Working with Flink jobs in Amazon EMR - Amazon EMR

Working with Flink jobs in Amazon EMR

There are several ways to interact with Flink on Amazon EMR: through the console, the Flink interface found on the ResourceManager Tracking UI, and at the command line. You can submit a JAR file to a Flink application with any of these. Once submit a JAR file, it becomes a job that is managed by the Flink JobManager. The JobManager is located on the YARN node that hosts the Flink session Application Master daemon.

You can run a Flink application as a YARN job on a long-running cluster or on a transient cluster. On a long-running cluster, you can submit multiple Flink jobs to one Flink cluster running on Amazon EMR. If you run a Flink job on a transient cluster, your Amazon EMR cluster exists only for the time it takes to run the Flink application, so you are only charged for the resources and time used. You can submit a Flink job with the Amazon EMR AddSteps API operation, as a step argument to the RunJobFlow operation, and through the AWS CLI add-steps or create-cluster commands.

To start a Flink application that multiple clients can submit work to through YARN API operations, you need to either create a cluster or add a Flink application an existing cluster. For instructions on how to create a new cluster, see Creating a cluster with Flink. To start a YARN session on an existing cluster, use the following steps from the console, AWS CLI, or Java SDK.


The flink-yarn-session command was added in Amazon EMR version 5.5.0 as a wrapper for the script to simplify execution. If you use an earlier version of Amazon EMR, substitute bash -c "/usr/lib/flink/bin/ -d" for Arguments in the console or Args. in the AWS CLI command.

To submit a Flink job on an existing cluster from the console

Submit the Flink session with the flink-yarn-session command in an existing cluster.

  1. Open the Amazon EMR console at

  2. In the cluster list, select the cluster you previously launched.

  3. In the cluster details page, choose Steps, Add Step.

  4. Use the guidelines that follow to enter the parameters, and then choose Add.

    Parameter Description

    Step type

    Custom JAR


    A name to help you identify the step. For example, <example-flink-step-name>.

    Jar location



    The flink-yarn-session command with arguments appropriate for your application. For example, flink-yarn-session -d starts a Flink session within your YARN cluster in a detached state (-d). See YARN setup in the latest Flink documentation for argument details.

To submit a Flink job on an existing cluster with the AWS CLI
  • Use the add-steps command to add a Flink job to a long-running cluster. The following example command specifies Args="flink-yarn-session", "-d" to start a Flink session within your YARN cluster in a detached state (-d). See YARN setup in the latest Flink documentation for argument details.

    aws emr add-steps --cluster-id <j-XXXXXXXX> --steps Type=CUSTOM_JAR,Name=<example-flink-step-name>,Jar=command-runner.jar,Args="flink-yarn-session","-d"

If you already have an existing Flink application on a long-running cluster, you can specify the cluster's Flink application ID in order to submit work to it. To obtain the application ID, run yarn application -list on the AWS CLI or through the YarnClient API operation:

$ yarn application -list 16/09/07 19:32:13 INFO client.RMProxy: Connecting to ResourceManager at ip-10-181-83-19.ec2.internal/ Total number of applications (application-types: [] and states: [SUBMITTED, ACCEPTED, RUNNING]):1 Application-Id Application-Name Application-Type User Queue State Final-State Progress Tracking-URL application_1473169569237_0002 Flink session with 14 TaskManagers (detached) Apache Flink hadoop default RUNNING UNDEFINED 100% http://ip-10-136-154-194.ec2.internal:33089

The application ID for this Flink session is application_1473169569237_0002, which you can use to submit work to the application from the AWS CLI or an SDK.

