Glad to see that the interest of this thread keeps going. And thanks Thomas, Gyula, and Marton for driving this effort.
I want to share my two cents about the Flink K8s operator. > Standalone deployment VS native K8s integration There is already some feature requirement issue[1] for the existing GoogleCloudPlatform/flink-on-k8s-operator to support native K8s integration. So I think it will be great if the new introduced K8s operator could support native K8s mode. I could imagine some advantages for using native mode. e.g. dynamic allocation, stability improvement, etc. Compared with standalone + reactive mode, the native K8s could not integrate with auto-scaling(allocate/remove TaskManager pods based on metrics) well. Since the reconcile behavior for standalone and native K8s mode will be different, I am not sure whether we will support them both at the very beginning. > Go VS Java Although most of the K8s operators are developed in Go, which could benefit from the prosperous ecosystem and various tools. I lean to develop the K8s operator under Flink umbrella using Java. Then the Flink contributors will be easier to get involved. We could use the same Kubernetes Java client with Flink. When Flink exposes some public deployment interfaces(e.g. ApplicationDeployer) in the future, the K8s operator will also benefit a lot from this. I already have a simple PoC project of this implementation[2]. Hope you could get some inspirations from this. [1]. https://github.com/GoogleCloudPlatform/flink-on-k8s-operator/issues/168 [2]. https://github.com/wangyang0918/flink-native-k8s-operator Best, Yang Xintong Song <tonysong...@gmail.com> 于2022年1月14日周五 15:47写道： > Thanks for volunteering to drive this effort, Marton, Thomas and Gyula. > > Looking forward to the public discussion. Please feel free to reach out if > there's anything you need from us. > > Thank you~ > > Xintong Song > > > > On Fri, Jan 14, 2022 at 8:27 AM Chenya Zhang <chenyazhangche...@gmail.com> > wrote: > >> Thanks Thomas, Gyula, and Marton for driving this effort! It would >> greatly ease the adoption of Apache Flink on Kubernetes and help to address >> the current operational pain points as mentioned. Look forward to the >> proposal and more discussions! >> >> Best, >> Chenya >> >> On Thu, Jan 13, 2022 at 12:15 PM Márton Balassi <balassi.mar...@gmail.com> >> wrote: >> >>> Hi All, >>> >>> I am pleased to see the level of enthusiasm and technical consideration >>> already emerging in this thread. I wholeheartedly support building an >>> operator and endorsing it via placing it under the Apache Flink umbrella >>> (as a separate repository) as the current lack of it is clearly becoming >>> an >>> adoption bottleneck for large scale Flink users. The next logical step is >>> to write a FLIP to agree on the technical details, so that we can put >>> forward the proposal to the Flink PMC for creating a new repository with >>> a >>> clear purpose in mind. I volunteer to work with Thomas and Gyula on the >>> initial wording on the proposal which we will put up for public >>> discussion >>> in the coming weeks. >>> >>> Best, >>> Marton >>> >>> On Thu, Jan 13, 2022 at 9:22 AM Konstantin Knauf <kna...@apache.org> >>> wrote: >>> >>> > Hi Thomas, >>> > >>> > Yes, I was referring to a separate repository under Apache Flink. >>> > >>> > Cheers, >>> > >>> > Konstantin >>> > >>> > On Thu, Jan 13, 2022 at 6:19 AM Thomas Weise <t...