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  1. Preface
  2. Understanding Pipeline Partitioning
  3. Partition Points
  4. Partition Types
  5. Pushdown Optimization
  6. Pushdown Optimization and Transformations
  7. Real-time Processing
  8. Commit Points
  9. Row Error Logging
  10. Workflow Recovery
  11. Stopping and Aborting
  12. Concurrent Workflows
  13. Grid Processing
  14. Load Balancer
  15. Workflow Variables
  16. Parameters and Variables in Sessions
  17. Parameter Files
  18. FastExport
  19. External Loading
  20. FTP
  21. Session Caches
  22. Incremental Aggregation
  23. Session Log Interface
  24. Understanding Buffer Memory
  25. High Precision Data

Advanced Workflow Guide

Advanced Workflow Guide

n:n Partitioning

n:n Partitioning

You can use
n
:
n
partitioning with Joiner transformations with sorted or unsorted input. When you use
n
:
n
partitioning for a Joiner transformation, you create
n
partitions in the master and detail pipelines. When the Integration Service processes the join, it compares the rows in a detail partition against the rows in the corresponding master partition, ignoring rows in other master partitions. When processing master and detail data for outer joins, the Integration Service outputs unmatched master rows after it processes the partition for each detail cache.
If the master source has a large number of rows, use
n
:
n
partitioning for better session performance.
To use
n
:
n
partitioning, you must create multiple partitions in the session and create a partition point at the Joiner transformation. You create the partition point at the Joiner transformation to create multiple partitions for both the master and detail source of the Joiner transformation.
If you create a partition point at the Joiner transformation, the Integration Service uses cache partitioning. It creates one memory cache for each partition. The memory cache for each partition contains only the rows needed by that partition. As a result, the Integration Service requires a portion of total cache memory for each partition.

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