[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16246811#comment-16246811 ] Marcelo Vanzin commented on SPARK-18838: I actually did a backport to our internal branches, here's the one for 2.1 (which IIRC is very similar to the 2.2 one): https://github.com/cloudera/spark/commit/a6b96c45afa227b48723ed872b692ceed9f9171d > 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: Marcelo Vanzin > Fix For: 2.3.0 > > Attachments: SparkListernerComputeTime.xlsx, perfResults.pdf > > > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16246804#comment-16246804 ] Anthony Truchet commented on SPARK-18838: - Agreed but at the same time this is "almost" a bug when you consider use of Spark at really large scale... I tried a naive cherry pick and obviously there are a huge lot of non trivial conflicts. Do you know how to get an overview of the change (and of what happened in the mean time since 2.2) to guide this conflict resolution? Or maybe you already have some insight about the best way to go for it? > 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: Marcelo Vanzin > Fix For: 2.3.0 > > Attachments: SparkListernerComputeTime.xlsx, perfResults.pdf > > > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16246783#comment-16246783 ] Marcelo Vanzin commented on SPARK-18838: It's kind of an intrusive change to backport to a maintenance release, but since it may help a lot of people it might be ok. > 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: Marcelo Vanzin > Fix For: 2.3.0 > > Attachments: SparkListernerComputeTime.xlsx, perfResults.pdf > > > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16234891#comment-16234891 ] Anthony Truchet commented on SPARK-18838: - I'm interested to work on a backport for Spark 2.2 we are using at Criteo. Any thoughts on that ? > 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: Marcelo Vanzin >Priority: Major > Fix For: 2.3.0 > > Attachments: SparkListernerComputeTime.xlsx, perfResults.pdf > > > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16186062#comment-16186062 ] Marcelo Vanzin commented on SPARK-18838: Not really. You could try to backport the patch (not trivial) and build your own 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: Marcelo Vanzin > Fix For: 2.3.0 > > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16185856#comment-16185856 ] Shahbaz Hussain commented on SPARK-18838: - Is there a work around to get it working before spark 2.3.0 releases. > 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: Marcelo Vanzin > Fix For: 2.3.0 > > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16173900#comment-16173900 ] Apache Spark commented on SPARK-18838: -- User 'vanzin' has created a pull request for this issue: https://github.com/apache/spark/pull/19297 > 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: Marcelo Vanzin > Fix For: 2.3.0 > > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16163695#comment-16163695 ] Apache Spark commented on SPARK-18838: -- User 'vanzin' has created a pull request for this issue: https://github.com/apache/spark/pull/19211 > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[
https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16137365#comment-16137365
]
Marcelo Vanzin commented on SPARK-18838:
[~milesc] after cleaning up a lot of noise from your logs, I could see a bunch
of errors, but I can't see how any of them are connected to the listener bus.
They seem to be more related to the way Spark is handling those errors and
making things worse, instead of correctly handling them. I don't doubt that
using the "block the listener bus" patch helps, but it probably helps because
of what I said before: that will have an effect on other parts of Spark, and
will probably make your application run slower, and will mask these issues. So
while that behavior may be better for you, it doesn't help us solve the
underlying issues in the code, and Spark running more slowly is hardly
something we want.
Here are a few choice errors from your log.
Shuffle files disappearing. There are quite a few of these.
{noformat}
2017-08-15 17:52:17,033 INFO org.apache.spark.scheduler.DAGScheduler:
ShuffleMapStage 80 (keyBy at MergeSourcedPapers.scala:94) failed in 266.509 s
due to org.apache.spark.shuffle.FetchFailedException:
java.io.FileNotFoundException:
/mnt/yarn/usercache/milesc/appcache/application_1502651039737_0038/blockmgr-9c34afa1-8738-4003-b532-dc85e04cfb45/26/shuffle_4_4428_0.index
(No such file or directory)
at java.io.FileInputStream.open0(Native Method)
at java.io.FileInputStream.open(FileInputStream.java:195)
at java.io.FileInputStream.init(FileInputStream.java:138)
at
org.apache.spark.shuffle.IndexShuffleBlockResolver.getBlockData(IndexShuffleBlockResolver.scala:199)
at
org.apache.spark.storage.BlockManager.getBlockData(BlockManager.scala:305)
at
org.apache.spark.network.netty.NettyBlockRpcServer$$anonfun$2.apply(NettyBlockRpcServer.scala:60)
{noformat}
More missing shuffle data:
{noformat}
2017-08-15 17:52:18,120 INFO org.apache.spark.MapOutputTrackerMaster: Size of
output statuses for shuffle 3 is 118757 bytes
2017-08-15 17:52:18,121 INFO org.apache.spark.MapOutputTrackerMaster: Epoch
changed, not caching!
