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https://issues.apache.org/jira/browse/SPARK-26941?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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liupengcheng updated SPARK-26941:
---------------------------------
    Component/s: YARN
        Summary: maxNumExecutorFailures should be computed with 
spark.streaming.dynamicAllocation.maxExecutors in ApplicationMaster for 
streaming   (was: maxNumExecutorFailures should be computed with 
spark.streaming.dynamicAllocation.maxExecutors in streaming )

> maxNumExecutorFailures should be computed with 
> spark.streaming.dynamicAllocation.maxExecutors in ApplicationMaster for 
> streaming 
> ---------------------------------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-26941
>                 URL: https://issues.apache.org/jira/browse/SPARK-26941
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core, YARN
>    Affects Versions: 2.1.0, 2.4.0
>            Reporter: liupengcheng
>            Priority: Major
>
> Currently, when enabled streaming dynamic allocation for streaming 
> applications, the maxNumExecutorFailures in ApplicationMaster is still 
> computed with `spark.dynamicAllocation.maxExecutors`. 
> Actually, we should consider `spark.streaming.dynamicAllocation.maxExecutors` 
> instead.
> Related codes:
> {code:java}
> private val maxNumExecutorFailures = {
>   val effectiveNumExecutors =
>     if (Utils.isStreamingDynamicAllocationEnabled(sparkConf)) {
>       sparkConf.get(STREAMING_DYN_ALLOCATION_MAX_EXECUTORS)
>     } else if (Utils.isDynamicAllocationEnabled(sparkConf)) {
>       sparkConf.get(DYN_ALLOCATION_MAX_EXECUTORS)
>     } else {
>       sparkConf.get(EXECUTOR_INSTANCES).getOrElse(0)
>     }
>   // By default, effectiveNumExecutors is Int.MaxValue if dynamic allocation 
> is enabled. We need
>   // avoid the integer overflow here.
>   val defaultMaxNumExecutorFailures = math.max(3,
>     if (effectiveNumExecutors > Int.MaxValue / 2) Int.MaxValue else (2 * 
> effectiveNumExecutors))
>   
> sparkConf.get(MAX_EXECUTOR_FAILURES).getOrElse(defaultMaxNumExecutorFailures)
> {code}



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