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. Deduplicate transformation
  9. Expression transformation
  10. Filter transformation
  11. Hierarchy Builder transformation
  12. Hierarchy Parser transformation
  13. Hierarchy Processor transformation
  14. Input transformation
  15. Java transformation
  16. Java transformation API reference
  17. Joiner transformation
  18. Labeler transformation
  19. Lookup transformation
  20. Mapplet transformation
  21. Normalizer transformation
  22. Output transformation
  23. Parse transformation
  24. Python transformation
  25. Rank transformation
  26. Router transformation
  27. Rule Specification transformation
  28. Sequence Generator transformation
  29. Sorter transformation
  30. SQL transformation
  31. Structure Parser transformation
  32. Transaction Control transformation
  33. Union transformation
  34. Velocity transformation
  35. Verifier transformation
  36. Web Services transformation

Transformations

Transformations

Sample transaction control mappings with multiple targets

Sample transaction control mappings with multiple targets

In a mapping with multiple targets, a Transaction Control transformation can be effective for one target and ineffective for another target. If each target is connected to an effective Transaction Control transformation, the mapping is valid. If one target in the mapping is not connected to an effective Transaction Control transformation, the mapping is invalid.
The following image shows a valid mapping with both an ineffective and an effective Transaction Control transformation:
The mapping contains two pipelines. The first pipeline contains the following transformations: Source, TransactionControl_1, Expression_1, Target_1. The second pipeline contains the following transformations: Source, TransactionControl_1, Aggregator, Expression_2, TransactionControl_2, Target_2.
Data Integration
processes TransactionControl_1, evaluates the transaction control expression, and creates transaction boundaries. The mapping does not include a transformation that drops transaction boundaries between TransactionControl_1 and Target_1, making TransactionControl_1 effective for Target_1.
Data Integration
uses the transaction boundaries defined by TransactionControl_1 for Target_1.
However, the mapping includes a transformation that drops transaction boundaries between TransactionControl_1 and Target_2, which makes TransactionControl_1 ineffective for Target_2. When
Data Integration
processes the Aggregator transformation, with transformation scope set to All Input, it drops the transaction boundaries defined by TransactionControl_1 and outputs all rows in an open transaction. Then
Data Integration
evaluates TransactionControl_2, creates transaction boundaries, and uses them for Target_2.
If a roll back occurs in TransactionControl_1,
Data Integration
rolls back only rows from Target_1. It does not roll back any rows from Target_2.
The following image shows an invalid mapping with both an ineffective and an effective Transaction Control transformation:
The mapping contains two pipelines. The first pipeline contains the following transformations: Source, mapplet "mplt_TransactionControl," Aggregator_1, Target_1. The second pipeline contains the following transformations: Source, mapplet "mplt_TransactionControl," Aggregator_2, Expression, TransactionControl_2, Target_2.
The mapplet, mplt_TransactionControl, contains a Transaction Control transformation. It is ineffective for Target_1 and Target_2. The transformation scope for Aggregator_1 is All Input. It is an active source for Target_1. The transformation scope for Aggregator_2 is All Input. It is an active source for Target_2. TransactionControl_2 is effective for Target_2.
The mapping is invalid because Target_1 is not connected to an effective Transaction Control transformation.