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https://issues.apache.org/jira/browse/SPARK-8632?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14733867#comment-14733867
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Justin Uang commented on SPARK-8632:
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Yea, I think that's the best solution for udfs, since the number of input rows
and output rows are the same per batch. So do you think we should create a
separate code path that uses this row-batch based engine specifically for UDFs?
It would also be nice because then we could switch to some language agnostic
data format like avro or protobufs, and then allow all language bindings to
support UDFs the same way.
> Poor Python UDF performance because of RDD caching
> --------------------------------------------------
>
> Key: SPARK-8632
> URL: https://issues.apache.org/jira/browse/SPARK-8632
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
> Affects Versions: 1.4.0
> Reporter: Justin Uang
> Assignee: Davies Liu
>
> {quote}
> We have been running into performance problems using Python UDFs with
> DataFrames at large scale.
> From the implementation of BatchPythonEvaluation, it looks like the goal was
> to reuse the PythonRDD code. It caches the entire child RDD so that it can do
> two passes over the data. One to give to the PythonRDD, then one to join the
> python lambda results with the original row (which may have java objects that
> should be passed through).
> In addition, it caches all the columns, even the ones that don't need to be
> processed by the Python UDF. In the cases I was working with, I had a 500
> column table, and i wanted to use a python UDF for one column, and it ended
> up caching all 500 columns.
> {quote}
> http://apache-spark-developers-list.1001551.n3.nabble.com/Python-UDF-performance-at-large-scale-td12843.html
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