Table of Contents

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  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Access Policy transformation
  6. B2B transformation
  7. Aggregator transformation
  8. Cleanse transformation
  9. Data Masking transformation
  10. Data Services transformation
  11. Deduplicate transformation
  12. Expression transformation
  13. Filter transformation
  14. Hierarchy Builder transformation
  15. Hierarchy Parser transformation
  16. Hierarchy Processor transformation
  17. Input transformation
  18. Java transformation
  19. Java transformation API reference
  20. Joiner transformation
  21. Labeler transformation
  22. Lookup transformation
  23. Machine Learning transformation
  24. Mapplet transformation
  25. Normalizer transformation
  26. Output transformation
  27. Parse transformation
  28. Python transformation
  29. Rank transformation
  30. Router transformation
  31. Rule Specification transformation
  32. Sequence Generator transformation
  33. Sorter transformation
  34. SQL transformation
  35. Structure Parser transformation
  36. Transaction Control transformation
  37. Union transformation
  38. Velocity transformation
  39. Verifier transformation
  40. Web Services transformation

Transformations

Transformations

Using Transaction Control transformations in mappings

Using Transaction Control transformations in mappings

Transaction Control transformations are transaction generators. They define and redefine transaction boundaries in a mapping. They drop any incoming transaction boundaries from an upstream active source or transaction generator, and they generate new transaction boundaries downstream.
Transaction Control transformations can be effective or ineffective for the downstream transformations and targets in the mapping. The Transaction Control transformation becomes ineffective for downstream transformations or targets if you put a transformation that drops incoming transaction boundaries after it. This includes any of the following active sources or transformations:
  • Aggregator transformation with the All Input level transformation scope
  • Java transformation with the All Input level transformation scope
  • Joiner transformation with the All Input level transformation scope
  • Rank transformation with the All Input level transformation scope
  • Sorter transformation with the All Input level transformation scope
  • SQL transformation that uses a saved or user-entered query and has the All Input level transformation scope
  • Transaction Control transformation
  • Any multiple input group transformation that is connected to multiple upstream transaction control points
Although a Transaction Control transformation may be ineffective for a target, it can be effective for downstream transformations. Downstream transformations with the Transaction level transformation scope can use the transaction boundaries defined by an upstream Transaction Control transformation.
The following image shows a valid mapping with a Transaction Control transformation that is effective for a Sorter transformation, but ineffective for the target:
The mapping contains the following transformations in a single pipeline: Source, TransactionControl_1, Sorter, Aggregator, Expression, TransactionControl_2, Target.
In this example, TransactionControl_1 is ineffective for the target, but effective for the Sorter transformation. The transformation scope for the Sorter transformation is Transaction. It uses the transaction boundaries defined by TransactionControl_1. The transformation scope for the Aggregator transformation is All Input. It drops transaction boundaries defined by TransactionControl_1. Transaction control transformation TransactionControl_2 is an effective Transaction Control transformation for the target.

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