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From Piotr Nowojski <pi...@data-artisans.com>
Subject Re: Flink memory leak
Date Thu, 09 Nov 2017 17:08:54 GMT
Hi,

Could you attach full logs from those task managers? At first glance I don’t see a connection
between those exceptions and any memory issue that you might had. It looks like a dependency
issue in one (some? All?) of your jobs.

Did you build your jars with -Pbuild-jar profile as described here:
https://ci.apache.org/projects/flink/flink-docs-release-1.3/quickstart/java_api_quickstart.html#build-project
<https://ci.apache.org/projects/flink/flink-docs-release-1.3/quickstart/java_api_quickstart.html#build-project>
? 

If that doesn’t help. Can you binary search which job is causing the problem? There might
be some Flink incompatibility between different versions and rebuilding a job’s jar with
a version matching to the cluster version might help.

Piotrek


> On 9 Nov 2017, at 17:36, ÇETİNKAYA EBRU ÇETİNKAYA EBRU <b20926247@cs.hacettepe.edu.tr>
wrote:
> 
> On 2017-11-08 18:30, Piotr Nowojski wrote:
>> Btw, Ebru:
>> I don’t agree that the main suspect is NetworkBufferPool. On your
>> screenshots it’s memory consumption was reasonable and stable: 596MB
>> -> 602MB -> 597MB.
>> PoolThreadCache memory usage ~120MB is also reasonable.
>> Do you experience any problems, like Out Of Memory errors/crashes/long
>> GC pauses? Or just JVM process is using more memory over time? You are
>> aware that JVM doesn’t like to release memory back to OS once it was
>> used? So increasing memory usage until hitting some limit (for example
>> JVM max heap size) is expected behaviour.
>> Piotrek
>>> On 8 Nov 2017, at 15:48, Piotr Nowojski <piotr@data-artisans.com>
>>> wrote:
>>> I don’t know if this is relevant to this issue, but I was
>>> constantly getting failures trying to reproduce this leak using your
>>> Job, because you were using non deterministic getKey function:
>>> @Override
>>> public Integer getKey(Integer event) {
>>> Random randomGen = new Random((new Date()).getTime());
>>> return randomGen.nextInt() % 8;
>>> }
>>> And quoting Java doc of KeySelector:
>>> "If invoked multiple times on the same object, the returned key must
>>> be the same.”
>>> I’m trying to reproduce this issue with following job:
>>> https://gist.github.com/pnowojski/b80f725c1af7668051c773438637e0d3
>>> Where IntegerSource is just an infinite source, DisardingSink is
>>> well just discarding incoming data. I’m cancelling the job every 5
>>> seconds and so far (after ~15 minutes) my memory consumption is
>>> stable, well below maximum java heap size.
>>> Piotrek
>>>> On 8 Nov 2017, at 15:28, Javier Lopez <javier.lopez@zalando.de>
>>>> wrote:
>>>> Yes, I tested with just printing the stream. But it could take a
>>>> lot of time to fail.
>>>> On Wednesday, 8 November 2017, Piotr Nowojski
>>>> <piotr@data-artisans.com> wrote:
>>>>> Thanks for quick answer.
>>>>> So it will also fail after some time with `fromElements` source
>>>> instead of Kafka, right?
>>>>> Did you try it also without a Kafka producer?
>>>>> Piotrek
>>>>> On 8 Nov 2017, at 14:57, Javier Lopez <javier.lopez@zalando.de>
>>>> wrote:
>>>>> Hi,
>>>>> You don't need data. With data it will die faster. I tested as
>>>> well with a small data set, using the fromElements source, but it
>>>> will take some time to die. It's better with some data.
>>>>> On 8 November 2017 at 14:54, Piotr Nowojski
>>>> <piotr@data-artisans.com> wrote:
>>>>>> Hi,
>>>>>> Thanks for sharing this job.
>>>>>> Do I need to feed some data to the Kafka to reproduce this
>>>> issue with your script?
>>>>>> Does this OOM issue also happen when you are not using the
>>>> Kafka source/sink?
