occurs
Modify the main job so if something goes wrong, it sends you an e-mail reporting the problem.
Doing so, you don't have to worry about checking the daily log to see if everything went fine.
Unless there is a problem with the e-mail server, you'll be notified whenever some error occurs.

Summary
In this chapter you created a set of jobs and transformations that loads a sales datamart.
Specifically, you learned how to load a fact table and to embed that process into a bigger
one—the process that loads a full datamart.
You also learned to automate PDI processes, which is useful to get rid of tedious and
repetitive manual tasks. In particular, you automated the loading of your sales datamart.
Beyond that, you must have found this chapter useful for reviewing all you learned since
the first chapter. If you can't wait for more, read the next chapter. There you will find useful
information for going further.

[ 403 ]

Taking it Further
The lessons learned in previous chapters gave you the basis of PDI. If you liked
working with PDI and intend to use it in your own projects, there is much more
ranging from applying best practices to using PDI integrated with the Pentaho
BI Suite.

This chapter points you the right direction for taking it further. The chapter begins by giving
you some advice to take into account in your daily work with PDI. After that it introduces you
some advanced PDI concepts for you to know to what extent you can use the tool beyond
the basics.

PDI best practices
If you intend to work seriously with PDI, knowing how to accomplish different tasks is not
enough. Here are some guidelines that will help you go in the right direction.


Outline your ideas on paper before creating a transformation or a job:
Don't drop steps randomly on the canvas trying to get things working. You could end
up with a transformation or job that is difficult to understand and even useless.



Document your work:
Write at least a simple description in the transformations and jobs setting windows.
Replace the default names of steps and job entries with meaningful ones. Use notes
to clarify the purpose of the transformations and jobs. Doing this, your work will be
quite self documented.

Taking it Further


Make your jobs and transformations clear to understand:
Arrange the elements in the canvas so that it doesn't look like a puzzle to solve.
Memorize the shortcuts for arrangement and alignment, and use them regularly.
You'll find a full list in Appendix D, Spoon shortcuts.



Organize PDI elements in folders:
Don't save all the transformations and jobs in the same folder. Organize them
according to their purpose.



Make your work flexible and reusable:
Make use of arguments, variables, and named parameters. If you identify tasks that
are going to be used in several situations, create subtransformations.



Make your work portable (ready for deployment):
This involves making sure even if you move your work to another machine or
another folder, or the paths to source or destination files change, or the connection
properties to the databases change, everything should work either with minimal
changes or without changes. In order to make ensure that, don't use fixed names
but variables. If you know the values for the variables beforehand, define the
variables in the kettle.properties file. For the name of the transformations and
jobs, use relative paths—use the ${Internal.Job.Filename.Directory} and
${Internal.Transformation.Filename.Directory} variables.



Avoid overloading your transformations:
A transformation should do a precise task. If it doesn't, think of splitting it in two
or more, or create subtransformations. Doing this will make your transformation
clearer and also reusable in the case of subtransformations.



Handle errors:
Try to figure out the kind of errors that may happen and trap them by validating and
handling errors, and taking appropriate actions such as fixing data, taking alternative
paths, sending friendly message to the log files, and so on.



Do everything you can to optimize the PDI performance:
You can find a full checklist at http://wiki.pentaho.com/display/COM/PDI+
Performance+tuning+check-list. As of version 3.1.0, PDI introduced a tool for
tracking the performance of individual steps in a transformation. You can find more
information at http://wiki.pentaho.com/display/EAI/Step+performance
+monitoring.

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Chapter 13


Keep track of jobs and transformations history:
You can use a versioning system such as subversion. Doing so, you could recover
older versions of your jobs and transformations or examine the history of how they
changed. For more on subversion, visit http://subversion.tigris.org/.
Bookmark the forum page and visit it frequently. The PDI forum is available
at http://forums.pentaho.org/forumdisplay.php?f=135.

The following is the main PDI forum page:

If you get stuck with something, search for a solution in the forum. If you don't find what you're
looking for, create a new thread, expose your doubts or scenario clearly, and you'll get a prompt
answer, as the Pentaho community, and particularly the PDI one, is quite active.

[ 407 ]

Taking it Further

Getting the most out of PDI
Throughout the book you learned, step by step, how to use PDI for accomplishing several
kinds of tasks— reading from different kinds of sources, writing back to them, transforming
data in several ways, loading data into databases, and even loading a full data mart. You
already have the knowledge and the experience to do anything you want or you need with
PDI from now on. However, PDI offers you some more features that may be useful for you as
well. The following sections will introduce them and will guide you so that you know where
to look for in case they want to put them into practice.

Extending Kettle with plugins
As you could see while learning Kettle, there is a large set of steps and job entries to choose
from when designing jobs and transformations. The number rises above 200 between steps
and entries! If you still feel like you need more, there are more options—plugins.
Kettle plugins are basically steps or job entries that you install separately. The available
plugins are listed at http://wiki.pentaho.org/display/EAI/List+of+Available+
Pentaho+Data+Integration+Plugins.
Most of the listed plugins can be downloaded and used for free. Some are so popular or
useful that they end up becoming standard steps of PDI—for example, the Formula step that
you used several times throughout the book.
There are other plugins that come as a trial version and you have to pay to use them.
It's also possible for you to develop your own plugins. The only prerequisite is knowing how
to develop code in Java. If you are interested in the subject, you can get more information at
http://wiki.pentaho.com/display/EAI/Writing+your+own+Pentaho+Data+Int
egration+Plug-In.

It's no coincidence that the author of those pages is Jens Bleuel. Jens used the
plugin architecture back in 2004, in order to connect Kettle with SAP, when he was
working at Proratio. The plugin support was incorporated in Kettle 2.0 and the
PRORATIO - SAP Connector, today available as a commercial plugin, was one of
the first developed Kettle plugins.
You should know that 3.x plugins no longer work on Kettle 4.0.

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Chapter 13

Have a go hero – listing the top 10 students by using the Head plugin step
Browse the plugin page and look for a plugin named Head. As described in the page, this
plugin is a step that keeps the first x rows of the stream. Download the plugin and install it.
The installation process is really straightforward. You have to copy a couple of *.jar files to
the libext directory inside the PDI installation folder, add the environment variable for the
PDI to find the libraries, and restart Spoon. The downloaded file includes a documentation
with full instructions. Once installed, the Head will appear as a new step within the
Transformation category of steps as shown here:

Create a transformation that reads the examination file that was used in the Time for Action
– reviewing examination by using the Calculator step section in Chapter 3 and some other
chapters as well. Generate an output file with the top 10 students by average score in
descending order. In order to keep the top 10, use the Head plugin.
Before knowing of the existence of this plugin, you used to do this kind of
filtering by using the JavaScript step. Another way to do it is by using an Add
sequence step followed by a Filter rows step. Note that none of these methods
use an ad hoc step.

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Taking it Further

Overcoming real world risks with some remote execution
In order to learn to use Kettle, you used very simple and small sets of data. It's worth saying
that all you learned can be also applied for processing huge files and databases with millions
of records. However, that's not for free! When you deal with such datasets, there are many
risks—your transformations slow down, you may run out of memory, and so on.
The first step in trying to overcome those problems is to do some remote execution. Suppose
you have to process a huge file located at a remote machine and that the only thing you
have to do with that file is to get some statistics such as the maximum, minimum, and
average value for a particular piece of data in the file. If you do it in the classic way, the data
in the file would travel along the network for being processed by Kettle in your machine,
loading the network unnecessarily.
PDI offers you the possibility to execute the tasks remotely. The remote execution capability
allows you to run the transformation in the machine where the data resides. Doing so, the
only data that would travel through the network will be the calculated data.
This kind of remote execution is done by Carte, a simple server that you can install in
a remote machine and that does nothing but run jobs and transformations on demand.
Therefore, it is called a slave server. You can start, monitor, and stop the execution of
jobs and transformations remotely as depicted here:

Starts
Monitors
Stops

Transformations
and Jobs
Executes

SPOON

CARTE

Kettle Engine

Network

You don't need to download additional software because Carte is distributed as part of the
Kettle software. For documentation on carte, follow this link: http://wiki.pentaho.
com/display/EAI/Carte+User+Documentation.

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Chapter 13

Scaling out to overcome bigger risks
As mentioned above, PDI can handle huge volumes of data. However, the bigger the volume
or complexity of your tasks, the bigger the risks. The solution not only lies in executing
remotely, but in order to enhance your performance and avoid undesirable situations, you'd
better increase your power. You basically have two options—you can either scale up or
scale out. Scaling up involves buying a better processor, more memory, or disks with more
capacity. Scaling out means to provide more processing power by distributing the work over
multiple machines.
With PDI you can scale out by executing jobs and transformations in a cluster. A cluster is a
group of Carte instances or slave servers that collectively execute a job or a transformation.
One of those servers is designed as the master and takes care of controlling the execution
across the cluster. Each server executes only a portion of the whole task.
Transformations
and Jobs
Executes

Executes

CARTE

Kettle Engine

MASTER

Slave server1

Slave server2

Slave serverN
Network

The list of servers that would make up a cluster may be known in advance, or you can have
dynamic clusters—clusters where the slave servers are known only at run time. This feature
allows you to hire resources—for example, server machines provided as a service over the
Internet, and run your jobs and transformations processes over those servers in a dynamic
cluster. This kind of Internet service is quite new and is known as cloud-computing, Amazon
EC2 being one of the most popular.
If you are interested in the subject, there is an interesting paper named Pentaho Data
Integration: Scaling Out Large Data Volume Processing in the Cloud or on Premise,
presented by the Pentaho partner Bayon Technologies. You can download it from
http://www.bayontechnologies.com.

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Taking it Further

Pop quiz – remote execution and clustering
For each of the following, decide if the sentence is true or false:
a. Carte is a graphical tool for designing jobs and transformations that are going to be
run remotely.
b. In order to run a transformation remotely you have to define a cluster.
c.

