5/1/2023 0 Comments Aws managed airflowThe current latest version of Airflow is 2.0.2. Outdated Airflow Version: At the time of writing (April 2021), the latest Airflow version available in MWAA was 1.10.12. For example, it has logging already integrated with Amazon CloudWatch. If more worker instances are needed to process tasks, MWAA takes care of it automatically and removes workers when they are no longer needed.Ĭompatibility with other AWS services: MWAA is part the AWS ecosystem and connects well with the tools and other resources required for your workflow if you are already hosting your infrastructure on AWS. ProsĪutoscaling workers: MWAA is a fully-managed version of Airflow, so it makes it easier to scale up Airflow. Pricing for MWAA or Astronomer Cloud isn’t mentioned below because the difference in pricing between the two wasn’t so drastically different that it was a deciding factor in choosing one over the other. Google Cloud has their version of a managed service for Airflow in Cloud Composer, but since Mage’s cloud infrastructure is built in AWS (future article on this coming soon), we won’t be mentioning the advantages or disadvantages of using Google Cloud Composer as it wasn’t an option for us. It’s backed by Amazon and meant to integrate with other Amazon services. MWAA is a managed service in AWS for Apache Airflow. Amazon Managed Workflows for Apache Airflow (MWAA) Managed Airflow alternativesĪirflow config can be a complicated process use these tools to help you manage it. We wanted to be able to scale up Airflow task processing efficiently, effectively, and with as little overhead and maintenance from the team as possible, and we were willing to pay for a managed service to help us with that. We needed a way to easily manage these resources and quickly deploy changes to our infrastructure if necessary. Managing the number of Airflow workers and resources allocated to the various AWS services required for Airflow was a bit cumbersome. We also had unnecessary costs due to over-provisioned Airflow workers, and it became a hassle to find the right resource allocation through the Amazon ECS UI. Challenges running Airflow in ECSĪs the number of DAGs in our Airflow cluster grew, we noticed that DAGs would sometimes have issues running successfully, or there would be problems with the scheduler. Refer to the Appendix at the bottom of this post for more details on the Airflow components. In addition, we had a Redis cluster in Amazon ElastiCache and a PostgreSQL database hosted in Amazon RDS. In this ECS cluster, we utilized five services: Workers, Webserver, Scheduler, Flower, and the Celery Executor. Heroku Our previous setup - Amazon ECS (Elastic Container Service)įor our previous setup, we used an Amazon ECS cluster running Airflow version 1.10.14. Here is a non-exhaustive list of various cloud services you can use (get ready for lots of acronyms): Amazon Web Services The other components of Airflow can be hosted in different cloud providers or on your own bare-metal server. Amazon’s Relational Database Service ( RDS) or Google’s Cloud SQL are two options for hosting the database. Though Airflow comes with SQLite by default, it will need a production database like PostgreSQL or MySQL for storing metadata. There are many different methods for running Airflow in production, depending on the needs of your project. Options for running Airflow in production We use Airflow to orchestrate the steps required in preparing training data, building new machine learning models, deploying machine learning models, processing streamed data, and more.Īirflow is an important part of making sure steps in our workflow happen in a certain order and on a recurring basis. Mage is a tool that helps developers use machine learning and their data to make predictions. How we use Airflow at MageĪirflow is an essential part of our software stack. With Airflow, let repeatable operations run by themselves.
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