58750024 datastage-student-guide

Contents - 2
Copyright © 2002 Ascential Software Corporation
Version 6.0: 09/01/02

Copyright
This document and the software described herein are the property of Ascential Software
Corporation and its licensors and contain confidential trade secrets. All rights to this
publication are reserved. No part of this document may be reproduced, transmitted,
transcribed, stored in a retrieval system or translated into any language, in any form or by any
means, without prior permission from Ascential Software Corporation.

Copyright © 2002 Ascential Software Corporation. All rights Reserved

Ascential Software Corporation reserves the right to make changes to this document and the
software described herein at any time and without notice. No warranty is expressed or
implied other than any contained in the terms and conditions of sale.

Ascential Software Corporation
50 Washington Street
Westboro, MA 01581-1021 USA
Phone: (508) 366-3888
Fax: (508) 389-8749

Ardent, Axielle, DataStage, Iterations, MetaBroker, MetaStage, and uniVerse are registered
trademarks of Ascential Software Corporation. Pick is a registered trademark of Pick
Systems. Ascential Software is not a licensee of Pick Systems. Other trademarks and
registered trademarks are the property of the respective trademark holder.

09-01-2002

Contents - 3

Contents - 4
Version 6.0: 09/01/02

Table of Contents
Module 1: Introduction to DataStage ............................ 1-01
Module 2: Installing DataStage ..................................... 2-01
Module 3: Configuring Projects ..................................... 3-01
Module 4: Designing and Running Jobs ........................ 4-01
Module 5: Working with Metadata................................. 5-01
Module 6: Working with Relational Data ....................... 6-01
Module 7: Constraints and Derivations .......................... 7-01
Module 8: Creating BASIC Expressions ........................ 8-01
Module 9: Troubleshooting ............................................ 9-01
Module 10: Defining Lookups ...................................... 10-01
Module 11: Aggregating Data ...................................... 11-01
Module 12: Job Control................................................ 12-01
Module 13: Working with Plug-Ins ............................... 13-01
Module 14: Scheduling and Reporting ........................ 14-01
Module 15: Optimizing Job Performance .................... 15-01
Module 16: Putting It All Together .............................. 16-01

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Contents - 6
Version 6.0: 09/01/02

Module 1

Introduction to DataStage

Module 1 – Introduction to DataStage DataStage 314Svr

Ascential software provides the enterprise with a full featured data integration
platform that can take data from any source and load it into any target. Sources
can range from customer relationship systems to legacy systems to data
warehouses -- in fact, any system that houses data. Target systems, likewise, can
consist of data in warehouses, real-time systems, Web services -- any application
that houses data.

Depending on your needs, source data can undergo scrutiny and transformation
through several stages:
1. Data profiling -- a discovery process where relevant information for target
enterprise applications is gathered
2. Data quality -- a preparation process where data can be cleansed and
corrected
3. Extract, Transform, Load -- a transformation process where data is
enriched and loaded into the target

Underlying these processes is an application framework that allows you to
1. Utilize parallel processing for maximum performance
2. Manage and share metadata amongst all the stages

Overlaying all of this is a command and control structure that allows you to tailor
your environment to your specific needs.

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DataStage Essentials Module 1 – Introduction to DataStage

1-3


A data warehouse is a central database that integrates data from many operational
sources within an organization. The data is transformed, summarized, and
organized to support business analysis and report generation.
• Repository of data
• Optimized for analysis
• Supports business:
− Projections
− Comparisons
− Assessments
• Extracted from operational sources
− Integrated
− Summarized
− Filtered
− Cleansed
− Denormalized
− Historical

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Data marts are like data warehouses but smaller in scope. Frequently an
organization will have both an enterprise-wide data warehouse and data marts that
extract data from it for specialized purposes.
• Like data warehouses but smaller in scope
• Organize data from a single subject area or department
• Solve a small set of business requirements
• Are cheaper and faster to build than a data warehouse
• Distribute data away from the data warehouse

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DataStage is a comprehensive tool for the fast, easy creation and maintenance of
data marts and data warehouses. It provides the tools you need to build, manage,
and expand them. With DataStage, you can build solutions faster and give users
access to the data and reports they need.
With DataStage you can:
• Design the jobs that extract, integrate, aggregate, load, and transform the data
for your data warehouse or data mart.
• Create and reuse metadata and job components.
• Run, monitor, and schedule these jobs.
• Administer your development and execution environments.

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DataStage is client/server software. The server stores all DataStage objects and
metadata in a repository, which consists of the UniVerse RDBMS. The clients
interface with the server.
The clients run on Windows 95 or later (Windows 98, NT, 2000). The server runs
on Windows NT 4.0 and Windows 2000. Most versions of UNIX are supported.
See the installation release notes for details.
The DataStage client components are:

Component Description
Administrator Administers DataStage projects and conducts
housekeeping on the server

Designer Creates DataStage jobs that are compiled into
executable programs

Director Used to run and monitor the DataStage jobs

Manager Allows you to view and edit the contents of the
repository

1-7


True or False? The DataStage Server and clients must be running on the
same machine.
True: Incorrect. Typically, there are many client machines each accessing the
same DataStage Server running on a separate machine. The Server can be
running on Windows NT or UNIX. The clients can be running on a variety of
Windows platforms.
False: Correct! Typically, there are many client machines each accessing the
same DataStage Server running on a separate machine. The Server can be
running on Windows NT or UNIX. The clients can be running on a variety of
Windows platforms.

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Use the Administrator to specify general server defaults, add and delete projects,
and to set project properties. The Administrator also provides a command
interface to the UniVerse repository.
• Use the Administrator Project Properties window to:
• Set job monitoring limits and other Director defaults on the General tab.
• Set user group privileges on the Permissions tab.
• Enable or disable server-side tracing on the Tracing tab.
• Specify a user name and password for scheduling jobs on the Schedule tab.
• Specify hashed file stage read and write cache sizes on the Tunables tab.
General server defaults can be set on the Administrator DataStage
Administration window (not shown):
• Change license information.
• Set server connection timeout.
The DataStage Administrator is discussed in detail in a later module.

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Use the Manager to store and manage reusable metadata for the jobs you define in
the Designer. This metadata includes table and file layouts and routines for
transforming extracted data.
Manager is also the primary interface to the DataStage repository. In addition to
table and file layouts, it displays the routines, transforms, and jobs that are defined
in the project. Custom routines and transforms can also be created in Manager.

