[jira] [Assigned] (SPARK-25004) Add spark.executor.pyspark.memory config to set resource.RLIMIT_AS

["Apache Spark (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-25004?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-25004:
------------------------------------

    Assignee: Apache Spark

> 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: Apache Spark
>            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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