How to Call Java/Scala Functions from Within a Spark Task

Calling a Java/Scala function from within a Spark task

Background

When using Scala, invoking DecisionTreeModel.predict as a part of the map transformation can result in an exception. The reason for this is related to the call to JavaModelWrapper.call method.

Understanding the issue

JavaModelWrapper.call requires access to the SparkContext, which, in the context of PySpark, runs on the driver. However, the map transformation runs on worker nodes, and hence calling JavaModelWrapper.call from within map is not permissible.

Solution using Java UDFs

One solution is to encapsulate the Java code as a user-defined function (UDF) and use it within Spark SQL. This avoids the issue of calling Java code from within Python tasks. However, this solution requires data conversion between Python and Scala and introduces additional complexity.

Solution using Java Service Wrappers

Another option is to create custom Java service wrappers that provide an interface to the Java code from Python. These wrappers can be registered with Py4j and accessed using org.apache.spark.api.java.JavaRDD.withContext to gain access to the SparkContext.

Conclusion

While solutions such as Java UDFs and Java service wrappers provide workarounds for calling Java/Scala functions from within Spark tasks, it is essential to consider the overhead and limitations associated with each approach before selecting the best solution for your specific use case.

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