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The DataStage Designer allows you to use familiar graphical point-and-click
techniques to develop processes for extracting, cleansing, transforming,
integrating and loading data into warehouse tables.
The Designer provides a “visual data flow” method to easily interconnect and
configure reusable components.
Use Designer to:
• Specify how the data is extracted.
• Specify data transformations.
• Decode (denormalize) data going into the data mart using reference lookups.
− For example, if the sales order records contain customer IDs, you can look
up the name of the customer in the CustomerMaster table.
− This avoids the need for a join when users query the data mart, thereby
speeding up the access.
• Aggregate data.
• Split data into multiple outputs on the basis of defined constraints.
You can easily move between the Director, Designer, and Manager by selecting
commands in the Tools menu.

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Use the Director to validate, run, schedule, and monitor your DataStage jobs.
You can also gather statistics as the job runs.

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• Define your project’s properties: Administrator
• Open (attach to) your project
• Import metadata that defines the format of data stores your jobs will read from
or write to: Manager
• Design the job: Designer
− Define data extractions (reads)
− Define data flows
− Define data integration
− Define data transformations
− Define data constraints
− Define data loads (writes)
− Define data aggregations
• Compile and debug the job: Designer
• Run and monitor the job: Director

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All your work is done in a DataStage project. Before you can do anything, other
than some general administration, you must open (attach to) a project.
Projects are created during and after the installation process. You can add
projects after installation on the Projects tab of Administrator.
A project is associated with a directory. The project directory is used by
DataStage to store your jobs and other DataStage objects and metadata.
You must open (attach to) a project before you can do any work in it.
Projects are self-contained. Although multiple projects can be open at the same
time, they are separate environments. You can, however, import and export
objects between them.
Multiple users can be working in the same project at the same time. However,
DataStage will prevent multiple users from accessing the same job at the same
time.

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DataStage Designer is used to build and compile your Extraction,
Transformation, and Load (ETL) jobs.
True: Correct! With Designer you can graphically build your job by placing
graphical components (called "stages") on a canvas. After you build it, your job
is compiled in Designer.
False: Incorrect. With Designer you can graphically build your job by placing
graphical components (called "stages") on a canvas. After you build it, your job
is compiled in Designer.

DataStage Manager is used to execute your jobs after you build them.
True: Incorrect. DataStage Manager is your primary interface to the DataStage
repository. Use Manager to manage metadata and other DataStage objects.
False: Correct! DataStage Manager is your primary interface to the DataStage
repository. Use Manager to manage metadata and other DataStage objects.

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DataStage Director is used to execute your jobs after they have been built.
True: Correct! Use Director to validate and run your jobs. You can also
monitor the job while it is running.
False: Incorrect. Use Director to validate and run your jobs. You can also
monitor the job while it is running.

DataStage Administrator is used to set global and project properties.
True: Correct! You can set some global properties such as connection timeout,
as well as project properties, such as permissions.
False: Incorrect. You can set some global properties such as connection timeout,
as well as project properties, such as permissions.

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Module 2

Installing DataStage

Module 2 – Installing DataStage DataStage 314Svr

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DataStage Essentials Module 2 – Installing DataStage

The DataStage server should be installed before the DataStage clients are
installed. The server can be installed on Windows NT (including Workstation
and Server), Windows 2000, or UNIX. This module describes the Windows NT
installation.
The exact system requirements depend on your version of DataStage. See the
installation CD for the latest system requirements.
To install the server you will need the installation CD and a license for the
DataStage server. The license contains the following information:
• Serial number
• Project count
− The maximum number of projects you can have installed on the server.
This includes new projects as well as previously created projects to be
upgraded.
• Expiration date
• Authorization code
− This information must be entered exactly as written in the license.

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The installation wizard guides you through the following steps:
• Enter license information
• Specify server directories
• Select program folder
• Create new projects and/or upgrade existing projects

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The DataStage services must be running on the server machine in order to run any
DataStage client applications. To start or stop the DataStage services in Windows
2000, open the DataStage Control Panel window in the Windows 2000 Control
Panel. Then click Start All Services (or Stop All Services). These services must
be stopped when installing or reinstalling DataStage.
UNIX note: In UNIX, these services are started and stopped using the uv.rc
script with the stop or start command options. The exact name varies by platform.
For SUN Solaris, it is /etc/rc2.d/S99uv.rc.

2-7


The DataStage clients should be installed after the DataStage server is installed.
The clients can be installed on Windows 95, Windows 98, Windows NT, or
Windows 2000.
There are two editions of DataStage.
• The Developer’s edition contains all the client applications (in addition to the
server).
• The Operator’s edition contains just the client applications needed to run and
monitor DataStage jobs (in addition to the server), namely, the Director and
Administrator.
To install the Developer’s edition you need a license for DataStage Developer.
To install the Operator’s edition you need a license for DataStage Director. The
license contains the following information:
• Serial number
• User limit
• Expiration date
• Authorization code
− This information must be entered exactly as written in the license.

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Module 3

Configuring Projects

Module 3 – Configuring Projects DataStage 314Svr

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DataStage Essentials Module 3 – Configuring Projects

In DataStage all development work is done within a project. Projects are created
during installation and after installation using Administrator.
Each project is associated with a directory. The directory stores the objects (jobs,
metadata, custom routines, etc.) created in the project.
Before you can work in a project you must attach to it (open it).
You can set the default properties of a project using DataStage Administrator.

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Click Properties on the DataStage Administration window to open the Project
Properties window. There are five active tabs. (The Mainframe tab is only
enabled if your license supports mainframe jobs.) The default is the General tab.
If you select the Enable job administration in Director box, you can perform
some administrative functions in Director without opening Administrator.
When a job is run in Director, events are logged describing the progress of the
job. For example, events are logged when a job starts, when it stops, and when it
aborts. The number of logged events can grow very large. The Auto-purge of
job log box tab allows you to specify conditions for purging these events.
You can limit the logged events either by number of days or number of job runs.

3-5


Use this page to set user group permissions for accessing and using DataStage.
All DataStage users must belong to a recognized user role before they can log on
to DataStage. This helps to prevent unauthorized access to DataStage projects.
There are three roles of DataStage user:
• DataStage Developer, who has full access to all areas of a DataStage project.
• DataStage Operator, who can run and manage released DataStage jobs.
• <None>, who does not have permission to log on to DataStage.
UNIX note: In UNIX, the groups displayed are defined in /etc/group.

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This tab is used to enable and disable server-side tracing.
The default is for server-side tracing to be disabled. When you enable it,
information about server activity is recorded for any clients that subsequently
attach to the project. This information is written to trace files. Users with in-depth
knowledge of the system software can use it to help identify the cause of a client
problem. If tracing is enabled, users receive a warning message whenever they
invoke a DataStage client.
Warning: Tracing causes a lot of server system overhead. This should only be
used to diagnose serious problems.

3-7


Use the Schedule tab to specify a user name and password for running scheduled
jobs in the selected project. If no user is specified here, the job runs under the
same user name as the system scheduler.

