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

Configure join conditions

Configure join conditions

When the output data format is hierarchical, you can define join conditions for the data sources. You must configure a join condition if an output group or field has multiple data sources. Configure a join condition to join the data from the input groups or incoming fields.
Configure the join conditions for the output groups on the
Hierarchy Processor
tab.
  1. Click the
    Join Conditions
    icon for the output group.
  2. Add a join condition.
  3. Select the left data source.
  4. Select the join type:
    • Inner. Includes rows with matching join conditions. Discards rows that do not match the join conditions.
    • Left Outer. Includes all rows from the right pipeline and the matching rows from the left pipeline. Discards the unmatched rows from the left pipeline.
    • Right Outer. Includes all rows from the left pipeline and the matching rows from the right pipeline. Discards the unmatched rows from the right pipeline.
    • Full Outer. Includes rows with matching join conditions and all incoming data from the left pipeline and right pipeline.
    If you select an outer join on a large data set, you might need to increase the Spark driver memory in the mapping task. For more information about Spark session properties, see
    Tasks
    .
  5. Select the right data source.
  6. Click
    Configure Join Condition
    .
  7. Select fields and built-in functions to create the expression.
  8. Validate the expression.
  9. Click
    Save
    .

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