[jira] [Resolved] (SPARK-11049) If a single executor fails to allocate memory, entire job fails

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-11049?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen resolved SPARK-11049.
-------------------------------
    Resolution: Not A Problem

Pending more info

> If a single executor fails to allocate memory, entire job fails
> ---------------------------------------------------------------
>
>                 Key: SPARK-11049
>                 URL: https://issues.apache.org/jira/browse/SPARK-11049
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core
>    Affects Versions: 1.4.0
>            Reporter: Brian
>
> To reproduce:
> * Create a spark cluster using start-master.sh and start-slave.sh (I believe this is the "standalone cluster manager?").  
> * Leave a process running on some nodes that take up about significant amounts of RAM.
> * Leave some nodes with plenty of RAM to run spark.
> * Run a job against this cluster with spark.executor.memory asking for all or most of the memory available on each node.
> On the node that has insufficient memory, there will of course be an error like:
> Error occurred during initialization of VM
> Could not reserve enough space for object heap
> Could not create the Java virtual machine.
> On the driver node, and in the spark master UI, I see that _all_ executors exit or are killed, and the entire job fails.  It would be better if there was an indication of which individual node is actually at fault.  It would also be better if the cluster manager could handle failing-over to nodes that are still operating properly and have sufficient RAM.



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