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On the Tunables tab, you can specify the sizes of the memory caches used when
reading rows in hashed files and when writing rows to hashed files. Hashed files
are mainly used for lookups and are discussed in a later module.
Active-to-Active link performance settings will be covered in detail in a later
module in this course.

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Module 4

Designing and Running Jobs

Module 4 – Designing and Running Jobs DataStage 314Svr

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DataStage Essentials Module 4 – Designing and Running Jobs

A job is an executable DataStage program. In DataStage, you can design and run
jobs that perform many useful data warehouse tasks, including data extraction,
data conversion, data aggregation, data loading, etc.
DataStage jobs are:
• Designed and built in Designer.
• Scheduled, invoked, and monitored in Director.
• Executed under the control of DataStage.

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In this module, you will go through the whole process with a simple job, except
for the first bullet. In this module you will manually define the metadata.

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In the center right is the Designer canvas. On it you place stages and links from
the Tools Palette on the right. On the bottom left is the Repository window,
which displays the branches in Manager. Items in Manager, such as jobs and
table definitions can be dragged to the canvas area. Click View>Repository to
display the Repository window.
Click View>Property Browser to display the Property Broswer window. This
window displays the properties of objects selected on the canvas.

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The toolbar at the top contains quick access to the main functions of Designer.

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The tool palette contains icons that represent the components you can add to your
job design.
Most of the stages shown here are automatically installed when you install
DataStage. You can also install additional stages called plug-ins for special
purposes. For example, there is a plug-in called sort that can be used to sort data.
Plug-ins are discussed in a later module.

4-7


There are two kinds of stages:
Passive stages define read and write access to data sources and repositories.
• Sequential
• ODBC
• Hashed
Active stages define how data is filtered and transformed.
• Transformer
• Aggregator
• Sort plug-in

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True or False? The Sequential stage is an active stage.
True: Incorrect. The Sequential stage is considered a passive stage because it is
used to extract or load sequential data from a file. It is not used to transform or
modify data.
False: Correct! The Sequential stage is considered a passive stage because it is
used to extract or load sequential data from a file. It is not used to transform or
modify data.

4-9


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The Sequential stage is used to extract data from a sequential file or to load data
into a sequential file.
The main things you need to specify when editing the sequential file stage are the
following:
• Path and name of file
• File format
• Column definitions
• If the sequential stage is being used as a target, specify the write action:
Overwrite the existing file or append to it.

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Defining a sequential target stage is similar to defining a sequential source stage.
You are defining the format of the data flowing into the stage, that is, from the
input links. Define each input link listed in the Input name box.
You are defining the file the job will write to. If the file doesn’t exist, it will be
created. Specify whether to overwrite or append the data in the Update action
set of buttons.
General Tab Filter command. Here you can specify a filter program for
processing the file you are extracting data from. This feature can be used, for
example, to unzip a compressed file before reading it. You can type in or browse
for the filter program, and specify any command line arguments it requires in the
text box. This text box is enabled only if you have selected the Stage uses filter
commands checkbox on the Stage page General tab. Note that, if you specify a
filter command, data browsing is not available so the View Data button is
disabled.

On the Format tab, you can specify a different format for the target file than you
specified for the source file.
If the target file doesn’t exist, you will not (of course!) be able to view its data
until after the job runs. If you click the View data button, DataStage will return a
“Failed to open …” error.

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The column definitions you defined in the source stage for a given (output) link
will appear already defined in the target stage for the corresponding (input) link.
Think of a link as like a pipe. What flows in one end flows out the other end.
The format going in is the same as the format going out.

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The Transformer stage is the primary active stage. Other active stages perform
more specialized types of transformations.
In the Transformer stage you can specify:
• Column mappings
• Derivations
• Constraints
A column mapping maps an input column to an output column. Values are
passed directly from the input column to the output column.
Derivations calculate the values to go into output columns based on values in zero
or more input columns.
Constraints specify the conditions under which incoming rows will be written to
output links.

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Notice the following elements of the transformer:
The top, left pane displays the columns of the input links. If there are multiple
input links, multiple sets of columns are displayed.
The top, right pane displays the contents of the output links. We haven’t defined
any fields here yet. If there are multiple output links, multiple sets of columns are
displayed.
For now, ignore the Stage Variables window in the top, right pane. This will be
discussed in a later module.
The bottom area shows the column definitions (metadata) for the input and output
links.
If there are multiple input and/or output links, there will be multiple tabs.

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Add one or more Annotation stages to the canvas to document your job.
An Annotation stage works like a text box with various formatting options. You
can optionally show or hide the Annotation stages by pressing a button on the
toolbar.
There are two Annotation stages. The Description Annotation stage is discussed
in a later module.

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Type the text in the box. Then specify the various options including:
• Text font and color
• Text box color
• Vertical and horizontal text justification

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Before you can run your job, you must compile it. This generates executable code
that can be run by the DataStage Server engine. To compile a job, click
File>Compile or click the Compile button on the toolbar. The Compile Job
window displays the status of the compile.
If an error occurs:
Click Show Error to identify the stage where the error occurred.
Click More to retrieve more information about the error.

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As you know, you run your jobs in Director. You can open Director from within
Designer by clicking Tools>Run Director.
In a similar way, you can move between Director, Manager, and Designer.
There are two methods for running a job:
• Run it immediately.
• Schedule it to run at a later time or date.
To run a job immediately:
• Select the job in the Job Status view. The job must have been compiled.
• Click Job>Run Now or click the Run Now button in the toolbar. The Job
Run Options window is displayed.

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This shows the Director Job Status view. To run a job, select it and then click
Job>Run Now.
Other views available:
• Job log – view messages from job run
• Schedule – view dates and times job is scheduled to run

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•

The Job Run Options window is displayed when you click Job>Run Now.
This window allows you to stop the job after:
• A certain number of rows.
• A certain number of warning messages.
You can validate your job before you run it. Validation performs some checks
that are necessary in order for your job to run successfully. These include:
• Verifying that connections to data sources can be made.
• Verifying that files can be opened.
• Verifying that SQL statements used to select data can be prepared.
Click Run to run the job after it is validated. The Status column displays the
status of the job run.

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Click the Log button in the toolbar to view the job log. The job log records
events that occur during the execution of a job.
These events include control events, such as the starting, finishing, and aborting
of a job; informational messages; warning messages; error messages; and
program-generated messages.

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Module 5

Working with Meta Data

Module 5 – Working with Meta Data DataStage 314Svr

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DataStage Essentials Module 5 – Working with Meta Data

DataStage Manager is a graphical tool for managing the contents of your
DataStage project repository, which contains metadata and other DataStage
components such as jobs and routines.
Metadata is “data about data” that describes the formats of sources and targets.
This includes general format information such as whether the record columns are
delimited and, if so, the delimiting character. It also includes the specific column
definitions.

