Thanks for feedback. Could you check whether I understand correctly? *Only using 'reactive' mode:* By manually adding TaskManager(TM) (such as using './bin/taskmanager.sh start'), parallelism will be increased. For example, when job parallelism is 1 and TM is 1, and if adding 1 new TM, JobManager will be restarted and parallelism will be 2. But the number of TM is not being controlled automatically.
*Autoscaler + non-reactive:* It can flexibilly control the number of TM by several metrics(CPU usage, throughput, ...), and JobManager will be restarted when scaling. But job parallelism is the same after the number of TM has been changed. *Autoscaler + 'reactive' mode*: It can control numbers of TM by metric, and increase/decrease job parallelism by changing TM. Regards, Jung 2023년 9월 1일 (금) 오후 8:16, Gyula Fóra <gyula.f...@gmail.com>님이 작성: > I would look at reactive scaling as a way to increase / decrease > parallelism. > > It’s not a way to automatically decide when to actually do it as you need > to create new TMs . > > The autoscaler could use reactive mode to change the parallelism but you > need the autoscaler itself to decide when new resources should be added > > On Fri, 1 Sep 2023 at 13:09, Dennis Jung <inylov...@gmail.com> wrote: > >> For now, the thing I've found about 'reactive' mode is that it >> automatically adjusts 'job parallelism' when TaskManager is >> increased/decreased. >> >> >> https://www.slideshare.net/FlinkForward/autoscaling-flink-with-reactive-mode >> >> Is there some other feature that only 'reactive' mode offers for scaling? >> >> Thanks. >> Regards. >> >> >> >> 2023년 9월 1일 (금) 오후 4:56, Dennis Jung <inylov...@gmail.com>님이 작성: >> >>> Hello, >>> Thank you for your response. I have few more questions in following: >>> https://nightlies.apache.org/flink/flink-docs-release-1.15/docs/deployment/elastic_scaling/ >>> >>> *Reactive Mode configures a job so that it always uses all resources >>> available in the cluster. Adding a TaskManager will scale up your job, >>> removing resources will scale it down. Flink will manage the parallelism of >>> the job, always setting it to the highest possible values.* >>> => Does this mean when I add/remove TaskManager in 'non-reactive' mode, >>> resource(CPU/Memory/Etc.) of the cluster is not being changed? >>> >>> *Reactive Mode restarts a job on a rescaling event, restoring it from >>> the latest completed checkpoint. This means that there is no overhead of >>> creating a savepoint (which is needed for manually rescaling a job). Also, >>> the amount of data that is reprocessed after rescaling depends on the >>> checkpointing interval, and the restore time depends on the state size.* >>> => As I know 'rescaling' also works in non-reactive mode, with restoring >>> checkpoint. What is the difference of using 'reactive' here? >>> >>> *The Reactive Mode allows Flink users to implement a powerful >>> autoscaling mechanism, by having an external service monitor certain >>> metrics, such as consumer lag, aggregate CPU utilization, throughput or >>> latency. As soon as these metrics are above or below a certain threshold, >>> additional TaskManagers can be added or removed from the Flink cluster.* >>> => Why is this only possible in 'reactive' mode? Seems this is more >>> related to 'autoscaler'. Are there some specific features/API which can >>> control TaskManager/Parallelism only in 'reactive' mode? >>> >>> Thank you. >>> >>> 2023년 9월 1일 (금) 오후 3:30, Gyula Fóra <gyula.f...@gmail.com>님이 작성: >>> >>>> The reactive mode reacts to available resources. The autoscaler reacts >>>> to changing load and processing capacity and adjusts resources. >>>> >>>> Completely different concepts and applicability. >>>> Most people want the autoscaler , but this is a recent feature and is >>>> specific to the k8s operator at the moment. >>>> >>>> Gyula >>>> >>>> On Fri, 1 Sep 2023 at 04:50, Dennis Jung <inylov...@gmail.com> wrote: >>>> >>>>> Hello, >>>>> Thanks for your notice. >>>>> >>>>> Than what is the purpose of using 'reactive', if this doesn't do >>>>> anything itself? >>>>> What is the difference if I use auto-scaler without 'reactive' mode? >>>>> >>>>> Regards, >>>>> Jung >>>>> >>>>> >>>>> >>>>> 2023년 8월 18일 (금) 오후 7:51, Gyula Fóra <gyula.f...@gmail.com>님이 작성: >>>>> >>>>>> Hi! >>>>>> >>>>>> I think what you need is probably not the reactive mode but a proper >>>>>> autoscaler. The reactive mode as you say doesn't do anything in itself, >>>>>> you >>>>>> need to build a lot of logic around it. >>>>>> >>>>>> Check this instead: >>>>>> https://nightlies.apache.org/flink/flink-kubernetes-operator-docs-main/docs/custom-resource/autoscaler/ >>>>>> >>>>>> The Kubernetes Operator has a built in autoscaler that can scale jobs >>>>>> based on kafka data rate / processing throughput. It also doesn't rely on >>>>>> the reactive mode. >>>>>> >>>>>> Cheers, >>>>>> Gyula >>>>>> >>>>>> On Fri, Aug 18, 2023 at 12:43 PM Dennis Jung <inylov...@gmail.com> >>>>>> wrote: >>>>>> >>>>>>> Hello, >>>>>>> Sorry for frequent questions. This is a question about 'reactive' >>>>>>> mode. >>>>>>> >>>>>>> 1. As far as I understand, though I've setup `scheduler-mode: >>>>>>> reactive`, it will not change parallelism automatically by itself, by >>>>>>> CPU >>>>>>> usage or Kafka consumer rate. It needs additional resource monitor >>>>>>> features >>>>>>> (such as Horizontal Pod Autoscaler, or else). Is this correct? >>>>>>> 2. Is it possible to create a custom resource monitor provider >>>>>>> application? For example, if I want to increase/decrease parallelism by >>>>>>> Kafka consumer rate, do I need to send specific API from outside, to >>>>>>> order >>>>>>> rescaling? >>>>>>> 3. If 2 is correct, what is the difference when using 'reactive' >>>>>>> mode? Because as far as I think, calling a specific API will rescale >>>>>>> either >>>>>>> using 'reactive' mode or not...(or is the API just working based on this >>>>>>> mode)? >>>>>>> >>>>>>> Thanks. >>>>>>> >>>>>>> Regards >>>>>>> >>>>>>>
