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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

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Cache Partitioning Lookup Transformations

Cache Partitioning Lookup Transformations

Use cache partitioning for static and dynamic caches, and named and unnamed caches. When you create a partition point at a connected Lookup transformation, use cache partitioning under the following conditions:
  • Use the hash auto-keys partition type for the Lookup transformation.
  • The lookup condition must contain only equality operators.
  • The database is configured for case-sensitive comparison.
    For example, if the lookup condition contains a string port and the database is not configured for case-sensitive comparison, the Integration Service does not perform cache partitioning and writes the following message to the session log:
    CMN_1799 Cache partitioning requires case sensitive string comparisons. Lookup will not use partitioned cache as the database is configured for case insensitive string comparisons.
The Integration Service uses cache partitioning when you create a hash auto-keys partition point at the Lookup transformation.
When the Integration Service creates cache partitions, it begins creating caches for the Lookup transformation when the first row of any partition reaches the Lookup transformation. If you configure the Lookup transformation for concurrent caches, the Integration Service builds all caches for the partitions concurrently.