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English
  • Data Engineering Integration 10.2.1
  • All Products

Table of Contents

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  1. Preface
  2. Introduction to Big Data Management Administration
  3. Big Data Management Engines
  4. Authentication and Authorization
  5. Running Mappings on a Cluster with Kerberos Authentication
  6. Configuring Access to an SSL/TLS-Enabled Cluster
  7. Cluster Configuration
  8. Cluster Configuration Privileges and Permissions
  9. Cloud Provisioning Configuration
  10. Queuing
  11. Tuning for Big Data Processing
  12. Connections
  13. Multiple Blaze Instances on a Cluster

Big Data Management Administrator Guide

Big Data Management Administrator Guide

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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.
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