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

Join condition

Join condition

The join condition defines when incoming rows are joined. It includes fields from both sources that must match to join source rows.
You define one or more conditions based on equality between the master and detail data. For example, if two sets of employee data contain employee ID numbers, the following condition matches rows with the same employee IDs in both sets of data:
EMP_ID1 = EMP_ID2
Use one or more join conditions. Additional join conditions increase the time necessary to join the data. When you use multiple join conditions, the
mapping
task evaluates the conditions in the order that you specify.
Both fields in a condition must have the same data type. If you need to use two fields with non-matching data types, convert the data types so they match.
For example, when you try to join Char and Varchar data, any spaces that pad Char values are included as part of the string. Both fields might include the value "Shoes," but because the Char(40) field includes 35 trailing spaces, the values do not match. To ensure that the values match, change the data type of one field to match the other.
When you use a Joiner transformation in an
elastic mapping
, the mapping becomes invalid if the join condition contains a binary data type.
The Joiner transformation does not match null values. To join rows with null values, you can replace null values with default values, and then join on the default values.