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https://issues.apache.org/jira/browse/SPARK-20658?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16001628#comment-16001628
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Paul Jones commented on SPARK-20658:
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Ah... This is using Amazons version of Hadoop 2.7.3
{noformat}
$ hadoop version
Hadoop 2.7.3-amzn-1
Subversion g...@aws157git.com:/pkg/Aws157BigTop -r
30eccced8ce8c483445f0aa3175ce725831ff06b
Compiled by ec2-user on 2017-02-17T17:59Z
Compiled with protoc 2.5.0
>From source with checksum 1833aada17b94cfb94ad40ccd02d3df8
This command was run using /usr/lib/hadoop/hadoop-common-2.7.3-amzn-1.jar
{noformat}
> spark.yarn.am.attemptFailuresValidityInterval doesn't seem to have an effect
> ----------------------------------------------------------------------------
>
> Key: SPARK-20658
> URL: https://issues.apache.org/jira/browse/SPARK-20658
> Project: Spark
> Issue Type: Bug
> Components: YARN
> Affects Versions: 2.1.0
> Reporter: Paul Jones
> Priority: Minor
>
> I'm running a job in YARN cluster mode using
> `spark.yarn.am.attemptFailuresValidityInterval=1h` specified in both
> spark-default.conf and in my spark-submit command. (This flag shows up in the
> environment tab of spark history server, so it seems that it's specified
> correctly).
> However, I just had a job die with with four AM failures (three of the four
> failures were over an hour apart). So, I'm confused as to what could be going
> on. I haven't figured out the cause of the individual failures, so is it
> possible that we always count certain types of failures? E.g. jobs that are
> killed due to memory issues always count?
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