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The left pane contains the project tree. There are eight main branches, but you
can create subfolders under each. Select a folder in the project tree to display its
contents. In this example, a folder named DS304 has been created that contains
some of the jobs in the project.
Data Elements branch: Lists the built-in and custom data elements. (Data
elements are extensions of data types, and are discussed in a later module.)
Jobs branch: Lists the jobs in the current project.
Routines branch: Lists the built-in and custom routines.
Routines are blocks of DataStage BASIC code that can be called within a job.
(Routines are discussed in a later module.)
Shared Containers branch: Shared Containers encapsulate sets of DataStage
components into a single stage. (Shared Containers are discussed in a later
module.)
Stage Types branch: Lists the types of stages that are available within a job.
Built-in stages include the sequential and transformer stages you used in
Designer.
Table Definitions branch: Lists the table definitions available for loading into a
job.

5-5


Transforms branch: Lists the built-in and custom transforms. Transforms are
functions you can use within a job for data conversion. Transforms are discussed
in a later module.

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DataStage Manager manages two different types of objects:
• Metadata describing sources and targets:
− Called table definitions in Manager. These are not to be confused with
relational tables. DataStage table definitions are used to describe the
format and column definitions of any type of source: sequential,
relational, hashed file, etc.
− Table definitions can be created in Manager or Designer and they can also
be imported from the sources or targets they describe.
• DataStage components
− Every object in DataStage (jobs, routines, table definitions, etc.) is stored
in the DataStage repository. Manager is the interface to this repository.
− DataStage components, including whole projects, can be exported from
and imported into Manager.

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Any set of DataStage objects, including whole projects, which are stored in the
Manager Repository, can be exported to a file. This export file can then be
imported back into DataStage.
Import and export can be used for many purposes, including:
• Backing up jobs and projects.
• Maintaining different versions of a job or project.
• Moving DataStage objects from one project to another. Just export the
objects, move to the other project, then re-import them into the new project.
• Sharing jobs and projects between developers. The export files, when zipped,
are small and can be easily emailed from one developer to another.

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Click Export>DataStage Components in Manager to begin the export process.
Any object in Manager can be exported to a file. Use this procedure to backup
your work or to move DataStage objects from one project to another.
Select the types of components to export. You can select either the whole project
or select a portion of the objects in the project.
Specify the name and path of the file to export to. By default, objects are
exported to a text file in a special format. By default, the extension is dsx.
Alternatively, you can export the objects to an XML document.
The directory you export to is on the DataStage client, not the server.

5-9


True or False? You can export DataStage objects such as jobs, but you can't
export metadata, such as field definitions of a sequential file.
True: Incorrect. Metadata describing files and relational tables are stored as
"Table Definitions". Table definitions can be exported and imported as any
DataStage objects can.
False: Correct! Metadata describing files and relational tables are stored as
"Table Definitions". Table definitions can be exported and imported as any
DataStage objects can.

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True or False? The directory you export to is on the DataStage client
machine, not on the DataStage server machine.
True: Correct! The directory you select for export must be addressible by your
client machine.
False: Incorrect. The directory you select for export must be addressible by your
client machine.

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To import DataStage components, click Import>DataStage Components.
Select the file to import. Click Import all to begin the import process or Import
selected to view a list of the objects in the import file. You can import selected
objects from the list. Select the Overwrite without query button to overwrite
objects with the same name without warning.

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Table definitions define the formats of a variety of data files and tables. These
definitions can then be used and reused in your jobs to specify the formats of data
stores.
For example, you can import the format and column definitions of the
Customers.txt file. You can then load this into the sequential source stage of a
job that extracts data from the Customers.txt file.
You can load this same metadata into other stages that access data with the same
format. In this sense the metadata is reusable. It can be used with any file or data
store with the same format.
If the column definitions are similar to what you need you can modify the
definitions and save the table definition under a new name.
You can also use the same table definition for different types of data stores with
the same format. For example, you can import a table definition from a sequential
file and use it to specify the format for an ODBC table. In this sense the metadata
is “loosely coupled” with the data whose format it defines.
You can import and define several different kinds of table definitions including:
Sequential files, ODBC data sources, UniVerse tables, hashed files.

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To start the import, click Import>Table Definitions>Sequential File
Definitions. The Import Meta Data (Sequential) window is displayed.
Select the directory containing the sequential files. The Files box is then
populated with the files you can import.
Select the file to import.
Select or specify a category (folder) to import into.
• The format is: <Category><Sub-category>
• <Category> is the first-level sub-folder under Table Definitions.
• <Sub-category> is (or becomes) a sub-folder under the type.

5 - 17


In Manager, select the category (folder) that contains the table definition.
Double-click the table definition to open the Table Definition window.
Click the Columns tab to view and modify any column definitions. Select the
Format tab to edit the file format specification.

5 - 18
09/01/02


5 - 19


5 - 20
09/01/02


5 - 21


5 - 22
09/01/02

Module 6

Working with Relational Data

Module 6 – Working with Relational Data DataStage 314Svr

6-2
09/01/02

DataStage Essentials Module 6 – Working with Relational Data

You can perform the same tasks with relational data that you can with sequential
data. You can extract, filter, and transform data from relational tables.
You can also load data into relational tables.
Although you can work with many relational databases through native drivers
(including UniVerse, UniData, and Oracle), you can access many more relational
databases using ODBC.
In the ODBC stage, you can either specify your query to one or more tables in the
database interactively or you can type the query or you can paste in an existing
query.

6-3


Before you can access data through ODBC you must define an ODBC data
source. In Windows NT, this can be done using the (32 bit) ODBC Data Source
Administrator in the Control Panel.
The ODBC Data Source Administrator has several tabs. For use with DataStage,
you should define your data sources on the System DSN tab (not User DSN).
You can install drivers for most of the common relational database systems from
the DataStage installation CD.
Click Add to define a new data source. When you click Add a list of available
drivers is displayed. Select the appropriate driver and then click Finish.
Different relational databases have different requirements. As an example, we
will define a Microsoft Access data source.
• Type the name of the data source in the Data Source Name box.
• Click Select to define a connection to an existing database. Type the name
and location of the database.
• Click Create to define a connection to a new database.

6-4
09/01/02


6-5


Importing table definitions from ODBC databases is similar to importing
sequential file definitions. Click Import>Table Definitions>ODBC Table
Definitions in Manager to start the process.
The DSN list displays the data sources that are defined for the DataStage Server.
Select the data source you want to import from and, if necessary, provide a user
name and password.
The Import Metadata window is displayed. It lists all tables in the database that
are available for import. Select one or more tables and a category to import to,
and then click OK.

6-6
09/01/02


Extracting data from a relational table is similar to extracting data from a
sequential file except that you use an ODBC stage instead of a sequential stage.
In this example, we’ll extract data from a relational table and load it into a
sequential file.

