Ascential Ds 324px - V70 - Student Reference
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DS324PX DATASTAGE PX
STUDENT REFERENCE
DS324PX
Copyright © 2004 Ascential Software Corporation 1/1/04
DataStage PX
DataStage PX
DS325PX
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. 01-01-2004
DS324PX
Copyright © 2004 Ascential Software Corporation 1/1/04
DataStage PX
DataStage PX
DS325PX
Table of Contents Intro Part 1: Introduction.................................................... 1 Intro Part 2: Configuring Project ...................................... 13 Intro Part 3: Managing Meta Data .................................... 25 Intro Part 4: Designing and Documenting Jobs ............... 45 Intro Part 5: Running Jobs................................................ 75 Module 1: Review.............................................................. 85 Module 2: Sequential Access ......................................... 125 Module 3: Standards ....................................................... 157 Module 4: DBMS Access ................................................. 175 Module 5: PX Platform Architecture .............................. 195 Module 6: Transforming Data ......................................... 229 Module 7: Sorting Data ................................................... 249 Module 8: Combining Data.............................................. 261 Module 9: Configuration Files......................................... 283 Module 10: Extending PX................................................ 305 Module 11: Meta Data..................................................... 347 Module 12: Job Control................................................... 369 Module 13: Testing ......................................................... 385
DS324PX
Copyright © 2004 Ascential Software Corporation 1/1/04
DataStage PX
Intro Part 1
Introduction to DataStage PX
Copyright ©2004 Ascential Software Corporation
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Ascential Platform
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What is DataStage? ~
~
~
~ ~
Design jobs for Extraction, Transformation, and Loading (ETL) Ideal tool for data integration projects – such as, data warehouses, data marts, and system migrations Import, export, create, and managed metadata for use within jobs Schedule, run, and monitor jobs all within DataStage Administer your DataStage development and execution environments
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 Server and Clients
The DataStage client components are: 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
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DataStage Administrator
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.
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Client Logon
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DataStage Manager
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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DataStage Designer
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.
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DataStage Director
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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Developing in DataStage ~
Define global and project properties in Administrator
~
Import meta data into Manager
~
Build job in Designer
~
Compile Designer
~
Validate, run, and monitor in Director
•
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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DataStage Projects
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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Quiz– True or False ~
~
~
~
DataStage Designer is used to build and compile your ETL jobs Manager is used to execute your jobs after you build them Director is used to execute your jobs after you build them Administrator is used to set global and project properties
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Intro Part 2
Configuring Projects
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Module Objectives ~
After this module you will be able to: – Explain how to create and delete projects – Set project properties in Administrator – Set PX global properties in Administrator
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Project Properties ~
~
Projects can be created and deleted in Administrator Project properties and defaults are set in Administrator
Recall from module 1: 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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Setting Project Properties ~
To set project properties, log onto Administrator, select your project, and then click “Properties”
The logon screen for Administrator does not provide the option to select a specific project (unlike the other DataStage clients).
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Project Properties General Tab
The General Tab is used to change DataStage license information and to adjust the amount of time a connection will be maintained between the DataStage server and clients.
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Projects General Tab
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.
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Environment Variables
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Permissions Tab
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.
•
, 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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Tracing Tab
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.
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Tunables Tab
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.
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Parallel Tab
You should enable OSH for viewing – OSH is generated when you compile a job.
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Intro Part 3
Managing Meta Data
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Module Objectives ~
After this module you will be able to: – Describe the DataStage Manager components and functionality – Import and export DataStage objects – Import metadata for a sequential file
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What Is Metadata?
Data
Source
Transform
Meta Data
Target Meta Data
Meta Data Repository
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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DataStage Manager
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. The left pane contains the project tree. There are seven main branches, but you can create subfolders under each. Select a folder in the project tree to display its contents.
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Manager Contents ~
~
Metadata describing sources and targets: Table definitions DataStage objects: jobs, routines, table definitions, etc.
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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Import and Export ~
Any object in Manager can be exported to a file
~
Can export whole projects
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Use for backup
~
Sometimes used for version control
~
~
Can be used to move DataStage objects from one project to another Use to share DataStage jobs and projects with other developers
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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Export Procedure ~
In Manager, click “Export>DataStage Components”
~
Select DataStage objects for export
~
Specified type of export: DSX, XML
~
Specify file path on client machine
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.
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Quiz: 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 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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Quiz: True or False? ~
The directory to which you export is on the DataStage client machine, not on the DataStage server machine.
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 addressable by your client machine. False: Incorrect. The directory you select for export must be addressable by your client machine.
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Exporting DataStage Objects
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Exporting DataStage Objects
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Import Procedure ~
~
In Manager, click “Import>DataStage Components” Select DataStage objects for import
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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Importing DataStage Objects
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Import Options
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Exercise ~
Import DataStage Component (table definition)
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Metadata Import ~
Import format and column destinations from sequential files
~
Import relational table column destinations
~
Imported as “Table Definitions”
~
Table definitions can be loaded into job stages
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 import and define several different kinds of table definitions including: Sequential files and ODBC data sources.
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Sequential File Import Procedure ~
~
~ ~
In Manager, click Import>Table Definitions>Sequential File Definitions Select directory containing sequential file and then the file Select Manager category Examined format and column definitions and edit is necessary
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:\<Sub-category>
•
is the first-level sub-folder under Table Definitions.
•
<Sub-category> is (or becomes) a sub-folder under the type.
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Manager Table Definition
In Manager, select the category (folder) that contains the table definition. Doubleclick 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.
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Importing Sequential Metadata
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Intro Part 4
Designing and Documenting Jobs
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Module Objectives ~
After this module you will be able to: – – – – – – – –
Describe what a DataStage job is List the steps involved in creating a job Describe links and stages Identify the different types of stages Design a simple extraction and load job Compile your job Create parameters to make your job flexible Document your job
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What Is a Job? ~ ~
Executable DataStage program Created in DataStage Designer, but can use components from Manager
~
Built using a graphical user interface
~
Compiles into Orchestrate shell language (OSH)
A job is an executable DataStage program. In DataStage, you can design and run jobs that perform many useful data integration 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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Job Development Overview ~
~
~
~
In Manager, import metadata defining sources and targets In Designer, add stages defining data extractions and loads And Transformers and other stages to defined data transformations Add lakes defining the flow of data from sources to targets
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Compiled the job
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In Director, validate, run, and monitor your job
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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Designer Work Area
The appearance of the designer work space is configurable; the graphic shown here is only one example of how you might arrange components. In the right center is the Designer canvas, where you create stages and links. On the 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.
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Designer Toolbar Provides quick access to the main functions of Designer Show/hide metadata markers
Job properties
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Compile
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Tools Palette
The tool palette contains icons that represent the components you can add to your job design. You can also install additional stages called plug-ins for special purposes.
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Adding Stages and Links ~
~
Stages can be dragged from the tools palette or from the stage type branch of the repository view Links can be drawn from the tools palette or by right clicking and dragging from one stage to another
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Sequential File Stage ~
Used to extract data from, or load data to, a sequential file
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Specify full path to the file
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Specify a file format: fixed width or delimited
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Specified column definitions
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Specify write action
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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Job Creation Example Sequence ~
Brief walkthrough of procedure
~
Presumes meta data already loaded in repository
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Designer - Create New Job
Several types of DataStage jobs: Server – not covered in this course. However, you can create server jobs, convert them to a container, then use this container in a parallel job. However, this has negative performance implications. Shared container (parallel or server) – contains reusable components that can be used by other jobs. Mainframe – DataStage 390, which generates Cobol code Parallel – this course will concentrate on parallel jobs. Job Sequence – used to create jobs that control execution of other jobs.
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Drag Stages and Links Using Palette
The tools palette may be shown as a floating dock or placed along a border. Alternatively, it may be hidden and the developer may choose to pull needed stages from the repository onto the design work area.
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Assign Meta Data
Meta data may be dragged from the repository and dropped on a link.
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Editing a Sequential Source Stage
Any required properties that are not completed will appear in red. You are defining the format of the data flowing out of the stage, that is, to the output link. Define the output link listed in the Output name box. You are defining the file from which the job will read. If the file doesn’t exist, you will get an error at run time. On the Format tab, you specify a format for the source file. You will be able to view its data using the View data button. Think of a link as like a pipe. What flows in one end flows out the other end (at the transformer stage).
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Editing a Sequential Target
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. 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. 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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Transformer Stage ~
~
~
Used to define constraints, derivations, and column mappings A column mapping maps an input column to an output column In this module will just defined column mappings (no derivations)
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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Transformer Stage Elements
There are two: transformer and basic transformer. Both look the same but access different routines and functions. Notice the following elements of the transformer: The top, left pane displays the columns of the input links. The top, right pane displays the contents of the stage variables. The lower, right pane displays the contents of the output link. Unresolved column mapping will show the output in red. 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.
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Create Column Mappings
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Creating Stage Variables
Stage variables are used for a variety of purposes: Counters Temporary registers for derivations Controls for constraints
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Result
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Adding Job Parameters ~
Makes the job more flexible
~
Parameters can be: – Used in constraints and derivations – Used in directory and file names
~
Parameter values are determined at run time
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Adding Job Documentation ~
Job Properties – Short and long descriptions – Shows in Manager
~
Annotation stage – Is a stage on the tool palette – Shows on the job GUI (work area)
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Job Properties Documentation
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Annotation Stage on the Palette
Two versions of the annotation stage are available: Annotation Annotation description The difference will be evident on the following slides.
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Annotation Stage Properties
You can type in whatever you want; the default text comes from the short description of the jobs properties you entered, if any. 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 slide.
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Final Job Work Area with Documentation
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Can be used to Document Specific Stages
To get the documentation to appear at the top of the text box, click the “top” button in the stage properties.
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Compiling a Job
Before you can run your job, you must compile it. To compile it, click File>Compile or click the Compile button on the toolbar. The Compile Job window displays the status of the compile. A compile will generate OSH.
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Errors or Successful Message
If an error occurs: Click Show Error to identify the stage where the error occurred. This will highlight the stage in error. Click More to retrieve more information about the error. This can be lengthy for parallel jobs.
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Intro Part 5
Running Jobs
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Module Objectives ~
After this module you will be able to: – – – – –
Validate your job Use DataStage Director to run your job Set to run options Monitor your job’s progress View job log messages
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Prerequisite to Job Execution Result from Designer compile
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DataStage Director ~ ~
Can schedule, validating, and run jobs Can be invoked from DataStage Manager or Designer – Tools > Run Director
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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Running Your Job
This shows the Director Status view. To run a job, select it and then click Job>Run Now. Better yet: Shift to log view from main Director screen. Then click green arrow to execute job.
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Run Options – Parameters and Limits
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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Director Log View
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 programgenerated messages.
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Message Details are Available
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Other Director Functions ~
Schedule job to run on a particular date/time
~
Clear job log
~
Set Director options – Row limits – Abort after x warnings
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Blank
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Module 1
DS324PX – DataStage PX Review
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Ascential’s Enterprise Data Integration Platform Command & Control
ANY SOURCE CRM ERP SCM RDBMS Legacy Real-time Client-server Web services Data Warehouse Other apps.
DISCOVER
PREPARE
TRANSFORM
Gather relevant information for target enterprise applications
Cleanse, correct and match input data
Standardize and enrich data and load to targets
Data Profiling
Data Quality
Extract, Transform, Load
ANY TARGET CRM ERP SCM BI/Analytics RDBMS Real-time Client-server Web services Data Warehouse Other apps.
Parallel Execution Meta Data Management
Ascential Software looked at what’s required across the whole lifecycle to solve enterprise data integration problems not just once for an individual project, but for multiple projects connecting any type of source (ERP, SCM, CRM, legacy systems, flat files, external data, etc) with any type of target. Ascential’s uniqueness comes from the combination of proven best-in-class functionality, across data profiling, data quality and matching, and ETL combined to provide a complete end-to-end data integration solution on a platform with unlimited scalability and performance through parallelization. We can therefore deal not only with gigabytes of data, but terabytes of data, and petabytes of data data volumes becoming more and more common………., and do so with complete management and integration of all the meta data in the enterprise environment. This is indeed an end-to-end solution, which customers can implement in whatever phases they choose, and which, by virtue of its completeness and breadth and robustness of solution, helps our customers get the quickest possible time to value and time to impact from strategic applications. It assures good data is being fed into the informational systems and that our solution provides the ROI and economic benefit customers expect from investments in strategic applications, which are very large investments and, which done right, should command very large returns.
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Course Objectives ~
You will learn to: – Build DataStage PX jobs using complex logic – Utilize parallel processing techniques to increase job performance – Build custom stages based on application needs
~
Course emphasis is: – Advanced usage of DataStage PX – Application job development – Best practices techniques
Students will gain hands-on experience with DSPX by building an application in class exercises. This application will use complex logic structures.
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Course Agenda ~
Day 1 – – – –
~
~
Review of PX Concepts Sequential Access Standards DBMS Access
Day 2 – PX Architecture – Transforming Data – Sorting Data
Day 3 – Combining Data – Configuration Files
~
Day 4 – – – –
Extending PX Meta Data Usage Job Control Testing
Suggested agenda – actual classes will vary.
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Module Objectives ~
~
~
Provide a background for completing work in the DSPX advanced course Ensure all students will have a successful advanced class Tasks – Review concepts covered in DSPX Essentials course
In this module we will review many of the concepts and ideas presented in the DataStage essentials course. At the end of this module students will be asked to complete a brief exercise demonstrating their mastery of that basic material.
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Review Topics ~
DataStage architecture
~
DataStage client review – – – –
Administrator Manager Designer Director
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Parallel processing paradigm
~
DataStage Parallel Extender (DSPX)
Our topics for review will focus on overall DataStage architecture and a review of the parallel processing paradigm in DataStage parallel extender.
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Client-Server Architecture Command & Control
Microsoft® Windows NT/2000/XP ANY TARGET
ANY SOURCE
Designer
Discover Extract
Director
Prepare Cleanse
Administrator
Transform Transform
Server
Repository Manager
Extend Integrate
CRM ERP SCM BI/Analytics RDBMS Real-Time Client-server Web services Data Warehouse Other apps.
Repository
Microsoft® Windows NT or UNIX Parallel Execution Meta Data Management
DataStage parallel extender consists of four clients connected to the DataStage parallel extender engine. The client applications are: Manager, Administrator, Designer, and Director. These clients connect to a server engine that is located on either a UNIX (parallel extender only) or NT operating system.
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Process Flow ~
Administrator – add/delete projects, set defaults
~
Manager – import meta data, backup projects
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Designer – assemble jobs, compile, and execute
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Director – execute jobs, examine job run logs
A typical DataStage workflow consists of: Setting up the project in Administrator Including metadata via Manager Building and assembling the job in Designer Executing and testing the job in Director.
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Administrator – Licensing and Timeout
Change licensing, if appropriate. Timeout period should be set to large number or choose “do not timeout” option.
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Administrator – Project Creation/Removal
Functions specific to a project.
Available functions: Add or delete projects. Set project defaults (properties button). Cleanup – perform repository functions. Command – perform queries against the repository.
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Administrator – Project Properties
RCP for parallel jobs should be enabled
Variables for parallel processing
Recommendations: Check enable job administration in Director Check enable runtime column propagation May check auto purge of jobs to manage messages in director log
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Administrator – Environment Variables
Variables are category specific
You will see different environment variables depending on which category is selected.
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OSH is what is run by the PX Framework
Reading OSH will be covered in a later module. Since DataStage parallel extender writes OSH, you will want to check this option.
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DataStage Manager
To attach to the DataStage Manager client, one first enters through the logon screen. Logons can be either by DNS name or IP address. Once logged onto Manager, users can import meta data; export all or portions of the project, or import components from another project’s export. Functions: Backup project Export Import Import meta data Table definitions Sequential file definitions Can be imported from metabrokers Register/create new stages
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Export Objects to MetaStage
Push meta data to MetaStage
DataStage objects can now be pushed from DataStage to MetaStage.
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Designer Workspace
Can execute the job from Designer
Job design process: Determine data flow Import supporting meta data Use designer workspace to create visual representation of job Define properties for all stages Compile Execute
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DataStage Generated OSH
The PX Framework runs OSH
The DataStage GUI now generates OSH when a job is compiled. This OSH is then executed by the parallel extender engine.
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Director – Executing Jobs
Messages from previous run in different color
Messages from previous runs are kept in different color from current run.
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Stages Can now customize the Designer’s palette
In Designer View > Customize palette This window will allow you to move icons into your Favorites folder plus many other customization features.
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Popular Stages
Row generator
Peek
The row generator and peek stages are especially useful during development.
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Row Generator ~
Can build test data Edit row in column tab
Repeatable property
Depending on the type of data, you can set values for each column in the row generator.
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Peek ~
Displays field values – Will be displayed in job log or sent to a file – Skip records option – Can control number of records to be displayed
~
Can be used as stub stage for iterative development (more later)
The peek stage will display column values in a job's output messages log.
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Why PX is so Effective ~
Parallel processing paradigm – More hardware, faster processing – Level of parallelization is determined by a configuration file read at runtime
~
Emphasis on memory – Data read into memory and lookups performed like hash table
PX takes advantage of the machines hardware architecture -- this can be changed at runtime.
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Scalable Systems ~
Parallel processing = executing your application on multiple CPUs – Scalable processing = add more resources (CPUs, RAM, and disks) to increase system performance 1
2
3
4
5
6
•
Example system containing 6 CPUs (or processing nodes) and disks
DataStage parallel extender can take advantage of multiple processing nodes to instantiate multiple instances of a DataStage job.
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Scaleable Systems: Examples Three main types of scalable systems ~
Symmetric Multiprocessors (SMP), shared memory
–Sun Starfire™ –IBM S80 –HP Superdome ~
Clusters: UNIX systems connected via networks
–Sun Cluster –Compaq TruCluster ~ note
MPP
You can describe an MPP as a bunch of connected SMPs.
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SMP: Shared Everything • Multiple CPUs with a single operating system • Programs communicate using shared memory • All CPUs share system resources (OS, memory with single linear address space, disks, I/O) When used with Parallel Extender: • Data transport uses shared memory • Simplified startup
cpu
cpu
cpu
cpu
Parallel Extender treats NUMA (NonUniform Memory Access) as plain SMP
A typical SMP machine has multiple CPUs that share both disks and memory.
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Traditional Batch Processing Operational Data
Transform
Clean
Load
Archived Data
Data Warehouse Disk
Disk
Disk
Source
Target
Traditional approach to batch processing: • Write to disk and read from disk before each processing operation • Sub-optimal utilization of resources • a 10 GB stream leads to 70 GB of I/O • processing resources can sit idle during I/O • Very complex to manage (lots and lots of small jobs) • Becomes impractical with big data volumes • disk I/O consumes the processing • terabytes of disk required for temporary staging
The traditional data processing paradigm involves dropping data to disk many times throughout a processing run.
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Pipeline Multiprocessing Data Pipelining • Transform, clean and load processes are executing simultaneously on the same processor • rows are moving forward through the flow Operational Data
Archived Data
Transform
Clean
Load
Data Warehouse
Target
Source • • • •
Start a downstream process while an upstream process is still running. This eliminates intermediate storing to disk, which is critical for big data. This also keeps the processors busy. Still has limits on scalability
Think of a conveyor belt moving the rows from process to process!
On the other hand, but parallel processing paradigm rarely drops data to disk unless necessary for business reasons -- such as backup and recovery.
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Partition Parallelism Data Partitioning • Break up big data into partitions • Run one partition on each processor Node 1
• 4X times faster on 4 processors With data big enough: 100X faster on 100 processors • This is exactly how the parallel databases work! • Data Partitioning requires the same transform to all partitions: Aaron Abbott and Zygmund Zorn undergo the same transform
Transform
A-F
Node 2
G- M Source Data
Transform
N-T U-Z
Node 3
Transform Node 4
Transform
Data may actually be partitioned in several ways -- range partitioning is only one example. We will explore others later.
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PX Program Elements
•
•
Dataset: uniform set of rows in the Framework's internal representation - Three flavors: 1. file sets *.fs : stored on multiple Unix files as flat files 2. persistent: *.ds : stored on multiple Unix files in Framework format read and written using the DataSet Stage 3. virtual: *.v : links, in Framework format, NOT stored on disk - The Framework processes only datasets—hence possible need for Import - Different datasets typically have different schemas - Convention: "dataset" = Framework data set. Partition: subset of rows in a dataset earmarked for processing by the same node (virtual CPU, declared in a configuration file). - All the partitions of a dataset follow the same schema: that of the dataset
Parallel extender deals with several different types and data: file sets data sets -- both in persistent and non-persistent forms.
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Combining Parallelism Types Putting It All Together: Parallel Dataflow
Source Data
Pa rtit i on
ing
Pipelining
Transform
Clean
Source
Load
Data Warehouse
Target
Pipelining and partitioning can be combined together to provide a powerful parallel processing paradigm.
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Repartitioning Putting It All Together: Parallel Dataflow with Repartioning on-the-fly
Re pa rtit ion ing
Pa rtit i on
Source Data
U-Z N-T G- M A-F
Re pa rtit ion ing
ing
Pipelining
Customer zip code
Credit card number
Transform
Customer last name
Clean
Source
Load
Data Warehouse
Target
Without Landing To Disk!
In addition, data can change partitioning from stage to stage. This can either happened explicitly at the desire of a programmer or implicitly performed by the engine.
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DSPX Overview • • • •
Build scalable applications from simple components Run native operators developed in C++ Run existing code written in C, COBOL, scripts, etc. Run existing sequential programs in parallel Parallel Extender Client • Parallel Extender will Legacy and 3rd Shell C++ manage parallel execution party programs scripts operators COBOL, C/C++, etc
The Framework UNIX UNIX UNIX UNIX UNIX Scalable Servers...
Parallel extender can interface with UNIX commands, shell scripts, and legacy programs to run on the parallel extender framework. Underlying this architecture is the UNIX operating system.
