Table of Contents

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  1. Preface
  2. Analyst Service
  3. Content Management Service
  4. Data Integration Service
  5. Data Integration Service Architecture
  6. Data Integration Service Management
  7. Data Integration Service Grid
  8. Data Integration Service Applications
  9. Mass Ingestion Service
  10. Metadata Access Service
  11. Metadata Manager Service
  12. Model Repository Service
  13. PowerCenter Integration Service
  14. PowerCenter Integration Service Architecture
  15. High Availability for the PowerCenter Integration Service
  16. PowerCenter Repository Service
  17. PowerCenter Repository Management
  18. PowerExchange Listener Service
  19. PowerExchange Logger Service
  20. SAP BW Service
  21. Search Service
  22. System Services
  23. Test Data Manager Service
  24. Test Data Warehouse Service
  25. Web Services Hub
  26. Application Service Upgrade
  27. Application Service Databases
  28. Connecting to Databases from Windows
  29. Connecting to Databases from UNIX
  30. Updating the DynamicSections Parameter of a DB2 Database

Processing Threads

Processing Threads

When the DTM runs mappings, it uses reader, transformation, and writer pipelines that run in parallel to extract, transform, and load data.
The DTM separates a mapping into pipeline stages and uses one reader thread, one transformation stage, and one writer thread to process each stage. Each pipeline stage runs in one of the following threads:
  • Reader thread that controls how the DTM extracts data from the source.
  • Transformation thread that controls how the DTM processes data in the pipeline.
  • Writer thread that controls how the DTM loads data to the target.
Because the pipeline contains three stages, the DTM can process three sets of rows concurrently and optimize mapping performance. For example, while the reader thread processes the third row set, the transformation thread processes the second row set, and the writer thread processes the first row set.
If you have the partitioning option, the Data Integration Service can maximize parallelism for mappings and profiles. When you maximize parallelism, the DTM separates a mapping into pipeline stages and uses multiple threads to process each stage.