0a4b5d8e49
* [main] CI: Upgrade important CI environment * Fix "prek run update-providers-build-files --all-files" * Fix CI breakages from the dependency upgrade - snowflake: snowflake-sqlalchemy (up to 1.11.1) cannot be imported on SQLAlchemy 2.1, which made ORMSelectCompileState private. Cap sqlalchemy<2.1 in the provider; the workspace lock resolves back to SQLAlchemy 2.0.54. Removing the cap is tracked in #75118. - airflow-core: type the dag_display_name and duration hybrid expressions against the class (type[...] -> ColumnElement) and fix _get_dialect's return type, so mypy holds on SQLAlchemy 2.0 and 2.1. - common.ai: declare the OpenAI Decisions and System One vendors added in pydantic-ai 2.53/2.55; reject sqlglot >= 30.22's exp.ListStage (Snowflake LIST/LS @stage) on the allow_writes path; adapt tests to pydantic-ai's streamed Anthropic requests and new request counting, and to sqlglot's MySQL "--+" tokenization. Generated-by: Claude Opus 5 * Type Backfill.duration expression against the class #75078 dropped the type: ignores in this expression, which only held while mypy did not check the body; typing cls as type[Backfill] keeps it clean on SQLAlchemy 2.0 and 2.1, as dag_display_name and DagRun.duration already are. Also annotate the Anthropic stream fixture so mypy can index its events. Generated-by: Claude Opus 5 --------- Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: Wei Lee <dev@wei-lee.me> Co-authored-by: Jarek Potiuk <jarek@potiuk.com>
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.