Kubernetes Setup

• Getting Started
  • Introduction
  • Preparation
  • Starting a Kubernetes Cluster (Session Mode)
• Deployment Modes
  • Deploy Application Cluster
  • Per-Job Cluster Mode
  • Session Mode
• Flink on Standalone Kubernetes Reference
  • Configuration
  • Accessing Flink in Kubernetes
  • Debugging and Log Access
  • High-Availability with Standalone Kubernetes
  • Enabling Queryable State
• Appendix
  • Common cluster resource definitions
  • Session cluster resource definitions
  • Application cluster resource definitions

Getting Started

This Getting Started guide describes how to deploy a Session cluster on Kubernetes.

Introduction

This page describes deploying a standalone Flink cluster on top of Kubernetes, using Flink’s standalone deployment. We generally recommend new users to deploy Flink on Kubernetes using native Kubernetes deployments.

Preparation

This guide expects a Kubernetes environment to be present. You can ensure that your Kubernetes setup is working by running a command like kubectl get nodes, which lists all connected Kubelets.

If you want to run Kubernetes locally, we recommend using MiniKube.

Note: If using MiniKube please make sure to execute minikube ssh 'sudo ip link set docker0 promisc on' before deploying a Flink cluster. Otherwise Flink components are not able to reference themselves through a Kubernetes service.

Starting a Kubernetes Cluster (Session Mode)

A Flink Session cluster is executed as a long-running Kubernetes Deployment. You can run multiple Flink jobs on a Session cluster. Each job needs to be submitted to the cluster after the cluster has been deployed.

A Flink Session cluster deployment in Kubernetes has at least three components:

• a Deployment which runs a JobManager
• a Deployment for a pool of TaskManagers
• a Service exposing the JobManager’s REST and UI ports

Using the file contents provided in the the common resource definitions, create the following files, and create the respective components with the kubectl command:

    # Configuration and service definition
    $ kubectl create -f flink-configuration-configmap.yaml
    $ kubectl create -f jobmanager-service.yaml
    # Create the deployments for the cluster
    $ kubectl create -f jobmanager-session-deployment.yaml
    $ kubectl create -f taskmanager-session-deployment.yaml

Next, we set up a port forward to access the Flink UI and submit jobs:

1. Run kubectl port-forward ${flink-jobmanager-pod} 8081:8081 to forward your jobmanager’s web ui port to local 8081.
2. Navigate to http://localhost:8081 in your browser.
3. Moreover, you could use the following command below to submit jobs to the cluster:

$ ./bin/flink run -m localhost:8081 
$ ./examples/streaming/TopSpeedWindowing.jar

You can tear down the cluster using the following commands:

    $ kubectl delete -f jobmanager-service.yaml
    $ kubectl delete -f flink-configuration-configmap.yaml
    $ kubectl delete -f taskmanager-session-deployment.yaml
    $ kubectl delete -f jobmanager-session-deployment.yaml

Deployment Modes

Deploy Application Cluster

A Flink Application cluster is a dedicated cluster which runs a single application.

A basic Flink Application cluster deployment in Kubernetes has three components:

• an Application which runs a JobManager
• a Deployment for a pool of TaskManagers
• a Service exposing the JobManager’s REST and UI ports

Check the Application cluster specific resource definitions and adjust them accordingly:

The args attribute in the jobmanager-job.yaml has to specify the main class of the user job. See also how to specify the JobManager arguments to understand how to pass other args to the Flink image in the jobmanager-job.yaml.

The job artifacts should be available from the job-artifacts-volume in the resource definition examples. The definition examples mount the volume as a local directory of the host assuming that you create the components in a minikube cluster. If you do not use a minikube cluster, you can use any other type of volume, available in your Kubernetes cluster, to supply the job artifacts. Alternatively, you can build a custom image which already contains the artifacts instead.

After creating the common cluster components, use the Application cluster specific resource definitions to launch the cluster with the kubectl command:

    $ kubectl create -f jobmanager-job.yaml
    $ kubectl create -f taskmanager-job-deployment.yaml

To terminate the single application cluster, these components can be deleted along with the common ones with the kubectl command:

    $ kubectl delete -f taskmanager-job-deployment.yaml
    $ kubectl delete -f jobmanager-job.yaml

Per-Job Cluster Mode

Flink on Standalone Kubernetes does not support the Per-Job Cluster Mode.