@apache.org> wrote: >>> > >>> >> Hi everyone, >>> >> >>> >> Thanks for the feedback and discussion. A few additional thoughts: >>> >> >>> >> [Konstantin] > With respect to common lifecycle management operations: >>> >> these features are >>> >> > not available (within Apache Flink) for any of the other resource >>> >> providers >>> >> > (YARN, Standalone) either. From this perspective, I wouldn't >>> consider >>> >> this >>> >> > a shortcoming of the Kubernetes integration. >>> >> >>> >> I think time and evolution of the ecosystem are factors to consider as >>> >> well. The state and usage of Flink was much different when YARN >>> >> integration was novel. Expectations are different today and the >>> >> lifecycle functionality provided by an operator may as well be >>> >> considered essential to support the concept of a Flink application on >>> >> k8s. After few years learning from operator experience outside of >>> >> Flink it might be a good time to fill the gap. >>> >> >>> >> [Konstantin] > I still believe that we should keep this focus on low >>> >> > level composable building blocks (like Jobs and Snapshots) in Apache >>> >> Flink >>> >> > to make it easy for everyone to build fitting higher level >>> abstractions >>> >> > like a FlinkApplication Custom Resource on top of it. >>> >> >>> >> I completely agree that it is important that the basic functions of >>> >> Flink are solid and continued focus is necessary. Thanks for sharing >>> >> the pointers, these are great improvements. At the same time, >>> >> ecosystem, contributor base and user spectrum are growing. There have >>> >> been significant additions in many areas of Flink including connectors >>> >> and higher level abstractions like statefun, SQL and Python. It's also >>> >> evident from additional repositories/subprojects that we have in Flink >>> >> today. >>> >> >>> >> [Konstantin] > Having said this, if others in the community have the >>> >> capacity to push and >>> >> > *maintain* a somewhat minimal "reference" Kubernetes Operator for >>> Apache >>> >> > Flink, I don't see any blockers. If or when this happens, I'd see >>> some >>> >> > clear benefits of using a separate repository (easier independent >>> >> > versioning and releases, different build system & tooling (go, I >>> >> assume)). >>> >> >>> >> Naturally different contributors to the project have different focus. >>> >> Let's find out if there is strong enough interest to take this on and >>> >> strong enough commitment to maintain. As I see it, there is a >>> >> tremendous amount of internal investment going into operationalizing >>> >> Flink within many companies. Improvements to the operational side of >>> >> Flink like the operator would complement Flink nicely. I assume that >>> >> you are referring to a separate repository within Apache Flink, which >>> >> would give it the chance to achieve better sustainability than the >>> >> existing external operator efforts. There is also the fact that some >>> >> organizations which are heavily invested in operationalizing Flink are >>> >> allowing contributing to Apache Flink itself but less so to arbitrary >>> >> github projects. Regarding the tooling, it could well turn out that >>> >> Java is a good alternative given the ecosystem focus and that there is >>> >> an opportunity for reuse in certain aspects (metrics, logging etc.). >>> >> >>> >> [Yang] > I think Xintong has given a strong point why we introduced >>> >> the native K8s integration, which is active resource management. >>> >> > I have a concrete example for this in the production. When a K8s >>> node >>> >> is down, the standalone K8s deployment will take longer >>> >> > recovery time based on the K8s eviction time(IIRC, default is 5 >>> >> minutes). For the native K8s integration, Flink RM could be aware of >>> the >>> >> > TM heartbeat lost and allocate a new one timely. >>> >> >>> >> Thanks for sharing this, we should evaluate it as part of a proposal. >>> >> If we can optimize recovery or scaling with active resource management >>> >> then perhaps it is worth to support it through the operator. >>> >> Previously mentioned operators all rely on the standalone model. >>> >> >>> >> Cheers, >>> >> Thomas >>> >> >>> >> On Wed, Jan 12, 2022 at 3:21 AM Konstantin Knauf <kna...