PowerCenter 7 Architecture and Performance Tuning

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1 PowerCenter 7 Architecture and Performance Tuning Erwin Dral Sales Consultant 1

2 Agenda PowerCenter Architecture Performance tuning step-by-step Eliminating Common bottlenecks 2

PowerCenter Architecture: Engine-based & Metadata-driven Client

Designer Metadata Reporter Metadata Exchange ODBC ODBC Erwin

SQL Server Sybase Informix DB2 UDB ODBC Flat File MainFrame XML

Native ODBC GDR TCP/IP Repository Server Repository Agent Native

Oracle API, SQL*Loader MS SQL Server, BCP Sybase, IQ Load Informix

SAS Flat File RealTime XML Remote Files PowerConnect PowerCenter

3 PowerCenter Architecture: Engine-based & Metadata-driven Client Tools Windows Workflow Manager Workflow Monitor Repository Manager Designer Metadata Reporter Metadata Exchange ODBC ODBC Erwin Designer 2000 Power Designer Heterogeneous CWM Sources Oracle MS SQL Server Sybase Informix DB2 UDB ODBC Flat File MainFrame XML VSAM/COBOL ERP Copybook SAS RealTime Remote Files Sources ODBC Native ODBC GDR TCP/IP Repository Server Repository Agent Native Metadata Repository Targets Native ODBC Heterogeneous JDBC Targets Oracle API, SQL*Loader MS SQL Server, BCP Sybase, IQ Load Informix DB2 UDB, Autoloader Teradata MainFrame fload, tpump, mpump ERP ODBC SAS Flat File RealTime XML Remote Files PowerConnect PowerCenter Server Engine TCP/IP PowerConnect Key Data Metadata UNIX, Windows Reader Buffers DTM Writer 3

4 Introducing PowerExchange On-Demand Data Access through Changed Data Capture Mainframe Real-time AS/400, HP3000 Change Relational File Formats, EAI Batch Change Bulk 4

5 PowerCenter Environment Disk Disk Disk Disk Disk Disk LAN/WAN DBMS OS Disk Disk Disk Disk Disk Disk PowerCenter Disk Disk This is a multi-vendor, multi-system environment There are many components involved Operating systems, databases, networks, I/O, PowerCenter Performance is determined by THE SLOWEST COMPONENT (the bottleneck) Usually need to monitor performance in several places Usually need to monitor outside PowerCenter 5

6 Server Architecture - Memory The PowerCenter Server utilizes two main processes Load Manager process (pmserver) Session process (DTM) The Load Manager process is a continuous listener process designed to handle tasks such as session start, scheduling, error reporting, , etc. Configured using the using the Load Manager Shared Memory parameter Set value to approximately 200K bytes per session multiplied by the max number of concurrent sessions 6

7 Server Architecture - Memory The DTM process uses shared memory to handle tasks such as reading, data transformation and writing Two session parameters control the DTM memory allocation DTM Buffer Pool Size Buffer Block Size DTM pipeline threads overlap when possible Reader Transformation Engine Writer 7

8 Server memory runtime Example 8

9 Server Architecture - Memory DTM Buffer Pool Size controls the total amount of memory used to buffer rows internally by the reader and writer This sets the total number of blocks available The optimal value is about 25MB If the block size is 64K, then you get 25M/64K = 390 blocks Buffer Block Size controls the size of the blocks that move in the pipeline Optimum size depends on the row size being processed 64KB 64 rows of 1KB 128KB 128 rows of 1KB 9

10 Server Architecture DTM Parameters The Session Task parameters control the processing pipeline and are found on the Properties and Config Object tabs 10

11 Server Architecture - Threads Assume a mapping with an Aggregator, a Rank, and other transformations in a session with two partitions. Pre and Post session commands would add one thread each. Load Manager Mapping Thread Reader Thread Reader Thread Thread Writer Thread Writer Thread Thread DTM Master Thread Transformation Transformation Thread Thread Process Memory Transformation Transformation Thread Thread Transformation Transformation Thread Thread Aggregator Threads Rank Threads 11

12 Performance tuning step-by-step 1. Determine Batch window 2. Measure Until elapsed time < batch window HINTS: 5. Run sessions Write down a log of every step If all resources are used 100%, buy more If the change doesn t help, UNDO 4. Make ONE change 3. Determine bottleneck 12

13 13 2. Measuring Performance Internal to Informatica

14 Measuring Performance - Internal Several types of Bottlenecks can affect session performance Network System Database Informatica Mapping and Session There several ways of measuring performance such as total amount of data (volume) per unit of time Volume can be measured as: Number of bytes Number of rows Time can be measured as: CPU or process time Lapsed time 14

15 Measuring Performance - Internal For the purpose of identifying bottlenecks use: Lapsed time as a relative measurement time Number of rows loaded over the period of time (rows per second) Rows per second allows performance measurement of a session over a period of time and with changes in the environment Rows per sec can have a very wide range depending on the size of the row (number of bytes), the type of source/target (flat file or relational) and underlying hardware 15

16 Measuring Performance - Internal Establishing the baseline using the Workflow Manager Run the session task to be measured View the session task Transformation Statistics detail window at the end of the session and record the number of rows loaded View the Session Task Properties window and record the start and end times of the session Subtract the start time from the end time of the session, convert to seconds to get the total session time Divide the number of rows loaded by the number of seconds of run time for the session 16

17 Measuring Performance - Internal Example Session Name Start/End Times Applied Rows 17

18 Measuring Performance - Internal Tips: Calculated rows per second are not the same as Write Throughput For multiple targets use sum of rows loaded for targets which are similar in row size For multiple partitions use the sum of rows loaded for all partitions Monitor background processes external to Informatica that will have an effect between test runs 18

19 19 Establishing Baselines Internal to Informatica

20 Establishing Baselines - Internal Each component in a production environment contributes to the overall session performance Performance is limited to the slowest component Knowing the physical data limits establishes the maximum data throughput Baseline measurement can be used for future comparisons LAN/ WAN DBMS OS PowerCenter 20

Establishing Baselines Read Throughput Mapping Read Throughput Mapping Use a database table to flat file mapping to establish a typical read rate Session

21 Establishing Baselines Read Throughput Mapping Read Throughput Mapping Use a database table to flat file mapping to establish a typical read rate Session Name Rows Loaded Rows Failed Start Time End Time Elapsed Time Rows Per Sec s_m_rdb_to_ff_test /18/ :00:58 AM 10/18/ :01:17 AM

22 Establishing Baselines - Historical Each Informatica Repository contains a history of each session run Use MX view REP_SESS_LOG to extract session information SUBJECT_AREA (Folder) SUCCESSFUL_ROWS (Rows Loaded) ACTUAL_START (Start Time) SESSION_NAME (Session) FAILED_ROWS (Rows Not Loaded) SESSION_TIMESTAMP (End Time) Note: simple query select * from rep_sess_log 22

23 2. Measure Performance Use repository views to establish performance Session elapsed time (in seconds) = (REP_SESS_LOG.SESSION_TIMESTAMP - REP_SESS_LOG.ACTUAL_START) * TIMESTAMPDIFF(2,CHAR (SESSION_LOG.SESSION_TIMESTAMP - SESSION_LOG.ACTUAL_START)) - Target Rows per second = SUCCESSFUL_ROWS / Session elapsed time OR: Use the MetaData Reporter! 23

24 3. Determine bottleneck Identifying Target Bottlenecks Identifying Source Bottlenecks Identifying Mapping Bottlenecks session parameters system resource allocation mapping/transformation design 24

25 3. Determine Target Bottlenecks Writing to a flat file usually does not cause a bottleneck Configure a session task to write to a flat file target (/dev/null) If write throughput increases significantly, then you have a target database bottleneck. 25

26 3. Determine Source or Mapping Bottlenecks Add a FILTER behind each source qualifier set filter condition to false Original Modified No faster Faster Source bottleneck mapping bottleneck 26

27 6. Make ONE change Very case-specific, here are some common bottlenecks Target Source Mapping Session System Only keep the changes that improve performance (maintaining changes is confusing and costly) 27

