docker-stack-docs/README.md - airflow - Git at Google


            
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            # Docker Image for Apache Airflow


            For the ease of deployment in production, the community releases a production-ready reference container

            
            image.


            The Apache Airflow community, releases Docker Images which are `reference images` for Apache Airflow.

            
            Every time a new version of Airflow is released, the images are prepared in the

            
            [apache/airflow DockerHub](https://hub.docker.com/r/apache/airflow)

            
            for all the supported Python versions.


            You can find the following images there (Assuming Airflow version `3.4.0`):


            * `apache/airflow:latest` - the latest released Airflow image with default Python version (3.12 currently)

            
            * `apache/airflow:latest-pythonX.Y` - the latest released Airflow image with specific Python version

            
            * `apache/airflow:3.4.0` - the versioned Airflow image with default Python version (3.12 currently)

            
            * `apache/airflow:3.4.0-pythonX.Y` - the versioned Airflow image with specific Python version


            Those are "reference" regular images. They contain the most common set of extras, dependencies and providers that are

            
            often used by the users and they are good to "try-things-out" when you want to just take Airflow for a spin,


            You can also use "slim" images that contain only core Airflow and are about half the size of the "regular" images

            
            but you need to add all the [Reference for package extras](https://airflow.apache.org/docs/apache-airflow/stable/extra-packages-ref.html) and providers that you need separately

            
            via [Building the image](https://airflow.apache.org/docs/docker-stack/build.html#build-build-image).


            * `apache/airflow:slim-latest`              - the latest released Airflow image with default Python version (3.12 currently)

            
            * `apache/airflow:slim-latest-pythonX.Y`    - the latest released Airflow image with specific Python version

            
            * `apache/airflow:slim-3.4.0`           - the versioned Airflow image with default Python version (3.12 currently)

            
            * `apache/airflow:slim-3.4.0-pythonX.Y` - the versioned Airflow image with specific Python version


            The Apache Airflow image provided as convenience package is optimized for size, and

            
            it provides just a bare minimal set of the extras and dependencies installed and in most cases

            
            you want to either extend or customize the image. You can see all possible extras in [Reference for package extras](https://airflow.apache.org/docs/apache-airflow/stable/extra-packages-ref.html).

            
            The set of extras used in Airflow Production image are available in the

            
            [Dockerfile](https://github.com/apache/airflow/blob/2c6c7fdb2308de98e142618836bdf414df9768c8/Dockerfile#L37).


            However, Airflow has more than 90 community-managed providers (installable via extras) and some of the

            
            default extras/providers installed are not used by everyone, sometimes others extras/providers

            
            are needed, sometimes (very often actually) you need to add your own custom dependencies,

            
            packages or even custom providers. You can learn how to do it in [Building the image](https://airflow.apache.org/docs/docker-stack/build.html#build-build-image).


            The production images are built in DockerHub from released version and release candidates. There

            
            are also images published from branches but they are used mainly for development and testing purpose.

            
            See [Airflow Git Branching](https://github.com/apache/airflow/blob/main/contributing-docs/10_working_with_git.rst#airflow-git-branches)

            
            for details.


            ## Usage


            The [`AIRFLOW_HOME`](https://airflow.apache.org/docs/apache-airflow/stable/cli-and-env-variables-ref.html#envvar-AIRFLOW_HOME) is set by default to ``/opt/airflow/`` - this means that dags

            
            are in default in the ``/opt/airflow/dags`` folder and logs are in the ``/opt/airflow/logs``


            The working directory is ``/opt/airflow`` by default.


            If no `AIRFLOW__DATABASE__SQL_ALCHEMY_CONN` variable is set then SQLite database is created in

            
            ``${AIRFLOW_HOME}/airflow.db``.


            For example commands that start Airflow see: [Executing commands](https://airflow.apache.org/docs/docker-stack/entrypoint.html#entrypoint-commands).


            Airflow requires many components to function as it is a distributed application. You may therefore also be interested

            
            in launching Airflow in the Docker Compose environment, see: [Running Airflow in Docker](https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html).


