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https://issues.apache.org/jira/browse/SPARK-17637?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16831014#comment-16831014
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Josh Rosen commented on SPARK-17637:
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I think this old feature suggestion is still very relevant and I'd love to see
someone pick this up and rework it for a newer version of Spark (or come up
with an alternative improvement).
Here's my use-case for packed scheduling of tasks:
I have a job shaped roughly like
{{input.flatMap(parse).repartition(40).map(transform).write.parquet()}}, where
I have ~1000 map tasks but only 40 reduce tasks (because I want to write
exactly 40 output files) and those reduce tasks are single-core CPU bound and
don't require much memory (so the tasks aren't really able to take advantage of
idle resources from unused task slots on their executors).
I want to run the initial map phase with huge parallelism (because my input
dataset is massive) and then want to scale down resource usage during the final
reduce phase such that I minimize the number of idle task slots.
I'm currently using dynamic allocation with Spark on YARN with the external
shuffle service enabled. What happens today is that the final reduce phase's
tasks get round-robin scheduled across the executors, spreading work thin and
leaving most of the executor cores idle. If these tasks were instead
densely-packed then I'd be able to scale down all but a couple of my executors,
freeing resources for other YARN applications. When the current current
round-robin policy is coupled with multi-core executors, it can sometimes cause
this shape of workload to be less resource efficient (in terms of
vcore_seconds) than an equivalent Scalding / Hadoop MapReduce job.
As a short-term workaround, I can reconfigure my application to run a larger
number of smaller executors, but that's somewhat painful compared to being able
to flip a single flag to toggle the task scheduling policy.
> Packed scheduling for Spark tasks across executors
> --------------------------------------------------
>
> Key: SPARK-17637
> URL: https://issues.apache.org/jira/browse/SPARK-17637
> Project: Spark
> Issue Type: Improvement
> Components: Scheduler
> Reporter: Zhan Zhang
> Assignee: Zhan Zhang
> Priority: Minor
>
> Currently Spark scheduler implements round robin scheduling for tasks to
> executors. Which is great as it distributes the load evenly across the
> cluster, but this leads to significant resource waste in some cases,
> especially when dynamic allocation is enabled.
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