org.apache.spark.shuffle.MetadataFetchFailedException: Missing an output
location for shuffle 3
at
org.apache.spark.MapOutputTracker$$anonfun$org$apache$spark$MapOutputTracker$$convertMapStatuses$2.apply(MapOutputTracker.scala:697)
at
org.apache.spark.MapOutputTracker$$anonfun$org$apache$spark$MapOutputTracker$$convertMapStatuses$2.apply(MapOutputTracker.scala:693)
at
scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:778)
at
scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
at
scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:777)
at
org.apache.spark.MapOutputTracker$.org$apache$spark$MapOutputTracker$$convertMapStatuses(MapOutputTracker.sca
{noformat}
Executors apparently dying:
{noformat}
2017-08-15 17:52:19,508 WARN
org.apache.spark.network.server.TransportChannelHandler: Exception in
connection from /10.12.1.121:51766
java.io.IOException: Connection reset by peer
at sun.nio.ch.FileDispatcherImpl.read0(Native Method)
at sun.nio.ch.SocketDispatcher.read(SocketDispatcher.java:39)
at sun.nio.ch.IOUtil.readIntoNativeBuffer(IOUtil.java:223)
at sun.nio.ch.IOUtil.read(IOUtil.java:192)
at sun.nio.ch.SocketChannelImpl.read(SocketChannelImpl.java:380)
at
io.netty.buffer.PooledUnsafeDirectByteBuf.setBytes(PooledUnsafeDirectByteBuf.java:221)
at io.netty.buffer.AbstractByteBuf.writeBytes(AbstractByteBuf.java:899)
{noformat}
Executor apparently dying, another type of error:
{noformat}
2017-08-15 17:52:26,774 WARN org.apache.spark.storage.BlockManagerMaster:
Failed to remove broadcast 56 with removeFromMaster = true - Failed to send RPC
4932545731330680871 to /10.12.1.121:51764:
java.nio.channels.ClosedChannelException
java.io.IOException: Failed to send RPC 4932545731330680871 to
/10.12.1.121:51764: java.nio.channels.ClosedChannelException
at
org.apache.spark.network.client.TransportClient$3.operationComplete(TransportClient.java:249)
at
org.apache.spark.network.client.TransportClient$3.operationComplete(TransportClient.java:233)
at
io.netty.util.concurrent.DefaultPromise.notifyListener0(DefaultPromise.java:514)
at
io.netty.util.concurrent.DefaultPromise.notifyListenersNow(DefaultPromise.java:488)
at
io.netty.util.concurrent.DefaultPromise.notifyListeners(DefaultPromise.java:427)
at
io.netty.util.concurrent.DefaultPromise.tryFailure(DefaultPromise.java:129)
{noformat}
Another type of executor going away message:
{noformat}
2017-08-15 17:52:32,011 INFO
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16129149#comment-16129149 ] Miles Crawford commented on SPARK-18838: [~imranr] and [~vanzin] Here is your log: https://dl.dropboxusercontent.com/u/231152/hunglog.gz This is a typically 5-hour-long spark job that staggered, retried, and eventually hung forever. I grabbed this log at the 24 hour mark, and killed the job. Looks like it gets stuck in some broadcast-related infinite loop? > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16126402#comment-16126402 ] Miles Crawford commented on SPARK-18838: [~imranr] We do runs tens of thousands of tasks per stage in a few stages, yes. We kept running into this issue: https://spark.apache.org/docs/latest/tuning.html#memory-usage-of-reduce-tasks in some of our operations, and had to increase our partition count very high to get around it. I'll try running with loglevel INFO on spark 2.2.0 now and share that log with you when it hangs. > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16126030#comment-16126030 ] Jason Dunkelberger commented on SPARK-18838: I just realized I have diverged from the original performance issue of this ticket. If there's a better issue to track this on, let me know. > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16126022#comment-16126022 ] Jason Dunkelberger commented on SPARK-18838: It's been about a week now, and we've have had 100% completed runs since the blocking change. We've forked spark for the moment with the hack mashed on top of 2.2.0. Here's the diff: https://github.com/apache/spark/compare/v2.2.0...allenai:v2.2.0-ai2-SNAPSHOT#diff-ca0fe05a42fd5edcab8a1bdaa8e58db9 To be clear I don't think I've actually fixed anything