Justin Uang created SPARK-8632: ---------------------------------- 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 -- 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