6-7


Specify the ODBC data source name in the Data source name box on the
General tab of the ODBC stage.
You can click the Get SQLInfo button to retrieve the quote character and schema
delimiters from the ODBC database.

6-8
09/01/02


Specify the table name on the General tab of the Outputs tab.
Select Generated query to define the SQL SELECT statement interactively using
the Columns and Selection tabs. Select User-defined SQL query to write your
own SQL SELECT statement to send to database.

6-9


Load the table definitions from Manager on the Columns tab. The procedure is
the same as for sequential files.
When you click Load, the Select Columns window is displayed. Select the
columns data is to be extracted from.

6 - 10
09/01/02


Optionally, specify a WHERE clause and other additional SQL clauses on the
Selection tab.
Other clauses can be anything else you wish to add to the Select clause, such as
ORDER BY.

6 - 11


The View SQL tab enables you to view the SELECT statement that will be used
to select the data from the table.
The SQL displayed in “read-only.” Click View Data to test the SQL statement
against the database.

6 - 12
09/01/02


If you want to define your own SQL query, click User-defined SQL query on
the General tab and then write or paste the query into the SQL for primary
inputs box on the SQL Query tab.

6 - 13


6 - 14
09/01/02


Editing an ODBC target stage is similar to editing an ODBC source stage. It
includes the following tasks:
• Specify the data source containing the target table.
• Specify the name of the table.
• Select the update action. You can choose from a variety of INSERT and/or
UPDATE actions.
• Optionally, create the table.
• Load the column definitions from the Manager table definition.

6 - 15


Some of the options are different in the ODBC stage when it is used as a target.
Select the type of action to perform from the Update action list.
You can optionally have DataStage create the target table or you can load to an
existing table.
On the View SQL tab you can view the SQL statement used to insert the data into
the target table.

6 - 16
09/01/02


On the Edit DDL tab you can generate and modify the CREATE TABLE
statement used to create the target table.
If you make any changes to column definitions, you need to regenerate the
CREATE TABLE statement by clicking the Create DDL button.

6 - 17


Transaction Handling: Allows you to specify a transaction isolation level for
read data. The isolation level specifies how potential conflicts between
transactions (i.e., dirty read, nonrepeatable reads, and phantom reads) are handled.

By default, all the rows are written to the target table before a COMMIT. In the
Rows per transaction box, you can specify a specific number of rows to write
before the COMMIT.

6 - 18
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6 - 19


True or False? Using a single ODBC stage, you can only extract data from a
single table.
True: Incorrect. You can join data from multiple tables within a single data
source.
False: Correct! You can join data from multiple tables within a single data
source.

6 - 20
09/01/02


6 - 21


6 - 22
09/01/02


The ORAOCI8 plug-in lets you rapidly and efficiently prepare and load streams
of tabular data from any DataStage stage (for example, the ODBC stage, the
Sequential File stage, and so forth) to and from tables of the target Oracle
database. The Oracle client on Windows NT or UNIX uses SQL*Net to access an
Oracle server on Windows NT or UNIX.

6 - 23


The plug-in appears as any other stage on the designer work area. It can extract or
write data contained in Oracle tables.
Features:
• Each ORAOCI8 plug-in stage is a passive stage that can have any number
of input, output, and reference output links.
• Input links specify the data you are writing, which is a stream of rows to
be loaded into an Oracle database. You can specify the data on an input
link using an SQL statement constructed by DataStage or a user-defined
SQL statement.
• Output links specify the data you are extracting, which is a stream of
rowsto be read from an Oracle database. You can also specify the data on
an output link using an SQL statement constructed by DataStage or a
userdefined SQL statement.
• Each reference output link represents a row that is key read from an
Oracle database (that is, it reads the record using the key field in the
WHERE clause of the SQL SELECT statement).

6 - 24
09/01/02


General Tab
This tab is displayed by default. It contains the following fields:

Table name. This required field is editable when the update action is not User-
defined SQL (otherwise, it is read-only). It is the name of the target Oracle table
the data is written to, and the table must exist or be created by choosing generate
DDL from the Create table action list. You must have insert, update, or delete
privileges, depending on input mode. You must specify Table name if you do not
specify User-defined SQL. There is no default. Click … (Browse button) to
browse the Repository to select the table.

Update action. Specifies which SQL statements are used to update the target
table. Some update actions require key columns to update or delete rows. There is
no default. Choose the option you want from the list.

Clear table then insert rows. Deletes the contents of the table and adds the new
rows, with slower performance because of transaction logging.

Truncate table then insert rows. Truncates the table with no transaction logging
and faster performance.

6 - 25


Insert rows without clearing. Inserts the new rows in the table.

Delete existing rows only. Deletes existing rows in the target table that have
identical keys in the source files.

Replace existing rows completely. Deletes the existing rows, then adds the new
rows to the table.

Update existing rows only. Updates the existing data rows. Any rows in the data
that do not exist in the table are ignored.

Update existing rows or insert new rows. Updates the existing data rows before
adding new rows. It is faster to update first when you have a large number of
records.

Insert new rows or update existing rows. Inserts the new rows before updating
existing rows. It is faster to insert first if you have only a few records.

User-defined SQL. Writes the data using a user-defined SQL statement,
which overrides the default SQL statement generated by the stage. If you
choose this option, you enter the SQL statement on the SQL tab.

User-defined SQL file. Reads the contents of the specified file to write
the data.

Transaction Isolation. Provides the necessary concurrency control between
transactions in the job and other transactions. Use one of the following transaction
isolation levels:
• Read committed. Takes exclusive locks on modified data and sharable
locks on all other data. Each query executed by a transaction sees only
data that was committed before the query (not the transaction) began.
Oracle queries never read dirty (uncommitted) data. This is the default.
• Serializable. Takes exclusive locks on modified data and sharable locks
on all other data. Serializable transactions see only the changes that were
committed at the time the transaction began.
Note: If Enable transaction grouping is selected on the
Transaction
Handling tab, only the Transaction Isolation value for the first
link is used for the entire group.
• Array size. Specifies the number of rows to be transferred in one call
between DataStage and Oracle before they are written. Enter a positive
integer to indicate how often Oracle performs writes at a time to the
database. The default value is 1, that is, each row is written in a separate
statement. Larger numbers use more memory on the client to cache the

6 - 26
09/01/02


rows. This minimizes server round trips and maximizes performance by
executingfewer statements. If this number is too large, the client may run
out of memory.
• Transaction size. This field exists for backward compatibility, but it is
ignored for version 3.0 and later of the plug-in. The transaction size for
new jobs is now handled by Rows per transaction on the Transaction
Handling tab.
• Create table action. Creates the target table in the specified database if
Generate DDL is selected. It uses the column definitions in the Columns
tab and the table name and the TABLESPACE and STORAGE properties
for the target table. The generated Create Table statement includes the
TABLESPACE and STORAGE keywords, which indicate the location
where the table is created and the storage expression for the Oracle
storage_clause. You must have CREATE TABLE privileges on your
schema. You can also specify your own CREATE TABLE SQL statement.
You must enter the storage clause in Oracle format. (Use the User-defined
DDL tab on the SQL tab for a complex statement.)