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DataStage PX Architecture DataStage: Provides data integration platform
Orchestrate Framework: Provides application scalability Orchestrate Program (sequential data flow)
Flat Files
Relational Data
Clean 1 Import
Analyze
Merge Clean 2
Centralized Error Handling and Event Logging
Configuration File
Performance Visualization
Orchestrate Application Framework and Runtime System
Parallel access to data in RDBMS Parallel pipelining Clean 1 Import Merge Clean 2
Parallel access to data in files
Analyze
Inter-node communications
Parallelization of operations
DataStage Parallel Extender: Best-of-breed scalable data integration platform No limitations on data volumes or throughput
The parallel extender engine was derived from DataStage and Orchestrate.
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Glossary Orchestrate
DataStage
schema
table definition
property
format
type
SQL type + length [and scale]
virtual dataset
link
record/field
row/column
operator
stage
step, flow, OSH command
job
Framework
DS engine
• GUI uses both terminologies • Program messages (warnings, errors) use Orchestrate terms
Note: Different uses of "import" and "constraint"
note
Orchestrate terms and DataStage terms have an equivalency. The GUI can log messages frequently used terms from both paradigms.
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Introduction to DataStage PX ~
DSPX: – Automatically scales to fit the machine – Handles data flow among multiple CPU’s and disks
~
With DSPX you can: – Create applications for SMP’s, clusters and MPP’s… Parallel Extender is architecture-neutral – Access relational databases in parallel – Execute external applications in parallel – Store data across multiple disks and nodes
Parallel extender is architecturally neutral -- it can run on SMP's, clusters, and MPP's. The configuration file determines how parallel extender will treat the hardware.
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Job Design VS. Execution User assembles the flow using the Designer (see below)
…and gets: parallel access, propagation, transformation, and load. The design is good for 1 node, 4 nodes, or N nodes. To change # nodes, just swap configuration file. No need to modify or recompile your design!
Much of the parallel processing paradigm is hidden from the programmer -- they simply designate process flow as shown in the upper portion of this diagram. Parallel extender, using the definitions in that configuration file, will actually execute UNIX processes that are partitioned and parallelized.
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DS-PX Program Elements
Partitioners and Collectors
~
Partitioners distribute rows into partitions – implement data-partition parallelism
~
~
Collectors = inverse partitioners Live on input links of stages running – in parallel (partitioners) – sequentially (collectors)
~
Use a choice of methods
Partitioners and collectors work in opposite directions -- however, many are frequently together in job designs.
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Example Partitioning Icons partitioner
Partitioners and collectors have no stage nor icons of their own. They live live on input links of stages running in parallel (resp. sequentially). Link markings indicate their presence. S----------------->S (no Marking) S----(fan out)--->P (partitioner) P----(fan in) ---->S (collector) P----(box)------->P (no reshuffling: partitioner using "SAME" method) P----(bow tie)--->P (reshuffling: partitioner using another method) Collectors = inverse partitioners recollect rows from partitions into a single input stream to a sequential stage They are responsible for some surprising behavior: The default (Auto) is "eager" to output rows and typically causes nondeterminism: row order may vary from run to run with identical input.
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Exercise
~
Complete exercises 1-1 and 1-2, and 1-3
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Module 2
DSPX Sequential Access
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Module Objectives ~
You will learn to: – Import sequential files into the PX Framework – Utilize parallel processing techniques to increase sequential file access – Understand usage of the Sequential, DataSet, FileSet, and LookupFileSet stages – Manage partitioned data stored by the Framework
In this module students will concentrate their efforts on sequential file access in parallel extender jobs.
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Types of Sequential Data Stages ~
Sequential – Fixed or variable length
~
File Set
~
Lookup File Set
~
Data Set
Several stages handle sequential data. Each stage as both advantages and differences from the other stages that handle sequential data. Sequential data can come in a variety of types -- including both fixed length and variable length.
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Sequential Stage Introduction ~ ~
~
~
The PX Framework processes only datasets For files other than datasets, such as flat files, Parallel Extender must perform import and export operations – this is performed by import and export OSH operators (generated by Sequential or FileSet stages) During import or export DataStage performs format translations – into, or out of, the PX internal format Data is described to the Framework in a schema
The DataStage sequential stage writes OSH – specifically the import and export Orchestrate operators. Q: Why import data into an Orchestrate data set? A: Partitioning works only with data sets. You must use data sets to distribute data to the multiple processing nodes of a parallel system. Every Orchestrate program has to perform some type of import operation, from: a flat file, COBOL data, an RDBMS, or a SAS data set. This section describes how to get your data into Orchestrate. Also talk about getting your data back out. Some people will be happy to leave data in Orchestrate data sets, while others require their results in a different format.
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How the Sequential Stage Works ~
Generates Import/Export operators
~
Types of transport – Performs direct C++ file I/O streams – Source programs which feed stdout (gunzip) send stdout into PX via sequential pipe
Behind each parallel stage is one or more Orchestrate operators. Import and Export are both operators that deal with sequential data.
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Using the Sequential File Stage Both import and export of general files (text, binary) are performed by the SequentialFile Stage.
Importing/Exporting Data
PX internal format
– Data import:
– Data export
PX internal format
When data is imported the imported operator translates that data into the parallel extender internal format. The export operator performs the reverse action.
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Working With Flat Files ~
Sequential File Stage – Normally will execute in sequential mode – Can execute in parallel if reading multiple files (file pattern option) – Can use multiple readers within a node on fixed width file – DSPX needs to know y y
How file is divided into rows How row is divided into columns
Both export and import operators are generated by the sequential stage -- which one you get depends on whether the sequential stage is used as source or target.
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Processes Needed to Import Data ~
Recordization – Divides input stream into records – Set on the format tab
~
Columnization – Divides the record into columns – Default set on the format tab but can be overridden on the columns tab – Can be “incomplete” if using a schema or not even specified in the stage if using RCP
These two processes must want together to correctly interpret data -- that is, to break a data string down into records and columns.
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File Format Example
Record delimiter Field 1
,
Field 1
,
Field 1
, Last field
nl
Final Delimiter = end Field Delimiter
Field 1
,
Field 1
,
Field 1
, Last field
, nl
Final Delimiter = comma
Fields all columns are defined my delimiters. Similarly, records are defined by terminating characters.
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Sequential File Stage ~
To set the properties, use stage editor – Page (general, input/output) – Tabs (format, columns)
~
Sequential stage link rules – One input link – One output links (except for reject link definition) – One reject link y
Will reject any records not matching meta data in the column definitions
The DataStage GUI allows you to determine properties that will be used to read and write sequential files.
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Job Design Using Sequential Stages
Stage categories
Source stage Multiple output links - however, note that one of the links is represented by a broken line. This is a reject link, not to be confused with a stream link or a reference link. Target One input link
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General Tab – Sequential Source
Multiple output links
Show records
If multiple links are present you'll need to down-click to see each.
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Properties – Multiple Files
Click to add more files having the same meta data.
If specified individually, you can make a list of files that are unrelated in name. If you select “read method” and choose file pattern, you effectively select an undetermined number of files.
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Properties - Multiple Readers
Multiple readers option allows you to set number of readers
To use multiple readers on a sequential file, must be fixed-length records.
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Format Tab
File into records
Record into columns
DXPX needs to know: How a file is divided into rows How a row is divided into columns Column properties set on this tab are defaults for each column; they can be overridden at the column level (from columns tab).
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Read Methods
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Reject Link ~
Reject mode = output
~
Source – All records not matching the meta data (the column definitions)
~
Target – All records that are rejected for any reason
~
Meta data – one column, datatype = raw
The sequential stage can have a single reject link. This is typically used when you are writing to a file and provides a location where records that have failed to be written to a file for some reason can be sent. When you are reading files, you can use a reject link as a destination for rows that do not match the expected column definitions.
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File Set Stage ~
Can read or write file sets
~
Files suffixed by .fs
~
File set consists of: 1. Descriptor file – contains location of raw data files + meta data 2. Individual raw data files
~
Can be processed in parallel
Number of raw data files depends on: the configuration file – more on configuration files later.
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File Set Stage Example
Descriptor file
The descriptor file shows both a record is metadata and the file's location. The location is determined by the configuration file.
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File Set Usage ~
Why use a file set? – 2G limit on some file systems – Need to distribute data among nodes to prevent overruns – If used in parallel, runs faster that sequential file
File sets, lower yielding faster access and simple text files, are not in the parallel extender internal format.
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Lookup File Set Stage ~
Can create file sets
~
Usually used in conjunction with Lookup stages
The lookup file set is similar to the file set but also contains information about the key columns. These keys will be used later in lookups.
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Lookup File Set > Properties
Key column specified
Key column dropped in descriptor file
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Data Set ~
Operating system (Framework) file
~
Suffixed by .ds
~
Referred to by a control file
~
Managed by Data Set Management utility from GUI (Manager, Designer, Director)
~
Represents persistent data
~
Key to good performance in set of linked jobs
Data sets represent persistent data maintained in the internal format.
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Persistent Datasets
~
Accessed from/to disk with DataSet Stage.
~
Two parts: – Descriptor file: y
– Data file(s) y y
input.ds
contains metadata, data location, but NOT the data itself
record ( partno: int32; description: string; )
contain the data multiple Unix files (one per node), accessible in parallel
node1:/local/disk1/… node2:/local/disk2/…
Accessed from/to disk with DataSet Stage.
Two parts:
Descriptor file User-specified name Contains table definition ("unformatted core" schema) Here is the icon of the DataSet Stage, used to access persistent datsets Descriptor file, e.g., "input.ds" contains: •Paths of data files •Metadata: unformatted table definition, no formats (unformatted schema) •Config file used to store the data Data file(s) Contain the data itself System-generated long file names, to avoid naming conflicts.
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Quiz!
• True or False? Everything that has been data-partitioned must be collected in same job
For example, if we have four nodes corresponding to four regions, we'll have four reports. No need to recollect if one does not need inter-region correlations.
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Data Set Stage
Is the data partitioned?
In both cases the answer is Yes.
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Engine Data Translation ~
Occurs on import – From sequential files or file sets – From RDBMS
~
Occurs on export – From datasets to file sets or sequential files – From datasets to RDBMS
~
Engine most efficient when processing internally formatted records (I.e. data contained in datasets)
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Managing DataSets ~
~
GUI (Manager, Designer, Director) – tools > data set management Alternative methods – Orchadmin Unix command line utility y List records y Remove data sets (will remove all components) y
– Dsrecords y
Lists number of records in a dataset
Both dsrecords and orchadmin are Unix command-line utilities. The DataStage Designer GUI provides a mechanism to view and manage data sets.
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Data Set Management
Display data
Schema
The screen is available (data sets management) from manager, designer, and director.
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Data Set Management From Unix ~
Alternative method of managing file sets and data sets – Dsrecords y
Gives record count – Unix command-line utility – $ dsrecords ds_name I.e.. $ dsrecords myDS.ds 156999 records
– Orchadmin y
Manages PX persistent data sets – Unix command-line utility I.e. $ orchadmin rm myDataSet.ds
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Exercise ~
Complete exercises 2-1, 2-2, 2-3, and 2-4.
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Blank
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Module 3
Standards and Techniques
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Objectives ~
~
Establish standard techniques for DSPX development Will cover: – – – – – – –
Job documentation Naming conventions for jobs, links, and stages Iterative job design Useful stages for job development Using configuration files for development Using environmental variables Job parameters
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Job Presentation
Document using the annotation stage
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Job Properties Documentation Organize jobs into categories
Description shows in DS Manager and MetaStage
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Naming conventions ~
Stages named after the – Data they access – Function they perform – DO NOT leave defaulted stage names like Sequential_File_0
~
Links named for the data they carry – DO NOT leave defaulted link names like DSLink3
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Stage and Link Names
Stages and links renamed to data they handle
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Create Reusable Job Components ~
Use parallel extender shared containers when feasible
Container
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Use Iterative Job Design ~ ~
~
Use copy or peek stage as stub Test job in phases – small first, then increasing in complexity Use Peek stage to examine records
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Copy or Peek Stage Stub
Copy stage
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Transformer Stage Techniques ~
Suggestions – Always include reject link. – Always test for null value before using a column in a function. – Try to use RCP and only map columns that have a derivation other than a copy. More on RCP later. – Be aware of Column and Stage variable Data Types. y
Often user does not pay attention to Stage Variable type.
– Avoid type conversions. y
Try to maintain the data type as imported.
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The Copy Stage With 1 link in, 1 link out:
the Copy Stage is the ultimate "no-op" (place-holder): – Partitioners – Sort / Remove Duplicates – Rename, Drop column
… can be inserted on: – –
input link (Partitioning): Partitioners, Sort, Remove Duplicates) output link (Mapping page): Rename, Drop.
Sometimes replace the transformer: R
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Developing Jobs 1.
Keep it simple •
2.
Start small and Build to final Solution • • •
3.
Use view data, copy, and peek. Start from source and work out. Develop with a 1 node configuration file.
Solve the business problem before the performance problem. •
4.
Jobs with many stages are hard to debug and maintain.
Don’t worry too much about partitioning until the sequential flow works as expected.
If you have to write to Disk use a Persistent Data set.
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Final Result
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Good Things to Have in each Job ~ ~
Use job parameters Some helpful environmental variables to add to job parameters – $APT_DUMP_SCORE y
Report OSH to message log
– $APT_CONFIG_FILE y
Establishes runtime parameters to PX engine; I.e. Degree of parallelization
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Setting Job Parameters
Click to add environment variables
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DUMP SCORE Output Setting APT_DUMP_SCORE yields: Double-click
Partitoner And Collector
Mapping Node--> partition
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Use Multiple Configuration Files ~
Make a set for 1X, 2X,….
~
Use different ones for test versus production
~
Include as a parameter in each job
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Exercise ~
Complete exercise 3-1
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Module 4
DBMS Access
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Objectives ~
Understand how DSPX reads and writes records to an RDBMS
~
Understand how to handle nulls on DBMS lookup
~
Utilize this knowledge to: – Read and write database tables – Use database tables to lookup data – Use null handling options to clean data
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Parallel Database Connectivity Traditional Client-Server
Client
Parallel Extender
Client
Sort
Client Client Client
Load
Client
Parallel RDBMS
Parallel RDBMS
Only RDBMS is running in parallel
Each application has only one connection Suitable only for small data volumes
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Parallel server runs APPLICATIONS Application has parallel connections to RDBMS Suitable for large data volumes Higher levels of integration possible
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RDBMS Access
Supported Databases
Parallel Extender provides high performance / scalable interfaces for: ~
DB2
~
Informix
~
Oracle
~
Teradata
Users must be granted specific privileges, depending on RDBMS.
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RDBMS Access
Supported Databases ~
Automatically convert RDBMS table layouts to/from Parallel Extender Table Definitions
~
RDBMS nulls converted to/from nullable field values
~
Support for standard SQL syntax for specifying: – field list for SELECT statement – filter for WHERE clause – open command, close command
~ ~
Can write an explicit SQL query to access RDBMS PX supplies additional information in the SQL query
RDBMS access is relatively easy because Orchestrate extracts the schema definition for the imported data set. Litle or no work is required from the user.
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RDBMS Stages ~
DB2/UDB Enterprise
~
Teradata Enterprise
~
Informix Enterprise
~
ODBC
~
Oracle Enterprise
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RDBMS Usage ~
As a source – Extract data from table (stream link) – Extract as table, generated SQL, or user-defined SQL – User-defined can perform joins, access views
– Lookup (reference link) – Normal lookup is memory-based (all table data read into memory) – Can perform one lookup at a time in DBMS (sparse option) – Continue/drop/fail options ~
As a target – Inserts – Upserts (Inserts and updates) – Loader
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RDBMS Source – Stream Link
Stream link
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DBMS Source - User-defined SQL
Columns in SQL statement must match the meta data in columns tab
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Exercise ~
User-defined SQL – Exercise 4-1
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DBMS Source – Reference Link
Reject link
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Lookup Reject Link
“Output” option automatically creates the reject link
All columns from the input link will be placed on the rejects link. Therefore, no column tab is available for the rejects link.
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Null Handling ~
~
Must handle null condition if lookup record is not found and “continue” option is chosen Can be done in a transformer stage
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Lookup Stage Mapping
Link name
The mapping tab will show all columns from the input link and the reference link (less the column used for key lookup).
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Lookup Stage Properties Reference link
Must have same column name in input and reference links. You will get the results of the lookup in the output column.
If the lookup results in a non-match and the action was set to continue, the output column will be null.
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DBMS as a Target
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DBMS As Target ~
Write Methods – – – –
~
Delete Load Upsert Write (DB2)
Write mode for load method – – – –
Truncate Create Replace Append
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Target Properties Generated code can be copied
Upsert mode determines options
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Checking for Nulls ~
~
Use Transformer stage to test for fields with null values (Use IsNull functions) In Transformer, can reject or load default value
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Exercise ~
Complete exercise 4-2
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Module 5
Platform Architecture
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Objectives ~
~
Understand how parallel extender Framework processes data You will be able to: – Read and understand OSH – Perform troubleshooting
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Concepts ~ ~
~ ~
The PX Platform OSH (generated by DataStage Parallel Canvas, and run by DataStage Director) Conductor,Section leaders,players. Configuration files (only one active at a time, describes H/W)
~
Schemas/tables
~
Schema propagation/RCP
~
Buildop,Wrapper
~
Datasets (data in Framework's internal representation)
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DS-PX Program Elements PX Stages Involve A Series Of Processing Steps Output Data Set schema: prov_num:int16; member_num:int8; custid:int32;
Input Data Set schema: prov_num:int16; member_num:int8; custid:int32;
• Piece of Application Logic Running Against Individual Records • Parallel or Sequential
Output Interface
Business Logic
Partitioner
Input Interface
PX Stage
• Three Sources – Ascential Supplied – Commercial tools/applications – Custom/Existing programs
Each Orchestrate component has input and output interface schemas, a partitioner and business logic. Interface schemas define the names and data types of the required fields of the component’s input and output Data Sets. The component’s input interface schema requires that an input Data Set have the named fields and data types exactly compatible with those specified by the interface schema for the input Data Set to be accepted by the component. A component ignores any extra fields in a Data Set, which allows the component to be used with any data set that has at least the input interface schema of the component. This property makes it possible to add and delete fields from a relational database table or from the Orchestrate Data Set without having to rewrite code inside the component. In the example shown here, Component has an interface schema that requires three fields with named fields and data types as shown in the example. In this example, the output schema for the component is the same as the input schema. This does not always have to be the case. The partitioner is key to Orchestrate’s ability to deliver parallelism and unlimited scalability. We’ll discuss exactly how the partitioners work in a few slides, but here it’s important to point out that partitioners are an integral part of Orchestrate components.
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DS-PX Program Elements Stage Execution Dual Parallelism Eliminates Bottlenecks! • PX Delivers Parallelism in Two Ways – Pipeline – Partition
• Block Buffering Between Components
Producer
– Eliminates Need for Program Load Balancing – Maintains Orderly Data Flow
Pipeline Consumer
Partition
There is one more point to be made about the Orchestrate execution model. Orchestrate achieves parallelism in two ways. We have already talked about partitioning the records and running multiple instances of each component to speed up program execution. In addition to this partition parallelism, Orchestrate is also executing pipeline parallelism. As shown in the picture on the left, as the Orchestrate program is executing, a producer component is feeding records to a consumer component without first writing the records to disk. Orchestrate is pipelining the records forward in the flow as they are being processed by each component. This means that the consumer component is processing records fed to it by the producer component before the producer has finished processing all of the records. Orchestrate provides block buffering between components so that producers cannot produce records faster than consumers can consume those records. This pipelining of records eliminates the need to store intermediate results to disk, which can provide significant performance advantages, particularly when operating against large volumes of data.
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Stages Control Partition Parallelism ~
Execution Mode (sequential/parallel) is controlled by Stage
– default = parallel for most Ascentialsupplied Stages – User can override default mode – Parallel Stage inserts the default partitioner (Auto) on its input links – Sequential Stage inserts the default collector (Auto) on its input links – user can override default y y
execution mode (parallel/sequential) of Stage (Advanced tab) choice of partitioner/collector (Input – Partitioning Tab)
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How Parallel Is It? ~
Degree of Parallelism is determined by the configuration file
– Total number of logical nodes in default pool, or a subset if using "constraints". y
Constraints are assigned to specific pools as defined in configuration file and can be referenced in the stage
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OSH ~
DataStage PX GUI generates OSH scripts – Ability to view OSH turned on in Administrator – OSH can be viewed in Designer using job properties
~
The Framework executes OSH
~
What is OSH? – Orchestrate shell – Has a UNIX command-line interface
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OSH Script ~
An osh script is a quoted string which specifies: – The operators and connections of a single Orchestrate step – In its simplest form, it is: osh “op < in.ds > out.ds”
~
Where: – op is an Orchestrate operator – in.ds is the input data set – out.ds is the output data set
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OSH Operators
~
Operator is an instance of a C++ class inheriting from APT_Operator
~
Developers can create new operators
~
Examples of existing operators: – Import – Export – RemoveDups
Operators are the basic functional units of an Orchestrate application. Operators read records from input data sets, perform actions on the input records, and write results to output data sets. An operator may perform an action as simple as copying records from an input data set to an output data set without modification. Alternatively, an operator may modify a record by adding, removing, or modifying fields during execution. Operators are the basic functional units of an Orchestrate application. Operators read records from input data sets, perform actions on the input records, and write results to output data sets. An operator may perform an action as simple as copying records from an input data set to an output data set without modification. Alternatively, an operator may modify a record by adding, removing, or modifying fields during execution. Operators are the basic functional units of an Orchestrate application. Operators read records from input data sets, perform actions on the input records, and write results to output data sets. An operator may perform an action as simple as copying records from an input data set to an output data set without modification. Alternatively, an operator may modify a record by adding, removing, or modifying fields during execution.
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Enable Visible OSH in Administrator
Will be enabled for all projects
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View OSH in Designer
Operator
Schema
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OSH Practice ~
Exercise 5-1
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Orchestrate May Add Operators to Your Command Let’s revisit the following OSH command:
$ osh "
echo 'Hello world!' [par]
> outfile "
1)“wrapper” (turning a Unix command into an DS/PX Operator)
4)export 2)partitioner
3)collector
(from dataset to Unix flat file)
The Framework silently inserts operators (steps 1,2,3,4)
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Elements of a Framework Program Steps, with internal and terminal datasets and links, described by schemas
• Step:
unit of OSH program
– one OSH command = one step – at end of step: synchronization, storage to disk
• Datasets: set of rows processed by Framework – Orchestrate data sets: – persistent (terminal) *.ds, and – virtual (internal) *.v. – Also: flat “file sets” *.fs
• Schema: data description (metadata) for datasets and links.
