[jira] [Commented] (SPARK-6415) Spark Streaming fail-fast: Stop scheduling jobs when a batch fails, and kills the app

["Yoel Amram (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-6415?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15044110#comment-15044110 ] 

Yoel Amram commented on SPARK-6415:
-----------------------------------

It would be useful to let the application decide when to fail-fast, I can see several relevant use cases:

1. one or more workers are out of memory (could be unbalanced load causing only one/several partitions to fail).
2. all workers fail because of unreachable destination (e.g. network issues with target database).

Also, maybe having a similar configuration to spark.task.maxFailures (i.e. spark.job.maxFailures) to retry job execution several times (with possible backoff period) before exiting the application.


> Spark Streaming fail-fast: Stop scheduling jobs when a batch fails, and kills the app
> -------------------------------------------------------------------------------------
>
>                 Key: SPARK-6415
>                 URL: https://issues.apache.org/jira/browse/SPARK-6415
>             Project: Spark
>          Issue Type: Improvement
>          Components: Streaming
>            Reporter: Hari Shreedharan
>
> Of course, this would have to be done as a configurable param, but such a fail-fast is useful else it is painful to figure out what is happening when there are cascading failures. In some cases, the SparkContext shuts down and streaming keeps scheduling jobs 



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