Session Mode

Deployment of a Session cluster is explained in the Getting Started guide at the top of this page.

Flink on Standalone Kubernetes Reference

Configuration

All configuration options are listed on the configuration page. Configuration options can be added to the flink-conf.yaml section of the flink-configuration-configmap.yaml config map.

Accessing Flink in Kubernetes

You can then access the Flink UI and submit jobs via different ways:

• kubectl proxy:

  1. Run kubectl proxy in a terminal.
  2. Navigate to http://localhost:8001/api/v1/namespaces/default/services/flink-jobmanager:webui/proxy in your browser.

• kubectl port-forward:

  1. Run kubectl port-forward ${flink-jobmanager-pod} 8081:8081 to forward your jobmanager’s web ui port to local 8081.
  2. Navigate to http://localhost:8081 in your browser.
  3. Moreover, you can use the following command below to submit jobs to the cluster:

    $ ./bin/flink run -m localhost:8081 
    $ ./examples/streaming/TopSpeedWindowing.jar

• Create a NodePort service on the rest service of jobmanager:
  1. Run kubectl create -f jobmanager-rest-service.yaml to create the NodePort service on jobmanager. The example of jobmanager-rest-service.yaml can be found in appendix.
  2. Run kubectl get svc flink-jobmanager-rest to know the node-port of this service and navigate to http://<public-node-ip>:<node-port> in your browser.
  3. If you use minikube, you can get its public ip by running minikube ip.
  4. Similarly to the port-forward solution, you can also use the following command below to submit jobs to the cluster:

    $ ./bin/flink run -m <public-node-ip>:<node-port> 
    $ ./examples/streaming/TopSpeedWindowing.jar

Debugging and Log Access

Many common errors are easy to detect by checking Flink’s log files. If you have access to Flink’s web user interface, you can access the JobManager and TaskManager logs from there.

If there are problems starting Flink, you can also use Kubernetes utilities to access the logs. Use kubectl get pods to see all running pods. For the quickstart example from above, you should see three pods:

$ kubectl get pods
NAME                                 READY   STATUS             RESTARTS   AGE
flink-jobmanager-589967dcfc-m49xv    1/1     Running            3          3m32s
flink-taskmanager-64847444ff-7rdl4   1/1     Running            3          3m28s
flink-taskmanager-64847444ff-nnd6m   1/1     Running            3          3m28s

You can now access the logs by running kubectl logs flink-jobmanager-589967dcfc-m49xv

High-Availability with Standalone Kubernetes

For high availability on Kubernetes, you can use the existing high availability services.

Session Mode and Application Mode clusters support using the Kubernetes high availability service. Users just need to add the following Flink config options to flink-configuration-configmap.yaml. All other yamls do not need to be updated.

Note The filesystem which corresponds to the scheme of your configured HA storage directory must be available to the runtime. Refer to custom Flink image and enable plugins for more information.

apiVersion: v1
kind: ConfigMap
metadata:
  name: flink-config
  labels:
    app: flink
data:
  flink-conf.yaml: |+
  ...
    kubernetes.cluster-id: <cluster-id>
    high-availability: org.apache.flink.kubernetes.highavailability.KubernetesHaServicesFactory
    high-availability.storageDir: hdfs:///flink/recovery
    restart-strategy: fixed-delay
    restart-strategy.fixed-delay.attempts: 10
  ...

Enabling Queryable State

You can access the queryable state of TaskManager if you create a NodePort service for it:

1. Run kubectl create -f taskmanager-query-state-service.yaml to create the NodePort service for the taskmanager pod. The example of taskmanager-query-state-service.yaml can be found in appendix.
2. Run kubectl get svc flink-taskmanager-query-state to get the <node-port> of this service. Then you can create the QueryableStateClient(<public-node-ip>, <node-port> to submit the state queries.

