Using Amazon MWAA with Amazon EMR

The following code sample demonstrates how to enable an integration using Amazon EMR and Amazon Managed Workflows for Apache Airflow.

Version

• The sample code on this page can be used with Apache Airflow v1 in Python 3.7.

Code sample

    """
    Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
     
    Permission is hereby granted, free of charge, to any person obtaining a copy of
    this software and associated documentation files (the "Software"), to deal in
    the Software without restriction, including without limitation the rights to
    use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
    the Software, and to permit persons to whom the Software is furnished to do so.
     
    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
    FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
    COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
    IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
    CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
    """
    from airflow import DAG
     
    from airflow.providers.amazon.aws.operators.emr import EmrAddStepsOperator
    from airflow.providers.amazon.aws.sensors.emr import EmrStepSensor
    from airflow.providers.amazon.aws.operators.emr import EmrCreateJobFlowOperator
     
    from airflow.utils.dates import days_ago
    from datetime import timedelta
    import os
     
    DAG_ID = os.path.basename(__file__).replace(".py", "")
     
    DEFAULT_ARGS = {
        'owner': 'airflow',
        'depends_on_past': False,
        'email': ['[e-mail]'],
        'email_on_failure': False,
        'email_on_retry': False,
    }
     
    SPARK_STEPS = [
        {
            'Name': 'calculate_pi',
            'ActionOnFailure': 'CONTINUE',
            'HadoopJarStep': {
                'Jar': 'command-runner.jar',
                'Args': ['/usr/lib/spark/bin/run-example', 'SparkPi', '10'],
            },
        }
    ]
     
    JOB_FLOW_OVERRIDES = {
        'Name': 'my-demo-cluster',
        'ReleaseLabel': 'emr-5.30.1',
        'Applications': [
            {
                'Name': 'Spark'
            },
        ],    
        'Instances': {
            'InstanceGroups': [
                {
                    'Name': "Master nodes",
                    'Market': 'ON_DEMAND',
                    'InstanceRole': 'MASTER',
                    'InstanceType': 'm5.xlarge',
                    'InstanceCount': 1,
                },
                {
                    'Name': "Slave nodes",
                    'Market': 'ON_DEMAND',
                    'InstanceRole': 'CORE',
                    'InstanceType': 'm5.xlarge',
                    'InstanceCount': 2,
                }
            ],
            'KeepJobFlowAliveWhenNoSteps': False,
            'TerminationProtected': False,
            'Ec2KeyName': 'mykeypair',
        },
        'VisibleToAllUsers': True,
        'JobFlowRole': 'EMR_EC2_DefaultRole',
        'ServiceRole': 'EMR_DefaultRole'
    }
     
    with DAG(
        dag_id=DAG_ID,
        default_args=DEFAULT_ARGS,
        dagrun_timeout=timedelta(hours=2),
        start_date=days_ago(1),
        schedule_interval='@once',
        tags=['emr'],
    ) as dag:
     
        cluster_creator = EmrCreateJobFlowOperator(
            task_id='create_job_flow', 
            job_flow_overrides=JOB_FLOW_OVERRIDES
        )
     
        step_adder = EmrAddStepsOperator(
            task_id='add_steps',
            job_flow_id="{{ task_instance.xcom_pull(task_ids='create_job_flow', key='return_value') }}",
            aws_conn_id='aws_default',
            steps=SPARK_STEPS,
        )
     
        step_checker = EmrStepSensor(
            task_id='watch_step',
            job_flow_id="{{ task_instance.xcom_pull('create_job_flow', key='return_value') }}",
            step_id="{{ task_instance.xcom_pull(task_ids='add_steps', key='return_value')[0] }}",
            aws_conn_id='aws_default',
        )
     
        cluster_creator >> step_adder >> step_checker