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From "Wenchen Fan (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (SPARK-20832) Standalone master should explicitly inform drivers of worker deaths and invalidate external shuffle service outputs
Date Thu, 22 Jun 2017 12:50:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-20832?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Wenchen Fan resolved SPARK-20832.
---------------------------------
       Resolution: Fixed
    Fix Version/s: 2.3.0

Issue resolved by pull request 18362
[https://github.com/apache/spark/pull/18362]

> Standalone master should explicitly inform drivers of worker deaths and invalidate external
shuffle service outputs
> -------------------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-20832
>                 URL: https://issues.apache.org/jira/browse/SPARK-20832
>             Project: Spark
>          Issue Type: Bug
>          Components: Deploy, Scheduler
>    Affects Versions: 2.0.0
>            Reporter: Josh Rosen
>             Fix For: 2.3.0
>
>
> In SPARK-17370 (a patch authored by [~ekhliang] and reviewed by me), we added logic to
the DAGScheduler to mark external shuffle service instances as unavailable upon task failure
when the task failure reason was "SlaveLost" and this was known to be caused by worker death.
If the Spark Master discovered that a worker was dead then it would notify any drivers with
executors on those workers to mark those executors as dead. The linked patch simply piggybacked
on this logic to have the executor death notification also imply worker death and to have
worker-death-caused-executor-death imply shuffle file loss.
> However, there are modes of external shuffle service loss which this mechanism does not
detect, leaving the system prone race conditions. Consider the following:
> * Spark standalone is configured to run an external shuffle service embedded in the Worker.
> * Application has shuffle outputs and executors on Worker A.
> * Stage depending on outputs of tasks that ran on Worker A starts.
> * All executors on worker A are removed due to dying with exceptions, scaling-down via
the dynamic allocation APIs, but _not_ due to worker death. Worker A is still healthy at this
point.
> * At this point the MapOutputTracker still records map output locations on Worker A's
shuffle service. This is expected behavior. 
> * Worker A dies at an instant where the application has no executors running on it.
> * The Master knows that Worker A died but does not inform the driver (which had no executors
on that worker at the time of its death).
> * Some task from the running stage attempts to fetch map outputs from Worker A but these
requests time out because Worker A's shuffle service isn't available.
> * Due to other logic in the scheduler, these preventable FetchFailures don't wind up
invaliding the now-invalid unavailable map output locations (this is a distinct bug / behavior
which I'll discuss in a separate JIRA ticket).
> * This behavior leads to several unsuccessful stage reattempts and ultimately to a job
failure.
> A simple way to address this would be to have the Master explicitly notify drivers of
all Worker deaths, even if those drivers don't currently have executors. The Spark Standalone
scheduler backend can receive the explicit WorkerLost message and can bubble up the right
calls to the task scheduler and DAGScheduler to invalidate map output locations from the now-dead
external shuffle service.
> This relates to SPARK-20115 in the sense that both tickets aim to address issues where
the external shuffle service is unavailable. The key difference is the mechanism for detection:
SPARK-20115 marks the external shuffle service as unavailable whenever any fetch failure occurs
from it, whereas the proposal here relies on more explicit signals. This JIRA ticket's proposal
is scoped only to Spark Standalone mode. As a compromise, we might be able to consider "all
of a single shuffle's outputs lost on a single external shuffle service" following a fetch
failure (to be discussed in separate JIRA). 



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