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. >> > > >
