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https://issues.apache.org/jira/browse/SPARK-24920?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen resolved SPARK-24920.
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Resolution: Fixed
Fix Version/s: 3.0.0
Issue resolved by pull request 23278
[https://github.com/apache/spark/pull/23278]
> Spark should allow sharing netty's memory pools across all uses
> ---------------------------------------------------------------
>
> Key: SPARK-24920
> URL: https://issues.apache.org/jira/browse/SPARK-24920
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 2.4.0
> Reporter: Imran Rashid
> Assignee: Attila Zsolt Piros
> Priority: Major
> Labels: memory-analysis
> Fix For: 3.0.0
>
>
> Spark currently creates separate netty memory pools for each of the following
> "services":
> 1) RPC Client
> 2) RPC Server
> 3) BlockTransfer Client
> 4) BlockTransfer Server
> 5) ExternalShuffle Client
> Depending on configuration and whether its an executor or driver JVM,
> different of these are active, but its always either 3 or 4.
> Having them independent somewhat defeats the purpose of using pools at all.
> In my experiments I've found each pool will grow due to a burst of activity
> in the related service (eg. task start / end msgs), followed another burst in
> a different service (eg. sending torrent broadcast blocks). Because of the
> way these pools work, they allocate memory in large chunks (16 MB by default)
> for each netty thread, so there is often a surge of 128 MB of allocated
> memory, even for really tiny messages. Also a lot of this memory is offheap
> by default, which makes it even tougher for users to manage.
> I think it would make more sense to combine all of these into a single pool.
> In some experiments I tried, this noticeably decreased memory usage, both
> onheap and offheap (no significant performance effect in my small
> experiments).
> As this is a pretty core change, as I first step I'd propose just exposing
> this as a conf, to let user experiment more broadly across a wider range of
> workloads
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