An updated version of puckel/docker-airflow

• Adds the following to the apache-airflow installation: gcs, salesforce, snowflake

How to use this

1. Clone this repo and cd into the repo
2. sudo docker build -t <name-you-want> .
3. docker run -d -p 8080:8080 -v /path/to/dags/folder/:/usr/local/airflow/dags -v /path/to/plugins/folder:/usr/local/airflow/plugins <name-you-want>

Modifying this further

1. Clone this repo (or create your own fork if you want to save your copy to your Github account)
2. Edit the Dockerfile/anything else as needed
3. Run the same docker build and docker run as described in steps 2 and 3 above
4. If forked, commit and push to your fork to save or build on in the future

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docker-airflow

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

Informations

• Based on Python (3.7-slim-stretch) official Image python:3.7-slim-stretch 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

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

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

or combined

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

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

Usage

By default, docker-airflow runs Airflow with SequentialExecutor :

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

docker run puckel/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_POSTGRES_MASTER=postgres://user:password@localhost:5432/master for a connection called "postgres_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

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)

UI Links

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

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

Wanna help?

Fork, improve and PR.