Justin Uang created SPARK-8632:
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Summary: 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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