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Orchestrate Datasets • Consist of Partitioned Data and Schema • Can be Persistent (*.ds) or Virtual (*.v, Link) • Overcome 2 GB File Limit What you program: GUI
=
What gets generated: OSH
What gets processed:
data files of x.ds
$ osh “operator_A > x.ds“
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Node 1
Node 2
Node 3
Node 4
Operator A
Operator A
Operator A
Operator A
. . .
Multiple files per partition Each file up to 2GBytes (or larger)
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Computing Architectures: Definition Dedicated Disk
Shared Disk
Disk
Disk
CPU
Memory
Uniprocessor
CPU CPU CPU CPU
Shared Memory
SMP System
Shared Nothing
Disk
Disk
Disk
Disk
CPU
CPU
CPU
CPU
Memory
Memory
Memory
Memory
Clusters and MPP Systems
(Symmetric Multiprocessor) • PC • Workstation • Single processor server
• IBM, Sun, HP, Compaq • 2 to 64 processors • Majority of installations
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• 2 to hundreds of processors • MPP: IBM and NCR Teradata • each node is a uniprocessor or SMP
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Job Execution: Orchestrate Conductor Node C
– – – –
Processing Node SL
P
P
Step Composer Creates Section Leader processes (one/node) Consolidates massages, outputs them Manages orderly shutdown.
• Section Leader P
– Forks Players processes (one/Stage) – Manages up/down communication.
• Players
Processing Node SL
P
• Conductor - initial DS/PX process
P
P
• Communication:
– The actual processes associated with Stages – Combined players: one process only – Send stderr to SL – Establish connections to other players for data flow – Clean up upon completion.
- SMP: Shared Memory - MPP: TCP
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Working with Configuration Files ~
You can easily switch between config files: y
'1-node' file
testing y y ~
- for sequential execution, lighter reports—handy for
'MedN-nodes' file - aims at a mix of pipeline and data-partitioned parallelism 'BigN-nodes' file - aims at full data-partitioned parallelism
Only one file is active while a step is running y
The Framework queries (first) the environment variable:
$APT_CONFIG_FILE
~
# nodes declared in the config file needs not match # CPUs y
Same configuration file can be used in development and target machines
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Scheduling Nodes, Processes, and CPUs ~
DS/PX does not: – know how many CPUs are available – schedule
~
Who knows what? Nodes
Ops
User
Y
N
Orchestrate
Y
Y
O/S ~
Nodes = # logical nodes declared in config. file Ops = # ops. (approx. # blue boxes in V.O.) Processes = # Unix processes CPUs = # available CPUs
Processes
CPUs
Nodes * Ops
N
"
Y
Who does what? – DS/PX creates (Nodes*Ops) Unix processes – The O/S schedules these processes on the CPUs
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Configuring DSPX – Node Pools {
3
4
1
2
node "n1" { fastname "s1" pool "" "n1" "s1" "app2" "sort" resource disk "/orch/n1/d1" {} resource disk "/orch/n1/d2" {} resource scratchdisk "/temp" {"sort"} } node "n2" { fastname "s2" pool "" "n2" "s2" "app1" resource disk "/orch/n2/d1" {} resource disk "/orch/n2/d2" {} resource scratchdisk "/temp" {} } node "n3" { fastname "s3" pool "" "n3" "s3" "app1" resource disk "/orch/n3/d1" {} resource scratchdisk "/temp" {} } node "n4" { fastname "s4" pool "" "n4" "s4" "app1" resource disk "/orch/n4/d1" {} resource scratchdisk "/temp" {} }
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Configuring DSPX – Disk Pools {
3
4
1
2
node "n1" { fastname "s1" pool "" "n1" "s1" "app2" "sort" resource disk "/orch/n1/d1" {} resource disk "/orch/n1/d2" {"bigdata"} resource scratchdisk "/temp" {"sort"} } node "n2" { fastname "s2" pool "" "n2" "s2" "app1" resource disk "/orch/n2/d1" {} resource disk "/orch/n2/d2" {"bigdata"} resource scratchdisk "/temp" {} } node "n3" { fastname "s3" pool "" "n3" "s3" "app1" resource disk "/orch/n3/d1" {} resource scratchdisk "/temp" {} } node "n4" { fastname "s4" pool "" "n4" "s4" "app1" resource disk "/orch/n4/d1" {} resource scratchdisk "/temp" {} }
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Re-Partitioning Parallel to parallel flow may incur reshuffling: Records may jump between nodes node 1
node 2
partitioner
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Re-Partitioning X-ray Partitioner with parallel import When a partitioner receives: node 1
node 2
N
• sequential input (1 partition), it creates N partitions
• parallel input (N partitions), it outputs N partitions*, may result in re-partitioning
* Assuming
N
no “constraints”
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Automatic Re-Partitioning partition 1
If Stage 2 runs in parallel, DS/PX silently inserts a partitioner upstream of it. If Stage 1 also runs in parallel, re-partitioning occurs.
partition 2
Stage 1
partitioner
Stage 2
In most cases, automatic repartitioning is benign (no reshuffling), preserving the same partitioning as upstream.
Re-partitioning can be forced to be benign, using either:
same preserve partitioning
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Partitioning Methods ~
Auto
~
Hash
~
Entire
~
Range
~
Range Map
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Collectors • Collectors combine partitions of a dataset into a single input stream to a sequential Stage ...
data partitions
collector
–Collectors do NOT synchronize data
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sequential Stage
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Partitioning and Repartitioning Are Visible On Job Design
Partitioners and collectors have no stage nor icons of their own. They live live on input links of stages running in parallel (resp. sequentially). Link markings indicate their presence. S----------------->S (no Marking) S----(fan out)--->P (partitioner) P----(fan in) ---->S (collector) P----(box)------->P (no reshuffling: partitioner using "SAME" method) P----(bow tie)--->P (reshuffling: partitioner using another method) Collectors = inverse partitioners recollect rows from partitions into a single input stream to a sequential stage
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Partitioning and Collecting Icons
Partitioner
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Collector
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Setting a Node Constraint in the GUI
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Reading Messages in Director ~
Set APT_DUMP_SCORE to true
~
Can be specified as job parameter
~
Messages sent to Director log
~
If set, parallel job will produce a report showing the operators, processes, and datasets in the running job
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Messages With APT_DUMP_SCORE = True
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Exercise ~
Complete exercise 5-2
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Blank
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Module 6
Transforming Data
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Module Objectives ~
~
Understand ways DataStage allows you to transform data Use this understanding to: – Create column derivations using user-defined code or system functions – Filter records based on business criteria – Control data flow based on data conditions
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Transformed Data ~
Transformed data is: – Outgoing column is a derivation that may, or may not, include incoming fields or parts of incoming fields – May be comprised of system variables
~
Frequently uses functions performed on something (ie. incoming columns) – Divided into categories – I.e. y y y y y
Date and time Mathematical Logical Null handling More
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Stages Review ~
Stages that can transform data – Transformer y y
Parallel Basic (from Parallel palette)
– Aggregator (discussed in later module)
~
Sample stages that do not transform data – – – –
Sequential FileSet DataSet DBMS
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Transformer Stage Functions ~
Control data flow
~
Create derivations
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Flow Control ~
~ ~
Separate record flow down links based on data condition – specified in Transformer stage constraints Transformer stage can filter records Other stages can filter records but do not exhibit advanced flow control – Sequential – Lookup – Filter
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Rejecting Data ~
Reject option on sequential stage – Data does not agree with meta data – Output consists of one column with binary data type
~
Reject links (from Lookup stage) result from the drop option of the property “If Not Found” – Lookup “failed” – All columns on reject link (no column mapping option)
~
Reject constraints are controlled from the constraint editor of the transformer – Can control column mapping – Use the “Other/Log” checkbox
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Rejecting Data Example
Contstraint – Other/log option Property Reject Mode = Output
“If Not Found” property
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Transformer Stage Properties
Link naming conventions are important because they identify appropriate links in the stage properties screen shown above. Four quadrants: Incoming data link (one only) Outgoing links (can have multiple) Meta data for all incoming links Meta data for all outgoing links – may have multiple tabs if there are multiple outgoing links Note the constraints bar – if you double-click on any you will get screen for defining constraints for all outgoing links.
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Transformer Stage Variables ~
First of transformer stage entities to execute
~
Execute in order from top to bottom – Can write a program by using one stage variable to point to the results of a previous stage variable
~
Multi-purpose – – – –
Counters Hold values for previous rows to make comparison Hold derivations to be used in multiple field dervations Can be used to control execution of constraints
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Stage Variables
Show/Hide button
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Transforming Data ~
Derivations – Using expressions – Using functions y
~
Date/time
Transformer Stage Issues – Sometimes require sorting before the transformer stage – I.e. using stage variable as accumulator and need to break on change of column value
~
Checking for nulls
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Checking for Nulls ~
~
Nulls can get introduced into the dataflow because of failed lookups and the way in which you chose to handle this condition Can be handled in constraints, derivations, stage variables, or a combination of these
If you perform a lookup from a lookup stage and choose the continue option for a failed lookup, you have the possibility of nulls entering your data flow.
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Nullability Can set the value of null; i.e.. If value of column is null put “NULL” in the outgoing column
Source Field
Destination Field
not_nullable
not_nullable
not_nullable
nullable
Source value propagates; destination value is never null.
nullable
not_nullable
nullable
nullable
WARNING messages in log. If source value is null,a fatal error occurs. Must handle in transformer. Source value or null propagates.
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Result Source value propagates to destination.
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Transformer Stage - Handling Rejects 1.
Constraint Rejects – All expressions are false and reject row is checked
2.
Expression Error Rejects –
Improperly Handled Null
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Transformer: Execution Order
• Derivations in stage variables are executed first • Constraints are executed before derivations • Column derivations in earlier links are executed before later links • Derivations in higher columns are executed before lower columns
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Two Transformers for the Parallel Palette ~
All > Processing >
~
Parallel > Processing
~
Transformer
~
Basic Transformer
~
~
~
Is the non-Universe transformer
~
Has a specific set of functions
~
No DS routines available
~
Makes server style transforms available on the parallel palette Can use DS routines No need for shared container to get Universe functionality on the parallel palette
•Program in Basic for both transformers
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Transformer Functions From Derivation Editor ~
Data & Time
~
Logical
~
Mathematical
~
Null Handling
~
Number
~
Raw
~
String
~
Type Conversion
~
Utility
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Timestamps and Dates ~
Data & Time
~
Also some in Type Conversion
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Exercise ~
Complete exercises 6-1, 6-2, and 6-3
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Module 7
Sorting Data
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Objectives ~ ~
Understand DataStage PX sorting options Use this understanding to create sorted list of data to enable functionality within a transformer stage
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Sorting Data ~
Important because – Transformer may be using stage variables for accumulators or control breaks and order is important – Other stages may run faster – I.e Aggregator – Facilitates the RemoveDups stage, order is important – Job has partitioning requirements
~
Can be performed – Option within stages (use input > partitioning tab and set partitioning to anything other than auto) – As a separate stage (more complex sorts)
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Sorting Alternatives
• Alternative representation of same flow:
One of the nation's largest direct marketing outfits has been using this simple program in DS-PX (and its previous instantiations) for years. Householding yields enormous savings by avoiding mailing the same material (in particular expensive catalogs) to the same household.
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Sort Option on Stage Link
Stable will not rearrange records that are already in a properly sorted data set. If set to false no prior ordering of records is guaranteed to be preserved by the sorting operation.
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Sort Stage
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Sort Utility ~
DataStage – the default
~
SyncSort
~
UNIX
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Sort Stage - Outputs ~
Specifies how the output is derived
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Sort Specification Options ~
Input Link Property
– Limited functionality – Max memory/partition is 20 MB, then spills to scratch ~
Sort Stage
– Tunable to use more memory before spilling to scratch. ~
Note: Spread I/O by adding more scratch file systems to each node of the APT_CONFIG_FILE
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Removing Duplicates ~
Can be done by Sort – Use unique option
OR
~
Remove Duplicates stage – Has more sophisticated ways to remove duplicates
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Exercise ~
Complete exercise 7-1
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Blank
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Module 8
Combining Data
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Objectives ~
~
Understand how DataStage can combine data using the Join, Lookup, Merge, and Aggregator stages Use this understanding to create jobs that will – Combine data from separate input streams – Aggregate data to form summary totals
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Combining Data ~
There are two ways to combine data: – Horizontally: Several input links; one output link (+ optional rejects) made of columns from different input links. E.g., y y y
Joins Lookup Merge
– Vertically: One input link, output with column combining values from all input rows. E.g., y
Aggregator
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Recall the Join, Lookup & Merge Stages ~
~
These "three Stages" combine two or more input links according to values of user-designated "key" column(s). They differ mainly in: – Memory usage – Treatment of rows with unmatched key values – Input requirements (sorted, de-duplicated)
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Joins - Lookup - Merge: Not all Links are Created Equal! • Parallel Extender distinguishes between: - The Primary Input (Framework port 0) - Secondary - in some cases "Reference" (other ports)
• Naming convention: Primary Input: port 0 Secondary Input(s): ports 1,…
Joins
Lookup
Merge
Left Right
Source LU Table(s)
Master Update(s)
Tip: Check "Input Ordering" tab to make sure intended Primary is listed first
The Framework concept of Port # is translated in the GUI by Primary/Reference
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Join Stage Editor
Link Order immaterial for Inner and Full Outer Joins (but VERY important for Left/Right Outer and Lookup and Merge)
One of four variants: – Inner – Left Outer – Right Outer – Full Outer
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Several key columns allowed
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1. The Join Stage Four types: • Inner • Left Outer • Right Outer • Full Outer
~
2 sorted input links, 1 output link – "left" on primary input, "right" on secondary input – Pre-sort make joins "lightweight": few rows need to be in RAM
Follow the RDBMS-style relational model: the operations Join and Load in RDBMS commute. x-products in case of duplicates, matching entries are reusable No fail/reject/drop option for missed matches
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2. The Lookup Stage Combines: – one source link with – one or more duplicate-free table links Source input
0 0
Output
One or more tables (LUTs)
1
no pre-sort necessary allows multiple keys LUTs flexible exception handling for source input rows with no match
2
Lookup
1
Reject
Contrary to Join, Lookup and Merge deal with missing rows. Obviously, a missing row cannot be captured, since it is missing. The closest thing one can capture is the corresponding unmatched row. Lookup can capture in a reject link unmatched rows from the primary input(Source). That is why it has only one reject link (there is only one primary). We'll see the reject option is exactly the opposite with the Merge stage.
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The Lookup Stage
~
Lookup Tables should be small enough to fit into physical memory (otherwise, performance hit due to paging)
– Space time trade-off: presort vs. in RAM table ~
~
On an MPP you should partition the lookup tables using entire partitioning method, or partition them the same way you partition the source link On an SMP, no physical duplication of a Lookup Table occurs
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The Lookup Stage ~
Lookup File Set – Like a persistent data set only it contains metadata about the key. – Useful for staging lookup tables
~
RDBMS LOOKUP – SPARSE y y
Select for each row. Might become a performance bottleneck.
– NORMAL y
Loads to an in memory hash table first.
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3. The Merge Stage ~
Combines – one sorted, duplicate-free master (primary) link with – one or more sorted update (secondary) links. – Pre-sort makes merge "lightweight": few rows need to be in RAM (as with joins, but opposite to lookup).
~
Follows the Master-Update model: – Master row and one or more updates row are merged if they have the same value in user-specified key column(s). – A non-key column occurs in several inputs? The lowest input port number prevails (e.g., master over update; update values are ignored) – Unmatched ("Bad") master rows can be either y y
kept dropped
– Unmatched ("Bad") update rows in input link can be captured in a "reject" link – Matched update rows are consumed.
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The Merge Stage
Master
One or more updates
0 0
1
2
1
2
Merge
Output
Allows composite keys Multiple update links Matched update rows are consumed Unmatched updates can be captured Lightweight Space/time tradeoff: presorts vs. in-RAM table
Rejects
Contrary to Lookup, Merges captures unmatched secondary (update) rows. Since there may be several update links, there may be several reject links.
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Synopsis: Joins, Lookup, & Merge Joins
Lookup
Merge
Model Memory usage
RDBMS-style relational light
Source - in RAM LU Table heavy
Master -Update(s) light
# and names of Inputs Mandatory Input Sort Duplicates in primary input Duplicates in secondary input(s) Options on unmatched primary Options on unmatched secondary On match, secondary entries are
exactly 2: 1 left, 1 right both inputs OK (x-product) OK (x-product) NONE NONE reusable
1 Source, N LU Tables
1 Master, N Update(s)
no OK Warning! [fail] | continue | drop | reject NONE reusable
all inputs Warning! OK only when N = 1 [keep] | drop capture in reject set(s) consumed
1 Nothing (N/A)
1 out, (1 reject) unmatched primary entries
1 out, (N rejects) unmatched secondary entries
# Outputs Captured in reject set(s)
In this table: • ,
= separator between primary and secondary input links (out and reject links)
This table contains everything one needs to know to use the three stages.
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The Aggregator Stage Purpose: Perform data aggregations Specify: ~
Zero or more key columns that define the aggregation units (or groups)
~
Columns to be aggregated
~
Aggregation functions: count (nulls/non-nulls) standard error sum of weights variance
~
sum max/min/range %coeff. of variation un/corrected sum of squares mean standard deviation
The grouping method (hash table or pre-sort) is a performance issue
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Grouping Methods ~
Hash: results for each aggregation group are stored in a hash table, and the table is written out after all input has been processed – doesn’t require sorted data – good when number of unique groups is small. Running tally for each group’s aggregate calculations need to fit easily into memory. Require about 1KB/group of RAM. – Example: average family income by state, requires .05MB of RAM
~
Sort: results for only a single aggregation group are kept in memory; when new group is seen (key value changes), current group written out. – requires input sorted by grouping keys – can handle unlimited numbers of groups – Example: average daily balance by credit card
WARNING! Hash has nothing to do with the Hash Partitioner! It says one hash table per group must be carried in RAM Sort has nothing to do with the Sort Stage. Just says expects sorted input.
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Aggregator Functions ~
Sum
~
Min, max
~
Mean
~
Missing value count
~
Non-missing value count
~
Percent coefficient of variation
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Aggregator Properties
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Aggregation Types
Aggregation types
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Containers ~
Two varieties – Local – Shared
~
Local – Simplifies a large, complex diagram
~
Shared – Creates reusable object that many jobs can include
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Creating a Container ~
Create a job
~
Select (loop) portions to containerize
~
Edit > Construct container > local or shared
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Using a Container ~
Select as though it were a stage
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Exercise ~
Complete exercise 8-1
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Module 9
Configuration Files
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Objectives ~
~
Understand how DataStage PX uses configuration files to determine parallel behavior Use this understanding to – Build a PX configuration file for a computer system – Change node configurations to support adding resources to processes that need them – Create a job that will change resource allocations at the stage level
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Configuration File Concepts ~
~
~
Determine the processing nodes and disk space connected to each node When system changes, need only change the configuration file – no need to recompile jobs When DataStage job runs, platform reads configuration file – Platform automatically scales the application to fit the system
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Processing Nodes Are ~
~ ~
Locations on which the framework runs applications Logical rather than physical construct Do not necessarily correspond to the number of CPUs in your system – Typically one node for two CPUs
~
Can define one processing node for multiple physical nodes or multiple processing nodes for one physical node
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Optimizing Parallelism ~
~
Degree of parallelism determined by number of nodes defined Parallelism should be optimized, not maximized – Increasing parallelism distributes work load but also increases Framework overhead
~
~
Hardware influences degree of parallelism possible System hardware partially determines configuration
The hardware that makes up your system partially determines configuration. For example, applications with large memory requirements, such as sort operations, are best assigned to machines with a lot of memory. Applications that will access an RDBMS must run on its server nodes; operators using other proprietary software, such as SAS or SyncSort, must run on nodes with licenses for that software.
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More Factors to Consider ~
Communication amongst operators – Should be optimized by your configuration – Operators exchanging large amounts of data should be assigned to nodes communicating by shared memory or high-speed link
~
SMP – leave some processors for operating system
~
Desirable to equalize partitioning of data
~
Use an experimental approach – Start with small data sets – Try different parallelism while scaling up data set sizes
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Factors Affecting Optimal Degree of Parallelism ~
CPU intensive applications – Benefit from the greatest possible parallelism
~
Applications that are disk intensive – Number of logical nodes equals the number of disk spindles being accessed
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PX Configuration File ~
Text file containing string data that is passed to the Framework – Sits on server side – Can be displayed and edited
~
~
Name and location found in environmental variable APT_CONFIG_FILE Components – – – –
Node Fast name Pools Resource
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Sample Configuration File { node “Node1" { fastname "BlackHole" pools "" "node1" resource disk "/usr/dsadm/Ascential/DataStage/Datasets" {pools "" } resource scratchdisk "/usr/dsadm/Ascential/DataStage/Scratch" {pools "" } } }
For a single node system node name usually set to value of UNIX command – uname –n Fastname attribute is the name of the node as it is referred to on the fastest network in the system, such as an IBM switch, FDDI, or BYNET. The fast name is the physical node name that operators use to open connections for high-volume data transfers. Typically this is the principal node name as returned by the UNIX command uname –n.
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Node Options •
Node name - name of a processing node used by PX – Typically the network name – Use command uname -n to obtain network name
~
Fastname – – Name of node as referred to by fastest network in the system – Operators use physical node name to open connections – NOTE: for SMP, all CPUs share single connection to network
~
Pools – Names of pools to which this node is assigned – Used to logically group nodes – Can also be used to group resources
~
Resource – Disk – Scratchdisk
Set of node pool reserved names: DB2 Oracle Informix Sas Sort Syncsort
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Node Pools ~
Node “node1" { fastname “server_name” pool "pool_name” }
~
"pool_name" is the name of the node pool. I.e. “extra”
~
Node pools group processing nodes based on usage. – Example: memory capacity and high-speed I/O.
