![]() ![]() Mount the folder as a volume by doing either of the following:.Create the plugins folders plugins/ with your custom plugins.In order to incorporate plugins into your docker container Documentation on plugins can be found here This will work for hooks etc, but won't show up in the "Ad-hoc Query" section unless an (empty) connection is also created in the DB Custom Airflow pluginsĪirflow allows for custom user-created plugins which are typically found in $/plugins folder. You can also define connections via environment variables by prefixing them with AIRFLOW_CONN_ - for example for a connection called "postgres_master". The general rule is the environment variable should be named AIRFLOW_, for example AIRFLOW_CORE_SQL_ALCHEMY_CONN sets the sql_alchemy_conn config option in the section.Ĭheck out the Airflow documentation for more details It's possible to set any configuration value for Airflow from environment variables, which are used over values from the airflow.cfg. Docker run puckel/docker-airflow python -c "from cryptography.fernet import Fernet FERNET_KEY = Fernet.generate_key().decode() print(FERNET_KEY)" The fourth launches a cluster of AWS EMR clients to execute a PySpark job.įrom airflow.The third configures the AWSCLI client with your aws credentials.The second installs an AWSCLI client on a remote machine using ssh.The first consists in showing in the logs the configuration of the AWSCLI of the Docker container.The objective of this article is to explore the technology by creating 4 DAGs: Attempting to run them with Docker Desktop for Windows will likely require some customisation.įor our exploration, we’ll be using Airflow on the Amazon Big Data platform AWS EMR. Please note that the containers detailed within this article were tested using Linux based Docker. They provide a working environment for Airflow using Docker where can explore what Airflow has to offer. Debug_Executor: the DebugExecutor is designed as a debugging tool and can be used from IDE.įor the purpose of this article, I relied on the airflow.cfg files, the Dockerfile as well as the docker-compose-LocalExecutor.yml which are available on the Mathieu ROISIL github.Kubernetes_Executor: this type of executor allows airflow to create or group tasks in Kubernetes pods.Dask_Executor: this type of executor allows airflow to launch these different tasks in a python cluster Dask.Celery_Executor: Celery is a types of executor prefers, in fact it makes it possible to distribute the processing in parallel over a large number of nodes.Local_Executor: launches tasks in parallel locally.Sequential_Executor: launches tasks one by one.There are six possible types of installation: The configuration of the different operators.Most of the configuration of Airflow is done in the airflow.cfg file. Once the Airflow webserver is running, go to the address localhost:8080 in your browser and activate the example DAG from the home page. # start the web server, default port is 8080Īirflow webserver -p 8080 # start the scheduler # Airflow installation guide : # airflow needs a home, ~/airflow is the default, # but you can lay foundation somewhere else if you prefer # (optional) export AIRFLOW_HOME =~/airflow
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