SAP Performance Optimization Guide

Documentation set for SAP Data Services

Become familiar with all the pieces of documentation that relate to your SAP Data Services product.

The latest Data Services documentation can be found on the SAP Help Portal.

Administrator Guide

Information about administrative tasks such as monitoring, lifecycle management, security, and so on.

Configuration Guide for CTS +

System landscape information and detailed steps for CTS+ configuration.

Customer Issues Fixed

Information about customer issues fixed in this release.

Note

In some releases, this information is displayed in the Release Notes.

Bottlenecks on database servers do not allow readers or loaders in jobs to use Job Server CPUs efficiently.

The Optimized SQL window displays the existing SQL statement in the repository. If you changed your data flow, save it so that the Optimized SQL window displays your current SQL statement.

If your data fits in memory, it is recommended that you use in-memory cache because pageable cache incurs an overhead cost.

If any one input schema has a cache setting other than Automatic specified in the Query editor, the Data Services Optimizer considers only Query editor cache settings and ignores all source editor cache settings.

Best practice is to define the join rank and cache settings in the Query editor.

In the Query editor, cache a source only if it is being used as an inner source in a join.

When the cache setting is such that data will be cached if possible, a source is used as an inner source in a join under the following conditions:

• The source is specified as the inner source of a left outer join.

• In an inner join between two tables, the source has a lower join rank.

Caching does not affect the order in which tables are joined.

If optimization conditions are such that the software is pushing down operations to the underlying database, it ignores your cache setting.

If a table becomes too large to fit in the cache, ensure that the cache type is pageable.

The default pageable cache directory is %LINKDIR\Log\PCache. If your data flows contain memoryconsuming operations, change this value to a pageable cache directory that:

• Contains enough disk space for the amount of data you plan to profile.

• Is on a separate disk or file system from the SAP Data Services system.

Change the directory in the Specify a directory with enough disk space for pageable cache option in the Server Manager, under Runtime resources configured for this computer.

The option Collect statistics for monitoring is costly to run because it determines the cache size for each row processed.

2. Run your job again with option Use collected statistics selected.

3. Look in the Trace Log to determine which cache type was used.

There are several messages that could appear at in the trace log. These messages indicate that the cache statistics are not available and the sub data flows use the default cache type, pageable.

Line breaks are added to the messages for readability. Execution time (sec) Time (in seconds) the object took to complete execution.

This statistics displays only if you selected Collect statistics for monitoring for the job execution.

If you imported an Oracle ○ Values can be literal numbers and strings or datetime types.

○ Column values must match column data types.

○ Literal strings must include single quotes: 'Director'.

○ For range partitions, the values for a partition must be greater than the values for the previous partition.

○ For the last partition, you can enter the value MAXVALUE to include all values.

7. Click OK.

If the validation rules described in the previous step are not met, you will see an error message. The number of partitions in a table equals the maximum number of parallel instances that the software can process for a source or target created from this table.

If you are loading to partitioned tables, a job will execute the load in parallel according to the number of partitions in the

Enabling CPU hyperthreading can negatively affect the performance of servers and is therefore not supported.

Performance Optimization Guide Using Parallel Execution P U B L I C

The default values for Start port and End port are 1025 and 32767, respectively. Change these values if you want to restrict the number of ports or if some of the ports are already in use.

When you execute the job, the Trace log displays messages such as the following that indicate the communication port for the data flow and the distribution level for each data flow. All of the sub data flows within a data flow run on the same computer.

Data flow communication using peer-to-peer method with the port range <1025> to <32767>. ... Peer-to-peer connection server for session process is listening at host <SJ-C>, port <1025>. Job <GroupBy_Q1_Q2_Job> is started. Starting data flow </GroupBy_Q1_Q2_Job/GroupBy_Q1_DF> on job server host <SJ-C>, port <3502>. Distribution level <Data flow>. Data flow submitted to server group <sg_direpo>. Load balancing algorithm <Least load>. Server group load statistics from job server <mssql_lap_js SJ-C 3502>: <mssql_lap_js SJ-C 3502> System Load <47%> Number of CPUs <1> <MSSQL2005_JS SJ-W-C 3500> System Load <70%> Number of CPUs <2> Process to execute data flow <GroupBy_Q1_DF> is started. Starting sub data flow <GroupBy_Q1_DF_1_1> on job server host <SJ-C>, port <3502>. Distribution level <Data flow>. Starting sub data flow <GroupBy_Q1_DF_1_2> on job server host <SJ-C>, port <3502>. Distribution level <Data flow>. Starting sub data flow <GroupBy_Q1_DF_1_3> on job server host <SJ-C>, port <3502>. Distribution level <Data flow>. Starting sub data flow <GroupBy_Q1_DF_1_4> on job server host <SJ-C>, port <3502>. Distribution level <Data flow>.

If you find that sending data across the network is causing your data flow to execute longer, you might want to change Distribution level from Sub data flow to Data flow or Job.

When you execute the job, the Trace log displays messages such as the following that indicate that the software selects a job server for each sub data flow based on the system load on each computer:

Starting sub data flow <GroupBy_DOP2_DF_1_1> on job server host <SJ-C>, port <3502>. Distribution level <Sub data flow>. Sub data flow submitted to server group <sg_direpo>. Load balancing algorithm <Least load>. Server group load statistics from job server <mssql_lap_js SJ-C 3502>: <mssql_lap_js SJ-C 3502> System Load <21%> Number of CPUs <1> <MSSQL2005_JS SJ-W-C 3500> System Load <70> Number of CPUs <1> Starting sub data flow <GroupBy_DOP2_DF_1_2> on job server host <SJ-C>, port <3502>. Distribution level <Sub data flow>. Sub data flow submitted to server group <sg_direpo>. Load balancing algorithm <Least load>. Server group load statistics from job server <mssql_lap_js SJ-C 3502>: <mssql_lap_js SJ-C 3502> System Load <21%> Number of CPUs <1> <MSSQL2005_JS SJ-W-C 3500> System Load <70> Number of CPUs <2>

The following messages show the communication port that each sub data flow uses:

Peer-to-peer connection server for sub data flow <GroupBy_DOP2_DF_1_1> is listening at host <SJ-C>, port <1027>. Process to execute sub data flow <GroupBy_DOP2_DF_1_4> is started. Peer-to-peer connection server for sub data flow <GroupBy_DOP2_DF_1_2> is listening at host <SJ-C>, port <1028>.

You cannot bulk load data to DB2 databases that run on AS/400 or z/OS (MVS) systems. • The DB2 UDB server and client must be Version 8.0 or later.

• Stops loading when it encounters the first rejected row.

SAP Data Services provides Informix bulk-loading support only for single-byte character ASCII delimited files (not for fixed-width files).

It is recommended that you set the Rows per commit and Network packet size parameters to avoid sending many partially filled packets over the network and ensure that the packet size contains all rows in the commit.

If this datastore is not being used specifically for Netezza bulk loading, the software ignores any FTP option entries.

4. If you are loading non-ASCII character data, set the Code page to latin-9.

If you are loading mulitbyte data, set the Code page to utf-8.

The Enable partitioning check box does not appear on the Options tab if the target table is not partitioned.

Product performance during this type of parallel loading depends on a number of factors such as distribution of incoming data and underlying DBMS capabilities. Under some circumstances it is possible that specifying parallel loaders can be detrimental to performance. Always test the parallel loading process before moving to production.

If you plan to use a partitioned table as a target, the physical table partitions in the database must match the metadata table partitions in SAP Data Services. If there is a mismatch, Data Services will not use the partition name to load partitions, which impacts processing time.

For the API method, the software records and displays error and trace logs as it does for any job. A monitor log records connection activity between components; however, it does not record activity while the API is handling the data.

If your Job Server is on a UNIX platform, to take advantage of bulk loading on Teradata 13 databases you must set the required environment parameters in the file $LINK_DIR/bin/td_env.config. Instructions are documented inside the file.

For detailed information about Teradata options and their behavior in the Teradata environment, see the relevant Teradata product documentation.

2. Teradata (Parallel Transporter or non-Parallel Transporter utility) creates named pipes to contain the data to load into a Teradata INSERT statements through the ODBC driver are simpler to use than a data file or pipe.

This method does not bulkload data.

Performance uaing this type of parallel loading depends on a number of factors such as distribution of incoming data and underlying DBMS capabilities. Under some circumstances, it is possible that specifying parallel loaders can be detrimental to performance. Always test the parallel loading process before moving to production. If you set Number of instances to 2 (load operators) and Number of DataConnector instances to 2 (read operators), Parallel Transporter will assign the first read operator instance to read one data file and the other instance to read another data file in parallel. The read operator instances then pass the data to the load operator instances for parallel loading into Teradata.

If you chose Data file, the value you specify for DataConnector instances (read operators) should be less than or equal to the number of data files.

5. If you chose Named pipes access module for File Option, specify Number of instances (load operators) in the loading scripts.

Teradata uses the value you specify in Number of loaders to determine the number of read operator instances, as well as the number of named pipes. The DataConnector instances is not applicable when you use Named Pipes Access Module.

For example, if you set Number of loaders to 2, Parallel Transporter generates two named pipes and assigns one read operator instance to read from one pipe and the other instance to read the other pipe in parallel. If you set Number of instances to 2 (load operators), the read operator instances pass the data to the load operator instances for parallel loading into Teradata.

6. If you specified Named pipes access module for File Option, you can override the default settings for the following Teradata Access Module parameters: Log directory, Log level, Block size, Fallback file name, Fallback directory, Signature checking.

The Teradata Access Module creates a log file to record the load status and writes information to fallback data files. If the job fails, the Teradata Access Module uses the fallback data files to restart the load. The Access Module log file differs from the build log that you specify in the Log directory option in the Teradata datastore.

Data Services sets the bulk loader directory as the default value for both Log Directory and Fallback Directory.

For more information about these parameters, see the relevant Teradata tools and utilities documentation.

By default, Data Services uses the bulk loader directory to store the script, data, error, log, and command (bat) files.

For more information about these parameters, see the Teradata FastLoad Reference.

5. If you specified Data file for File Option, you can increase the Number of loaders on the Options tab, which increases the number of data files. The software can use parallel processing to write data to multiple data files in batches of 999 rows.

If you specified Generic named pipe or Named pipes access module, Data Services supports only one loader and disables the Number of loaders option.

By default, Data Services uses the bulk loader directory to store the script, data, error, log, and command (bat) files.

For more information about these parameters, see the Teradata MultiLoad Reference.

5. If you specified Data file in File Option, you can increase the Number of loaders in the Options tab which increase the number of data files. Data Services can use parallel processing to write data to multiple data files in batches of 999 rows.

If you specified Generic named pipe or Named pipes access module, Data Services supports only one loader and disables the Number of loaders option.

By default, SAP Data Services uses the bulk loader directory to store the script, data, error, log, and command (bat) files.

5. If you specified Data file in File Option, you can increase the Number of loaders in the Options tab which increase the number of data files. The software can use parallel processing to write data to multiple data files in batches of 999 rows.

If you specified Generic named pipe or Named pipe access module, Data Services supports only one loader and disables the Number of loaders option.

These tuning techniques require that you make small adjustments, carefully monitor the change in performance, and make more small adjustments, and so on until you get the desired results.

Join ranks set in sources are not displayed in the Query editor. If a join rank value is specified in the source, to find the value you must open the source editor.

The Data Services Optimizer gives preference to settings entered in the Query editor over settings entered in a source editor. If any one input schema has a join rank specified in the Query editor, then the Data Services Optimizer considers only Query editor join rank settings and ignores all source editor join rank settings.

Additionally, in a data flow containing multiple adjacent Query transforms, upstream join rank settings may be considered.

The join rank determination for multiple adjacent Query transforms can be complex because the Data Services Optimizer may combine these Query transforms into a single Query transform.

Higher array fetch settings will consume more processing memory proportionally to the length of the data in each row and the number of rows in each fetch.

Regardless of the array fetch setting, sources reading columns with an Oracle LONG data type cannot take advantage of this feature. If a selected data column is of type LONG, the array fetch size internally defaults to 1 row per request. The Array Fetch Size option does not support long column data types. If the SELECT list contains a long column, the software sets the Array Fetch Size to 1 and reads one row of data at a time from the database.

Designer Guide

Information about how to use Data Services Designer.

Documentation Map

Information about available Data Services books, languages, and locations.

Installation Guide for UNIX

Information about and procedures for installing Data Services in a UNIX environment.

Installation Guide for Win dows

Information about and procedures for installing Data Services in a Windows environment.