Appendix

Common cluster resource definitions

flink-configuration-configmap.yaml

apiVersion: v1
kind: ConfigMap
metadata:
  name: flink-config
  labels:
    app: flink
data:
  flink-conf.yaml: |+
    jobmanager.rpc.address: flink-jobmanager
    taskmanager.numberOfTaskSlots: 2
    blob.server.port: 6124
    jobmanager.rpc.port: 6123
    taskmanager.rpc.port: 6122
    queryable-state.proxy.ports: 6125
    jobmanager.memory.process.size: 1600m
    taskmanager.memory.process.size: 1728m
    parallelism.default: 2
  log4j-console.properties: |+
    # This affects logging for both user code and Flink
    rootLogger.level = INFO
    rootLogger.appenderRef.console.ref = ConsoleAppender
    rootLogger.appenderRef.rolling.ref = RollingFileAppender
    # Uncomment this if you want to _only_ change Flink's logging
    #logger.flink.name = org.apache.flink
    #logger.flink.level = INFO
    # The following lines keep the log level of common libraries/connectors on
    # log level INFO. The root logger does not override this. You have to manually
    # change the log levels here.
    logger.akka.name = akka
    logger.akka.level = INFO
    logger.kafka.name= org.apache.kafka
    logger.kafka.level = INFO
    logger.hadoop.name = org.apache.hadoop
    logger.hadoop.level = INFO
    logger.zookeeper.name = org.apache.zookeeper
    logger.zookeeper.level = INFO
    # Log all infos to the console
    appender.console.name = ConsoleAppender
    appender.console.type = CONSOLE
    appender.console.layout.type = PatternLayout
    appender.console.layout.pattern = %d{yyyy-MM-dd HH:mm:ss,SSS} %-5p %-60c %x - %m%n
    # Log all infos in the given rolling file
    appender.rolling.name = RollingFileAppender
    appender.rolling.type = RollingFile
    appender.rolling.append = false
    appender.rolling.fileName = ${sys:log.file}
    appender.rolling.filePattern = ${sys:log.file}.%i
    appender.rolling.layout.type = PatternLayout
    appender.rolling.layout.pattern = %d{yyyy-MM-dd HH:mm:ss,SSS} %-5p %-60c %x - %m%n
    appender.rolling.policies.type = Policies
    appender.rolling.policies.size.type = SizeBasedTriggeringPolicy
    appender.rolling.policies.size.size=100MB
    appender.rolling.strategy.type = DefaultRolloverStrategy
    appender.rolling.strategy.max = 10
    # Suppress the irrelevant (wrong) warnings from the Netty channel handler
    logger.netty.name = org.apache.flink.shaded.akka.org.jboss.netty.channel.DefaultChannelPipeline
    logger.netty.level = OFF

jobmanager-service.yaml

apiVersion: v1
kind: Service
metadata:
  name: flink-jobmanager
spec:
  type: ClusterIP
  ports:
  - name: rpc
    port: 6123
  - name: blob-server
    port: 6124
  - name: webui
    port: 8081
  selector:
    app: flink
    component: jobmanager

jobmanager-rest-service.yaml. Optional service, that exposes the jobmanager rest port as public Kubernetes node’s port.

apiVersion: v1
kind: Service
metadata:
  name: flink-jobmanager-rest
spec:
  type: NodePort
  ports:
  - name: rest
    port: 8081
    targetPort: 8081
    nodePort: 30081
  selector:
    app: flink
    component: jobmanager

taskmanager-query-state-service.yaml. Optional service, that exposes the TaskManager port to access the queryable state as a public Kubernetes node’s port.

apiVersion: v1
kind: Service
metadata:
  name: flink-taskmanager-query-state
spec:
  type: NodePort
  ports:
  - name: query-state
    port: 6125
    targetPort: 6125
    nodePort: 30025
  selector:
    app: flink
    component: taskmanager

