Table of Contents


  1. Preface
  2. Introduction to Informatica Big Data Management
  3. Connections
  4. Mappings in the Hadoop Environment
  5. Mapping Objects in the Hadoop Environment
  6. Processing Hierarchical Data on the Spark Engine
  7. Stateful Computing on the Spark Engine
  8. Monitoring Mappings in the Hadoop Environment
  9. Mappings in the Native Environment
  10. Profiles
  11. Native Environment Optimization
  12. Data Type Reference
  13. Complex File Data Object Properties
  14. Function Reference
  15. Parameter Reference

Mapping Execution Plans

Mapping Execution Plans

The Data Integration Service generates an execution plan to run mappings on a Blaze, Spark, or Hive engine. The Data Integration Service translates the mapping logic into code that the run-time engine can execute. You can view the plan in the Developer tool before you run the mapping and in the Administrator tool after you run the mapping.
The Data Integration Service generates mapping execution plans to run on the following engines:
Informatica Blaze engine
The Blaze engine execution plan simplifies the mapping into segments. It contains tasks to start the mapping, run the mapping, and clean up the temporary tables and files. It contains multiple tasklets and the task recovery strategy. It also contains pre- and post-grid task preparation commands for each mapping before running the main mapping on a Hadoop cluster. A pre-grid task can include a task such as copying data to HDFS. A post-grid task can include tasks such as cleaning up temporary files or copying data from HDFS.
Spark engine
The Spark execution plan shows the run-time Scala code that runs the mapping logic. A translation engine translates the mapping into an internal representation of the logic. The internal representation is rendered into Scala code that accesses the Spark API. You can view the Scala code in the execution plan to debug the logic.
Hive engine
The Hive execution plan is a series of Hive queries. The plan contains tasks to start the mapping, run the mapping, and clean up the temporary tables and files. You can view the Hive execution plan that the Data Integration Service generates before you run the mapping. When the Data Integration Service pushes the mapping to the Hive engine, it has a Hive executor that can process the mapping. The Hive executor simplifies the mapping to an equivalent mapping with a reduced set of instructions and generates a Hive execution plan.

Updated December 13, 2018