Example SDK for Java
List<StepConfig> stepConfigs = new ArrayList<StepConfig>(); HadoopJarStepConfig flinkWordCountConf = new HadoopJarStepConfig() .withJar("command-runner.jar") .withArgs("flink", "run", "-m", "yarn-cluster", "-yid", "application_1473169569237_0002", "-yn", "2", "/usr/lib/flink/examples/streaming/WordCount.jar", "--input", "s3://myBucket/pg11.txt", "--output", "s3://myBucket/alice2/"); StepConfig flinkRunWordCount = new StepConfig() .withName("Flink add a wordcount step") .withActionOnFailure("CONTINUE") .withHadoopJarStep(flinkWordCountConf); stepConfigs.add(flinkRunWordCount); AddJobFlowStepsResult res = emr.addJobFlowSteps(new AddJobFlowStepsRequest() .withJobFlowId("myClusterId") .withSteps(stepConfigs));
Example AWS CLI
aws emr add-steps --cluster-id <j-XXXXXXXX> \ --steps Type=CUSTOM_JAR,Name=Flink_Submit_To_Long_Running,Jar=command-runner.jar,\ Args="flink","run","-m","yarn-cluster","-yid","application_1473169569237_0002",\ "/usr/lib/flink/examples/streaming/WordCount.jar",\ "--input","s3://myBucket/pg11.txt","--output","s3://myBucket/alice2/" \ --region <region-code>

The following examples launch a transient cluster that runs a Flink job and then terminates on completion.

Example SDK for Java
import java.util.ArrayList; import java.util.List; import com.amazonaws.AmazonClientException; import com.amazonaws.auth.AWSCredentials; import com.amazonaws.auth.AWSStaticCredentialsProvider; import com.amazonaws.auth.profile.ProfileCredentialsProvider; import; import; import*; public class Main_test { public static void main(String[] args) { AWSCredentials credentials_profile = null; try { credentials_profile = new ProfileCredentialsProvider("default").getCredentials(); } catch (Exception e) { throw new AmazonClientException( "Cannot load credentials from .aws/credentials file. " + "Make sure that the credentials file exists and the profile name is specified within it.", e); } AmazonElasticMapReduce emr = AmazonElasticMapReduceClientBuilder.standard() .withCredentials(new AWSStaticCredentialsProvider(credentials_profile)) .withRegion(Regions.US_WEST_1) .build(); List<StepConfig> stepConfigs = new ArrayList<StepConfig>(); HadoopJarStepConfig flinkWordCountConf = new HadoopJarStepConfig() .withJar("command-runner.jar") .withArgs("bash", "-c", "flink", "run", "-m", "yarn-cluster", "-yn", "2", "/usr/lib/flink/examples/streaming/WordCount.jar", "--input", "s3://path/to/input-file.txt", "--output", "s3://path/to/output/"); StepConfig flinkRunWordCountStep = new StepConfig() .withName("Flink add a wordcount step and terminate") .withActionOnFailure("CONTINUE") .withHadoopJarStep(flinkWordCountConf); stepConfigs.add(flinkRunWordCountStep); Application flink = new Application().withName("Flink"); RunJobFlowRequest request = new RunJobFlowRequest() .withName("flink-transient") .withReleaseLabel("emr-5.20.0") .withApplications(flink) .withServiceRole("EMR_DefaultRole") .withJobFlowRole("EMR_EC2_DefaultRole") .withLogUri("s3://path/to/my/logfiles") .withInstances(new JobFlowInstancesConfig() .withEc2KeyName("myEc2Key") .withEc2SubnetId("subnet-12ab3c45") .withInstanceCount(3) .withKeepJobFlowAliveWhenNoSteps(false) .withMasterInstanceType("m4.large") .withSlaveInstanceType("m4.large")) .withSteps(stepConfigs); RunJobFlowResult result = emr.runJobFlow(request); System.out.println("The cluster ID is " + result.toString()); } }
Example AWS CLI

Use the create-cluster subcommand to create a transient cluster that terminates when the Flink job completes:

aws emr create-cluster --release-label emr-5.2.1 \ --name "Flink_Transient" \ --applications Name=Flink \ --configurations file://./configurations.json \ --region us-east-1 \ --log-uri s3://myLogUri \ --auto-terminate --instance-type m5.xlarge \ --instance-count 2 \ --service-role EMR_DefaultRole_V2 \ --ec2-attributes KeyName=<YourKeyName>,InstanceProfile=EMR_EC2_DefaultRole \ --steps Type=CUSTOM_JAR,Jar=command-runner.jar,Name=Flink_Long_Running_Session,\ Args="bash","-c","\"flink run -m yarn-cluster /usr/lib/flink/examples/streaming/WordCount.jar --input s3://myBucket/pg11.txt --output s3://myBucket/alice/""