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https://issues.apache.org/jira/browse/SPARK-22683?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16289046#comment-16289046
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Julien Cuquemelle commented on SPARK-22683:
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Hi [~xuefuz],
Thanks for noticing you also experience resource usage efficiency issues when
using dynamic allocation. I'll take the opportunity to provide a few
clarifications:
- regarding tuning, the study I made is precisely about tuning for a type of
workload and not specific jobs. Our users launch several hundreds of such jobs
per day (the size of which spanning a wide range as already stated); the
figures I gave where computed over a representative sample of such jobs.
- The resource waste is not specific to MR-style jobs, as this over-allocation
of executors will happen every time a new stage is launched with more tasks
than available taskSlots. I think that it would be great to allow the
dynamicAllocation to be resource efficient, while still giving the benefit of
elasticity over different stages.
- According to the tuning guide, Spark has been designed for larger executors
(5 cores regularly reported as a sweet spot) that should execute a large number
of small tasks ("as short as 200ms"), to mutualize / mitigate various
overheads,IOs, ..., so I'm not sure going back to smaller executors is the
right move
> 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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