specific. I've just changed the possible failures away from whatever leaves it hanging. [~irashid] I'll look into what you suggest. For now the goal was stability, which we've achieved with the naive change. I'm pretty confident that total performance has gone down, but again, that's still better than hanging altogether. One other thought, I looked at subclasses of SparkListener which all go through LiveListenerBus (?). These seem pretty critical: ExecutorAllocationListener, BlockStatusListener/StorageListener (?) I didn't say before but we run on EMR via Yarn which may be relevant. > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16119275#comment-16119275 ] Imran Rashid commented on SPARK-18838: -- +1 to Marcelo's request for logs from cases where listener bus dropping events leads to spark hanging. I wouldn't expect that either (given his caveats above -- no Dynamic Allocation and UI-weirdness). [~milesc] -- from the log snippet you posted, it looks like ~40k messages get dropped in a minute. I expect most messages are related to tasks starting & completing, so I assume the number of tasks you have is in the same ballpark? for those considering experimenting with making it blocking ([~dirkraft]) -- one relatively simple alternative to just making the queue blocking would be to instead stop the TaskSchedulerImpl from scheduling any more tasks when the listener bus is over half full. I wouldn't want to keep scheduling tasks if the listener bus is full, because then you'll just create a huge backlog in other parts of the scheduler as you they all wait to get things on the listener bus. This would help avoid some of the increased memory usage that Marcelo was mentioning above. Obviously, it'll still decrease scheduler throughput, but maybe that's OK. Also this is just an idea off the top of my head, so YMMV, but as long as you're experimenting with other approaches ... > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[
https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16119265#comment-16119265
]
Li Yuanjian commented on SPARK-18838:
-
I'm facing the same problem with [~milesc]
{quote}
We do not use dynamic allocation, and our applications frequently hang
completely after seeing this log message, not just the UI.
{quote}
Should we solve these problems by dividing in 2 parts. The first is we should
ensure not to drop the event and the second is like discussion above, improve
the performance by several ways. We try to do the first in SPARK-21560 and the
corresponding PR is testing in our pre-online env, do you have any suggestions
for that? [~vanzin] Thanks.
> 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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[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16116930#comment-16116930 ] Jason Dunkelberger commented on SPARK-18838: Thanks for your quick outline [~vanzin]. It gives a much better idea of the intention, which might help me see something significant. > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16116922#comment-16116922 ] Marcelo Vanzin commented on SPARK-18838: I'd like to see something that backs your assertion that the app is not making progress; that will probably just mean looking at all the logs. Those messages are expected when your listener bus is backed up; what is not expected is the app to stop making progress. > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[
https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16116916#comment-16116916
]
Miles Crawford commented on SPARK-18838:
Can I get specific direction on the logs you'd like to see? I see bursts of
lines like this:
{{2017-08-07 17:33:45,911 WARN org.apache.spark.scheduler.LiveListenerBus:
Dropped 48996 SparkListenerEvents since Mon Aug 07 17:32:45 UTC 2017
2017-08-07 17:34:45,911 WARN org.apache.spark.scheduler.LiveListenerBus:
Dropped 86057 SparkListenerEvents since Mon Aug 07 17:33:45 UTC 2017}}
And after a while we get in a state where every executor is idle, and the
driver appears to still be waiting for results from executors, right in the
middle of a stage.
