+1 (non-binding) - rolled out to thousands of router jobs in our test env - tested with a large-state job. Did simple resilience and checkpoint/savepoint tests. General performance metrics look on par. - tested with a high-parallelism stateless transformation job. General performance metrics look on par.
On Sat, Jul 4, 2020 at 7:39 AM Zhijiang <wangzhijiang...@aliyun.com.invalid> wrote: > Hi Thomas, > > Thanks for the further update information. > > I guess we can dismiss the network stack changes, since in your case the > downstream and upstream would probably be deployed in the same slot > bypassing the network data shuffle. > Also I guess release-1.11 will not bring general performance regression in > runtime engine, as we also did the performance testing for all general > cases by [1] in real cluster before and the testing results should fit the > expectation. But we indeed did not test the specific source and sink > connectors yet as I known. > > Regarding your performance regression with 40%, I wonder it is probably > related to specific source/sink changes (e.g. kinesis) or environment > issues with corner case. > If possible, it would be helpful to further locate whether the regression > is caused by kinesis, by replacing the kinesis source & sink and keeping > the others same. > > As you said, it would be efficient to contact with you directly next week > to further discuss this issue. And we are willing/eager to provide any help > to resolve this issue soon. > > Besides that, I guess this issue should not be the blocker for the > release, since it is probably a corner case based on the current analysis. > If we really conclude anything need to be resolved after the final > release, then we can also make the next minor release-1.11.1 come soon. > > [1] https://issues.apache.org/jira/browse/FLINK-18433 > > Best, > Zhijiang > > > ------------------------------------------------------------------ > From:Thomas Weise <t...@apache.org> > Send Time:2020年7月4日(星期六) 12:26 > To:dev <dev@flink.apache.org>; Zhijiang <wangzhijiang...@aliyun.com> > Cc:Yingjie Cao <kevin.ying...@gmail.com> > Subject:Re: [VOTE] Release 1.11.0, release candidate #4 > > Hi Zhijiang, > > It will probably be best if we connect next week and discuss the issue > directly since this could be quite difficult to reproduce. > > Before the testing result on our side comes out for your respective job > case, I have some other questions to confirm for further analysis: > - How much percentage regression you found after switching to 1.11? > > ~40% throughput decline > > - Are there any network bottleneck in your cluster? E.g. the network > bandwidth is full caused by other jobs? If so, it might have more effects > by above [2] > > The test runs on a k8s cluster that is also used for other production jobs. > There is no reason be believe network is the bottleneck. > > - Did you adjust the default network buffer setting? E.g. > "taskmanager.network.memory.floating-buffers-per-gate" or > "taskmanager.network.memory.buffers-per-channel" > > The job is using the defaults, i.e we don't configure the settings. If you > want me to try specific settings in the hope that it will help to isolate > the issue please let me know. > > - I guess the topology has three vertexes "KinesisConsumer -> Chained > FlatMap -> KinesisProducer", and the partition mode for "KinesisConsumer -> > FlatMap" and "FlatMap->KinesisProducer" are both "forward"? If so, the edge > connection is one-to-one, not all-to-all, then the above [1][2] should no > effects in theory with default network buffer setting. > > There are only 2 vertices and the edge is "forward". > > - By slot sharing, I guess these three vertex parallelism task would > probably be deployed into the same slot, then the data shuffle is by memory > queue, not network stack. If so, the above [2] should no effect. > > Yes, vertices share slots. > > - I also saw some Jira changes for kinesis in this release, could you > confirm that these changes would not effect the performance? > > I will need to take a look. 1.10 already had a regression introduced by the > Kinesis producer update. > > > Thanks, > Thomas > > > On Thu, Jul 2, 2020 at 11:46 PM Zhijiang <wangzhijiang...