~
One node can be assigned to multiple pools.
Default node pool (” ") is made up of each node defined in the config file, unless it’s qualified as belonging to a different pool and it is not designated as belonging to the default pool (see following example).
~
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Resource Disk and Scratchdisk node “node_0" { fastname “server_name” pool "pool_name” resource disk “path” {pool “pool_1”} resource scratchdisk “path” {pool “pool_1”} ... }
~ ~
Resource type can be disk(s) or scratchdisk(s) "pool_1" is the disk or scratchdisk pool, allowing you to group disks and/or scratchdisks.
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Disk Pools ~
Disk pools allocate storage
~
Pooling applies to both disk types
pool "bigdata"
~
By default, PX uses the default pool, specified by “”
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Sorting Requirements
Resource pools can also be specified for sorting: ~
~
The Sort stage looks first for scratch disk resources in a “sort” pool, and then in the default disk pool Sort uses as many scratch disks as defined in the first pool it finds
Recommendations: Each logical node defined in the configuration file that will run sorting operations should have its own sort disk. Each logical node's sorting disk should be a distinct disk drive or a striped disk, if it is shared among nodes. In large sorting operations, each node that performs sorting should have multiple disks, where a sort disk is a scratch disk available for sorting that resides in either the sort or default disk pool.
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Configuration File: Example { node "n1" { fastname “s1" pool "" "n1" "s1" "sort" resource disk "/data/n1/d1" {} resource disk "/data/n1/d2" {} resource scratchdisk "/scratch" } node "n2" { fastname "s2" pool "" "n2" "s2" "app1" resource disk "/data/n2/d1" {} resource scratchdisk "/scratch" } node "n3" { fastname "s3" pool "" "n3" "s3" "app1" resource disk "/data/n3/d1" {} resource scratchdisk "/scratch" } node "n4" { fastname "s4" pool "" "n4" "s4" "app1" resource disk "/data/n4/d1" {} resource scratchdisk "/scratch" } ...
6
4
5
2
3 1
{"sort"}
{}
{}
{}
}
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Resource Types ~
Disk
~
Scratchdisk
~
DB2
~
Oracle
~
Saswork
~
Sortwork
~
Can exist in a pool – Groups resources together
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Using Different Configurations
Lookup stage where DBMS is using a sparse lookup type
In this instance, since a sparse lookup is viewed as the bottleneck, the stage has been set to execute on multiple nodes.
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Building a Configuration File ~
Scoping the hardware: – Is the hardware configuration SMP, Cluster, or MPP? – Define each node structure (an SMP would be single node): y y y y y
Number of CPUs CPU speed Available memory Available page/swap space Connectivity (network/back-panel speed)
– Is the machine dedicated to PX? If not, what other applications are running on it? – Get a breakdown of the resource usage (vmstat, mpstat, iostat) – Are there other configuration restrictions? E.g. DB only runs on certain nodes and ETL cannot run on them?
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To Create Hardware Specifications Complete one per disk subsystem
Complete one per node _____________ TCP Addr
_____________
Hardware Type ________________
Switch Addr
Shared between nodes
Hardware Type ___________ Node Name _____________ # CPUs
_____________
# channels/ controllers____ Throughput ____
CPU Speed _____________ Memory
_____________
Page Space _____________
Y or N
Storage Type
________________
Storage Size
________________
Read Cache size _______________ Write Cache size _______________ Read hit ratio
_______________
Write hit ratio
_______________
I/O rate (R/W)
_______________
Swap Space _____________
In addition, record all coexistence usage (other applications or subsystems sharing this disk subsystem).
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“Ballpark” Tuning As a rule of thumb, generally expect the application to be I/O constrained. First try to parallelize (spread out) the I/O as much as possible. To validate that you’ve achieved this: 1.
2. 3.
4. 5.
Calculate the theoretic I/O bandwidth available (don’t forget to reduce this by the amount other applications on this machine or others are impacting the I/O subsystem). See DS Performance and tuning for calculation methods. Determine the I/O bandwidth being achieved by the DS application (rows/sec * bytes/row). If the I/O rate isn’t approximately equal to the theoretical, there is probably a bottleneck elsewhere (CPU, Memory, etc). Attempt to tune to the I/O bandwidth. Pay particular attention to I/O intensive competing workloads such as database logging, paging/swapping, etc.
Some useful commands: iostat (I/O activity) vmstat (system activity) mpstat (processor utilization sar (resource usage)
When running out of capacity: Tuning: sequentially removing bottlenecks. Look at throughout on the I/O subsystem: Take amount of data moved times number of I/O – gives bandwith Look at sar, iostat, or mpstat.
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Exercise ~
Complete exercise 9-1 and 9-2
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Blank
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Module 10
Extending DataStage PX
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Objectives ~
~
Understand the methods by which you can add functionality to PX Use this understanding to: – Build a DataStage PX stage that handles special processing needs not supplied with the vanilla stages – Build a DataStage PX job that uses the new stage
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PX Extensibility Overview Sometimes it will be to your advantage to leverage PX’s extensibility. This extensibility includes: ~
Wrappers
~
Buildops
~
Custom Stages
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When To Leverage PX Extensibility Types of situations: Complex business logic, not easily accomplished using standard PX stages Reuse of existing C, C++, Java, COBOL, etc…
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Wrappers vs. Buildop vs. Custom ~
~
~
Wrappers are good if you cannot or do not want to modify the application and performance is not critical. Buildops: good if you need custom coding but do not need dynamic (runtime-based) input and output interfaces. Custom (C++ coding using framework API): good if you need custom coding and need dynamic input and output interfaces.
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Building “Wrapped” Stages You can “wrapper” a legacy executable: y Binary y Unix command y Shell script … and turn it into a Parallel Extender stage capable, among other things, of parallel execution… As long as the legacy executable is: y amenable to data-partition parallelism y
y
no dependencies between rows
pipe-safe y y
can read rows sequentially no random access to data
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Wrappers (Cont’d)
Wrappers are treated as a black box ~ ~
~
~
~
PX has no knowledge of contents PX has no means of managing anything that occurs inside the wrapper PX only knows how to export data to and import data from the wrapper User must know at design time the intended behavior of the wrapper and its schema interface If the wrappered application needs to see all records prior to processing, it cannot run in parallel.
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LS Example
~
Can this command be wrappered?
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Creating a Wrapper
To create the “ls” stage
Used in this job ---Æ
Example: wrapping ls Unix command: Ls /opt/AdvTrain/dnich would yield a list of files and subdirectories. The wrapper is thus comprised of the command and a parameter that contains a disk location.
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Wrapper Starting Point
Creating Wrapped Stages From Manager: Right-Click on Stage Type > New Parallel Stage > Wrapped
We will "Wrapper” an existing Unix executables – the ls command
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Wrapper - General Page
Name of stage
Unix command to be wrapped
Unix ls command can take several arguments but we will use it in its simplest form: Ls location Where location will be passed into the stage with a job parameter.
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The "Creator" Page
Conscientiously maintaining the Creator page for all your wrapped stages will eventually earn you the thanks of others.
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Wrapper – Properties Page ~
If your stage will have properties appear, complete the Properties page
This will be the name of the property as it appears in your stage
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Wrapper - Wrapped Page
Interfaces – input and output columns these should first be entered into the table definitions meta data (DS Manager); let’s do that now.
The Interfaces > input and output describe the meta data for how you will communicate with the wrapped application.
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Interface schemas • Layout interfaces describe what columns the stage: – Needs for its inputs (if any) – Creates for its outputs (if any) – Should be created as tables with columns in Manager
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Column Definition for Wrapper Interface
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How Does the Wrapping Work?
– Define the schema for export and import y Schemas become interface schemas of the operator and allow for by-name column access – Define multiple inputs/outputs required by UNIX executable
input schema export stdin or named pipe UNIX executable stdout or named pipe import output schema
QUIZ: Why does export precede import?
Answer: You must first EXIT the DS-PX environment to access the vanilla Unix environment. Then you must reenter the DS-PX environment.
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Update the Wrapper Interfaces ~
This wrapper will have no input interface – i.e. no input link. The location will come as a job parameter that will be passed to the appropriate stage property.
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Resulting Job
Wrapped stage
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Job Run ~
Show file from Designer palette
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Wrapper Story: Cobol Application ~
Hardware Environment: – IBM SP2, 2 nodes with 4 CPU’s per node.
~
Software: – DB2/EEE, COBOL, PX
~
Original COBOL Application: – Extracted source table, performed lookup against table in DB2, and Loaded results to target table. – 4 hours 20 minutes sequential execution
~
Parallel Extender Solution: – Used PX to perform Parallel DB2 Extracts and Loads – Used PX to execute COBOL application in Parallel – PX Framework handled data transfer between DB2/EEE and COBOL application – 30 minutes 8-way parallel execution
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Buildops Buildop provides a simple means of extending beyond the functionality provided by PX, but does not use an existing executable (like the wrapper) Reasons to use Buildop include: ~ ~
Speed / Performance Complex business logic that cannot be easily represented using existing stages – Lookups across a range of values – Surrogate key generation – Rolling aggregates
~
~
Build once and reusable everywhere within project, no shared container necessary Can combine functionality from different stages into one
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BuildOps – The user performs the fun tasks: encapsulate the business logic in a custom operator – The Parallel Extender interface called “buildop” automatically performs the tedious, error-prone tasks: invoke needed header files, build the necessary “plumbing” for a correct and efficient parallel execution. – Exploits extensibility of PX Framework
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BuildOp Process Overview
From Manager (or Designer): Repository pane: Right-Click on Stage Type > New Parallel Stage > {Custom | Build | Wrapped}
• "Build" stages from within Parallel Extender • "Wrapping” existing “Unix” executables
That is the fun one! There is programming! This section just sows the flavor of what Buildop can do.
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General Page Identical to Wrappers, except:
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Under the Build Tab, your program!
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Logic Tab for Business Logic
Enter Business C/C++ logic and arithmetic in four pages under the Logic tab Main code section goes in Per-Record page- it will be applied to all rows
NOTE: Code will need to be Ansi C/C++ compliant. If code does not compile outside of PX, it won’t compile within PX either!
The four tabs are Definitions, Pre-Loop, Per-Record, Post-Loop. The main action is in Per-record. Definitions is used to declare and initialize variables Pre-loop has code to be performed prior to processing the first row. Post-loop has code to be performed after processing the last row
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Code Sections under Logic Tab
Temporary variables declared [and initialized] here
Logic here is executed once BEFORE processing the FIRST row
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Logic here is executed once AFTER processing the LAST row
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I/O and Transfer Under Interface tab: Input, Output & Transfer pages
First line: output 0
Optional renaming of output port from default "out0"
Write row Input page: 'Auto Read' Read next row
In-Repository Table Definition
'False' setting, not to interfere with Transfer page
This is the Output page. This Input page is the same, except it has an "Auto Read," instead of the "Auto Write" column. The Input/Output interface TDs must be prepared in advance and put in the repository.
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I/O and Transfer
First line: Transfer of index 0
• Transfer from input in0 to output out0. • If page left blank or Auto Transfer = "False" (and RCP = "False") Only columns in output Table Definition are written
The role of Transfer will be made clearer soon with examples.
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Building Stages Simple Example
~
Example - sumNoTransfer – Add input columns "a" and "b"; ignores other columns that might be present in input – Produces a new "sum" column – Do not transfer input columns
a:int32; b:int32
sumNoTransfer sum:int32
Only the column(s) explicitly listed in the output TD survive.
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No Transfer
From Peek: NO TRANSFER
• Causes: - RCP set to "False" in stage definition and - Transfer page left blank, or Auto Transfer = "False"
• Effects: - input columns "a" and "b" are not transferred - only new column "sum" is transferred
Compare with transfer ON…
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Transfer
TRANSFER
• Causes: - RCP set to "True" in stage definition or - Auto Transfer set to "True"
• Effects: - new column "sum" is transferred, as well as - input columns "a" and "b" and - input column "ignored" (present in input, but not mentioned in stage)
All the columns in the input link are transferred, irrespective of what the Input/Output TDs say.
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Adding a Column With Row ID
Out Table;
Output
Hint (resp. solution) of quiz on next slide (resp. note)
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Columns vs. Temporary C++ Variables Columns DS-PX type
~ ~
Defined in Table Definitions
Temp C++ variables
~ ~
~ ~
Value refreshed from row to row
C/C++ type Need declaration (in Definitions or Pre-Loop page) Value persistent throughout "loop" over rows, unless modified in code
ANSWER to QUIZ Replacing index = count++; with index++ ; would result in index=1 throughout. See bottom bullet in left column.
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Exercise ~
Complete exercise 10-1 and 10-2
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Exercise ~
Complete exercises 10-3 and 10-4
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Custom Stage ~
Reasons for a custom stage: – Add PX operator not already in DataStage PX – Build your own Operator and add to DataStage PX
~ ~
Use PX API Use Custom Stage to add new operator to PX canvas
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Custom Stage DataStage Manager > select Stage Types branch > right click
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Custom Stage
Number of input and output links allowed
Name of Orchestrate operator to be used
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Custom Stage – Properties Tab
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The Result
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Blank
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Module 11
Meta Data in DataStage PX
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Objectives ~
~
Understand how PX uses meta data, particularly schemas and runtime column propagation Use this understanding to: – Build schema definition files to be invoked in DataStage jobs – Use RCP to manage meta data usage in PX jobs
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Establishing Meta Data ~
Data definitions – Recordization and columnization – Fields have properties that can be set at individual field level y
Data types in GUI are translated to types used by PX
– Described as properties on the format/columns tab (outputs or inputs pages) OR – Using a schema file (can be full or partial) ~
Schemas – Can be imported into Manager – Can be pointed to by some job stages (i.e. Sequential)
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Data Formatting – Record Level ~
Format tab
~
Meta data described on a record basis
~
Record level properties
To view documentation on each of these properties, open a stage > input or output > format. Now hover your cursor over the property in question and help text will appear.
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Data Formatting – Column Level ~
Defaults for all columns
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Column Overrides ~
Edit row from within the columns tab
~
Set individual column properties
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Extended Column Properties
Field and string settings
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Extended Properties – String Type ~
Note the ability to convert ASCII to EBCDIC
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Editing Columns
Properties depend on the data type
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Schema ~
Alternative way to specify column definitions for data used in PX jobs
~
Written in a plain text file
~
Can be written as a partial record definition
~
record {final_delim=end, delim=",", quote=double} Can be imported into the DataStage repository ( first_name: string; last_name: string; gender: string; birth_date: date; income: decimal[9,2]; state: string; )
The format of each line describing a column is: column_name:[nullability]datatype; column_name. This is the name that identifies the column. Names must start with a letter or an underscore (_), and can contain only alphanumeric or underscore characters. The name is not case sensitive. The name can be of any length. nullability. You can optionally specify whether a column is allowed to contain a null value, or whether this would be viewed as invalid. If the column can be null, insert the word ’nullable’. By default columns are not nullable. You can also include ’nullable’ at record level to specify that all columns are nullable, then override the setting for individual columns by specifying ‘not nullable’. For example: record nullable ( name:not nullable string[255]; value1:int32; Copyright ©2004 Ascential Software Corporation
date:date)
datatype. This is the data type of the column.
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Creating a Schema ~
Using a text editor – Follow correct syntax for definitions – OR
~
Import from an existing data set or file set – On DataStage Manager import > Table Definitions > Orchestrate Schema Definitions – Select checkbox for a file with .fs or .ds
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Importing a Schema
Schema location can be on the server or local work station
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Data Types ~
Date
~
Vector
~
Decimal
~
Subrecord
~
Floating point
~
Raw
~
Integer
~
Tagged
~
String
~
Time
~
Timestamp
Raw – collection of untyped bytes Vector – elemental array Subrecord – a record within a record (elements of a group level) Tagged – column linked to another column that defines its datatype
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Partial Schemas ~
~
Only need to define column definitions that you are actually going to operate on Allowed by stages with format tab – – – – –
Sequential file stage File set stage External source stage External target stage Column import stage
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Runtime Column Propagation ~
DataStage PX is flexible about meta data. It can cope with the situation where meta data isn’t fully defined. You can define part of your schema and specify that, if your job encounters extra columns that are not defined in the meta data when it actually runs, it will adopt these extra columns and propagate them through the rest of the job. This is known as runtime column propagation (RCP).
~
RCP is always on at runtime.
~
Design and compile time column mapping enforcement.
– RCP is off by default. – Enable first at project level. (Administrator project properties) – Enable at job level. (job properties General tab) – Enable at Stage. (Link Output Column tab)
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Enabling RCP at Project Level
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Enabling RCP at Job Level
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Enabling RCP at Stage Level ~ ~
Go to output link’s columns tab For transformer you can find the output links columns tab by first going to stage properties
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Using RCP with Sequential Stages ~
~
To utilize runtime column propagation in the sequential stage you must use the “use schema” option Stages with this restriction: – – – –
Sequential File Set External Source External Target
What is runtime column propagation? Runtime column propagation (RCP) allows DataStage to be flexible about the columns you define in a job. If RCP is enabled for a project, you can just define the columns you are interested in using in a job, but ask DataStage to propagate the other columns through the various stages. So such columns can be extracted from the data source and end up on your data target without explicitly being operated on in between. Sequential files, unlike most other data sources, do not have inherent column definitions, and so DataStage cannot always tell where there are extra columns that need propagating. You can only use RCP on sequential files if you have used the Schema File property (see Link\xd2 ” on page - and on page - ) to specify a schema which describes all the columns in the sequential file. You need to specify the same schema file for any similar stages in the job where you want to propagate columns. Stages that will require a schema file are: Sequential File File Set External Source External Target Column Import Column Export Copyright ©2004 Ascential Software Corporation
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Runtime Column Propagation ~
When RCP is Disabled – DataStage Designer will enforce Stage Input Column to Output Column mappings. – At job compile time modify operators are inserted on output links in the generated osh.
Modify operators can add or change columns in a data flow.
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Runtime Column Propagation ~
When RCP is Enabled – DataStage Designer will not enforce mapping rules. – No Modify operator inserted at compile time. – Danger of runtime error if column names incoming do not match column names outgoing link – case sensitivity.
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Exercise ~
Complete exercises 11-1 and 11-2
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Module 12
Job Control Using the Job Sequencer
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Objectives ~
~
Understand how the DataStage job sequencer works Use this understanding to build a control job to run a sequence of DataStage jobs
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Job Control Options ~
Manually write job control – Code generated in Basic – Use the job control tab on the job properties page – Generates basic code which you can modify
~
Job Sequencer – Build a controlling job much the same way you build other jobs – Comprised of stages and links – No basic coding
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Job Sequencer ~
Build like a regular job
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Type “Job Sequence”
~
Has stages and links
~
~
Job Activity stage represents a DataStage job Links represent passing control Stages
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Example Job Activity stage – contains conditional triggers
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Job Activity Properties
Job to be executed – select from dropdown
Job parameters to be passed
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Job Activity Trigger
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Trigger appears as a link in the diagram
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Custom options let you define the code
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Options ~
Use custom option for conditionals – Execute if job run successful or warnings only
~ ~
Can add “wait for file” to execute Add “execute command” stage to drop real tables and rename new tables to current tables
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Job Activity With Multiple Links
Different links having different triggers
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Sequencer Stage ~
Build job sequencer to control job for the collections application
Can be set to all or any
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Notification Stage
Notification
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Notification Activity
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Sample DataStage log from Mail Notification ~
Sample DataStage log from Mail Notification
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Notification Activity Message ~
E-Mail Message
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Exercise ~
Complete exercise 12-1
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Blank
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Module 13
Testing and Debugging
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Objectives ~
~
Understand spectrum of tools to perform testing and debugging Use this understanding to troubleshoot a DataStage job
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Environment Variables
Environment fall in broad categories, listed in the left pane. We'll see these categories one by one. All environments values listed in the ADMINISTRATOR are the project-wide defaults. Can be modified by DESIGNER per job, and again, by DIRECTOR per run. The default values are reasonable ones, there is no need for the beginning user to modify them, or even to know much about them--with one possible exception: APT_CONFIG_FILE-- see next slide.
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Parallel Environment Variables
Highlighted: APT_CONFIG_FILE, contains the path (on the server) of the active config file. The main aspect of a given configuration file is the # of nodes it declares. In the Labs, we used two files: One with one node declared; for use in sequential execution One with two nodes declared; for use in parallel execution
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Environment Variables
Stage Specific
The correct settings for these should be set at install. If you need to modify them, first check with your DBA.
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Environment Variables
These are for the user to play with. Easy: they take only TRUE/FALSE values. Control the verbosity of the log file. The defaults are set for minimal verbosity. The top one, APT_DUMP_SCORE, is an old favorite. It tracks datasets, nodes, partitions, and combinations --- all TBD soon. APT_RECORDS_COUNT helps you detect load imbalance. APT_PRINT_SCHEMAS shows the textual representation of the unformatted metadata at all stages. Online descriptions with the "Help" button.
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Environment Variables
Compiler
You need to have these right to use the Transformer and the Custom stages. Only these stages invoke the C++ compiler. The correct values are listed in the Release Notes.
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The Director Typical Job Log Messages: ~
Environment variables
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Configuration File information
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Framework Info/Warning/Error messages
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Output from the Peek Stage
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Additional info with "Reporting" environments
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Tracing/Debug output
– Must compile job in trace mode – Adds overhead
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Job Level Environmental Variables • Job Properties, from Menu Bar of Designer • Director will prompt you before each run
Project-wide environments set by ADMINISTRATOR can be modified on a job basis in DESIGNER's Job Properties, and on a run basis by DIRECTOR. Provides great flexibility.
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Troubleshooting If you get an error during compile, check the following: ~
~
Compilation problems – If Transformer used, check C++ compiler, LD_LIRBARY_PATH – If Buildop errors try buildop from command line – Some stages may not support RCP – can cause column mismatch . – Use the Show Error and More buttons – Examine Generated OSH – Check environment variables settings Very little integrity checking during compile, should run validate from Director.
