Using Amazon MWAA with Amazon EMR - Amazon Managed Workflows for Apache Airflow

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.


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

Code sample

""" Copyright, 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.contrib.operators.emr_add_steps_operator import EmrAddStepsOperator from airflow.contrib.operators.emr_create_job_flow_operator import EmrCreateJobFlowOperator from airflow.contrib.sensors.emr_step_sensor import EmrStepSensor 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': [''], '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