> 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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[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16116903#comment-16116903 ] Marcelo Vanzin commented on SPARK-18838: I'd be interested in seeing logs from an application not using dynamic allocation that hangs because of this issue. I was under the impression that the scheduler was pretty isolated from the listener bus and things should work, even if you can't tell that they are from the UI. I think there's a consensus about what the solution is (implementing queues so that slow listeners can be isolated), and perhaps once that's done we can even think about adding blocking (since we can then control which listeners can block the listener bus - e.g. we can make the dynamic allocation listener blocking, but the event log listener is allowed to drop events). There are even a couple of PRs opened, but since this is a very core part of the Spark code, there's going to be more scrutiny about the code that is being written. > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16116896#comment-16116896 ] Miles Crawford commented on SPARK-18838: We do not use dynamic allocation, and our applications frequently hang completely after seeing this log message, not just the UI. Can I gather any information from an application in this state that would inform the priority of this issue, or the possible fixes? > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16116892#comment-16116892 ] Marcelo Vanzin commented on SPARK-18838: bq. although that is guaranteed to happen when the wrong events are dropped I think there are, mainly, two different things that go on when the listener bus gets backed up: - events dropped mess up the UI - events dropped mess up dynamic allocation The UI being messed up doesn't mean the application is not making progress. The scheduler does not use the event bus nor UI information to decide what to do next. Dynamic allocation getting messed up is a real problem; currently the best course is probably to disable it, or to play with settings to increase the queue size and disable other expensive listeners. A blocking strategy is not out of the picture, but it needs to be properly studied to understand its effects. At the very least, it will cause memory usage to increase and will slow down the scheduler, even if it does not cause actual errors. It's kinda sub-optimal to slow down the whole Spark app because some listener in the driver is doing I/O. > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16116886#comment-16116886 ] Miles Crawford commented on SPARK-18838: This seems to be the core issue for a large number of bugs. It's causing regular stability issues for us as well. Is a more thorough look in progress? What is the recourse for people getting bit by this bug? > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16115147#comment-16115147 ] Jason Dunkelberger commented on SPARK-18838: 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 1. 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16115139#comment-16115139 ] Marcelo Vanzin commented on SPARK-18838: I think the main issue with a blocking bus is that you'd be affecting scheduler's ability to process incoming messages from executors. A solution like that requires a more thorough look at how events are generated, from the executors down to the event bus, and making sure nothing in that path gets broken (memory usage going up, RPCs failing because of time outs, etc). Also I'm pretty sure there's already a PR open to add that functionality, although I don't have a link handy. > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16115129#comment-16115129 ] Jason Dunkelberger commented on SPARK-18838: I cut a PR here which just makes this queue blocking if full and logs a warning if a put is "too slow". So messages never get dropped anymore. Made our spark jobs much more stable (so far). I am not sure why it's a bad idea, so am looking for that kind of feedback. Or maybe, it is the way to go forward, at least for stability until something more performant/more-complicated is added. https://github.com/apache/spark/pull/18850 > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16113067#comment-16113067 ] Miles Crawford commented on SPARK-18838: We have disabled our eventlog listener, which is unfortunate, but seemed to help alot. Nevertheless, we still get dropped events, which causes the UI to screw up, jobs to hang, and so forth. Can we do anything to identify which listener is backing up? > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16084169#comment-16084169 ] Arun Achuthan commented on SPARK-18838: --- We are facing an issue where randomly some jobs are stuck either at processing level or queued up. The subsequentt jobs are processed. For very job getting stuck we see an error at the same time in driver logs mentioning org.apache.spark.scheduler.LiveListenerBus: Dropped 1 SparkListenerEvents since Thu Jan 01 00:00:00 UTC 1970 > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16075600#comment-16075600 ] Anthony Truchet commented on SPARK-18838: - Hello, We (Criteo Predictive Search team with http://labs.criteo.com/about-us/) are critically running into this issue and I'm willing to work on it ASAP. Is there any update not listed here ? Is the plan described in the ticket kind of agreed upon ? I'll start diving in the code ; in the mean-time all design / return on experience / ... inputs are welcome :-) > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16051109#comment-16051109 ] Marcelo Vanzin commented on SPARK-18838: 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16051097#comment-16051097 ] Marcelo Vanzin commented on SPARK-18838: [~sitalke...