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https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16051109#comment-16051109
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Marcelo Vanzin commented on SPARK-18838:
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bq. If we're careful in performance optimization of Spark's core internal
listeners ... then it might be okay to publish events directly to those
listeners
While it's possible to squeeze better performance out of current code, it's
hard to make statements without an actual goal in mind. What are the
requirements for a listener to be considered for this feature? e.g., no more
than "x" us processing time per event, or something like that? How many
listeners do you allow to operate in this manner before they start to slow down
the code that's publishing events?
I actually spent a whole lot of time refactoring and optimizing listener code
as part of the work in SPARK-18085 (which I'm slowly sending out for review),
but there's just so much you can do in certain cases. The executor allocation
listener is probably the only one I'd even consider for this category. Any
other listener, especially ones that collect data in one way or another, ends
up hitting slow paths every once in a while - e.g. you're collecting things in
some data structure and suddenly you need to resize / copy a bunch of things,
and there goes your processing time.
> 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
> Attachments: perfResults.pdf, SparkListernerComputeTime.xlsx
>
>
> 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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