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Matt Cheah commented on SPARK-24135: ------------------------------------ Put up the PR< see above - created a separate setting for this class of errors. > [K8s] Executors that fail to start up because of init-container errors are > not retried and limit the executor pool size > ----------------------------------------------------------------------------------------------------------------------- > > Key: SPARK-24135 > URL: https://issues.apache.org/jira/browse/SPARK-24135 > Project: Spark > Issue Type: Bug > Components: Kubernetes > Affects Versions: 2.3.0 > Reporter: Matt Cheah > Priority: Major > > In KubernetesClusterSchedulerBackend, we detect if executors disconnect after > having been started or if executors hit the {{ERROR}} or {{DELETED}} states. > When executors fail in these ways, they are removed from the pending > executors pool and the driver should retry requesting these executors. > However, the driver does not handle a different class of error: when the pod > enters the {{Init:Error}} state. This state comes up when the executor fails > to launch because one of its init-containers fails. Spark itself doesn't > attach any init-containers to the executors. However, custom web hooks can > run on the cluster and attach init-containers to the executor pods. > Additionally, pod presets can specify init containers to run on these pods. > Therefore Spark should be handling the {{Init:Error}} cases regardless if > Spark itself is aware of init-containers or not. > This class of error is particularly bad because when we hit this state, the > failed executor will never start, but it's still seen as pending by the > executor allocator. The executor allocator won't request more rounds of > executors because its current batch hasn't been resolved to either running or > failed. Therefore we end up with being stuck with the number of executors > that successfully started before the faulty one failed to start, potentially > creating a fake resource bottleneck. -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org