How to Call Java/Scala Functions from Apache Spark Tasks in PySpark?

Accessing Java/Scala Functions from Apache Spark Tasks

In PySpark, calling Java/Scala functions within tasks can be challenging due to limitations with the Py4J gateway.

Underlying Issue

Py4J gateway, which facilitates communication between Python and Java/Scala, only runs on the driver and is not accessible to workers. Certain operations, such as DecisionTreeModel.predict, use JavaModelWrapper.call to invoke Java functions that require direct access to SparkContext.

Workarounds

While default Py4J communication is not feasible, there are several workarounds:

• Spark SQL Data Sources API:

  • Integrate JVM code as a custom data source.
  • Pros: High-level, supported, requires no internal PySpark access.
  • Cons: Verbose, limited documentation.

• Scala UDFs:

  • Define Scala functions that can be applied to DataFrames.
  • Pros: Easy implementation, minimal data conversion, minimal Py4J access.
  • Cons: Requires internal Py4J and API access, limited to Spark SQL.

• Scala Interfaces:

  • Create custom Scala interfaces similar to those in MLlib.
  • Pros: Flexible, complex code execution, options for DataFrame or RDD integration.
  • Cons: Low-level, data conversion required, not supported.

• External Workflow Management:

  • Use tools to manage transitions between Python and Scala/Java, passing data through a distributed file system.
  • Pros: Easy implementation, minimal code changes.
  • Cons: Additional storage costs.

• Shared SQLContext:

  • Utilize a shared SQLContext to communicate through temporary tables.
  • Pros: Suitable for interactive analysis.
  • Cons: May not be ideal for batch jobs.

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