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https://issues.apache.org/jira/browse/SPARK-22683?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16289656#comment-16289656
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Thomas Graves commented on SPARK-22683:
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I am also curious, when you are comparing spark to MR based jobs and assuming
you are running hive or pig, are you comparing spark to resource usage across
the multiple MR jobs? Or are you running straight MR job vs spark app? You
have to look across the jobs because its using resources by having to write to
hdfs and read from hdfs between jobs.
> DynamicAllocation wastes resources by allocating containers that will barely
> be used
> ------------------------------------------------------------------------------------
>
> Key: SPARK-22683
> URL: https://issues.apache.org/jira/browse/SPARK-22683
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 2.1.0, 2.2.0
> Reporter: Julien Cuquemelle
> Labels: pull-request-available
>
> let's say an executor has spark.executor.cores / spark.task.cpus taskSlots
> The current dynamic allocation policy allocates enough executors
> to have each taskSlot execute a single task, which minimizes latency,
> but wastes resources when tasks are small regarding executor allocation
> and idling overhead.
> By adding the tasksPerExecutorSlot, it is made possible to specify how many
> tasks
> a single slot should ideally execute to mitigate the overhead of executor
> allocation.
> PR: https://github.com/apache/spark/pull/19881
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