>>>>>> Piotrek
>>>>>> On 8 Nov 2017, at 14:08, Javier Lopez <javier.lopez@zalando.de>
>>>> wrote:
>>>>>> Hi,
>>>>>> This is the test flink job we created to trigger this leak
>>>> https://gist.github.com/javieredo/c6052404dbe6cc602e99f4669a09f7d6
>>>>>> And this is the python script we are using to execute the job
>>>> thousands of times to get the OOM problem
>>>> https://gist.github.com/javieredo/4825324d5d5f504e27ca6c004396a107
>>>>>> The cluster we used for this has this configuration:
>>>>>> Instance type: t2.large
>>>>>> Number of workers: 2
>>>>>> HeapMemory: 5500
>>>>>> Number of task slots per node: 4
>>>>>> TaskMangMemFraction: 0.5
>>>>>> NumberOfNetworkBuffers: 2000
>>>>>> We have tried several things, increasing the heap, reducing the
>>>> heap, more memory fraction, changes this value in the
>>>> taskmanager.sh "TM_MAX_OFFHEAP_SIZE="2G"; and nothing seems to
>>>> work.
>>>>>> Thanks for your help.
>>>>>> On 8 November 2017 at 13:26, ÇETİNKAYA EBRU ÇETİNKAYA EBRU
>>>> <b20926247@cs.hacettepe.edu.tr> wrote:
>>>>>>> On 2017-11-08 15:20, Piotr Nowojski wrote:
>>>>>>>> Hi Ebru and Javier,
>>>>>>>> Yes, if you could share this example job it would be helpful.
>>>>>>>> Ebru: could you explain in a little more details how does
>>>> your Job(s)
>>>>>>>> look like? Could you post some code? If you are just using
>>>> maps and
>>>>>>>> filters there shouldn’t be any network transfers involved,
>>>> aside
>>>>>>>> from Source and Sink functions.
>>>>>>>> Piotrek
>>>>>>>>> On 8 Nov 2017, at 12:54, ebru
>>>> <b20926247@cs.hacettepe.edu.tr> wrote:
>>>>>>>>> Hi Javier,
>>>>>>>>> It would be helpful if you share your test job with us.
>>>>>>>>> Which configurations did you try?
>>>>>>>>> -Ebru
>>>>>>>>> On 8 Nov 2017, at 14:43, Javier Lopez
>>>> <javier.lopez@zalando.de>
>>>>>>>>> wrote:
>>>>>>>>> Hi,
>>>>>>>>> We have been facing a similar problem. We have tried
some
>>>> different
>>>>>>>>> configurations, as proposed in other email thread by
Flavio
>>>> and
>>>>>>>>> Kien, but it didn't work. We have a workaround similar
to
>>>> the one
>>>>>>>>> that Flavio has, we restart the taskmanagers once they
reach
>>>> a
>>>>>>>>> memory threshold. We created a small test to remove all
of
>>>> our
>>>>>>>>> dependencies and leave only flink native libraries. This
>>>> test reads
>>>>>>>>> data from a Kafka topic and writes it back to another
topic
>>>> in
>>>>>>>>> Kafka. We cancel the job and start another every 5 seconds.
>>>> After
>>>>>>>>> ~30 minutes of doing this process, the cluster reaches
the
>>>> OS memory
>>>>>>>>> limit and dies.
>>>>>>>>> Currently, we have a test cluster with 8 workers and
8 task
>>>> slots
>>>>>>>>> per node. We have one job that uses 56 slots, and we
cannot
>>>> execute
>>>>>>>>> that job 5 times in a row because the whole cluster dies.
If
>>>> you
>>>>>>>>> want, we can publish our test job.
>>>>>>>>> Regards,
>>>>>>>>> On 8 November 2017 at 11:20, Aljoscha Krettek
>>>> <aljoscha@apache.org>
>>>>>>>>> wrote:
>>>>>>>>> @Nico & @Piotr Could you please have a look at this?
You
>>>> both
>>>>>>>>> recently worked on the network stack and might be most
>>>> familiar with
>>>>>>>>> this.
>>>>>>>>> On 8. Nov 2017, at 10:25, Flavio Pompermaier
>>>> <pompermaier@okkam.it>
>>>>>>>>> wrote:
>>>>>>>>> We also have the same problem in production. At the moment
>>>> the
>>>>>>>>> solution is to restart the entire Flink cluster after
every
>>>> job..
