Hi Gyula, Max, John! Thanks for the great FLIP, it's very useful for flink users.
> Ideally the autoscaler is a separate process (an outside observer) Could we finally use the autoscaler as a outside tool? or run it as a separate java process? If it's complex, can the part that detects the job and suggests parallelism be a separate java process? Since a large number of Flink jobs are still using Flink on yarn, this feature is also useful for them. I was wondering if some logs or advice can be provided if automatic scala is not working for Flink on yarn. For example: the parallelism suggested by vertex_1 is 100, and the parallelism suggested by vertex_2 is 150. With this information, the flink user can manually set reasonable parallelism. Or some flink platforms can integrate this tool and use `pipeline.jobvertex-parallelism-overrides`[1] to make autoscaler work on Flink on yarn. > By adding it to the operator, the autoscaler can potentially work on > older Flink versions as well As I understand, `pipeline.jobvertex-parallelism-overrides`[1] is supported in Flink 1.17, so old flink versions can only detect, not auto scala, right? [1] https://issues.apache.org/jira/browse/FLINK-29501 Best Rui Fan On Fri, Nov 25, 2022 at 4:54 AM Gyula Fóra <gyula.f...@gmail.com> wrote: > Hi John! > > Thank you for the excellent question. > > There are few reasons why we felt that the operator is the right place for > this component: > > - Ideally the autoscaler is a separate process (an outside observer) , and > the jobmanager is very much tied to the lifecycle of the job. The operator > is a perfect example of such an external process that lives beyond > individual jobs. > - Scaling itself might need some external resource management (for > standalone clusters) that the jobmanager is not capable of, and the logic > is already in the operator > - Adding this to the operator allows us to integrate this fully in the > lifecycle management of the application. This guarantees that scaling > decisions do not interfere with upgrades, suspends etc. > - By adding it to the operator, the autoscaler can potentially work on > older Flink versions as well > - The jobmanager is a component designed to handle Flink individual jobs, > but the autoscaler component needs to work on a higher abstraction layer to > be able to integrate with user job upgrades etc. > > These are some of the main things that come to my mind :) > > Having it in the operator ties this logic to Kubernetes itself but we feel > that an autoscaler is mostly relevant in an elastic cloud environment > anyways. > > Cheers, > Gyula > > On Thu, Nov 24, 2022 at 9:40 PM John Roesler <vvcep...@apache.org> wrote: > > > Hi Max, > > > > Thanks for the FLIP! > > > > I’ve been curious about one one point. I can imagine some good reasons > for > > it but wonder what you have in mind. What’s the reason to add auto > scaling > > to the Operator instead of to the JobManager? > > > > It seems like adding that capability to the JobManager would be a bigger > > project, but it also would create some interesting opportunities. > > > > This is certainly not a suggestion, just a question. > > > > Thanks! > > John > > > > On Wed, Nov 23, 2022, at 10:12, Maximilian Michels wrote: > > > Thanks for your comments @Dong and @Chen. It is true that not all the > > > details are contained in the FLIP. The document is meant as a general > > > design concept. > > > > > > As for the rescaling time, this is going to be a configurable setting > for > > > now but it is foreseeable that we will provide auto-tuning of this > > > configuration value by observing the job restart time. Same goes for > the > > > scaling decision itself which can learn from previous decisions. But we > > > want to keep it simple for the first version. > > > > > > For sources that do not support the pendingRecords metric, we are > > planning > > > to either give the user the choice to set a manual target rate, or > scale > > it > > > purely based on its utilization as reported via busyTimeMsPerSecond. In > > > case of legacy sources, we will skip scaling these branches entirely > > > because they support neither of these metrics. > > > > > > -Max > > > > > > On Mon, Nov 21, 2022 at 11:27 AM Maximilian Michels <m...