@gmail.com] I requested your PR be closed. It was sitting there with unaddressed feedback and multiple people asking for updates for about 6 months. If you're still interested in updating it you can re-open it and, more importantly, actually update it. > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16046957#comment-16046957 ] Sital Kedia commented on SPARK-18838: - [~joshrosen] - The PR for my change to multi-thread the event processor has been closed inadvertently as a stale PR. Can you take a look at the approach and let me know what you think. Please note that this change is very critical for running large jobs in our environment. https://github.com/apache/spark/pull/16291 > 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. -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16044827#comment-16044827 ] Apache Spark commented on SPARK-18838: -- User 'bOOm-X' has created a pull request for this issue: https://github.com/apache/spark/pull/18253 > 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. -- This message was sent by Atlassian JIRA (v6.3.15#6346) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16032788#comment-16032788 ] Antoine PRANG commented on SPARK-18838: --- Hi, Can someone take a look at my pull request ? > 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. -- This message was sent by Atlassian JIRA (v6.3.15#6346) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16024690#comment-16024690 ] Antoine PRANG commented on SPARK-18838: --- [~joshrosen] I uploaded the new timings and new version of my commits. I think that the bottleneck in the StorageListener execution time is the call to the method "storageStatusList" in the "StorageStatusListener". This call is done for each BlockUpdate (very frequent) and in my case (I have 1000 executors) will re-create a huge Seq for each call. I try to do something for that. But it is not so conclusive (see the last timings that I uploaded). So I did not put this commit in my pull request. But it is in my metrics branch in my fork if you want to take a look). Can I have a review on my pull request ? We should definitely transform the EventLoggingListener in an asynchronous fashion. No events are dropped anymore in my test case, the global execution time of the messages is significantly decreased. Thanks > 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. -- This message was sent by Atlassian JIRA (v6.3.15#6346) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16017560#comment-16017560 ] Antoine PRANG commented on SPARK-18838: --- [~mathieude]]: Yep, I introduced a blocking strategy for the LiveListenerBus (If the queue is full, we wait for space instead of dropping events). This is not the default strategy but it can be activated through a settings. The default strategy remains the dropping one. > 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: 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. -- This message was sent by Atlassian JIRA (v6.3.15#6346) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16017528#comment-16017528 ] Mathieu D commented on SPARK-18838: --- I'm not very familiar with this part of Spark, but I'd like to share a thought. In my experience (SPARK-18881) when events start to be dropped because of full event queues, it's much more serious than just a failed job. The Spark driver became useless, I had to relaunch. So, besides the improvement of existing bus, listeners and threads, wouldn't be a kind of back-pressure mechanism (on tasks emission) better than dropping events ? I mean, this would obviously degrade the job performance, but it's still better than compromising the whole job or even the driver health. my2cent > 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: 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. -- This message was sent by Atlassian JIRA (v6.3.15#6346) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16017120#comment-16017120 ] Antoine PRANG commented on SPARK-18838: --- [~joshrosen] I uploaded the timings I get. I put some counters in the code. You can take a look at the metrics branch of my forks. I do not have exact profile of the methods. First the StorageListener "really" execute a lot of message. It has not a no-op received method for the most frequent messages (SparkListenerBlockUpdated) if i understand well. they are not logged in the EventLoggingListener for example. But the StorageStatusListener listens to this kind of events too, and its execution time is not comparable. But it seems to do much more work (with the parent classes). there is a lot of synchronization which may be avoided in my mind. > 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: 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. -- This message was sent by Atlassian JIRA (v6.3.15#6346) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[
https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16016664#comment-16016664
]
Ruslan Dautkhanov commented on SPARK-18838:
---
my 2 cents. Would be nice to explore idea of storing offsets of consumed
messages in listeners themselves, very much like Kafka consumers.
(based on my limited knowledge of spark event queue listeners, assuming each
listeners don't depend on each other and can read from the queue
asynchronously) - so then if one of the "non-critical" listeners can't keep up,
messages will be lost just for that one listener, and it wouldn't affect rest
of listeners.