>>>>>>>>> We've tried to reproduce this problem with a test (see
>>>>>>>>> https://issues.apache.org/jira/browse/FLINK-7845 [1])
but we
>>>> don't
>>>>>>>>> know whether the error produced by the test and the leak
are
>>>>>>>>> correlated..
>>>>>>>>> Best,
>>>>>>>>> Flavio
>>>>>>>>> On Wed, Nov 8, 2017 at 9:51 AM, ÇETİNKAYA EBRU ÇETİNKAYA
>>>> EBRU
>>>>>>>>> <b20926247@cs.hacettepe.edu.tr> wrote:
>>>>>>>>> On 2017-11-07 16:53, Ufuk Celebi wrote:
>>>>>>>>> Do you use any windowing? If yes, could you please share
>>>> that code?
>>>>>>>>> If
>>>>>>>>> there is no stateful operation at all, it's strange where
>>>> the list
>>>>>>>>> state instances are coming from.
>>>>>>>>> On Tue, Nov 7, 2017 at 2:35 PM, ebru
>>>> <b20926247@cs.hacettepe.edu.tr>
>>>>>>>>> wrote:
>>>>>>>>> Hi Ufuk,
>>>>>>>>> We don’t explicitly define any state descriptor. We
only
>>>> use map
>>>>>>>>> and filters
>>>>>>>>> operator. We thought that gc handle clearing the flink’s
>>>> internal
>>>>>>>>> states.
>>>>>>>>> So how can we manage the memory if it is always increasing?
>>>>>>>>> - Ebru
>>>>>>>>> On 7 Nov 2017, at 16:23, Ufuk Celebi <uce@apache.org>
wrote:
>>>>>>>>> Hey Ebru, the memory usage might be increasing as long
as a
>>>> job is
>>>>>>>>> running.
>>>>>>>>> This is expected (also in the case of multiple running
>>>> jobs). The
>>>>>>>>> screenshots are not helpful in that regard. :-(
>>>>>>>>> What kind of stateful operations are you using? Depending
on
>>>> your
>>>>>>>>> use case,
>>>>>>>>> you have to manually call `clear()` on the state instance
in
>>>> order
>>>>>>>>> to
>>>>>>>>> release the managed state.
>>>>>>>>> Best,
>>>>>>>>> Ufuk
>>>>>>>>> On Tue, Nov 7, 2017 at 12:43 PM, ebru
>>>>>>>>> <b20926247@cs.hacettepe.edu.tr> wrote:
>>>>>>>>> Begin forwarded message:
>>>>>>>>> From: ebru <b20926247@cs.hacettepe.edu.tr>
>>>>>>>>> Subject: Re: Flink memory leak
>>>>>>>>> Date: 7 November 2017 at 14:09:17 GMT+3
>>>>>>>>> To: Ufuk Celebi <uce@apache.org>
>>>>>>>>> Hi Ufuk,
>>>>>>>>> There are there snapshots of htop output.
>>>>>>>>> 1. snapshot is initial state.
>>>>>>>>> 2. snapshot is after submitted one job.
>>>>>>>>> 3. Snapshot is the output of the one job with 15000 EPS.
And
>>>> the
>>>>>>>>> memory
>>>>>>>>> usage is always increasing over time.
>>>>>>>>> <1.png><2.png><3.png>
>>>>>>>>> On 7 Nov 2017, at 13:34, Ufuk Celebi <uce@apache.org>
wrote:
>>>>>>>>> Hey Ebru,
>>>>>>>>> let me pull in Aljoscha (CC'd) who might have an idea
what's
>>>> causing
>>>>>>>>> this.
>>>>>>>>> Since multiple jobs are running, it will be hard to
>>>> understand to
>>>>>>>>> which job the state descriptors from the heap snapshot
>>>> belong to.
>>>>>>>>> - Is it possible to isolate the problem and reproduce
the
>>>> behaviour
>>>>>>>>> with only a single job?
