Table of Contents

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  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Access Policy transformation
  6. Aggregator transformation
  7. B2B transformation
  8. Chunking transformation
  9. Cleanse transformation
  10. Data Masking transformation
  11. Data Services transformation
  12. Deduplicate transformation
  13. Expression transformation
  14. Filter transformation
  15. Hierarchy Builder transformation
  16. Hierarchy Parser transformation
  17. Hierarchy Processor transformation
  18. Input transformation
  19. Java transformation
  20. Java transformation API reference
  21. Joiner transformation
  22. Labeler transformation
  23. Lookup transformation
  24. Machine Learning transformation
  25. Mapplet transformation
  26. Normalizer transformation
  27. Output transformation
  28. Parse transformation
  29. Python transformation
  30. Rank transformation
  31. Router transformation
  32. Rule Specification transformation
  33. Sequence transformation
  34. Sorter transformation
  35. SQL transformation
  36. Structure Parser transformation
  37. Transaction Control transformation
  38. Union transformation
  39. Vector Embedding transformation
  40. Velocity transformation
  41. Verifier transformation
  42. 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.
In advanced mode, the join condition can’t contain a binary data type or evaluate to 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.

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