Table of Contents

Search

  1. Preface
  2. Introduction to Informatica Data Engineering Integration
  3. Mappings
  4. Mapping Optimization
  5. Sources
  6. Targets
  7. Transformations
  8. Python Transformation
  9. Data Preview
  10. Cluster Workflows
  11. Profiles
  12. Monitoring
  13. Hierarchical Data Processing
  14. Hierarchical Data Processing Configuration
  15. Hierarchical Data Processing with Schema Changes
  16. Intelligent Structure Models
  17. Blockchain
  18. Stateful Computing
  19. Appendix A: Connections Reference
  20. Appendix B: Data Type Reference
  21. Appendix C: Function Reference

Mapping Execution Plans

Mapping Execution Plans

The Data Integration Service generates an execution plan to run mappings on a Blaze, Spark, or Databricks Spark 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 compute 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.
Databricks Spark engine
The Databricks 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.


Updated November 10, 2020