@apache.org> >>> >> wrote: >>> >> > >>> >> > cc dev@ >>> >> > >>> >> > Hi Thomas, Hi everyone, >>> >> > >>> >> > Thank you for starting this discussion and sorry for chiming in >>> late. >>> >> > >>> >> > I agree with Thomas' and David's assessment of Flink's "Native >>> >> Kubernetes >>> >> > Integration", in particular, it does actually not integrate well >>> with >>> >> the >>> >> > Kubernetes ecosystem despite being called "native" (tooling, >>> security >>> >> > concerns). >>> >> > >>> >> > With respect to common lifecycle management operations: these >>> features >>> >> are >>> >> > not available (within Apache Flink) for any of the other resource >>> >> providers >>> >> > (YARN, Standalone) either. From this perspective, I wouldn't >>> consider >>> >> this >>> >> > a shortcoming of the Kubernetes integration. Instead, we have been >>> >> focusing >>> >> > our efforts in Apache Flink on the operations of a single Job, and >>> left >>> >> > orchestration and lifecycle management that spans multiple Jobs to >>> >> > ecosystem projects. I still believe that we should keep this focus >>> on >>> >> low >>> >> > level composable building blocks (like Jobs and Snapshots) in Apache >>> >> Flink >>> >> > to make it easy for everyone to build fitting higher level >>> abstractions >>> >> > like a FlinkApplication Custom Resource on top of it. For example, >>> we >>> >> are >>> >> > currently contributing multiple improvements [1,2,3,4] to the REST >>> API >>> >> and >>> >> > Application Mode that in our experience will make it easier to >>> manage >>> >> > Apache Flink with a Kubernetes operator. Given this background, I >>> >> suspect a >>> >> > Kubernetes Operator in Apache Flink would not be a priority for us >>> at >>> >> > Ververica - at least right now. >>> >> > >>> >> > Having said this, if others in the community have the capacity to >>> push >>> >> and >>> >> > *maintain* a somewhat minimal "reference" Kubernetes Operator for >>> Apache >>> >> > Flink, I don't see any blockers. If or when this happens, I'd see >>> some >>> >> > clear benefits of using a separate repository (easier independent >>> >> > versioning and releases, different build system & tooling (go, I >>> >> assume)). >>> >> > >>> >> > Looking forward to your thoughts, >>> >> > >>> >> > Konstantin >>> >> > >>> >> > [1] https://issues.apache.org/jira/browse/FLINK-24275 >>> >> > [2] https://issues.apache.org/jira/browse/FLINK-24208 >>> >> > [3] >>> >> > >>> >> >>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-194%3A+Introduce+the+JobResultStore >>> >> > [4] https://issues.apache.org/jira/browse/FLINK-24113 >>> >> > >>> >> > On Mon, Jan 10, 2022 at 2:11 PM Gyula Fóra <gyf...@apache.org> >>> wrote: >>> >> > >>> >> > > Hi All! >>> >> > > >>> >> > > This is a very interesting discussion. >>> >> > > >>> >> > > I think many users find it confusing what deployment mode to >>> choose >>> >> when >>> >> > > considering a new production application on Kubernetes. With all >>> the >>> >> > > options of native, standalone and different operators this can get >>> >> tricky :) >>> >> > > >>> >> > > I really like the idea that Thomas brought up to have at least a >>> >> minimal >>> >> > > operator implementation in Flink itself to cover the most common >>> >> production >>> >> > > job lifecycle management scenarios. I think the Flink community >>> has a >>> >> very >>> >> > > strong experience in this area to create a successful >>> implementation >>> >> that >>> >> > > would benefit most production users on Kubernetes. >>> >> > > >>> >> > > Cheers, >>> >> > > Gyula >>> >> > > >>> >> > > On Mon, Jan 10, 2022 at 4:29 AM Yang Wang <danrtsey...@gmail.com> >>> >> wrote: >>> >> > > >>> >> > >> Thanks all for this fruitful discussion. >>> >> > >> >>> >> > >> I think Xintong has given a strong point why we introduced the >>> >> native K8s >>> >> > >> integration, which is active resource management. >>> >> > >> I have a concrete example for this in the production. When a K8s >>> >> node is >>> >> > >> down, the standalone K8s deployment will take longer >>> >> > >> recovery time based on the K8s eviction time(IIRC, default is 5 >>> >> minutes). >>> >> > >> For the native K8s integration, Flink RM could be aware of the >>> >> > >> TM heartbeat