Table of Contents

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  1. Preface
  2. Analyst Service
  3. Catalog Service
  4. Content Management Service
  5. Data Integration Service
  6. Data Integration Service Architecture
  7. Data Integration Service Management
  8. Data Integration Service Grid
  9. Data Integration Service REST API
  10. Data Integration Service Applications
  11. Data Privacy Management Service
  12. Enterprise Data Preparation Service
  13. Interactive Data Preparation Service
  14. Informatica Cluster Service
  15. Mass Ingestion Service
  16. Metadata Access Service
  17. Metadata Manager Service
  18. Model Repository Service
  19. PowerCenter Integration Service
  20. PowerCenter Integration Service Architecture
  21. High Availability for the PowerCenter Integration Service
  22. PowerCenter Repository Service
  23. PowerCenter Repository Management
  24. PowerExchange Listener Service
  25. PowerExchange Logger Service
  26. SAP BW Service
  27. Search Service
  28. System Services
  29. Test Data Manager Service
  30. Test Data Warehouse Service
  31. Web Services Hub
  32. Application Service Upgrade
  33. Appendix A: Application Service Databases
  34. Appendix B: Connecting to Databases from Windows
  35. Appendix C: Connecting to Databases from UNIX or Linux
  36. Appendix D: 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.