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https://issues.apache.org/jira/browse/SPARK-8632?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15231585#comment-15231585
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Davies Liu commented on SPARK-8632:
-----------------------------------
[~bijay697] Python UDFs had been improved a lot recently in master, see
https://issues.apache.org/jira/browse/SPARK-14267 and
https://issues.apache.org/jira/browse/SPARK-14215.
Could you try master ?
> 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
> Priority: Blocker
> Fix For: 1.5.1, 1.6.0
>
>
> {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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