[jira] [Commented] (SPARK-8632) Poor Python UDF performance because of RDD caching

["Justin Uang (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-8632?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14601288#comment-14601288 ] 

Justin Uang commented on SPARK-8632:
------------------------------------

[~davies], my current plan is to switch to a synchronous model so that we can avoid deadlock. From a quick benchmark on my machine of loading pickled data and converting it to a python object, 95% of time is spent on cPickle and 5% on IO. I think the performance drawbacks of a synchronous model are trivial enough that the conceptual simplicity is worth it.

> 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
>
> {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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