Integrator Guide

Information for third-party developers to access Data Services functionality using web services and APIs.

Management Console Guide Information about how to use Data Services Administrator and Data Services Metadata Reports.

Document What this document provides

Master Guide Information about the application, its components and scenarios for planning and designing your system landscape. Information about SAP Information Steward is also provided in this guide.

Information platform services Administrator Guide Information for administrators who are responsible for config uring, managing, and maintaining an Information platform services installation.

Performance Optimization Guide

Information about how to improve the performance of Data Services.

Using grid computing to distribute data flow execution P U B L I C Peer-to-peer connection server for sub data flow <GroupBy_DOP2_DF_1_3> is listening at host <SJ-C>, port <1029>. Peer-to-peer connection server for sub data flow <GroupBy_DOP2_DF_1_4> is listening at host <SJ-C>, port <1030>.

Reference Guide

Detailed reference material for Data Services Designer.

Release Notes

Important information you need before installing and deploying this version of Data Services.

Sizing Guide

Guidelines and recommendations on hardware requirements and software considerations for your implementation.

Technical Manuals

A compiled, searchable, "master" PDF of core Data Services books:

• Administrator Guide Information about building dictionaries and extraction rules to create your own extraction pat terns to use with Text Data Processing transforms.

Text Data Processing Lan guage Reference Guide

Information about the linguistic analysis and extraction processing features that the Text Data Processing component provides, as well as a reference section for each language supported.

Tutorial

A step-by-step introduction to using Data Services.

Upgrade Guide

Information to help you upgrade from previous releases of Data Services and release-specific product behavior changes from earlier versions of Data Services to the latest release.

What's New

Highlights of new key features in this SAP Data Services release. This document is not updated for support package or patch releases.

Workbench Guide

Provides users with information about how to use the Workbench to migrate data and database schema information between different database systems.

In addition, you may need to refer to several Supplemental Guides.

Supplement for Adapters

Information about how to install, configure, and use Data Services adapters.

Supplement for Google BigQuery

Information about interfaces between Data Services and Google BigQuery.

Document

What this document provides

Supplement for J.D. Edwards

Information about interfaces between Data Services and J.D. Edwards World and J.D. Edwards OneWorld.

Supplement for Oracle Applications

Information about the interface between Data Services and Oracle Applications.

Supplement for PeopleSoft

Information about interfaces between Data Services and PeopleSoft.

Supplement for SAP

Information about interfaces between Data Services, SAP Applications, and SAP Net Weaver BW.

Supplement for Siebel

Information about the interface between Data Services and Siebel.

We also include these manuals for information about SAP BusinessObjects Information platform services. Table 3:

Information platform services Installation Guide for UNIX

Installation procedures for SAP BusinessObjects Information platform services on a UNIX environment.

Information platform services Installation Guide for Windows

Installation procedures for SAP BusinessObjects Information platform services on a Windows environment.

Accessing documentation from the Web

You can access the complete documentation set for SAP Data Services from the SAP Business Users Support site.

To do this, go to http://help.sap.com/bods.

You can view the PDFs online or save them to your computer.

SAP information resources

A list of information resource links.

A global network of SAP technology experts provides customer support, education, and consulting to ensure maximum information management benefit to your business.

Useful addresses at a glance: The means with which to contribute content, post comments, and organize information in a hierarchical manner to so that information is easy to find.

Product Availability Matrix (PAM) https://apps.support.sap.com/sap/support/pam Information about supported platforms for SAP Data Services with a search function to quickly find information related to your platform.

Blueprints https://wiki.scn.sap.com/wiki/display/EIM/SAP+Data +Services+Blueprints Blueprints for you to download and modify to fit your needs. Each blueprint contains the necessary SAP Data Services project, jobs, data flows, file formats, sample data, template tables, and custom functions to run the data flows in your en vironment with only a few modifications.

Environment Test Strategy

Suggested methods for tuning source and target database applications, their operating systems, and the network used by your SAP Data Services environment.

To test and tune jobs, work with all of the following components in the order listed:

• Source OS and database server

• Target OS and database server

• Network

• Job Server OS and job options

In addition, you can use your UNIX or Windows operating system and database server documentation for specific techniques, commands, and utilities that can help you measure and tune the SAP Data Services environment.

Related Information

The source OS and database server [

Processes

Analyzing log files for task duration [page 21]

Degree of parallelism [page 60] Splitting a dataflow into sub data flows [page 72]

Using Caches [page 46]

Monitoring and tuning in-memory and pageable caches [page 52]

Managing

Maximizing

Using Parallel Execution [page 55]

Using grid computing to distribute data flow execution [page 80]

Managing

Managing

Maximizing

Maximizing

Maximizing

Managing

Operations that cannot be pushed down [page 30]

Software Data Services supports push-down operations using linked remote server on all Windows and UNIX platforms. It supports Sybase IQ target and source datastore and SAP ASE source datastore.

Example: Push-down with linked datastores [page 41]

Example: Push-down with linked remote servers [page 43]

Caching joins [page 47]

Join rank settings [page 116]

Using

To automatically choose the cache type [page 52]

Monitoring and tuning in-memory and pageable caches [page 52] Performance Optimization Guide Using Caches P U B L I C

Monitoring and tuning caches [page 51]

Monitoring and tuning in-memory and pageable caches [page 52]

Monitoring and tuning in-memory and pageable caches [page 52]

Monitoring and tuning caches [page 51]

To enable partition settings in a source or target

To set the degree of parallelism for a data flow [page 63]

To view SQL [page 35]

Using Parallel Execution [page 55]

Performance Optimization Guide Distributing Data Flow Execution P U B L I C To define sub data flows to push down a join of a file and SELECT "Data_Transfer_Orders_Flatfile"."PRODUCTID" , "ORDERS"."SHIPCOUNTRY" , "ORDERS"."SHIPREGION" , "Data_Transfer_Orders_Flatfile"."ORDERID" FROM "DBO"."ORDERS_FROMFILE" "Data_Transfer_Orders_Flatfile","DBO"."ORDERS""ORDERS" WHERE ("Data_Transfer_Orders_Flatfile"."ORDERID" = "ORDERS"."ORDERID") SAP Data Services can push down many operations without using the Data_Transfer transform.

5. When you execute the job, the Trace Log shows messages that indicate that the software created two sub data flows with different Pids to run the different operations serially.

To define sub data flows to push down another operation [page 75]

TPump commands for Data Services parameters [page 109]

Push-down operations [page 28]

Caching sources [page 46] 11.3 Job design performance options

The source OS and database server

Tune the source operating system and database to quickly read data from disks.

Operating system

Make the input and output (I/O) operations as fast as possible. The read-ahead protocol, offered by most operating systems, can greatly improve performance. This portocol allows you to set the size of each I/O operation. Usually its default value is 4 to 8 kilobytes which is too small. Set it to at least 64K on most platforms.

Make the input and output operations as fast as possible. For example, the asynchronous I/O, offered by most operating systems, can greatly improve performance. Turn on the asynchronous I/O.

SAP Data Services jobs are multi-threaded applications. Typically a single data flow in a job initiates one al_engine process that in turn initiates at least 4 threads.

For maximum performance benefits:

• Consider a design that will run one al_engine process per CPU at a time.

• Tune the Job Server OS so that threads spread to all available CPUs.

Database

Tune your database on the source side to perform SELECTs as quickly as possible.

In the database layer, you can improve the performance of SELECTs in several ways, such as the following:

• Create indexes on appropriate columns, based on your data flows.

• Increase the size of each I/O from the database server to match the OS read-ahead I/O size.

• Increase the size of the shared buffer to allow more data to be cached in the database server.

• Cache tables that are small enough to fit in the shared buffer. For example, if jobs access the same piece of data on a database server, then cache that data. Caching data on database servers will reduce the number of I/O operations and speed up access to database tables.

See your database server documentation for more information about techniques, commands, and utilities that can help you measure and tune the the source databases in your jobs.

Tune your database on the target side to perform INSERTS and UPDATES as quickly as possible.

In the database layer, there are several ways to improve the performance of these operations.

Here are some examples from Oracle:

• Turn off archive logging.

• Turn off redo logging for all tables.

• Tune rollback segments for better performance.

• Place redo log files and data files on a raw device, if possible.

• Increase the size of the shared buffer.

See your database server documentation for more information about techniques, commands, and utilities that can help you measure and tune the the target databases in your jobs.

The target OS and database server

Tune the target operating system and database to quickly write data to disks.

The network

Information about tuning your network for environment testing.

When reading and writing data involves going through your network, its ability to efficiently move large amounts of data with minimal overhead is very important. Do not underestimate the importance of network tuning (even if you have a very fast network with lots of bandwidth).

Set network buffers to reduce the number of round trips to the database servers across the network. For example, adjust the size of the network buffer in the database client so that each client request completely fills a small number of network packets.

Job Server OS and job options

Suggestions for tuning your operating system and jobs for improved performance.

Tune the Job Server operating system and set job execution options to improve performance and take advantage of self-tuning features of SAP Data Services.

Jobs

You can tune job execution options after you have done the following:

• Tune the database and operating system on the source and the target computers.

• Adjust the size of the network buffer.

• Make sure that your data flow design seems optimal.

You can tune the following execution options to improve the performance of your jobs:

• Monitor sample rate

• Collect and use statistics for optimization 3 Measuring Performance

Data Services processes and threads

Processes and threads and how they affect performence.

Data Services uses processes and threads to execute jobs that extract data from sources, transform the data, and load data into a data warehouse. The number of concurrently executing processes and threads affects the performance of Data Services jobs.

Processes

The processes used to run jobs in SAP Data Services. Table 5:

Process Description al_jobserver The al_jobserver initiates one process for each Job Server configured on a computer. This process does not use much CPU power because it is only responsible for launching each job and monitoring the job's execution.

al_engine For batch jobs, an al_engine process runs when a job starts and for each of its data flows. Real-time jobs run as a single process.

The number of processes a batch job initiates also depends upon the number of:

• parallel work flows

• parallel data flows

• sub data flows

Threads

Threads and how they affect performance.

A data flow typically initiates one al_engine process, which creates one thread per data flow object. A data flow object can be a source, transform, or target. For example, two sources, a query, and a target could initiate four threads.

If you are using parallel objects in data flows, the thread count will increase to approximately one thread for each source or target table partition. If you set the Degree of parallelism (DOP) option for your data flow to a value greater than one, the thread count per transform will increase. For example, a DOP of 5 allows five concurrent threads for a Query transform. To run objects within data flows in parallel, use the following features:

• Table partitioning • Degree of parallelism for data flows

• File multithreading

Measuring performance of jobs

There are several techniques to measure performance of SAP Data Services jobs.

You can make adjustments to your jobs to enhance job performance by analyzing the following aspects of your jobs:

• Check system utilization

Checking system utilization

The system resources to check for system resource utilization.

The number of processes and threads concurrently executing affects the utilization of system resources. Therefore job performance can be enhanced by checking the utilization of the following system resources:

To monitor these system resources, use the tools listed in the table below for your operating system. Depending on the performance of your jobs and the utilization of system resources, you might want to adjust the number of processes and threads. The following sections describe different situations and suggests features to adjust the number of processes and threads for each situation.

CPU utilization

SAP Data Services is designed to maximize the use of CPUs and memory available to run the job.

The total number of concurrent threads a job can run depends upon job design and environment. Test your job while watching multi-threaded processes to see how much CPU and memory the job requires. Make needed adjustments to your job design and environment and test again to confirm improvements.

For example, if you run a job and see that the CPU utilization is very high, you might decrease the DOP value or run less parallel jobs or data flows. Otherwise, CPU thrashing might occur.

For another example, if you run a job and see that only half a CPU is being used, or if you run eight jobs on an eight-way computer and CPU usage is only 50%, you can interpret this CPU utilization in several ways:

• The software is able to push most of the processing down to source and/or target databases.

• There are bottlenecks in the database server or the network connection.

CPU bottlenecks

What to look at to determine CPU bottlenecks.

To eliminate bottlenecks, you need to determine the source of the bottleneck. The table below contains several factors to help you determine bottlenecks. Number of threads per process allowed on each database server operating system.

See the example below this table.

Network connection speed Determine the rate that your data is being transferred across your network.

If the network is a bottle neck, you might change your job exe cution distribution level from sub data flow to data flow or job to execute the entire data flow on the local Job Server.

If the capacity of your network is much larger, you might re trieve multiple rows from source databases using fewer re quests.

Factor

Additional notes Under-utilized system In this case, you might increase the value for the Degree of parallelism option and increase the number of parallel jobs and data flows.

