Apache airflow docker operator9/13/2023 ![]() Environmental variables are more important than variables from the airflow.cfg file, so they will be overwritten by the default ones provided by the image. You can specify these settings in airflow.cfg and mount that file, but Airflow has variable hierarchy. A simple and convenient way to do this is by attaching the env_file field in docker-compose. To configure Airflow to use Celery, we need to specify some variables. data/airflow/dags:/usr/local/airflow/dags To improve readability and maintainability, it’s recommended to split commands over separate lines using backslashes. ![]() It’s good practise to chain commands together, as this will reduce the number of image layers, resulting in a smaller image since Docker caches every image layer. Take a closer look at the “ &” mark in the RUN command. You can create a different Dockerfile for every service to avoid installing unnecessary libraries, since you will generally only need them on workers. If you need to install any other libraries, you can do it in Dockerfile, as shown below. This image contains a pre-installed Celery library, which we plan to use anyway. From several images available in Docker Hub, we choose puckel/docker-airflow. To run Airflow in Docker we need an Airflow image. Airflow deployment on single host with Docker Now you can combine strength of both executors. But this is not end! In Airflow 2.0 new executor was added - Celery Kubernetes. Thanks to K8S you can leverage Keda to autoscale Celery workers. It’s worth mention that Airflow with Celery executor can be deployed in Kubernetes. It would be nice if this two kinds of a job get different amount of resources.Īll of mentioned problems can be solved using Airflow with Kubernetes executor. On the other hand you can run machine learning model training as Airflow task. Some tasks like SparkSubmitOperator require small amount of resources, as they only watch process running on external system. resource allocation - you can’t configure available resources per each Airflow task.Nobody will allow you to install your favorite library without corporate process. In big company most of the time you’re only user of Airflow. library management - you need to install all your dependencies (like Java) beforehand on all of your workers.Maybe it would be better to give this resources to other parts of the system? waste of resources - Celery workers are created on start-up.autoscaling - there is no mechanism to scale Celery workers based on task queue usage out of the box,.Nothing is perfect, let’s have a look on limitation of Celery executors: prioritization - you can give priority to your critical tasks.horizontal scalability - new Airflow workers can be added anytime,.speed - workers are always ready to use immediately,.Running Airflow with Celery executor has several major advantages: Starting from Airflow 2.0 webserver works only in stateless mode. This means DAGs are now fetched from the database, instead of being parsed by Webserver as before. Starting from Airflow version 1.10.7, webserver can be stateless.
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