lost and allocate a new one timely. >>> >> > >> >>> >> > >> Also when introducing the native K8s integration, another hit is >>> >> that we >>> >> > >> should make the users are easy enough to migrate from YARN >>> >> deployment. >>> >> > >> They already have a production-ready job life-cycle management >>> >> system, >>> >> > >> which is using Flink CLI to submit the Flink jobs. >>> >> > >> So we provide a consistent command "bin/flink run-application -t >>> >> > >> kubernetes-application/yarn-application" to start a Flink >>> >> application and >>> >> > >> "bin/flink cancel/stop ..." >>> >> > >> to terminate a Flink application. >>> >> > >> >>> >> > >> >>> >> > >> Compared with K8s operator, I know that this is not a K8s >>> >> > >> native mechanism. Hence, I also agree that we still need a >>> powerful >>> >> K8s >>> >> > >> operator which >>> >> > >> could work with both standalone and native K8s modes. The major >>> >> > >> difference between them is how to start the JM and TM pods. For >>> >> standalone, >>> >> > >> they are managed by K8s job/deployment. For native, maybe we >>> could >>> >> simply >>> >> > >> create a submission carrying the "flink run-application" >>> arguments >>> >> > >> which is derived from the Flink application CR. >>> >> > >> >>> >> > >> Make the Flink's active resource manager can talk to the K8s >>> >> operator is >>> >> > >> an interesting option, which could support both standalone and >>> >> native. >>> >> > >> Then Flink RM just needs to declare the resource >>> requirement(e.g. 2 * >>> >> > >> <2G, 1CPU>, 2 * <4G, 1CPU>) and defer the resource >>> >> allocation/de-allocation >>> >> > >> to the K8s operator. It feels like an intermediate form between >>> >> native >>> >> > >> and standalone mode :) >>> >> > >> >>> >> > >> >>> >> > >> >>> >> > >> Best, >>> >> > >> Yang >>> >> > >> >>> >> > >> >>> >> > >> >>> >> > >> Xintong Song <tonysong...@gmail.com> 于2022年1月7日周五 12:02写道： >>> >> > >> >>> >> > >>> Hi folks, >>> >> > >>> >>> >> > >>> Thanks for the discussion. I'd like to share my two cents on >>> this >>> >> topic. >>> >> > >>> >>> >> > >>> Firstly, I'd like to clarify my understanding of the concepts >>> >> "native >>> >> > >>> k8s integration" and "active resource management". >>> >> > >>> - Native k8s integration means Flink's master interacts with >>> k8s' >>> >> api >>> >> > >>> server directly. It acts like embedding an operator inside >>> Flink's >>> >> master, >>> >> > >>> which manages the resources (pod, deployment, configmap, etc.) >>> and >>> >> watches >>> >> > >>> / reacts to related events. >>> >> > >>> - Active resource management means Flink can actively start / >>> >> terminate >>> >> > >>> workers as needed. Its key characteristic is that the resource a >>> >> Flink >>> >> > >>> deployment uses is decided by the job's execution plan, unlike >>> the >>> >> opposite >>> >> > >>> reactive mode (resource available to the deployment decides the >>> >> execution >>> >> > >>> plan) or the standalone mode (both execution plan and deployment >>> >> resources >>> >> > >>> are predefined). >>> >> > >>> >>> >> > >>> Currently, we have the yarn and native k8s deployments (and the >>> >> recently >>> >> > >>> removed mesos deployment) in active mode, due to their ability >>> to >>> >> request / >>> >> > >>> release worker resources from the underlying cluster. And all >>> the >>> >> existing >>> >> > >>> operators, AFAIK, work with a Flink standalone deployment, where >>> >> Flink >>> >> > >>> cannot request / release resources by itself. >>> >> > >>> >>> >> > >>> From this perspective, I think a large part of the native k8s >>> >> > >>> integration advantages come from the active mode: being able to >>> >> better >>> >> > >>> understand the job's resource requirements and adjust the >>> deployment >>> >> > >>> resource accordingly. Both fine-grained resource management >>> >> (customizing TM >>> >> > >>> resources for different tasks / operators) and adaptive batch >>> >> scheduler >>> >> > >>> (rescale the deployment w.r.t. different stages) fall into this >>> >> category. >>> >> > >>> >>> >> > >>> I'm wondering if we can have an operator that also works with >>> the >>> >> active >>> >> > >>> mode. Instead of talking to the api server directly for adding / >>> >> deleting >>> >> > >>> resources, Flink's active resource manager can talk to the >>> operator >>> >> (via >>> >> > >>> CR) about the resources the deployment needs, and let the >>> operator >>> >> to >>> >> > >>> actually add / remove the resources. The operator should be >>> able to >>> >> work >>> >> > >>> with (active) or without (standalone) the information of >>> >> deployment's >>> >> > >>> resource requirements. In this way, users are free to choose >>> >> between active >>> >> > >>> and reactive (e.g., HPA) rescaling, while always benefiting >>> from the >>> >> > >>> beyond-deployment lifecycle (upgrades, savepoint management, >>> etc.) >>> >> and >>> >> > >>> alignment with the K8s ecosystem (Flink client free, operating >>> via >>> >> kubectl, >>> >> > >>> etc.). >>> >> > >>> >>> >> > >>> Thank you~ >>> >> > >>> >>> >> > >>> Xintong Song >>> >> > >>> >>> >> > >>> >>> >> > >>> >>> >> > >>> On Thu, Jan 6, 2022 at 1:06 PM Thomas Weise <t...@apache.org> >>> wrote: >>> >> > >>> >>> >> > >>>> Hi David, >>> >> > >>>> >>> >> > >>>> Thank you for the reply and context! >>> >> > >>>> >>> >> > >>>> As for workload types and where native integration might fit: I >>> >> think >>> >> > >>>> that any k8s native solution that satisfies category 3) can >>> also >>> >> take >>> >> > >>>> care of 1) and 2) while the native integration by itself can't >>> >> achieve >>> >> > >>>> that. Existence of [1] might serve as further indication. >>> >> > >>>> >>> >> > >>>> The k8s operator pattern would be an essential building block >>> for a >>> >> > >>>> k8s native solution that is interoperable with k8s ecosystem >>> >> tooling >>> >> > >>>> like kubectl, which is why [2] and subsequent derived art were >>> >> > >>>> created. Specifically the CRD allows us to directly express the >>> >> > >>>> concept of a Flink application consisting of job manager and >>> task >>> >> > >>>> manager pods along with associated create/update/delete >>> operations. >>> >> > >>>> >>> >> > >>>> Would it make sense to gauge interest to have such an operator >>> as >>> >> part >>> >> > >>>> of Flink? It appears so from discussions like [3]. I think such >>> >> > >>>> addition would significantly lower the barrier to adoption, >>> since >>> >> like >>> >> > >>>> you mentioned one cannot really run mission critical streaming >>> >> > >>>> workloads with just the Apache Flink release binaries alone. >>> While >>> >> it >>> >> > >>>> is great to have multiple k8s operators to choose from that are >>> >> > >>>> managed outside Flink, it is unfortunately also evident that >>> >> today's >>> >> > >>>> hot operator turns into tomorrow's tech debt. I think such fate >>> >> would >>> >> > >>>> be less likely within the project, when multiple parties can >>> join >>> >> > >>>> forces and benefit from each other's contributions. There were >>> >> similar >>> >> > >>>> considerations and discussions around Docker images in the >>> past. >>> >> > >>>> >>> >> > >>>> Out of the features that you listed it is particularly the >>> >> application >>> >> > >>>> upgrade that needs to be solved through an external process >>> like >>> >> > >>>> operator. The good thing is that many folks have already >>> thought >>> >> hard >>> >> > >>>> about this and in existing implementations we see different >>> >> strategies >>> >> > >>>> that have their merit and production mileage (certainly >>> applies to >>> >> > >>>> [2]). We could combine the best of these ideas into a unified >>> >> > >>>> implementation as part of Flink itself as starting point. >>> >> > >>>> >>> >> > >>>> Cheers, >>> >> > >>>> Thomas >>> >> > >>>> >>> >> > >>>> >>> >> > >>>> [1] https://github.com/wangyang0918/flink-native-k8s-operator >>> >> > >>>> [2] https://github.com/lyft/flinkk8soperator >>> >> > >>>> [3] >>> >> https://lists.apache.org/thread/fhcr5gj1txcr0fo4s821jkp6d4tk6080 >>> >> > >>>> >>> >> > >>>> >>> >> > >>>> On Tue, Jan 4, 2022 at 4:04 AM David Morávek <d...