When you have very complex transformations or huge datasets you have to execute
in a cluster because PDI doesn’t support that load in a single machine.

d. To run a transformation in a cluster you have to know the list of servers in advance.
e. If you want to run jobs or transformations remotely or in a cluster you need the PDI
Enterprise Edition.

Integrating PDI and the Pentaho BI suite
In this book you learned to use PDI standalone, but as mentioned in the first chapter, it is
possible to use it integrated with the rest of the suite. There are a couple of options for
doing so.

PDI as a process action
In Chapter 1 you were introduced to the Pentaho platform. Everything in the Pentaho
platform is made by action sequences. An action sequence is, as its name suggests, a
sequence of atomic actions that together accomplish small business processes.
Look at the following sample with regard to the Puzzle business:
Consider that you regularly receive updated price lists (one for each manufacturer) and you
drop the files in a given folder. When you decide to hike the prices, you process one of those
files and get a web-based report with the updated prices. You can implement that process
with an action sequence.
Get list of files
to process

Prompt for the file

Update prices

Run web-based
report

There are four atomic actions in this sequence. You already know how to do the first and
third actions (building the list of available price lists and updating the prices) with PDI. You
can create transformations or jobs that perform these tasks and then use them as actions
in an action sequence. The following is a sample screenshot of Design Studio, the action
sequence editor:
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Chapter 13

The screenshot shows how the action sequence editor looks like while editing the explained
action sequence. In the tree at the left side, you can see the list of actions, while the right
section allows you to configure each action. The action being edited in the screenshot is the
PDI transformation that updates the prices.

PDI as a datasource
You already created several transformations that, after doing some data manipulation,
generated plain files or Excel sheets. What if, instead of these types of output files, you
wanted the same data displayed as a more attractive, colorful, and interactive web-based
report? You can't do it with PDI alone. With the newest Pentaho report engine you can take
the data that came out of a transformation and use it as the data source for your report.
Having the data, the reporting tool allows you to generate any kind of output.

[ 413 ]

Taking it Further

If you want to learn more about Pentaho reporting, you can start by visiting the wiki at
http://wiki.pentaho.com/display/Reporting/Pentaho+Reporting+Communi
ty+Documentation. Or you can buy the book Pentaho Reporting 3.5 for Java Developers

(ISBN: 3193), authored by Will Gorman, published by Packt Publishing. Despite its name, it
is not just a book for developers; it's a great book for those who are unfamiliar with the tool
and who want to learn how to create reports with it.
Data coming out of a transformation can also be used as a source data for a CDF dashboard.
A dashboard is an application that shows you visual indicators such as charts, traffic lights, or
dials. A CDF dashboard is a dashboard created with a toolkit, known as Community Dashboard
Framework, which is developed by members of the Pentaho community. The CDF dashboards,
recently incorporated as part of the Pentaho suite, accept many types of data sources, PDI
transformations being one of them. The only restriction (at least for now) is that they only
accept transformations stored in a repository (see Chapter 1 and Appendix A for details). For
more about CDF here is a link to the wiki page: http://wiki.pentaho.com/display/
COM/Community+Dashboard+Framework.

More about the Pentaho suite
The options mentioned earlier for using PDI integrated with other components of the suite
are a good starting point to begin working with the Pentaho BI suite. By putting into practice
those examples, you can gradually get familiarized with the suite.
There is much more to learn once you get started. Look at the following sample screen:

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Chapter 13

This represents a multidimensional view of your sales data mart. Here you can see cross-tab
information for puzzle sales in August and September for three specific manufacturers across
different regions, countries, and cities. It looks really useful for exploring your sales numbers,
doesn't it? Well, this is just an example of what you can do with Pentaho beyond using PDI
reporting and dashboard tools mentioned earlier.
For more about the suite, you can visit the wiki page http://wiki.pentaho.com/ or the
Pentaho site (www.pentaho.com). If, instead of browsing here and there, you prefer to
read it all in a single place, there is also a new book that brings you a good introduction to
the whole suite. The book is titled Pentaho Solutions (Wiley publishing), authored by Roland
Bouman and Jos van Dongen—two seasoned Pentaho community members.

PDI Enterprise Edition and Kettle Developer Support
Pentaho offers an Enterprise Edition of the Pentaho BI Suite and also for PDI. The PDI
Enterprise Edition adds an Enterprise Console for performance monitoring, remote
administration, and alerting. There are also a growing number of extra plugins for
enterprise customers. In addition to the PDI extensions, customers get services and support,
indemnification, software maintenance (fix versions, e.g. 3.2.2), and a knowledge base with
additional technical resources.
Since the end of 2009, Pentaho also offers Kettle Developer Support for the Community
Edition. With this, you can get direct assistance from the product experts for the design,
development, and testing phases of the ETL lifecycle. This option is perfect for getting
started, removing roadblocks, and troubleshooting ETL processes.
For further information, check the Pentaho site (www.pentaho.com).

[ 415 ]

Taking it Further

Summary
This chapter provided you with a list of best practices to apply while working with PDI. If
you follow the given advice, your work will not only be useful, but also flexible, reusable,
documented, and neatly presented.
You were introduced to PDI plugins, a mechanism that allows you to customize the tool.
A quick review about remote execution and clustering was given for those interested in
developing PDI in large environments.
Finally, an introduction was given showing you how PDI can be used not only as a standalone
tool but can also be integrated with the Pentaho BI suite.
Some links and references were provided for those of you who, after reading the book and
particularly this chapter, are anxious to learn more.
I hope you enjoyed reading the book and learning PDI, and will start using PDI to solve all
your data requirements.

[ 416 ]

Working with Repositories
Spoon allows you to store your transformations and jobs under two
different configurations—file based and repository based. In contrast to the
file-based configuration that keeps the transformations and jobs in XML format
such as *.ktr and *.kjb files in the local file system, the repository-based
configuration keeps the same information in tables in a relational database.

While working with the file-based system is simple and practical, the repository-based
system can be convenient in some situations. The following is a list of some of the distinctive
repository features:




Repositories implement security. In order to work with a repository, you need
credentials. You can create users and profiles with different permissions on the
repository; however, keep in mind that the kind of permissions you may apply is
limited.
Repositories are prepared for basic team development. The elements you create
(transformations, jobs, database connections, and so on) are shared by all repository
users as soon as you create them.
If you want to use PDI as the input source in dashboards made with the CDF (refer to
Chapter 13 for details), the only way you have is by working with repositories.
PDI 4, in its Enterprise version, will include a lot of new repository features such as
version control.

Working with Repositories

Before you decide on working with a repository, you have to be aware of the file-based
system benefits that you may lose out on. Here are some examples:




When working with the repository-based system, you need access to the repository
database. If, for some reason, you cannot access the database (due to a network
problem or any other issue), you will not be able to work. You don't have this
restriction when working with files—you need only the software and the
.ktr/.kjb files.
When working with repositories, it is difficult to keep track of the changes. On
the other hand, when you work with the file system, it's easier to know which
jobs or transformations are modified. If you use Subversion, you even have
version control.
Suppose you want to search and replace some text in all jobs and transformations.
If you are working with repositories, you would have to do it for each table in the
repository database. When working with the file-based system, this task is quite
simple—you could create an Eclipse project, load the root directory of your jobs
and transformations, and do the task by using the Eclipse utilities.

This appendix explains how to create a repository and how to work with it. You can give
repositories a try and decide for yourself which method, repository-based or file-based, suits
you best.

Creating a repository
If you want to work with the repository-based configuration, you have to create a repository
in advance.

Time for action – creating a PDI repository
To create a repository, follow these steps:

Open MySQL Command Line Client.

In the command window, type the following:
CREATE DATABASE PDI_REPO;

Open Spoon.

If the repository dialog appears, skip to step 6.

Open the repository dialog from the Repository | Connect to repository menu.

Click on New to create a new repository. The repository information dialog shows
up. Click on New to create a new database connection.
[ 418 ]

Appendix A

The database connection window appears. Define a connection to the database
you have just created and give a name to the connection— PDI_REPO_CONN
in this case.
If you want to refer to the steps on creating the database
connection, check out Time for action – creating a connection to
the Steel Wheels database section in Chapter 8.

Test the connection to see that it is properly configured.

Click OK to close the database connection window. The Select database connection
box will show the created connection.

10.

Give the name MY_REPO to the repository. As description, type My first repository.

11.

Click on Create or Upgrade.

12.

PDI will ask you if you are sure you want to create the repository on the specified
database connection. Answer Yes if you are sure of the settings you entered.

13.

A dialog appears asking if you want to do a dry run to evaluate the generated SQL
before execution.

14.

Answer No unless you want to preview the SQL that will create the reposProgress
progress window appears showing you the progress while the repository is
being created.

15.

Finally, you see a window with the message Kettle created the repository on the
specified connection. Close the dialog window.

[ 419 ]

Working with Repositories

16.

Click on OK to close the repository information window. You will be back in the
repository dialog, this time with a new repository available in the repository
drop-down list.

17.

If you want to start working with the created repository, please refer to the Working
with the repository storage system section. If not, click on No Repository. This will
close the window.

What just happened?
In MySQL you created a new database named PDI_REPO. Then you used that database to
create a PDI repository.

Creating repositories to store your transformations and jobs
A Kettle repository is a database that provides you with a storage system for your
transformations and jobs. The repository is the alternative to the *.ktr and *.kjb
file-based system.
In order to create a new repository, a database must have been created previously. In the
tutorial, the repository was created in a MySQL RDBMS. However, you can create your
repositories in any relational database.
The PDI repository database should be used exclusively for its purpose!

Note that if the repository has already been created from another machine or by another
user, that is, another profile in the operating system, you don't have to create the repository
again. In that case, just define the connection to the repository but don't create it again. In
other words, follow all the instructions but don't click the Create or Upgrade button.
Once you have created a repository, its name, description, and connection information are
stored in a file named repositories.xml, which is located in the PDI home directory.
The repository database is populated with a bunch of tables with familiar names such as
transformation, job, steps, and steps_type.
[ 420 ]

Appendix A

Note that you may have more than one repository—different repositories for different
projects, different repositories for different versions of a project, a repository just for testing
new PDI features, and another for serious development, and so on. Therefore, it is important
that you give the repositories meaningful names and descriptions so that you don't get
confused if you have more than one.