>>>>>>>>> – Ufuk
>>>>>>>>> On Tue, Nov 7, 2017 at 10:27 AM, ÇETİNKAYA EBRU
>>>> ÇETİNKAYA EBRU
>>>>>>>>> <b20926247@cs.hacettepe.edu.tr> wrote:
>>>>>>>>> Hi,
>>>>>>>>> We are using Flink 1.3.1 in production, we have one job
>>>> manager and
>>>>>>>>> 3 task
>>>>>>>>> managers in standalone mode. Recently, we've noticed
that we
>>>> have
>>>>>>>>> memory
>>>>>>>>> related problems. We use docker container to serve Flink
>>>> cluster. We
>>>>>>>>> have
>>>>>>>>> 300 slots and 20 jobs are running with parallelism of
10.
>>>> Also the
>>>>>>>>> job
>>>>>>>>> count
>>>>>>>>> may be change over time. Taskmanager memory usage always
>>>> increases.
>>>>>>>>> After
>>>>>>>>> job cancelation this memory usage doesn't decrease. We've
>>>> tried to
>>>>>>>>> investigate the problem and we've got the task manager
jvm
>>>> heap
>>>>>>>>> snapshot.
>>>>>>>>> According to the jam heap analysis, possible memory leak
was
>>>> Flink
>>>>>>>>> list
>>>>>>>>> state descriptor. But we are not sure that is the cause
of
>>>> our
>>>>>>>>> memory
>>>>>>>>> problem. How can we solve the problem?
>>>>>>>>> We have two types of Flink job. One has no state full
>>>> operator
>>>>>>>>> contains only maps and filters and the other has time
window
>>>> with
>>>>>>>>> count trigger.
>>>>>>>> * We've analysed the jvm heaps again in different
>>>> conditions. First
>>>>>>>> we analysed the snapshot when no flink jobs running on
>>>> cluster. (image
>>>>>>>> 1)
>>>>>>>> * Then, we analysed the jvm heap snapshot when the flink
job
>>>> that has
>>>>>>>> no state full operator is running. And according to the
>>>> results, leak
>>>>>>>> suspect was NetworkBufferPool (image 2)
>>>>>>>> *   Last analys, there were both two types of jobs running
>>>> and leak
>>>>>>>> suspect was again NetworkBufferPool. (image 3)
>>>>>>>> In our system jobs are regularly cancelled and resubmitted
so
>>>> we
>>>>>>>> noticed that when job is submitted some amount of memory
>>>> allocated and
>>>>>>>> after cancelation this allocated memory never freed. So over
>>>> time
>>>>>>>> memory usage is always increasing and exceeded the limits.
>>>>>>>> Links:
>>>>>>>> ------
>>>>>>>> [1] https://issues.apache.org/jira/browse/FLINK-7845
>>>>>>> Hi Piotr,
>>>>>>> There are two types of jobs.
>>>>>>> In first, we use Kafka source and Kafka sink, there isn't any
>>>> window operator.
>>>>>>> In second job, we use Kafka source, filesystem sink and
>>>> elastic search sink and window operator for buffering.
> Hi Piotrek,
> 
> Thanks for your reply.
> 
> We've tested our link cluster again. We have 360 slots, and our cluster configuration
is like this;
> 
> jobmanager.rpc.address: %JOBMANAGER%
> jobmanager.rpc.port: 6123
> jobmanager.heap.mb: 1536
> taskmanager.heap.mb: 1536
> taskmanager.numberOfTaskSlots: 120
> taskmanager.memory.preallocate: false
> parallelism.default: 1
> jobmanager.web.port: 8081
> state.backend: filesystem
> state.backend.fs.checkpointdir: file:///storage/%CHECKPOINTDIR%
> state.checkpoints.dir: file:///storage/%CHECKPOINTDIR%
> taskmanager.network.numberOfBuffers: 5000
> 
> We are using docker based Flink cluster.