@aliyun.com > .invalid> > wrote: > > > Hi Thomas, > > > > Thanks for your reply with rich information! > > > > We are trying to reproduce your case in our cluster to further verify it, > > and @Yingjie Cao is working on it now. > > As we have not kinesis consumer and producer internally, so we will > > construct the common source and sink instead in the case of backpressure. > > > > Firstly, we can dismiss the rockdb factor in this release, since you also > > mentioned that "filesystem leads to same symptoms". > > > > Secondly, if my understanding is right, you emphasis that the regression > > only exists for the jobs with low checkpoint interval (10s). > > Based on that, I have two suspicions with the network related changes in > > this release: > > - [1]: Limited the maximum backlog value (default 10) in subpartition > > queue. > > - [2]: Delay send the following buffers after checkpoint barrier on > > upstream side until barrier alignment on downstream side. > > > > These changes are motivated for reducing the in-flight buffers to speedup > > checkpoint especially in the case of backpressure. > > In theory they should have very minor performance effect and actually we > > also tested in cluster to verify within expectation before merging them, > > but maybe there are other corner cases we have not thought of before. > > > > Before the testing result on our side comes out for your respective job > > case, I have some other questions to confirm for further analysis: > > - How much percentage regression you found after switching to 1.11? > > - Are there any network bottleneck in your cluster? E.g. the network > > bandwidth is full caused by other jobs? If so, it might have more effects > > by above [2] > > - Did you adjust the default network buffer setting? E.g. > > "taskmanager.network.memory.floating-buffers-per-gate" or > > "taskmanager.network.memory.buffers-per-channel" > > - I guess the topology has three vertexes "KinesisConsumer -> > Chained > > FlatMap -> KinesisProducer", and the partition mode for "KinesisConsumer > -> > > FlatMap" and "FlatMap->KinesisProducer" are both "forward"? If so, the > edge > > connection is one-to-one, not all-to-all, then the above [1][2] should no > > effects in theory with default network buffer setting. > > - By slot sharing, I guess these three vertex parallelism task would > > probably be deployed into the same slot, then the data shuffle is by > memory > > queue, not network stack. If so, the above [2] should no effect. > > - I also saw some Jira changes for kinesis in this release, could you > > confirm that these changes would not effect the performance? > > > > Best, > > Zhijiang > > > > > > ------------------------------------------------------------------ > > From:Thomas Weise <t...@apache.org> > > Send Time:2020年7月3日(星期五) 01:07 > > To:dev <dev@flink.apache.org>; Zhijiang <wangzhijiang...@aliyun.com> > > Subject:Re: [VOTE] Release 1.11.0, release candidate #4 > > > > Hi Zhijiang, > > > > The performance degradation manifests in backpressure which leads to > > growing backlog in the source. I switched a few times between 1.10 and > 1.11 > > and the behavior is consistent. > > > > The DAG is: > > > > KinesisConsumer -> (Flat Map, Flat Map, Flat Map) -------- forward > > ---------> KinesisProducer > > > > Parallelism: 160 > > No shuffle/rebalance. > > > > Checkpointing config: > > > > Checkpointing Mode Exactly Once > > Interval 10s > > Timeout 10m 0s > > Minimum Pause Between Checkpoints 10s > > Maximum Concurrent Checkpoints 1 > > Persist Checkpoints Externally Enabled (delete on cancellation) > > > > State backend: rocksdb (filesystem leads to same symptoms) > > Checkpoint size is tiny (500KB) > > > > An interesting difference to another job that I had upgraded successfully > > is the low checkpointing interval. > > > > Thanks, > > Thomas > > > > > > On Wed, Jul 1, 2020 at 9:02 PM Zhijiang <wangzhijiang...@aliyun.com > > .invalid> > > wrote: > > > > > Hi Thomas, > > > > > > Thanks for the efficient feedback. > > > > > > Regarding the suggestion of adding the release notes document, I agree > > > with your point. Maybe we should adjust the vote template accordingly > in > > > the respective wiki to guide the following release processes. > > > > > > Regarding the performance regression, could you provide some more > details > > > for our better measurement or reproducing on our sides? > > > E.g. I guess the topology only includes two vertexes source and sink? > > > What is the parallelism for every vertex? > > > The upstream shuffles data to the downstream via rebalance partitioner > or > > > other? > > > The checkpoint mode is exactly-once with rocksDB state backend? > > > The backpressure happened in this case? > > > How much percentage regression in this case? > > > > > > Best, > > > Zhijiang > > > > > > > > > > > > ------------------------------------------------------------------ > > > From:Thomas Weise <t...