>From my experience, java profilers are sometimes not accurate enough to find out the performance regression root cause. In this case, I would suggest you try out intel vtune amplifier to watch more detailed metrics.
Best, Kurt On Fri, Jul 24, 2020 at 8:51 AM Thomas Weise <t...@apache.org> wrote: > The cause of the issue is all but clear. > > Previously I had mentioned that there is no suspect change to the Kinesis > connector and that I had reverted the AWS SDK change to no effect. > > https://issues.apache.org/jira/browse/FLINK-17496 actually fixed another > regression in the previous release and is present before and after. > > I repeated the run with 1.11.0 core and downgraded the entire Kinesis > connector to 1.10.1: Nothing changes, i.e. the regression is still present. > Therefore we will need to look elsewhere for the root cause. > > Regarding the time spent in snapshotState, repeat runs reveal a wide range > for both versions, 1.10 and 1.11. So again this is nothing pointing to a > root cause. > > At this point, I have no ideas remaining other than doing a bisect to find > the culprit. Any other suggestions? > > Thomas > > > On Thu, Jul 16, 2020 at 9:19 PM Zhijiang <wangzhijiang...@aliyun.com > .invalid> > wrote: > > > Hi Thomas, > > > > Thanks for your further profiling information and glad to see we already > > finalized the location to cause the regression. > > Actually I was also suspicious of the point of #snapshotState in previous > > discussions since it indeed cost much time to block normal operator > > processing. > > > > Based on your below feedback, the sleep time during #snapshotState might > > be the main concern, and I also digged into the implementation of > > FlinkKinesisProducer#snapshotState. > > while (producer.getOutstandingRecordsCount() > 0) { > > producer.flush(); > > try { > > Thread.sleep(500); > > } catch (InterruptedException e) { > > LOG.warn("Flushing was interrupted."); > > break; > > } > > } > > It seems that the sleep time is mainly affected by the internal > operations > > inside KinesisProducer implementation provided by amazonaws, which I am > not > > quite familiar with. > > But I noticed there were two upgrades related to it in release-1.11.0. > One > > is for upgrading amazon-kinesis-producer to 0.14.0 [1] and another is for > > upgrading aws-sdk-version to 1.11.754 [2]. > > You mentioned that you already reverted the SDK upgrade to verify no > > changes. Did you also revert the [1] to verify? > > [1] https://issues.apache.org/jira/browse/FLINK-17496 > > [2] https://issues.apache.org/jira/browse/FLINK-14881 > > > > Best, > > Zhijiang > > ------------------------------------------------------------------ > > From:Thomas Weise <t...@apache.org> > > Send Time:2020年7月17日(星期五) 05:29 > > To:dev <dev@flink.apache.org> > > Cc:Zhijiang <wangzhijiang...@aliyun.com>; Stephan Ewen <se...@apache.org > >; > > Arvid Heise <ar...@ververica.com>; Aljoscha Krettek <aljos...@apache.org > > > > Subject:Re: Kinesis Performance Issue (was [VOTE] Release 1.11.0, release > > candidate #4) > > > > Sorry for the delay. > > > > I confirmed that the regression is due to the sink (unsurprising, since > > another job with the same consumer, but not the producer, runs as > > expected). > > > > As promised I did CPU profiling on the problematic application, which > gives > > more insight into the regression [1] > > > > The screenshots show that the average time for snapshotState increases > from > > ~9s to ~28s. The data also shows the increase in sleep time during > > snapshotState. > > > > Does anyone, based on changes made in 1.11, have a theory why? > > > > I had previously looked at the changes to the Kinesis connector and also > > reverted the SDK upgrade, which did not change the situation. > > > > It will likely be necessary to drill into the sink / checkpointing > details > > to understand the cause of the problem. > > > > Let me know if anyone has specific questions that I can answer from the > > profiling results. > > > > Thomas > > > > [1] > > > > > https://docs.google.com/presentation/d/159IVXQGXabjnYJk3oVm3UP2UW_5G-TGs_u9yzYb030I/edit?usp=sharing > > > > On Mon, Jul 13, 2020 at 11:14 AM Thomas Weise <t...@apache.org> wrote: > > > > > + dev@ for visibility > > > > > > I will investigate further today. > > > > > > > > > On Wed, Jul 8, 2020 at 4:42 AM Aljoscha Krettek <aljos...