6 - 27

Module 7

Constraints and Derivations

Module 7 – Constraints and Derivations DataStage 314Svr

7-2
09/01/02

DataStage Essentials Module 7 – Constraints and Derivations

A constraint specifies the condition under which data flows through a link. For
example, suppose you want to split the data in the jobs file into separate files
based on the job level.
We need to define a constraint on each link so that only jobs within a certain level
range are written to each file.

7-3


Click the Constraints button in the toolbar at the top of the Transformer Stage
window to open the Transformer Stage Contraints window.
The Transformer Stage Contraints window lists all the links out of the
transformer. Double-click on the cell next to a link to create the constraint.
• Rows that are not written out to previous rows are written to a rejects link.
• A row of data is sent down all the links it satisfies.
• If there is no constraint on a (non-rejects) link, all rows will be sent down the
link.

7-4
09/01/02


This shows the Constraints window. Constraints are defined for each of the top
three links. The Reject Row box is selected for the last link. All rows that fail to
satisfy the top three links will be sent down this link.

7-5


True or False? A constraint specifies a condition under which incoming rows
of data will be written to an output link
True: Correct! You can separately define a constraint for each output link. If no
constraint is written for a particular output link, then all rows will be written to
that link.
False: Incorrect. You can separately define a constraint for each output link. If
no constraint is written for a particular output link, then all rows will be written to
that link.

7-6
09/01/02


True or False? A Rejects link can be placed anywhere in the link ordering.
True: Incorrect. A Rejects link should be placed last in the link ordering, if it is
to get every row that doesn't satisfy any of the other constraints.
False: Correct! A Rejects link should be placed last in the link ordering, if it is
to get every row that doesn't satisfy any of the other constraints.

7-7


7-8
09/01/02


A derivation is an expression that specifies the value to be moved into a target
column (field).
Every target column must have a derivation. The simplest derivation is an input
column. The value in the input column is moved to the target column.
To construct a derivation for a target column double-click on the derivation cell
next to the target column.
Derivations are constructed in the same way that constraints are constructed:
• Type constants.
• Type or enter operators from Operator shortcut menu.
• Type or enter operands from Operand shortcut menu.
What’s the difference between derivations and constraints?
• Constraints apply to links; derivations apply to columns.
• Constraints are conditions, either true or false; derivations specify a value to
go into a target column.

7-9


In this example the concatenation of several fields is moved into the FullName
target field.
The colon (:) is the concatenation operator. You can insert this from the Operator
menu or type it in.

7 - 10
09/01/02


True or False? If the constraint for a particular link is not satisified, then the
derivations defined for that link are not executed.
True: Correct! Constraints have precedence over derivations. Derivations in an
output link are only executed if the constraint is satisfied.
False: Incorrect. Constraints have precedence over derivations. Derivations in
an output link are only executed if the constraint is satisfied.

7 - 11


7 - 12
09/01/02


You can create stage variables for use in your column derivations and constraints.
Stage variables store values without writing them out to a target file or table.
They can be used in expressions just like constants, input columns, and other
operands.
Stage variables retain their values across reads. This allows them to be used as
counters and accumulators. You can also use them to compare a current input
value to a previous input value.
To create a new stage variable, click the right mouse button over the Stage
Variables window and then click Append New Stage Variable (or Insert New
Stage Variable).
After you create it, you specify a derivation for it in the same way as for columns.

7 - 13


This lists the execution order:
• Derivations in stage variables are executed before constraints. This allows
them to be used in constraints.
• Next constraints are executed.
• Then column derivations are executed.
• Derivations in higher columns are executed before lower columns.

7 - 14
09/01/02


Note the output link reordering icon available on the toolbar from within the
Transformer stage.

7 - 15


To get to the link ordering screen, open the transformer stage, then click on the
output link execution order icon. The above screen will appear. Select a link
and use the arrow buttons to reposition a link in the execution order.

7 - 16
09/01/02


Derivations for stage variables are executed before derivations for any
output link columns.
True: Correct! So you can be sure that the derivations for any of the stage
variables referenced in column derivations will have already been executed.
False: Incorrect. The derivations for stage variables are executed first. So you
can be sure that the derivations for any of the stage variables referenced in column
derivations will have already been executed.

7 - 17


7 - 18
09/01/02


7 - 19


7 - 20
09/01/02

Module 8

Creating Basic Expressions

Module 8 – Creating Basic Expressions DataStage 304

8-2
03/01/02

DataStage Essentials Module 8 – Creating Basic Expressions

DataStage BASIC is a form of BASIC that has been customized to work with
DataStage.
In the previous module you learned how to define constraints and derivations.
Derivations and constraints are written using DataStage BASIC.
Job control routines, which are discussed in a later module, are also written in
DataStage BASIC.
This module will not attempt to teach you BASIC programming. Our focus is on
what you need to know in order to construct complex DataStage constraints and
derivations.

8-3


For more information about BASIC operators than is provided here, search for
“BASIC Operators” in Help. You can insert these operators from the Operators
menu (except for the IF operator, which is on the Operands menu).
• Arithmetic operators: -, +, *, /
• Relational operators: =, <, >, <=, >=
• Logical operators: AND, OR, NOT
• IF operator:
− IF min_lvl < 0 THEN “Out of Range” ELSE “In Range”
• Concatenation operator (:)
− “The employee’s name is ” : lname : “, ” : fname
• Substring operator ([start, length]). First character is 1 (not 0).
− “APPL3245”[1, 4] → “APPL”
− “APPL3245”[5, 2] → “32”

8-4
03/01/02


For more information about BASIC functions than is provided here, look up
Alphabetical List BASIC Functions and Statements in Help. BASIC functions
include the standard Pick BASIC functions. Click Function from the Operands
menu to insert a function.
Here are a few of the more common functions:
• TRIM(string), TRIM(string, character), TRIMF, TRIMB
− TRIM(“ xyz ” ) → “xyz”
• LEN(string)
• UPCASE(string), DOWNCASE(string)
• ICONV, OCONV
− ICONV is used to convert values to an internal format
− OCONV is used to convert values from an internal format
− Very powerful functions. Often used for date and time conversions and
manipulations.
− These functions are discussed later in the module.

