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From "ASF GitHub Bot (JIRA)" <>
Subject [jira] [Commented] (SPARK-24920) Spark should allow sharing netty's memory pools across all uses
Date Mon, 10 Dec 2018 23:14:00 GMT


ASF GitHub Bot commented on SPARK-24920:

SparkQA commented on issue #23278: [SPARK-24920][Core] Allow sharing Netty's memory pool allocators
   **[Test build #99938 has started](**
for PR 23278 at commit [`ae8fa8e`](

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> Spark should allow sharing netty's memory pools across all uses
> ---------------------------------------------------------------
>                 Key: SPARK-24920
>                 URL:
>             Project: Spark
>          Issue Type: Improvement
>          Components: Spark Core
>    Affects Versions: 2.4.0
>            Reporter: Imran Rashid
>            Priority: Major
>              Labels: memory-analysis
> 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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