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https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16013317#comment-16013317
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Josh Rosen commented on SPARK-18838:
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I think that SPARK-20776 / https://github.com/apache/spark/pull/18008 might
help here: it addresses a nasty performance bug in
JobProgressListener.onTaskStart caused by the unnecessary creation of empty
TaskMetrics objects.
[~sitalke...@gmail.com] [~zsxwing], I'd be interested to see if we can do a
coarse-grained split between users' custom listeners and Spark's own internal
listeners. If we're careful in performance optimization of Spark's core
internal listeners (such as ExecutorAllocationManagerListener) then it might be
okay to publish events directly to those listeners (without buffering) and use
buffering only for third-party listeners where we don't want to risk perf. bugs
slowing down the cluster.
Alternatively, we could use two queues, one for internal listeners and another
for external ones. This wouldn't be as fine-grained as thread-per-listener but
might buy us a lot of the benefits with perhaps less code needed.
> 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
>
> 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 this delay might
> hurt the job performance significantly or even fail the job. For example, a
> significant delay in receiving the `SparkListenerTaskStart` might cause
> `ExecutorAllocationManager` manager to mistakenly remove an executor which is
> not idle.
> The problem is that 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.
> This single threaded processor often becomes the bottleneck for large jobs.
> Also, if one of the Listener is very slow, all the listeners will pay the
> price of delay incurred by the slow listener. In addition to that a slow
> listener can cause events to be dropped from the event queue which might be
> fatal to the job.
> To solve the above problems, we propose to get rid of the event queue and the
> single threaded event processor. Instead each listener will have its own
> dedicate single threaded executor service . When ever an event is posted, it
> will be submitted to executor service of all the listeners. The Single
> threaded executor service will guarantee in order processing of the events
> per listener. The queue used for the executor service will be bounded to
> guarantee we do not grow the memory indefinitely. The downside of this
> approach is separate event queue per listener will increase the driver memory
> footprint.
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