@apache.org> > > wrote: > > > > > >> >Do we think the scaler could be a plugin or hard coded ? > > >> > > >> +1 For pluggable scaling logic. > > >> > > >> On Mon, Nov 21, 2022 at 3:38 AM Chen Qin <qinnc...@gmail.com> wrote: > > >> > > >>> On Sun, Nov 20, 2022 at 7:25 AM Gyula Fóra <gyula.f...@gmail.com> > > wrote: > > >>> > > >>> > Hi Chen! > > >>> > > > >>> > I think in the long term it makes sense to provide some pluggable > > >>> > mechanisms but it's not completely trivial where exactly you would > > plug > > >>> in > > >>> > your custom logic at this point. > > >>> > > > >>> sounds good, more specifically would be great if it can accept input > > >>> features > > >>> (including previous scaling decisions) and output decisions. > > >>> Folks might keep their own secret sauce and avoid patching oss fork. > > >>> > > >>> > > > >>> > In any case the problems you mentioned should be solved robustly by > > the > > >>> > algorithm itself without any customization: > > >>> > - We need to be able to detect ineffective scaling decisions, > let\s > > >>> say we > > >>> > scaled up (expecting better throughput with a higher parallelism) > > but we > > >>> > did not get a better processing capacity (this would be the > external > > >>> > service bottleneck) > > >>> > > > >>> sounds good, so we would at least try restart job once (optimistic > > path) > > >>> as > > >>> design choice. > > >>> > > >>> > - We are evaluating metrics in windows, and we have some flexible > > >>> > boundaries to avoid scaling on minor load spikes > > >>> > > > >>> yes, would be great if user can feed in throughput changes over > > different > > >>> time buckets (last 10s, 30s, 1 min,5 mins) as input features > > >>> > > >>> > > > >>> > Regards, > > >>> > Gyula > > >>> > > > >>> > On Sun, Nov 20, 2022 at 12:28 AM Chen Qin <qinnc...@gmail.com> > > wrote: > > >>> > > > >>> > > Hi Gyula, > > >>> > > > > >>> > > Do we think the scaler could be a plugin or hard coded ? > > >>> > > We observed some cases scaler can't address (e.g async io > > dependency > > >>> > > service degradation or small spike that doesn't worth restarting > > job) > > >>> > > > > >>> > > Thanks, > > >>> > > Chen > > >>> > > > > >>> > > On Fri, Nov 18, 2022 at 1:03 AM Gyula Fóra <gyula.f...@gmail.com > > > > >>> wrote: > > >>> > > > > >>> > > > Hi Dong! > > >>> > > > > > >>> > > > Could you please confirm that your main concerns have been > > >>> addressed? > > >>> > > > > > >>> > > > Some other minor details that might not have been fully > > clarified: > > >>> > > > - The prototype has been validated on some production > workloads > > yes > > >>> > > > - We are only planning to use metrics that are generally > > available > > >>> and > > >>> > > are > > >>> > > > previously accepted to be standardized connector metrics (not > > Kafka > > >>> > > > specific). This is actually specified in the FLIP > > >>> > > > - Even if some metrics (such as pendingRecords) are not > > accessible > > >>> the > > >>> > > > scaling algorithm works and can be used. For source scaling > > based on > > >>> > > > utilization alone we still need some trivial modifications on > the > > >>> > > > implementation side. > > >>> > > > > > >>> > > > Cheers, > > >>> > > > Gyula > > >>> > > > > > >>> > > > On Thu, Nov 17, 2022 at 5:22 PM Gyula Fóra < > gyula.f...