Working with the repository storage system
In order to work with a repository, you must have created at least one. If you haven't, please
refer to the section Creating a repository.
If you already have a repository and you want to work with it, the first thing you have to do is
to log into it. The next tutorial helps you do this.

Time for action – logging into a repository
To log into an existent repository, follow these instructions:

1.
2.

Launch Spoon.
If the repository dialog window doesn't show up, select Repository | Connect to
repository from the main menu. The repository dialog window appears.

3.
4.

In the drop-down list, select the repository you want to log into.

You will now be logged into the repository. You will see the name of the repository
in the upper-left corner of Spoon:

Type your username and password. If you have never created any users, use the
default username and password—admin and admin. Click on OK.

What just happened?
You opened Spoon and logged into a repository. In order to do that, you provided the name
of the repository and proper credentials. Once you did it, you were ready to start working
with the repository.
[ 421 ]

Working with Repositories

Logging into a repository by using credentials
If you want to work with the repository storage system, you have to log into the repository
before you begin your work. In order to do that, you have to choose the repository and
provide a repository username and password.
The repository dialog that allows you to log into the repository can be opened from the
main Spoon menu. If you intend to log into the repository often, you'd better select Edit |
Options... and check the general option Show repository dialog at startup?. This will cause
the repository dialog to always show up when you launch Spoon.
It is possible to log into the repository automatically. Let's assume you have a repository
named MY_REPO and you use the default user. Add the following lines to the
kettle.properties file:
KETTLE_REPOSITORY=MY_REPO
KETTLE_USER=admin
KETTLE_PASSWORD=admin

The next time you launch Spoon, you will be logged into the repository automatically.
For details about the kettle.properties file, refer to the section on
Kettle variables in Chapter 2.

Because the log information is exposed, auto login is not recommended.

Defining repository user accounts
To log into a repository, you need a user account. Every repository user has a profile
that dictates the permissions that the user has on the repository. There are three
predefined profiles:
Profile

Permissions

Read-only
User

Cannot create nor modify any element in the repository

Administrator

Can create, modify, and delete any object in the repository excepting
users and profiles
Has full permissions, including creating new users and profiles

[ 422 ]

Appendix A

There are also two predefined users:




admin: A user with Administrator profile. This is the user you used to log into the
repository for the first time. It has full permissions on the repository.
guest: A user with Read-only profile.

If you have Administrator profile, you can create, modify, rename, or delete users and
profiles from the Repository explorer. For details, please refer to the section Examining and
modifying the contents of a repository with the Repository explorer, later in this chapter. Any
user may change his/her own user information both from the Repository explorer and from
the Repository | Edit current user menu option.

Creating transformations and jobs in repository folders
In a repository, the jobs and transformations are organized in folders. A folder in a repository
fulfills the same purpose as a folder in your drive—it allows you to keep your work organized.
Once you create a folder, you can save both transformations and jobs in it.
While connected to a repository you design, preview, and run jobs and transformations
just as you do with files. However, there are some differences when it comes to opening,
creating, or saving your work. So, let's summarize how you do those tasks when logged
into a repository:
Task

Procedure

Open a transformation / job Select File | Open. The Repository explorer shows up. Navigate the
repository until you find the transformation or job you want to open.
Double-click it.
Create a folder
Select Repository | Explore repository, expand the transformation
or job tree, locate the parent folder, right-click and create the folder.
Alternatively, double-click the parent folder.
Create a transformation
Select File | New | Transformation or press Ctrl+N.
Create a Job
Select File | New | Job or press Ctrl+Alt+N.
Save a transformation
Press Ctrl+T. Give a name to the transformation. In the Directory
textbox, select the folder where the transformation is going to be saved.
Press Ctrl+S. The transformation will now be saved in the selected
directory under the given name.
Save a job
Press Ctrl+J. Give a name to the job. In the Directory textbox, select the
folder where the job is going to be saved. Press Ctrl+S. The job will be
saved in the selected directory under the given name.

[ 423 ]

Working with Repositories

Creating database connections, partitions, servers, and clusters
Besides users, profiles, jobs, and transformations, there are some additional PDI elements
that you can define:
Element
Database connections
Partition schemas

Slave servers
Clusters

Description
Connection definitions to relational databases. These are covered in
Chapter 8.
Partitioning is a mechanism by which you send individual rows
to different copies of the same step—for example, based on a
field value.
This is an advanced topic not covered in this book.
Slave servers are installed in remote machines to execute jobs and
transformations remotely. They are introduced in Chapter 13.
Clusters are groups of slave servers that collectively execute a job or
a transformation. They are also introduced in Chapter 13.

All these elements can also be created, modified, and deleted from the Repository explorer.
Once you create any of these elements, it is automatically shared by all repository users.

Backing up and restoring a repository
A PDI repository is a database. As such, you may regularly backup it with the utilities
provided by the RDBMS. However, PDI offers you a method for creating a backup in
an XML file.
You create a backup from the Repository explorer. Right-click the name of the repository and
select Export all objects to an XML file. You will be asked for the name and location of the
XML file that will contain the backup data. In order to back up a single folder, instead of rightclicking the repository name, right-click the name of the folder.
You can restore a backup made in an XML file also from the Repository explorer. Right-click
the name of the repository and select Import all objects from an XML file. You will be asked
for the name and location of the XML file that contains the backup.

Examining and modifying the contents of a repository
with the Repository explorer
The Repository explorer shows you a tree view of the repository to which you are
connected. From the main Spoon menu, select Repository | Explore Repository and you
get to the explorer window. The following screenshot shows you a sample Repository
explorer screen:
[ 424 ]

Appendix A

In the tree you can see: Database connections, Partition schemas, Slave servers (slaves in
the tree), Clusters, Transformations, Jobs, Users, and Profiles.
You can sort the different elements by name, user, changed data, or description by just
clicking on the appropriate column header: Name, User, Changed date, or Description. The
sort is made within each folder.
The Repository explorer not only shows you these elements, but also allows you to create,
modify, rename, and delete them. The following table summarizes the available actions:
Action
Create a new element
(any but
transformations and
jobs)
Open an element for
editing
Delete an element

Procedure
Double-click the name of the
element at the top of the list.
Alternatively, right-click any
element in its category and
select the New option.
Right-click it and select the Open
option. Alternatively, doubleclick it.
Right-click it and select the
Delete option.

[ 425 ]

Example
In order to create a new user,
double-click the word Users at the
top of the users list, or right-click any
user and select New User.
In order to edit a job, double-click it,
or right-click and select Open job.
In order to delete a user, right-click it
and select Delete user.

Working with Repositories

When you explore the repository, you don't see jobs and transformations
mixed. Consequently, the whole folder tree appears twice—once under
Transformations and then under Jobs.

In order to confirm your work, click on Commit changes. If you make a mistake, click on
Rollback changes.

Migrating from a file-based system to a repository-based
system and vice-versa
No matter which storage system you are using, file based or repository based, you may want
to move your work to the other system. The following tables summarize the procedure for
doing that:
Migrating from file-based configuration to repository-based configuration:
PDI element

Procedure for migrating from file to repository

Transformations or jobs From File | Import from an XML file, browse to locate the .ktr/.kjb file
to import and open it. Once the file has been imported, you can save it into
the repository as usual.
Database connections,
partition schemas,
slaves, and clusters

When importing from XML, a job or transformation that uses the database
connection, the connection is imported as well. The same applies to
partitions, slave servers, and clusters.

Migrating from file-based configuration to repository-based configuration:
PDI element

Procedure for migrating from repository to file

Single transformation
or job

Open the job or transformation, select File | Export to an XML file, browse
to the folder where you want to save the job or transformation, and save
it. Once it has been exported, it will be available to work with under the file
storage method or to import from another repository.

All transformations
saved in a folder

In the Repository explorer, right-click the name of the folder and select
Export transformations. You will be asked to select the directory where the
folder along with all its subfolders and transformations will be exported to.
If you right-click the name of the repository or the root folder in the
transformation tree, you may export all the transformations.

[ 426 ]

Appendix A

PDI element

Procedure for migrating from repository to file

All jobs saved in a folder In the Repository explorer, right-click the name of the folder and select
Export Jobs. You will be asked to select the directory where the folder
along with all its subfolders and jobs will be exported to.
If you right-click the name of the repository or the root folder in the job
tree, you may export all the jobs.
Database connections,
partition schemas,
slaves and clusters

When exporting to XML a job or transformation that uses the database
connection, the connection is exported as well (it's saved as part of the
KTR/KJB file). The same applies to partitions, slave servers, and clusters.

You have to be logged into the repository in order to perform any of the
explained operations.

If you share a database connection, a partition schema, a slave server, or a cluster, it will
be available for using both from a file and from any repository, as the shared elements are
always saved in the shared.xml file in the Kettle home directory.

Summary
This appendix covered the basics concepts for working with repositories. Besides the topics
covered here, working with repositories is pretty much the same as working with files.
Although the tutorials in this book were explained assuming that you work with files, all of
them can be implemented under a repository-based configuration with minimal changes. For
example, instead of saving a transformation in c:\pdi_labs\hello.ktr, you could save it
in a folder named pdi_labs with the name hello. Besides these tiny details, you shouldn't
have any trouble in developing and testing the exercises.

[ 427 ]

Pan and Kitchen: Launching
Transformations and Jobs from the
Command Line
All the transformations and jobs you design in Spoon end up being used as part of batch
processes—for example, processes that run every night in a scheduled fashion. When it
comes to running them in that way, you need Pan and Kitchen.




Pan is a command line program that lets you launch the transformations designed in
Spoon, both from .ktr files and from a repository.
The counterpart to Pan is Kitchen that allows you to run jobs both from .kjb files
and from a repository.

This appendix shows you different options you have to run these commands.

