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 >> <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>) 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. >>> >>> >> >> >