Memory

Factors that affect memory utilization.

There are several cases in which memory utilization is affected.

Utilizing memory

Actions to take when you have a low amount of physical memory, or when you have a large amount of memory but it is under-utilized.

Low amount of memory

Try the following solutions when you have a low amount of memory:

• Add more memory to the Job Server.

• Redesign your data flow to run memory-consuming operations in separate sub data flows that each use a smaller amount of memory, and distribute the sub data flows over different Job Servers to access memory on multiple machines. • Redesign your data flow to push down memory-consuming operations to the database.

Under-utilized memory

When you have a large amount of physical memory, it may be under-utilized. To better-utilize your memory, cache more data. Caching data can improve the performance of data transformations because it reduces the number of times the system must access the database.

There are two types of caches available: in-memory and pageable.

Changing default paging limits

Actions to take when paging occurs.

Pageable cache is the default cache type for data flows. On Windows, UNIX, and Linux, the virtual memory available to the al_engine process is 3.5 gigabytes. (500 megabytes of virtual memory is reserved for other engine operations that total 4GB). You can change this default limit by increasing the value of the MAX_64BIT_PROCESS_VM_IN_MB parameter in the DSConfig.txt file.

If more memory than the default cache is needed, the software starts paging to continue executing the data flow.

You can avoid paging by taking advantage of one of the following features:

• Split the data flow into sub data flows that can each use the amount of memory set by the virtual memory limits. Each data flow or each memory-intensive operation within a data flow can run as a separate process that uses separate memory from each other to improve performance and throughput.

• Push-down memory-intensive operations to the database server so that less memory is used on the Job Server computer.

Analyzing log files for task duration

The trace log shows the progress of an execution through each component (object) of a job. The following depiction of a trace log shows a separate process ID (Pid) and time stamp for the job, data flow, and each sub data flow.

Reading the Monitor Log for execution statistics

The Monitor log file indicates how many rows SAP Data Services produces or loads for a job. By viewing this log during job execution, you can observe the progress of row-counts to determine the location of bottlenecks. You can use the Monitor log to answer questions such as the following:

• What transform is running at any moment?

• How many rows have been processed so far?

The frequency that the Monitor log refreshes the statistics is based on Monitor sample rate. • How long does it take to build the cache for a lookup or comparison table? How long does it take to process the cache? If take long time to build the cache, use persistent cache.

• How long does it take to sort? If take long time to sort, you can redesign your data flow to push down the sort operation to the database. • How much time elapses before a blocking operation sends out the first row?

If your data flow contains resource-intensive operations after the blocking operation, you can add Data_Transfer transforms to push-down the resource-intensive operations.

You can view the Monitor log from the following tools:

• The Designer, as the job executes, when you click the Monitor icon.

• The Administrator of the Management Console, when you click the Monitor link for a job from the Batch Job Status page.

The Monitor log in the Designer shows the path for each object in the job, the number of rows processed, and the elapsed time for each object. The Absolute time column displays the total time from the start of the job to when the software completes the execution of the data flow object.

Reading the Performance Monitor for execution statistics

The Performance Monitor displays execution information for each work flow, data flow, and sub data flow within a job. You can display the execution times in a table format. You can use the Performance Monitor to answer questions such as the following:

• Which data flows might be bottlenecks?

• How much time did a a data flow or sub data flow take to execute?

• How many rows did the data flow or sub data flow process?

• How much memory did a specific data flow use?

Reading Operational Dashboards for execution statistics

The Operational Dashboard provides graphical depictions of SAP Data Services job execution statistics. It allows you to view, at a glance, the status and performance of your job executions across repositories over a given time period (for example, the last day or week).

The Operational Dashboard provides the job history statistics information you need to have better control over your jobs and to increase performance. You can use the information in the detailed visualized dashboard to:

• Determine how many jobs succeeded or failed over a given time period.

• Identify your performance by checking the CPU or buffer uses of dataflow.

• Monitor your job scheduling and management for better performance.

Comparing execution times for the same job over time

Use the Operational Dashboard and the Job Execution History information, which can be found in the Administrator, to compare execution times.

Job execution strategies

Descriptions of different Data Services tuning options.

There are several strategies for executing jobs that help maximize performance:

• Manage push-down operations

• Improve throughput

• Advanced tuning

Managing push-down operations

Design your dataflow for maximum push-down operations.. SAP Data Services generates SQL SELECT statements to retrieve data from source databases. The software automatically distributes the processing workload by pushing down as much as possible to the source database server.

Data flow design influences the number of operations that the software can push to the database. Before running a job, you can view the SQL that is generated and adjust your design to maximize the SQL that is pushed down to improve performance. You can set your data flow design to perform partial and full push-down operations.

Improving throughput

Use the following features to improve throughput:

• Using caches for faster access to data You can improve the performance of data transformations by caching as much data as possible. By caching data in memory, you limit the number of times the system must access the database.

Using advanced tuning options

If your jobs have CPU-intensive and memory-intensive operations, you can use the advanced tuning features in the table below to improve performance. Table 9:

Advanced tuning feature Description

Parallel processes Individual work flows and data flows can execute in parallel if you do not connect them in the Designer workspace.

Parallel threads

The software supports partitioned source tables, partitioned target tables, and degree of parallelism. These options allow you to control the number of instances for a source, target, and transform that can run in parallel within a data flow. Each instance runs as a separate thread and can run on a separate CPU.

Advanced tuning feature Description Server groups and distribution levels You can group Job Servers on different computers into a logi cal component called a server group. A server group automat ically measures resource availability on each Job Server in the group and distributes scheduled batch jobs to the computer with the lightest load at runtime. This functionality also pro vides a hot backup method. If one Job Server in a server group is down, another Job Server in the group processes the job.

You can distribute the execution of data flows or sub data flows within a batch job across multiple Job Servers within a Server Group to better balance resource-intensive operations.

Maximizing Push-Down Operations

An overview of the advantage of push-down operations.

For SQL sources and targets, SAP Data Services creates database-specific SQL statements based on the data flow diagrams in a job. The software generates SQL SELECT statements to retrieve the data from source databases. To optimize performance, the software pushes down as many SELECT operations as possible to the source database and combines as many operations as possible into one request to the database. It can push down SELECT operations such as joins, Group By, and common functions such as decode and string functions.

You can take advantage of certain push-down operations::

• Use the power of the database server to take advantage of certain optimized SELECT operations and common functions. Often the database is optimized for operations such as joins and Group By, and common functions such as decode and string functions. • Include filters or aggregations in the SQL statements to minimize the amount of data sent over the network so that fewer rows are retrieved.

Push-down operations

By pushing down operations to the source database, Data Services reduces the number of rows and operations that the engine must retrieve and process, which improves performance. When determining which operations to push to the database, Data Services examines the database and its environment.

The software performs two types of push-down operations: Full and partial.

Full push-down operations

The Optimizer always first tries to do a full push-down operation. A full push-down operation is when all transform operations can be pushed down to the databases and the data streams directly from the source database to the target database. SAP Data Services sends SQL INSERT INTO... SELECT statements to the target database where SELECT retrieves data from the source.

The software does a full push-down operation to the source and target databases when the following conditions are met:

• All of the operations between the source table and target table can be pushed down.

• The source and target tables are from the same datastore, they are in datastores that have a database link defined between them, or if the datastore has linked remote servers.

To enable a full push-down from the source to the target, you can also use the following features:

• Data_Transfer transform • Database links

• Use Linked Remote Servers option

For database targets that support the Allow merge or upsert option, when all other operations in the data flow can be pushed down to the source database, the auto-correct loading operation may also be pushed down for a full push-down operation to the target. The software sends a SQL MERGE INTO <target> statement that implements the Ignore columns with value and Ignore columns with null options.

Partial push-down operations

When a full push-down operation is not possible, SAP Data Services still pushes down the SELECT statement to the source database. The operations within the SELECT statement that the software can push to the database are listed in the table below.

Operation Description

Aggregations Aggregate functions, typically used with a Group by state ment, always produce a data set smaller than or the same size as the original data set.

Distinct rows When you select Distinct rows from the Select tab in the query editor, the software will only output unique rows.

Filtering

Filtering can produce a data set smaller than or equal to the original data set.

Joins

Joins typically produce a data set smaller than or similar in size to the original tables. The software can push down joins when either of the following conditions exist:

• The source tables are in the same datastore

• The source tables are in datastores that have a database link defined between them

Ordering

Ordering does not affect data-set size. The software can effi ciently sort data sets that fit in memory. It is recommended that you push down the Order By for very large data sets.

Projection

Projection is the subset of columns that you map on the Mapping tab in the query editor. Projection normally produces a smaller data set because it only returns columns needed by subsequent operations in a data flow.

Functions

Most functions that have equivalents in the underlying data base are appropriately translated. These functions include de code, aggregation, and string functions.

Operations that cannot be pushed down

SAP Data Services cannot push some transform operations to the database. For example:

• Expressions that include functions that do not have database correspondents

• Load operations that contain triggers

• Transforms other than Query

• Joins between sources that are on different database servers that do not have database links defined between them.

Similarly, the software cannot always combine operations into single requests. For example, when a stored procedure contains a COMMIT statement or does not return a value, the software cannot combine the stored procedure SQL with the SQL for other operations in a query.

The software can only push operations supported by the DBMS down to that DBMS. Therefore, for best performance, try not to intersperse SAP Data Services transforms among operations that can be pushed down to the database.

Push-down examples

The examples in this section are typical push-down scenarios.

Example 2: Full push down from source to target

A full push-down where the INSERT into the target uses a SELECT from the source.

If the source and target are in the same datastore, the software can do a full push-down operation where the INSERT into the target uses a SELECT from the source. If the source and target are not in the same datastore, the software can also do a full push-down operation if you use one of the following features:

• Add a Data _Transfer transform before the target.

• Define a database link between the two datastores.

Example 3: Full push down for auto correct load to target

The Optimizer does a full push-down operation.

For supported databases, if you enable the Auto correct load and Allow merge or upsert options, the Optimizer may be able to do a full push-down operation where the SQL statement is a MERGE into the target with a SELECT from the source.

In order for the Allow merge or upsert option to generate a MERGE statement, the primary key of the source table must be a subset of the primary key of the target table and the source row must be unique on the target key. In other words, there cannot be duplicate rows in the source data. If this condition is not met, the Optimizer pushes down the operation using a database-specific method to identify, update, and insert rows into the target table.

Example 4: Partial push down to source

Operations that cannot be passed to the DBMS.

If the data flow contains operations that cannot be passed to the DBMS, the software optimizes the transformation differently than Examples 2 and 3.

Example 5: Push-down SQL join

Example where an entire query is pushed down to the DBMS.

If the tables to be joined in a query meet the requirements for a push-down operation, then the entire query is pushed down to the DBMS.

To confirm that the query will be pushed down, look at the Optimized SQL. If the query shows a single SELECT statement, then it will be pushed down.

To view SQL

Process to view the SQL code for table sources in data flows before running a job.

Before running a job, you can view the SQL code that SAP Data Services generates for table sources in data flows. By examining the SQL code, you can verify that the software generates the commands you expect. If necessary, you can alter your design to improve the data flow.

1. Validate and save data flows.

2. Open a data flow in the workspace.

3. Select Display Optimized SQL from the Validation menu.

Alternately, you can right-click a data flow in the object library and select Display Optimized SQL.

The Optimized SQL window opens and shows a list of datastores and the optimized SQL code for the selected datastore. By default, the Optimized SQL window selects the first datastore.

The software only shows the SELECT generated for INSERT INTO "DBO"."ORDER_AGG" ("SHIPCOUNTRY","SHIPREGION", "SALES_AGG") SELECT "TS_Query_Lookup"."SHIPCOUNTRY" , "TS_Query_Lookup"."SHIPREGION" ,sum("TS_Query_Lookup"."SALES") FROM"DBO"."TRANS2""TS_Query_Lookup" GROUP BY "TS_Query_Lookup"."SHIPCOUNTRY" , "TS_Query_Lookup"."SHIPREGION"

In the Optimized SQL window you can:

○ Use the Find button to perform a search on the SQL displayed.

○ Use the Save As button to save the text as a .sql file.

If you try to use the Display Optimized SQL command when there are no SQL sources in your data flow, the software alerts you. Examples of non-SQL sources include:

• Message sources

• File sources

• IDoc sources

If a data flow is not valid when you click the Display Optimized SQL option, the software alerts you.