@gmail.com > > > > > >>> > wrote: > > >>> > > > > > >>> > > > > Hi Dong! > > >>> > > > > > > >>> > > > > This is not an experimental feature proposal. The > > implementation > > >>> of > > >>> > the > > >>> > > > > prototype is still in an experimental phase but by the time > the > > >>> FLIP, > > >>> > > > > initial prototype and review is done, this should be in a > good > > >>> stable > > >>> > > > first > > >>> > > > > version. > > >>> > > > > This proposal is pretty general as autoscalers/tuners get as > > far > > >>> as I > > >>> > > > > understand and there is no history of any alternative effort > > that > > >>> > even > > >>> > > > > comes close to the applicability of this solution. > > >>> > > > > > > >>> > > > > Any large features that were added to Flink in the past have > > gone > > >>> > > through > > >>> > > > > several iterations over the years and the APIs have evolved > as > > >>> they > > >>> > > > matured. > > >>> > > > > Something like the autoscaler can only be successful if there > > is > > >>> > enough > > >>> > > > > user exposure and feedback to make it good, putting it in an > > >>> external > > >>> > > > repo > > >>> > > > > will not get us anywhere. > > >>> > > > > > > >>> > > > > We have a prototype implementation ready that works well and > > it is > > >>> > more > > >>> > > > or > > >>> > > > > less feature complete. We proposed this FLIP based on > something > > >>> that > > >>> > we > > >>> > > > see > > >>> > > > > as a working solution, please do not underestimate the effort > > that > > >>> > went > > >>> > > > > into this proposal and the validation of the ideas. So in > this > > >>> sense > > >>> > > our > > >>> > > > > approach here is the same as with the Table Store and > > Kubernetes > > >>> > > Operator > > >>> > > > > and other big components of the past. On the other hand it's > > >>> > impossible > > >>> > > > to > > >>> > > > > sufficiently explain all the technical depth/implementation > > >>> details > > >>> > of > > >>> > > > such > > >>> > > > > complex components in FLIPs to 100%, I feel we have a good > > >>> overview > > >>> > of > > >>> > > > the > > >>> > > > > algorithm in the FLIP and the implementation should cover all > > >>> > remaining > > >>> > > > > questions. We will have an extended code review phase > following > > >>> the > > >>> > > FLIP > > >>> > > > > vote before this make it into the project. > > >>> > > > > > > >>> > > > > I understand your concern regarding the stability of Flink > > >>> Kubernetes > > >>> > > > > Operator config and metric names. We have decided to not > > provide > > >>> > > > guarantees > > >>> > > > > there yet but if you feel that it's time for the operator to > > >>> support > > >>> > > such > > >>> > > > > guarantees please open a separate discussion on that topic, I > > >>> don't > > >>> > > want > > >>> > > > to > > >>> > > > > mix the two problems here. > > >>> > > > > > > >>> > > > > Regards, > > >>> > > > > Gyula > > >>> > > > > > > >>> > > > > On Thu, Nov 17, 2022 at 5:07 PM Dong Lin < > lindon...@gmail.com> > > >>> > wrote: > > >>> > > > > > > >>> > > > >> Hi Gyula, > > >>> > > > >> > > >>> > > > >> If I understand correctly, this autopilot proposal is an > > >>> > experimental > > >>> > > > >> feature and its configs/metrics are not mature enough to > > provide > > >>> > > > backward > > >>> > > > >> compatibility yet. And the proposal provides high-level > ideas > > of > > >>> the > > >>> > > > >> algorithm but it is probably too complicated to explain it > > >>> > end-to-end. > > >>> > > > >> > > >>> > > > >> On the one hand, I do agree that having an auto-tuning > > prototype, > > >>> > even > > >>> > > > if > > >>> > > > >> not mature, is better than nothing for Flink users. On the > > other > > >>> > > hand, I > > >>> > > > >> am > > >>> > > > >> concerned that this FLIP seems a bit too experimental, and > > >>> starting > > >>> > > with > > >>> > > > >> an > > >>> > > > >> immature design might make it harder for us to reach a > > >>> > > production-ready > > >>> > > > >> and > > >>> > > > >> generally applicable auto-tuner in the future. And > introducing > > >>> too > > >>> > > > >> backward > > >>> > > > >> incompatible changes generally hurts users' trust in the > Flink > > >>> > > project. > > >>> > > > >> > > >>> > > > >> One alternative might be to develop and experiment with this > > >>> feature > > >>> > > in > > >>> > > > a > > >>> > > > >> non-Flink repo. You can iterate fast without worrying about > > >>> > typically > > >>> > > > >> backward compatibility requirement as required for most > Flink > > >>> public > > >>> > > > >> features. And once the feature is reasonably evaluated and > > mature > > >>> > > > enough, > > >>> > > > >> it will be much easier to explain the design and address all > > the > > >>> > > issues > > >>> > > > >> mentioned above. For example, Jingsong implemented a Flink > > Table > > >>> > Store > > >>> > > > >> prototype > > >>> > > > >> < > > >>> https://github.com/JingsongLi/flink/tree/table_storage/flink-table > > >>> > > > > >>> > > > >> before > > >>> > > > >> proposing FLIP-188 in this thread > > >>> > > > >> < > > >>> https://lists.apache.org/thread/dlhspjpms007j2ynymsg44fxcx6fm064>. > > >>> > > > >> > > >>> > > > >> I don't intend to block your progress. Just my two cents. It > > >>> will be > > >>> > > > great > > >>> > > > >> to hear more from other developers (e.g. in the voting > > thread). > > >>> > > > >> > > >>> > > > >> Thanks, > > >>> > > > >> Dong > > >>> > > > >> > > >>> > > > >> > > >>> > > > >> On Thu, Nov 17, 2022 at 1:24 AM Gyula Fóra < > > gyula.f...@gmail.com > > >>> > > > >>> > > > wrote: > > >>> > > > >> > > >>> > > > >> > Hi Dong, > > >>> > > > >> > > > >>> > > > >> > Let me address your comments. > > >>> > > > >> > > > >>> > > > >> > Time for scale / backlog processing time derivation: > > >>> > > > >> > We can add some more details to the Flip but at this point > > the > > >>> > > > >> > implementation is actually much simpler than the algorithm > > to > > >>> > > describe > > >>> > > > >> it. > > >>> > > > >> > I would not like to add more equations etc because it just > > >>> > > > >> overcomplicates > > >>> > > > >> > something relatively simple in practice. > > >>> > > > >> > > > >>> > > > >> > In a nutshell: Time to recover == lag / > > >>> > > > processing-rate-after-scaleup. > > >>> > > > >> > It's fairly easy to see where this is going, but best to > > see in > > >>> > > code. > > >>> > > > >> > > > >>> > > > >> > Using pendingRecords and alternative mechanisms: > > >>> > > > >> > True that the current algorithm relies on pending records > to > > >>> > > > effectively > > >>> > > > >> > compute the target source processing rates and therefore > > scale > > >>> > > > sources. > > >>> > > > >> > This is available for Kafka which is by far the most > common > > >>> > > streaming > > >>> > > > >> > source and is used by the majority of streaming > applications > > >>> > > > currently. > > >>> > > > >> > It would be very easy to add alternative purely > utilization > > >>> based > > >>> > > > >> scaling > > >>> > > > >> > to the sources. We can start with the current proposal and > > add > > >>> > this > > >>> > > > >> along > > >>> > > > >> > the way before the first version. > > >>> > > > >> > > > >>> > > > >> > Metrics, Configs and Public API: > > >>> > > > >> > The autoscaler feature is proposed for the Flink > Kubernetes > > >>> > Operator > > >>> > > > >> which > > >>> > > > >> > does not have the same API/config maturity and thus does > not > > >>> > provide > > >>> > > > the > > >>> > > > >> > same guarantees. > > >>> > > > >> > We currently support backward compatibilty for the CRD > > itself > > >>> and > > >>> > > not > > >>> > > > >> the > > >>> > > > >> > configs or metrics. This does not mean that we do not aim > to > > >>> do so > > >>> > > but > > >>> > > > >> at > > >>> > > > >> > this stage we still have to clean up the details of the > > newly > > >>> > added > > >>> > > > >> > components. In practice this means that if we manage to > get > > the > > >>> > > > metrics > > >>> > > > >> / > > >>> > > > >> > configs right at the first try we will keep them and > provide > > >>> > > > >> compatibility, > > >>> > > > >> > but if we feel that we missed something or we don't need > > >>> something > > >>> > > we > > >>> > > > >> can > > >>> > > > >> > still remove it. It's a more pragmatic approach for such a > > >>> > component > > >>> > > > >> that > > >>> > > > >> > is likely to evolve than setting everything in stone > > >>> immediately. > > >>> > > > >> > > > >>> > > > >> > Cheers, > > >>> > > > >> > Gyula > > >>> > > > >> > > > >>> > > > >> > > > >>> > > > >> > > > >>> > > > >> > On Wed, Nov 16, 2022 at 6:07 PM Dong Lin < > > lindon...