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https://issues.apache.org/jira/browse/YARN-5479?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15412177#comment-15412177
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Jason Lowe commented on YARN-5479:
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Agree the proposals are interesting. I'd love to get the overhead of Resource
reduced, since as you and Ray point out it's used everywhere.
Minimally I think we should approach this as two (or more) separate JIRAs since
there are two vastly different approaches to improving performance here. One
is optimizing the existing algorithm while the other is proposing to change the
requirements to allow more optimization. I don't think we should start
loosening the guarantees of the scheduler for performance reasons until we've
exhausted the other ways we can improve performance. So personally I'd rather
see the Resource-related improvements before the others that change the
guarantees to which users have grown accustomed.
> FairScheduler: Scheduling performance improvement
> -------------------------------------------------
>
> Key: YARN-5479
> URL: https://issues.apache.org/jira/browse/YARN-5479
> Project: Hadoop YARN
> Issue Type: Improvement
> Components: fairscheduler, resourcemanager
> Affects Versions: 2.6.0
> Reporter: He Tianyi
> Assignee: He Tianyi
>
> Currently ResourceManager uses a single thread to handle async events for
> scheduling. As number of nodes grows, more events need to be processed in
> time in FairScheduler. Also, increased number of applications & queues slows
> down processing of each single event.
> There are two cases that slow processing of nodeUpdate events is problematic:
> A. global throughput is lower than number of nodes through heartbeat rounds.
> This keeps resource from being allocated since the inefficiency.
> B. global throughput meets the need, but for some of these rounds, events of
> some nodes cannot get processed before next heartbeat. This brings
> inefficiency handling burst requests (i.e. newly submitted MapReduce
> application cannot get its all task launched soon given enough resource).
> Pretty sure some people will encounter the problem eventually after a single
> cluster is scaled to several K of nodes (even with {{assignmultiple}}
> enabled).
> This issue proposes to perform several optimization towards performance in
> FairScheduler {{nodeUpdate}} method. To be specific:
> A. trading off fairness with efficiency, queue & app sorting can be skipped
> (or should this be called 'delayed sorting'?). we can either start another
> dedicated thread to do the sorting & updating, or actually perform sorting
> after current result have been used several times (say sort once in every 100
> calls.)
> B. performing calculation on {{Resource}} instances is expensive, since at
> least 2 objects ({{ResourceImpl}} and its proto builder) is created each time
> (using 'immutable' apis). the overhead can be eliminated with a
> light-weighted implementation of Resource, which do not instantiate a builder
> until necessary, because most instances are used as intermediate result in
> scheduler instead of being exchanged via IPC. Also, {{createResource}} is
> using reflection, which can be replaced by a plain {{new}} (for scheduler
> usage only). furthermore, perhaps we could 'intern' resource to avoid
> allocation.
> C. other minor changes: such as move {{updateRootMetrics}} call to
> {{update}}, making root queue metrics eventual consistent (which may
> satisfies most of the needs). or introduce counters to {{getResourceUsage}}
> and make changing of resource incrementally instead of recalculate each time.
> With A and B, I was looking at 4 times improvement in a cluster with 2K nodes.
> Suggestions? Opinions?
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