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

Dynamic lookup cache

Dynamic lookup cache

Use a dynamic lookup cache to keep the lookup cache synchronized with the target.
When you enable lookup caching, a
mapping
task builds the lookup cache when it processes the first lookup request. The cache can be static or dynamic. If the cache is static, the data in the lookup cache doesn't change as the mapping task runs. If the task uses the cache multiple times, the task uses the same data. If the cache is dynamic, the task updates the cache based on the actions in the task, so if the task uses the lookup multiple times, downstream transformations can use updated data.
You can use a dynamic cache with most types of lookup sources. You cannot use a dynamic cache with flat file or Salesforce lookups. For more information about using a dynamic cache with a specific type of lookup source, see the help for the appropriate connector.
Based on the results of the lookup query, the row type, and the Lookup transformation properties, the
mapping
task performs one of the following actions on the dynamic lookup cache when it reads a row from the source:
Inserts the row into the cache
The
mapping
task inserts the row when the row is not in the cache. The
mapping
task flags the row as insert.
Updates the row in the cache
The
mapping
task updates the row when the row exists in the cache. The
mapping
task updates the row in the cache based on the input fields. The
mapping
task flags the row as an update row.
Makes no change to the cache
The
mapping
task makes no change when the row is in the cache and nothing changes. The
mapping
task flags the row as unchanged.
The dynamic Lookup transformation includes the return field, NewLookupRow, which describes the changes the task makes to each row in the cache. Based on the value of the NewLookupRow, you can also configure a Router or Filter transformation with the dynamic Lookup transformation to route insert or update rows to the target table. You can route unchanged rows to another target table or flat file, or you can drop them.
You cannot use a parameterized source, target, or lookup with a Lookup transformation that uses a dynamic cache.

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