Data_Transfer transform for push-down operations

Use the Data_Transfer transform to move data from a source or from another transform into the target datastore and enable a full push-down operation (INSERT INTO... SELECT) to the target. You can use the Data_Transfer transform to push-down resource-intensive operations that occur anywhere within a data flow to the database. Resource-intensive operations include joins, GROUP BY, ORDER BY, and DISTINCT.

Push down an operation after a blocking operation

You can place a Data_Transfer transform after a blocking operation to enable Data Services to push down a subsequent operation. A blocking operation is an operation that the software cannot push down to the database, and prevents ("blocks") operations after it from being pushed down.

Using Data_Transfer tables to speed up auto correct loads

Data_Transfer tables can speed up auto correct loads.

Auto correct loading ensures that the same row is not duplicated in a target table, which is useful for data recovery operations. However, an auto correct load prevents a full push-down operation from the source to the target when the source and target are in different datastores.

For large loads using database targets that support the Allow merge or upsert option for auto correct load, you can add a Data_Transfer transform before the target to enable a full push-down from the source to the target.

Make the following settings so that the Allow merge or upsert option generates a MERGE statement and prevents duplicate rows in the source data:

• The primary key of the source table must be a subset of the primary key of the target table.

• The source row must be unique on the target key.

If this condition is not met and there are duplicate rows in the source data, the Optimizer pushes down the operation using a database-specific method to identify, update, and insert rows into the target table.

If the MERGE statement can be used, SAP Data Services generates a SQL MERGE INTO <target> statement that implements the following:

• Ignore columns with value value (if a value is specified in the target transform editor).

• Ignore columns with null Yes/No setting.

Database link and linked remote server support for pushdown operations across datastores

Various database vendors support one-way communication paths from one database server to another. SAP Data Services refers to communication paths between databases as database links. The datastores in a database link relationship are called linked datastores, or, in the case of Sybase IQ, linked remote server.

The software uses linked datastores to enhance its performance by pushing down operations to a target database using a target datastore. Pushing down operations to a database not only reduces the amount of information that needs to be transferred between the databases and SAP Data Services but also allows the software to take advantage of the various DMBS capabilities, such as various join algorithms.

With support for database links, the software pushes processing down from different datastores, which can also refer to the same or different database type. Linked datastores allow a one-way path for data. For example, if you import a database link from target database B and link datastore B to datastore A, the software pushes the load operation down to database B, not to database A.

To take advantage of linked datastores

1. Create a database link on a database server that you intend to use as a target in a job.

The following database software is required. See Supported Platforms (Product Availability Matrix) for specific version numbers. 2. Create a database datastore connection to your target database.

Example: Push-down with linked datastores

Linked datastores enable a full push-down operation (INSERT INTO... SELECT) to the target if all the sources are linked with the target. The sources and target can be in datastores that use the same database type or different database types.

To take advantage of linked remote servers

1. On the Sybase IQ server that you intend to use as a target for a job, create a remote server to the Sybase IQ or SAP ASE server that you intend to use as a source for the job.

2. On the target server, create an external login to the same server that you configured the remote server for.

Refer to the Sybase documentation to set up the connectivity between the two servers that you want to communicate with.

3. Create a datastore connection to the target database and source database using the same server that you used to create the remote server.

Linked datastores enable a full push-down operation (INSERT INTO... SELECT) to a target if all of the sources are linked with the target. The sources and target can be in datastores that use the same database type or different database types.

Example: Push-down with linked remote servers

Linked remote servers enable a full push-down operation (INSERT…LOCATION) to the target if all the target servers have remote links to the respective source server. Sources can be in Sybase IQ or SAP ASE datastores and targets can be in Sybase IQ datastores.

Generated SQL statements

To see how SAP Data Services optimizes SQL statements, use Display Optimized SQL from the Validation menu when a data flow is open in the workspace.

• For DB2, it uses nicknames to refer to remote table references in the SQL display.

• For Oracle, it uses the following syntax to refer to remote table references:

<remote_table>@<dblink_name>. • For SQL Server, it uses the following syntax to refer to remote table references:

<linked_server>.<remote_database>.<remote_user>.<remote_table>.

Tuning performance at the data flow or Job Server level Linked datastore push-downs

You might want to turn off linked-datastore push downs in cases where you do not notice performance improvements.

For example, the underlying database might not process operations from different data sources well. Data Services pushes down Oracle stored procedures and external functions. If these are in a job that uses database links, it will not impact expected performance gains. However, Data Services does not push down functions imported from other databases (such as DB2). In this case, although you may be using database links, Data Services cannot push the processing down.

Test your assumptions about individual job designs before committing to a large development effort using database links.

Data flow level

On the data flow properties dialog, Data Services enables the Use database links option by default to allow pushdown operations using linked datastores. If you do not want to use linked datastores in a data flow to push down processing, deselect the check box.

Data Services can perform push downs using datastore links if the tables involved share the same database type and database connection name, or datasource name, even if the tables have different schema names. However, problems with enabling this feature could arise, for example, if the user of one datastore does not have access privileges to the tables of another datastore, causing a data access problem. In such a case, you can disable this feature. 6 Using Caches

When Distribution level has the value Data flow, all of the processes that belong to each data flow can execute on a different computer. For example, the following GroupBy_Q1_Q2_Job has two data flows: GroupQ1_DF and GroupQ1_DF that process orders for the first quarter and second quarter, respectively.

• The solid blue lines enclose each process that can execute on a separate Job Server. In this example, each data flow can execute on a different computer than the computer where the job started.

• SAP Data Services uses Inter-Process Communications (IPC) to send data between the job and data flows on the different computers. IPC uses the peer-to-peer port numbers specified on the Start port and End port options in the Server Manager.

Caching data

You can improve the performance of data transformations that occur in memory by caching as much data as possible. By caching data, you limit the number of times the system must access the database.

SAP Data Services provides the following types of caches that your data flow can use for all of the operations it contains:

• In-memory Use in-memory cache when your data flow processes a small amount of data that fits in memory.

• Pageable cache Use pageable cache when your data flow processes a very large amount of data that does not fit in memory. When memory-intensive operations (such as Group By and Order By) exceed available memory, the software uses pageable cache to complete the operation.

Pageable cache is the default cache type. To change the cache type, use the Cache type option on the data flow Properties window.

Caching sources

By default, the Cache option is set to Yes in a source table or file editor to specify that data from the source is cached using memory on the Job Server computer. When sources are joined using the Query transform, the cache setting in the Query transform takes precedence over the setting in the source.

The default value for Cache type for data flows is Pageable.

It is recommended that you cache small tables in memory. Calculate the approximate size of a table with the following formula to determine if you should use a cache type of Pageable or In-memory. Compute row count and table size on a regular basis, especially when:

• You are aware that a table has significantly changed in size.

• You experience decreased system performance.

If the table fits in memory, change the value of the Cache type option to In-memory in the Properties window of the data flow.

Caching joins

The Cache setting indicates whether the software should read the required data from the source and load it into memory or pageable cache.

When sources are joined using the Query transform, the cache setting in the Query transform takes precedence over the setting in the source. In the Query editor, the cache setting is set to Automatic by default. The automatic setting carries forward the setting from the source table. The following table shows the relationship between cache settings in the source, Query editor, and whether the software will load the data in the source table into cache.

To change cache type for a data flow

You can improve the performance of data transformations that occur in memory by caching as much data as possible. By caching data, you limit the number of times the system must access the database.

To change the cache type for a data flow:

1. In the object library, select the data flow name.

2. Right-click and choose Properties.

3. On the General tab of the Properties window, select the desired cache type in the drop-down list for the Cache type option.

Caching lookups

Cache data when you look up individual values from tables and files.

You can improve performance by caching data when looking up individual values from tables and files. Use a Lookup function in a query or use a source table set as an outer join.

Using a Lookup function in a query

SAP Data Services has three Lookup functions: lookup, lookup_seq, and lookup_ext. The lookup and lookup_ext functions have cache options. Caching lookup sources improves performance because the software avoids the expensive task of creating a database query or full file scan on each row.

You can set cache options when you specify a lookup function. There are three caching options: When there are many values and some values might be missing, demand-load caching is sig nificantly less efficient than caching the entire source.

Using a source table and setting it as the outer join

Although you can use lookup functions inside SAP Data Services queries, an alternative is to expose the translate (lookup) table as a source table in the data flow diagram, and use an outer join (if necessary) in the query to look up the required data. This technique has some advantages:

• You can graphically see the table the job will search on the diagram, making the data flow easier to maintain.

• The software can push the execution of the join down to the underlying RDBMS (even if you need an outer join).

This technique also has some disadvantages:

• You cannot specify default values in an outer join (default is always null), but you can specify a default value in lookup_ext. • If an outer join returns multiple rows, you cannot specify what to return, (you can specify MIN or MAX in lookup_ext).

• The workspace can become cluttered if there are too many objects in the data flow.

• There is no option to use DEMAND_LOAD caching, which is useful when looking up only a few repetitive values in a very large table.

Tip

If you use the lookup table in multiple jobs, you can create a persistent cache that multiple data flows can access. For more information, see Using persistent cache [page 50].

If you want to sort the input to the table comparison transform, then choose the Sorted input option for comparison.

If the input is not sorted, then choose the Cached comparison table option.

If DOP is greater than one, select either job or data flow for the Distribution level option when you execute the job. If you execute the job with the value sub data flow for Distribution level, the Hash Split sends data to the replicated queries that might be executing on different Job Servers. Because the data is sent on the network between different Job Servers, the entire data flow might be slower.

If the target is not partitioned, set the Number of loaders option equal to the DOP value. Depending on the number of CPUs available, set the DOP value equal to the number of source partitions as a general rule. This produces a data flow without the Merge Round Robin Split and each partition pipes the data directly into the consuming transform.

If the number of target partitions is not equal to the number of source partitions, set the Number of loaders option equal to the DOP value and do not enable partitioning for the target. Depending on the number of CPUs available, set the DOP value equal to the number of source partitions as a general rule. This produces a data flow without the Merge Round Robin Split and each partition pipes the data directly into the consuming transform.

When your data flow has a DOP of greater than one, select either job or data flow for the Distribution level option when you execute the job. If you execute the job with the value sub data flow for Distribution level, the Round-Robin Split or Hash Split sends data to the replicated queries that might be executing on different job servers. Because the data is sent on the network between different job servers, the entire data flow might be slower.

The optimal number for Array fetch size depends on the size of your table rows (the number and type of columns involved) as well as the network round-trip time involved in the database requests and responses. If your computing environment is very powerful, (meaning that the computers running the Job Server, related databases, and connections are extremely fast), then try higher values for Array fetch size and test the performance of your jobs to find the best setting.

Performance Optimization Guide Tuning Techniques for performance options P U B L I C

Caching table comparisons

You can improve the performance of a Table_Comparison transform by caching the comparison table. There are three modes of comparisons:

Of the three, Row-by-row select will likely be the slowest and Sorted input the fastest.

Specifying a pageable cache directory

If the memory-consuming operations in your data flow exceed the available memory, SAP Data Services uses pageable cache to complete the operation. Memory-intensive operations include the following operations:

• Distinct

• Functions such as count_distinct and lookup_ext

• Group By

• Hierarchy_Flattening

• Order By

Using persistent cache

Persistent cache datastores provide the following benefits for data flows that process large volumes of data.

• You can store a large amount of data in persistent cache which SAP Data Services quickly pages into memory each time the job executes. For example, you can access a lookup

Using persistent cache tables as sources

After you create a persistent cache table as a target in one data flow, you can use the persistent cache table as a source in any data flow. You can also use it as a lookup table or comparison table.

Monitoring and tuning caches

Determine cache type automatically through the software or manually.

SAP Data Services uses cache statistics collected from previous job runs to automatically determine which cache type to use for a data flow. Cache statistics include the number of rows processed.

The default cache type is pageable. The software can switch to in-memory cache when it determines that your data flow processes a small amount of data that fits in memory.

You can also monitor and choose the cache type to use for the data flow.

To automatically choose the cache type

Steps to run jobs so that Data Services determines cache type automatically.

1. Run your job with the option Collect statistics for optimization selected.

2. Run your job again with the option Use collected statistics (this option is selected by default).

Monitoring and tuning in-memory and pageable caches

Steps to monitor and choose the cache type to use for the data flow.

1. Test run your job with options Collect statistics for optimization and Collect statistics for monitoring selected.

Row Count

Number of rows that this object processed.

Cache Size (KB) Size (in kilobytes) of the cache that was used to process this object.

Using Parallel Execution

You can set SAP Data Services to perform data extraction, transformation, and loads in parallel by setting parallel options for sources, transforms, and targets. In addition, you can set individual data flows and work flows to run in parallel by simply not connecting them in the workspace. If the Job Server is running on a multi-processor computer, it takes full advantage of available CPUs.

Parallel data flows and work flows

You can explicitly execute different data flows and work flows in parallel by not connecting them in a work flow or job. SAP Data Services coordinates the parallel steps, then waits for all steps to complete before starting the next sequential step.

For example, use parallel processing to load dimension tables by calling work flows in parallel. Then specify that your job creates dimension tables before the fact table by moving it to the left of a second (parent) work flow and connecting the flows.

Parallel engine processes execute the parallel data flow processes. Note that if you have more than eight CPUs on your Job Server computer, you can increase Maximum number of engine processes to improve performance. To change the maximum number of parallel engine processes, use the Job Server options ( Tools Options> Job Server Environment ).

Performance Optimization Guide Using Parallel Execution P U B L I C

Parallel execution in data flows

For batch jobs, SAP Data Services allows you to execute parallel threads in data flows.

Table partitioning

SAP Data Services processes data flows with partitioned tables based on the amount of partitioning defined.

Data flow with source partitions only

If you have a data flow with a source that has two partitions connected to a query and a target, it appears in the workspace as shown in the following diagram:

At runtime, the software translates this data flow to:

The software instantiates a source thread for each partition, and these threads run in parallel. The data from these threads later merges into a single stream by an internal merge transform before processing the query.

Data flow with target partitions only

If you have a data flow with a target that has two partitions connected to a query and a source, it appears in the workspace as shown in the following diagram:

At runtime, the software translates this data flow to:

The software inserts an internal Round Robin Split (RRS) transform after the Query transform, which routes incoming rows in a round-robin fashion to internal Case transforms. The Case transforms evaluate the rows to determine the partition ranges. Finally, an internal Merge transform collects the incoming rows from different Case transforms and outputs a single stream of rows to the target threads. The Case transforms, Merge transform, and target threads execute in parallel.

Data flow with source and target partitions

If you have a data flow with a source that has two partitions connected to a query and a target that has two partitions, it appears in the workspace as shown in the following diagram:

At runtime, the software translates this data flow to:

The source threads execute in parallel and the Case transforms, Merge transforms, and targets execute in parallel.

Viewing, creating, and enabling table partitions

Oracle databases support range, list, and hash partitioning. You can import this information as table metadata and use it to extract data in parallel. You can use range and list partitions to load data to Oracle targets. You can also specify logical range and list partitions using SAP Data Services metadata for Oracle tables.

SAP HANA supports partition support for column store tables. SAP Data Services supports the SAP HANA partition feature for parallel reading and loading using physical partitions and logical partitions. You can import an SAP HANA partition table metadata for has and range partitioned tables used for parallel reading and loading. For a logical partition of SAP HANA tables, range mixed with list (similar to physical range partition syntax) is supported.

In addition, SAP Data Services provides the ability to specify logical range and list partitions for DB2, Microsoft SQL Server, SAP R/3, SAP ASE, and SAP Sybase IQ tables by modifying imported table metadata.

SAP Data Services uses partition information by instantiating a thread at runtime for each partition. These threads execute in parallel. To maximize performance benefits, use a multi-processor environment.

To view partition information

Steps to view partition information to inprove performance.

SAP Data Services uses partition information by instantiating a thread at runtime for each partition. To view partition information:

1. Import a table into SAP Data Services.

2. In the Datastores tab of the object library, right-click the table name and select Properties.

3. Click the Partitions tab.

When you import partitioned tables from your database, you will find these partitions displayed on the Partitions tab of the table's Properties window. The partition name appears in the first column. The columns that are used for partitioning appear as column headings in the second row. 3. Select a partition type.

Range

Each partition contains a set of rows with column values less than those specified.

For example, if the value of column one is 100,000, then the data set for partition one will include rows with values less than 100,000 in column one.

List

Each partition contains a set of rows that contain the specified column values.

To enable partition settings in a source or target table

Steps to enable the partitions settings.

In addition to importing partitions or creating and editing partition metadata, enable the partition settings when you configure sources and targets by following these steps: When the job executes, the softwarre generates parallel instances based on the partition information.

Degree of parallelism

The degree of parallelism (DOP) is a property of a data flow that defines how many times each transform defined in the data flow replicates for use on a parallel subset of data. If there are multiple transforms in a data flow, SAP Data Services chains them together until it reaches a merge point.

You can run transforms in parallel by entering a number in the Degree of Parallelism option on a data flow's Properties window. The number is used to replicate transforms in the data flow which run as separate threads when the Job Server processes the data flow.

Local and Global settings

The default value for degree of parallelism is 0. If you set an individual data flow's degree of parallelism to this default value (0), then you can control it using a Global_DOP value which affects all data flows run by a given Job Server. If you use any other value for a data flow's degree of parallelism, it overrides the Global_DOP value.

7.2.2.1

Before you use DOP for enhanced performance DOP can degrade performance if you do not use it judiciously. The best value to choose depends on the complexity of the flow and number of CPUs available.

Degree of parallelism and transforms

The Query transform always replicates when you set the DOP to a value greater than 1. SAP Data Services also replicates query operations such as Order By, Group By, join, and functions such as lookup_ext.

The • Map_Operation

• History_Preserving

• Pivot

There are two basic scenarios:

• DOP and a data flow with a single transform

• DOP and a data flow with multiple transforms

DOP and a data flow with a single transform

The following figures show runtime instances of a data flow with a DOP of 1, and the same data flow with a DOP of 2. With a DOP greater than 1, the software inserts an internal Round Robin Split (RRS) that transfers data to each of the replicated queries. The replicated queries execute in parallel, and the results merge into a single stream by an internal Merge transform.

DOP and a data flow with multiple transforms

The following figures show runtime instances of a data flow with a DOP of 1, and the same data flow with a DOP of 2. Notice multiple transforms in a data flow replicate and chain when the DOP is greater than 1. When there are multiple transforms in a data flow and the DOP is greater than 1, the software carries the replicated stream as far as possible, then merges the data into a single stream.

To set the degree of parallelism for a data flow

The degree of parallelism (DOP) is a data flow property that acts on transforms added to the data flow.

1. In the object library, select the Data Flow tab.

2. Right-click the data flow icon and select Properties.

Enter a number in the

Degree of parallelism and joins

If your Query transform joins sources, DOP determines the number of times the join replicates to process a parallel subset of data.

This section describes two scenarios:

• DOP and executing a join as a single process

• DOP and executing a join as multiple processes

DOP and executing a join as a single process

The following figures show runtime instances of a data flow that contains a join with a DOP of 1 and the same data flow with a DOP of 2. You use join ranks to define the outer source and inner source. In both data flows, the inner source is cached in memory. With a DOP greater than one, the software inserts an internal Round Robin Split (RRS) that transfers data to each of the replicated joins. The inner source is cached once, and each half of the outer source joins with the cached data in the replicated joins. The replicated joins execute in parallel, and the results merge into a single stream by an internal Merge transform.

DOP and executing a join as multiple processes

When you select the Run JOIN as a separate process in the Query transform, you can split the execution of a join among multiple processes. the software creates a sub data flow for each separate process.

The following figure shows a runtime instance of a data flow that contains a join with a DOP of 2 and the Run JOIN as a separate process option selected. • The first sub data flow uses an internal hash algorithm to split the data.

• The next two sub data flows are the replicated joins that run as separate processes.

• The last sub data flow merges the data and loads the target.

Degree of parallelism and functions

You can set stored procedures and custom functions to replicate with the transforms in which they are used. To specify this option, select the Enable parallel execution check box on the function's Properties window. If this option is not selected and you add the function to a transform, the transform will not replicate and run in parallel even if its parent data flow has a Degree of parallelism value greater than 1.

When enabling functions to run in parallel, verify that:

• Your database will allow a stored procedure to run in parallel.

• A custom function set to run in parallel will improve performance.

Most built-in functions replicate if the transform they are used in replicates because of the DOP value. However, the following functions will not replicate because of the DOP value: Table 20:

To enable stored procedures to run in parallel

Use the Enable parallel execution option to set functions to run in parallel when the transforms in which they are used execute in parallel.

1. In the Datastores tab of the object library, expand a Datastore node. For all the scenarios, the data flow appears as follows:

Expand its

Two source partitions and a DOP of three

When a source has two partitions, it replicates twice. The input feeds into a merge-round-robin splitter (MRRS) that merges the input streams and splits them into a number equal to the value for DOP (in this case, three outputs to the query transform). The stream then merges and feeds into the target.

Two source partitions and a DOP of two

When the number of source partitions is the same as the value for DOP, the engine merges before the target (or before any operation that requires a merge, such as aggregation operations) and proceeds in a single stream to complete the flow.

Two source partitions, DOP of three, two target partitions

When the number of source partitions is less then the value for DOP, the input feeds into a merge-round-robin splitter (MRRS) that merges the input streams and splits them into a number equal to the value for DOP. The engine then merges the data before the target to equal the number of target partitions, then proceeds to complete the flow.

Two source partitions, DOP of two, and two target partitions

The best case situation is when the following conditions exist:

• The source and target are partitioned the same way.

• The source and target have the same number of partitions.

• DOP is equal to the same number of partitions.

When a source has two partitions, it replicates twice. Because the DOP value is two, the Query transform replicates twice. When a target has two partitions, it replicates twice. The following figure shows that each source partition feeds directly into a replicated Query transform, and the output from each query feeds directly into a replicated target.

File multi-threading

Multi-threading and software behavior without multi-threading set.

You can set the number of threads used to process some sources and targets by setting the Parallel process threads option. The is available on the Parallel process threads option is available in the following editors:

• File format editor

• Source file editor

• Target file editor

Parallel process threads is also available in the Properties window of an ABAP data flow Without the Parallel process threads option enabled, the software processes sources and targets without the benefits of multi-threading for the following processes:

• Delimited file reading. Without multi-threading enabled, SAP Data Services reads a block of data from the file system and then scans each character to determine if it is a column delimiter, a row delimiter, or a text delimiter. Then it builds a row using an internal format. • Positional file reading, Without multi-threading enabled, the software does not scan character by character, but it still builds a row using an internal format.

• File loading. Without multi-threading enabled, processing involves building a character-based row from the internal row format.

You can set these time-consuming operations to run in parallel by setting the Parallel process threads option to specify how many threads to execute in parallel to process the I/O blocks.

7.2.4.1

Tuning performance

The Parallel process threads option is a performance enhancement for some sources and targets. Performance is defined as the total elapsed time used to read a file source.

A multi-threaded file source or target achieves high performance by maximizing the utilization of the CPUs on your Job Server computer. You will notice higher CPU usage when you use this feature. You might also notice higher memory usage because the number of process threads you set (each consisting of blocks of rows that use 128 kilobytes) reside in memory at the same time.

To tune performance, adjust the value for Parallel process threads. Ideally, have at least as many CPUs as process threads. For example, if you enter the value 4 for Parallel process threads, have at least four CPUs on your Job Server computer.

However, increasing the value for process threads does not necessarily improve performance. The file reads and loads achieve their best performance when the work load is distributed evenly among all the CPUs and the speed of the file's input/output (I/O) thread is comparable with the speed of the process threads.

The I/O thread for a file source reads data from a file and feeds it to process threads. The I/O thread for a file target takes data from process threads and loads it to a file. Therefore, if a source file's I/O thread is too slow to keep the process threads busy, there is no need to increase the number of process threads.

If there is more than one process thread on one CPU, that CPU will need to switch between the threads. There is an overhead incurred in creating these threads and switching the CPU between them.

Flat file sources

To use the Parallel process threads option, the following conditions must be met:

• In the file format editor:

○ For delimited files, no text delimiters are defined.

For fixed-width files, having a text delimiter defined does not prevent the file from being read by parallel process threads. You can set SAP Data Services to read flat file data in parallel in most cases because the majority of jobs use fixed-width or column-delimited source files that do not have text delimiters specified. ○ An end-of-file (EOF) marker for the file's input/output style is not specified.

○ The value of the row delimiter is not set to {none}. A row delimiter can be {none} only if the file is a fixedwidth file. ○ If the file has a multi-byte locale and you want to take advantage of parallel process threads, set the row delimiter as follows: ○ The length of the row delimiter must be 1. If the codepage of the file is UTF-16, the length of the row delimiter can be 2. ○ The row delimiter hex value must be less than 0x40.

• In the Source File Editor, no number has been entered for Rows to read.

The Rows to read option indicates the maximum number of rows that the software reads. It is normally used for debugging. Its default value is none.

• The maximum row size does not exceed 128 KB.