@apache.org> >>> >> wrote: >>> >> > >>>> > >>> >> > >>>> > Hi Thomas, >>> >> > >>>> > >>> >> > >>>> > AFAIK there are no specific plans in this direction with the >>> >> native >>> >> > >>>> integration, but I'd like to share some thoughts on the topic >>> >> > >>>> > >>> >> > >>>> > In my understanding there are three major groups of >>> workloads in >>> >> > >>>> Flink: >>> >> > >>>> > >>> >> > >>>> > 1) Batch workloads >>> >> > >>>> > 2) Interactive workloads (Both Batch and Streaming; eg. SQL >>> >> Gateway / >>> >> > >>>> Zeppelin Notebooks / VVP ...) >>> >> > >>>> > 3) "Mission Critical" Streaming workloads >>> >> > >>>> > >>> >> > >>>> > I think the native integration fits really well in the first >>> two >>> >> > >>>> categories. Let's talk about these first: >>> >> > >>>> > >>> >> > >>>> > 1) Batch workloads >>> >> > >>>> > >>> >> > >>>> > You don't really need to address the upgrade story here. The >>> >> > >>>> interesting topic is how to "dynamically" adjust parallelism >>> as the >>> >> > >>>> workload can change between stages. This is where the Adaptive >>> >> Batch >>> >> > >>>> Scheduler [1] comes into play. To leverage the scheduler to the >>> >> full >>> >> > >>>> extend, it needs to be deployed with the remote shuffle >>> service in >>> >> place >>> >> > >>>> [2], so the Flink's Resource Manager can free TaskManagers that >>> >> are no >>> >> > >>>> longer needed. >>> >> > >>>> > >>> >> > >>>> > This can IMO work really well with the native integration as >>> >> there is >>> >> > >>>> really clear approach on how the Resource Manager should >>> decide on >>> >> what >>> >> > >>>> resources should be allocated. >>> >> > >>>> > >>> >> > >>>> > 2) Interactive workloads >>> >> > >>>> > >>> >> > >>>> > Again, the upgrade story is not really interesting in this >>> >> scenario. >>> >> > >>>> For batch workloads, it's basically the same as the above. For >>> >> streaming >>> >> > >>>> one this gets tricky. The main initiative that we current have >>> in >>> >> terms of >>> >> > >>>> auto scaling / re-scaling of the streaming workloads is the >>> >> reactive mode >>> >> > >>>> (adaptive scheduler) [3]. >>> >> > >>>> > >>> >> > >>>> > I can totally see how the reactive mode could be integrated >>> in >>> >> the >>> >> > >>>> native integration, but with the application mode, which is not >>> >> really >>> >> > >>>> suitable for the interactive workloads. For integration with >>> >> session >>> >> > >>>> cluster, we'd first need to address the "scheduling" problem of >>> >> how to >>> >> > >>>> distribute newly available resources between multiple jobs. >>> >> > >>>> > >>> >> > >>>> > What's pretty neat here is that the interpreter (zeppelin, >>> sql >>> >> gw, >>> >> > >>>> ...) have a really convenient way of spinning up a new cluster >>> >> inside k8s. >>> >> > >>>> > >>> >> > >>>> > 3) "Mission Critical" Streaming workloads >>> >> > >>>> > >>> >> > >>>> > This one is IMO the primary reason why one would consider >>> >> building a >>> >> > >>>> new operator these days as this needs a careful lifecycle >>> >> management of the >>> >> > >>>> pipeline. I assume this is also the use case that you're >>> >> investigating, am >>> >> > >>>> I correct? >>> >> > >>>> > >>> >> > >>>> > I'd second the requirements that you've already stated: >>> >> > >>>> > a) Resource efficiency - being able to re-scale based on the >>> >> > >>>> workload, in order to keep up with the input / not waste >>> resources >>> >> > >>>> > b) Fast recovery >>> >> > >>>> > c) Application upgrades >>> >> > >>>> > >>> >> > >>>> > I personally don't think that the native integration is >>> really >>> >> > >>>> suitable here. The direction that we're headed is with the >>> >> standalone >>> >> > >>>> deployment on Kubernetes + the reactive mode (adaptive >>> scheduler). >>> >> > >>>> > >>> >> > >>>> > In theory, if we want to build a really cloud (Kubernetes) >>> native >>> >> > >>>> stream processor, deploying the pipeline should be as simple as >>> >> deploying >>> >> > >>>> any other application. It should be also simple to integrate >>> with >>> >> CI & CD >>> >> > >>>> environment and the fast / frequent deploy philosophy. >>> >> > >>>> > >>> >> > >>>> > Let's see where we stand and where we can expand from there: >>> >> > >>>> > >>> >> > >>>> > a) Resource efficiency >>> >> > >>>> > >>> >> > >>>> > We already have the reactive mode in place. This allows you >>> to >>> >> add / >>> >> > >>>> remove task managers by adjusting the TM deployment (`kubectl >>> >> scale ...`) >>> >> > >>>> and Flink will automatically react to the available resources. >>> >> This is >>> >> > >>>> currently only supported with the Application Mode, that is >>> >> limited to a >>> >> > >>>> single job (which should be enough for this kind of workload). >>> >> > >>>> > >>> >> > >>>> > The re-scaling logic is left completely up to the user and >>> can >>> >> be as >>> >> > >>>> simple as setting up a HPA (Horizontal Pod Autoscaler). I tend >>> to >>> >> think in >>> >> > >>>> the direction, that we might want to provide a custom k8s >>> metrics >>> >> server, >>> >> > >>>> that allows HPA to query the metrics from JM, to make this more >>> >> flexible >>> >> > >>>> and easy to use. >>> >> > >>>> > >>> >> > >>>> > As this looks really great in theory, there are still some >>> >> > >>>> shortcomings that we're actively working on addressing. For >>> this >>> >> feature to >>> >> > >>>> be really widely adopted, we need to make the re-scaling >>> >> experience as fast >>> >> > >>>> as possible, so we can re-scale often to react to the input >>> rate. >>> >> This >>> >> > >>>> could be currently a problem with large RocksDB states as this >>> >> involves >>> >> > >>>> full re-balance of the state (splitting / merging RocksDB >>> >> instances). The >>> >> > >>>> k8s operator approach has the same / even worse limitation as >>> it >>> >> involves >>> >> > >>>> taking a savepoint a re-building the state from it. >>> >> > >>>> > >>> >> > >>>> > b) Fast recovery >>> >> > >>>> > >>> >> > >>>> > This is IMO not as different from the native mode (although >>> I'd >>> >> have >>> >> > >>>> to check whether RM failover can reuse task managers). This >>> >> involves >>> >> > >>>> frequent and fast checkpointing, local recovery (which is >>> still not >>> >> > >>>> supported in reactive mode, but this will be hopefully >>> addressed >>> >> soon) and >>> >> > >>>> working directory efforts [4]. >>> >> > >>>> > >>> >> > >>>> > c) Application upgrades >>> >> > >>>> > >>> >> > >>>> > This is the topic I'm still struggling with a little. >>> >> Historically >>> >> > >>>> this involves external lifecycle management (savepoint + >>> >> submitting a new >>> >> > >>>> job). I think at the end of the day, with application mode on >>> >> standalone >>> >> > >>>> k8s, it could be as simple as updating the docker image of the >>> JM >>> >> > >>>> deployment. >>> >> > >>>> > >>> >> > >>>> > If I think about the simplest upgrade scenario, simple >>> in-place >>> >> > >>>> restore from the latest checkpoint, it may be fairly simple to >>> >> implement. >>> >> > >>>> What I'm struggling with are the more complex upgrade scenarios >>> >> such as >>> >> > >>>> dual, blue / green deployment. >>> >> > >>>> > >>> >> > >>>> > >>> >> > >>>> > To sum this up, I'd really love if Flink could provide great >>> >> out-of >>> >> > >>>> the box experience with standalone mode on k8s, that makes the >>> >> experience >>> >> > >>>> as close to running / operating any other application as >>> possible. >>> >> > >>>> > >>> >> > >>>> > I'd really appreciate to hear your thoughts on this topic. >>> >> > >>>> > >>> >> > >>>> > [1] >>> >> > >>>> >>> >> >>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-187%3A+Adaptive+Batch+Job+Scheduler >>> >> > >>>> > [2] https://github.com/flink-extended/flink-remote-shuffle >>> >> > >>>> > [3] >>> >> > >>>> >>> >> >>> https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/deployment/elastic_scaling/ >>> >> > >>>> > [4] >>> >> > >>>> >>> >> >>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-198%3A+Working+directory+for+Flink+processes >>> >> > >>>> > >>> >> > >>>> > Best, >>> >> > >>>> > D. >>> >> > >>>> > >>> >> > >>>> > On Tue, Jan 4, 2022 at 12:44 AM Thomas Weise <t...