@gmail.com> > > >>> > > wrote: > > >>> > > > >> > > > >>> > > > >> > > Thanks for the update! Please see comments inline. > > >>> > > > >> > > > > >>> > > > >> > > On Tue, Nov 15, 2022 at 11:46 PM Maximilian Michels < > > >>> > > m...@apache.org > > >>> > > > > > > >>> > > > >> > > wrote: > > >>> > > > >> > > > > >>> > > > >> > > > Of course! Let me know if your concerns are addressed. > > The > > >>> > wiki > > >>> > > > page > > >>> > > > >> > has > > >>> > > > >> > > > been updated. > > >>> > > > >> > > > > > >>> > > > >> > > > >It will be great to add this in the FLIP so that > > reviewers > > >>> > can > > >>> > > > >> > > understand > > >>> > > > >> > > > how the source parallelisms are computed and how the > > >>> algorithm > > >>> > > > works > > >>> > > > >> > > > end-to-end. > > >>> > > > >> > > > > > >>> > > > >> > > > I've updated the FLIP page to add more details on how > > the > > >>> > > > >> backlog-based > > >>> > > > >> > > > scaling works (2). > > >>> > > > >> > > > > > >>> > > > >> > > > > >>> > > > >> > > The algorithm is much more informative now. The > algorithm > > >>> > > currently > > >>> > > > >> uses > > >>> > > > >> > > "Estimated time for rescale" to derive new source > > >>> parallelism. > > >>> > > Could > > >>> > > > >> we > > >>> > > > >> > > also specify in the FLIP how this value is derived? > > >>> > > > >> > > > > >>> > > > >> > > The algorithm currently uses pendingRecords to derive > > source > > >>> > > > >> parallelism. > > >>> > > > >> > > It is an optional metric and KafkaSource currently > reports > > >>> this > > >>> > > > >> metric. > > >>> > > > >> > So > > >>> > > > >> > > it means that only the proposed algorithm currently only > > >>> works > > >>> > > when > > >>> > > > >> all > > >>> > > > >> > > sources of the job are KafkaSource, right? > > >>> > > > >> > > > > >>> > > > >> > > This issue considerably limits the applicability of this > > >>> FLIP. > > >>> > Do > > >>> > > > you > > >>> > > > >> > think > > >>> > > > >> > > most (if not all) streaming source will report this > > metric? > > >>> > > > >> > Alternatively, > > >>> > > > >> > > any chance we can have a fallback solution to evaluate > the > > >>> > source > > >>> > > > >> > > parallelism based on e.g. cpu or idle ratio for cases > > where > > >>> this > > >>> > > > >> metric > > >>> > > > >> > is > > >>> > > > >> > > not available? > > >>> > > > >> > > > > >>> > > > >> > > > > >>> > > > >> > > > >These metrics and configs are public API and need to > be > > >>> > stable > > >>> > > > >> across > > >>> > > > >> > > > minor versions, could we document them before > finalizing > > >>> the > > >>> > > FLIP? > > >>> > > > >> > > > > > >>> > > > >> > > > Metrics and config changes are not strictly part of > the > > >>> public > > >>> > > API > > >>> > > > >> but > > >>> > > > >> > > > Gyula has added a section. > > >>> > > > >> > > > > > >>> > > > >> > > > > >>> > > > >> > > Hmm... if metrics are not public API, then it might > happen > > >>> that > > >>> > we > > >>> > > > >> change > > >>> > > > >> > > the mbean path in a minor release and break users' > > monitoring > > >>> > > tool. > > >>> > > > >> > > Similarly, we might change configs in a minor release > that > > >>> break > > >>> > > > >> user's > > >>> > > > >> > job > > >>> > > > >> > > behavior. We probably want to avoid these breaking > > changes in > > >>> > > minor > > >>> > > > >> > > releases. > > >>> > > > >> > > > > >>> > > > >> > > It is documented here > > >>> > > > >> > > < > > >>> > > > >> > > > > >>> > > > >> > > > >>> > > > >> > > >>> > > > > > >>> > > > > >>> > > > >>> > > > https://cwiki.apache.org/confluence/display/FLINK/Flink+Improvement+Proposals > > >>> > > > >> > > > > > >>> > > > >> > > that > > >>> > > > >> > > "Exposed monitoring information" and "Configuration > > settings" > > >>> > are > > >>> > > > >> public > > >>> > > > >> > > interfaces of the project. > > >>> > > > >> > > > > >>> > > > >> > > Maybe we should also specify the metric here so that > users > > >>> can > > >>> > > > safely > > >>> > > > >> > setup > > >>> > > > >> > > dashboards and tools to track how the autopilot is > > working, > > >>> > > similar > > >>> > > > to > > >>> > > > >> > how > > >>> > > > >> > > metrics are documented in FLIP-33 > > >>> > > > >> > > < > > >>> > > > >> > > > > >>> > > > >> > > > >>> > > > >> > > >>> > > > > > >>> > > > > >>> > > > >>> > > > https://cwiki.apache.org/confluence/display/FLINK/FLIP-33%3A+Standardize+Connector+Metrics > > >>> > > > >> > > > > > >>> > > > >> > > ? > > >>> > > > >> > > > > >>> > > > >> > > > > >>> > > > >> > > > -Max > > >>> > > > >> > > > > > >>> > > > >> > > > On Tue, Nov 15, 2022 at 3:01 PM Dong Lin < > > >>> lindon...@gmail.com > > >>> > > > > >>> > > > >> wrote: > > >>> > > > >> > > > > > >>> > > > >> > > > > Hi Maximilian, > > >>> > > > >> > > > > > > >>> > > > >> > > > > It seems that the following comments from the > previous > > >>> > > > discussions > > >>> > > > >> > have > > >>> > > > >> > > > not > > >>> > > > >> > > > > been addressed yet. Any chance we can have them > > addressed > > >>> > > before > > >>> > > > >> > > starting > > >>> > > > >> > > > > the voting thread? > > >>> > > > >> > > > > > > >>> > > > >> > > > > Thanks, > > >>> > > > >> > > > > Dong > > >>> > > > >> > > > > > > >>> > > > >> > > > > On Mon, Nov 7, 2022 at 2:33 AM Gyula Fóra < > > >>> > > gyula.f...@gmail.com > > >>> > > > > > > >>> > > > >> > > wrote: > > >>> > > > >> > > > > > > >>> > > > >> > > > > > Hi Dong! > > >>> > > > >> > > > > > > > >>> > > > >> > > > > > Let me try to answer the questions :) > > >>> > > > >> > > > > > > > >>> > > > >> > > > > > 1 : busyTimeMsPerSecond is not specific for CPU, > it > > >>> > measures > > >>> > > > the > > >>> > > > >> > time > > >>> > > > >> > > > > > spent in the main record processing loop for an > > >>> operator > > >>> > if > > >>> > > I > > >>> > > > >> > > > > > understand correctly. This includes IO operations > > too. > > >>> > > > >> > > > > > > > >>> > > > >> > > > > > 2: We should add this to the FLIP I agree. It > would > > be > > >>> a > > >>> > > > >> Duration > > >>> > > > >> > > > config > > >>> > > > >> > > > > > with the expected catch up time after rescaling > > (let's > > >>> > say 5 > > >>> > > > >> > > minutes). > > >>> > > > >> > > > It > > >>> > > > >> > > > > > could be computed based on the current data rate > and > > >>> the > > >>> > > > >> calculated > > >>> > > > >> > > max > > >>> > > > >> > > > > > processing rate after the rescale. > > >>> > > > >> > > > > > > > >>> > > > >> > > > > > > >>> > > > >> > > > > It will be great to add this in the FLIP so that > > >>> reviewers > > >>> > can > > >>> > > > >> > > understand > > >>> > > > >> > > > > how the source parallelisms are computed and how the > > >>> > algorithm > > >>> > > > >> works > > >>> > > > >> > > > > end-to-end. > > >>> > > > >> > > > > > > >>> > > > >> > > > > > > >>> > > > >> > > > > > 3: In the current proposal we don't have per > > operator > > >>> > > configs. > > >>> > > > >> > Target > > >>> > > > >> > > > > > utilization would apply to all operators > uniformly. > > >>> > > > >> > > > > > > > >>> > > > >> > > > > > 4: It should be configurable, yes. > > >>> > > > >> > > > > > > > >>> > > > >> > > > > > > >>> > > > >> > > > > Since this config is a public API, could we update > the > > >>> FLIP > > >>> > > > >> > accordingly > > >>> > > > >> > > > to > > >>> > > > >> > > > > provide this config? > > >>> > > > >> > > > > > > >>> > > > >> > > > > > > >>> > > > >> > > > > > > > >>> > > > >> > > > > > 5,6: The names haven't been finalized