If a file source needs to read more than one file (for example, *.txt is specified for the File(s) option in the file format editor), the software processes the data in the first file before the data in the next file. It performs file multithreading one file at a time.

Flat file targets

If you enter a positive value for Parallel process threads, Data Services parallel processes flat file targets when the maximum row size does not exceed 128KB.

Tips

The best value for Parallel process threads depends on the complexity of your data flow and the number of available processes. If your Job Server is on a computer with multiple CPUs, the values for file sources and targets should be set to at least two.

After that, experiment with different values to determine the best value for your environment.

Here are some additional guidelines:

• If Parallel process threads is set to none, then flat file reads and loads are not processed in parallel.

• If Parallel process threads is set to 1, (meaning that one process thread will spawn) and your Job Server computer has one CPU, then reads and loads can occur faster than single-threaded file reads and loads because SAP Data Services reads the I/O thread separately and concurrently with the process thread. • If Parallel process threads is set to 4, four process threads will spawn. You can run these threads on a single CPU. However, using four CPUs would more likely maximize the performance of flat file reads and loads.

Performance Optimization Guide Using Parallel Execution P U B L I C

Distributing Data Flow Execution

SAP Data Services can run a single process as multiple threads that run in parallel on a multiprocessor computer. By using multiple threads and degree of parallelism (DOP), it can execute each thread on a separate CPU on the computer.

Data Services can also split a process (data flow) into multiple processes (sub data flows) that can take advantage of more memory across multiple computers or on the same computer that has more than two gigabytes of memory. For example, if your computer has eight gigabytes of memory, you can have four sub data flows that each can use up to two gigabytes.

With this capability, the software can distribute CPU-intensive and memory-intensive operations (such as join, grouping, table comparison and lookups). This distribution of data flow execution provides the following potential benefits:

• Better memory management by taking advantage of more CPU power and physical memory.

• Better job performance and scalability by taking advantage of grid computing.

You can create sub data flows so that the software does not need to process the entire data flow in memory at one time. You can also distribute the sub data flows to different job servers within a server group to use additional memory and CPU resources.

Splitting a data flow into sub data flows

When you specify multiple Run as separate process options in objects in a data flow, SAP Data Services splits the data flow into sub data flows that run in parallel.

Use the Run as separate process options for data flows that contain multiple resource-intensive operations. Then the software runs each operation as a separate process (sub data flow) that uses separate resources (memory and computer) from each other to improve performance and throughput.

The Run as a separate process option is available on resource-intensive operations that include the following:

• Hierarchy_Flattening transform

Multiple processes with Data_Transfer

The following are typical scenarios of when you might use the Data_Transfer transform to split a data flow into sub data flows to push down operations to the database server.

• Scenario 1: Sub data flow to push down join of file and

Scenario 1: Sub data flow to push down join of file and table sources

Your data flow might join the Orders flat file and Orders table, perform a lookup_ext function to obtain sales subtotals, and use another Query transform to group the results by country and region.

Scenario 2: Sub data flow to push down memoryintensive operations

You can use the Data_Transfer transform to push down memory-intensive operations such as Group By or Order By.

For the sample data flow in Scenario 1: Sub data flow to push down join of file and

To define sub data flows to push down another operation

1. Add a Data_Transfer transform between the Lookup and GroupBy Query transforms, as the following diagram shows. INSERT INTO "DBO"."JOINTARGET"("PRODUCTID","SHIPCOUNTRY","SHIPREGION","SALES") SELECT "Data_Transfer_1_Lookup"."PRODUCTID", "Data_Transform_1_Lookup"."SHIPCOUNTRY", "Data_Transfer_1_Lookup"."SHIPREGION",sum("Data_Transfer_1_Lookup"."SALES") FROM "DBO"."GROUPTRANS""Data_Transfer_1_Lookup" GROUP BY "Data_Transfer_1_Lookup"."PRODUCTID","Data_Transfer_1_Lookup"."SHIPCOUNTRY", "Data_Transfer_1_Lookup"."SHIPREGION"

Select the value

The software can push down many operations without using the Data_Transfer transform.

5. When you execute the job, the messages indicate that the software creates three sub data flows to run the different operations serially.

Push-down operations [page 28]

Scenario 2: Sub data flow to push down memory-intensive operations [page 75]

Multiple processes for a data flow

A data flow can contain multiple resource-intensive operations that each require large amounts of memory or CPU utilization. You can run each resource-intensive operation as a separate process that can use more memory on a different computer or on the same computer that has more than two gigabytes of memory.

Scenario 1: Run multiple sub data flows with DOP set to 1

Using the example in Multiple processes for a data flow [page 76], the following diagram shows how SAP Data Services splits this data flow into two sub data flows when you specify the Run as a separate process option for either the Lookup_ext function or the Group By.

The software generates sub data flow names that follow this format:

<DFName_executionGroupNumber_indexInExecutionGroup >

• <DFName> is the name of the data flow.

• <executionGroupNumber> is the order that the software executes a group of sub data flows • <indexInExecutionGroup> is the sub data flow within an execution group.

When you execute the job, the trace log shows two sub data flows that execute in parallel and have different process IDs (Pids). For example, the following depiction of the trace log shows two sub data flows GroupBy_DF_1_1 and GroupBy_DF_1_2 that each start at the same time and have different Pids than the parent data flow GroupBy_DF.

Scenario 2: Run multiple sub data flows with DOP greater than 1

Using the example in Multiple processes for a data flow [page 76], when the degree of parallelism (DOP) is set to a value greater than 1, each transform defined in the data flow replicates for use on a parallel subset of data.

Set DOP to a value greater than 1 on the data flow Properties window.

The following diagram shows the sub data flows that Data Services generates for GroupBy_DOP2_Job when the Run GROUP BY as a separate process is selected and DOP set to 2.

When you execute the job, the trace log shows that the software creates sub data flows that execute in parallel with different process IDs (Pids). For example, the following table shows what the trace log would display for the following four sub data flows that start concurrently, each with a different Pid than the parent data flow GroupBy_DOP2_DF:

• GroupBy_DOP2_DF_1_1

• GroupBy_DOP2_DF_1_2

• GroupBy_DOP2_DF_1_3

• GroupBy_DOP2_DF_1_4

Degree of parallelism [page 60]

Using grid computing to distribute data flow execution [page 80]

Data_Transfer transform

The Data_Transfer transform creates transfer tables in datastores to enable the software to push down operations to the database server. The Data_Transfer transform creates two sub data flows and uses the transfer table to distribute the data from one sub data flow to the other sub data flow. The sub data flows execute serially. 9 Using grid computing to distribute data flow execution SAP Data Services takes advantage of grid computing when you:

• Define a group of Job Servers (called a Server Group [page 80]) that acts as a server grid. The software leverages available CPU and memory on the computers where the Job Servers execute. • Specify Distribution levels for data flow execution [page 80] to process smaller data sets or fewer transforms on different Job Servers in a Server Group. Each data flow or sub data flow consumes less virtual memory.

Server Group

You can distribute the execution of a job or a part of a job across multiple Job Servers within a Server Group to better balance resource-intensive operations. A server group automatically measures resource availability on each Job Server in the group and distributes scheduled batch jobs to the Job Server with the lightest load at runtime.

Distribution levels for data flow execution

When you execute a job, you can specify the following values on the Distribution level option:

• Job level -An entire job can execute on an available Job Server.

• Data flow level -Each data flow within a job can execute on an available Job Server and can take advantage of additional memory (up to two gigabytes) for both in-memory and pageable cache on another computer.

• Sub data flow level -A resource-intensive operation (such as a sort, table comparison, or table lookup) within a data flow can execute on an available Job Server. Each operation can take advantage of up to two gigabytes additional memory for both in-memory and pageable cache on another computer.

Job level

When you choose a Server Group to execute your job, the default distribution level is Job.

When Distribution level has the value Job, all of the processes that belong to the job execute on the same computer. For example, section Scenario 2: Run multiple sub data flows with DOP greater than 1 [page 78] describes the data flow GroupBy_DOP2_DF which is designed to generate four sub data flows as follows.

When you execute the job, the following Trace log messages indicate the distribution level for each sub data flow:

Starting sub data flow <GroupBy_DOP2_DF_1_1> on job server host <SJ-C>, port <3502>. Distribution level <Job>. Starting sub data flow <GroupBy_DOP2_DF_1_2> on job server host <SJ-C>, port <3502>. Distribution level <Job>. Starting sub data flow <GroupBy_DOP2_DF_1_3> on job server host <SJ-C>, port <3502>. Distribution level <Job>. Starting sub data flow <GroupBy_DOP2_DF_1_4> on job server host <SJ-C>, port <3502>. Distribution level <Job>.

When Distribution level is Job, the software uses named pipes to send data between the sub data flow processes on the same computer, as the following diagram indicates with the blue arrows.

Sub data flow level

When Distribution level has the value Sub data flow, each sub data flow within a data flow can execute on a different computer. In the example that section Scenario 2: Run multiple sub data flows with DOP greater than 1 [page 78] describes, the GroupBy_DOP2_Job has four sub data flows as follows.

• The solid blue lines enclose each process that can execute on a separate Job Server. In this example, each sub data flow can execute on a different computer than the computer where the job started.

• The yellow arrows indicate the Inter-Process Communications (IPC) that SAP Data Services uses to send data between the job and sub data flows on the different computers. IPC the peer-to-peer port numbers specified on the Start port and End port options in the Server Manager. The default values for Start port and End port are 1025 and 32767, respectively. Change these values if you want to restrict the number of ports or if some of the ports are already in use.

84

P U B L I C Performance Optimization Guide Using grid computing to distribute data flow execution SAP Data Services supports capabilities present in most supported databases that enable you to load and in some cases read data in bulk rather than using SQL statements. Some general considerations when using bulk loading and reading are:

• Specify bulk-loading options on the Data Services target table editor on the Options and Bulk Loader Options tabs.

• Specify Teradata reading options on the source table editor Teradata options tab.

• Most databases do not support bulk loading with a template table.

• Operation code support can differ between databases. The following table lists databases and the operation codes they support when using bulk loading:

Import

Loads a large volume of data by using a SQL IN SERT statement to write data from an input file into a table or view.

• Recovery is enabled automat ically because DB2 logging occurs during import.

• Performs referential integrity or table constraint checking in addition to unique key con straint checking.

• Because DB2 logs each INSERT state ment, this method is the slowest way to bulk load data.

• The Data Services Job Server and DB2 UDB server must be on the same com puter.

Using the DB2 CLI load method

The DB2 Call Level Interface (CLI) load method performs faster than the bulk load or import utilities because it does not write the data to an intermediate file. Instead, the CLI load method writes the data from memory (where SAP Data Services extracted or transformed the data) directly to the table on the DB2 server.

To configure your system to use the CLI load method

1. Enter the appropriate information in the datastore editor, on the DB2 Properties tab.

Fields include: ○ Bulk loader user name: The user name SAP Data Services uses when loading data with the CLI load option. For bulk loading, you might specify a different user name, for example one with import and load permissions. ○ Bulk loader password: The password SAP Data Services uses when loading with the CLI load option.

2. To use a different bulk loader working directory than the default (<DS_COMMON_DIR>\log\bulkloader), specify the directory name in the datastore editor on the Connections tab.

Using the import utility

SAP Data Services also supports bulk loading in the DB2 Universal Database 5.2 environment using the import utility. For the software to initiate DB2 bulk loading by this method directly, the Job Server and DB2 must be located on the same system. If they are not, use the following procedure to initiate bulk loading:

1. Generate a control file and data file. Check Generate files only in the target table editor on the Bulk Loader Options tab.

2. Manually move the control file and data file to the system where the target database is located.

3. Start the execution of the bulk loader manually. For the software to initiate Informix bulk loading directly, the Job Server and the target database must be located on the same system.

Bulk loading in Informix

Setting Informix server variables

For Windows platforms, configure the environment variables in the $LINK_DIR\bin\dbloadIfmx.bat script.

Bulk loading in Microsoft SQL Server

SAP Data Services supports Microsoft SQL Server bulk loading through the SQL Server ODBC bulk copy API. For detailed information about the SQL Server ODBC bulk copy API options and their behavior in the Microsoft SQL Server DBMS environment, see the relevant Microsoft SQL Server product documentation.

To use the SQL Server ODBC bulk copy API

To enable bulk loading on the default job server, do the following:

1. From the Tools menu, select Options Job Server General .

2. For Section, enter AL_Engine.

3. For Key, enter UseSQLServerBulkCopy.

4. Select TRUE (default) or FALSE. If you leave the default, the software uses the SQL Server ODBC bulk copy API. If you set this parameter to FALSE, the software overrides the default and uses the SQLBulkOperations API (this method is slower than the default).

