Hi, Mazen AFAIK, we now have two K8s integration, native[1] and standalone[2]. I guess the native K8s integration is what you mean by active K8S integration.
Regarding the reactive mode, I think it is still working in progress, you could refer to [3]. [1] https://ci.apache.org/projects/flink/flink-docs-release-1.11/ops/deployment/native_kubernetes.html [2] https://ci.apache.org/projects/flink/flink-docs-release-1.11/ops/deployment/kubernetes.html [3] https://issues.apache.org/jira/browse/FLINK-10407 Best, Yangze Guo On Mon, Aug 24, 2020 at 6:20 PM Mazen <mazen.ezzedd...@etu.unice.fr> wrote: > > To the best of my knowledge, for Flink deployment on Kubernetes we have two > options as of now : (1) active K8S integration with separate job manager per > job and (2) reactive container mode with auto rescale based on some metrics: > Could you please give me on the hint on the below: > > A - Are the two integrations already integrated to Flink recent releases? > Any documentation on that? > > B - In all cases it is necessary to kill and restart the job which is a > concern for some critical use cases? Can a rolling upgrade be used to have a > zero down time while recalling/upgrading? > > C- In such recasle mechanism, does Kubernetes/Flink identify which stream > operator is the source of load/utilization and rescale it individually, or > the rescaling is done at the granularity of whole job. > > D- for stateful operators/jobs, how the state repartitioning and assignment > to new instances is performed? Does this repartitioning/reassignment is time > consuming especially for large states? > > Thank you. > > > > -- > Sent from: http://apache-flink-mailing-list-archive.1008284.n3.nabble.com/
