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From "min shi (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-14168) Managed Memory Leak Msg Should Only Be a Warning
Date Tue, 08 Aug 2017 10:09:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-14168?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16118128#comment-16118128
] 

min shi commented on SPARK-14168:
---------------------------------

this affects spark 1.6.0 too, what do we do if we can not upgrade our version yet? shall we
increase allowing the number of errors?

> Managed Memory Leak Msg Should Only Be a Warning
> ------------------------------------------------
>
>                 Key: SPARK-14168
>                 URL: https://issues.apache.org/jira/browse/SPARK-14168
>             Project: Spark
>          Issue Type: Improvement
>          Components: Spark Core
>    Affects Versions: 1.6.1
>            Reporter: Imran Rashid
>            Assignee: Imran Rashid
>            Priority: Minor
>
> When a task is completed, executors check to see if all managed memory for the task was
correctly released, and logs an error when it wasn't.  However, it turns out its OK for there
to be memory that wasn't released when an Iterator isn't read to completion, eg., with {{rdd.take()}}.
 This results in a scary error msg in the executor logs:
> {noformat}
> 16/01/05 17:02:49 ERROR Executor: Managed memory leak detected; size = 16259594 bytes,
TID = 24
> {noformat}
> Furthermore, if tasks fails for any reason, this msg is also triggered.  This can lead
users to believe that the failure was from the memory leak, when the root cause could be entirely
different.  Eg., the same error msg appears in executor logs with this clearly broken user
code run with {{spark-shell --master 'local-cluster[2,2,1024]'}}
> {code}
> sc.parallelize(0 to 10000000, 2).map(x => x % 10000 -> x).groupByKey.mapPartitions
{ it => throw new RuntimeException("user error!") }.collect
> {code}
> We should downgrade the msg to a warning and link to a more detailed explanation.
> See https://issues.apache.org/jira/browse/SPARK-11293 for more reports from users (and
perhaps a true fix)



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