[ https://issues.apache.org/jira/browse/SPARK-40281?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-40281. ---------------------------------- Fix Version/s: 3.4.0 Resolution: Fixed Issue resolved by pull request 38584 [https://github.com/apache/spark/pull/38584] > Memory Profiler on Executors > ---------------------------- > > Key: SPARK-40281 > URL: https://issues.apache.org/jira/browse/SPARK-40281 > Project: Spark > Issue Type: New Feature > Components: PySpark > Affects Versions: 3.4.0 > Reporter: Xinrong Meng > Assignee: Xinrong Meng > Priority: Major > Fix For: 3.4.0 > > > The ticket proposes to implement PySpark memory profiling on executors. See > more > [design|https://docs.google.com/document/d/e/2PACX-1vR2K4TdrM1eAjNDC1bsflCNRH67UWLoC-lCv6TSUVXD91Ruksm99pYTnCeIm7Ui3RgrrRNcQU_D8-oh/pub]. > There are many factors in a PySpark program’s performance. Memory, as one of > the key factors of a program’s performance, had been missing in PySpark > profiling. A PySpark program on the Spark driver can be profiled with [Memory > Profiler|https://www.google.com/url?q=https://pypi.org/project/memory-profiler/&sa=D&source=editors&ust=1668027860192689&usg=AOvVaw1t4LRcObEGuhaTr5oHEUwU] > as a normal Python process, but there was not an easy way to profile memory > on Spark executors. > PySpark UDFs, one of the most popular Python APIs, enable users to run custom > code on top of the Apache Spark™ engine. However, it is difficult to optimize > UDFs without understanding memory consumption. > The ticket proposes to introduce the PySpark memory profiler, which profiles > memory on executors. It provides information about total memory usage and > pinpoints which lines of code in a UDF attribute to the most memory usage. > That will help optimize PySpark UDFs and reduce the likelihood of > out-of-memory errors. -- This message was sent by Atlassian Jira (v8.20.10#820010) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org