Hi Eleanore, That does't sound like a scaling issue. It's probably a data skew, that the data volume on some of the keys are significantly higher than others. I'm not familiar with this area though, and have copied Jark for you, who is one of the community experts in this area.
Thank you~ Xintong Song On Wed, Mar 11, 2020 at 10:37 AM Eleanore Jin <eleanore....@gmail.com> wrote: > _Hi Xintong, > > Thanks for the prompt reply! To answer your question: > > - Which Flink version are you using? > > v1.8.2 > > - Is this skew observed only after a scaling-up? What happens if the > parallelism is initially set to the scaled-up value? > > I also tried this, it seems skew also happens even I do > not change the parallelism, so it may not caused by scale-up/down > > - Keeping the job running a while after the scale-up, does the skew > ease? > > So the skew happens in such a way that: some partitions > lags down to 0, but other partitions are still at level of 10_000, and I am > seeing the back pressure is ok. > > Thanks a lot! > Eleanore > > > On Tue, Mar 10, 2020 at 7:03 PM Xintong Song <tonysong...@gmail.com> > wrote: > >> Hi Eleanore, >> >> I have a few more questions regarding your issue. >> >> - Which Flink version are you using? >> - Is this skew observed only after a scaling-up? What happens if the >> parallelism is initially set to the scaled-up value? >> - Keeping the job running a while after the scale-up, does the skew >> ease? >> >> I suspect the performance difference might be an outcome of some warming >> up issues. E.g., the existing TMs might have some file already localized, >> or some memory buffers already promoted to the JVM tenured area, while the >> new TMs have not. >> >> Thank you~ >> >> Xintong Song >> >> >> >> On Wed, Mar 11, 2020 at 9:25 AM Eleanore Jin <eleanore....@gmail.com> >> wrote: >> >>> Hi Experts, >>> I have my flink application running on Kubernetes, initially with 1 Job >>> Manager, and 2 Task Managers. >>> >>> Then we have the custom operator that watches for the CRD, when the CRD >>> replicas changed, it will patch the Flink Job Manager deployment >>> parallelism and max parallelism according to the replicas from CRD >>> (parallelism can be configured via env variables for our application). >>> which causes the job manager restart. hence a new Flink job. But the >>> consumer group does not change, so it will continue from the offset >>> where it left. >>> >>> In addition, operator will also update Task Manager's deployment >>> replicas, and will adjust the pod number. >>> >>> In case of scale up, the existing task manager pods do not get killed, >>> but new task manager pods will be created. >>> >>> And we observed a skew in the partition offset consumed. e.g. some >>> partitions have huge lags and other partitions have small lags. (observed >>> from burrow) >>> >>> This is also validated by the metrics from Flink UI, showing the >>> throughput differs for slotss >>> >>> Any clue why this is the case? >>> >>> Thanks a lot! >>> Eleanore >>> >>