28 6. Eliminate Target Bottlenecks Databases indexes and constraints Disable indexes and constraints before the load, and enable afterward (connection/target pre- & post SQL) Check the database space allocation for indexes indexes should be on a different disk if possible Use a loader connection Check the commit interval Very small commit intervals cause excessive overhead Make sure you have allocated plenty of rollback space (PC6: connection Rollback segment) Good Commit interval is 50,000 28

29 6. Eliminate Target Bottlenecks PowerCenter updates and deletes Updates and deletes can be extremely slow without an index or key Bitmap Indexes on columns you are updating cause very slow performance (usually less than 100 rows/sec) Do NOT use an Update Strategy transformation if all rows are treated the same (DD_INSERT, DD_UPDATE). The writer cannot do block inserts or block updates 29

30 3. Eliminate Source Bottlenecks Discuss with your DBA how to optimize your Source Qualifier SQL (in the session log file) standard DBMS tuning: explain plan, add indexes, estimate statistics (regularly) alter database parameters, etc Optimize the query to begin returning rows early the total query time may be longer, but PowerCenter processing can overlap with the query execution 30

31 3. Eliminate Mapping Bottlenecks Reduce I/O times Cache in memory Use fast disks for Cache, BadFiles, SessionLogs etc. Check your Sequence Generator Reduce amount of data to transform Filter early Aggregator or joiner: prefix with a sorter 31

32 6. Optimize expression performance Use numeric ports instead of string ports Reduce (hidden) Data type conversions Simplify expressions Factor out common logic to transformation variables or even mapping variables or parameters Simplify nested IIFs when possible or use DECODE statements 32

33 6. Optimize Lookup Performance Reduce the number of lookup rows. where clause in lookup sql Use persistent lookup caches When a nightly batch has several sessions that use the same lookup Build the persistent cachefile in a separate session Lookup with date-range: lookup/filter combo Lookup against large dimension with few changes: PoweExchange Changed Data Capture checksum AEP plus lookup (devnet.informatica.com) Remove the lookup, use update else insert 33

34 6. Session Optimizing Set the DTM Buffer Pool Size and Buffer Block Size Large row sizes may require a larger buffer block size Default buffer pool is b = 12 Mb, recommended is 24Mb Buffer Block Size controls the size of the blocks that move in the pipeline Buffer Block size should hold about 100 rows 64K (64,000) 64 rows of 1Kb 128K (128,000) 128 rows of 1Kb Extremely large DTM may SLOW DOWN session! 34

35 6. Session Memory Settings Set cache memory larger than the size of the cachefile on disk Set the server variable directories (Badfiles, Cache, SessLogs, etc.) to point to high performance disk arrays Reduce transformation errors (& error logging) 35

36 For those that are still on PowerCenter 5 PowerCenter 6 Performance highlights More efficient server New Sorter transformation Sorted Input switch for aggregator & joiner More bulk loaders Pipeline Partitioning (PowerCenter only) Upgrade! 36

37 For those that are still on PowerCenter 6 PowerCenter 7 Performance highlights Block DTM Enables moving/transforming a block of rows at a time at each transformation Accelerates ALL sessions with: Mapping bottleneck AND (Lots of transformations OR Lots of string ports) Superior XML reading and writing Easy GUI for partitioning Max 64 partitions per partition point 64-bit version Upgrade! Server Grid (workflow load balancing across several servers) Change Data Capture (MVS, Oracle 9i and MS SQL server) 37

38 Performance tuning step-by-step 1. Determine Batch window 2. Measure Until elapsed time < batch window HINTS: 5. Run sessions - Write down a log of every step 3. If all resources are used 100%, buy more 4. If the change doesn t help, UNDO 4. Make ONE change 3. Determine bottleneck 38

Performance Tuning. Chapter 25

Performance Tuning. Chapter 25 Chapter 25 Performance Tuning This chapter covers the following topics: Overview, 618 Identifying the Performance Bottleneck, 619 Optimizing the Target Database, 624 Optimizing the Source Database, 627