Highlights source of error
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Generating Test Data
~
Row Generator stage can be used – Column definitions – Data type dependent
~
Row Generator plus lookup stages provides good way to create robust test data from pattern files
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STUDENT REFERENCE
DS324PX
Copyright © 2004 Ascential Software Corporation 1/1/04
DataStage PX
DataStage PX
DS325PX
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. 01-01-2004
DS324PX
Copyright © 2004 Ascential Software Corporation 1/1/04
DataStage PX
DataStage PX
DS325PX
Table of Contents Intro Part 1: Introduction.................................................... 1 Intro Part 2: Configuring Project ...................................... 13 Intro Part 3: Managing Meta Data .................................... 25 Intro Part 4: Designing and Documenting Jobs ............... 45 Intro Part 5: Running Jobs................................................ 75 Module 1: Review.............................................................. 85 Module 2: Sequential Access ......................................... 125 Module 3: Standards ....................................................... 157 Module 4: DBMS Access ................................................. 175 Module 5: PX Platform Architecture .............................. 195 Module 6: Transforming Data ......................................... 229 Module 7: Sorting Data ................................................... 249 Module 8: Combining Data.............................................. 261 Module 9: Configuration Files......................................... 283 Module 10: Extending PX................................................ 305 Module 11: Meta Data..................................................... 347 Module 12: Job Control................................................... 369 Module 13: Testing ......................................................... 385
DS324PX
Copyright © 2004 Ascential Software Corporation 1/1/04
DataStage PX
Intro Part 1
Introduction to DataStage PX
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Ascential Platform
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What is DataStage? ~
~
~
~ ~
Design jobs for Extraction, Transformation, and Loading (ETL) Ideal tool for data integration projects – such as, data warehouses, data marts, and system migrations Import, export, create, and managed metadata for use within jobs Schedule, run, and monitor jobs all within DataStage Administer your DataStage development and execution environments
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 Server and Clients
The DataStage client components are: 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
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DataStage Administrator
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.
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Client Logon
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DataStage Manager
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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DataStage Designer
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.
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DataStage Director
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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Developing in DataStage ~
Define global and project properties in Administrator
~
Import meta data into Manager
~
Build job in Designer
~
Compile Designer
~
Validate, run, and monitor in Director
•
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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DataStage Projects
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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Quiz– True or False ~
~
~
~
DataStage Designer is used to build and compile your ETL jobs Manager is used to execute your jobs after you build them Director is used to execute your jobs after you build them Administrator is used to set global and project properties
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Intro Part 2
Configuring Projects
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Module Objectives ~
After this module you will be able to: – Explain how to create and delete projects – Set project properties in Administrator – Set PX global properties in Administrator
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Project Properties ~
~
Projects can be created and deleted in Administrator Project properties and defaults are set in Administrator
Recall from module 1: 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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Setting Project Properties ~
To set project properties, log onto Administrator, select your project, and then click “Properties”
The logon screen for Administrator does not provide the option to select a specific project (unlike the other DataStage clients).
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Project Properties General Tab
The General Tab is used to change DataStage license information and to adjust the amount of time a connection will be maintained between the DataStage server and clients.
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Projects General Tab
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.
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Environment Variables
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Permissions Tab
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.
•
UNIX note: In UNIX, the groups displayed are defined in /etc/group.
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Tracing Tab
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.
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Tunables Tab
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.
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Parallel Tab
You should enable OSH for viewing – OSH is generated when you compile a job.
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Intro Part 3
Managing Meta Data
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Module Objectives ~
After this module you will be able to: – Describe the DataStage Manager components and functionality – Import and export DataStage objects – Import metadata for a sequential file
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What Is Metadata?
Data
Source
Transform
Meta Data
Target Meta Data
Meta Data Repository
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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DataStage Manager
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. The left pane contains the project tree. There are seven main branches, but you can create subfolders under each. Select a folder in the project tree to display its contents.
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Manager Contents ~
~
Metadata describing sources and targets: Table definitions DataStage objects: jobs, routines, table definitions, etc.
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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Import and Export ~
Any object in Manager can be exported to a file
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Can export whole projects
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Use for backup
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Sometimes used for version control
~
~
Can be used to move DataStage objects from one project to another Use to share DataStage jobs and projects with other developers
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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Export Procedure ~
In Manager, click “Export>DataStage Components”
~
Select DataStage objects for export
~
Specified type of export: DSX, XML
~
Specify file path on client machine
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.
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Quiz: 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 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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Quiz: True or False? ~
The directory to which you export is on the DataStage client machine, not on the DataStage server machine.
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 addressable by your client machine. False: Incorrect. The directory you select for export must be addressable by your client machine.
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Exporting DataStage Objects
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Exporting DataStage Objects
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Import Procedure ~
~
In Manager, click “Import>DataStage Components” Select DataStage objects for import
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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Importing DataStage Objects
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Import Options
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Exercise ~
Import DataStage Component (table definition)
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Metadata Import ~
Import format and column destinations from sequential files
~
Import relational table column destinations
~
Imported as “Table Definitions”
~
Table definitions can be loaded into job stages
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 import and define several different kinds of table definitions including: Sequential files and ODBC data sources.
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Sequential File Import Procedure ~
~
~ ~
In Manager, click Import>Table Definitions>Sequential File Definitions Select directory containing sequential file and then the file Select Manager category Examined format and column definitions and edit is necessary
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:
•
•
<Sub-category> is (or becomes) a sub-folder under the type.
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Manager Table Definition
In Manager, select the category (folder) that contains the table definition. Doubleclick 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.
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Importing Sequential Metadata
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Intro Part 4
Designing and Documenting Jobs
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Module Objectives ~
After this module you will be able to: – – – – – – – –
Describe what a DataStage job is List the steps involved in creating a job Describe links and stages Identify the different types of stages Design a simple extraction and load job Compile your job Create parameters to make your job flexible Document your job
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What Is a Job? ~ ~
Executable DataStage program Created in DataStage Designer, but can use components from Manager
~
Built using a graphical user interface
~
Compiles into Orchestrate shell language (OSH)
A job is an executable DataStage program. In DataStage, you can design and run jobs that perform many useful data integration 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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Job Development Overview ~
~
~
~
In Manager, import metadata defining sources and targets In Designer, add stages defining data extractions and loads And Transformers and other stages to defined data transformations Add lakes defining the flow of data from sources to targets
~
Compiled the job
~
In Director, validate, run, and monitor your job
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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Designer Work Area
The appearance of the designer work space is configurable; the graphic shown here is only one example of how you might arrange components. In the right center is the Designer canvas, where you create stages and links. On the 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.
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Designer Toolbar Provides quick access to the main functions of Designer Show/hide metadata markers
Job properties
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Tools Palette
The tool palette contains icons that represent the components you can add to your job design. You can also install additional stages called plug-ins for special purposes.
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Adding Stages and Links ~
~
Stages can be dragged from the tools palette or from the stage type branch of the repository view Links can be drawn from the tools palette or by right clicking and dragging from one stage to another
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Sequential File Stage ~
Used to extract data from, or load data to, a sequential file
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Specify full path to the file
~
Specify a file format: fixed width or delimited
~
Specified column definitions
~
Specify write action
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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Job Creation Example Sequence ~
Brief walkthrough of procedure
~
Presumes meta data already loaded in repository
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Designer - Create New Job
Several types of DataStage jobs: Server – not covered in this course. However, you can create server jobs, convert them to a container, then use this container in a parallel job. However, this has negative performance implications. Shared container (parallel or server) – contains reusable components that can be used by other jobs. Mainframe – DataStage 390, which generates Cobol code Parallel – this course will concentrate on parallel jobs. Job Sequence – used to create jobs that control execution of other jobs.
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Drag Stages and Links Using Palette
The tools palette may be shown as a floating dock or placed along a border. Alternatively, it may be hidden and the developer may choose to pull needed stages from the repository onto the design work area.
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Assign Meta Data
Meta data may be dragged from the repository and dropped on a link.
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Editing a Sequential Source Stage
Any required properties that are not completed will appear in red. You are defining the format of the data flowing out of the stage, that is, to the output link. Define the output link listed in the Output name box. You are defining the file from which the job will read. If the file doesn’t exist, you will get an error at run time. On the Format tab, you specify a format for the source file. You will be able to view its data using the View data button. Think of a link as like a pipe. What flows in one end flows out the other end (at the transformer stage).
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Editing a Sequential Target
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. 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. 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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Transformer Stage ~
~
~
Used to define constraints, derivations, and column mappings A column mapping maps an input column to an output column In this module will just defined column mappings (no derivations)
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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Transformer Stage Elements
There are two: transformer and basic transformer. Both look the same but access different routines and functions. Notice the following elements of the transformer: The top, left pane displays the columns of the input links. The top, right pane displays the contents of the stage variables. The lower, right pane displays the contents of the output link. Unresolved column mapping will show the output in red. 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.
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Create Column Mappings
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Creating Stage Variables
Stage variables are used for a variety of purposes: Counters Temporary registers for derivations Controls for constraints
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Result
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Adding Job Parameters ~
Makes the job more flexible
~
Parameters can be: – Used in constraints and derivations – Used in directory and file names
~
Parameter values are determined at run time
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Adding Job Documentation ~
Job Properties – Short and long descriptions – Shows in Manager
~
Annotation stage – Is a stage on the tool palette – Shows on the job GUI (work area)
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Job Properties Documentation
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Annotation Stage on the Palette
Two versions of the annotation stage are available: Annotation Annotation description The difference will be evident on the following slides.
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Annotation Stage Properties
You can type in whatever you want; the default text comes from the short description of the jobs properties you entered, if any. 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 slide.
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Final Job Work Area with Documentation
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Can be used to Document Specific Stages
To get the documentation to appear at the top of the text box, click the “top” button in the stage properties.
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Compiling a Job
Before you can run your job, you must compile it. To compile it, click File>Compile or click the Compile button on the toolbar. The Compile Job window displays the status of the compile. A compile will generate OSH.
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Errors or Successful Message
If an error occurs: Click Show Error to identify the stage where the error occurred. This will highlight the stage in error. Click More to retrieve more information about the error. This can be lengthy for parallel jobs.
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Intro Part 5
Running Jobs
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Module Objectives ~
After this module you will be able to: – – – – –
Validate your job Use DataStage Director to run your job Set to run options Monitor your job’s progress View job log messages
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Prerequisite to Job Execution Result from Designer compile
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DataStage Director ~ ~
Can schedule, validating, and run jobs Can be invoked from DataStage Manager or Designer – Tools > Run Director
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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Running Your Job
This shows the Director Status view. To run a job, select it and then click Job>Run Now. Better yet: Shift to log view from main Director screen. Then click green arrow to execute job.
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Run Options – Parameters and Limits
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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Director Log View
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 programgenerated messages.
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Message Details are Available
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Other Director Functions ~
Schedule job to run on a particular date/time
~
Clear job log
~
Set Director options – Row limits – Abort after x warnings
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Blank
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Module 1
DS324PX – DataStage PX Review
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Ascential’s Enterprise Data Integration Platform Command & Control
ANY SOURCE CRM ERP SCM RDBMS Legacy Real-time Client-server Web services Data Warehouse Other apps.
DISCOVER
PREPARE
TRANSFORM
Gather relevant information for target enterprise applications
Cleanse, correct and match input data
Standardize and enrich data and load to targets
Data Profiling
Data Quality
Extract, Transform, Load
ANY TARGET CRM ERP SCM BI/Analytics RDBMS Real-time Client-server Web services Data Warehouse Other apps.
Parallel Execution Meta Data Management
Ascential Software looked at what’s required across the whole lifecycle to solve enterprise data integration problems not just once for an individual project, but for multiple projects connecting any type of source (ERP, SCM, CRM, legacy systems, flat files, external data, etc) with any type of target. Ascential’s uniqueness comes from the combination of proven best-in-class functionality, across data profiling, data quality and matching, and ETL combined to provide a complete end-to-end data integration solution on a platform with unlimited scalability and performance through parallelization. We can therefore deal not only with gigabytes of data, but terabytes of data, and petabytes of data data volumes becoming more and more common………., and do so with complete management and integration of all the meta data in the enterprise environment. This is indeed an end-to-end solution, which customers can implement in whatever phases they choose, and which, by virtue of its completeness and breadth and robustness of solution, helps our customers get the quickest possible time to value and time to impact from strategic applications. It assures good data is being fed into the informational systems and that our solution provides the ROI and economic benefit customers expect from investments in strategic applications, which are very large investments and, which done right, should command very large returns.
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Course Objectives ~
You will learn to: – Build DataStage PX jobs using complex logic – Utilize parallel processing techniques to increase job performance – Build custom stages based on application needs
~
Course emphasis is: – Advanced usage of DataStage PX – Application job development – Best practices techniques
Students will gain hands-on experience with DSPX by building an application in class exercises. This application will use complex logic structures.
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Course Agenda ~
Day 1 – – – –
~
~
Review of PX Concepts Sequential Access Standards DBMS Access
Day 2 – PX Architecture – Transforming Data – Sorting Data
Day 3 – Combining Data – Configuration Files
~
Day 4 – – – –
Extending PX Meta Data Usage Job Control Testing
Suggested agenda – actual classes will vary.
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Module Objectives ~
~
~
Provide a background for completing work in the DSPX advanced course Ensure all students will have a successful advanced class Tasks – Review concepts covered in DSPX Essentials course
In this module we will review many of the concepts and ideas presented in the DataStage essentials course. At the end of this module students will be asked to complete a brief exercise demonstrating their mastery of that basic material.
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Review Topics ~
DataStage architecture
~
DataStage client review – – – –
Administrator Manager Designer Director
~
Parallel processing paradigm
~
DataStage Parallel Extender (DSPX)
Our topics for review will focus on overall DataStage architecture and a review of the parallel processing paradigm in DataStage parallel extender.
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Client-Server Architecture Command & Control
Microsoft® Windows NT/2000/XP ANY TARGET
ANY SOURCE
Designer
Discover Extract
Director
Prepare Cleanse
Administrator
Transform Transform
Server
Repository Manager
Extend Integrate
CRM ERP SCM BI/Analytics RDBMS Real-Time Client-server Web services Data Warehouse Other apps.
Repository
Microsoft® Windows NT or UNIX Parallel Execution Meta Data Management
DataStage parallel extender consists of four clients connected to the DataStage parallel extender engine. The client applications are: Manager, Administrator, Designer, and Director. These clients connect to a server engine that is located on either a UNIX (parallel extender only) or NT operating system.
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Process Flow ~
Administrator – add/delete projects, set defaults
~
Manager – import meta data, backup projects
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Designer – assemble jobs, compile, and execute
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Director – execute jobs, examine job run logs
A typical DataStage workflow consists of: Setting up the project in Administrator Including metadata via Manager Building and assembling the job in Designer Executing and testing the job in Director.
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Administrator – Licensing and Timeout
Change licensing, if appropriate. Timeout period should be set to large number or choose “do not timeout” option.
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Administrator – Project Creation/Removal
Functions specific to a project.
Available functions: Add or delete projects. Set project defaults (properties button). Cleanup – perform repository functions. Command – perform queries against the repository.
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Administrator – Project Properties
RCP for parallel jobs should be enabled
Variables for parallel processing
Recommendations: Check enable job administration in Director Check enable runtime column propagation May check auto purge of jobs to manage messages in director log
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Administrator – Environment Variables
Variables are category specific
You will see different environment variables depending on which category is selected.
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OSH is what is run by the PX Framework
Reading OSH will be covered in a later module. Since DataStage parallel extender writes OSH, you will want to check this option.
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DataStage Manager
To attach to the DataStage Manager client, one first enters through the logon screen. Logons can be either by DNS name or IP address. Once logged onto Manager, users can import meta data; export all or portions of the project, or import components from another project’s export. Functions: Backup project Export Import Import meta data Table definitions Sequential file definitions Can be imported from metabrokers Register/create new stages
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Export Objects to MetaStage
Push meta data to MetaStage
DataStage objects can now be pushed from DataStage to MetaStage.
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Designer Workspace
Can execute the job from Designer
Job design process: Determine data flow Import supporting meta data Use designer workspace to create visual representation of job Define properties for all stages Compile Execute
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DataStage Generated OSH
The PX Framework runs OSH
The DataStage GUI now generates OSH when a job is compiled. This OSH is then executed by the parallel extender engine.
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Director – Executing Jobs
Messages from previous run in different color
Messages from previous runs are kept in different color from current run.
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Stages Can now customize the Designer’s palette
In Designer View > Customize palette This window will allow you to move icons into your Favorites folder plus many other customization features.
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Popular Stages
Row generator
Peek
The row generator and peek stages are especially useful during development.
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Row Generator ~
Can build test data Edit row in column tab
Repeatable property
Depending on the type of data, you can set values for each column in the row generator.
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Peek ~
Displays field values – Will be displayed in job log or sent to a file – Skip records option – Can control number of records to be displayed
~
Can be used as stub stage for iterative development (more later)
The peek stage will display column values in a job's output messages log.
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Why PX is so Effective ~
Parallel processing paradigm – More hardware, faster processing – Level of parallelization is determined by a configuration file read at runtime
~
Emphasis on memory – Data read into memory and lookups performed like hash table
PX takes advantage of the machines hardware architecture -- this can be changed at runtime.
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Scalable Systems ~
Parallel processing = executing your application on multiple CPUs – Scalable processing = add more resources (CPUs, RAM, and disks) to increase system performance 1
2
3
4
5
6
•
Example system containing 6 CPUs (or processing nodes) and disks
DataStage parallel extender can take advantage of multiple processing nodes to instantiate multiple instances of a DataStage job.
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Scaleable Systems: Examples Three main types of scalable systems ~
Symmetric Multiprocessors (SMP), shared memory
–Sun Starfire™ –IBM S80 –HP Superdome ~
Clusters: UNIX systems connected via networks
–Sun Cluster –Compaq TruCluster ~ note
MPP
You can describe an MPP as a bunch of connected SMPs.
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SMP: Shared Everything • Multiple CPUs with a single operating system • Programs communicate using shared memory • All CPUs share system resources (OS, memory with single linear address space, disks, I/O) When used with Parallel Extender: • Data transport uses shared memory • Simplified startup
cpu
cpu
cpu
cpu
Parallel Extender treats NUMA (NonUniform Memory Access) as plain SMP
A typical SMP machine has multiple CPUs that share both disks and memory.
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Traditional Batch Processing Operational Data
Transform
Clean
Load
Archived Data
Data Warehouse Disk
Disk
Disk
Source
Target
Traditional approach to batch processing: • Write to disk and read from disk before each processing operation • Sub-optimal utilization of resources • a 10 GB stream leads to 70 GB of I/O • processing resources can sit idle during I/O • Very complex to manage (lots and lots of small jobs) • Becomes impractical with big data volumes • disk I/O consumes the processing • terabytes of disk required for temporary staging
The traditional data processing paradigm involves dropping data to disk many times throughout a processing run.
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Pipeline Multiprocessing Data Pipelining • Transform, clean and load processes are executing simultaneously on the same processor • rows are moving forward through the flow Operational Data
Archived Data
Transform
Clean
Load
Data Warehouse
Target
Source • • • •
Start a downstream process while an upstream process is still running. This eliminates intermediate storing to disk, which is critical for big data. This also keeps the processors busy. Still has limits on scalability
Think of a conveyor belt moving the rows from process to process!
On the other hand, but parallel processing paradigm rarely drops data to disk unless necessary for business reasons -- such as backup and recovery.
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Partition Parallelism Data Partitioning • Break up big data into partitions • Run one partition on each processor Node 1
• 4X times faster on 4 processors With data big enough: 100X faster on 100 processors • This is exactly how the parallel databases work! • Data Partitioning requires the same transform to all partitions: Aaron Abbott and Zygmund Zorn undergo the same transform
Transform
A-F
Node 2
G- M Source Data
Transform
N-T U-Z
Node 3
Transform Node 4
Transform
Data may actually be partitioned in several ways -- range partitioning is only one example. We will explore others later.
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PX Program Elements
•
•
Dataset: uniform set of rows in the Framework's internal representation - Three flavors: 1. file sets *.fs : stored on multiple Unix files as flat files 2. persistent: *.ds : stored on multiple Unix files in Framework format read and written using the DataSet Stage 3. virtual: *.v : links, in Framework format, NOT stored on disk - The Framework processes only datasets—hence possible need for Import - Different datasets typically have different schemas - Convention: "dataset" = Framework data set. Partition: subset of rows in a dataset earmarked for processing by the same node (virtual CPU, declared in a configuration file). - All the partitions of a dataset follow the same schema: that of the dataset
Parallel extender deals with several different types and data: file sets data sets -- both in persistent and non-persistent forms.
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Combining Parallelism Types Putting It All Together: Parallel Dataflow
Source Data
Pa rtit i on
ing
Pipelining
Transform
Clean
Source
Load
Data Warehouse
Target
Pipelining and partitioning can be combined together to provide a powerful parallel processing paradigm.
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Repartitioning Putting It All Together: Parallel Dataflow with Repartioning on-the-fly
Re pa rtit ion ing
Pa rtit i on
Source Data
U-Z N-T G- M A-F
Re pa rtit ion ing
ing
Pipelining
Customer zip code
Credit card number
Transform
Customer last name
Clean
Source
Load
Data Warehouse
Target
Without Landing To Disk!
In addition, data can change partitioning from stage to stage. This can either happened explicitly at the desire of a programmer or implicitly performed by the engine.
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DSPX Overview • • • •
Build scalable applications from simple components Run native operators developed in C++ Run existing code written in C, COBOL, scripts, etc. Run existing sequential programs in parallel Parallel Extender Client • Parallel Extender will Legacy and 3rd Shell C++ manage parallel execution party programs scripts operators COBOL, C/C++, etc
The Framework UNIX UNIX UNIX UNIX UNIX Scalable Servers...
Parallel extender can interface with UNIX commands, shell scripts, and legacy programs to run on the parallel extender framework. Underlying this architecture is the UNIX operating system.
