Table of Contents


  1. Preface
  2. Introduction to Informatica Big Data Management
  3. Mappings in the Hadoop Environment
  4. Mapping Sources in the Hadoop Environment
  5. Mapping Targets in the Hadoop Environment
  6. Mapping Transformations in the Hadoop Environment
  7. Processing Hierarchical Data on the Spark Engine
  8. Configuring Transformations to Process Hierarchical Data
  9. Processing Unstructured and Semi-structured Data with an Intelligent Structure Model
  10. Stateful Computing on the Spark Engine
  11. Monitoring Mappings in the Hadoop Environment
  12. Mappings in the Native Environment
  13. Profiles
  14. Native Environment Optimization
  15. Cluster Workflows
  16. Connections
  17. Data Type Reference
  18. Function Reference
  19. Parameter Reference

Spark Engine Architecture

Spark Engine Architecture

The Data Integration Service can use the Spark engine on a Hadoop cluster to run Model repository mappings.
To run a mapping on the Spark engine, the Data Integration Service sends a mapping application to the Spark executor. The Spark executor submits the job to the Hadoop cluster to run.
The following image shows how a Hadoop cluster processes jobs sent from the Spark executor:
The following events occur when Data Integration Service runs a mapping on the Spark engine:
  1. The Logical Data Transformation Manager translates the mapping into a Scala program, packages it as an application, and sends it to the Spark executor.
  2. The Spark executor submits the application to the Resource Manager in the Hadoop cluster and requests resources to run the application.
    When you run mappings on the HDInsight cluster, the Spark executor launches a spark-submit script. The script requests resources to run the application.
  3. The Resource Manager identifies the Node Managers that can provide resources, and it assigns jobs to the data nodes.
  4. Driver and Executor processes are launched in data nodes where the Spark application runs.

Updated October 23, 2019