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https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16115147#comment-16115147
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Jason Dunkelberger commented on SPARK-18838:
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I saw a couple of PRs to the same effect, but then they also added a bunch of
other stuff. I was just looking for the minimum change to stability, giving up
some possible performance.
I can understand the upstream processes getting hung up, although that is
guaranteed to happen when the wrong events are dropped. For whatever reason (I
guess these are the questions) spark hasn't completely hung up on us with this
change, even still using the default queue size of only 10000. Whatever
failures do happen (probably some of the things you mention) spark is able to
recover from, so its turned out ok. Without the change, spark was hanging up
completely; it's like the recovery mechanisms also lost key messages and so
there was no recovery for the recovery.
I would also rather have a component fail outright, then be left in a terminal,
silent state, which is what seems to happen when the wrong events get dropped
(crash is better than hang). So I still think it's an improvement. We'll be
dealing with this either way, so if we find anything else meaningful I'll bring
it back here.
> 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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