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https://issues.apache.org/jira/browse/APEXCORE-392?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15207364#comment-15207364
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Chandni Singh edited comment on APEXCORE-392 at 3/22/16 9:37 PM:
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[~ilganeli] I highly suspect that it can be caused by errors in HDFS
environment. I think if not hadoop binaries then maybe incompatible version of
other dependent jars. Anyways I guess we can close the issue by marking it
"Cannot Reproduce"
was (Author: csingh):
[~ilganeli] I highly suspect that it can be caused by errors in HDFS
environment. I think if not hadoop binaries then maybe kryo incompatible
version. Anyways I guess we can close the issue by marking it "Cannot Reproduce"
> 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
>
> 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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