Ash Berlin-Taylor
46e20a1a09
Previously to see the same error in docs build as you would get in CI, you would have to run both the docs build and the spellchecking build. The issue was that the spellcheck (using sphinx-contrib's spelling) was defined as a new builder, meaning you could either build HTML, or you can check spelling. On CI, this didn't result in any extra wall-clock time as the jobs were parallelized (but this change does remove a job from the overall matrix), but locally that was almost always not the case, so this would mean full build to view the HTML, and spell checking build to look for errors, resulting in it taking twice as long. The upstream spellchecker was also slower than it should be mainly because its importable-module filter calls find_spec on every distinct identifier-like word before consulting the dictionary. Here it runs only for words that are still misspelled. Word lists, filters and `:spelling:ignore:` behave as before. This also handles errors a lot more nicely (hence all the code deletions): - They are written natively to a JSON file, not scraped from a stream - They also show up as GH annotations (Where we can attribute it to a real file) to make fixing them easier on PRs. Please note: this is lightly breaking for Airflow contributors as `breeze build-docs --docs-only` and `--spellcheck-only` are gone -- every build checks spelling now as it is essentially free (on the order of milliseconds per module).
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.