![]() ![]() Whilst this works, I begin to think that maybe I can fix the ECS Operator and add the new “launchtype” of “EXTERNAL”. Second_task=PythonOperator(task_id='run_task', python_callable=run_task, provide_context=True, dag=dag) With DAG('airflow_ecsanywhere_boto3', catchup=False, default_args=default_args, schedule_interval=None) as dag:įirst_task=PythonOperator(task_id='create_taskdef', python_callable=create_task, provide_context=True, dag=dag) Next I change the DAG, setting the launchtype of “EXTERNAL” results in an error when triggering the workflow as follows: '.format(taskdef=new_taskdef) As expected, the ECS task takes the parameters and runs the script, exporting a file to my Amazon S3 bucket. I first create an ECS Cluster running on EC2, set the launchtype to “EC2” and the trigger the DAG. Task_definition="airflow-hybrid-ecs-task:3", With DAG('airflow_dag_test', catchup=False, default_args=default_args, schedule_interval=None) as dag: from airflow import DAGįrom .operators.ecs import ECSOperator ![]() This is the simple workflow we create just to test how it works. We know that there is a third launchtype of “EXTERNAL”, but that does not appear to be listed in the docs. Reading the docs, the two supported launchtypes for this Apache Airflow operator are “EC2” and “FARGATE”. One of the key ones is “launchtype” (as mentioned before, this is how the ECS control plane knows where to run your tasks. The ECS Operator takes a number of parameters. That operator is called the ECS Operator. Now that I have my ETL script, I can use an Apache Airflow operator that integrates with Amazon ECS to orchestrate this. FROM /docker/library/python:latestĮNTRYPOINT īefore testing this in Apache Airflow, I package up the container image, push it up to Amazon ECR and then test that it runs from the command. I planned to create an ETL script, and ensure the script can take parameters, to maximise reuse and flexibility. My solution was to use Apache Airflow and create a new workflow to orchestrate this. However, in my particular use case, I want to be able to control what data is moved to the data lake. In this instance, it is a MySQL database, and the MySQL database contains different data across the two environments.Īs part of building out my data lake on Amazon S3, I am pulling data from both these environments. In this environment, I have various data silos that reside in both my AWS environment, and on my local network. This allows you to run container images anywhere where you can deploy the ECS Anywhere agent, and uses a specific configuration parameter (launchtype=“EXTERNAL”) in order to know where to run your container “Tasks”. In a previous post ( Creating a multi architecture CI/CD solution with Amazon ECS and ECS Anywhere) I set up a typical environment that you might come across with customers that are looking at a Hybrid approach to managing and deploying their workloads. I will post regular updates as a series of posts, as the journey unfolds.īut as always, we need to set the stage and start with our reason for doing so. The purpose is to show you how typical open source projects like Apache Airflow work, how you engage with the community to orchestrate change and hopefully inspire more people to contribute to this open source project. In this series of posts, I am going to share what I learn as embark on my first upstream contribution to the Apache Airflow project. Contributing to Apache Airflow Introduction ![]()
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