Hey Sonam, I'm very happy to hear that you are interested in reactive mode. Your understanding of the limitations for 1.13 is correct. Note that you can deploy standalone Flink on Kubernetes [1]. I'm actually currently preparing a demo for this [2].
We are certainly aware that support for active deployments is a much desired feature. The "problem" with the 1.13 implementation of reactive mode is that it will try to acquire infinite resources from an active resource manager. For integration with an active deployment, how would you like to control the scaling behavior of Flink? (for example via a REST API call to Flink's JobManager, or via a programmatic scaling policy, or a configured scaling policy? If you prefer a scaling policy, which metric would you like to consider?) Best, Sonam [1] https://ci.apache.org/projects/flink/flink-docs-master/docs/deployment/resource-providers/standalone/kubernetes/ [2] https://github.com/rmetzger/flink-reactive-mode-k8s-demo (attention, this is really work in progress!) On Wed, Mar 10, 2021 at 5:32 PM Sonam Mandal <soman...@linkedin.com> wrote: > Hello, > > We were going through FlIP-159 > <https://cwiki.apache.org/confluence/display/FLINK/FLIP-159%3A+Reactive+Mode> > and FLIP-160 > <https://cwiki.apache.org/confluence/display/FLINK/FLIP-160%3A+Adaptive+Scheduler> > and > found this feature of interest to us for auto-scaling purposes. The > limitations indicate that Flink 1.13 will release this for standalone only > and for application mode deployments only. > > Will this be extended in future releases to other active deployments such > as Native Flink on Kubernetes? What about session mode? > > Thanks, > Sonam >
