Running a Flink application - Amazon EMR
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Running a Flink application

With Amazon EMR 6.13.0 and higher, you can run a Flink application with the Flink Kubernetes operator in Application mode on Amazon EMR on EKS. With Amazon EMR 6.15.0 and higher, you can also run a Flink application in Session mode. This page describes both methods that you can use to run a Flink application with Amazon EMR on EKS.

Topics
    Note

    You must have an Amazon S3 bucket created to store the high-availability metadata when you submit your Flink job. If you don’t want to use this feature, you can disable it. It's enabled by default.

    Prerequisite – Before you can run a Flink application with the Flink Kubernetes operator, complete the steps in Setting up the Flink Kubernetes operator for Amazon EMR on EKS and Installing the operator.

    Application mode

    With Amazon EMR 6.13.0 and higher, you can run a Flink application with the Flink Kubernetes operator in Application mode on Amazon EMR on EKS.

    1. Create a FlinkDeployment file definition file basic-example-app-cluster.yaml with the following example content:

      apiVersion: flink.apache.org/v1beta1
kind: FlinkDeployment
metadata:
  name: basic-example-app-cluster
spec:
  flinkConfiguration:
    taskmanager.numberOfTaskSlots: "2"
    state.checkpoints.dir: CHECKPOINT_S3_STORAGE_PATH
    state.savepoints.dir: SAVEPOINT_S3_STORAGE_PATH 
  flinkVersion: v1_17
  executionRoleArn: JOB_EXECUTION_ROLE_ARN
  emrReleaseLabel: "emr-6.13.0-flink-latest" # 6.13 or higher
  jobManager:
    storageDir: HIGH_AVAILABILITY_STORAGE_PATH
    resource:
      memory: "2048m"
      cpu: 1
  taskManager:
    resource:
      memory: "2048m"
      cpu: 1
  job:
    # if you have your job jar in S3 bucket you can use that path as well
    jarURI: local:///opt/flink/examples/streaming/StateMachineExample.jar
    parallelism: 2
    upgradeMode: savepoint
    savepointTriggerNonce: 0
  monitoringConfiguration:    
    cloudWatchMonitoringConfiguration:
       logGroupName: LOG_GROUP_NAME

    2. Submit the Flink deployment with the following command. This will also create a FlinkDeployment object named basic-example-app-cluster.

      kubectl create -f basic-example-app-cluster.yaml -n <NAMESPACE>

    3. Access the Flink UI.

      kubectl port-forward deployments/basic-example-app-cluster 8081 -n NAMESPACE

    4. Open localhost:8081 to view your Flink jobs locally.

    5. Clean up the job. Remember to clean up the S3 artifacts that were created for this job, such as checkpointing, high-availability, savepointing metadata, and CloudWatch logs.

    For more information on submitting applications to Flink through the Flink Kubernetes operator, see Flink Kubernetes operator examples in the apache/flink-kubernetes-operator folder on GitHub.

    Session mode

    With Amazon EMR 6.15.0 and higher, you can run a Flink application with the Flink Kubernetes operator in Session mode on Amazon EMR on EKS.

    1. Create a FlinkDeployment file definition file basic-example-session-cluster.yaml with the following example content:

      apiVersion: flink.apache.org/v1beta1
kind: FlinkDeployment
metadata:
  name: basic-example-session-cluster
spec:
  flinkConfiguration:
    taskmanager.numberOfTaskSlots: "2"
    state.checkpoints.dir: CHECKPOINT_S3_STORAGE_PATH
    state.savepoints.dir: SAVEPOINT_S3_STORAGE_PATH
  flinkVersion: v1_17
  executionRoleArn: JOB_EXECUTION_ROLE_ARN
  emrReleaseLabel: "emr-6.15.0-flink-latest"
  jobManager:
    storageDir: HIGH_AVAILABILITY_S3_STORAGE_PATH
    resource:
      memory: "2048m"
      cpu: 1
  taskManager:
    resource:
      memory: "2048m"
      cpu: 1
  monitoringConfiguration:    
    s3MonitoringConfiguration:
       logUri: 
    cloudWatchMonitoringConfiguration:
       logGroupName: LOG_GROUP_NAME

    2. Submit the Flink deployment with the following command. This will also create a FlinkDeployment object named basic-example-session-cluster.

      kubectl create -f basic-example-app-cluster.yaml -n NAMESPACE

    3. Use the following command to confirm that the session cluster LIFECYCLE is STABLE:

      kubectl get flinkdeployments.flink.apache.org basic-example-session-cluster -n NAMESPACE

      The output should be similar to the following example:

      NAME                              JOB STATUS   LIFECYCLE STATE
basic-example-session-cluster                          STABLE

    4. Create a FlinkSessionJob custom definition resource file basic-session-job.yaml with the following example content:

      apiVersion: flink.apache.org/v1beta1
kind: FlinkSessionJob
metadata:
  name: basic-session-job
spec:
  deploymentName: basic-session-deployment
  job:
    # If you have your job jar in an S3 bucket you can use that path.
    # To use jar in S3 bucket, set 
    # OPERATOR_EXECUTION_ROLE_ARN (--set emrContainers.operatorExecutionRoleArn=$OPERATOR_EXECUTION_ROLE_ARN)
    # when you install Spark operator
    jarURI: https://repo1.maven.org/maven2/org/apache/flink/flink-examples-streaming_2.12/1.16.1/flink-examples-streaming_2.12-1.16.1-TopSpeedWindowing.jar
    parallelism: 2
    upgradeMode: stateless

    5. Submit the Flink session job with the following command. This will create a FlinkSessionJob object basic-session-job.

      kubectl apply -f basic-session-job.yaml -n $NAMESPACE

    6. Use the following command to confirm that the session cluster LIFECYCLE is STABLE, and the JOB STATUS is RUNNING:

      kubectl get flinkdeployments.flink.apache.org basic-example-session-cluster -n NAMESPACE

      The output should be similar to the following example:

      NAME                              JOB STATUS   LIFECYCLE STATE
basic-example-session-cluster     RUNNING      STABLE

    7. Access the Flink UI.

      kubectl port-forward deployments/basic-example-session-cluster 8081 -n NAMESPACE

    8. Open localhost:8081 to view your Flink jobs locally.

    9. Clean up the job. Remember to clean up the S3 artifacts that were created for this job, such as checkpointing, high-availability, savepointing metadata, and CloudWatch logs.