@apache.org> > > > wrote: > > > > > >> On 06.07.20 20:39, Stephan Ewen wrote: > > >> > - Did sink checkpoint notifications change in a relevant way, for > > >> example > > >> > due to some Kafka issues we addressed in 1.11 (@Aljoscha maybe?) > > >> > > >> I think that's unrelated: the Kafka fixes were isolated in Kafka and > the > > >> one bug I discovered on the way was about the Task reaper. > > >> > > >> > > >> On 07.07.20 17:51, Zhijiang wrote: > > >> > Sorry for my misunderstood of the previous information, Thomas. I > was > > >> assuming that the sync checkpoint duration increased after upgrade as > it > > >> was mentioned before. > > >> > > > >> > If I remembered correctly, the memory state backend also has the > same > > >> issue? If so, we can dismiss the rocksDB state changes. As the slot > > sharing > > >> enabled, the downstream and upstream should > > >> > probably deployed into the same slot, then no network shuffle > effect. > > >> > > > >> > I think we need to find out whether it has other symptoms changed > > >> besides the performance regression to further figure out the scope. > > >> > E.g. any metrics changes, the number of TaskManager and the number > of > > >> slots per TaskManager from deployment changes. > > >> > 40% regression is really big, I guess the changes should also be > > >> reflected in other places. > > >> > > > >> > I am not sure whether we can reproduce the regression in our AWS > > >> environment by writing any Kinesis jobs, since there are also normal > > >> Kinesis jobs as Thomas mentioned after upgrade. > > >> > So it probably looks like to touch some corner case. I am very > willing > > >> to provide any help for debugging if possible. > > >> > > > >> > > > >> > Best, > > >> > Zhijiang > > >> > > > >> > > > >> > ------------------------------------------------------------------ > > >> > From:Thomas Weise <t...@apache.org> > > >> > Send Time:2020年7月7日(星期二) 23:01 > > >> > To:Stephan Ewen <se...@apache.org> > > >> > Cc:Aljoscha Krettek <aljos...@apache.org>; Arvid Heise < > > >> ar...@ververica.com>; Zhijiang <wangzhijiang...@aliyun.com> > > >> > Subject:Re: Kinesis Performance Issue (was [VOTE] Release 1.11.0, > > >> release candidate #4) > > >> > > > >> > We are deploying our apps with FlinkK8sOperator. We have one job > that > > >> works as expected after the upgrade and the one discussed here that > has > > the > > >> performance regression. > > >> > > > >> > "The performance regression is obvious caused by long duration of > sync > > >> checkpoint process in Kinesis sink operator, which would block the > > normal > > >> data processing until back pressure the source." > > >> > > > >> > That's a constant. Before (1.10) and upgrade have the same sync > > >> checkpointing time. The question is what change came in with the > > upgrade. > > >> > > > >> > > > >> > > > >> > On Tue, Jul 7, 2020 at 7:33 AM Stephan Ewen <se...@apache.org> > wrote: > > >> > > > >> > @Thomas Just one thing real quick: Are you using the standalone > setup > > >> scripts (like start-cluster.sh, and the former "slaves" file) ? > > >> > Be aware that this is now called "workers" because of avoiding > > >> sensitive names. > > >> > In one internal benchmark we saw quite a lot of slowdown initially, > > >> before seeing that the cluster was not a distributed cluster any more > > ;-) > > >> > > > >> > > > >> > On Tue, Jul 7, 2020 at 9:08 AM Zhijiang <wangzhijiang...@aliyun.com > > > > >> wrote: > > >> > Thanks for this kickoff and help analysis, Stephan! > > >> > Thanks for the further feedback and investigation, Thomas! > > >> > > > >> > The performance regression is obvious caused by long duration of > sync > > >> checkpoint process in Kinesis sink operator, which would block the > > normal > > >> data processing until back pressure the source. > > >> > Maybe we could dig into the process of sync execution in checkpoint. > > >> E.g. break down the steps inside respective operator#snapshotState to > > >> statistic which operation cost most of the time, then > > >> > we might probably find the root cause to bring such cost. > > >> > > > >> > Look forward to the further progress. :) > > >> > > > >> > Best, > > >> > Zhijiang > > >> > > > >> > ------------------------------------------------------------------ > > >> > From:Stephan Ewen <se...@apache.org> > > >> > Send Time:2020年7月7日(星期二) 14:52 > > >> > To:Thomas Weise <t...@apache.org> > > >> > Cc:Stephan Ewen <se...@apache.org>; Zhijiang < > > >> wangzhijiang...@aliyun.com>; Aljoscha Krettek <aljos...@apache.org>; > > >> Arvid Heise <ar...@ververica.com> > > >> > Subject:Re: Kinesis Performance Issue (was [VOTE] Release 1.11.0, > > >> release candidate #4) > > >> > > > >> > Thank you for the digging so deeply. > > >> > Mysterious think this regression. > > >> > > > >> > On Mon, Jul 6, 2020, 22:56 Thomas Weise <t...@apache.org> wrote: > > >> > @Stephan: yes, I refer to sync time in the web UI (it is unchanged > > >> between 1.10 and 1.11 for the specific pipeline). > > >> > > > >> > I verified that increasing the checkpointing interval does not make > a > > >> difference. > > >> > > > >> > I looked at the Kinesis connector changes since 1.10.1 and don't see > > >> anything that could cause this. > > >> > > > >> > Another pipeline that is using the Kinesis consumer (but not the > > >> producer) performs as expected. > > >> > > > >> > I tried reverting the AWS SDK version change, symptoms remain > > unchanged: > > >> > > > >> > diff --git a/flink-connectors/flink-connector-kinesis/pom.xml > > >> b/flink-connectors/flink-connector-kinesis/pom.xml > > >> > index a6abce23ba..741743a05e 