Table of Contents

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  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Aggregator transformation
  6. Cleanse transformation
  7. Data Masking transformation
  8. Data Services transformation
  9. Deduplicate transformation
  10. Expression transformation
  11. Filter transformation
  12. Hierarchy Builder transformation
  13. Hierarchy Parser transformation
  14. Hierarchy Processor transformation
  15. Input transformation
  16. Java transformation
  17. Java transformation API reference
  18. Joiner transformation
  19. Labeler transformation
  20. Lookup transformation
  21. Machine Learning transformation
  22. Mapplet transformation
  23. Normalizer transformation
  24. Output transformation
  25. Parse transformation
  26. Python transformation
  27. Rank transformation
  28. Router transformation
  29. Rule Specification transformation
  30. Sequence Generator transformation
  31. Sorter transformation
  32. SQL transformation
  33. Structure Parser transformation
  34. Transaction Control transformation
  35. Union transformation
  36. Velocity transformation
  37. Verifier transformation
  38. Web Services transformation

Transformations

Transformations

Transformation caches

Transformation caches

Data Integration
allocates cache memory for Aggregator, Joiner, Lookup, Rank, and Sorter transformations in a mapping.
You can configure the cache sizes for these transformations. The cache size determines how much memory
Data Integration
allocates for each transformation cache at the start of a mapping run.
If the cache size is larger than the available memory on the machine,
Data Integration
cannot allocate enough memory and the task fails.
If the cache size is smaller than the amount of memory required to run the transformation,
Data Integration
processes some of the transformation in memory and stores overflow data in cache files. When
Data Integration
pages cache files to the disk, processing time increases. For optimal performance, configure the cache size so that
Data Integration
can process the transformation data in the cache memory.
By default,
Data Integration
automatically calculates the memory requirements at run time based on the maximum amount of memory that it can allocate. After you run a mapping in auto cache mode, you can tune the cache sizes for each transformation.

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