Running transformations and jobs stored in files
In order to run a transformation or job stored as a .ktr / .kjb file, follow these steps:
1. Open a terminal window.
2. Go to the Kettle installation directory.
3. Run the proper command according to the following table:
Running a ...
transformation
job

Windows
pan.bat /file:
kitchen.bat /file:

Unix-like system
pan.sh /file:
kitchen.sh /file:

Download from Wow! eBook

Pan and Kitchen: Launching Transformations and Jobs from the Command Line

When specifying the .ktr/.kjb filename, you must include the full path. If the name
contains spaces, surround it with double quotes.
Here are some examples:


Suppose that you work with Windows and that your Kettle installation directory is
c:\pdi-ce. In order to execute a transformation stored in the file c:\pdi_labs\
hello.ktr, you have to type the following commands:
C:
cd \pdi-ce
pan.bat /file:"c:\pdi_labs\hello.ktr"



Suppose that you work with a Unix-like system and that your Kettle installation
directory is /home/yourself/pdi-ce. In order to execute a job stored in the file
/home/pdi_labs/hellojob.kjb, you have to type the following commands:
cd /home/yourself/pdi-ce
kitchen.sh /file:"/home/yourself/pdi-ce/hellojob.kjb"

If you have a repository with auto login (refer Appendix A), as
part of the command, add /norep. This will avoid that PDI
login to the repository.

Running transformations and jobs from a repository
In order to run a transformation or job stored in a repository follow these steps:
1. Open a terminal window.
2. Go to the Kettle installation directory.
3. Run the proper command according to the following table:
Running a ...
transformation

job

Windows
pan.bat /rep:
/user:
/pass:
/trans:
/dir:
kitchen.bat /rep:
/user:
/pass:
/job:
/dir:

[ 430 ]

Unix-like system
pan.sh /rep:
/user:
/pass:
/trans:
/dir:
kitchen.sh /rep:
/user:
/pass:
/job:
/dir:

Appendix B

In this preceding table:
•

rep is the name of the repository to log into

•

user and pass are the credentials to log into the repository

•

trans and job are the names of the transformation or job to run

•

dir is the name of the directory where the transformation or job is located

The parameters are shown on different lines for you to clearly identify all the options.
When you type the command, you have to write all the parameters on
the same line.

Suppose that you work on Windows, you have a repository named MY_REPO, and you log
into the repository with user PDI_USER and password 1234. To run a transformation named
Hello located in a directory named MY_WORK in that repository, type the following:
pan.bat /rep:"MY_REPO" /user:"PDI_USER" /pass:"1234" /trans:"Hello" /
dir:"/MY_WORK/"

If you defined auto-login, you don't need to provide the repository
information— the rep, user, and pass command line parameters—
as part of the command.

Specifying command line options
In the examples provided in this appendix, all options are specified by using the /option:
value syntax—for example, /trans:"Hello".

Instead of /, you can also use -. Between the name of the option and the value, you can also
use =. This means the options /trans:"Hello" and -trans="Hello" are equivalents.
You may use any combination of /,-, :, and =.
In Windows, the use of - and = may cause problems; it's
recommended that you use the /option:value syntax.

If there are spaces in the values, you can use quotes ('') or double quotes ("") to keep the
values together. If there are no spaces, the quotes are optional.

[ 431 ]

Pan and Kitchen: Launching Transformations and Jobs from the Command Line

Checking the exit code
Both Pan and Kitchen return an error code based on how the execution went. To check the
exit code of Pan or Kitchen under Windows, type the following command:
echo %ERRORLEVEL%

To check the exit code of Pan or Kitchen under Unix-like systems, type the
following command:
echo $?

If you get a zero, it means that there are no errors, whereas a value greater than zero
implies failure. To understand the meaning of the error, please refer to the Pan / Kitchen
documentation; URL references are provided at the end of the appendix.

Providing options when running Pan and Kitchen
When you execute a transformation or a job with Spoon, you have the option to provide
additional information such as named parameters. The following Spoon dialog window
shows you an example of that:

[ 432 ]

Appendix B

When you execute the transformation or job with Pan or Kitchen respectively, you provide
this same information as options in the command line. This is how you do it compared
side-by-side with Spoon:

Log details
Spoon
You specify the log level in
the drop-down list inside
the Details box.

Pan/Kitchen option
/level:

Example
/level:Detailed

where the logging level can be
one of the following:

The log appears in the terminal
window, but you can use the
command language of your
operating system to redirect
it to a file.

When the transformation or Error, Nothing, Minimal,
job runs, the log is shown
Basic, Detailed, Debug, or
in the Execution Results
Rowlevel.
window.

Named parameters
Spoon
You specify the named
parameters in the
Parameters box. The
window shows you the
name of the defined named
parameters for you to fill
the values or keep the
default values.

Pan/Kitchen option
/param:

Example
/param:

"REPORTS_FOLDER=

c:\my_rep\"

PAN/Kitchen option
You type them in order as part of
the command.

Example
20091001 20091031

Arguments
Spoon
You specify the command
line arguments in the
Arguments grid. Each line
corresponds to a different
argument.

Variables
Spoon

Pan/Kitchen

The grid named Variables shows the
variables used in the transformation/job
as well as their current values. At the
time of the execution, you can type
different values.

You cannot set variables either in the Pan or in the
Kitchen command. The variables have to exist. You may
define them in the kettle.properties file. To
get the details of this file, refer to the Kettle Variables
section in Chapter 2.
[ 433 ]

Pan and Kitchen: Launching Transformations and Jobs from the Command Line

Suppose that the sample transformation shown in the screenshot is located at
c:\pdi_labs\sales_report.ktr. Then the following Pan command
pan.bat /file:"c:\pdi_labs\sales_report.ktr" 20091001 20091031 /level:Detailed > c:\pdi_labs\logs\sales_report.log

executes the transformation with the same options shown in the screenshot. The command
redirects the log to the file c:\pdi_labs\logs\sales_report.log.
Besides these, both Pan and Kitchen have additional options. For a full list and more
examples, visit the Pan and Kitchen documentation at http://wiki.pentaho.com/
display/EAI/Pan+User+Documentation and http://wiki.pentaho.com/
display/EAI/Kitchen+User+Documentation.

[ 434 ]

Quick Reference:
Steps and Job Entries
This appendix summarizes the purpose of the steps and job entries used in the tutorials
throughout the book. For each of them, you can see the name of the Time for action section
where it was introduced and also a reference to the chapters where you can find more
examples that use it.
How to use this reference
Suppose you are inside Spoon, editing a Transformation. If the transformation
uses a step that you don't know and you want to understand what it does or
how to use it, double-click the step and take note of the title of the settings
window; that title is the name of the step. Then search for that name in the
transformation steps reference table. The steps are listed in alphabetical order
so that you can find them quickly. The last column will take you to the place in
the book where the step is explained.
The same applies to jobs. If you see in a job an unknown entry, double-click the
entry and take note of the title of the settings window; that title is the name of
the entry. Then search for that name in the job entries reference table. The job
entries are also listed in alphabetical order.

Quick Reference: Steps and Job Entries

Transformation steps
The following table includes all the transformation steps used in the book. For a full list
of steps and their descriptions, select Help | Show step plug-in information in Spoon's
main menu.
You can also visit http://wiki.pentaho.com/display/EAI/Pentaho+Data+Integra
tion+v3.2.+Steps for a full step reference along with some examples.
Icon

Name
Abort
Add constants

Purpose
Aborts a transformation

Time for action
Aborting when there are too
many errors (Chapter 7); also in
Chapters 11 and 12
Adds one or more constant Gathering progress and
fields to the stream
merging all together (Chapter
4); also in Chapters 7, 8, and 9

Add sequence

Gets the next value from a
sequence

Assigning tasks by Distributing
(Chapter 4); also in Chapters 6
and 11

Append
streams

Appends two streams in
an ordered way

Giving priority to Bouchard
by using Append Stream
(Chapter 4)

Calculator

Creates new fields by
performing simple
calculations

Database join

Executes a database query
using stream values as
parameters

Using a Database join step
to create a list of suggested
products to buy (Chapter 9)

Database
lookup

Looks up values in a
database table

Using a Database lookup step to
create a list of products to buy
(Chapter 9), also in Chapter 12

Reviewing examination by
using the Calculator step
(Chapter 3); also in
Chapters 6 and 8
Combination
Updates a junk dimension. Loading a region dimension
lookup/update Alternatively, it can be
with a Combination lookup/
used to update
update step (Chapter 9); also in
Type I SCD.
Chapter 12
Copy rows to
Write rows to the
Splitting the generation of top
result
executing job. The
scores by copying and getting
information will then be
rows (Chapter 11)
passed to the next entry in
the job.
Data Validator Validates fields based on a Checking films file with the
set of rules
Data Validator (Chapter 7)

[ 436 ]

Appendix C

Icon

Name
Delay row

Delete

Purpose
For each incoming row,
waits a given time before
giving the row to the
next step
Delete data in a database
table

Dimension
Updates or looks up a
lookup/update Type II SCD. Alternatively,
it can be used to update
Type I SCD or hybrid
dimensions.
Dummy (do
This step type doesn't do
nothing)
anything! However it is
used often.

Time for action
Generating custom files by
executing a transformation for
every input row (Chapter 11)
Deleting data about
discontinued items (Chapter 8)
Keeping a history of product
changes with the Dimension
lookup/update step
(Chapter 9), also in Chapter 12
Creating a hello world
transformation (Chapter 1),
also in Chapters 2, 3, 7, and 9

Excel Input

Reads data from a
Browsing PDI new features by
Microsoft Excel (.xls) file copying a dataset (Chapter 4);
also in Chapter 8

Excel Output

Writes data to a Microsoft
Excel (.xls) file

Filter rows

Splits the stream in two
upon a given condition.
Alternatively, it is used to
let pass just the rows that
meet the condition.
Fixed file input Reads data from a fixed
width file
Formula

Creates new fields by
using formulas. It uses
Pentaho's libformula.

