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https://issues.apache.org/jira/browse/SPARK-26679?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16750660#comment-16750660
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Imran Rashid commented on SPARK-26679:
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the java side has the same problem. Spark has no idea how much memory the user
needs for their own code. That's why there is a spark.memory.fraction option.
I think the new option "spark.executor.pyspark.memory" is close to
"spark.executor.memory" (which sets -Xmx for each executor) on the java side.
Most users don't mess w/ spark.memory.fraction, but its there to cover those
extreme cases.
> Deconflict spark.executor.pyspark.memory and spark.python.worker.memory
> -----------------------------------------------------------------------
>
> Key: SPARK-26679
> URL: https://issues.apache.org/jira/browse/SPARK-26679
> Project: Spark
> Issue Type: Improvement
> Components: PySpark
> Affects Versions: 2.4.0
> Reporter: Ryan Blue
> Priority: Major
>
> In 2.4.0, spark.executor.pyspark.memory was added to limit the total memory
> space of a python worker. There is another RDD setting,
> spark.python.worker.memory that controls when Spark decides to spill data to
> disk. These are currently similar, but not related to one another.
> PySpark should probably use spark.executor.pyspark.memory to limit or default
> the setting of spark.python.worker.memory because the latter property
> controls spilling and should be lower than the total memory limit. Renaming
> spark.python.worker.memory would also help clarity because it sounds like it
> should control the limit, but is more like the JVM setting
> spark.memory.fraction.
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