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DataStage PX Architecture DataStage: Provides data integration platform
Orchestrate Framework: Provides application scalability Orchestrate Program (sequential data flow)
Flat Files
Relational Data
Clean 1 Import
Analyze
Merge Clean 2
Centralized Error Handling and Event Logging
Configuration File
Performance Visualization
Orchestrate Application Framework and Runtime System
Parallel access to data in RDBMS Parallel pipelining Clean 1 Import Merge Clean 2
Parallel access to data in files
Analyze
Inter-node communications
Parallelization of operations
DataStage Parallel Extender: Best-of-breed scalable data integration platform No limitations on data volumes or throughput
The parallel extender engine was derived from DataStage and Orchestrate.
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Glossary Orchestrate
DataStage
schema
table definition
property
format
type
SQL type + length [and scale]
virtual dataset
link
record/field
row/column
operator
stage
step, flow, OSH command
job
Framework
DS engine
• GUI uses both terminologies • Program messages (warnings, errors) use Orchestrate terms
Note: Different uses of "import" and "constraint"
note
Orchestrate terms and DataStage terms have an equivalency. The GUI can log messages frequently used terms from both paradigms.
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Introduction to DataStage PX ~
DSPX: – Automatically scales to fit the machine – Handles data flow among multiple CPU’s and disks
~
With DSPX you can: – Create applications for SMP’s, clusters and MPP’s… Parallel Extender is architecture-neutral – Access relational databases in parallel – Execute external applications in parallel – Store data across multiple disks and nodes
Parallel extender is architecturally neutral -- it can run on SMP's, clusters, and MPP's. The configuration file determines how parallel extender will treat the hardware.
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Job Design VS. Execution User assembles the flow using the Designer (see below)
…and gets: parallel access, propagation, transformation, and load. The design is good for 1 node, 4 nodes, or N nodes. To change # nodes, just swap configuration file. No need to modify or recompile your design!
Much of the parallel processing paradigm is hidden from the programmer -- they simply designate process flow as shown in the upper portion of this diagram. Parallel extender, using the definitions in that configuration file, will actually execute UNIX processes that are partitioned and parallelized.
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DS-PX Program Elements
Partitioners and Collectors
~
Partitioners distribute rows into partitions – implement data-partition parallelism
~
~
Collectors = inverse partitioners Live on input links of stages running – in parallel (partitioners) – sequentially (collectors)
~
Use a choice of methods
Partitioners and collectors work in opposite directions -- however, many are frequently together in job designs.
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Example Partitioning Icons partitioner
Partitioners and collectors have no stage nor icons of their own. They live live on input links of stages running in parallel (resp. sequentially). Link markings indicate their presence. S----------------->S (no Marking) S----(fan out)--->P (partitioner) P----(fan in) ---->S (collector) P----(box)------->P (no reshuffling: partitioner using "SAME" method) P----(bow tie)--->P (reshuffling: partitioner using another method) Collectors = inverse partitioners recollect rows from partitions into a single input stream to a sequential stage They are responsible for some surprising behavior: The default (Auto) is "eager" to output rows and typically causes nondeterminism: row order may vary from run to run with identical input.
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Exercise
~
Complete exercises 1-1 and 1-2, and 1-3
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Module 2
DSPX Sequential Access
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Module Objectives ~
You will learn to: – Import sequential files into the PX Framework – Utilize parallel processing techniques to increase sequential file access – Understand usage of the Sequential, DataSet, FileSet, and LookupFileSet stages – Manage partitioned data stored by the Framework
In this module students will concentrate their efforts on sequential file access in parallel extender jobs.
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Types of Sequential Data Stages ~
Sequential – Fixed or variable length
~
File Set
~
Lookup File Set
~
Data Set
Several stages handle sequential data. Each stage as both advantages and differences from the other stages that handle sequential data. Sequential data can come in a variety of types -- including both fixed length and variable length.
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Sequential Stage Introduction ~ ~
~
~
The PX Framework processes only datasets For files other than datasets, such as flat files, Parallel Extender must perform import and export operations – this is performed by import and export OSH operators (generated by Sequential or FileSet stages) During import or export DataStage performs format translations – into, or out of, the PX internal format Data is described to the Framework in a schema
The DataStage sequential stage writes OSH – specifically the import and export Orchestrate operators. Q: Why import data into an Orchestrate data set? A: Partitioning works only with data sets. You must use data sets to distribute data to the multiple processing nodes of a parallel system. Every Orchestrate program has to perform some type of import operation, from: a flat file, COBOL data, an RDBMS, or a SAS data set. This section describes how to get your data into Orchestrate. Also talk about getting your data back out. Some people will be happy to leave data in Orchestrate data sets, while others require their results in a different format.
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How the Sequential Stage Works ~
Generates Import/Export operators
~
Types of transport – Performs direct C++ file I/O streams – Source programs which feed stdout (gunzip) send stdout into PX via sequential pipe
Behind each parallel stage is one or more Orchestrate operators. Import and Export are both operators that deal with sequential data.
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Using the Sequential File Stage Both import and export of general files (text, binary) are performed by the SequentialFile Stage.
Importing/Exporting Data
PX internal format
– Data import:
– Data export
PX internal format
When data is imported the imported operator translates that data into the parallel extender internal format. The export operator performs the reverse action.
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Working With Flat Files ~
Sequential File Stage – Normally will execute in sequential mode – Can execute in parallel if reading multiple files (file pattern option) – Can use multiple readers within a node on fixed width file – DSPX needs to know y y
How file is divided into rows How row is divided into columns
Both export and import operators are generated by the sequential stage -- which one you get depends on whether the sequential stage is used as source or target.
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Processes Needed to Import Data ~
Recordization – Divides input stream into records – Set on the format tab
~
Columnization – Divides the record into columns – Default set on the format tab but can be overridden on the columns tab – Can be “incomplete” if using a schema or not even specified in the stage if using RCP
These two processes must want together to correctly interpret data -- that is, to break a data string down into records and columns.
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File Format Example
Record delimiter Field 1
,
Field 1
,
Field 1
, Last field
nl
Final Delimiter = end Field Delimiter
Field 1
,
Field 1
,
Field 1
, Last field
, nl
Final Delimiter = comma
Fields all columns are defined my delimiters. Similarly, records are defined by terminating characters.
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Sequential File Stage ~
To set the properties, use stage editor – Page (general, input/output) – Tabs (format, columns)
~
Sequential stage link rules – One input link – One output links (except for reject link definition) – One reject link y
Will reject any records not matching meta data in the column definitions
The DataStage GUI allows you to determine properties that will be used to read and write sequential files.
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Job Design Using Sequential Stages
Stage categories
Source stage Multiple output links - however, note that one of the links is represented by a broken line. This is a reject link, not to be confused with a stream link or a reference link. Target One input link
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General Tab – Sequential Source
Multiple output links
Show records
If multiple links are present you'll need to down-click to see each.
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Properties – Multiple Files
Click to add more files having the same meta data.
If specified individually, you can make a list of files that are unrelated in name. If you select “read method” and choose file pattern, you effectively select an undetermined number of files.
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Properties - Multiple Readers
Multiple readers option allows you to set number of readers
To use multiple readers on a sequential file, must be fixed-length records.
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Format Tab
File into records
Record into columns
DXPX needs to know: How a file is divided into rows How a row is divided into columns Column properties set on this tab are defaults for each column; they can be overridden at the column level (from columns tab).
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Read Methods
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Reject Link ~
Reject mode = output
~
Source – All records not matching the meta data (the column definitions)
~
Target – All records that are rejected for any reason
~
Meta data – one column, datatype = raw
The sequential stage can have a single reject link. This is typically used when you are writing to a file and provides a location where records that have failed to be written to a file for some reason can be sent. When you are reading files, you can use a reject link as a destination for rows that do not match the expected column definitions.
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File Set Stage ~
Can read or write file sets
~
Files suffixed by .fs
~
File set consists of: 1. Descriptor file – contains location of raw data files + meta data 2. Individual raw data files
~
Can be processed in parallel
Number of raw data files depends on: the configuration file – more on configuration files later.
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File Set Stage Example
Descriptor file
The descriptor file shows both a record is metadata and the file's location. The location is determined by the configuration file.
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File Set Usage ~
Why use a file set? – 2G limit on some file systems – Need to distribute data among nodes to prevent overruns – If used in parallel, runs faster that sequential file
File sets, lower yielding faster access and simple text files, are not in the parallel extender internal format.
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Lookup File Set Stage ~
Can create file sets
~
Usually used in conjunction with Lookup stages
The lookup file set is similar to the file set but also contains information about the key columns. These keys will be used later in lookups.
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Lookup File Set > Properties
Key column specified
Key column dropped in descriptor file
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Data Set ~
Operating system (Framework) file
~
Suffixed by .ds
~
Referred to by a control file
~
Managed by Data Set Management utility from GUI (Manager, Designer, Director)
~
Represents persistent data
~
Key to good performance in set of linked jobs
Data sets represent persistent data maintained in the internal format.
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Persistent Datasets
~
Accessed from/to disk with DataSet Stage.
~
Two parts: – Descriptor file: y
– Data file(s) y y
input.ds
contains metadata, data location, but NOT the data itself
record ( partno: int32; description: string; )
contain the data multiple Unix files (one per node), accessible in parallel
node1:/local/disk1/… node2:/local/disk2/…
Accessed from/to disk with DataSet Stage.
Two parts:
Descriptor file User-specified name Contains table definition ("unformatted core" schema) Here is the icon of the DataSet Stage, used to access persistent datsets Descriptor file, e.g., "input.ds" contains: •Paths of data files •Metadata: unformatted table definition, no formats (unformatted schema) •Config file used to store the data Data file(s) Contain the data itself System-generated long file names, to avoid naming conflicts.
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Quiz!
• True or False? Everything that has been data-partitioned must be collected in same job
For example, if we have four nodes corresponding to four regions, we'll have four reports. No need to recollect if one does not need inter-region correlations.
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Data Set Stage
Is the data partitioned?
In both cases the answer is Yes.
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Engine Data Translation ~
Occurs on import – From sequential files or file sets – From RDBMS
~
Occurs on export – From datasets to file sets or sequential files – From datasets to RDBMS
~
Engine most efficient when processing internally formatted records (I.e. data contained in datasets)
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Managing DataSets ~
~
GUI (Manager, Designer, Director) – tools > data set management Alternative methods – Orchadmin Unix command line utility y List records y Remove data sets (will remove all components) y
– Dsrecords y
Lists number of records in a dataset
Both dsrecords and orchadmin are Unix command-line utilities. The DataStage Designer GUI provides a mechanism to view and manage data sets.
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Data Set Management
Display data
Schema
The screen is available (data sets management) from manager, designer, and director.
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Data Set Management From Unix ~
Alternative method of managing file sets and data sets – Dsrecords y
Gives record count – Unix command-line utility – $ dsrecords ds_name I.e.. $ dsrecords myDS.ds 156999 records
– Orchadmin y
Manages PX persistent data sets – Unix command-line utility I.e. $ orchadmin rm myDataSet.ds
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Exercise ~
Complete exercises 2-1, 2-2, 2-3, and 2-4.
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Blank
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Module 3
Standards and Techniques
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Objectives ~
~
Establish standard techniques for DSPX development Will cover: – – – – – – –
Job documentation Naming conventions for jobs, links, and stages Iterative job design Useful stages for job development Using configuration files for development Using environmental variables Job parameters
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Job Presentation
Document using the annotation stage
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Job Properties Documentation Organize jobs into categories
Description shows in DS Manager and MetaStage
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Naming conventions ~
Stages named after the – Data they access – Function they perform – DO NOT leave defaulted stage names like Sequential_File_0
~
Links named for the data they carry – DO NOT leave defaulted link names like DSLink3
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Stage and Link Names
Stages and links renamed to data they handle
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Create Reusable Job Components ~
Use parallel extender shared containers when feasible
Container
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Use Iterative Job Design ~ ~
~
Use copy or peek stage as stub Test job in phases – small first, then increasing in complexity Use Peek stage to examine records
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Copy or Peek Stage Stub
Copy stage
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Transformer Stage Techniques ~
Suggestions – Always include reject link. – Always test for null value before using a column in a function. – Try to use RCP and only map columns that have a derivation other than a copy. More on RCP later. – Be aware of Column and Stage variable Data Types. y
Often user does not pay attention to Stage Variable type.
– Avoid type conversions. y
Try to maintain the data type as imported.
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The Copy Stage With 1 link in, 1 link out:
the Copy Stage is the ultimate "no-op" (place-holder): – Partitioners – Sort / Remove Duplicates – Rename, Drop column
… can be inserted on: – –
input link (Partitioning): Partitioners, Sort, Remove Duplicates) output link (Mapping page): Rename, Drop.
Sometimes replace the transformer: R
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Developing Jobs 1.
Keep it simple •
2.
Start small and Build to final Solution • • •
3.
Use view data, copy, and peek. Start from source and work out. Develop with a 1 node configuration file.
Solve the business problem before the performance problem. •
4.
Jobs with many stages are hard to debug and maintain.
Don’t worry too much about partitioning until the sequential flow works as expected.
If you have to write to Disk use a Persistent Data set.
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Final Result
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Good Things to Have in each Job ~ ~
Use job parameters Some helpful environmental variables to add to job parameters – $APT_DUMP_SCORE y
Report OSH to message log
– $APT_CONFIG_FILE y
Establishes runtime parameters to PX engine; I.e. Degree of parallelization
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Setting Job Parameters
Click to add environment variables
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DUMP SCORE Output Setting APT_DUMP_SCORE yields: Double-click
Partitoner And Collector
Mapping Node--> partition
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Use Multiple Configuration Files ~
Make a set for 1X, 2X,….
~
Use different ones for test versus production
~
Include as a parameter in each job
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Exercise ~
Complete exercise 3-1
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Module 4
DBMS Access
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Objectives ~
Understand how DSPX reads and writes records to an RDBMS
~
Understand how to handle nulls on DBMS lookup
~
Utilize this knowledge to: – Read and write database tables – Use database tables to lookup data – Use null handling options to clean data
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Parallel Database Connectivity Traditional Client-Server
Client
Parallel Extender
Client
Sort
Client Client Client
Load
Client
Parallel RDBMS
Parallel RDBMS
Only RDBMS is running in parallel
Each application has only one connection Suitable only for small data volumes
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Parallel server runs APPLICATIONS Application has parallel connections to RDBMS Suitable for large data volumes Higher levels of integration possible
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RDBMS Access
Supported Databases
Parallel Extender provides high performance / scalable interfaces for: ~
DB2
~
Informix
~
Oracle
~
Teradata
Users must be granted specific privileges, depending on RDBMS.
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RDBMS Access
Supported Databases ~
Automatically convert RDBMS table layouts to/from Parallel Extender Table Definitions
~
RDBMS nulls converted to/from nullable field values
~
Support for standard SQL syntax for specifying: – field list for SELECT statement – filter for WHERE clause – open command, close command
~ ~
Can write an explicit SQL query to access RDBMS PX supplies additional information in the SQL query
RDBMS access is relatively easy because Orchestrate extracts the schema definition for the imported data set. Litle or no work is required from the user.
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RDBMS Stages ~
DB2/UDB Enterprise
~
Teradata Enterprise
~
Informix Enterprise
~
ODBC
~
Oracle Enterprise
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RDBMS Usage ~
As a source – Extract data from table (stream link) – Extract as table, generated SQL, or user-defined SQL – User-defined can perform joins, access views
– Lookup (reference link) – Normal lookup is memory-based (all table data read into memory) – Can perform one lookup at a time in DBMS (sparse option) – Continue/drop/fail options ~
As a target – Inserts – Upserts (Inserts and updates) – Loader
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RDBMS Source – Stream Link
Stream link
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DBMS Source - User-defined SQL
Columns in SQL statement must match the meta data in columns tab
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Exercise ~
User-defined SQL – Exercise 4-1
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DBMS Source – Reference Link
Reject link
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Lookup Reject Link
“Output” option automatically creates the reject link
All columns from the input link will be placed on the rejects link. Therefore, no column tab is available for the rejects link.
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Null Handling ~
~
Must handle null condition if lookup record is not found and “continue” option is chosen Can be done in a transformer stage
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Lookup Stage Mapping
Link name
The mapping tab will show all columns from the input link and the reference link (less the column used for key lookup).
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Lookup Stage Properties Reference link
Must have same column name in input and reference links. You will get the results of the lookup in the output column.
If the lookup results in a non-match and the action was set to continue, the output column will be null.
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DBMS as a Target
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DBMS As Target ~
Write Methods – – – –
~
Delete Load Upsert Write (DB2)
Write mode for load method – – – –
Truncate Create Replace Append
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Target Properties Generated code can be copied
Upsert mode determines options
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Checking for Nulls ~
~
Use Transformer stage to test for fields with null values (Use IsNull functions) In Transformer, can reject or load default value
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Exercise ~
Complete exercise 4-2
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Module 5
Platform Architecture
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Objectives ~
~
Understand how parallel extender Framework processes data You will be able to: – Read and understand OSH – Perform troubleshooting
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Concepts ~ ~
~ ~
The PX Platform OSH (generated by DataStage Parallel Canvas, and run by DataStage Director) Conductor,Section leaders,players. Configuration files (only one active at a time, describes H/W)
~
Schemas/tables
~
Schema propagation/RCP
~
Buildop,Wrapper
~
Datasets (data in Framework's internal representation)
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DS-PX Program Elements PX Stages Involve A Series Of Processing Steps Output Data Set schema: prov_num:int16; member_num:int8; custid:int32;
Input Data Set schema: prov_num:int16; member_num:int8; custid:int32;
• Piece of Application Logic Running Against Individual Records • Parallel or Sequential
Output Interface
Business Logic
Partitioner
Input Interface
PX Stage
• Three Sources – Ascential Supplied – Commercial tools/applications – Custom/Existing programs
Each Orchestrate component has input and output interface schemas, a partitioner and business logic. Interface schemas define the names and data types of the required fields of the component’s input and output Data Sets. The component’s input interface schema requires that an input Data Set have the named fields and data types exactly compatible with those specified by the interface schema for the input Data Set to be accepted by the component. A component ignores any extra fields in a Data Set, which allows the component to be used with any data set that has at least the input interface schema of the component. This property makes it possible to add and delete fields from a relational database table or from the Orchestrate Data Set without having to rewrite code inside the component. In the example shown here, Component has an interface schema that requires three fields with named fields and data types as shown in the example. In this example, the output schema for the component is the same as the input schema. This does not always have to be the case. The partitioner is key to Orchestrate’s ability to deliver parallelism and unlimited scalability. We’ll discuss exactly how the partitioners work in a few slides, but here it’s important to point out that partitioners are an integral part of Orchestrate components.
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DS-PX Program Elements Stage Execution Dual Parallelism Eliminates Bottlenecks! • PX Delivers Parallelism in Two Ways – Pipeline – Partition
• Block Buffering Between Components
Producer
– Eliminates Need for Program Load Balancing – Maintains Orderly Data Flow
Pipeline Consumer
Partition
There is one more point to be made about the Orchestrate execution model. Orchestrate achieves parallelism in two ways. We have already talked about partitioning the records and running multiple instances of each component to speed up program execution. In addition to this partition parallelism, Orchestrate is also executing pipeline parallelism. As shown in the picture on the left, as the Orchestrate program is executing, a producer component is feeding records to a consumer component without first writing the records to disk. Orchestrate is pipelining the records forward in the flow as they are being processed by each component. This means that the consumer component is processing records fed to it by the producer component before the producer has finished processing all of the records. Orchestrate provides block buffering between components so that producers cannot produce records faster than consumers can consume those records. This pipelining of records eliminates the need to store intermediate results to disk, which can provide significant performance advantages, particularly when operating against large volumes of data.
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Stages Control Partition Parallelism ~
Execution Mode (sequential/parallel) is controlled by Stage
– default = parallel for most Ascentialsupplied Stages – User can override default mode – Parallel Stage inserts the default partitioner (Auto) on its input links – Sequential Stage inserts the default collector (Auto) on its input links – user can override default y y
execution mode (parallel/sequential) of Stage (Advanced tab) choice of partitioner/collector (Input – Partitioning Tab)
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How Parallel Is It? ~
Degree of Parallelism is determined by the configuration file
– Total number of logical nodes in default pool, or a subset if using "constraints". y
Constraints are assigned to specific pools as defined in configuration file and can be referenced in the stage
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OSH ~
DataStage PX GUI generates OSH scripts – Ability to view OSH turned on in Administrator – OSH can be viewed in Designer using job properties
~
The Framework executes OSH
~
What is OSH? – Orchestrate shell – Has a UNIX command-line interface
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OSH Script ~
An osh script is a quoted string which specifies: – The operators and connections of a single Orchestrate step – In its simplest form, it is: osh “op < in.ds > out.ds”
~
Where: – op is an Orchestrate operator – in.ds is the input data set – out.ds is the output data set
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OSH Operators
~
Operator is an instance of a C++ class inheriting from APT_Operator
~
Developers can create new operators
~
Examples of existing operators: – Import – Export – RemoveDups
Operators are the basic functional units of an Orchestrate application. Operators read records from input data sets, perform actions on the input records, and write results to output data sets. An operator may perform an action as simple as copying records from an input data set to an output data set without modification. Alternatively, an operator may modify a record by adding, removing, or modifying fields during execution. Operators are the basic functional units of an Orchestrate application. Operators read records from input data sets, perform actions on the input records, and write results to output data sets. An operator may perform an action as simple as copying records from an input data set to an output data set without modification. Alternatively, an operator may modify a record by adding, removing, or modifying fields during execution. Operators are the basic functional units of an Orchestrate application. Operators read records from input data sets, perform actions on the input records, and write results to output data sets. An operator may perform an action as simple as copying records from an input data set to an output data set without modification. Alternatively, an operator may modify a record by adding, removing, or modifying fields during execution.
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Enable Visible OSH in Administrator
Will be enabled for all projects
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View OSH in Designer
Operator
Schema
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OSH Practice ~
Exercise 5-1
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Orchestrate May Add Operators to Your Command Let’s revisit the following OSH command:
$ osh "
echo 'Hello world!' [par]
> outfile "
1)“wrapper” (turning a Unix command into an DS/PX Operator)
4)export 2)partitioner
3)collector
(from dataset to Unix flat file)
The Framework silently inserts operators (steps 1,2,3,4)
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Elements of a Framework Program Steps, with internal and terminal datasets and links, described by schemas
• Step:
unit of OSH program
– one OSH command = one step – at end of step: synchronization, storage to disk
• Datasets: set of rows processed by Framework – Orchestrate data sets: – persistent (terminal) *.ds, and – virtual (internal) *.v. – Also: flat “file sets” *.fs
• Schema: data description (metadata) for datasets and links.
