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Posted to issues@spark.apache.org by "Marcelo Vanzin (JIRA)" <ji...@apache.org> on 2019/03/04 18:02:00 UTC
[jira] [Assigned] (SPARK-24135) [K8s] Executors that fail to start
up because of init-container errors are not retried and limit the executor
pool size
[ https://issues.apache.org/jira/browse/SPARK-24135?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Marcelo Vanzin reassigned SPARK-24135:
--------------------------------------
Assignee: Marcelo Vanzin
> [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
> Assignee: Marcelo Vanzin
> 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.
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