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Chapter 1. Overview of the Kubeflow Spark Operator (KSO)

The Kubeflow Spark Operator allows you to run Spark data processing applications in a distributed environment on Red Hat OpenShift AI. You can create custom resources (CRs) for specifying, running and surfacing Spark applications.

KSO for Apache Spark currently supports the following:

  • Spark versions 4.0.1 and above.
  • When a SparkApplication custom resource is created, the operator creates and runs a spark-submit job that starts Spark driver and executor pods.
  • Native Cron support for running scheduled applications.
  • Customization of Spark pods, including: mounting ConfigMaps/volumes and setting pod affinity.
  • Automatic application restart with custom policies.
  • Collecting and exporting application metrics and driver metrics to Prometheus.

1.1. Kubeflow Spark Operator (KSO) architecture
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The Spark operator consists of the following:

  • SparkApplication Controller: A job that watches Create, Update and Delete events in SparkApplication resources.
  • Submission Runner: When a SparkApplication custom resource is created, the operator creates and runs a spark-submit job that starts Spark driver and executor pods.
  • Spark Pod Monitor: Observes the Driver and Executor pods and updates the .status field of the SparkApplication CR.
  • Mutating Admission Webhook: A component that intercepts pod creation requests and injects ConfigMap mounts or Volumes into the Driver and Executor pods before they are scheduled.
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