            You can use this image in [Helm Chart](https://airflow.apache.org/docs/helm-chart/stable/index.html) as well.
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	# Docker Image for Apache Airflow

	For the ease of deployment in production, the community releases a production-ready reference container
	image.

	The Apache Airflow community, releases Docker Images which are `reference images` for Apache Airflow.
	Every time a new version of Airflow is released, the images are prepared in the
	[apache/airflow DockerHub](https://hub.docker.com/r/apache/airflow)
	for all the supported Python versions.

	You can find the following images there (Assuming Airflow version `3.4.0`):

	* `apache/airflow:latest` - the latest released Airflow image with default Python version (3.12 currently)
	* `apache/airflow:latest-pythonX.Y` - the latest released Airflow image with specific Python version
	* `apache/airflow:3.4.0` - the versioned Airflow image with default Python version (3.12 currently)
	* `apache/airflow:3.4.0-pythonX.Y` - the versioned Airflow image with specific Python version

	Those are "reference" regular images. They contain the most common set of extras, dependencies and providers that are
	often used by the users and they are good to "try-things-out" when you want to just take Airflow for a spin,

	You can also use "slim" images that contain only core Airflow and are about half the size of the "regular" images
	but you need to add all the [Reference for package extras](https://airflow.apache.org/docs/apache-airflow/stable/extra-packages-ref.html) and providers that you need separately
	via [Building the image](https://airflow.apache.org/docs/docker-stack/build.html#build-build-image).

	* `apache/airflow:slim-latest` - the latest released Airflow image with default Python version (3.12 currently)
	* `apache/airflow:slim-latest-pythonX.Y` - the latest released Airflow image with specific Python version
	* `apache/airflow:slim-3.4.0` - the versioned Airflow image with default Python version (3.12 currently)
	* `apache/airflow:slim-3.4.0-pythonX.Y` - the versioned Airflow image with specific Python version

	The Apache Airflow image provided as convenience package is optimized for size, and
	it provides just a bare minimal set of the extras and dependencies installed and in most cases
	you want to either extend or customize the image. You can see all possible extras in [Reference for package extras](https://airflow.apache.org/docs/apache-airflow/stable/extra-packages-ref.html).
	The set of extras used in Airflow Production image are available in the
	[Dockerfile](https://github.com/apache/airflow/blob/2c6c7fdb2308de98e142618836bdf414df9768c8/Dockerfile#L37).

	However, Airflow has more than 90 community-managed providers (installable via extras) and some of the
	default extras/providers installed are not used by everyone, sometimes others extras/providers
	are needed, sometimes (very often actually) you need to add your own custom dependencies,
	packages or even custom providers. You can learn how to do it in [Building the image](https://airflow.apache.org/docs/docker-stack/build.html#build-build-image).

	The production images are built in DockerHub from released version and release candidates. There
	are also images published from branches but they are used mainly for development and testing purpose.
	See [Airflow Git Branching](https://github.com/apache/airflow/blob/main/contributing-docs/10_working_with_git.rst#airflow-git-branches)
	for details.

	## Usage

	The [`AIRFLOW_HOME`](https://airflow.apache.org/docs/apache-airflow/stable/cli-and-env-variables-ref.html#envvar-AIRFLOW_HOME) is set by default to ``/opt/airflow/`` - this means that dags
	are in default in the ``/opt/airflow/dags`` folder and logs are in the ``/opt/airflow/logs``

	The working directory is ``/opt/airflow`` by default.

	If no `AIRFLOW__DATABASE__SQL_ALCHEMY_CONN` variable is set then SQLite database is created in
	``${AIRFLOW_HOME}/airflow.db``.

	For example commands that start Airflow see: [Executing commands](https://airflow.apache.org/docs/docker-stack/entrypoint.html#entrypoint-commands).

	Airflow requires many components to function as it is a distributed application. You may therefore also be interested
	in launching Airflow in the Docker Compose environment, see: [Running Airflow in Docker](https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html).

	You can use this image in [Helm Chart](https://airflow.apache.org/docs/helm-chart/stable/index.html) as well.