8-5


For more information about BASIC system variables than is provided here, look
up System Variables in Help. Click System Variable from the Operands menu
to insert a system variable.
• @DATE, @TIME Date/time job started
− @YEAR, @MONTH, @DAY Extracted from @DATE
• @INROWNUM row counter - incoming link
• @OUTROWNUM row counter - outgoing link
• @LOGNAME User logon name
• @NULL NULL value
• @TRUE, @FALSE
• @WHO Name of current project

8-6
03/01/02


True or False? TRIM is a system variable.
True: Incorrect. TRIM is a DataStage function that removes surrounding spaces
in a character string.
False: Correct! TRIM is a DataStage function that removes surrounding spaces
in a character string.

8-7


True or False? @INROWNUM is a DataStage function.
True: Incorrect. System variables all begin with the @-sign. @INROWNUM is
a system variable that contains the number of the last row read from the input
link.
False: Correct! System variables all begin with the @-sign. @INROWNUM is
a system variable that contains the number of the last row read from the input
link.

8-8
03/01/02


DataStage is supplied with a number of functions you can use to obtain
information about your jobs and projects. You can insert these functions into
derivations.
DS functions and macros are discussed in a later module.

8-9


DS (DataStage) routines are defined in DataStage Manager. There are several
types of DS routines. The type you can insert into your derivations and
constraints are of the Transform Function type. A DS Transform Function
Routine consists of a predefined block of BASIC statements that takes one or
more arguments and returns a single value.
DS routines are defined in DataStage Manager. You can define your own
routines, but there are also a number of pre-built routines that are supplied with
DataStage.
The pre-built routines include a number of routines for manipulating dates, such
as ConvertMonth, QuarterTag, and Timestamp.

8 - 10
03/01/02


8 - 11


Data elements are extended data types. For example, a phone number is a kind of
string. You could define a data element called PHONE.NUMBER to precisely
define this type.
Data elements are defined in DataStage Manager. A number of built-in types are
supplied with DataStage. For example MONTH.TAG represents a string of the
form “YYYY-MM”.

8 - 12
03/01/02


8 - 13


DS Transforms are similar to DS Transform Function routines. They take one or
more arguments and return a single value. There are two primary differences:
• The argument(s) and return value have specific data elements associated with
them. In this sense, they transform data from one data element type to
another data element type.
• Unlike DS routines, they do not consist of blocks of BASIC statements.
Rather, they consist of a single (though possibly very complex) BASIC
expression.
You can define your own DS Transforms, but there are also a number of pre-built
transforms that are supplied with DataStage.
The pre-built transforms include a number of routines for manipulating strings
and dates.

8 - 14
03/01/02


8 - 15


8 - 16
03/01/02


8 - 17


Date manipulation in DataStage can be done in several ways:
• Using the Iconv and Oconv functions using the “D” conversion code.
• Using the built-in date Transforms.
• Using the built-in date routines.
• Using routines in the DataStage Software Development Kit (SDK)
Using routines in the DataStage Software Development Kit (SDK) is covered in
another DataStage course. Your instructor can provide further details. The SDK
routines are installed in the Manager Routinessdk folder.

8 - 18
03/01/02


For detailed help on Iconv and Oconv, see their entries in the Alphabetical List
of BASIC Functions and Statements in Help.
Use Iconv to convert a string date in a variety of formats to the internal DataStage
integer format. Use Oconv to convert an internal date to a string date in a variety
of formats. Use these two functions together to covert a string date from one
format to another.
The internal format for a date is based on a reference date of December 31, 1967,
which is day 0. Dates before are negative integers; dates after are positive
integers.
Use the “D” conversion code to specify the format of the date to be converted to
an internal date by Iconv or the format of the date to be output by Oconv.

8 - 19


For detailed help (more than you probably want), see D Code under Iconv or
Oconv in Help.
“D4-MDY[2,2,4]”
• D Date conversion code
• 4 Number of digits in year
• - Separator
• MDY Ordering is month, day, year
• [2,2,4] Number of digits for M,D,Y, respectively
Note:
• The number in brackets for “Y” (namely 4) overrides the number following
“D”.
• Iconv ignores some of the characters.
− Any separator will do.
− Number of characters is ignored if there are separators.

8 - 20
03/01/02


• Iconv(“12-31-67”, “D2-MDY[2,2,2]”) → 0
• Iconv(“12311967”, “D MDY[2,2,4]”) → 0
• Iconv(“31-12-1967”, “D-DMY[2,2,4]”) → 0
• Oconv(0, “D2-MDY[2,2,4]”) → “12-31-1967”
• Oconv(0, “D2/DMY[2,2,2]”) → “31/12/67”
• Oconv(10, “D/YDM[4,2,A10]”) → “1968/10/JANUARY”
− This example illustrates the use of an additional formatting option. The
“A10” options says to alphabetically express the name, length 10
characters.
• Oconv( Iconv(“12-31-67”, “D2-MDY[2,2,2]”), “D/YDM[4,2,A10]”)
→ “1967/31/DECEMBER”
− This example shows how to convert from one string representation to
another.

8 - 21


8 - 22
03/01/02


DataStage provides a number of built-in transforms you can use for date
conversions.
The following data elements are used with the built-in transforms:

Data element String format Example
DATE.TAG YYYY-MM-DD 1999-02-24
WEEK.TAG YYYYWnn 1999W06
MONTH.TAG YYYY-MM 1999-02
QUARTER.TAG YYYYQn 1999Q4
YEAR.TAG YYYY 1999

8 - 23


True or False? You can use Oconv to convert a string date from one format
to another.
True: Incorrect. Oconv by itself can't do this. You would first use Iconv to
convert the input string into a day integer. Then you can use Oconv to convert the
day integer into the output string.
False: Correct! Oconv by itself can't do this. You would first use Iconv to
convert the input string into a day integer. Then you can use Oconv to convert the
day integer into the output string.