Generate Rows Generates a number of
equal rows
Get data from
XML

Gets data from XML files

[ 437 ]

Getting data from an XML
file with information about
countries (Chapter 2); also in
Chapters 4 and10
Counting frequent words by
filtering (Chapter 3); also in
Chapters 4, 6, 7, 9, 11, and 12
Calculating Scores with
JavaScript (Chapter 5)
Reviewing examination by
using the Formula step
(Chapter 3); also in
Chapters 10 and 11
Creating a hello world
transformation (Chapter 1);
also in Chapters 6, 9, and 10
Getting data from an XML
file with information about
countries(Chapter 2); also in
chapters 3 and 9

Quick Reference: Steps and Job Entries

Icon

Name
Get rows from
result

Purpose
Reads rows from a
previous entry in a job

Time for action
Splitting the generation of top
scores by copying and getting
rows (Chapter 11)

Get System
Info

Gets information from the
system like system date,
arguments, etc.

Updating a file with news about
examination (Chapter 2) also in
Chapters 7, 8, 10, 11, and12

Get Variables

Takes the values of
environment or Kettle
variables and adds them as
fields in the stream
Group by
Builds aggregates in a
group by fashion. This
works only on a sorted
input. If the input is
not sorted, only double
consecutive rows are
handled correctly
If field value is If a field is null, it changes
null
its value to a constant. It
can be applied to all fields
of a same data type, or to
particular fields
Insert / Update Updates or inserts rows in
a database table

Creating the time dimension
dataset(Chapter 6)

Mapping (sub- Runs a subtransformation
transformation)

Calculating the top scores with a
subtransformation (Chapter 11)

Mapping input Specifies the input
specification
interface of a
sub-transformation

Calculating the top scores with a
subtransformation (Chapter 11)

Mapping
output
specification

Specifies the output
interface of a
sub-transformation

Calculating the top scores with a
subtransformation (Chapter 11)

Modified Java
Script Value

Allows you to code
Javascript to modify or
create new fields. It's also
possible to code Java
Creates ranges based on a
numeric field

Calculating Scores with
JavaScript(Chapter 5); also in
Chapters 6, 7, and 11

Number range

[ 438 ]

Calculating World Cup statistics
by grouping data (Chapter 3);
also in Chapters 4, 7, and 9

Enhancing a films file by
converting rows to columns
(Chapter 6)
Inserting new products or
updating existent ones
(Chapter 8)

Capturing errors while
calculating the age of a film
(Chapter 7); also in Chapter 8

Appendix C

Icon

Name
Regex
Evaluation

Purpose
Evaluates a field with a
regular expression

Row
denormaliser

Denormalises rows by
Enhancing a films file by
looking up key-value pairs converting rows to columns
(Chapter 6)

Row
Normaliser

Normalises data
de-normalised

Enhancing the matches file
by normalizing the dataset
(Chapter 6)

Select values

Selects, reorders, or
removes fields. Also
allows you to change the
metadata of fields

Set Variables

Sets Kettle variables based
on a single input row

Sort rows

Sorts rows based upon
field values, ascending or
descending

Split field to
rows

Splits a single string field
and creates a new row for
each split term

Reading all your files at a
time using a single Text file
input step (Chapter 2); also in
Chapters 3, 4, 6, 7, 8, 9, 11,
and 12
Updating a file with news
about examinations by setting
a variable with the name of the
file (Chapter 11); also in
Chapter 12
Reviewing examinations by
using the Calculator step
(Chapter 3); also in Chapters 4,
6, 7, 8, 9, and 11
Counting frequent words by
filtering (Chapter 3)

Split Fields

Splits a single field into
more than one

Stream lookup

Looks up values coming
Finding out which language
from another stream in the people speak (Chapter 3); also
transformation
in Chapter 6

Switch / Case

Switches a row to a certain Assigning tasks by filtering
target step based on the
priorities with the Switch/ Case
value of a field
step (Chapter 4)

Table input

Reads data from a database Getting data about shipped
table
orders (Chapter 8); also in
Chapters 9, 10, and 12

Table output

Writes data to a database
table

[ 439 ]

Time for action
Validating Genres with a Regex
Evaluation step (Chapter 7); also
in Chapter 12

Calculating World Cup statistics
by grouping data (Chapter 3);
also in Chapters 6 and 11

Loading a table with a list of
manufacturers (Chapter 8), also
in Chapters 9 and 12

Quick Reference: Steps and Job Entries

Icon

Name
Text file input

Purpose
Reads data from a text file

Text file output Writes data to a text file

Time for action
Reading all your files at a
time using a single Text file
input step (Chapter 2); also in
Chapters 3, 5, 6, 7, 8, and 11
Sending the results of matches
to a plain file (Chapter 2); also in
Chapters 3, 7, 9, 10, and 11

Update

Updates data in a database Loading a region dimension
table
with a Combination lookup/
update step (Chapter 9)

Value Mapper

Maps values of a certain
field from one value to
another

Browsing PDI new features by
copying a dataset (Chapter 4)

Job entries
The following table includes all the job entries used in the book. For a full list of job
entries and their descriptions, select Help | Show job entries plug-in information in
Spoon's main menu.
You can also visit http://wiki.pentaho.com/display/EAI/Pentaho+Data+Integra
tion+v3.2.+Job+Entries for more information.
There you'll find a full job entries reference and some examples as well.
Icon

Name

Purpose

Time for action

Abort job

Aborts the job

Updating a file with news
about examinations by setting
a variable with the name of the
file (Chapter 11)

Create a folder

Creates a folder

Creating a simple Hello world
job (Chapter 10)

Delete file

Deletes a file

Generating custom files by
executing a transformation for
every input row (Chapter 11)

Evaluate rows
number in a
table

Evaluates the content of a
table

Loading the dimensions for the
sales datamart (Chapter 12)

[ 440 ]

Appendix C

Icon

Name

Purpose

Time for action

File Exists

Checks if a file exists

Updating a file with news
about examinations by setting
a variable with the name of the
file (Chapter 11)

Job

Executes a job

Generating the files with top
scores by nesting jobs (Chapter
11); also in Chapter 12

Mail

Sends an e-mail

Sending a sales report and
warning the administrator
if something were wrong
(Chapter 10)

Special entries

Start job entry; mandatory
at the beginning of a job

Creating a simple Hello
world job (Chapter 10); also in
Chapters 11 and 12

Success

Forces the success of a job
execution

Updating a file with news
about examinations by setting
a variable with the name of the
file (Chapter 11); also in
Chapter 12

Transformation Executes a transformation

Creating a simple Hello
world job (Chapter 10); also in
Chapters 11 and 12

Note that this appendix is just a quick reference. It's not meant at all for learning
to use PDI. In order to learn from scratch, you should read the book starting from
the first chapter.

[ 441 ]

Spoon Shortcuts
The following tables summarize the main Spoon shortcuts. Have this appendix handy; it will
save a lot of time while working with Spoon.
If you are a Mac user, please be aware that a mixture of Windows and Mac keys
is used. Thus, the shortcut keys are not always what you expect. For example, in
some cases you copy with Ctrl+C, while in others you do it with Command+C.

General shortcuts
The following table lists general Spoon shortcuts:
Action
New job
New transformation
Open a job/transformation
Save a job/transformation
Close a job/transformation
Run a job/transformation
Preview a transformation
Debug a transformation
Verify a transformation
Job settings
Transformation settings
Search metadata
Set environment variables
Show environment variables
Show arguments

Shortcut

Ctrl+Alt+N
Ctrl+N
Ctrl+O
Ctrl+S
Ctrl+F4
F9
F10
Shift+F10
F11
Ctrl+J
Ctrl+T
Ctrl+F
Ctrl+Alt+J
Ctrl+L
Ctrl+Alt+U

Spoon Shortcuts

Designing transformations and jobs
The following are the shortcuts that help the design of transformations and jobs:
Action

Shortcut

New step/job entry

Drag the step/job entry icon to the work
area and drop it there

Edit step/job entry

Double-click

Edit step description

Double-click the middle mouse button

New hop

Click a step and drag toward the second
step while holding down the middle
mouse button or while pressing Shift and
holding down the left mouse button

Edit a hop

Double-click in transformations,
right-click in jobs

Split a hop

Drag a step over the hop until it
gets wider

Select some steps/job entries

Ctrl+click

Select all steps

Ctrl+A

Clear selection

Esc

Copy selected steps/job entries to clipboard

Ctrl+C

Paste from clipboard to work area

Ctrl+V

Delete selected steps/job entries

Del

Align selected steps/job entries to top

Ctrl+Up

Align selected steps/job entries to bottom

Ctrl+Down

Align selected steps/job entries to left

Ctrl+Left

Align selected steps/job entries to right

Ctrl+Right

Distribute selected steps/job entries horizontally

Alt+Right

Distribute selected steps/job entries vertically

Alt+Up

Zoom in

Page up

Zoom out

Page down

Zoom 100%

Home

Snap to grid

Alt+Home

Undo

Ctrl+Z

Redo

Ctrl+Y

Show output stream (only available in transformations) Position the mouse cursor over the step;
then press Space bar

[ 444 ]

Appendix D

Grids
Action

Shortcut

Move a row up

Ctrl+Up

Move a row down

Ctrl+Down

Resize all columns to see the full values (header included)

Resize all columns to see the full values (header excluded)

Select all rows

Ctrl+A

Clear selection

Esc

Copy selected lines to clipboard

Ctrl+C

Paste from clipboard to grid

Ctrl+V

Cut selected lines

Ctrl+X

Delete selected lines

Del

Keep only selected lines

Ctrl+K

Undo

Ctrl+Z

Redo

Ctrl+Y

Repositories
Action

Shortcut

Connect to repository

Ctrl+R

Disconnect repository

Ctr+D

Explore repository

Ctrl+E

Edit current user

Ctrl+U

[ 445 ]

Introducing PDI 4 Features
While writing this book, version 4.0 of PDI was still under development. Kettle 4.0 was
mainly created to provide a new API for the future—the API that is cleaned up, flexible, more
pluggable, and so on. Beside those architectural changes, Kettle 4.0 also includes some new
functional features. This appendix will quickly introduce you to those features.

