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. Metadata Manager Service
  10. Model Repository Service
  11. PowerCenter Integration Service
  12. PowerCenter Integration Service Architecture
  13. High Availability for the PowerCenter Integration Service
  14. PowerCenter Repository Service
  15. PowerCenter Repository Management
  16. PowerExchange Listener Service
  17. PowerExchange Logger Service
  18. SAP BW Service
  19. Search Service
  20. System Services
  21. Test Data Manager Service
  22. Web Services Hub
  23. Application Service Upgrade
  24. Application Service Databases
  25. Connecting to Databases from Windows
  26. Connecting to Databases from UNIX
  27. 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.