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Orchestrate Datasets • Consist of Partitioned Data and Schema • Can be Persistent (*.ds) or Virtual (*.v, Link) • Overcome 2 GB File Limit What you program: GUI
=
What gets generated: OSH
What gets processed:
data files of x.ds
$ osh “operator_A > x.ds“
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Node 1
Node 2
Node 3
Node 4
Operator A
Operator A
Operator A
Operator A
. . .
Multiple files per partition Each file up to 2GBytes (or larger)
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Computing Architectures: Definition Dedicated Disk
Shared Disk
Disk
Disk
CPU
Memory
Uniprocessor
CPU CPU CPU CPU
Shared Memory
SMP System
Shared Nothing
Disk
Disk
Disk
Disk
CPU
CPU
CPU
CPU
Memory
Memory
Memory
Memory
Clusters and MPP Systems
(Symmetric Multiprocessor) • PC • Workstation • Single processor server
• IBM, Sun, HP, Compaq • 2 to 64 processors • Majority of installations
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• 2 to hundreds of processors • MPP: IBM and NCR Teradata • each node is a uniprocessor or SMP
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Job Execution: Orchestrate Conductor Node C
– – – –
Processing Node SL
P
P
Step Composer Creates Section Leader processes (one/node) Consolidates massages, outputs them Manages orderly shutdown.
• Section Leader P
– Forks Players processes (one/Stage) – Manages up/down communication.
• Players
Processing Node SL
P
• Conductor - initial DS/PX process
P
P
• Communication:
– The actual processes associated with Stages – Combined players: one process only – Send stderr to SL – Establish connections to other players for data flow – Clean up upon completion.
- SMP: Shared Memory - MPP: TCP
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Working with Configuration Files ~
You can easily switch between config files: y
'1-node' file
testing y y ~
- for sequential execution, lighter reports—handy for
'MedN-nodes' file - aims at a mix of pipeline and data-partitioned parallelism 'BigN-nodes' file - aims at full data-partitioned parallelism
Only one file is active while a step is running y
The Framework queries (first) the environment variable:
$APT_CONFIG_FILE
~
# nodes declared in the config file needs not match # CPUs y
Same configuration file can be used in development and target machines
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Scheduling Nodes, Processes, and CPUs ~
DS/PX does not: – know how many CPUs are available – schedule
~
Who knows what? Nodes
Ops
User
Y
N
Orchestrate
Y
Y
O/S ~
Nodes = # logical nodes declared in config. file Ops = # ops. (approx. # blue boxes in V.O.) Processes = # Unix processes CPUs = # available CPUs
Processes
CPUs
Nodes * Ops
N
"
Y
Who does what? – DS/PX creates (Nodes*Ops) Unix processes – The O/S schedules these processes on the CPUs
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Configuring DSPX – Node Pools {
3
4
1
2
node "n1" { fastname "s1" pool "" "n1" "s1" "app2" "sort" resource disk "/orch/n1/d1" {} resource disk "/orch/n1/d2" {} resource scratchdisk "/temp" {"sort"} } node "n2" { fastname "s2" pool "" "n2" "s2" "app1" resource disk "/orch/n2/d1" {} resource disk "/orch/n2/d2" {} resource scratchdisk "/temp" {} } node "n3" { fastname "s3" pool "" "n3" "s3" "app1" resource disk "/orch/n3/d1" {} resource scratchdisk "/temp" {} } node "n4" { fastname "s4" pool "" "n4" "s4" "app1" resource disk "/orch/n4/d1" {} resource scratchdisk "/temp" {} }
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Configuring DSPX – Disk Pools {
3
4
1
2
node "n1" { fastname "s1" pool "" "n1" "s1" "app2" "sort" resource disk "/orch/n1/d1" {} resource disk "/orch/n1/d2" {"bigdata"} resource scratchdisk "/temp" {"sort"} } node "n2" { fastname "s2" pool "" "n2" "s2" "app1" resource disk "/orch/n2/d1" {} resource disk "/orch/n2/d2" {"bigdata"} resource scratchdisk "/temp" {} } node "n3" { fastname "s3" pool "" "n3" "s3" "app1" resource disk "/orch/n3/d1" {} resource scratchdisk "/temp" {} } node "n4" { fastname "s4" pool "" "n4" "s4" "app1" resource disk "/orch/n4/d1" {} resource scratchdisk "/temp" {} }
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Re-Partitioning Parallel to parallel flow may incur reshuffling: Records may jump between nodes node 1
node 2
partitioner
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Re-Partitioning X-ray Partitioner with parallel import When a partitioner receives: node 1
node 2
N
• sequential input (1 partition), it creates N partitions
• parallel input (N partitions), it outputs N partitions*, may result in re-partitioning
* Assuming
N
no “constraints”
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Automatic Re-Partitioning partition 1
If Stage 2 runs in parallel, DS/PX silently inserts a partitioner upstream of it. If Stage 1 also runs in parallel, re-partitioning occurs.
partition 2
Stage 1
partitioner
Stage 2
In most cases, automatic repartitioning is benign (no reshuffling), preserving the same partitioning as upstream.
Re-partitioning can be forced to be benign, using either:
same preserve partitioning
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Partitioning Methods ~
Auto
~
Hash
~
Entire
~
Range
~
Range Map
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Collectors • Collectors combine partitions of a dataset into a single input stream to a sequential Stage ...
data partitions
collector
–Collectors do NOT synchronize data
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sequential Stage
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Partitioning and Repartitioning Are Visible On Job Design
Partitioners and collectors have no stage nor icons of their own. They live live on input links of stages running in parallel (resp. sequentially). Link markings indicate their presence. S----------------->S (no Marking) S----(fan out)--->P (partitioner) P----(fan in) ---->S (collector) P----(box)------->P (no reshuffling: partitioner using "SAME" method) P----(bow tie)--->P (reshuffling: partitioner using another method) Collectors = inverse partitioners recollect rows from partitions into a single input stream to a sequential stage
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Partitioning and Collecting Icons
Partitioner
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Collector
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Setting a Node Constraint in the GUI
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Reading Messages in Director ~
Set APT_DUMP_SCORE to true
~
Can be specified as job parameter
~
Messages sent to Director log
~
If set, parallel job will produce a report showing the operators, processes, and datasets in the running job
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Messages With APT_DUMP_SCORE = True
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Exercise ~
Complete exercise 5-2
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Blank
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Module 6
Transforming Data
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Module Objectives ~
~
Understand ways DataStage allows you to transform data Use this understanding to: – Create column derivations using user-defined code or system functions – Filter records based on business criteria – Control data flow based on data conditions
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Transformed Data ~
Transformed data is: – Outgoing column is a derivation that may, or may not, include incoming fields or parts of incoming fields – May be comprised of system variables
~
Frequently uses functions performed on something (ie. incoming columns) – Divided into categories – I.e. y y y y y
Date and time Mathematical Logical Null handling More
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Stages Review ~
Stages that can transform data – Transformer y y
Parallel Basic (from Parallel palette)
– Aggregator (discussed in later module)
~
Sample stages that do not transform data – – – –
Sequential FileSet DataSet DBMS
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Transformer Stage Functions ~
Control data flow
~
Create derivations
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Flow Control ~
~ ~
Separate record flow down links based on data condition – specified in Transformer stage constraints Transformer stage can filter records Other stages can filter records but do not exhibit advanced flow control – Sequential – Lookup – Filter
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Rejecting Data ~
Reject option on sequential stage – Data does not agree with meta data – Output consists of one column with binary data type
~
Reject links (from Lookup stage) result from the drop option of the property “If Not Found” – Lookup “failed” – All columns on reject link (no column mapping option)
~
Reject constraints are controlled from the constraint editor of the transformer – Can control column mapping – Use the “Other/Log” checkbox
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Rejecting Data Example
Contstraint – Other/log option Property Reject Mode = Output
“If Not Found” property
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Transformer Stage Properties
Link naming conventions are important because they identify appropriate links in the stage properties screen shown above. Four quadrants: Incoming data link (one only) Outgoing links (can have multiple) Meta data for all incoming links Meta data for all outgoing links – may have multiple tabs if there are multiple outgoing links Note the constraints bar – if you double-click on any you will get screen for defining constraints for all outgoing links.
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Transformer Stage Variables ~
First of transformer stage entities to execute
~
Execute in order from top to bottom – Can write a program by using one stage variable to point to the results of a previous stage variable
~
Multi-purpose – – – –
Counters Hold values for previous rows to make comparison Hold derivations to be used in multiple field dervations Can be used to control execution of constraints
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Stage Variables
Show/Hide button
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Transforming Data ~
Derivations – Using expressions – Using functions y
~
Date/time
Transformer Stage Issues – Sometimes require sorting before the transformer stage – I.e. using stage variable as accumulator and need to break on change of column value
~
Checking for nulls
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Checking for Nulls ~
~
Nulls can get introduced into the dataflow because of failed lookups and the way in which you chose to handle this condition Can be handled in constraints, derivations, stage variables, or a combination of these
If you perform a lookup from a lookup stage and choose the continue option for a failed lookup, you have the possibility of nulls entering your data flow.
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Nullability Can set the value of null; i.e.. If value of column is null put “NULL” in the outgoing column
Source Field
Destination Field
not_nullable
not_nullable
not_nullable
nullable
Source value propagates; destination value is never null.
nullable
not_nullable
nullable
nullable
WARNING messages in log. If source value is null,a fatal error occurs. Must handle in transformer. Source value or null propagates.
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Result Source value propagates to destination.
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Transformer Stage - Handling Rejects 1.
Constraint Rejects – All expressions are false and reject row is checked
2.
Expression Error Rejects –
Improperly Handled Null
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Transformer: Execution Order
• Derivations in stage variables are executed first • Constraints are executed before derivations • Column derivations in earlier links are executed before later links • Derivations in higher columns are executed before lower columns
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Two Transformers for the Parallel Palette ~
All > Processing >
~
Parallel > Processing
~
Transformer
~
Basic Transformer
~
~
~
Is the non-Universe transformer
~
Has a specific set of functions
~
No DS routines available
~
Makes server style transforms available on the parallel palette Can use DS routines No need for shared container to get Universe functionality on the parallel palette
•Program in Basic for both transformers
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Transformer Functions From Derivation Editor ~
Data & Time
~
Logical
~
Mathematical
~
Null Handling
~
Number
~
Raw
~
String
~
Type Conversion
~
Utility
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Timestamps and Dates ~
Data & Time
~
Also some in Type Conversion
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Exercise ~
Complete exercises 6-1, 6-2, and 6-3
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Module 7
Sorting Data
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Objectives ~ ~
Understand DataStage PX sorting options Use this understanding to create sorted list of data to enable functionality within a transformer stage
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Sorting Data ~
Important because – Transformer may be using stage variables for accumulators or control breaks and order is important – Other stages may run faster – I.e Aggregator – Facilitates the RemoveDups stage, order is important – Job has partitioning requirements
~
Can be performed – Option within stages (use input > partitioning tab and set partitioning to anything other than auto) – As a separate stage (more complex sorts)
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Sorting Alternatives
• Alternative representation of same flow:
One of the nation's largest direct marketing outfits has been using this simple program in DS-PX (and its previous instantiations) for years. Householding yields enormous savings by avoiding mailing the same material (in particular expensive catalogs) to the same household.
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Sort Option on Stage Link
Stable will not rearrange records that are already in a properly sorted data set. If set to false no prior ordering of records is guaranteed to be preserved by the sorting operation.
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Sort Stage
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Sort Utility ~
DataStage – the default
~
SyncSort
~
UNIX
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Sort Stage - Outputs ~
Specifies how the output is derived
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Sort Specification Options ~
Input Link Property
– Limited functionality – Max memory/partition is 20 MB, then spills to scratch ~
Sort Stage
– Tunable to use more memory before spilling to scratch. ~
Note: Spread I/O by adding more scratch file systems to each node of the APT_CONFIG_FILE
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Removing Duplicates ~
Can be done by Sort – Use unique option
OR
~
Remove Duplicates stage – Has more sophisticated ways to remove duplicates
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Exercise ~
Complete exercise 7-1
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Blank
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Module 8
Combining Data
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Objectives ~
~
Understand how DataStage can combine data using the Join, Lookup, Merge, and Aggregator stages Use this understanding to create jobs that will – Combine data from separate input streams – Aggregate data to form summary totals
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Combining Data ~
There are two ways to combine data: – Horizontally: Several input links; one output link (+ optional rejects) made of columns from different input links. E.g., y y y
Joins Lookup Merge
– Vertically: One input link, output with column combining values from all input rows. E.g., y
Aggregator
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Recall the Join, Lookup & Merge Stages ~
~
These "three Stages" combine two or more input links according to values of user-designated "key" column(s). They differ mainly in: – Memory usage – Treatment of rows with unmatched key values – Input requirements (sorted, de-duplicated)
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Joins - Lookup - Merge: Not all Links are Created Equal! • Parallel Extender distinguishes between: - The Primary Input (Framework port 0) - Secondary - in some cases "Reference" (other ports)
• Naming convention: Primary Input: port 0 Secondary Input(s): ports 1,…
Joins
Lookup
Merge
Left Right
Source LU Table(s)
Master Update(s)
Tip: Check "Input Ordering" tab to make sure intended Primary is listed first
The Framework concept of Port # is translated in the GUI by Primary/Reference
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Join Stage Editor
Link Order immaterial for Inner and Full Outer Joins (but VERY important for Left/Right Outer and Lookup and Merge)
One of four variants: – Inner – Left Outer – Right Outer – Full Outer
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Several key columns allowed
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1. The Join Stage Four types: • Inner • Left Outer • Right Outer • Full Outer
~
2 sorted input links, 1 output link – "left" on primary input, "right" on secondary input – Pre-sort make joins "lightweight": few rows need to be in RAM
Follow the RDBMS-style relational model: the operations Join and Load in RDBMS commute. x-products in case of duplicates, matching entries are reusable No fail/reject/drop option for missed matches
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2. The Lookup Stage Combines: – one source link with – one or more duplicate-free table links Source input
0 0
Output
One or more tables (LUTs)
1
no pre-sort necessary allows multiple keys LUTs flexible exception handling for source input rows with no match
2
Lookup
1
Reject
Contrary to Join, Lookup and Merge deal with missing rows. Obviously, a missing row cannot be captured, since it is missing. The closest thing one can capture is the corresponding unmatched row. Lookup can capture in a reject link unmatched rows from the primary input(Source). That is why it has only one reject link (there is only one primary). We'll see the reject option is exactly the opposite with the Merge stage.
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The Lookup Stage
~
Lookup Tables should be small enough to fit into physical memory (otherwise, performance hit due to paging)
– Space time trade-off: presort vs. in RAM table ~
~
On an MPP you should partition the lookup tables using entire partitioning method, or partition them the same way you partition the source link On an SMP, no physical duplication of a Lookup Table occurs
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The Lookup Stage ~
Lookup File Set – Like a persistent data set only it contains metadata about the key. – Useful for staging lookup tables
~
RDBMS LOOKUP – SPARSE y y
Select for each row. Might become a performance bottleneck.
– NORMAL y
Loads to an in memory hash table first.
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3. The Merge Stage ~
Combines – one sorted, duplicate-free master (primary) link with – one or more sorted update (secondary) links. – Pre-sort makes merge "lightweight": few rows need to be in RAM (as with joins, but opposite to lookup).
~
Follows the Master-Update model: – Master row and one or more updates row are merged if they have the same value in user-specified key column(s). – A non-key column occurs in several inputs? The lowest input port number prevails (e.g., master over update; update values are ignored) – Unmatched ("Bad") master rows can be either y y
kept dropped
– Unmatched ("Bad") update rows in input link can be captured in a "reject" link – Matched update rows are consumed.
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The Merge Stage
Master
One or more updates
0 0
1
2
1
2
Merge
Output
Allows composite keys Multiple update links Matched update rows are consumed Unmatched updates can be captured Lightweight Space/time tradeoff: presorts vs. in-RAM table
Rejects
Contrary to Lookup, Merges captures unmatched secondary (update) rows. Since there may be several update links, there may be several reject links.
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Synopsis: Joins, Lookup, & Merge Joins
Lookup
Merge
Model Memory usage
RDBMS-style relational light
Source - in RAM LU Table heavy
Master -Update(s) light
# and names of Inputs Mandatory Input Sort Duplicates in primary input Duplicates in secondary input(s) Options on unmatched primary Options on unmatched secondary On match, secondary entries are
exactly 2: 1 left, 1 right both inputs OK (x-product) OK (x-product) NONE NONE reusable
1 Source, N LU Tables
1 Master, N Update(s)
no OK Warning! [fail] | continue | drop | reject NONE reusable
all inputs Warning! OK only when N = 1 [keep] | drop capture in reject set(s) consumed
1 Nothing (N/A)
1 out, (1 reject) unmatched primary entries
1 out, (N rejects) unmatched secondary entries
# Outputs Captured in reject set(s)
In this table: • ,
= separator between primary and secondary input links (out and reject links)
This table contains everything one needs to know to use the three stages.
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The Aggregator Stage Purpose: Perform data aggregations Specify: ~
Zero or more key columns that define the aggregation units (or groups)
~
Columns to be aggregated
~
Aggregation functions: count (nulls/non-nulls) standard error sum of weights variance
~
sum max/min/range %coeff. of variation un/corrected sum of squares mean standard deviation
The grouping method (hash table or pre-sort) is a performance issue
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Grouping Methods ~
Hash: results for each aggregation group are stored in a hash table, and the table is written out after all input has been processed – doesn’t require sorted data – good when number of unique groups is small. Running tally for each group’s aggregate calculations need to fit easily into memory. Require about 1KB/group of RAM. – Example: average family income by state, requires .05MB of RAM
~
Sort: results for only a single aggregation group are kept in memory; when new group is seen (key value changes), current group written out. – requires input sorted by grouping keys – can handle unlimited numbers of groups – Example: average daily balance by credit card
WARNING! Hash has nothing to do with the Hash Partitioner! It says one hash table per group must be carried in RAM Sort has nothing to do with the Sort Stage. Just says expects sorted input.
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Aggregator Functions ~
Sum
~
Min, max
~
Mean
~
Missing value count
~
Non-missing value count
~
Percent coefficient of variation
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Aggregator Properties
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Aggregation Types
Aggregation types
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Containers ~
Two varieties – Local – Shared
~
Local – Simplifies a large, complex diagram
~
Shared – Creates reusable object that many jobs can include
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Creating a Container ~
Create a job
~
Select (loop) portions to containerize
~
Edit > Construct container > local or shared
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Using a Container ~
Select as though it were a stage
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Exercise ~
Complete exercise 8-1
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Module 9
Configuration Files
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Objectives ~
~
Understand how DataStage PX uses configuration files to determine parallel behavior Use this understanding to – Build a PX configuration file for a computer system – Change node configurations to support adding resources to processes that need them – Create a job that will change resource allocations at the stage level
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Configuration File Concepts ~
~
~
Determine the processing nodes and disk space connected to each node When system changes, need only change the configuration file – no need to recompile jobs When DataStage job runs, platform reads configuration file – Platform automatically scales the application to fit the system
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Processing Nodes Are ~
~ ~
Locations on which the framework runs applications Logical rather than physical construct Do not necessarily correspond to the number of CPUs in your system – Typically one node for two CPUs
~
Can define one processing node for multiple physical nodes or multiple processing nodes for one physical node
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Optimizing Parallelism ~
~
Degree of parallelism determined by number of nodes defined Parallelism should be optimized, not maximized – Increasing parallelism distributes work load but also increases Framework overhead
~
~
Hardware influences degree of parallelism possible System hardware partially determines configuration
The hardware that makes up your system partially determines configuration. For example, applications with large memory requirements, such as sort operations, are best assigned to machines with a lot of memory. Applications that will access an RDBMS must run on its server nodes; operators using other proprietary software, such as SAS or SyncSort, must run on nodes with licenses for that software.
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More Factors to Consider ~
Communication amongst operators – Should be optimized by your configuration – Operators exchanging large amounts of data should be assigned to nodes communicating by shared memory or high-speed link
~
SMP – leave some processors for operating system
~
Desirable to equalize partitioning of data
~
Use an experimental approach – Start with small data sets – Try different parallelism while scaling up data set sizes
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Factors Affecting Optimal Degree of Parallelism ~
CPU intensive applications – Benefit from the greatest possible parallelism
~
Applications that are disk intensive – Number of logical nodes equals the number of disk spindles being accessed
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PX Configuration File ~
Text file containing string data that is passed to the Framework – Sits on server side – Can be displayed and edited
~
~
Name and location found in environmental variable APT_CONFIG_FILE Components – – – –
Node Fast name Pools Resource
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Sample Configuration File { node “Node1" { fastname "BlackHole" pools "" "node1" resource disk "/usr/dsadm/Ascential/DataStage/Datasets" {pools "" } resource scratchdisk "/usr/dsadm/Ascential/DataStage/Scratch" {pools "" } } }
For a single node system node name usually set to value of UNIX command – uname –n Fastname attribute is the name of the node as it is referred to on the fastest network in the system, such as an IBM switch, FDDI, or BYNET. The fast name is the physical node name that operators use to open connections for high-volume data transfers. Typically this is the principal node name as returned by the UNIX command uname –n.
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Node Options •
Node name - name of a processing node used by PX – Typically the network name – Use command uname -n to obtain network name
~
Fastname – – Name of node as referred to by fastest network in the system – Operators use physical node name to open connections – NOTE: for SMP, all CPUs share single connection to network
~
Pools – Names of pools to which this node is assigned – Used to logically group nodes – Can also be used to group resources
~
Resource – Disk – Scratchdisk
Set of node pool reserved names: DB2 Oracle Informix Sas Sort Syncsort
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Node Pools ~
Node “node1" { fastname “server_name” pool "pool_name” }
~
"pool_name" is the name of the node pool. I.e. “extra”
~
Node pools group processing nodes based on usage. – Example: memory capacity and high-speed I/O.