Agile BI
Pentaho Agile Business Intelligence (Agile BI) is a new, iterative design approach to BI
development. Agile BI provides an integrated solution that enables you, as an ETL designer,
to work iteratively, modeling the data, visualizing it, and finally providing the data to users
for self-service reporting and analysis. Agile BI is delivered as a plugin to Pentaho Data
Integration. You can learn more about Agile BI at http://wiki.pentaho.com/display/
AGILEBI/Documentation.

Visual improvements for designing transformations and
jobs
The new version of the product includes mainly Enterprise or advanced features. There are,
however, a couple of novelties in the Community Edition that will catch your attention as
soon as you start using the new version of the software. In this section you will learn about
those novelties.

Experiencing the mouse-over assistance
The mouse-over assistance is the first new feature you will notice. It assists you while editing
jobs and transformations. Let's see it working.

Introducing PDI 4 Features

Time for action – creating a hop with the mouse-over assistance
You already know several ways to create a hop between two job entries or two steps. Now
you will learn a new way:

Create a job and drag two job entries to the canvas. Name the entries A and B.

Position the mouse cursor over the entry named A and wait until a tiny toolbar
shows up below the entry icon as shown:

Click on the output connector (the last icon in the toolbar), and drag toward the
entry named B. A grayed hop is displayed.

When the mouse cursor is over the B entry, release the mouse button. A hop is
created from the A entry to the B entry.

What just happened?
You created a hop between two job entries by using the mouse-over assistance—a feature
incorporated in PDI 4.

Using the mouse-over assistance toolbar
When you position the mouse cursor over a step in a transformation or a job entry in a job, a
tiny toolbar shows up to assist you. The following diagram depicts its options:

[ 448 ]

Appendix E

The following table explains each button in this toolbar:
Button
Edit
Menu

Description
Equivalent to double-clicking the job entry/step to edit it.
Equivalent to right-clicking the job entry/step to bring up the
contextual menu.
Input
Assistant for creating hops leaving from this job entry/step. If the job
connector entry/step doesn't accept any input (that is, START entry job or Generate
Rows step), the input connector is disabled.
Output
Assistant for creating hops directed toward this job entry/step. It's used as
connector shown in the tutorial, but the direction of the created hop is the opposite.

In the tutorial, you created a simple hop between two job entries. You can create hops
between steps in the same way. In this case, depending on the kind of source step, you might
be prompted for the kind of hop to create. For example, when leaving a Filter rows step, you
will be asked if the destination step is where you'll send the "true" data, or where you will
send the "false" data, or if this is the main output of the step.

Experiencing the sniff-testing feature
The sniff-testing feature allows you to see the rows that are coming into or out of a step
in real time. While a transformation is running, right-click a step, select Sniff test during
execution | Sniff test output rows. A window appears showing you the output data as it
is being processed. In the same way, you can select Sniff test during execution | Sniff test
input rows to see the incoming rows.
Note that the sniff-testing feature slows down the transformation and
its use is recommended just for debugging purposes.

Experiencing the job drill-down feature
In Chapters 10 and 11, you learned how to nest jobs and transformations. You even learned
how to create subtransformations. Whichever the case, when you ran the main job or
transformation, there was a single log tab showing the log for the main and all nested jobs
and transformations.
In PDI 4.0, when a job entry is running, you can drill-down into that. Drilling down means
opening that entry and seeing what's going on inside that job or transformation. In a
separate window, you'll see both the step metrics and the log. If there are more nested
transformations or jobs, you can continue drilling down. You can go even further into a
running subtransformation. In any of these jobs or transformations, you may sniff test
as well, as described above.

[ 449 ]

Introducing PDI 4 Features

Drilling down is useful, for example, to understand why your jobs or transformations don't
behave as expected or to find out where a performance problem is.
You can see the job drill-down and sniff-testing in action in two videos made by Matt Casters,
Kettle chief leader and author of these features at: http://www.ibridge.be/?p=179.

Experiencing even more visual changes
Besides the features that we have just seen, there are some other UI improvements
worth mentioning:




Enhanced notes editor: Now you can apply different fonts and colors to the notes
you create in Spoon.
Color-coded logs: Now it is easier to read a log, as different colors allow you to
quickly identify different kinds of log messages.
Revamped Repository explorer: The Repository explorer has been completely
redesigned, making this a major UI improvement in Kettle 4.0.

Enterprise features
As said, most of the functional features included in Kettle 4.0 apply only to the Enterprise
version of the product. Among those features, the following are the most remarkable:


Job and transformation versioning and locking



Robust security and administration capabilities



Ability to schedule jobs and transformations from Spoon



Enhanced logging architecture for real-time monitoring and debugging of
transformations

Summary
This appendix introduced you to the main features included in Kettle 4.0. All the explanations
and exercises in this book have been developed, explained, and tested in the latest stable
version 3.2. However, as the new version of the product includes mainly Enterprise or
advanced features, working with Kettle 4.0 Community Edition is not so different from
working with Kettle 3.2. You can try all the examples and exercises in the book in
Kettle 4.0 if you want to. You shouldn't have any difficulties.

[ 450 ]

Pop Quiz Answers
Chapter 1
PDI data sources
5

PDI prerequisites
1

1 and 3

PDI basics
1

False (Spoon is the only graphical tool)

True

False (Spoon doesn't generate code, but interprets Transformation and Jobs)

False (The grid size is intended to line up steps in the screen)

False (As an example the transformation in this chapter created the rows of data
from scratch; it didn't use external data)

Pop Quiz Answers

Chapter 2
formatting data
1

(a) and (b). The field is already a Number, so you may define the output
field as a Number, taking care of the format you apply. If you define
the output field as a String and you don't set a format, Kettle will send
the field to the output as 1.0, 2.0, 3.0, etc., which clearly is not
the same as your code. Just to confirm this, create a single file and a
transformation to see the results for yourself.

Chapter 3
concatenating strings
1

(a) and (c). The calculator allows you to use the + operator both for
adding numbers and for concatenating text. The Formula step makes a
difference: To add numbers you use
+; to concatenate text you have to use & instead.

Chapter 4
data movement (copying and distributing)
1

(b). In the second transformation the rows are copied, so all the
unassigned rows reach the dummy step. In the first transformation the
rows are distributed, so to the filter step arrives half of the rows. When
you do the preview, you see only the unassigned tasks for this half; you
don't see the unassigned tasks that went to the other stream.

splitting a stream
1

(c). Both (a) and (b) solve the situation.

[ 452 ]

Appendix F

Chapter 5
finding the seven errors
1. The type of log a doesn't exist. Look at the sample provided for the
function to see the valid options.

2. The variable uSkill is not defined. Its definition is required if you want
to add it to the list of new fields.
3. setValue() cause an error without compatibility mode. To change the
value of the Country field, a new variable should be used instead.
4. A variable named average is calculated but wAverage is used as the
new field.
5. It is not trans_status; it is trans_Status.
6. No data type was specified for the totalScore field.
7. The sentence writeToLog(‘Ready to calculate averages...')
will be written for every row. To write it at the beginning, you have to put it
in a Start script, not in the main.

Chapter 6
using Kettle variables inside transformations
1

(a). You don't need a Get Variables step in this case. As name of the file you simply type
hello_${user.name} or hello_%%user.name%%.

In (b) and (c) you need to add the variables ${user.name} and ${user.
language} respectively as fields of your dataset. You do it with a Get
Variables step.

Chapter 7
PDI error handling
1

(c). With PDI you cannot avoid unexpected errors; you can capture them avoiding the
crash of the transformation. After that, discarding or treating the bad rows is up to you.

[ 453 ]

Pop Quiz Answers

Chapter 8
defining database connections
1

(c)

database datatypes versus PDI datatypes
1

(b)

Insert/Update step versus Table Output/Update steps
1

(a) If an incoming row belongs to a product that doesn't exist in the products table,
both the Insert/Update step and the Table output step will insert the record.
If an incoming row belongs to a product that already exist in the products table,
the Insert/Update step updates it. In this alternative version, the Table output will
fail (there cannot be two products with the same value for the primary key) but the
failing row goes to the Update step that updates the record.
If an incoming row contains invalid data (for example, a price with a non numeric
value), neither of the Insert/Update step, the Table output step, and the Update
step would insert or update the table with this product.

filtering the first 10 rows
1

(c). To limit the number of rows in MySQL you use the clause LIMIT. (a) and (b) are
dialects: (a) is valid in HSQLDB. (b) is valid in Oracle. If you put any of this options in
a Table Input for querying the js database, the transformation would fail

Chapter 9
loading slowly changing dimensions
1

(a). The decision for the kind of dimension is not related to data you have.
You just have to know your business, so the last option is out. You don't
need to keep history for the name of the film. If the name changes it is because it was
misspelled, or because you want to change the name to upper case, or something like
that. It doesn't have sense to keep the old value. So you create
a Type I SCD.

(c). You can use any of these steps for loading a Type I SCD. In the tutorial for loading
a type I SCD you used a Combination L/U, but you could have used the other too, as
explained above.
[ 454 ]

Appendix F

loading type III slowly changing dimensions
1

(b). With a Database lookup to get the current value stored in the dimension. If there is
no data in the dimension table, the lookup fails and returns null; that is not a problem.
After that, you compare the found data with the new one and set the proper values for
the dimension columns. Then you load the dimension either with a Combination L/U or
with a Dimension lookup, just as you do for a regular Type I SCD.

Chapter 10
defining PDI jobs
1

(b)

All the given options are True. Simply explore the Job entries tree and you'll find the
answers.

