Table of Contents


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