[jira] [Updated] (FLINK-16225) Metaspace Out Of Memory should be handled as Fatal Error in TaskManager

["Andrey Zagrebin (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/FLINK-16225?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Andrey Zagrebin updated FLINK-16225:
------------------------------------
    Fix Version/s: 1.10.2

> Metaspace Out Of Memory should be handled as Fatal Error in TaskManager
> -----------------------------------------------------------------------
>
>                 Key: FLINK-16225
>                 URL: https://issues.apache.org/jira/browse/FLINK-16225
>             Project: Flink
>          Issue Type: Improvement
>          Components: Runtime / Task
>    Affects Versions: 1.10.0
>            Reporter: Stephan Ewen
>            Assignee: Andrey Zagrebin
>            Priority: Critical
>              Labels: pull-request-available, usability
>             Fix For: 1.11.0, 1.10.2
>
>
> When an {{OutOfMemory (Metaspace)}} exception happens, there is usually no way to recover. This is often the result of user code or libraries that have subtle class loading leaks.
> The one way to recover is to kill the TaskManagers and to let the resource orchestrators (K8s, Yarn, Mesos) restart them. Flink's fault tolerance should then be able to recover the job.
> I would suggest to implement this the following way:
> * The user code ClassLoader takes an "OOM Handler", which is called when class loading causes an OOM exception.
> * The handler wraps this into an Exception with a good error message (see below) and invokes the TaskManager's {{FatalErrorHandler}}.
> * The {{FatalErrorHandler}} in turn should attempt to cancel everything and notify the JM before shutting down. That way, we get decent error reporting and users can see what is going on.
> The error message should describe the following:
> * If user sees the error consistently on the first deploy, then the metaspace is simply too small for their application, and they need to explicitly increase it in the configuration
> * If the user sees occasionally TaskManagers in a session cluster failing with that exception when deploying new jobs, then some user code or library probably has a class leak. The TM failure / restart is done in order to forcefully clean up.



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