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 > <b20926...@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 <pi...@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.lo...@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 >>>> <pi...@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.lo...@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 >>>> <pi...@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.lo...@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 >>>> <b20926...@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 >>>> <b20926...@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.lo...@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 >>>> <aljos...@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 >>>> <pomperma...@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 >>>>>>>>> <b20926...@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 >>>> <b20926...@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 <u...@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 >>>>>>>>> <b20926...@cs.hacettepe.edu.tr> wrote: >>>>>>>>> Begin forwarded message: >>>>>>>>> From: ebru <b20926...@cs.hacettepe.edu.tr> >>>>>>>>> Subject: Re: Flink memory leak >>>>>>>>> Date: 7 November 2017 at 14:09:17 GMT+3 >>>>>>>>> To: Ufuk Celebi <u...@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 <u...@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 >>>>>>>>> <b20926...@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
