Table of Contents


  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Access Policy transformation
  6. Aggregator transformation
  7. Cleanse transformation
  8. Data Masking transformation
  9. Data Services transformation
  10. Deduplicate transformation
  11. Expression transformation
  12. Filter transformation
  13. Hierarchy Builder transformation
  14. Hierarchy Parser transformation
  15. Hierarchy Processor transformation
  16. Input transformation
  17. Java transformation
  18. Java transformation API reference
  19. Joiner transformation
  20. Labeler transformation
  21. Lookup transformation
  22. Machine Learning transformation
  23. Mapplet transformation
  24. Normalizer transformation
  25. Output transformation
  26. Parse transformation
  27. Python transformation
  28. Rank transformation
  29. Router transformation
  30. Rule Specification transformation
  31. Sequence Generator transformation
  32. Sorter transformation
  33. SQL transformation
  34. Structure Parser transformation
  35. Transaction Control transformation
  36. Union transformation
  37. Velocity transformation
  38. Verifier transformation
  39. Web Services transformation





In advanced mode, when you load data to some types of partitioned targets that you create at runtime, you can configure the partition key fields. For some target types, you can use partitions to optimize loading data to the target.
You can configure partition key fields and the partitioning method on the
tab. The
tab is displayed for targets in advanced mode.

Partition key fields

When you load data to certain types of partitioned targets that you create at runtime, you can configure the fields to be used as partition keys. You might need to configure partition key fields when you write data to complex file targets.
For example, you can create a mapping that loads data to an Amazon S3 V2 target that you create at runtime. The target is a partitioned Hive table that is backed by Avro data files. You want to write the data files in directories that are partitioned based on the columns YEAR, MONTH, and DAY. Configure the fields YEAR, MONTH, and DAY as partition keys.
Configure the fields to be used as partition keys in the Partition Fields area on the
tab. You can add, delete, and change the order of the partition key fields.
For more information about configuring partition key fields for different target types, see the help for the appropriate connector.

Partitioning methods

If a
task loads large data sets, the task can take a long time to load data. When you use multiple partitions, the
task divides data into partitions and loads the data in each partition concurrently, which can optimize performance. Not all target types support partitioning.
If a target in advanced mode supports partitioning, you can select the partitioning method in the Parallel Processing area on the
tab. The partitioning methods that you can select vary based on the target type. For more information about partitioning different types of targets, see the help for the appropriate connector.
You can select one of the following partitioning methods based on the target type:
task loads all data in a single partition. This is the default option.
task distributes rows of data based on the number of partitions that you specify. You can specify up to 64 partitions.
Consider the number of records to be passed to the target to determine an appropriate number of target partitions. For a small number of records, partitioning might not be advantageous.
Pass through
task processes data without redistributing rows among partitions. All rows in a single partition stay in the partition. Choose pass-through partitioning when you want to create additional partitions to improve performance, but do not want to change the distribution of data across partitions.
task determines the optimal number of partitions to create at runtime.


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