8 - 24
03/01/02


The transforms can be grouped into the following categories:
• String to day number
− Formatted string → internal date integer
• Day number to date string
− Internal date integer → formatted string
• Date string to date string
− DATE.TAG string → formatted string

8 - 25


The following transforms convert strings of the specified format (MONTH.TAG,
QUARTER.TAG, …) to an internal date representing the first or last day of the
period.
Function Tag Description

MONTH.FIRST MONTH.TAG Returns a numeric internal date
corresponding to the first/last day
MONTH.LAST
of a month
QUARTER.FIRST QUARTER.TAG Returns a numeric internal date
QUARTER.LAST
of a quarter
WEEK.FIRST WEEK.TAG Returns a numeric internal date
corresponding to the first day
WEEK.LAST
(Monday) / last day (Sunday) of a
week
YEAR.FIRST YEAR.TAG Returns a numeric internal date
YEAR.LAST
of a year

8 - 26
03/01/02


Examples:
MONTH.FIRST(“1993-02”) → 9164
MONTH.LAST(“1993-02”) → 9191

8 - 27


The following functions convert internal dates to strings in various formats
(DATE.TAG, MONTH.TAG, …).
Function Argument type Description
DATE.TAG Internal date Converts internal date to string in
DATE.TAG format
MONTH.TAG Internal date Converts internal date to string in
MONTH.TAG format
QUARTER.TAG Internal date Converts internal date to string in
QUARTER.TAG format
WEEK.TAG Internal date Converts internal date to string in
WEEK.TAG format

Examples:
MONTH.TAG(9177) → “1993-02”
DATE.TAG(9177) → “1993-02-14”

8 - 28
03/01/02


The following functions convert strings in DATE.TAG format to strings in
various other formats (DAY.TAG, MONTH.TAG, …).
Function Tag Description
TAG.TO.MONTH DATE.TAG Convert DATE.TAG to
MONTH.TAG
TAG.TO.QUARTER DATE.TAG Convert DATE.TAG to
QUARTER.TAG
TAG.TO.WEEK DATE.TAG Convert DATE.TAG to
WEEK.TAG
TAG.TO.DAY DATE.TAG Convert DATE.TAG to
DAY.TAG

Examples:
TAG.TO.MONTH(“1993-02-14”) → “1993-02”
TAG.TO.QUARTER(“1993-02-14”) → “1993Q1”

8 - 29


8 - 30
03/01/02


8 - 31


8 - 32
03/01/02

Module 9 – Troubleshooting DataStage 314Svr

9-2
09/01/02

DataStage Essentials Module 9 – Troubleshooting

Events are logged to the job log file when a job is validated, run, or reset. You
can use the log file to troubleshoot jobs that fail during validation or a run.
Various entries are written to the log, including when:
• The job starts
• The job finishes
• An active stage starts
• An active stage finishes
• Rows are rejected (yellow icons)
• Errors occur (red icons)
• DataStage informational reports are logged
• User-invoked messages are displayed

09 - 3


The event window shows the events that are logged for a job during its run.
The job log contains the following information:
Column Name Description
Occurred Time the event occurred
On date Date the event occurred
Type Info Informational. No action required.
Warning An error occurred. Investigate the cause of the
warning, as this may indicate a serious error.
Fatal A fatal error occurred.
Control The job starts and finishes.
Reject Rejected rows are output.
Reset A job or the log is reset.
Event A message describing the event. The system displays the
first line of the message. If a message has an ellipsis (…)
at the end, it contains more than one line. You can view
the full message in the Event Detail window.

9-4
09/01/02


Clearing the log
To clear the log, click Job>Clear Log.

09 - 5


Double-click on an event to open the Event Detail window. This window gives
you more information.
When an active stage finishes, DataStage logs an informational message that
describes how many rows were read in to the stage and how many were written.
This provides you with valuable information that can indicate possible errors.

9-6
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The Monitor can be used to display information about a job while it is running.
To start the Monitor, click Tools>New Monitor. Once in Monitor, click the right
mouse button and then select Show links to display information about each of the
input and output links.

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When you are testing a job, you can save time by limiting the number of rows and
warnings.

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Server side tracing is enabled in Administrator. It is designed to be used to help
customer support analysts troubleshoot serious problems. When enabled, it logs a
record to a trace file whenever DataStage clients interact with the server.
Caution: Because of the overhead caused by server side tracing it should only be
used when working with customer support.

09 - 9


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DataStage provides a debugger for testing and debugging your job designs. The
debugger runs within Designer. With the DataStage debugger you can:
• Set breakpoints on job links, including conditional breakpoints.
• Step through your job link-by-link or row-by-row.
• Watch the values going into link columns.

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To begin debugging a program, click View>Debug Bar to display the debug
toolbar. The toolbar provides access to all of the debugging functions.

Stop Toggle
breakpoint
Next link Debug job
parameters
View job log

Clear breakpoints

Go Debug window
Next row Edit breakpoints

Button Description
Go Start/continue debugging.
Next Link The job continues until the next action occurs on
the link.
Next Row The job continues until the next row is processed
or ntil another link ith a breakpoint is
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or until another link with a breakpoint is
encountered.
Stop Job Stops the job at the point it is at. Click Go to
continue.
Job Parameters Set limits on rows and warnings.
Edit Breakpoints Displays the Edit Breakpoints window, in which
you can edit existing breakpoints.
Toggle Breakpoint Set or clear a breakpoint on a selected link.
Clear All Breakpoints Removes breakpoints from all links.
View job log Open Director and view the job log.
Debug Window Show/hide the Debug Window, which displays
link column values.

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To set a breakpoint on a link, select the link and then click the Toggle
Breakpoint button. A black circle appears on the link.

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Click the Edit Breakpoints button to open the Edit Breakpoints window.
Existing breakpoints are listed in the lower pane.
To set a condition for a breakpoint, select the breakpoint and then specify the
condition in the above pane. You can either specify the number of rows before
breaking or specify an expression to break upon when it’s true.

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Click the Debug Window button to open the Debug Window.
• The top pane lists all the columns defined for all links.
• The Local Data column lists the data currently in the column.
• The Current Break box at the top of the window lists the link where
execution stopped.
• To add a column to the lower pane (where it is isolated), select the column
and then click Add Watch.
• If a breakpoint is set, execution stops at that link when a row is written to the
link.

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You can step through row-by-row or step-by-step.
• Next Row extracts a row of data and stops at the next link with a breakpoint
that the row is written to.
− For example, if a breakpoint is set on the MexicoCustomersOut link,
execution stops at the MexicoCustomersOut link when a Mexican
customer is read.
− If a breakpoint is not set on the MexicoCustomersOut link, execution
will not stop at the MexicoCustomersOut link when a Mexican customer
is read.
− Execution will stop at the CustomersIn link (even if there is no
breakpoint set on it) because all rows are read through that link.
• Next Link stops at the next link that data is written to.

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Module 10 – Defining Lookups DataStage 314Svr

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09/01/02

DataStage Essentials Module 10 – Defining Lookups

A hashed file is a file that distributes records in one or more evenly-sized groups
based on a primary key. The primary key value is processed by a "hashing
algorithm" to determine the location of the record.
The number of groups in the file is referred to as its modulus.
In this example, there are 5 groups (modulus 5).
Hashed files are used for reference lookups in DataStage because of their fast
performance. The hashing algorithm determines the group the record is in. The
groups contain a small number of records, so the record can be quickly located
within the group.
If write caching is enabled, DataStage does not write hashed file records directly
to disk. Instead it caches the records in memory, and writes the cached records to
disk when the cache is full. This improved performance. You can specify the
size of the cache on the Tunables tab in Administrator.

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