@apache.org> > > > Send Time:2020年7月2日(星期四) 09:54 > > > To:dev <dev@flink.apache.org> > > > Subject:Re: [VOTE] Release 1.11.0, release candidate #4 > > > > > > Hi Till, > > > > > > Yes, we don't have the setting in flink-conf.yaml. > > > > > > Generally, we carry forward the existing configuration and any change > to > > > default configuration values would impact the upgrade. > > > > > > Yes, since it is an incompatible change I would state it in the release > > > notes. > > > > > > Thanks, > > > Thomas > > > > > > BTW I found a performance regression while trying to upgrade another > > > pipeline with this RC. It is a simple Kinesis to Kinesis job. Wasn't > able > > > to pin it down yet, symptoms include increased checkpoint alignment > time. > > > > > > On Wed, Jul 1, 2020 at 12:04 AM Till Rohrmann <trohrm...@apache.org> > > > wrote: > > > > > > > Hi Thomas, > > > > > > > > just to confirm: When starting the image in local mode, then you > don't > > > have > > > > any of the JobManager memory configuration settings configured in the > > > > effective flink-conf.yaml, right? Does this mean that you have > > explicitly > > > > removed `jobmanager.heap.size: 1024m` from the default configuration? > > If > > > > this is the case, then I believe it was more of an unintentional > > artifact > > > > that it worked before and it has been corrected now so that one needs > > to > > > > specify the memory of the JM process explicitly. Do you think it > would > > > help > > > > to explicitly state this in the release notes? > > > > > > > > Cheers, > > > > Till > > > > > > > > On Wed, Jul 1, 2020 at 7:01 AM Thomas Weise <t...@apache.org> wrote: > > > > > > > > > Thanks for preparing another RC! > > > > > > > > > > As mentioned in the previous RC thread, it would be super helpful > if > > > the > > > > > release notes that are part of the documentation can be included > [1]. > > > > It's > > > > > a significant time-saver to have read those first. > > > > > > > > > > I found one more non-backward compatible change that would be worth > > > > > addressing/mentioning: > > > > > > > > > > It is now necessary to configure the jobmanager heap size in > > > > > flink-conf.yaml (with either jobmanager.heap.size > > > > > or jobmanager.memory.heap.size). Why would I not want to do that > > > anyways? > > > > > Well, we set it dynamically for a cluster deployment via the > > > > > flinkk8soperator, but the container image can also be used for > > testing > > > > with > > > > > local mode (./bin/jobmanager.sh start-foreground local). That will > > fail > > > > if > > > > > the heap wasn't configured and that's how I noticed it. > > > > > > > > > > Thanks, > > > > > Thomas > > > > > > > > > > [1] > > > > > > > > > > > > > > > > > > > > https://ci.apache.org/projects/flink/flink-docs-release-1.11/release-notes/flink-1.11.html > > > > > > > > > > On Tue, Jun 30, 2020 at 3:18 AM Zhijiang < > wangzhijiang...@aliyun.com > > > > > .invalid> > > > > > wrote: > > > > > > > > > > > Hi everyone, > > > > > > > > > > > > Please review and vote on the release candidate #4 for the > version > > > > > 1.11.0, > > > > > > as follows: > > > > > > [ ] +1, Approve the release > > > > > > [ ] -1, Do not approve the release (please provide specific > > comments) > > > > > > > > > > > > The complete staging area is available for your review, which > > > includes: > > > > > > * JIRA release notes [1], > > > > > > * the official Apache source release and binary convenience > > releases > > > to > > > > > be > > > > > > deployed to dist.apache.org [2], which are signed with the key > > with > > > > > > fingerprint 2DA85B93244FDFA19A6244500653C0A2CEA00D0E [3], > > > > > > * all artifacts to be deployed to the Maven Central Repository > [4], > > > > > > * source code tag "release-1.11.0-rc4" [5], > > > > > > * website pull request listing the new release and adding > > > announcement > > > > > > blog post [6]. > > > > > > > > > > > > The vote will be open for at least 72 hours. It is adopted by > > > majority > > > > > > approval, with at least 3 PMC affirmative votes. > > > > > > > > > > > > Thanks, > > > > > > Release Manager > > > > > > > > > > > > [1] > > > > > > > > > > > > > > > > > > > > > https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12315522&version=12346364 > > > > > > [2] > https://dist.apache.org/repos/dist/dev/flink/flink-1.11.0-rc4/ > > > > > > [3] https://dist.apache.org/repos/dist/release/flink/KEYS > > > > > > [4] > > > > > > > > > > > > https://repository.apache.org/content/repositories/orgapacheflink-1377/ > > > > > > [5] > > https://github.com/apache/flink/releases/tag/release-1.11.0-rc4 > > > > > > [6] https://github.com/apache/flink-web/pull/352 > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >
