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https://issues.apache.org/jira/browse/SPARK-40281?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon resolved SPARK-40281.
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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.
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