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 for all the supported Python versions.

You can find the following images there (Assuming Airflow version 2.7.3):

  • apache/airflow:latest - the latest released Airflow image with default Python version (3.8 currently)

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

  • apache/airflow:2.7.3 - the versioned Airflow image with default Python version (3.8 currently)

  • apache/airflow:2.7.3-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 and providers that you need separately via Building the image.

  • apache/airflow:slim-latest - the latest released Airflow image with default Python version (3.8 currently)

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

  • apache/airflow:slim-2.7.3 - the versioned Airflow image with default Python version (3.8 currently)

  • apache/airflow:slim-2.7.3-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. The set of extras used in Airflow Production image are available in the Dockerfile.

However, Airflow has more than 60 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.

The production images are build 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 for details.

Fixing images at release time

The released “versioned” reference images are mostly fixed when we release Airflow version and we only update them in exceptional circumstances. For example when we find out that there are dependency errors that might prevent important Airflow or embedded provider’s functionalities working. In normal circumstances, the images are not going to change after release, even if new version of Airflow dependencies are released - not even when those versions contain critical security fixes. The process of Airflow releases is designed around upgrading dependencies automatically where applicable but only when we release a new version of Airflow, not for already released versions.

If you want to make sure that Airflow dependencies are upgraded to the latest released versions containing latest security fixes in the image you use, you should implement your own process to upgrade those yourself when you build custom image based on the Airflow reference one. Airflow usually does not upper-bound versions of its dependencies via requirements, so you should be able to upgrade them to the latest versions - usually without any problems. And you can follow the process described in Building the image to do it (even in automated way).

Obviously - since we have no control over what gets released in new versions of the dependencies, we cannot give any guarantees that tests and functionality of those dependencies will be compatible with Airflow after you upgrade them - testing if Airflow still works with those is in your hands, and in case of any problems, you should raise issue with the authors of the dependencies that are problematic. You can also - in such cases - look at the Airflow issues Airflow Pull Requests and Airflow Discussions, searching for similar problems to see if there are any fixes or workarounds found in the main version of Airflow and apply them to your custom image.

The easiest way to keep-up with the latest released dependencies is however, to upgrade to the latest released Airflow version via switching to newly released images as base for your images, when a new version of Airflow is released. Whenever we release a new version of Airflow, we upgrade all dependencies to the latest applicable versions and test them together, so if you want to keep up with those tests - staying up-to-date with latest version of Airflow is the easiest way to update those dependencies.


The reference Docker Image supports the following platforms and database:

Intel platform (x86_64)

  • Postgres Client

  • MySQL Client

  • MSSQL Client

ARM platform (aarch64)

ARM support is experimental, might change any time.

  • Postgres Client

  • MySQL Client (MySQL 8)

  • MSSQL Client

Note that MySQL on arm has experimental support through MariaDB client library.


The AIRFLOW_HOME is set by default to /opt/airflow/ - this means that DAGs are by 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.

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.

You can use this image in Helm Chart as well.

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