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From "Chandni Singh (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (APEXCORE-392) Stack Overflow when launching jobs
Date Mon, 21 Mar 2016 21:59:25 GMT

    [ https://issues.apache.org/jira/browse/APEXCORE-392?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15205239#comment-15205239
] 

Chandni Singh commented on APEXCORE-392:
----------------------------------------

Created an application with 20 input/output operators and changed the default locality. 

Also created an application test in order to reproduce the error locally. Also launched the
application on cluster and don't see this happening. 

> Stack Overflow when launching jobs
> ----------------------------------
>
>                 Key: APEXCORE-392
>                 URL: https://issues.apache.org/jira/browse/APEXCORE-392
>             Project: Apache Apex Core
>          Issue Type: Bug
>    Affects Versions: 3.2.0, 3.3.0
>            Reporter: Ilya Ganelin
>            Assignee: Chandni Singh
>            Priority: Blocker
>
> I’m running into a very frustrating issue where certain DAG configurations cause the
following error log (attached). When this happens, my application even fails to launch. This
does not seem to be a YARN issue since this occurs even with a relatively small number of
partitions/memory. 
> This issue DOES appear to be related to HDFS input/output operations since the specific
parameter that appears to affect things is the number of physical partitions for the HDFS
input/output operators.
> I’ve also attached the input and output operators in question:
> https://gist.github.com/ilganeli/7f770374113b40ffa18a
> I can get this to occur predictable by
>   1.  Increasing the partition count on my input operator (reads from HDFS) - values
above 20 cause this error
>   2.  Increase the partition count on my output operator (writes to HDFS) - values above
20 cause this error
>   3.  Set stream locality from the default to either thread local, node local, or container_local
on the output operator
> This behavior is very frustrating as it’s preventing me from partitioning my HDFS I/O
appropriately, thus allowing me to scale to higher throughputs.



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