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Paul Jones commented on SPARK-20658: ------------------------------------ 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? -- This message was sent by Atlassian JIRA (v6.3.15#6346) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org