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English
  • Data Engineering Integration 10.5.7
  • All Products

Table of Contents

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  1. Preface
  2. Introduction to Data Engineering Administration
  3. Authentication
  4. Running Mappings on a Cluster with Kerberos Authentication
  5. Authorization
  6. Cluster Configuration
  7. Cloud Provisioning Configuration
  8. Data Integration Service Processing
  9. Appendix A: Connections Reference
  10. Appendix B: Monitoring REST API

Data Engineering Administrator Guide

Data Engineering Administrator Guide

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Run-time Process on the Spark Engine

Run-time Process on the Spark Engine

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:
This image shows the Spark engine architectural diagram. Jobs sent from the Spark executor are submitted to the Resource Manager in the Hadoop cluster. The Resource Manager identifies the Node Managers and assigns jobs to the data nodes.
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.
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