docker-airflow

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

Informations

• Based on Debian Jessie official Image debian:jessie and uses the official Postgres as backend and Redis as queue
• Install Docker
• Install Docker Compose
• Following the Airflow release from Python Package Index

Installation

Pull the image from the Docker repository.

    docker pull puckel/docker-airflow

Build

For example, if you need to install Extra Packages, edit the Dockerfile and then build it.

    docker build --rm -t puckel/docker-airflow .

Usage

By default, docker-airflow runs Airflow with SequentialExecutor :

    docker run -d -p 8080:8080 puckel/docker-airflow

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 don't want to have DAGs example loaded (default=True), you've to set the following environment variable :

LOAD_EX=n

    docker run -d -p 8080:8080 -e LOAD_EX=n puckel/docker-airflow

If you want to use Ad hoc query, make sure you've configured connections: Go to Admin -> Connections and Edit "postgres_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 :

    python -c "from cryptography.fernet import Fernet; FERNET_KEY = Fernet.generate_key().decode(); print FERNET_KEY"

Check Airflow Documentation

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
• The entrypoint.sh script execute the pip install command (with --user option)

UI Links

• Airflow: localhost:8080
• Flower: localhost:5555

Scale the number of workers

Easy scaling using docker-compose:

    docker-compose scale worker=5

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

Wanna help?

Fork, improve and PR. ;-)