Hi John, hi Prasanna, hi Rui, Gyula already gave great answers to your questions, just adding to it:
>What’s the reason to add auto scaling to the Operator instead of to the JobManager? As Gyula mentioned, the JobManager is not the ideal place, at least not until Flink supports in-place autoscaling which is a related but ultimately very different problem because it involves solving job reconfiguration at runtime. I believe the AdaptiveScheduler has moved into this direction and there is nothing preventing us from using it in the future once it has evolved further. For now, going through the job redeployment route seems like the easiest and safest way. >Could we finally use the autoscaler as a outside tool? or run it as a separate java process? I think we could but I wouldn't make it a requirement for the first version. There is nothing preventing the autoscaler from running as a separate k8s/yarn deployment which would provide some of the same availability guarantees as the operator or any deployment has on k8s/yarn. However, I think this increases complexity by a fair bit because the operator already has all the configuration and tooling to manage Flink jobs. I'm not at all opposed to coming up with a way to allow the autoscaler to run separately as well as with the k8s operator. I just think it is out of scope for the first version to keep the complexity and scope under control. -Max On Fri, Nov 25, 2022 at 8:16 AM Rui Fan <1996fan...@gmail.com> wrote: > Hi Gyula > > Thanks for the clarification! > > Best > Rui Fan > > On Fri, Nov 25, 2022 at 1:50 PM Gyula Fóra <gyula.f...@gmail.com> wrote: > > > Rui, Prasanna: > > > > I am afraid that creating a completely independent autoscaler process > that > > works with any type of Flink clusters is out of scope right now due to > the > > following reasons: > > > > If we were to create a new general process, we would have to implement > high > > availability and a pluggable mechanism to durably store metadata etc. The > > process itself would also have to run somewhere so we would have to > provide > > integrations. > > > > It would also not be able to scale clusters easily without adding > > Kubernetes-operator-like functionality to it, and if the user has to do > it > > manually most of the value is already lost. > > > > Last but not least this would have the potential of interfering with > other > > actions the user might be currently doing, making the autoscaler itself > > complex and more unreliable. > > > > These are all prohibitive reasons at this point. We already have a > > prototype that tackle these smoothly as part of the Kubernetes operator. > > > > Instead of trying to put the autoscaler somewhere else we might also > > consider supporting different cluster types within the Kubernetes > operator. > > While that might sound silly at first, it is of similar scope to your > > suggestions and could help the problem. > > > > As for the new config question, we could collectively decide to backport > > this feature to enable the autoscaler as it is a very minor change. > > > > Gyula > > > > On Fri, 25 Nov 2022 at 06:21, John Roesler <vvcep...@apache.org> wrote: > > > > > Thanks for this answer, Gyula! > > > -John > > > > > > On Thu, Nov 24, 2022, at 14:53, Gyula Fóra 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 > > > >> >>> > > > >> > > > > >> > > > >> >>> > > > >> > > > > >> > > > >> >>> > > > >> > > > > >> > > > >> >>> > > > >> > > > > > > > >> >>> > > > >> > > > > > > >> >>> > > > >> > > > > > >> >>> > > > >> > > > > >> >>> > > > >> > > > >> >>> > > > > > > > >> >>> > > > > > > >> >>> > > > > > >> >>> > > > > >> >>> > > > >> >> > > > >> > > > > > >
