Apache Flink Kubernetes Operator 0.1.0 Release Announcement

Lifecycle management for Apache Flink deployments using native Kubernetes tooling

03 Apr 2022 Gyula Fora (@GyulaFora)

The Apache Flink Community is pleased to announce the preview release of the Apache Flink Kubernetes Operator (0.1.0)

The Flink Kubernetes Operator allows users to easily manage their Flink deployment lifecycle using native Kubernetes tooling.

The operator takes care of submitting, savepointing, upgrading and generally managing Flink jobs using the built-in Flink Kubernetes integration. This way users do not have to use the Flink Clients (e.g. CLI) or interact with the Flink jobs manually, they only have to declare the desired deployment specification and the operator will take care of the rest. It also make it easier to integrate Flink job management with CI/CD tooling.

Core Features

  • Deploy and monitor Flink Application and Session deployments
  • Upgrade, suspend and delete Flink deployments
  • Full logging and metrics integration

Overview 1


Getting started

For a detailed getting started guide please check the documentation site.

FlinkDeployment CR overview

When using the operator, users create FlinkDeployment objects to describe their Flink application and session clusters deployments.

A minimal application deployment yaml would look like this:

apiVersion: flink.apache.org/v1alpha1
kind: FlinkDeployment
metadata:
  namespace: default
  name: basic-example
spec:
  image: flink:1.14
  flinkVersion: v1_14
  flinkConfiguration:
    taskmanager.numberOfTaskSlots: "2"
  serviceAccount: flink
  jobManager:
    replicas: 1
    resource:
      memory: "2048m"
      cpu: 1
  taskManager:
    resource:
      memory: "2048m"
      cpu: 1
  job:
    jarURI: local:///opt/flink/examples/streaming/StateMachineExample.jar
    parallelism: 2
    upgradeMode: stateless

Once applied to the cluster using kubectl apply -f your-deployment.yaml the operator will spin up the application cluster for you. If you would like to upgrade or make changes to your application, you can simply modify the yaml and submit it again, the operator will execute the necessary steps (savepoint, shutdown, redeploy etc.) to upgrade your application.

To stop and delete your application cluster you can simply call kubectl delete -f your-deployment.yaml.

You can read more about the job management features on the documentation site.

What’s Next?

The community is currently working on hardening the core operator logic, stabilizing the APIs and adding the remaining bits for making the Flink Kubernetes Operator production ready.

In the upcoming 1.0.0 release you can expect (at-least) the following additional features:

  • Support for Session Job deployments
  • Job upgrade rollback strategies
  • Pluggable validation logic
  • Operator deployment customization
  • Improvements based on feedback from the preview release

In the medium term you can also expect:

  • Support for standalone / reactive deployment modes
  • Support for other job types such as SQL or Python

Please give the preview release a try, share your feedback on the Flink mailing list and contribute to the project!

Release Resources

The source artifacts and helm chart are now available on the updated Downloads page of the Flink website.

The official 0.1.0 release archive doubles as a Helm repository that you can easily register locally:

$ helm repo add flink-kubernetes-operator-0.1.0 https://archive.apache.org/dist/flink/flink-kubernetes-operator-0.1.0/
$ helm install flink-kubernetes-operator flink-kubernetes-operator-0.1.0/flink-kubernetes-operator --set webhook.create=false

You can also find official Kubernetes Operator Docker images of the new version on Dockerhub.

For more details, check the updated documentation and the release notes. We encourage you to download the release and share your feedback with the community through the Flink mailing lists or JIRA.

List of Contributors

The Apache Flink community would like to thank each and every one of the contributors that have made this release possible:

Aitozi, Biao Geng, Gyula Fora, Hao Xin, Jaegu Kim, Jaganathan Asokan, Junfan Zhang, Marton Balassi, Matyas Orhidi, Nicholas Jiang, Sandor Kelemen, Thomas Weise, Yang Wang, 愚鲤