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

Partitioning rules and guidelines

Partitioning rules and guidelines

Before you partition a mapping, note the following rules and guidelines:
  • Consider the types of transformations in the mapping and the order in which transformations appear so that you do not get unexpected results. You can partition a mapping if the
    mapping
    task can maintain data consistency when it processes the partitioned data.
  • For flat file partitioning, session performance is optimal with large source files. The load may be unbalanced if the amount of input data is small.
  • When a Sequence Generator transformation is in a mapping with partitioning enabled, ensure that you set up caching in the Sequence Generator transformation. Otherwise, the sequence numbers the task generates for each partition are not consecutive.
  • Sequence numbers generated by Normalizer and Sequence Generator transformations might not be sequential for a partitioned source, but they are unique.
  • When a Sorter transformation is in a mapping with partitioning enabled, the task sorts data in each partition separately.
  • A Sorter transformation must be placed before any Joiner transformation or Aggregator transformation that is configured to use sorted data.
  • You cannot use in-out parameters for key range values.
  • If your mapping has more than eight partitions,
    mapping
    task performance might degrade. You can configure the Buffer Block Size and DTM Buffer Size advanced properties in the
    mapping
    task to improve performance.
  • On Linux, if a target table name includes a unicode character, you need to set the default locale to UTF-8 to support multibyte data. To set the default locale to UTF-8, see the following examples:
    • For bash and related UNIX shells:
      export LC_ALL=en_US.UTF-8
    • For csh and related UNIX shells:
      setenv LC_ALL en_US.UTF-8
  • If you use key range partitioning on a source in a mapping in advanced mode, the mapping fails if all of the following conditions are true:
    • The Data Integration Server processes the source.
    • An
      advanced cluster
      processes a midstream transformation.
    • The
      advanced cluster
      runs in a Google Cloud or Microsoft Azure environment.

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