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https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-18838:
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Assignee: Apache Spark
> High latency of event processing for large jobs
> -----------------------------------------------
>
> Key: SPARK-18838
> URL: https://issues.apache.org/jira/browse/SPARK-18838
> Project: Spark
> Issue Type: Improvement
> Affects Versions: 2.0.0
> Reporter: Sital Kedia
> Assignee: Apache Spark
>
> Currently we are observing the issue of very high event processing delay in
> driver's `ListenerBus` for large jobs with many tasks. Many critical
> component of the scheduler like `ExecutorAllocationManager`,
> `HeartbeatReceiver` depend on the `ListenerBus` events and these delay is
> causing job failure. For example, a significant delay in receiving the
> `SparkListenerTaskStart` might cause `ExecutorAllocationManager` manager to
> remove an executor which is not idle. The event processor in `ListenerBus`
> is a single thread which loops through all the Listeners for each event and
> processes each event synchronously
> https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/scheduler/LiveListenerBus.scala#L94.
>
> The single threaded processor often becomes the bottleneck for large jobs.
> In addition to that, if one of the Listener is very slow, all the listeners
> will pay the price of delay incurred by the slow listener.
> To solve the above problems, we plan to have a per listener single threaded
> executor service and separate event queue. That way we are not bottlenecked
> by the single threaded event processor and also critical listeners will not
> be penalized by the slow listeners. The downside of this approach is separate
> event queue per listener will increase the driver memory footprint.
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