~
One node can be assigned to multiple pools.
Default node pool (” ") is made up of each node defined in the config file, unless it’s qualified as belonging to a different pool and it is not designated as belonging to the default pool (see following example).
~
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Resource Disk and Scratchdisk node “node_0" { fastname “server_name” pool "pool_name” resource disk “path” {pool “pool_1”} resource scratchdisk “path” {pool “pool_1”} ... }
~ ~
Resource type can be disk(s) or scratchdisk(s) "pool_1" is the disk or scratchdisk pool, allowing you to group disks and/or scratchdisks.
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Disk Pools ~
Disk pools allocate storage
~
Pooling applies to both disk types
pool "bigdata"
~
By default, PX uses the default pool, specified by “”
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Sorting Requirements
Resource pools can also be specified for sorting: ~
~
The Sort stage looks first for scratch disk resources in a “sort” pool, and then in the default disk pool Sort uses as many scratch disks as defined in the first pool it finds
Recommendations: Each logical node defined in the configuration file that will run sorting operations should have its own sort disk. Each logical node's sorting disk should be a distinct disk drive or a striped disk, if it is shared among nodes. In large sorting operations, each node that performs sorting should have multiple disks, where a sort disk is a scratch disk available for sorting that resides in either the sort or default disk pool.
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Configuration File: Example { node "n1" { fastname “s1" pool "" "n1" "s1" "sort" resource disk "/data/n1/d1" {} resource disk "/data/n1/d2" {} resource scratchdisk "/scratch" } node "n2" { fastname "s2" pool "" "n2" "s2" "app1" resource disk "/data/n2/d1" {} resource scratchdisk "/scratch" } node "n3" { fastname "s3" pool "" "n3" "s3" "app1" resource disk "/data/n3/d1" {} resource scratchdisk "/scratch" } node "n4" { fastname "s4" pool "" "n4" "s4" "app1" resource disk "/data/n4/d1" {} resource scratchdisk "/scratch" } ...
6
4
5
2
3 1
{"sort"}
{}
{}
{}
}
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Resource Types ~
Disk
~
Scratchdisk
~
DB2
~
Oracle
~
Saswork
~
Sortwork
~
Can exist in a pool – Groups resources together
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Using Different Configurations
Lookup stage where DBMS is using a sparse lookup type
In this instance, since a sparse lookup is viewed as the bottleneck, the stage has been set to execute on multiple nodes.
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Building a Configuration File ~
Scoping the hardware: – Is the hardware configuration SMP, Cluster, or MPP? – Define each node structure (an SMP would be single node): y y y y y
Number of CPUs CPU speed Available memory Available page/swap space Connectivity (network/back-panel speed)
– Is the machine dedicated to PX? If not, what other applications are running on it? – Get a breakdown of the resource usage (vmstat, mpstat, iostat) – Are there other configuration restrictions? E.g. DB only runs on certain nodes and ETL cannot run on them?
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To Create Hardware Specifications Complete one per disk subsystem
Complete one per node _____________ TCP Addr
_____________
Hardware Type ________________
Switch Addr
Shared between nodes
Hardware Type ___________ Node Name _____________ # CPUs
_____________
# channels/ controllers____ Throughput ____
CPU Speed _____________ Memory
_____________
Page Space _____________
Y or N
Storage Type
________________
Storage Size
________________
Read Cache size _______________ Write Cache size _______________ Read hit ratio
_______________
Write hit ratio
_______________
I/O rate (R/W)
_______________
Swap Space _____________
In addition, record all coexistence usage (other applications or subsystems sharing this disk subsystem).
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“Ballpark” Tuning As a rule of thumb, generally expect the application to be I/O constrained. First try to parallelize (spread out) the I/O as much as possible. To validate that you’ve achieved this: 1.
2. 3.
4. 5.
Calculate the theoretic I/O bandwidth available (don’t forget to reduce this by the amount other applications on this machine or others are impacting the I/O subsystem). See DS Performance and tuning for calculation methods. Determine the I/O bandwidth being achieved by the DS application (rows/sec * bytes/row). If the I/O rate isn’t approximately equal to the theoretical, there is probably a bottleneck elsewhere (CPU, Memory, etc). Attempt to tune to the I/O bandwidth. Pay particular attention to I/O intensive competing workloads such as database logging, paging/swapping, etc.
Some useful commands: iostat (I/O activity) vmstat (system activity) mpstat (processor utilization sar (resource usage)
When running out of capacity: Tuning: sequentially removing bottlenecks. Look at throughout on the I/O subsystem: Take amount of data moved times number of I/O – gives bandwith Look at sar, iostat, or mpstat.
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Exercise ~
Complete exercise 9-1 and 9-2
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Blank
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Module 10
Extending DataStage PX
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Objectives ~
~
Understand the methods by which you can add functionality to PX Use this understanding to: – Build a DataStage PX stage that handles special processing needs not supplied with the vanilla stages – Build a DataStage PX job that uses the new stage
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PX Extensibility Overview Sometimes it will be to your advantage to leverage PX’s extensibility. This extensibility includes: ~
Wrappers
~
Buildops
~
Custom Stages
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When To Leverage PX Extensibility Types of situations: Complex business logic, not easily accomplished using standard PX stages Reuse of existing C, C++, Java, COBOL, etc…
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Wrappers vs. Buildop vs. Custom ~
~
~
Wrappers are good if you cannot or do not want to modify the application and performance is not critical. Buildops: good if you need custom coding but do not need dynamic (runtime-based) input and output interfaces. Custom (C++ coding using framework API): good if you need custom coding and need dynamic input and output interfaces.
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Building “Wrapped” Stages You can “wrapper” a legacy executable: y Binary y Unix command y Shell script … and turn it into a Parallel Extender stage capable, among other things, of parallel execution… As long as the legacy executable is: y amenable to data-partition parallelism y
y
no dependencies between rows
pipe-safe y y
can read rows sequentially no random access to data
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Wrappers (Cont’d)
Wrappers are treated as a black box ~ ~
~
~
~
PX has no knowledge of contents PX has no means of managing anything that occurs inside the wrapper PX only knows how to export data to and import data from the wrapper User must know at design time the intended behavior of the wrapper and its schema interface If the wrappered application needs to see all records prior to processing, it cannot run in parallel.
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LS Example
~
Can this command be wrappered?
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Creating a Wrapper
To create the “ls” stage
Used in this job ---Æ
Example: wrapping ls Unix command: Ls /opt/AdvTrain/dnich would yield a list of files and subdirectories. The wrapper is thus comprised of the command and a parameter that contains a disk location.
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Wrapper Starting Point
Creating Wrapped Stages From Manager: Right-Click on Stage Type > New Parallel Stage > Wrapped
We will "Wrapper” an existing Unix executables – the ls command
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Wrapper - General Page
Name of stage
Unix command to be wrapped
Unix ls command can take several arguments but we will use it in its simplest form: Ls location Where location will be passed into the stage with a job parameter.
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The "Creator" Page
Conscientiously maintaining the Creator page for all your wrapped stages will eventually earn you the thanks of others.
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Wrapper – Properties Page ~
If your stage will have properties appear, complete the Properties page
This will be the name of the property as it appears in your stage
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Wrapper - Wrapped Page
Interfaces – input and output columns these should first be entered into the table definitions meta data (DS Manager); let’s do that now.
The Interfaces > input and output describe the meta data for how you will communicate with the wrapped application.
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Interface schemas • Layout interfaces describe what columns the stage: – Needs for its inputs (if any) – Creates for its outputs (if any) – Should be created as tables with columns in Manager
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Column Definition for Wrapper Interface
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How Does the Wrapping Work?
– Define the schema for export and import y Schemas become interface schemas of the operator and allow for by-name column access – Define multiple inputs/outputs required by UNIX executable
input schema export stdin or named pipe UNIX executable stdout or named pipe import output schema
QUIZ: Why does export precede import?
Answer: You must first EXIT the DS-PX environment to access the vanilla Unix environment. Then you must reenter the DS-PX environment.
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Update the Wrapper Interfaces ~
This wrapper will have no input interface – i.e. no input link. The location will come as a job parameter that will be passed to the appropriate stage property.
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Resulting Job
Wrapped stage
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Job Run ~
Show file from Designer palette
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Wrapper Story: Cobol Application ~
Hardware Environment: – IBM SP2, 2 nodes with 4 CPU’s per node.
~
Software: – DB2/EEE, COBOL, PX
~
Original COBOL Application: – Extracted source table, performed lookup against table in DB2, and Loaded results to target table. – 4 hours 20 minutes sequential execution
~
Parallel Extender Solution: – Used PX to perform Parallel DB2 Extracts and Loads – Used PX to execute COBOL application in Parallel – PX Framework handled data transfer between DB2/EEE and COBOL application – 30 minutes 8-way parallel execution
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Buildops Buildop provides a simple means of extending beyond the functionality provided by PX, but does not use an existing executable (like the wrapper) Reasons to use Buildop include: ~ ~
Speed / Performance Complex business logic that cannot be easily represented using existing stages – Lookups across a range of values – Surrogate key generation – Rolling aggregates
~
~
Build once and reusable everywhere within project, no shared container necessary Can combine functionality from different stages into one
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BuildOps – The user performs the fun tasks: encapsulate the business logic in a custom operator – The Parallel Extender interface called “buildop” automatically performs the tedious, error-prone tasks: invoke needed header files, build the necessary “plumbing” for a correct and efficient parallel execution. – Exploits extensibility of PX Framework
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BuildOp Process Overview
From Manager (or Designer): Repository pane: Right-Click on Stage Type > New Parallel Stage > {Custom | Build | Wrapped}
• "Build" stages from within Parallel Extender • "Wrapping” existing “Unix” executables
That is the fun one! There is programming! This section just sows the flavor of what Buildop can do.
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General Page Identical to Wrappers, except:
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Under the Build Tab, your program!
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Logic Tab for Business Logic
Enter Business C/C++ logic and arithmetic in four pages under the Logic tab Main code section goes in Per-Record page- it will be applied to all rows
NOTE: Code will need to be Ansi C/C++ compliant. If code does not compile outside of PX, it won’t compile within PX either!
The four tabs are Definitions, Pre-Loop, Per-Record, Post-Loop. The main action is in Per-record. Definitions is used to declare and initialize variables Pre-loop has code to be performed prior to processing the first row. Post-loop has code to be performed after processing the last row
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Code Sections under Logic Tab
Temporary variables declared [and initialized] here
Logic here is executed once BEFORE processing the FIRST row
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Logic here is executed once AFTER processing the LAST row
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I/O and Transfer Under Interface tab: Input, Output & Transfer pages
First line: output 0
Optional renaming of output port from default "out0"
Write row Input page: 'Auto Read' Read next row
In-Repository Table Definition
'False' setting, not to interfere with Transfer page
This is the Output page. This Input page is the same, except it has an "Auto Read," instead of the "Auto Write" column. The Input/Output interface TDs must be prepared in advance and put in the repository.
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I/O and Transfer
First line: Transfer of index 0
• Transfer from input in0 to output out0. • If page left blank or Auto Transfer = "False" (and RCP = "False") Only columns in output Table Definition are written
The role of Transfer will be made clearer soon with examples.
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Building Stages Simple Example
~
Example - sumNoTransfer – Add input columns "a" and "b"; ignores other columns that might be present in input – Produces a new "sum" column – Do not transfer input columns
a:int32; b:int32
sumNoTransfer sum:int32
Only the column(s) explicitly listed in the output TD survive.
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No Transfer
From Peek: NO TRANSFER
• Causes: - RCP set to "False" in stage definition and - Transfer page left blank, or Auto Transfer = "False"
• Effects: - input columns "a" and "b" are not transferred - only new column "sum" is transferred
Compare with transfer ON…
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Transfer
TRANSFER
• Causes: - RCP set to "True" in stage definition or - Auto Transfer set to "True"
• Effects: - new column "sum" is transferred, as well as - input columns "a" and "b" and - input column "ignored" (present in input, but not mentioned in stage)
All the columns in the input link are transferred, irrespective of what the Input/Output TDs say.
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Adding a Column With Row ID
Out Table;
Output
Hint (resp. solution) of quiz on next slide (resp. note)
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Columns vs. Temporary C++ Variables Columns DS-PX type
~ ~
Defined in Table Definitions
Temp C++ variables
~ ~
~ ~
Value refreshed from row to row
C/C++ type Need declaration (in Definitions or Pre-Loop page) Value persistent throughout "loop" over rows, unless modified in code
ANSWER to QUIZ Replacing index = count++; with index++ ; would result in index=1 throughout. See bottom bullet in left column.
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Exercise ~
Complete exercise 10-1 and 10-2
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Exercise ~
Complete exercises 10-3 and 10-4
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Custom Stage ~
Reasons for a custom stage: – Add PX operator not already in DataStage PX – Build your own Operator and add to DataStage PX
~ ~
Use PX API Use Custom Stage to add new operator to PX canvas
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Custom Stage DataStage Manager > select Stage Types branch > right click
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Custom Stage
Number of input and output links allowed
Name of Orchestrate operator to be used
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Custom Stage – Properties Tab
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The Result
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Module 11
Meta Data in DataStage PX
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Objectives ~
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Understand how PX uses meta data, particularly schemas and runtime column propagation Use this understanding to: – Build schema definition files to be invoked in DataStage jobs – Use RCP to manage meta data usage in PX jobs
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Establishing Meta Data ~
Data definitions – Recordization and columnization – Fields have properties that can be set at individual field level y
Data types in GUI are translated to types used by PX
– Described as properties on the format/columns tab (outputs or inputs pages) OR – Using a schema file (can be full or partial) ~
Schemas – Can be imported into Manager – Can be pointed to by some job stages (i.e. Sequential)
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Data Formatting – Record Level ~
Format tab
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Meta data described on a record basis
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Record level properties
To view documentation on each of these properties, open a stage > input or output > format. Now hover your cursor over the property in question and help text will appear.
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Data Formatting – Column Level ~
Defaults for all columns
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Column Overrides ~
Edit row from within the columns tab
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Set individual column properties
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Extended Column Properties
Field and string settings
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Extended Properties – String Type ~
Note the ability to convert ASCII to EBCDIC
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Editing Columns
Properties depend on the data type
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Schema ~
Alternative way to specify column definitions for data used in PX jobs
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Written in a plain text file
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Can be written as a partial record definition
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record {final_delim=end, delim=",", quote=double} Can be imported into the DataStage repository ( first_name: string; last_name: string; gender: string; birth_date: date; income: decimal[9,2]; state: string; )
The format of each line describing a column is: column_name:[nullability]datatype; column_name. This is the name that identifies the column. Names must start with a letter or an underscore (_), and can contain only alphanumeric or underscore characters. The name is not case sensitive. The name can be of any length. nullability. You can optionally specify whether a column is allowed to contain a null value, or whether this would be viewed as invalid. If the column can be null, insert the word ’nullable’. By default columns are not nullable. You can also include ’nullable’ at record level to specify that all columns are nullable, then override the setting for individual columns by specifying ‘not nullable’. For example: record nullable ( name:not nullable string[255]; value1:int32; Copyright ©2004 Ascential Software Corporation
date:date)
datatype. This is the data type of the column.
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Creating a Schema ~
Using a text editor – Follow correct syntax for definitions – OR
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Import from an existing data set or file set – On DataStage Manager import > Table Definitions > Orchestrate Schema Definitions – Select checkbox for a file with .fs or .ds
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Importing a Schema
Schema location can be on the server or local work station
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Data Types ~
Date
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Vector
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Decimal
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Subrecord
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Floating point
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Raw
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Integer
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Tagged
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String
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Time
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Timestamp
Raw – collection of untyped bytes Vector – elemental array Subrecord – a record within a record (elements of a group level) Tagged – column linked to another column that defines its datatype
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Partial Schemas ~
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Only need to define column definitions that you are actually going to operate on Allowed by stages with format tab – – – – –
Sequential file stage File set stage External source stage External target stage Column import stage
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Runtime Column Propagation ~
DataStage PX is flexible about meta data. It can cope with the situation where meta data isn’t fully defined. You can define part of your schema and specify that, if your job encounters extra columns that are not defined in the meta data when it actually runs, it will adopt these extra columns and propagate them through the rest of the job. This is known as runtime column propagation (RCP).
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RCP is always on at runtime.
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Design and compile time column mapping enforcement.
– RCP is off by default. – Enable first at project level. (Administrator project properties) – Enable at job level. (job properties General tab) – Enable at Stage. (Link Output Column tab)
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Enabling RCP at Project Level
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Enabling RCP at Job Level
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Enabling RCP at Stage Level ~ ~
Go to output link’s columns tab For transformer you can find the output links columns tab by first going to stage properties
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Using RCP with Sequential Stages ~
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To utilize runtime column propagation in the sequential stage you must use the “use schema” option Stages with this restriction: – – – –
Sequential File Set External Source External Target
What is runtime column propagation? Runtime column propagation (RCP) allows DataStage to be flexible about the columns you define in a job. If RCP is enabled for a project, you can just define the columns you are interested in using in a job, but ask DataStage to propagate the other columns through the various stages. So such columns can be extracted from the data source and end up on your data target without explicitly being operated on in between. Sequential files, unlike most other data sources, do not have inherent column definitions, and so DataStage cannot always tell where there are extra columns that need propagating. You can only use RCP on sequential files if you have used the Schema File property (see Link\xd2 ” on page - and on page - ) to specify a schema which describes all the columns in the sequential file. You need to specify the same schema file for any similar stages in the job where you want to propagate columns. Stages that will require a schema file are: Sequential File File Set External Source External Target Column Import Column Export Copyright ©2004 Ascential Software Corporation
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Runtime Column Propagation ~
When RCP is Disabled – DataStage Designer will enforce Stage Input Column to Output Column mappings. – At job compile time modify operators are inserted on output links in the generated osh.
Modify operators can add or change columns in a data flow.
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Runtime Column Propagation ~
When RCP is Enabled – DataStage Designer will not enforce mapping rules. – No Modify operator inserted at compile time. – Danger of runtime error if column names incoming do not match column names outgoing link – case sensitivity.
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Exercise ~
Complete exercises 11-1 and 11-2
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Module 12
Job Control Using the Job Sequencer
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Objectives ~
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Understand how the DataStage job sequencer works Use this understanding to build a control job to run a sequence of DataStage jobs
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Job Control Options ~
Manually write job control – Code generated in Basic – Use the job control tab on the job properties page – Generates basic code which you can modify
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Job Sequencer – Build a controlling job much the same way you build other jobs – Comprised of stages and links – No basic coding
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Job Sequencer ~
Build like a regular job
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Type “Job Sequence”
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Has stages and links
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Job Activity stage represents a DataStage job Links represent passing control Stages
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Example Job Activity stage – contains conditional triggers
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Job Activity Properties
Job to be executed – select from dropdown
Job parameters to be passed
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Job Activity Trigger
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Trigger appears as a link in the diagram
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Custom options let you define the code
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Options ~
Use custom option for conditionals – Execute if job run successful or warnings only
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Can add “wait for file” to execute Add “execute command” stage to drop real tables and rename new tables to current tables
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Job Activity With Multiple Links
Different links having different triggers
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Sequencer Stage ~
Build job sequencer to control job for the collections application
Can be set to all or any
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Notification Stage
Notification
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Notification Activity
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Sample DataStage log from Mail Notification ~
Sample DataStage log from Mail Notification
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Notification Activity Message ~
E-Mail Message
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Exercise ~
Complete exercise 12-1
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Module 13
Testing and Debugging
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Objectives ~
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Understand spectrum of tools to perform testing and debugging Use this understanding to troubleshoot a DataStage job
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Environment Variables
Environment fall in broad categories, listed in the left pane. We'll see these categories one by one. All environments values listed in the ADMINISTRATOR are the project-wide defaults. Can be modified by DESIGNER per job, and again, by DIRECTOR per run. The default values are reasonable ones, there is no need for the beginning user to modify them, or even to know much about them--with one possible exception: APT_CONFIG_FILE-- see next slide.
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Parallel Environment Variables
Highlighted: APT_CONFIG_FILE, contains the path (on the server) of the active config file. The main aspect of a given configuration file is the # of nodes it declares. In the Labs, we used two files: One with one node declared; for use in sequential execution One with two nodes declared; for use in parallel execution
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Environment Variables
Stage Specific
The correct settings for these should be set at install. If you need to modify them, first check with your DBA.
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Environment Variables
These are for the user to play with. Easy: they take only TRUE/FALSE values. Control the verbosity of the log file. The defaults are set for minimal verbosity. The top one, APT_DUMP_SCORE, is an old favorite. It tracks datasets, nodes, partitions, and combinations --- all TBD soon. APT_RECORDS_COUNT helps you detect load imbalance. APT_PRINT_SCHEMAS shows the textual representation of the unformatted metadata at all stages. Online descriptions with the "Help" button.
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Environment Variables
Compiler
You need to have these right to use the Transformer and the Custom stages. Only these stages invoke the C++ compiler. The correct values are listed in the Release Notes.
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The Director Typical Job Log Messages: ~
Environment variables
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Configuration File information
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Framework Info/Warning/Error messages
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Output from the Peek Stage
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Additional info with "Reporting" environments
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Tracing/Debug output
– Must compile job in trace mode – Adds overhead
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Job Level Environmental Variables • Job Properties, from Menu Bar of Designer • Director will prompt you before each run
Project-wide environments set by ADMINISTRATOR can be modified on a job basis in DESIGNER's Job Properties, and on a run basis by DIRECTOR. Provides great flexibility.
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Troubleshooting If you get an error during compile, check the following: ~
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Compilation problems – If Transformer used, check C++ compiler, LD_LIRBARY_PATH – If Buildop errors try buildop from command line – Some stages may not support RCP – can cause column mismatch . – Use the Show Error and More buttons – Examine Generated OSH – Check environment variables settings Very little integrity checking during compile, should run validate from Director.
Highlights source of error
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Generating Test Data
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Row Generator stage can be used – Column definitions – Data type dependent
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Row Generator plus lookup stages provides good way to create robust test data from pattern files
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