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Version: v2.10.0

JAR Job

A JAR job is best suited for production-grade workloads that require compiled code, well-structured Spark logic, and reusable applications. It enables efficient execution of complex data processing tasks using pre-built artifacts. JAR jobs allow you to execute Java or Scala-based Spark applications packaged as a .jar file.

Jar Job

Required Fields​

  • Class Name: Fully qualified class containing the main method
  • Arguments: Space-separated arguments passed to the application
  • Cluster: Target cluster where the job runs
  • Path: Location of the JAR file in the repository

Advanced Options​

You can further customize your job configuration using the following options:

  • Additional JARs – Include extra dependencies required for execution
  • Repositories – Specify custom repositories for dependency resolution
  • Spark Configs – Define Spark configuration properties
  • Driver Configs – Configure driver-level settings
  • Archives – Add archive files needed during execution
  • Files – Attach additional files required by the job
  • Packages – Include external libraries or packages

Advanced Options

When to Use JAR Jobs​

Use JAR jobs in the following scenarios:

  • Performance-critical workloads
  • Stable and versioned data pipelines
  • CI/CD-driven Spark applications