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https://issues.apache.org/jira/browse/YARN-11411?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated YARN-11411:
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Labels: pull-request-available (was: )
> [Umbrella] Build Concurrent Yarn Scheduler
> ------------------------------------------
>
> Key: YARN-11411
> URL: https://issues.apache.org/jira/browse/YARN-11411
> Project: Hadoop YARN
> Issue Type: New Feature
> Reporter: Krishan Goyal
> Assignee: Krishan Goyal
> Priority: Major
> Labels: pull-request-available
>
> We operate multiple yarn clusters with each cluster capped to ~ 10k nodes
> which is its scalability limit. We expect multiple benefits with fewer
> clusters & larger cluster sizes (better elasticity, operational simplicity,
> larger queues).
> Thus, we want to scale a single yarn cluster to as much as possible in terms
> of number of nodes heartbeating to the cluster (& proportionally increase
> container allocation rate) without degradation in overall quantiles (p50 /
> p75 / p95) of container allocation delay
> The scalability limit of a yarn cluster is primarily driven by RM’s
> processing of node heartbeats & container allocation. The CPU usage of our RM
> is < 10% & RM is primarily bottlenecked on global queue & user read/write
> locks for container allocation
> By removing these locks (through a very naive & incorrect implementation), we
> were able to scale RM to 25k nodes (& proportional increase in container
> allocs/sec) with avg RM CPU utilization of 20% (so there is still room for
> improvement to use more CPU / scale up further).
> This primarily requires
> # Async scheduling to decouple scheduling from node heartbeats (existing
> feature)
> # Removing global write locks in scheduler path (primarily to maintain
> queues and users)
> # Multi threaded event queue dispatcher to process events parallelly
> Additionally we need to probably scale RPC handling, DT management,
> preemption flows, Timeline server, RM HA failover.
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