~n0mn0m/airflow-docker

Airflow setup to run inside of docker.
fd700370 — Alex Hagerman 1 year, 7 months ago
Updated README file with Conda documentation. Added env template. Updated Dockerfile to use conda and the intel Python distro.
973f449a — Alex Hagerman 1 year, 7 months ago
Documentation updates and docker link location updates.
fe8dd675 — Alex Hagerman 1 year, 7 months ago
Merge pull request #1 from AlexHagerman/rabbitmq-sql-server

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docker-airflow

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This repository contains Dockerfile of apache-airflow for Docker's automated build published to the public Docker Hub Registry.

Informations

Installation

Pull the image from the Docker repository.

docker pull alexhagerman/docker-airflow

Build

Optionally install Extra Airflow Packages and/or python/conda dependencies at build time :

docker build --rm --build-arg AIRFLOW_DEPS="datadog,dask" -t alexhagerman/docker-airflow .
docker build --rm --build-arg PYTHON_DEPS="flask_oauthlib>=0.9" -t alexhagerman/docker-airflow .
docker build --rm --build-arg CONDA_DEPS="hdfs3 libhdfs3" -t alexhagerman/docker-airflow .

or combined

docker build --rm --build-arg AIRFLOW_DEPS="datadog,dask" --build-arg PYTHON_DEPS="flask_oauthlib>=0.9" CONDA_DEPS="libhdfs3" -t alexhagerman/docker-airflow .

Don't forget to update the airflow images in the docker-compose files to alexhagerman/docker-airflow:latest.

Usage

By default, docker-airflow runs Airflow with SequentialExecutor :

docker run -d -p 8080:8080 alexhagerman/docker-airflow webserver

If you want to run another executor, use the other docker-compose.yml files provided in this repository.

For LocalExecutor :

docker-compose -f docker-compose-LocalExecutor.yml up -d

For CeleryExecutor :

docker-compose -f docker-compose-CeleryExecutor.yml up -d

NB : If you want to have DAGs example loaded (default=False), you've to set the following environment variable :

LOAD_EX=n

docker run -d -p 8080:8080 -e LOAD_EX=y alexhagerman/docker-airflow

If you want to use Ad hoc query, make sure you've configured connections: Go to Admin -> Connections and Edit "mssql_default" set this values (equivalent to values in airflow.cfg/docker-compose*.yml) :

  • Host : postgres
  • Schema : airflow
  • Login : airflow
  • Password : airflow

For encrypted connection passwords (in Local or Celery Executor), you must have the same fernet_key. By default docker-airflow generates the fernet_key at startup, you have to set an environment variable in the docker-compose (ie: docker-compose-LocalExecutor.yml) file to set the same key accross containers. To generate a fernet_key :

docker run alexhagerman/docker-airflow python -c "from cryptography.fernet import Fernet; FERNET_KEY = Fernet.generate_key().decode(); print(FERNET_KEY)"

Configuring Airflow

It's possible to set any configuration value for Airflow from environment variables, which are used over values from the airflow.cfg.

The general rule is the environment variable should be named AIRFLOW__<section>__<key>, for example AIRFLOW__CORE__SQL_ALCHEMY_CONN sets the sql_alchemy_conn config option in the [core] section.

Check out the Airflow documentation for more details

You can also define connections via environment variables by prefixing them with AIRFLOW_CONN_ - for example AIRFLOW_CONN_MSSQL_MASTER=mssql+pyodbc://user:password@localhost:1433/master?driver for a connection called "mssql_master". The value is parsed as a URI. 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

Configuring the Docker Environment

docker-compose supports using a .env file to setup default environment variables. A sample .env-template is available in the repo. Using this file allows you to define your airflow, celery and rabbitmq default settings in one location.

Custom Airflow plugins

Airflow allows for custom user-created plugins which are typically found in ${AIRFLOW_HOME}/plugins folder. Documentation on plugins can be found here

In order to incorporate plugins into your docker container

  • Create the plugins folders plugins/ with your custom plugins.
  • Mount the folder as a volume by doing either of the following:
    • Include the folder as a volume in command-line -v $(pwd)/plugins/:/usr/local/airflow/plugins
    • Use docker-compose-LocalExecutor.yml or docker-compose-CeleryExecutor.yml which contains support for adding the plugins folder as a volume

Install custom python package

  • Create a file "requirements.txt" with the desired python modules
  • Mount this file as a volume -v $(pwd)/requirements.txt:/requirements.txt (or add it as a volume in docker-compose file)
  • The entrypoint.sh script execute the pip install command (with --user option)

Scale the number of workers

Easy scaling using docker-compose:

docker-compose -f docker-compose-CeleryExecutor.yml scale worker=5

This can be used to scale to a multi node setup using docker swarm.

Running other airflow commands

If you want to run other airflow sub-commands, such as list_dags or clear you can do so like this:

docker run --rm -ti alexhagerman/docker-airflow airflow list_dags

or with your docker-compose set up like this:

docker-compose -f docker-compose-CeleryExecutor.yml run --rm webserver airflow list_dags

You can also use this to run a bash shell or any other command in the same environment that airflow would be run in:

docker run --rm -ti alexhagerman/docker-airflow bash
docker run --rm -ti alexhagerman/docker-airflow ipython

Wanna help?

Fork, improve and PR. ;-)