Network packet size option

When loading to SQL Server, the client caches rows until it either fills a network packet or reaches the commit size (regardless of whether the packet is full). Then the client sends the packet to the server. You can affect performance by tuning commit size and network packet size. You can change these sizes on the Bulk Loader Options tab for SQL Server:

• Rows per commit This option lets you specify the number of rows to put in the cache before issuing a commit. • Network packet size This option lets you specify network packet size in kilobytes. The default packet size is 4 kilobytes.

Maximum rejects option

The Maximum rejects parameter (on the Bulk Loader Options page) can also affect your SQL Server bulk-loading performance. When you set Maximum rejects to 0, SAP Data Services stops at the first error it encounters and does not cache rows in the transaction (caching rows in a transaction allows the software to process each row even if an error occurs during the transaction commit process.) When you do not specify a value for Maximum rejects, the software ignores the rejected rows, logs warnings, and continues processing.

Bulk loading in Netezza

SAP Data Services supports bulk loading to Netezza Performance Servers.

For detailed information about Netezza loading options and their behavior in the Netezza environment, see the relevant Netezza product documentation.

Netezza recommends using the bulk-loading method to load data for faster performance. Unlike some other bulk loaders, the SAP Data Services bulk loader for Netezza supports UPDATE and DELETE as well as INSERT operations, which allows for more flexibility and performance.

Netezza bulk-loading process

To bulk load to a Netezza target table, SAP Data Services:

• Creates an external table that is associated with a local file or named pipe.

• Loads data from the source into the file or named pipe.

• Loads data from the external

Options overview

From the Bulk Loader Options tab of your Netezza target table editor, select one of these methods depending on your Netezza environment:

• Named pipe: SAP Data Services streams data as it is written to the named pipe through the external table to the staging table. For files that are larger than 4 GB in size, select this option for faster performance.

• File: SAP Data Services writes the data to a file before loading through the external table to the staging table.

For files that are smaller than 4 GB in size, select this option for faster performance.

• None: SAP Data Services does not use bulk loading.

Because the bulk loader for Netezza also supports UPDATE and DELETE operations, the following options (on the target table editor Options tab) are also available for Netezza bulk loading.

• Column comparison

To configure bulk loading for Netezza

First configure the bulk loader and log directories in the datastore editor, then enable and configure bulk loading in the target table editor:

1. In the Netezza datastore editor, click Advanced.

2. Click in the field to the right of Bulk loader directory and type the directory path or click Browse to where the software should write SQL and data files for bulk loading.

3. In the FTP category, enter the FTP host name, login user name, login password, and host working directory.

These options are used to transfer the Netezza nzlog and nzbad files.

Click

Netezza log files

When writing from the external table to the staging table, Netezza generates logs (nzlog and nzbad files) and writes them to a database server working directory. You can use these logs to troubleshoot your jobs. (If you do not enter a Database server working directory in the datastore editor, Netezza uses the temp directory on its server, /tmp, to store the nzlog and nzbad files.)

For SAP Data Services to access and manage these logs, configure the FTP parameters in the datastore editor. After a load, trhe software copies these files from the specified Netezza Database server working directory to the specified Bulk loader directory and deletes them from the Netezza server.

For successful loads, SAP Data Services then deletes these log files from the Bulk loader directory (assuming the Clean up bulk loader directory after load option is checked in the target table editor).

For failed loads, the software does not delete the log files from the Bulk loader directory even if the Clean up bulk loader directory after load option is checked in the target table editor.

Bulk loading in Oracle

SAP Data Services supports Oracle bulk loading.

Bulk-loading methods

You can bulk load to Oracle using an API or a staging file:

• If you select the API method, SAP Data Services accesses the direct path engine of Oracle's database server associated with the target table and connected to the target database. Using Oracle's Direct-Path Load API, input data feeds directly into database files. To use this option, you must have Oracle version 8.1 or later. • If you select the File method, Data Services writes an intermediate staging file, control file, and log files to the local disk and invokes the Oracle SQL*Loader. This method requires more processing time than the API method. For detailed information about the Oracle SQL*Loader options, see the relevant Oracle product documentation.

Bulk-loading modes

Bulk loading in Oracle supports two modes of data loading: conventional-path and direct-path. Conventional-path loading is implicit for the File option if you do not select Direct-path on the Bulk Loader Options tab in the target table editor. SAP Data Services always uses direct-path loading for the API option.

• Conventional-path loading: Conventional-path loads use the SQL INSERT statements to load data to tables.

• Direct-path loading:Direct-path loads use multiple buffers for a number of formatted blocks that load data directly to database files associated with tables.

Bulk-loading parallel-execution options

Parallel-execution options for bulk loading are on the Options tab.

Bulk-loading scenarios

With two bulk-loading methods, two load modes, and two parallel load options, there are several scenarios you can configure: Here are some tips for using these scenarios:

• The API method always uses the direct-path load type, and when it is used with a partitioned target table, SAP Data Services processes loads in parallel. The software instantiates multiple loaders based on the number of partitions in a table. Each loader receives rows that meet the conditions specified by the partition. • With the File method, direct-path is faster than conventional load, but the File method is slower than using an API because of the need to generate a staging file, logs, and invoke Oracle's SQL*Loader.

• With the File method, when you use a value of greater than one for either the Number of Loaders or the Enable partitioning option, loads cannot truly run in parallel. The creation of a staging file and log for each loader is serialized.

Using bulk-loading options

As seen in the table on the previous page, there are many ways to set up bulk loading for an Oracle database. The following sections describe two scenarios in detail.

Direct-path loads using Number of Loaders and File method

In the Options tab of the target table editor, when you enter a value for Number of loaders, SAP Data Services instantiates multiple loaders. Each loader receives rows equal to the amount specified in the Rows per commit box on the Bulk Loader Options tab. The loaders pipe rows to a staging file, then call the SQL*Loader to load the staging file contents into the table.

This process occurs in "round-robin" fashion. For example, if you set Rows per commit to 5000 and Number of loaders to 2, then the first loader receives 5000 rows, writes them to the staging file, and then invokes the SQL*Loader to load the data into the table.

Meanwhile, the second loader receives the second batch of 5000 rows, writes them to a staging file, and then waits for the first loader to complete the loading. When the first loader completes the bulk load, the second loader starts, and while the second loader is loading, the first loader receives the third batch of 5000 rows. This process continues until all the data loads.

The SQL*Loader uses a control file to read staging files and load data. The software either creates this control file at runtime or uses one that is specified on the Bulk Loader Options tab at design time.

For parallel loading, the generated control files, data files, and log files are named as follows:

<TableNameTIDPID_LDNUM_BATCHNUM >

Where:

<TableName:> The name of the table into which data loads.

<TID> : The thread ID.

<PID> : The process ID.

<LDNUM> : The loader number, which ranges from 0 to number of loaders minus 1. For single loaders, LDNUM is always 0.

<BATCHNUM> : The batch number the loader is processing. For single loaders the <BATCHNUM> is always 0.

Direct-path loads using partitioned tables and API method

You can import partitioned tables as SAP Data Services metadata.

In the Options tab of the target table editor, when you select Enable partitioning, the software instantiates multiple loaders based on the number of partitions in a table. Each loader receives rows that meet the conditions specified by the partition. In addition, commits occur based on the number specified in the Rows per commit box on the Bulk Loader Options tab.

For example:

• If you Rows per commit to 5000, the number of partitions is set 2, and your first partition includes 2500 rows, then the first loader commits after receiving all possible rows (2500) while concurrently processing the second loader. • If you Rows per commit to 5000, the number of partitions is set 2, and your first partition includes 10,000 rows, then the first loader commits twice. Once after receiving 5000 rows and again after receiving the second batch of 5000 rows. Meanwhile, the second loader is processing its rows.

The loaders pipe rows directly to Oracle database files by using Oracle direct-path load APIs (installed with the Oracle client) that are associated with the target database.

The API method allows the software to bypass the use of the SQL* Loader (and the control and staging files it needs). In addition, by using table partitioning, bulk loaders can pass data to different partitions in the same target table at the same time. Using the API method with partitioned tables fully optimizes performance.

Bulk loading in SAP HANA

SAP Data Services supports bulk loading to the SAP HANA database.

For improved performance when using changed-data capture or auto correct load, Data Services uses a temporary staging table to load the target table. Data Services first loads the data to the staging table, then it applies the operation codes (INSERT, UPDATE, and DELETE) to update the target table. With the Bulk load option selected in the target table editor, any one of the following conditions triggers the staging mechanism:

• The data flow contains a Map_CDC_Operation transform.

• The data flow contains a Map_Operation transform that outputs UPDATE or DELETE rows.

• The data flow contains a Table_Comparison transform.

• The Auto correct load option in the target table editor is set to Yes.

If none of these conditions are met, that means the input data contains only INSERT rows. Therefore Data Services does only a bulk insert operation, which does not require a staging table or the need to execute any additional SQL.

By default, Data Services automatically detects the SAP HANA target table type and updates the table accordingly for optimal performance.

Because the bulk loader for SAP HANA is scalable and supports UPDATE and DELETE operations, the following options (target table editor Options Advanced Update control ) are also available for bulk loading:

• Use input keys • Auto correct load

Bulk loading in SAP ASE

SAP Data Services supports bulk loading of SAP ASE databases through the SAP ASE bulk copy utility. For detailed information about the SAP ASE bulk loader options and their behavior in the SAP ASE DBMS environment, see the relevant SAP ASE product documentation.

Bulk loading in SAP Sybase IQ

SAP Data Services supports bulk loading to SAP Sybase IQ databases via the SAP Sybase IQ LOAD For improved performance when using changed-data capture or auto correct load, Data Services uses a termporary staging table to load the target table. Data Services first loads the data to the staging table, then it applies the operation codes (INSERT, UPDATE, and DELETE) to update the target table. With the Bulk load option selected in the target table editor, any one of the following conditions triggers the staging mechanism:

• The data flow contains a Map_CDC_Operation transform.

• The data flow contains a Map_Operation transform that outputs UPDATE or DELETE rows.

• The Auto correct load option in the target table editor is set to Yes.

If none of these conditions are met, that means the input data contains only INSERT rows. Therefore, Data Services does only a bulk INSERT operation, which does not require a staging table or the need to execute any additional SQL.

Note that because the bulk loader for SAP Sybase IQ also supports UPDATE and DELETE operations, the following options (target table editor Options Advanced Update control ) are also available for bulk loading:

• Use input keys • Auto correct load

To configure bulk loading for SAP Sybase IQ

First configure the bulk loader and log directories in the datastore editor, then enable and configure bulk loading in the target

SAP Sybase IQ log files

After a job executes, Data Services stores the SAP Sybase IQ message and row logs in the Bulk loader directory specified in the datastore editor (regardless of the setting for the JS and DB on same machine option). A data file will also be present if you do not use the named pipe option. If you do not specify a Bulk loader directory, Data Services by default writes the files to the directory <DS_COMMON_DIR>\log\bulkloader.

The logs include:

• message log-Records constraint violations specified in the Error handling section of the target table Bulk

Loader Options tab. • row log-Contains the data from the violating row. The data in the row log is delimited by the Field delimiter character specified on the Bulk Loader Options tab.

If you select Clean up bulk loader directory after load, Data Services deletes the data file and log files after loading completes. If you choose not to clean up the bulk loader directory or if your job results in errors captured in the logs, the software does not delete the data file and log files.

Bulk loading and reading in Teradata

SAP Data Services supports the following bulk loading and reading tools:

Parallel Transporter method

SAP Data Services supports Teradata's Parallel Transporter, an ETL tool that consolidates bulk-loading utilities into a single interface.

When you use the Parallel Transporter method, you can leverage Data Services' powerful parallel processing capabilities to specify a number of source and target options including the number of files or pipes for the software to use when processing large quantities of data.

Source performance tuning

Teradata source options are on the Teradata options tab in the source table editor. Here you can select the reading mode plus a number of advanced options.

You can tune the following options in the Teradata source editor to improve performance: Table 27:

Option Description

Maximum number of sessions

For large volumes of data, more sessions allows Data Services to read more data parallel. Ideally this number should equal the number of AMPs.

Number of export operator instances

When reading data in parallel, it can be consumed by multiple export instances for better performance. Ideally this value should equal the number of CPUs.

Parallel process threads

These internal threads break buffered data into rows and columns, which can im prove performance by maximizing CPU usage on the Job Server computer. Ideally this number should equal the number of CPUs.

