Ta3

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Ta3

  1. 1. PowerCenter® 7 Architecture and Performance Tuning Erwin Dral Sales Consultant1
  2. 2. Agenda ° PowerCenter Architecture ° Performance tuning step-by-step ° Eliminating Common bottlenecks2
  3. 3. PowerCenter Architecture: Engine-based & Metadata-driven Metadata Client Tools Workflow Workflow Repository Designer Windows Manager Monitor Manager Reporter ODBC ODBC Metadata Exchange Erwin Designer 2000 TCP/IP Power Designer Heterogeneous CWM Heterogeneous JDBC ODBC Targets Sources Repository Server Oracle Repository Agent Oracle API, SQL*Loader MS SQL Server MS SQL Server, BCP Sybase Native Informix Sybase, IQ Load DB2 UDB Targets Informix ODBC Sources Flat File DB2 UDB, Autoloader MainFrame XML Teradata fload, tpump, MainFrame , fload, tpump VSAM/COBOL ERP Metadata mpumpERP Copybook GDR Native ODBC SAS SAS Native ODBC Repository ODBC Flat File RealTime RealTimeRemote Files XML Remote Files TCP/IP PowerConnect PowerConnect PowerCenter Server Engine Buffers UNIX, Windows Reader DTM WriterKey Data Metadata3
  4. 4. Introducing PowerExchange On-Demand Data Access through Changed Data Capture Mainframe Real-time AS/400, HP3000 Change Relational Batch File Formats, EAI Change Bulk4
  5. 5. PowerCenter Environment Disk Disk Disk Disk Disk Disk DBMS LAN/WAN Disk OS Disk Disk Disk Disk PowerCenter Disk 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 PowerCenter5
  6. 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, email, 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 sessions6
  7. 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 Transformation Reader Writer Engine7
  8. 8. Server memory runtime ° Example8
  9. 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 1KB9
  10. 10. Server Architecture – DTM Parameters The Session Task parameters control the processing pipeline and are found on the Properties and Config Object tabs10
  11. 11. Server Architecture - Threads Assume a mapping with an Aggregator, a Rank, and other transformations in a session with two partitions. Pre and Load Manager Post session commands would add one thread each. DTM Master Thread Mapping Thread Transformation Transformation Thread Thread Reader Rank Thread Threads Reader Thread Thread Transformation Thread Transformation Transformation Writer Thread Thread Transformation Thread Thread Writer Thread Thread Aggregator Process Memory Threads11
  12. 12. Performance tuning step-by-step 1. Determine Batch window Until elapsed time 2. < batch window Measure 5. 3. Run Determine sessions bottleneck HINTS: •Write down a log of every step 4. •If all resources are used 100%, buy more Make ONE change •If the change doesn’t help, UNDO12
  13. 13. 2. Measuring Performance Internal to Informatica13
  14. 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 time14
  15. 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 hardware15
  16. 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 session16
  17. 17. Measuring Performance - Internal Example Session Name Start/End Times Applied Rows17
  18. 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 runs18
  19. 19. Establishing Baselines Internal to Informatica19
  20. 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/ DBMS WAN OS PowerCenter20
  21. 21. Establishing Baselines – Read Throughput Mapping ° Read Throughput Mapping – Use a database table to flat file mapping to establish a typical read rate Rows Session Name Rows Rows Start End Elapsed Per Loaded Failed Time Time Time Sec s_m_RDB_TO_FF_TEST 249995 0 10/18/2002 10/18/2002 19 13158 11:00:58 AM 11:01:17 AM21
  22. 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_log22
  23. 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) * 86400 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. 24. 3. Determine bottleneck ° Identifying Target Bottlenecks ° Identifying Source Bottlenecks ° Identifying Mapping Bottlenecks − session parameters − system resource allocation − mapping/transformation design24
  25. 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. 26. 3. Determine Source or Mapping Bottlenecks Add a FILTER behind each source qualifier set filter condition to false Original Modified No faster  Source bottleneck Faster  mapping bottleneck26
  27. 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. 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 50,00028
  29. 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 updates29
  30. 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 execution30
  31. 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 sorter31
  32. 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 statements32
  33. 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. 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 12000000b = 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. 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. 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 Upgrade! (PowerCenter only)36
  37. 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 Upgrade! ° Easy GUI for partitioning ° Max 64 partitions per partition point ° 64-bit version ° Server Grid (workflow load balancing across several servers) ° Change Data Capture (MVS, Oracle 9i and MS SQL server)37
  38. 38. Performance tuning step-by-step 1. Determine Batch window Until elapsed time 2. < batch window Measure 5. 3. Run Determine sessions bottleneck HINTS: - Write down a log of every step 4. 3. If all resources are used 100%, buy more Make ONE change 4. If the change doesn’t help, UNDO38

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