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From "Anirudh Ramanathan (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-24135) [K8s] Executors that fail to start up because of init-container errors are not retried and limit the executor pool size
Date Wed, 02 May 2018 06:06:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-24135?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16460559#comment-16460559
] 

Anirudh Ramanathan commented on SPARK-24135:
--------------------------------------------

+1 to detecting all pod error states and doing something about them. We should try and account
for as many possible error conditions as possible. For example, there are many types of just
[image pull errors|https://github.com/kubernetes/kubernetes/blob/886e04f1fffbb04faf8a9f9ee141143b2684ae68/pkg/kubelet/images/types.go#L25-L43].
It is sometimes unclear if they are framework or application errors. I think making them count
towards job failure is the easiest and most conservative behavior to start with. 

> [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.



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