> WE submitted 36 jobs with the parallelism of 10. After all slots became full. Memory
usage were increasing by the time and one by one task managers start to die. And the exception
was like this;
> Taskmanager1 log:
> Uncaught error from thread [flink-akka.actor.default-dispatcher-17] shutting down JVM
since 'akka.jvm-exit-on-fatal-error' is enabled for ActorSystem[flink]
> java.lang.NoClassDefFoundError: org/apache/kafka/common/metrics/stats/Rate$1
>   at org.apache.kafka.common.metrics.stats.Rate.convert(Rate.java:93)
>   at org.apache.kafka.common.metrics.stats.Rate.measure(Rate.java:62)
>   at org.apache.kafka.common.metrics.KafkaMetric.value(KafkaMetric.java:61)
>   at org.apache.kafka.common.metrics.KafkaMetric.value(KafkaMetric.java:52)
>   at org.apache.flink.streaming.connectors.kafka.internals.metrics.KafkaMetricWrapper.getValue(KafkaMetricWrapper.java:35)
>   at org.apache.flink.streaming.connectors.kafka.internals.metrics.KafkaMetricWrapper.getValue(KafkaMetricWrapper.java:26)
>   at org.apache.flink.runtime.metrics.dump.MetricDumpSerialization.serializeGauge(MetricDumpSerialization.java:213)
>   at org.apache.flink.runtime.metrics.dump.MetricDumpSerialization.access$200(MetricDumpSerialization.java:50)
>   at org.apache.flink.runtime.metrics.dump.MetricDumpSerialization$MetricDumpSerializer.serialize(MetricDumpSerialization.java:138)
>   at org.apache.flink.runtime.metrics.dump.MetricQueryService.onReceive(MetricQueryService.java:109)
>   at akka.actor.UntypedActor$$anonfun$receive$1.applyOrElse(UntypedActor.scala:167)
>   at akka.actor.Actor$class.aroundReceive(Actor.scala:467)
>   at akka.actor.UntypedActor.aroundReceive(UntypedActor.scala:97)
>   at akka.actor.ActorCell.receiveMessage(ActorCell.scala:516)
>   at akka.actor.ActorCell.invoke(ActorCell.scala:487)
>   at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:238)
>   at akka.dispatch.Mailbox.run(Mailbox.scala:220)
>   at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:397)
>   at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
>   at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
>   at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
>   at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
> Caused by: java.lang.ClassNotFoundException: org.apache.kafka.common.metrics.stats.Rate$1
>   at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
>   at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
>   at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
>   ... 22 more
> 
> Taskmanager2 log:
> Uncaught error from thread [flink-akka.actor.default-dispatcher-17] shutting down JVM
since 'akka.jvm-exit-on-fatal-error' is enabled for ActorSystem[flink]
> Java.lang.NoClassDefFoundError: org/apache/flink/streaming/connectors/kafka/internals/AbstractFetcher$1
>   at org.apache.flink.streaming.connectors.kafka.internals.AbstractFetcher$OffsetGauge.getValue(AbstractFetcher.java:492)
>   at org.apache.flink.streaming.connectors.kafka.internals.AbstractFetcher$OffsetGauge.getValue(AbstractFetcher.java:480)
>   at org.apache.flink.runtime.metrics.dump.MetricDumpSerialization.serializeGauge(MetricDumpSerialization.java:213)
>   at org.apache.flink.runtime.metrics.dump.MetricDumpSerialization.access$200(MetricDumpSerialization.java:50)
>   at org.apache.flink.runtime.metrics.dump.MetricDumpSerialization$MetricDumpSerializer.serialize(MetricDumpSerialization.java:138)
>   at org.apache.flink.runtime.metrics.dump.MetricQueryService.onReceive(MetricQueryService.java:109)
>   at akka.actor.UntypedActor$$anonfun$receive$1.applyOrElse(UntypedActor.scala:167)
>   at akka.actor.Actor$class.aroundReceive(Actor.scala:467)
>   at akka.actor.UntypedActor.aroundReceive(UntypedActor.scala:97)
>   at akka.actor.ActorCell.receiveMessage(ActorCell.scala:516)
>   at akka.actor.ActorCell.invoke(ActorCell.scala:487)
>   at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:238)
>   at akka.dispatch.Mailbox.run(Mailbox.scala:220)
>   at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:397)
>   at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
>   at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
>   at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
>   at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
> Caused by: java.lang.ClassNotFoundException: org.apache.flink.streaming.connectors.kafka.internals.AbstractFetcher$1
>   at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
>   at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
>   at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
>   ... 18 more
> 
> 
> -Ebru


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