Table of Contents

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  1. Preface
  2. Introduction to Transformations
  3. Transformation Ports
  4. Transformation Caches
  5. Address Validator Transformation
  6. Aggregator Transformation
  7. Association Transformation
  8. Bad Record Exception Transformation
  9. Case Converter Transformation
  10. Classifier Transformation
  11. Comparison Transformation
  12. Consolidation Transformation
  13. Data Masking Transformation
  14. Data Processor Transformation
  15. Decision Transformation
  16. Duplicate Record Exception Transformation
  17. Expression Transformation
  18. Filter Transformation
  19. Hierarchical to Relational Transformation
  20. Java Transformation
  21. Java Transformation API Reference
  22. Java Expressions
  23. Joiner Transformation
  24. Key Generator Transformation
  25. Labeler Transformation
  26. Lookup Transformation
  27. Lookup Caches
  28. Dynamic Lookup Cache
  29. Match Transformation
  30. Match Transformations in Field Analysis
  31. Match Transformations in Identity Analysis
  32. Normalizer Transformation
  33. Merge Transformation
  34. Parser Transformation
  35. Python Transformation
  36. Rank Transformation
  37. Read Transformation
  38. Relational to Hierarchical Transformation
  39. REST Web Service Consumer Transformation
  40. Router Transformation
  41. Sequence Generator Transformation
  42. Sorter Transformation
  43. SQL Transformation
  44. Standardizer Transformation
  45. Union Transformation
  46. Update Strategy Transformation
  47. Web Service Consumer Transformation
  48. Parsing Web Service SOAP Messages
  49. Generating Web Service SOAP Messages
  50. Weighted Average Transformation
  51. Window Transformation
  52. Write Transformation
  53. Appendix A: Transformation Delimiters

Developer Transformation Guide

Developer Transformation Guide

Joiner Caches

Joiner Caches

When you run a mapping that uses a Joiner transformation, the Data Integration Service creates an index cache and data cache in memory to run the transformation. If the Data Integration Service requires more space than available in the memory cache, it stores overflow data in cache files.
When you run a mapping that uses a Joiner transformation, the Data Integration Service reads rows from the master and detail sources concurrently and builds index and data caches based on the master rows. The Data Integration Service performs the join based on the detail source data and the cached master data.
The type of Joiner transformation determines the number of rows that the Data Integration Service stores in the cache.
The following table describes the information that the Data Integration Service stores in the caches for different types of Joiner transformations:
Joiner Transformation Type
Index Cache
Data Cache
Unsorted Input
Stores all master rows in the join condition with unique index keys.
Stores all master rows.
Sorted Input with Different Sources
Stores 100 master rows in the join condition with unique index keys.
Stores master rows that correspond to the rows stored in the index cache. If the master data contains multiple rows with the same key, the Data Integration Service stores more than 100 rows in the data cache.
Sorted Input with the Same Source
Stores all master or detail rows in the join condition with unique keys. Stores detail rows if the Data Integration Service processes the detail pipeline faster than the master pipeline. Otherwise, stores master rows. The number of rows it stores depends on the processing rates of the master and detail pipelines. If one pipeline processes its rows faster than the other, the Data Integration Service caches all rows that have already been processed. The service keeps the rows cached until the other pipeline finishes processing its rows.
Stores data for the rows stored in the index cache. If the index cache stores keys for the master pipeline, the data cache stores the data for master pipeline. If the index cache stores keys for the detail pipeline, the data cache stores data for detail pipeline.

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