[jira] [Commented] (SPARK-25004) Add spark.executor.pyspark.memory config to set resource.RLIMIT_AS
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https://issues.apache.org/jira/browse/SPARK-25004?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17062333#comment-17062333
]
Xiaochen Ouyang commented on SPARK-25004:
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[~rdblue] This configuration can only control the worker.py process, and the
maximum memory limit of the derived child process cannot be controlled.
Worker(JVM) --> Executor–> python.demon–>python.demon , the last python demon
process can not be controlled by this configuration.
> Add spark.executor.pyspark.memory config to set resource.RLIMIT_AS
> --
>
> Key: SPARK-25004
> URL: https://issues.apache.org/jira/browse/SPARK-25004
> Project: Spark
> Issue Type: Bug
> Components: PySpark
>Affects Versions: 2.3.0
>Reporter: Ryan Blue
>Assignee: Ryan Blue
>Priority: Major
> Fix For: 2.4.0
>
>
> Some platforms support limiting Python's addressable memory space by limiting
> [{{resource.RLIMIT_AS}}|https://docs.python.org/3/library/resource.html#resource.RLIMIT_AS].
> We've found that adding a limit is very useful when running in YARN because
> when Python doesn't know about memory constraints, it doesn't know when to
> garbage collect and will continue using memory when it doesn't need to.
> Adding a limit reduces PySpark memory consumption and avoids YARN killing
> containers because Python hasn't cleaned up memory.
> This also improves error messages for users, allowing them to see when Python
> is allocating too much memory instead of YARN killing the container:
> {code:lang=python}
> File "build/bdist.linux-x86_64/egg/package/library.py", line 265, in
> fe_engineer
> fe_eval_rec.update(f(src_rec_prep, mat_rec_prep))
> File "build/bdist.linux-x86_64/egg/package/library.py", line 163, in fe_comp
> comparisons = EvaluationUtils.leven_list_compare(src_rec_prep.get(item,
> []), mat_rec_prep.get(item, []))
> File "build/bdist.linux-x86_64/egg/package/evaluationutils.py", line 25, in
> leven_list_compare
> permutations = sorted(permutations, reverse=True)
> MemoryError
> {code}
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[jira] [Commented] (SPARK-25004) Add spark.executor.pyspark.memory config to set resource.RLIMIT_AS
[
https://issues.apache.org/jira/browse/SPARK-25004?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16567396#comment-16567396
]
Apache Spark commented on SPARK-25004:
--
User 'rdblue' has created a pull request for this issue:
https://github.com/apache/spark/pull/21977
> Add spark.executor.pyspark.memory config to set resource.RLIMIT_AS
> --
>
> Key: SPARK-25004
> URL: https://issues.apache.org/jira/browse/SPARK-25004
> Project: Spark
> Issue Type: Bug
> Components: PySpark
>Affects Versions: 2.3.0
>Reporter: Ryan Blue
>Priority: Major
>
> Some platforms support limiting Python's addressable memory space by limiting
> [{{resource.RLIMIT_AS}}|https://docs.python.org/3/library/resource.html#resource.RLIMIT_AS].
> We've found that adding a limit is very useful when running in YARN because
> when Python doesn't know about memory constraints, it doesn't know when to
> garbage collect and will continue using memory when it doesn't need to.
> Adding a limit reduces PySpark memory consumption and avoids YARN killing
> containers because Python hasn't cleaned up memory.
> This also improves error messages for users, allowing them to see when Python
> is allocating too much memory instead of YARN killing the container:
> {code:lang=python}
> File "build/bdist.linux-x86_64/egg/package/library.py", line 265, in
> fe_engineer
> fe_eval_rec.update(f(src_rec_prep, mat_rec_prep))
> File "build/bdist.linux-x86_64/egg/package/library.py", line 163, in fe_comp
> comparisons = EvaluationUtils.leven_list_compare(src_rec_prep.get(item,
> []), mat_rec_prep.get(item, []))
> File "build/bdist.linux-x86_64/egg/package/evaluationutils.py", line 25, in
> leven_list_compare
> permutations = sorted(permutations, reverse=True)
> MemoryError
> {code}
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