100644 > > >> > --- a/flink-connectors/flink-connector-kinesis/pom.xml > > >> > +++ b/flink-connectors/flink-connector-kinesis/pom.xml > > >> > @@ -33,7 +33,7 @@ under the License. > > >> > > > >> > <artifactId>flink-connector-kinesis_${scala.binary.version}</artifactId> > > >> > <name>flink-connector-kinesis</name> > > >> > <properties> > > >> > - <aws.sdk.version>1.11.754</aws.sdk.version> > > >> > + <aws.sdk.version>1.11.603</aws.sdk.version> > > >> > > > >> <aws.kinesis-kcl.version>1.11.2</aws.kinesis-kcl.version> > > >> > > > >> <aws.kinesis-kpl.version>0.14.0</aws.kinesis-kpl.version> > > >> > > > >> > > > <aws.dynamodbstreams-kinesis-adapter.version>1.5.0</aws.dynamodbstreams-kinesis-adapter.version> > > >> > > > >> > I'm planning to take a look with a profiler next. > > >> > > > >> > Thomas > > >> > > > >> > > > >> > On Mon, Jul 6, 2020 at 11:40 AM Stephan Ewen <se...@apache.org> > > wrote: > > >> > Hi all! > > >> > > > >> > Forking this thread out of the release vote thread. > > >> > From what Thomas describes, it really sounds like a sink-specific > > >> issue. > > >> > > > >> > @Thomas: When you say sink has a long synchronous checkpoint time, > you > > >> mean the time that is shown as "sync time" on the metrics and web UI? > > That > > >> is not including any network buffer related operations. It is purely > the > > >> operator's time. > > >> > > > >> > Can we dig into the changes we did in sinks: > > >> > - Kinesis version upgrade, AWS library updates > > >> > > > >> > - Could it be that some call (checkpoint complete) that was > > >> previously (1.10) in a separate thread is not in the mailbox and this > > >> simply reduces the number of threads that do the work? > > >> > > > >> > - Did sink checkpoint notifications change in a relevant way, for > > >> example due to some Kafka issues we addressed in 1.11 (@Aljoscha > maybe?) > > >> > > > >> > Best, > > >> > Stephan > > >> > > > >> > > > >> > On Sun, Jul 5, 2020 at 7:10 AM Zhijiang <wangzhijiang...@aliyun.com > > .invalid> > > >> wrote: > > >> > Hi Thomas, > > >> > > > >> > Regarding [2], it has more detail infos in the Jira description ( > > >> https://issues.apache.org/jira/browse/FLINK-16404). > > >> > > > >> > I can also give some basic explanations here to dismiss the > concern. > > >> > 1. In the past, the following buffers after the barrier will be > > >> cached on downstream side before alignment. > > >> > 2. In 1.11, the upstream would not send the buffers after the > > >> barrier. When the downstream finishes the alignment, it will notify > the > > >> downstream of continuing sending following buffers, since it can > process > > >> them after alignment. > > >> > 3. The only difference is that the temporary blocked buffers are > > >> cached either on downstream side or on upstream side before alignment. > > >> > 4. The side effect would be the additional notification cost for > > >> every barrier alignment. If the downstream and upstream are deployed > in > > >> separate TaskManager, the cost is network transport delay (the effect > > can > > >> be ignored based on our testing with 1s checkpoint interval). For > > sharing > > >> slot in your case, the cost is only one method call in processor, can > be > > >> ignored also. > > >> > > > >> > You mentioned "In this case, the downstream task has a high > average > > >> checkpoint duration(~30s, sync part)." This duration is not reflecting > > the > > >> changes above, and it is only indicating the duration for calling > > >> `Operation.snapshotState`. > > >> > If this duration is beyond your expectation, you can check or > debug > > >> whether the source/sink operations might take more time to finish > > >> `snapshotState` in practice. E.g. you can > > >> > make the implementation of this method as empty to further verify > > the > > >> effect. > > >> > > > >> > Best, > > >> > Zhijiang > > >> > > > >> > > > >> > ------------------------------------------------------------------ > > >> > From:Thomas Weise <t...@apache.org> > > >> > Send Time:2020年7月5日(星期日) 12:22 > > >> > 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, > > >> > > > >> > Could you please point me to more details regarding: "[2]: Delay > > send > > >> the > > >> > following buffers after checkpoint barrier on upstream side until > > >> barrier > > >> > alignment on downstream side." > > >> > > > >> > In this case, the downstream task has a high average checkpoint > > >> duration > > >> > (~30s, sync part). If there was a change to hold buffers depending > > on > > >> > downstream performance, could this possibly apply to this case > (even > > >> when > > >> > there is no shuffle that would require alignment)? > > >> > > > >> > Thanks, > > >> > Thomas > > >> > > > >> > > > >> > 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 > > >> > > > > > > > > > >> > > > > > > > > > >> > > > > > > > > >> > > > > > > > >> > > > > > > >> > > > > > > >> > > > > > >> > > > > > >> > > > > >> > > > > >> > > > >> > > > >> > > > >> > > >> > > > > >