Session cluster resource definitions

jobmanager-session-deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: flink-jobmanager
spec:
  replicas: 1
  selector:
    matchLabels:
      app: flink
      component: jobmanager
  template:
    metadata:
      labels:
        app: flink
        component: jobmanager
    spec:
      containers:
      - name: jobmanager
        image: flink:1.12.0-scala_2.11
        args: ["jobmanager"]
        ports:
        - containerPort: 6123
          name: rpc
        - containerPort: 6124
          name: blob-server
        - containerPort: 8081
          name: webui
        livenessProbe:
          tcpSocket:
            port: 6123
          initialDelaySeconds: 30
          periodSeconds: 60
        volumeMounts:
        - name: flink-config-volume
          mountPath: /opt/flink/conf
        securityContext:
          runAsUser: 9999  # refers to user _flink_ from official flink image, change if necessary
      volumes:
      - name: flink-config-volume
        configMap:
          name: flink-config
          items:
          - key: flink-conf.yaml
            path: flink-conf.yaml
          - key: log4j-console.properties
            path: log4j-console.properties

taskmanager-session-deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: flink-taskmanager
spec:
  replicas: 2
  selector:
    matchLabels:
      app: flink
      component: taskmanager
  template:
    metadata:
      labels:
        app: flink
        component: taskmanager
    spec:
      containers:
      - name: taskmanager
        image: flink:1.12.0-scala_2.11
        args: ["taskmanager"]
        ports:
        - containerPort: 6122
          name: rpc
        - containerPort: 6125
          name: query-state
        livenessProbe:
          tcpSocket:
            port: 6122
          initialDelaySeconds: 30
          periodSeconds: 60
        volumeMounts:
        - name: flink-config-volume
          mountPath: /opt/flink/conf/
        securityContext:
          runAsUser: 9999  # refers to user _flink_ from official flink image, change if necessary
      volumes:
      - name: flink-config-volume
        configMap:
          name: flink-config
          items:
          - key: flink-conf.yaml
            path: flink-conf.yaml
          - key: log4j-console.properties
            path: log4j-console.properties

Application cluster resource definitions

jobmanager-application.yaml

apiVersion: batch/v1
kind: Job
metadata:
  name: flink-jobmanager
spec:
  template:
    metadata:
      labels:
        app: flink
        component: jobmanager
    spec:
      restartPolicy: OnFailure
      containers:
        - name: jobmanager
          image: flink:1.12.0-scala_2.11
          env:
          args: ["standalone-job", "--job-classname", "com.job.ClassName", <optional arguments>, <job arguments>] # optional arguments: ["--job-id", "<job id>", "--fromSavepoint", "/path/to/savepoint", "--allowNonRestoredState"]
          ports:
            - containerPort: 6123
              name: rpc
            - containerPort: 6124
              name: blob-server
            - containerPort: 8081
              name: webui
          livenessProbe:
            tcpSocket:
              port: 6123
            initialDelaySeconds: 30
            periodSeconds: 60
          volumeMounts:
            - name: flink-config-volume
              mountPath: /opt/flink/conf
            - name: job-artifacts-volume
              mountPath: /opt/flink/usrlib
          securityContext:
            runAsUser: 9999  # refers to user _flink_ from official flink image, change if necessary
      volumes:
        - name: flink-config-volume
          configMap:
            name: flink-config
            items:
              - key: flink-conf.yaml
                path: flink-conf.yaml
              - key: log4j-console.properties
                path: log4j-console.properties
        - name: job-artifacts-volume
          hostPath:
            path: /host/path/to/job/artifacts

taskmanager-job-deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: flink-taskmanager
spec:
  replicas: 2
  selector:
    matchLabels:
      app: flink
      component: taskmanager
  template:
    metadata:
      labels:
        app: flink
        component: taskmanager
    spec:
      containers:
      - name: taskmanager
        image: flink:1.12.0-scala_2.11
        env:
        args: ["taskmanager"]
        ports:
        - containerPort: 6122
          name: rpc
        - containerPort: 6125
          name: query-state
        livenessProbe:
          tcpSocket:
            port: 6122
          initialDelaySeconds: 30
          periodSeconds: 60
        volumeMounts:
        - name: flink-config-volume
          mountPath: /opt/flink/conf/
        - name: job-artifacts-volume
          mountPath: /opt/flink/usrlib
        securityContext:
          runAsUser: 9999  # refers to user _flink_ from official flink image, change if necessary
      volumes:
      - name: flink-config-volume
        configMap:
          name: flink-config
          items:
          - key: flink-conf.yaml
            path: flink-conf.yaml
          - key: log4j-console.properties
            path: log4j-console.properties
      - name: job-artifacts-volume
        hostPath:
          path: /host/path/to/job/artifacts