Special considerations

Be aware of the following limitations when using the Teradata Parallel Transporter.

• It is not always possible to use the Parallel Transporter method to read from a source. In certain situations, Teradata does not support certain SQL constructs and you must use the ODBC method instead. In the following scenarios, Data Services will automatically switch the source mode to ODBC regardless of the actual mode selected on the Teradata options tab in the source editor: ○ The WHERE clause of a Query contains <primary key>=<value> predicate(s). Such a primary key can be a single column or a composite key. ○ The input schema contains columns of the LOB (CLOB or BLOB) data type.

• Unique secondary index columns are not allowed in the WHERE clause of a Query. However, because Data Services does not have the information as to whether a WHERE clause predicate is part of a unique secondary index, the WHERE clause gets pushed down to the Parallel Transporter reader. In this situation, a run-time error will occur, and you can manually set the source mode to None (ODBC

Target performance tuning

SAP Data Services provides the option for parallel processing when you bulk load data using the Parallel Transporter method.

In the target table editor using a combination of choices from the Options and Bulk Loader Options tabs, you can specify the number of data files or named pipes as well as the number of read and load Operator Instances. The Number of Loaders option distributes the workload while the Operators perform parallel processing.

In the target table Options tab, specify the Number of Loaders to control the number of data files or named pipes that Data Services or Parallel Transporter generates. Data Services writes data to these files in batches of 999 rows. For example, if you set Number of Loaders to 2, the software would generate two data files, writing 999 rows to the first file, then writing the next 999 rows to the second file. If there are more rows to process, the software continues, writing to the first file again, then the second, and so forth.

On the Bulk Loader Options tab, specify the number of instances in the loading scripts. If you set Number of DataConnector instances to 2 and Number of instances to 2, Parallel Transporter will assign the first read operator instance to read one data file and the other instance to read another data file in parallel. The DataConnector (read operator) instances then pass the data to the load operator instances for parallel loading into Teradata.

The Parallel Transporter uses a control file to read staging files or pipes and load data.

To configure the bulk loader for parallel processing

Teradata standalone utilities

In addition to the Parallel Transporter interface, SAP Data Services supports several Teradata utilities that load to and extract from the Teradata database. Each load utility is a separate executable designed to move data into a Teradata database. Choose from the following bulk loader utilities:

MultiLoad

Loads large quantities of data into populated tables. MultiLoad also supports bulk inserts, updates, up serts, and deletions against populated tables.

TPump

Uses standard SQL/DML to maintain data in tables. It also contains a method that you can use to spec ify the percentage of system resources necessary for operations on tables. Allows background mainte nance for insert, update, upsert, and delete operations to take place at any time you specify. Used with small data volumes.

Load Utility Invokes one of the above utilities (MultiLoad, FastLoad, or TPump) with the interface prior to Data Services version 11.5.1.

To bulk load a table using FastLoad

This procedure describes how to bulk load a table using the Teradata FastLoad utility.

Ensure that your Teradata datastore specifies a value in TdpId (Teradata Director Program Identifier). This option identifies the name of the Teradata database to load and is mandatory for bulk loading. 3. You can specify the following FastLoad parameters: For more information about these parameters, see the Teradata FastLoad Reference.

4. In Attributes, you can usually use the default settings for the following attributes in the FastLoad script that SAP Data Services generates.

To bulk load a table using MultiLoad

This procedure describes how to bulk load a table using the Teradata MultiLoad utility.

Ensure that your Teradata datastore specifies a value in TdpId (Teradata Director Program Identifier). This option identifies the name of the Teradata database to load and is mandatory for bulk loading. 3. You can specify the following MultiLoad parameters: Table 31:

MultiLoad parameter Short description

Reduced print output The default is not to reduce print output.

Data Encryption

The default is not to encrypt all sessions.

Character set Particular mapping between characters and byte strings (such as ASCII or UTF-8).

For more information about these parameters, see the Teradata MultiLoad Reference.

4. In Attributes, you can usually use the default settings for the following attributes in the MultiLoad script that SAP Data Services generates.

To bulk load a table using TPump

This procedure describes how to bulk load a table using the Teradata TPump utility.

Ensure that your Teradata datastore specifies a value in TdpId (Teradata Director Program Identifier). This option identifies the name of the Teradata database to load and is mandatory for bulk loading. 3. You can specify the following TPump parameters:

TPump commands for Data Services parameters

Descriptions of the TPump command and the corresponding Data Services parameters in Attributes.

Using the UPSERT bulk-loading operation

The purpose of the Teradata UPSERT operation is to update a row, but if no row matches the update, the row is inserted.

In SAP Data Services, you can only use the Teradata UPSERT operation with the following Bulk loader methods:

• MultiLoad

• TPump After selecting Upsert, note that you can also enable the Use input keys option on the target editor's Options tab.

Bulk loading using DataDirect's Wire Protocol SQL Server ODBC driver

Use the DataDirect's Wire Protocol SQL Server ODBC driver bulk load feature to quickly insert and update a large number of records into a database. You don't need to use a separate database load utility because the bulk load feature is built into the driver. DataDirect drivers are included in the Data Services installation.

For more detailed information about the Wire Protocol SQL Server ODBC driver, see the DataDirect documentation.

Some general considerations when using the bulk load feature:

• Enable the bulk load option only when you want to optimize load performance. Leaving the bulk load option enabled at all times could lead to undesired results or even corrupt data.

To enable the DataDirect bulk load feature in Windows

Procedure to enable the DataDirect bulk load feature for MS SQL Server on Windows.

1. Open the ODBC Data Source Administrator and go to the Use DSN tab. Click Add.

2. In the Create New Data Source window, select the DataDirect SQL Server Wire Protocol driver and click Finish.

The ODBC SQL Server Wire Protocol Driver Setup window opens.

3. Enter driver setup information, such as the name of the data source, the host number, and so on.

4. On the Bulk tab, select the Enable Bulk Loading option.

Set the Bulk Options.

Option Description

Keep Identity

Keeps source identity values.

Check Constraints

Checks constraints while data is being inserted into the database.

A value of 16 checks constraints while data is being inserted into the database.

Keep Nulls

Keeps null values in the destination table.

A value of 64 keeps null values in the destination table.

Table Lock

Locks the table while the bulk copy operation is taking place. This option is checked by default.

A value of 2 locks the table while the bulk copy operation is taking place.

Fire Triggers

Executes a trigger each time a row is being inserted into the database.

A value of 32 executes triggers when a row is being inserted into the database.

Bulk Binary Threshold (KB)

Maximum amount of data that you want exported to the bulk data file.

Batch Size

Number of rows you want the driver to send to the database at one time.

Bulk Character Threshhold (KB)

Maximum amount of character data that you want exported to the bulk data file.

To enable the DataDirect bulk load feature in UNIX

Procedure to enable the DataDirect bulk load feature for MS SQL Server on UNIX.

1. Using a text editor, open the odbc.ini file associated with the DSN for which you are bulk loading.

2. Set the EnableBulkLoad option to 1.

3. Enter a BulkLoadOptions value. The value you enter depends on what options you enable.

Option Description

Keep Identity A value of 1 keeps source identity values.

Tuning Techniques for performance options

Overview of the performance options that you can use to tune your system's performance.

Thre are three areas where you can make adjustments to tune performance:

• Source

• Target

• Job design

Source-based performance options

There are three areas that you can adjust in for data sources when you set up jobs:

• Join order

• Extracted data

• Array fetch size Performance Optimization Guide Tuning Techniques for performance options P U B L I C 115

Join rank settings

Use join rank to control the order in which sources are joined.

Join rank indicates the rank of a source relative to other tables and files joined in a data flow. When considering join rank, the Data Services Optimizer joins sources with higher join ranks before joining sources with lower join ranks.

Join rank must be a non-negative integer. The default value is 0.

Although it is possible to set join rank in FROM tab of the Query editor or in a source editor, the best practice is to specify the join rank directly in the Query editor.

Join ranking scenario 2

See the example in Join rank settings [page 116] for a picture of the data flow.

In Query_2, if no join rank value is specified, then the default join rank of 0 is applied to both the Query_1 result set and T3. The join rank values are set as follows: Then the combined query, Query_2', would have the following join rank values: The join rank value for T3 is adjusted to 40 because in the original Query_2 T3 has the same join rank value as the result of T1 join T2 (Query_1 result set).

Join ranking scenario 3

See the example in Join rank settings [page 116] for a picture of the data flow.

Assume join ranks are not set in the source tables. In Query_1, if no join rank value is specified, then the default join rank of 0 is applied to both T1 and T2. Values are set in the Query_2 Query editor as follows: Then the combined query, Query_2, would have the following join rank values: If join rank values are not specified in the Query_1 and Query_2 Query editors, then the combined query, Query_2', would have no join rank values specified (default=0).

Minimizing extracted data

The best way to minimize the amount of data extracted from the source systems is to retrieve only the data that has changed since the last time you performed the extraction. This technique is called changed-data capture.

Using array fetch size

SAP Data Services provides an easy way to request and obtain multiple data rows from source databases. The array fetch size feature allows you to retrieve data using fewer requests, thereby significantly reducing traffic on your network. Tuning array fetch size can also reduce the amount of CPU use on the Job Server computer.

The array fetch feature lowers the number of database requests by "fetching" multiple rows (an array) of data with each request. Enter the number of rows to fetch per request in the Array fetch size option on any source table editor or SQL transform editor. The default setting is 1000, meaning that with each database request, The software will automatically fetch 1000 rows of data from your source database. The maximum array fetch size that you can specify is 5000 bytes.

It is recommended that you set the array fetch size based on network speed.

To set the Array fetch size parameter

2. Click OK.

Target-based performance options

Loading method

You can choose to use regular loading or bulk loading. For a regular load, the Parameterized SQL option is automatically selected when generating, parsing, and compiling the statement. By using parameterized SQL, the software can minimize these efforts by using one handle for a set of values instead of one handle per value.

Many databases do not support bulk loading with the following options; see the specific options for your target database in the Reference Guide.

• Auto-correct load

• Enable Partitioning

• Number of Loaders

• Full push down to a database

The software automatically selects this optimizer process when the following conditions are met: ○ The source and target in a data flow are on the same database.

○ The database supports the operations in the data flow.

If the optimizer pushes down source or target operations, then it ignores the performance options set for sources (Array fetch size, Caching, and Join rank) because it is not solely processing the data flow. • Overflow file

• Transactional loading

To improve performance for a regular load (parameterized SQL), you can select the following options from the target table editor. Note that if you use one, you cannot use the others for the same target.

• Enable Partitioning

Parallel loading option. The number of parallel loads is determined by the number of partitions in the target table.

• Number of Loaders Parallel loading option. The number of parallel loads is determined by the number you enter for this option.

Rows per commit

Rows per commit for regular loading defaults to 1000 rows. Setting the Rows per commit value significantly affects job performance. Adjust the rows per commit value in the target table editor's Options tab, noting the following rules:

• Do not use negative number signs and other non-numeric characters.

• If you enter nothing or 0, the text box will automatically display 1000.

• If you enter a number larger than 5000, the text box automatically displays 5000.

It is recommended that you set rows per commit between 500 and 2000 for best performance. You might also want to calculate a value. To do this, use the following formula: max_IO_size/row size (in bytes)

For most platforms, max_IO_size is 64K. For Solaris, max_IO_size is 1024K.

Note that even with a value greater than one set for Rows per commit, SAP Data Services will submit data one row at a time if the following conditions exist:

• You are loading into a database (this scenario does not apply to Oracle databases), and have a column with a LONG datatype attribute.

• You are using an overflow file where the transaction failed. However, once all the rows are loaded successfully, the commit size reverts to the number you entered. In this case, depending on how often a load error happens, performance might be come worse than setting Rows per commit to 1.

Loading only changed data

Identifying and loading only changed data is called changed-data capture (CDC), which includes only incremental data that has changed since the last refresh cycle. Performance improves because with less data to extract, transform, and load, the job typically takes less time.

Minimizing data type conversion

SAP Data Services offers very robust and easy-to-use data type conversions via column mappings of different data types. It is recommended that you:

• Avoid unnecessary data conversions.

• Verify that Data Services is performing the implicit conversions (selected when you drag and drop columns from input to output schemas in the query transform) as expected. This can be done by looking at the warnings generated during job validation.

Performance Optimization Guide Tuning Techniques for performance options P U B L I C

Minimizing locale conversion

If your jobs do not require the use of different or multi-byte locales, you can increase performance by ensuring that locales are single-byte and not mismatched.

Table 1 :

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