[ https://issues.apache.org/jira/browse/SPARK-8632?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Josh Rosen resolved SPARK-8632. ------------------------------- Resolution: Fixed Fix Version/s: 1.5.1 1.6.0 Issue resolved by pull request 8835 [https://github.com/apache/spark/pull/8835] > 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: 1.6.0, 1.5.1 > > > {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 -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org