Can You Call Java/Scala Functions from a PySpark Task?

Calling Java/Scala Functions from PySpark Task

In PySpark, leveraging functionality implemented in Java or Scala can present challenges. While the Scala API provides a recommended workaround for calling DecisionTreeModel.predict, a more general solution is sought.

Technical Context

The issue arises when calling Java functions from PySpark tasks, specifically due to the involvement of JavaModelWrapper.call. This method attempts to access SparkContext, which is unavailable in worker code.

Elegant Solution

An elegant solution remains elusive. Two heavyweight options exist:

• Extending Spark classes through implicit conversions or wrappers
• Direct usage of the Py4j gateway

Alternative Approaches

Instead, consider alternative approaches:

• Using Spark SQL Data Sources API: Wrap JVM code, but with verbose implementation and limited input scope.
• Operating on DataFrames with Scala UDFs: Execute complex code on DataFrames, avoiding Python/Scala data conversion but requiring Py4j access.
• Creating Scala Interface: Build a Scala interface for arbitrary code execution, offering flexibility but requiring low-level implementation details and data conversion.
• External Workflow Management Tool: Switch between Python/Scala jobs and pass data through a Distributed File System (DFS), avoiding data conversion but incurring I/O costs.
• Shared SQLContext: Pass data between guest languages through temporary tables, optimized for interactive analysis but not ideal for batch jobs.

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