Chapter 11
using the Add sequence step
1

(e) None of the proposed solution gives you the same results you obtained in the
tutorial. The Add sequence step gives you the next value in a sequence which
can be a database sequence or transformation counter. In the tutorial you used a
transformation counter. In the options (b) and (c), instead of four sequences from 1
to 10, a single sequence from 1 to 40 would have been generated. No matter which
method you use for generating the sequence, if you use the same name of sequence in
more than one Add sequence step, the sequence is the same and is shared by all those
steps. Therefore, the option (a) also would have generated a single sequence from 1 to
40 shared by the four streams.
Besides these details about the generation of sequences, the (b) option introduces an
extra inconvenience. By distributing rows, you cannot be sure that the rows will go to
the proper stream. PDI would have distributed them in its own fashion.

deciding the scope of variables
1

All the options are valid. In the tutorial you had just a transformation and its parent
job, that is also the root job. So (a) is valid. The grand-parent job scope includes the
parent job so option (b) is valid too. Option (c) includes all the other options, so it is a
valid option too.

[ 455 ]

Pop Quiz Answers

Chapter 12
modifying a star model and loading the star with PDI
1

iii

As mentioned in Chapter 9, despite being
designed for building Type II SCDs, the
Dimension L/U step can be used for building
Type I SCDs as well. So, you have two options:
Reuse the table (modifying the transformation
that loads it) and get the surrogate key with
a Dimension L/U step, or use another table
without all fields specific to Type II dimensions
and, for getting the surrogate key, use a DB
Lookup step.
In any case, you may reuse the id_region
field, as it is a integer and serves in any
situation.

iii

The dimension table has to have one record by
month. Therefore a different table is needed.
For the key you could use a string with the
format yyyymm. If you don't want to change
the fact table, you may reuse the dt field
leaving blank the last two characters, but it
would be more appropriate to have a string
field with just 6 positions. For getting the
surrogate key you use a Select values step
changing the metadata but this time you put as
format the new mask yyyymm.
The product_type field is a string; it's not
the proper field for referencing a surrogate key
from a fact table, so you have to define a new
field for that purpose. For getting the right key
you use a Database lookup step.

Chapter 13
remote execution and clustering
1

None of the sentences are true.

[ 456 ]

Index
Symbols
${} notation 193
%%%% notation 193
*.kjb format 417
*.ktr format 417
.kjb file jobs 429, 430
.ktr file transformations
running 429, 430
/option:value syntax 431

A
action sequence 412
administrative tasks
getting rid of 399
sales datamart loading, automating 399-402
work backup, creating automatically 402
Agile BI 447

B
basic calculation
calculator step, using 74
data, sorting 81
Dummy step 81
examination review, calculator step used 74,
78, 80
field modification, PDI used 82
fields, modifying 82
Select values step, using 81
basic modification
Group by step 94
business keys to surrogate keys, sales fact table
junk dimension surrogate key, obtaining 391
time dimension surrogate key, obtaining 391
translating 388-391

TypeII SCD surrogate key, obtaining 389, 390
Type I SCD surrogate key, obtaining 388, 389

C
calculator step used, basic calculation
about 74
average, taking 74-77
editing 78
examination, reviewing 80, 81
final preview 80
preview 78
Select Values step 79
Sort rows Step 78
Carte 410
CDF 414
change history, maintaining
Dimension lookup/update step, using 286-288
steps 286
transformation, testing 288, 289
cloud-computing 411
cluster 411, 424
coding
disadvantages 166
command line argument
named parameters, differentiating between
317, 318
passing, to transformation 315, 316
use, analyzing 318
Community Dashboard Framework. See CDF
Community Edition 415
complex lookups, data
customers list, rebuilding 275
database to stream data, joining 272-274
performing 270
suggested products list, creating 270, 272

columns 218
connecting, with RDBMS 222, 223
constellation 376
custom time dimension dataset
creating 187-191
generating, Kettle variables used 186
Get Variables step 191-193

D
dashboard
screenshot 414
data
normalizing 180, 181
normalizing, Row Normalizer step used 182,
184
reading, from files 35
data, database
complex lookups, doing 270
looking up 266
simple lookups, doing 266
data, reading
football match results, reading 36-40
from files 35
grids 46
input files 41
input files, properties 41, 42
multiple files, reading at once 42, 43
multiple files reading, single Text file input step
used 43, 44
reading files, troubleshooting 45, 46
regular expressions 44
data, sending to database
data, inserting 246-251
data, updating 246-251
Insert/Update step, using 251-253
inserting, table output step used 245
table list, loading 239-244
data, XML files
Get Data From XML input step, configuring 69
node, selecting 69
obtaining 68
path expression, examples 69
XPath, using 68
database connections 424
database connections See also connecting, with
RDBMS

database explorer, using 228
database operations 261
database querying
data, working with 229-231
data obtaining, table input step used 231, 232
SELECT statement, using 232, 233
data cleaning. See data cleansing
data cleansing
about 213, 214
example 214
PDI step, using 214
data elimination, from database
Delete step using 259, 260
steps 256-258
data manipulation
basic calculation 73
filtering 97
Data Manipulation Language. See DML
datamart
about 275, 367
datawarehouse, difference 368
sales datamart 368
data scrubbing. See data cleansing
dataset
custom time dimension dataset, generating 186
data, normalizing 180
modifying, Row Normalizer step used 182, 184
rows, converting to columns 169
data to files, transferring
about 47
field 50
field, deleting 52
field, selecting 52
field metadata, changing 52
match results, sending 47, 49
output files 49
row 50
rowset 50
streams 51
data transformation 141
data type, system information
date field 58, 59
date formats, using 62
number 99.55, formatting 62
numeric fields 59, 60
transformation, executing 60, 61

[ 458 ]

data validation
example 208
films, checking 209, 210
need for 208
simple validation rules, defining 211-213
datawarehouse
about 275
datamart, difference 368
DDL
about 226
example 226
degenerate dimension 370
Design Studio
screenshot 413
dimensional modeling
about 275, 276
datamart 275
datawarehouse 275
dimension tables 275
fact table 275
junk dimension 369
mini dimension 285
SCD 282
star schema 275
dimensions, sales datamart
about 186
loading 371-376
Dimension tables 275
dimension tables, with data
about 275
change history, maintaining 286
dimension data, describing 281, 282
loading 276
loading, combination lookup/update step used
276-281
DML
about 226
example 226, 227
dynamic clusters 411

E
E4X 148
Eclipse 418
Enterprise Console 415
Enterprise features 450
entity relationship diagram. See ERD
ERD 264

errors, capturing
Abort step, using 203
about 195
captured errors, fixing 203, 204
error handling functionality, using 200, 201
film age, calculating 196-199
PDI error handling functionality, activities 205
rows, treating 205, 206
transformation, aborting 201-203
ETL 7
exit code
checking, under Unix-based systems 432
checking, under Windows 432
EXtensible Markup Language. See XML
Extracting, Transforming and Loading. See ETL

F
facts 275
fact table
about 275
loading, date ranges used 394-396
field 50
field modification, basic modification
add constants field 82
calculator step 83
examples 89-92
Formula step 84-87
number range field 82
replace in string field 82
split fields 82
student, listing 88
User Defined Java Expression 83
Value Mapper 82
file
data, reading from 35
file result list, creating 326
file result list, using 326
output files, writing 52
updating 53-56
file-based system
migrating, to repository-based system 426
file result 326
filtering
frequent words, counting 97-102
rows 104, 105
rows, filter rows used 103

[ 459 ]

spoken language, identifying 105-109
Stream lookup step 109
word count, discarding commonly used 105
filter rows step
using, for filtering row 103
first transformation, Spoon
hello world transformation, creating 20-24
interface, exploring 26
Kettle engine, directing 25
previewing 27, 28
previewing, results in Execution Results window
28
running 27, 28
structure, viewing 26
flow-control oriented 305
foreign keys (FK) 218
formula step 165

G
grain 370
grid shortcuts
Ctrl+A 445
Ctrl+C 445
Ctrl+Down 445
Ctrl+K 445
Ctrl+Up 445
Ctrl+V 445
Ctrl+X 445
Ctrl+Y 445
Ctrl+Z 445
Del 445
Esc 445
F3 445
F4 445
Group by step, basic modification
about 94
fields, reviewing 95
preview 96
tasks 96

H
hash table algorithm 111
hop
about 25, 305
creating 298-300
hop color 325

HSQLDB 222
Hybrid SCD 293
HyperSQL DataBase. See HSQLDB

I
id_junk_sales key 388
id_manufacturer key 388
id_region key 388
installing
MySQL 29
MySQL, on Ubuntu 32-34
MySQL, on Windows 29-31
PDI 14, 15

J
JavaScript
advantages 166
JavaScript code, inserting
about 148
average calculations, testing 152, 153
Clone() function 165
code, testing 151
compatibility switch, turning on 151
fields, adding 150, 151
fields, modifying 150, 151
getProcessCount() function 161
Input fields branch 149
LoadScriptFormTab() function 159
new average calculations, testing 153
script, testing 153
Transform Functions 149
JavaScript step
about 154
End Script 159
JavaScript code, inserting 148
Java code, using 161
Main script 159
named parameters, using 158
scores, calculating 142-147
simple tasks, doing 142
Start Script 159
transformation predefined constants, using
159-161, 453
transformations, modifying 154-157
using, in PDI 147

[ 460 ]

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jigsaw puzzle database
buy_methods table 265
cities table 265
countries table 265
customers table 265
exploring 264, 265
invoices table 265
manufactures table 265
payment_methods table 265
populating 261-264
products table 265
job
designing, shortcuts 444
flexible version, creating 309-311
hello world file, customizing 309-311
hello world job, creating 298-304
named parameters, using 312
processes, executing 305
running, from repository 430, 431
transformation job entry, using 307, 308
job, creating as process flow
data flow, modifying 353
data transfer, copy/get rows mechanism used
352, 353
transformation, splitting 348-351
job, running from repository
command line options, specifying 431
steps 430, 431
job, running from terminal window
steps 313
job entry
abort job 440
about 305
create a folder 440
delete file 440
evaluate rows number in a table 440
File Exists 441
Job 441
mail 441
special entries 441
success 441
transformation 441
job entry, executing
execution flow, modifying 324, 325
launching, in parallel 308

sales report, sending 318-323
job iteration
about 357
custom files, executing 358-361
every input row, executing 361-366
jobs, nesting
files, generating 354, 355
job, running inside another job 355
join 385
junk dimension 369

