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https://issues.apache.org/jira/browse/SPARK-15369?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15549990#comment-15549990
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Reynold Xin commented on SPARK-15369:
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So while I'm sure you can improve performance for some UDFs, the limitation of
Jython is pretty severe and I worry we are building on a shaky foundation with
this approach. Maybe a better approach is to speed up serialization for Python,
e.g. by introducing block oriented UDFs that return numpy arrays or Pandas data
frames.
> Investigate selectively using Jython for parts of PySpark
> ---------------------------------------------------------
>
> Key: SPARK-15369
> URL: https://issues.apache.org/jira/browse/SPARK-15369
> Project: Spark
> Issue Type: Improvement
> Components: PySpark
> Reporter: holdenk
> Priority: Minor
>
> Transferring data from the JVM to the Python executor can be a substantial
> bottleneck. While Jython is not suitable for all UDFs or map functions, it
> may be suitable for some simple ones. We should investigate the option of
> using Jython to accelerate these small functions.
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