but I think > > these > > >>> > are > > >>> > > > >> minor > > >>> > > > >> > > > > details. > > >>> > > > >> > > > > > We could add concrete names to the FLIP :) > > >>> > > > >> > > > > > > > >>> > > > >> > > > > > > >>> > > > >> > > > > These metrics and configs are public API and need to > > be > > >>> > stable > > >>> > > > >> across > > >>> > > > >> > > > minor > > >>> > > > >> > > > > versions, could we document them before finalizing > the > > >>> FLIP? > > >>> > > > >> > > > > > > >>> > > > >> > > > > > > >>> > > > >> > > > > > > > >>> > > > >> > > > > > Cheers, > > >>> > > > >> > > > > > Gyula > > >>> > > > >> > > > > > > > >>> > > > >> > > > > > > > >>> > > > >> > > > > > On Sun, Nov 6, 2022 at 5:19 PM Dong Lin < > > >>> > > lindon...@gmail.com> > > >>> > > > >> > wrote: > > >>> > > > >> > > > > > > > >>> > > > >> > > > > >> Hi Max, > > >>> > > > >> > > > > >> > > >>> > > > >> > > > > >> Thank you for the proposal. The proposal tackles > a > > >>> very > > >>> > > > >> important > > >>> > > > >> > > > issue > > >>> > > > >> > > > > >> for Flink users and the design looks promising > > >>> overall! > > >>> > > > >> > > > > >> > > >>> > > > >> > > > > >> I have some questions to better understand the > > >>> proposed > > >>> > > > public > > >>> > > > >> > > > > interfaces > > >>> > > > >> > > > > >> and the algorithm. > > >>> > > > >> > > > > >> > > >>> > > > >> > > > > >> 1) The proposal seems to assume that the > operator's > > >>> > > > >> > > > busyTimeMsPerSecond > > >>> > > > >> > > > > >> could reach 1 sec. I believe this is mostly true > > for > > >>> > > > cpu-bound > > >>> > > > >> > > > > operators. > > >>> > > > >> > > > > >> Could you confirm that this can also be true for > > >>> io-bound > > >>> > > > >> > operators > > >>> > > > >> > > > > such as > > >>> > > > >> > > > > >> sinks? For example, suppose a Kafka Sink subtask > > has > > >>> > > reached > > >>> > > > >> I/O > > >>> > > > >> > > > > bottleneck > > >>> > > > >> > > > > >> when flushing data out to the Kafka clusters, > will > > >>> > > > >> > > busyTimeMsPerSecond > > >>> > > > >> > > > > >> reach 1 sec? > > >>> > > > >> > > > > >> > > >>> > > > >> > > > > >> 2) It is said that "users can configure a maximum > > >>> time to > > >>> > > > fully > > >>> > > > >> > > > process > > >>> > > > >> > > > > >> the backlog". The configuration section does not > > seem > > >>> to > > >>> > > > >> provide > > >>> > > > >> > > this > > >>> > > > >> > > > > >> config. Could you specify this? And any chance > this > > >>> > > proposal > > >>> > > > >> can > > >>> > > > >> > > > provide > > >>> > > > >> > > > > >> the formula for calculating the new processing > > rate? > > >>> > > > >> > > > > >> > > >>> > > > >> > > > > >> 3) How are users expected to specify the > > per-operator > > >>> > > configs > > >>> > > > >> > (e.g. > > >>> > > > >> > > > > >> target utilization)? For example, should users > > >>> specify it > > >>> > > > >> > > > > programmatically > > >>> > > > >> > > > > >> in a DataStream/Table/SQL API? > > >>> > > > >> > > > > >> > > >>> > > > >> > > > > >> 4) How often will the Flink Kubernetes operator > > query > > >>> > > metrics > > >>> > > > >> from > > >>> > > > >> > > > > >> JobManager? Is this configurable? > > >>> > > > >> > > > > >> > > >>> > > > >> > > > > >> 5) Could you specify the config name and default > > value > > >>> > for > > >>> > > > the > > >>> > > > >> > > > proposed > > >>> > > > >> > > > > >> configs? > > >>> > > > >> > > > > >> > > >>> > > > >> > > > > >> 6) Could you add the name/mbean/type for the > > proposed > > >>> > > > metrics? > > >>> > > > >> > > > > >> > > >>> > > > >> > > > > >> > > >>> > > > >> > > > > >> Cheers, > > >>> > > > >> > > > > >> Dong > > >>> > > > >> > > > > >> > > >>> > > > >> > > > > >> > > >>> > > > >> > > > > >> > > >>> > > > >> > > > > > > >>> > > > >> > > > > > >>> > > > >> > > > > >>> > > > >> > > > >>> > > > >> > > >>> > > > > > > >>> > > > > > >>> > > > > >>> > > > >>> > > >> > > >
