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 > <mailto: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 >> <mailto:javier.lo...@zalando.de>> wrote: >> >> Hi, >> >> This is the test flink job we created to trigger this leak >> https://gist.github.com/javieredo/c6052404dbe6cc602e99f4669a09f7d6 >> <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 >> <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 <mailto: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 >> <mailto: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 >> <mailto: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 >> <mailto: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 >> <mailto: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 >> <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 <mailto: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 >> <mailto: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 >> <mailto: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 <mailto:b20926...@cs.hacettepe.edu.tr>> wrote: >> >> Begin forwarded message: >> >> From: ebru <b20926...@cs.hacettepe.edu.tr >> <mailto: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 <mailto: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 >> <mailto: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 <mailto: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 >> <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. >> > >