@apache.org >>> > >>> >> wrote: >>> >> > >>>> >> >>> >> > >>>> >> Hi, >>> >> > >>>> >> >>> >> > >>>> >> I was recently looking at the Flink native Kubernetes >>> >> integration [1] >>> >> > >>>> >> to get an idea how it relates to existing operator based >>> >> solutions >>> >> > >>>> >> [2], [3]. >>> >> > >>>> >> >>> >> > >>>> >> Part of the native integration's motivations was simplicity >>> (no >>> >> extra >>> >> > >>>> >> component to install), but arguably that is also a >>> shortcoming. >>> >> The >>> >> > >>>> >> k8s operator model can offer support for application >>> lifecycle >>> >> like >>> >> > >>>> >> upgrade and rescaling, as well as job submission without a >>> Flink >>> >> > >>>> >> client. >>> >> > >>>> >> >>> >> > >>>> >> When using the Flink native integration it would still be >>> >> necessary >>> >> > >>>> to >>> >> > >>>> >> provide that controller functionality. Is the idea to use >>> the >>> >> native >>> >> > >>>> >> integration for task manager resource allocation in tandem >>> with >>> >> an >>> >> > >>>> >> operator that provides the external controller >>> functionality? If >>> >> > >>>> >> anyone using the Flink native integration can share >>> experience, >>> >> I >>> >> > >>>> >> would be curious to learn more about the specific setup and >>> if >>> >> there >>> >> > >>>> >> are plans to expand the k8s native integration capabilities. >>> >> > >>>> >> >>> >> > >>>> >> For example: >>> >> > >>>> >> >>> >> > >>>> >> * Application upgrade with features such as [4]. Since the >>> job >>> >> > >>>> manager >>> >> > >>>> >> is part of the deployment it cannot orchestrate the >>> deployment. >>> >> It >>> >> > >>>> >> needs to be the responsibility of an external process. Has >>> >> anyone >>> >> > >>>> >> contemplated adding such a component to Flink itself? >>> >> > >>>> >> >>> >> > >>>> >> * Rescaling: Theoretically a parallelism change could be >>> >> performed >>> >> > >>>> w/o >>> >> > >>>> >> restart of the job manager pod. Hence, building blocks to >>> >> trigger and >>> >> > >>>> >> apply rescaling could be part of Flink itself. Has this been >>> >> explored >>> >> > >>>> >> further? >>> >> > >>>> >> >>> >> > >>>> >> Yang kindly pointed me to [5]. Is the >>> recommendation/conclusion >>> >> that >>> >> > >>>> >> when a k8s operator is already used, then let it be in >>> charge >>> >> of the >>> >> > >>>> >> task manager resource allocation? If so, what scenario was >>> the >>> >> native >>> >> > >>>> >> k8s integration originally intended for? >>> >> > >>>> >> >>> >> > >>>> >> Thanks, >>> >> > >>>> >> Thomas >>> >> > >>>> >> >>> >> > >>>> >> [1] >>> >> > >>>> >>> >> >>> https://nightlies.apache.org/flink/flink-docs-release-1.13/docs/deployment/resource-providers/native_kubernetes/#deployment-modes >>> >> > >>>> >> [2] https://github.com/lyft/flinkk8soperator >>> >> > >>>> >> [3] https://github.com/spotify/flink-on-k8s-operator >>> >> > >>>> >> [4] >>> >> > >>>> >>> >> >>> https://github.com/lyft/flinkk8soperator/blob/master/docs/state_machine.md >>> >> > >>>> >> [5] >>> >> https://lists.apache.org/thread/8cn99f6n8nhr07n5vqfo880tpm624s5d >>> >> > >>>> >>> >> > >>> >>> >> > >>> >> > -- >>> >> > >>> >> > Konstantin Knauf >>> >> > >>> >> > https://twitter.com/snntrable >>> >> > >>> >> > https://github.com/knaufk >>> >> >>> > >>> > >>> > -- >>> > >>> > Konstantin Knauf >>> > >>> > https://twitter.com/snntrable >>> > >>> > https://github.com/knaufk >>> > >>> >>
