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 > >> > > > > > > > > >> > > > > > > > > >> > > > > > > > >> > > > > > > >> > > > > > >> > > > > > >> > > > > >> > > > > >> > > > >> > > > >> > > >> > > >> > > >> > >> > >
