Shahar Epstein
f92281e1d0
* Drop Python 3.10 from CI, Breeze and the container images Python 3.10 reaches end of life in October 2026 and more and more of Airflow's dependencies have already dropped it, which makes the 3.10 leg the slowest and most fragile part of CI. This is the first layer of the stack that moves the baseline to Python 3.11: Breeze stops offering 3.10, the default Python for CI, Breeze and the images moves to 3.11, and the PGP path for verifying the Python tarball goes away because every remaining version ships Sigstore bundles. Python 3.10 stays in the historical version lists that Breeze uses to reason about older Airflow releases, and in the CI image restore action that release branches still rely on. The distributions still declare Python 3.10 in this layer; layer 7 bumps them once the code no longer carries 3.10-only constructs. Until then the provider metadata generator keeps its own 3.10-inclusive version list, and the prek minimum-Python check keeps 3.10 because its script header still pins it to a 3.10 interpreter until layer 2. The shared-distribution and scripts test jobs run uv without an interpreter pin, so uv picks the runner's system Python, which is 3.10 on the current images. Pinning them to the default CI Python keeps them on a supported interpreter regardless of the runner image. The image build speed check builds the CI image from the registry cache within a fixed budget. A PR that changes install_os_dependencies.sh or the embedded GPG keys misses the cache for the layer that compiles Python from source. That compile took about 4.5 minutes for Python 3.10 but 11 to 18 minutes for 3.11 and newer on hosted runners, so the budget is raised to fit a cold Python 3.11+ build. * Explain when to drop a Python version from the CI version list The comment above ALL_PYTHON_MAJOR_MINOR_VERSIONS described only keeping dropped versions so cherry-picks to older branches stay tested. Python 3.10 is removed from the list because the 3.10 drop is also cherry-picked to v3-4-test, so no release branch that still receives cherry-picks from main supports 3.10. The comment now spells out both phases so the next version drop follows the same rule. The Docker image changelog gets its Python 3.10 entry under Airflow 3.4.0 here, next to the image change itself, as was done for Python 3.9.
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 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 versionapache/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 and providers that you need separately via Building the 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 versionapache/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. The set of extras used in Airflow Production image are available in the Dockerfile.
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.
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 for details.
Usage
The 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.
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.