K
KDE Extraction, Transportation, Transformation
and loading Environment. See Kettle
Kettle 9
kettle.properties file 62, 63, 264
Kettle 4.0, features
Agile BI 447
Enterprise features 450
visual improvements 447
Kettle Developer Support 415
Kettle repository 420
Kettle variables, XML files
about 70
exploring 71
Get Variable step 193
scope types 357
using 70
variables, getting 192
work documentation 71
Key Performance Indicators. See KPIs
Kitchen
about 429
arguments 433
documentation 434
log details 433
named parameters 433
running, options 432
sales datamart loading, automating 399-402
variables 433
KPIs 8

L
LoadScriptFromTab() function 159

[ 461 ]

M
mapping 345
master 411
mini-dimension
loading 285
Modified JavaScript Values step. See JavaScript
step
mouse-over assistance
toolbar, using 448, 449
working 448
MySQL
installing 29
installing, on Ubuntu 32-34
installing, on Windows 29-31
MySQL, installing
onUbuntu 32-34
on Windows 29-32

N
named parameters
command line argument, differentiating
between 317, 318
passing, to transformation 315, 316
use, analysing 318
using 158

O
OLTP 275
On-Line Transaction Processing. See OLTP
output files
output steps 50

P
Pan
about 429
arguments 433
documentation 434
examination transformation, executing from
terminal window 60, 61
log details 433
named parameters 433
running, options 432
variables 433
partition schemas 424

PDI
about 7
and Pentaho BI Suite 7
best practices 405
cloud-computing 411
cluster 411
dynamic clusters 411
features 408-411
integrating, with Pentaho BI suite 412
graphic designer, launching 15-18
installing 14, 15
job 297
Kettle 9
Kettle plug-ins 408, 409
master 411
PDI 2.3 10
PDI 2.4 10
PDI 2.5 10
PDI 3.0 10
PDI 3.1 10
PDI 3.2 10
PDI 4.0 10
real world risk, overcoming 410
scaling out 411
scaling up 411
Spoon 15
using, in real world scenarios 11
PDI, using in real world scenarios
data, cleansing 12
data, exporting 13
data, integrating 12
datamart, loading 11, 12
datawarehouse, loading 11, 12
information, migration 13
integrating, Pentaho BI used 13
PDI best practices 405-407
PDI elements
clusters 424
database connections 424
partial schemas 424
slave servers 424
PDI Enterprise Edition 415
PDI features
browsing 114
browsing, dataset copied 114-119
PDI graphic designer. See Spoon

[ 462 ]

PDI integration, with Pentaho BI suite
about 412
as datasource 413
as process action 412, 413
Pentaho suite 414, 415
PDI options, stream merge
Bouchard’s rows 137, 138
choosing 134, 135
tasks, merging 138
tasks, sorting 138
union, creating 134
PDI, steps
about 184
films file, normalizing 185, 186
normalize benefits, verifying 185
scores, calculating 186
Pentaho Agile Business Intelligence. See Agile BI
Pentaho BI Suite
analysis engine 7
and PDI 7
dashboards 8
data integration 8
data mining 8
Pentaho BI Platform 8
reporting engine 8
Pentaho BI suite integration, with PDI
about 412-415
as datasource 413
as process action 412, 413
Pentaho Data Integration. See PDI
plug-in
Kettle plug-in 408, 409
primary key (PK) 218
process execution, PDI job
about 305
hop 305
job design, comparing with job transformation
306
job entry 305
job running, Spoon used 306
puzzles fact table
loading 393

R
RDBMS 222
records 218
regular expressions 44

relational database 218
Relational Database Management System. See
RDBMS
repository
backing up 424
creating 418-420
details, storing 420
features 417
file-based system benefits 418
jobs in folders, creating 42
Kettle repository 420
logging into 421
logging into, credentials used 422
restoring 424
storage system, working with 421
tasks 423
transformation in folders, creating 423
user accounts, using 422, 423
shortcuts 445
repository-based system
migrating, to file-based system 426, 427
repository explorer
element, creating 425
element, deleting 425
element, opening 425
using, for content examination 424, 425
using, for content modification 424, 425
repository shortcuts
Ctr+D 445
Ctrl+E 445
Ctrl+R 445
Ctrl+U 445
Rhino engine 147
root-job 365
row 50
Row denormalizer
about 173
data, aggregating 176-179
working 173
rows, converting to columns
about 169
data, aggregating 176-179
films file, enhancing 170-172
Row denormalizer step, using 173-176
total scores, calculating 177-179
rows, Stream split
copying 119, 120

[ 463 ]

distributing 120
tasks, assigning 121-124
rowset 51

S
sales datamart
degenerate dimension 370
dimensions 368
dimensions, loading 370
exploring 369
granularity level, determining 370
junk dimension 369
model 376
sales datamart model
about 376, 377
added dimensions 376
added dimensions, loading 378
sales fact table
business keys to surrogate keys, translating 388
information obtaining, SQL queries used
384-387
loading 378
scaling out 411
scaling up 411
SCD
about 282
Type II SCD 289
Type I SCD 282
SELECT statement
aggregate function 386
Kettle variables, advantages 238
Kettle variables, using 236, 237
Kettle variables, using in queries 238
parameters, adding 235, 236
parameters, using 234, 235
using 232, 233
simple lookups, data
buyers product list, creating 266, 267
database values, looking up 268, 269
performing 266
slave server 410, 424
Slowly Changing Dimensions. See SCD
sniff-testing feature 449
sorting data 74
split field to rows step 165

Spoon
about 18
files method 19
first transformation, creating 20
jobs, storing 19
launching 16, 17, 18
method, choosing 20
options window preference, setting 18
repository method 19
shortcuts 443
starting 15
transformation, storing 19
Spoon Interface
Design view 26, 306
exploring 26
View perspective 26, 307
transformation structure 26
Spoon shortcuts
Ctrl+Alt+J 443
Ctrl+Alt+N 443
Ctrl+Alt+U 443
Ctrl+F 443
Ctrl+F4 443
Ctrl+J 443
Ctrl+L 443
Ctrl+N 443
Ctrl+O 443
Ctrl+S 443
Ctrl+T 443
F10 443
F11 443
F9 443
of job design 444
of transformation design 444
Shift+F10 443
SQL
about 225
cast function 385
DDL 226
DML 226
star schema 275
Steel Wheels database
about 217, 218
configured database exploring, database
explorer used 228
connecting, with RDBMS 222, 223
connecting to 219

[ 464 ]

connection, creating 219-221
sample database, exploring 224, 225
SQL 225
tables 218
stream, merging
about 131
PDI options 134, 135
progress, gathering 132-134
Stream lookup step, filtering
using 109, 110
word counting, precisely 111
streams
merging 131
splitting 113
splitting, based on condition 126
Stream split
PDI features, browsing 113
rows, copying 119, 120
rows, distributing 120-124
Stream split, based on condition
PDI, steps 128
task, assigning 128
tasks assignment, Filter rows step used 126,
127
tasks assignment, Switch/Case step used 129,
130
Structured Query Language. See SQL
subtransformation, transformation design
about 345
redefining 347
using 345
working 346, 347
scores, calculating 341-345
Subversion 418
surrogate key 281
system information
data type 58
examination news file, updating 53-56
Get System Info step 57

T
table 218
time dimension 186
transformation
command line arguments, using 314-317
designing, shortcuts 444

running, from repository 430, 431
steps 436
named parameters, using 314-317
transformation, designing
color-coded logs 450
enhanced notes editor 450
job drill-down feature 449
mouse-over assistance 447, 448
mouse-over assistance toolbar, using 448
revamped repository explorer 450
sniff-testing feature 449
transformation, enhancing
variables, setting 335, 336
variables, using 330-335
transformation, running from repository
command line options, specifying 431
steps 430, 431
transformation design, enhancing
example 337-340
job, creating as process flow 348
jobs, nesting 354
transformation iteration. See job iteration
transformation steps
abort 436
add constants 436
add sequence 436
analytic query step 165
append streams 436
calculator 436
combination lookup/update 436
copy rows to result 436
database join 436
database lookup 436
data Validator 436
delay row 437
delete 437
dimension lookup/update 437
dummy 437
excel input 437
excel output 437
filter rows 437
fixed file input 437
formula 437
generate rows 437
get data from XML 437
get rows from result 438
Get System Info 438
[ 465 ]

Get Variables 438
Group by 438
If field value is null 438
Insert / Update 438
mapping (sub-transformation) 438
mapping input specification 438
mapping output specification 438
Merge Rows (diff) 136
Modified Java Script Value 438
Number range 438
Regex Evaluation 439
Replace in String 82
Row denormaliser 439
RowFlattener 184
Row Normaliser 439
select values 439
Set Variables 439
Sorted Merge 136
Sort rows 439
Split Fields 439
Split field to rows 439
stream lookup 439
Switch / Case 439
table input 439
table output 439
text file input 440
text file output 440
update 440
Unique rows 184, 214
Univariate Statistics 184
User Defined Java Expression 83
Value Mapper 440
trap detector 135
Type II SCD
about 289-291
loading, Dimension lookup/update step used
291-294
using, to maintain entire history 289-291, 294
Type I SCD
loading, with combination lookup/update step
282-284
manufactures dimension, loading 284, 285
regions, adding 284

U
Ubuntu
MySQL, installing 32-34
unexpected errors, avoiding
data, cleansing 213
data, validating 206-210
genres field, validating 206, 207
unstructured files
contest files, modifying 165
modifying 164
previous rows, viewing 164
reading 162, 163
user accounts, repository
administrator 422
defining 422, 423
predefined user, admin 423
predefined user, guest 423
read-only 422
user 422

V
variables. See Kettle variables, XML files

W
Windows
MySQL, installing 29-31

X
XML
about 67
PDI transformations files 68
XML files
about 62
basic country information, building 62-66
data, obtaining 68
Kettle variables 70

[ 466 ]

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