Table of Contents

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

Transformations

Transformations

Partitions

Partitions

In an elastic mapping, 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
Partitions
tab. The
Partitions
tab is displayed for targets in elastic mappings.

Partition key fields

In an elastic mapping, 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 create an elastic 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
Partitions
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
mapping
task loads large data sets, the task can take a long time to load data. When you use multiple partitions, the
mapping
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 an elastic mapping supports partitioning, you can select the partitioning method in the Parallel Processing area on the
Partitions
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:
None
The
mapping
task loads all data in a single partition. This is the default option.
Fixed
The
mapping
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
The
mapping
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.
Dynamic
The
mapping
task determines the optimal number of partitions to create at runtime.