{quote}Alternatively, we could use two queues, one for internal listeners and
another for external ones{quote}Making a parallel with Kafka again, looks like
we're talking here about two "topics"
> 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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[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16016663#comment-16016663 ] Josh Rosen commented on SPARK-18838: [~bOOmX], do you have CPU-time profiling within each of those listeners? I'm wondering why StorageListener is so slow. Although it's not a real solution, I bet that we could make a significant constant-factor improvement to StorageListener (perhaps by using more imperative Java-style code instead of Scala collections). > 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. -- This message was sent by Atlassian JIRA (v6.3.15#6346) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16016579#comment-16016579 ] Antoine PRANG commented on SPARK-18838: --- [~joshrosen][~sitalke...@gmail.com] I have measured the listener execution time on my test case. You can ind below for each listener the average percentage of the total execution time for a message (I have instrumented ListenerBus) EnvironmentListener :0.0 EventLoggingListener :48.2 ExecutorsListener :0.1 HeartbeatReceiver :0.0 JobProgressListener :6.9 PepperdataSparkListener :2.7 RDDOperationGraphListener :0.1 StorageListener :38.3 StorageStatusListener :0.4 The execution time is concentrated on 2 Listeners: EventLoggingListener, StorageListener. I think that putting parallelization at the listener bus is not a so good idea. Duplicating the messages in 2 queues will change the current synchronization contract (all listeners receive each message in the same time, they are ahead or behind other listener from 1 and only 1 message). For me the best idea would be to keep the listener bus as simple as now (N producers - 1 consumer) to be able to take advantage of that to dequeue as fast as possible and introduce parallelisation at the listener level - being aware of the synchronization contract - when it is possible. The EventLoggingListener can for example be executed asynchronously. I am doing a commit to do that right now. > 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. -- This message was sent by Atlassian JIRA (v6.3.15#6346) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16016217#comment-16016217 ] Sital Kedia commented on SPARK-18838: - [~joshrosen] - >> 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. Actually that is exactly what my PR is doing. https://github.com/apache/spark/pull/16291. I have not been able to work on it recently, but you can take a look and let me know how it looks. I can prioritize working on it. > 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. -- This message was sent by Atlassian JIRA (v6.3.15#6346) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16013317#comment-16013317 ] Josh Rosen commented on SPARK-18838: 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. -- This message was sent by Atlassian JIRA (v6.3.15#6346) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16012813#comment-16012813 ] Apache Spark commented on SPARK-18838: -- User 'bOOm-X' has created a pull request for this issue: https://github.com/apache/spark/pull/18004 > 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. -- This message was sent by Atlassian JIRA (v6.3.15#6346) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15932238#comment-15932238 ] Danny Ruchman commented on SPARK-18838: --- Hi, We also have an issue where we query huge amount of parquet file with spark sql and experiencing dramatic slowdown after the process reach around 100K tasks. Until this point it works in very impressive speed. The query creates over 2MM tasks so this practically prevents us from being able to query this amount of data using spark 2.0 and above. It works with spark 1.6 though but the execution time per task is slower than the execution per task in spark 2.0 (until the process starts to slow down) It seems very much related to the issue described in this ticket. Is anyone working on a solution for this? > 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. -- This message was sent by Atlassian JIRA (v6.3.15#6346) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15753535#comment-15753535 ] Sital Kedia commented on SPARK-18838: - cc - [~kayousterhout] > 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15750217#comment-15750217 ] Sital Kedia commented on SPARK-18838: - [~zsxwing] - Its not only the ExecutorAllocationManager, other critical listeners like HeartbeatReceiver also depend on it. In addition to that there might be some latency sensitive user added listener. Making the event processing faster by multi-threading will fix all theses issues. I have an initial version of the PR for this, would appreciate if you can take a look and give feedback on the overall design. > 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 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15750209#comment-15750209 ] Apache Spark commented on SPARK-18838: -- User 'sitalkedia' has created a pull request for this issue: https://github.com/apache/spark/pull/16291 > 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 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15746217#comment-15746217 ] Shixiong Zhu commented on SPARK-18838: -- [~sitalke...@gmail.com] Instead of your proposal, I prefer to make ExecutorAllocationManager not depend on the listener bus, which may drop critical events. > 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 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18838) High latency of event processing for large jobs
[ https://issues.apache.org/jira/browse/SPARK-18838?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15744086#comment-15744086 ] Sital Kedia commented on SPARK-18838: - [~rxin], [~zsxwing] - Any thoughts on this? > 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 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 single threaded executor > service and separate event queue per listener. That way we are not > bottlenecked by the single threaded 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
