Airflow Docker images
- PROD image
- CI image
Image naming conventions
Building docker images from current sources
Building PROD docker images from released PIP packages
Using cache during builds
Choosing image registry
- Using GitHub Container Registry as build cache
Customizing the CI image
- CI image build arguments
- CI Image manifests
Working with the images

Airflow Docker images

Airflow has two main images (build from Dockerfiles):

Production image (Dockerfile) - that can be used to build your own production-ready Airflow installation You can read more about building and using the production image in the Production Deployments document. The image is built using Dockerfile

CI image (Dockerfile.ci) - used for running tests and local development. The image is built using Dockerfile.ci

The PROD image is a multi-segment image. The first segment "airflow-build-image" contains all the build essentials and related dependencies that allow to install airflow locally. By default the image is build from a released version of Airflow from GitHub, but by providing some extra arguments you can also build it from local sources. This is particularly useful in CI environment where we are using the image to run Kubernetes tests. See below for the list of arguments that should be provided to build production image from the local sources.

The image is primarily optimised for size of the final image, but also for speed of rebuilds - the 'airflow-build-image' segment uses the same technique as the CI builds for pre-installing PIP dependencies. It first pre-installs them from the right GitHub branch and only after that final airflow installation is done from either local sources or remote location (PIP or GitHub repository).

You can read more details about building, extending and customizing the PROD image in the Latest documentation

CI image

The CI image is used by Breeze as shell image but it is also used during CI build. The image is single segment image that contains Airflow installation with "all" dependencies installed. It is optimised for rebuild speed. It installs PIP dependencies from the current branch first - so that any changes in setup.py do not trigger reinstalling of all dependencies. There is a second step of installation that re-installs the dependencies from the latest sources so that we are sure that latest dependencies are installed.

Image naming conventions

The images are named as follows:

apache/airflow-ci:<BRANCH>-python<PYTHON_MAJOR_MINOR_VERSION>[-ci][-manifest]

For production images tagged with official releases:

apache/airflow:<TAG>-python<PYTHON_MAJOR_MINOR_VERSION>

And for production images with latest tag:

``apache/airflow:latest[-python<PYTHON_MAJOR_MINOR_VERSION>]

where:

BRANCH_OR_TAG - branch or tag used when creating the image. Examples: main, v2-1-test, 2.1.0. The main, v2-*-test labels are built from branches so they change over time. The 2.*.* labels are built from git tags and they are "fixed" once built.
PYTHON_MAJOR_MINOR_VERSION - version of Python used to build the image. Examples: 3.6, 3.7, 3.8, 3.9
The -ci suffix is added for CI images
The -manifest is added for manifest images (see below for explanation of manifest images)

We also store (to increase speed of local build/pulls) Python images that were used to build the CI images. Each CI image, when built uses current Python version of the base images. Those python images are regularly updated (with bugfixes/security fixes), so for example Python 3.8 from last week might be a different image than Python 3.8 today. Therefore whenever we push CI image to airflow repository, we also push the Python image that was used to build it this image is stored as apache/airflow-ci:python<PYTHON_MAJOR_MINOR_VERSION>-<BRANCH_OR_TAG>.

Since those are simply snapshots of the existing Python images, DockerHub does not create a separate copy of those images - all layers are mounted from the original Python images and those are merely labels pointing to those.

Building docker images from current sources

The easy way to build the CI/PROD images is to use BREEZE.rst. It uses a number of optimization and caches to build it efficiently and fast when you are developing Airflow and need to update to latest version.

CI image, airflow package is always built from sources. When you execute the image, you can however use the --use-airflow-version flag (or USE_AIRFLOW_VERSION environment variable) to remove the preinstalled source version of Airflow and replace it with one of the possible installation methods:

"none" airflow is removed and not installed
"wheel" airflow is removed and replaced with "wheel" version available in dist
"sdist" airflow is removed and replaced with "sdist" version available in dist
"<VERSION>" airflow is removed and installed from PyPI (with the specified version)

For PROD image by default production image is built from the latest sources when using Breeze, but when you use it via docker build command, it uses the latest installed version of airflow and providers. However, you can choose different installation methods as described in Building PROD docker images from released PIP packages. Detailed reference for building production image from different sources can be found in: Build Args reference

You can build the CI image using current sources this command:

./breeze build-image

You can build the PROD image using current sources with this command:

./breeze build-image --production-image

By adding --python <PYTHON_MAJOR_MINOR_VERSION> parameter you can build the image version for the chosen Python version.

The images are build with default extras - different extras for CI and production image and you can change the extras via the --extras parameters and add new ones with --additional-extras. You can see default extras used via ./breeze flags.

For example if you want to build Python 3.7 version of production image with "all" extras installed you should run this command:

./breeze build-image --python 3.7 --extras "all" --production-image

If you just want to add new extras you can add them like that:

./breeze build-image --python 3.7 --additional-extras "all" --production-image

The command that builds the CI image is optimized to minimize the time needed to rebuild the image when the source code of Airflow evolves. This means that if you already have the image locally downloaded and built, the scripts will determine whether the rebuild is needed in the first place. Then the scripts will make sure that minimal number of steps are executed to rebuild parts of the image (for example, PIP dependencies) and will give you an image consistent with the one used during Continuous Integration.

The command that builds the production image is optimised for size of the image.

Building PROD docker images from released PIP packages

You can also build production images from PIP packages via providing --install-airflow-version parameter to Breeze:

./breeze build-image --python 3.7 --additional-extras=trino \
    --production-image --install-airflow-version=2.0.0

This will build the image using command similar to:

pip install \
  apache-airflow[async,amazon,celery,cncf.kubernetes,docker,dask,elasticsearch,ftp,grpc,hashicorp,http,ldap,google,microsoft.azure,mysql,postgres,redis,sendgrid,sftp,slack,ssh,statsd,virtualenv]==2.0.0 \
  --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-2.0.0/constraints-3.6.txt"

Note

Only pip installation is currently officially supported.

While they are some successes with using other tools like poetry or pip-tools, they do not share the same workflow as pip - especially when it comes to constraint vs. requirements management. Installing via Poetry or pip-tools is not currently supported.

If you wish to install airflow using those tools you should use the constraint files and convert them to appropriate format and workflow that your tool requires.

You can also build production images from specific Git version via providing --install-airflow-reference parameter to Breeze (this time constraints are taken from the constraints-main branch which is the HEAD of development for constraints):

pip install "https://github.com/apache/airflow/archive/<tag>.tar.gz#egg=apache-airflow" \
  --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-main/constraints-3.6.txt"

You can also skip installing airflow and install it from locally provided files by using --install-from-docker-context-files parameter and --disable-pypi-when-building to Breeze:

./breeze build-image --python 3.7 --additional-extras=trino \
    --production-image --disable-pypi-when-building --install-from-docker-context-files

In this case you airflow and all packages (.whl files) should be placed in docker-context-files folder.

Using cache during builds

Default mechanism used in Breeze for building CI images uses images pulled from DockerHub or GitHub Image Registry. This is done to speed up local builds and CI builds - instead of 15 minutes for rebuild of CI images, it takes usually less than 3 minutes when cache is used. For CI builds this is usually the best strategy - to use default "pull" cache. This is default strategy when BREEZE.rst builds are performed.

For Production Image - which is far smaller and faster to build, it's better to use local build cache (the standard mechanism that docker uses. This is the default strategy for production images when BREEZE.rst builds are performed. The first time you run it, it will take considerably longer time than if you use the pull mechanism, but then when you do small, incremental changes to local sources, Dockerfile image= and scripts further rebuilds with local build cache will be considerably faster.

You can also disable build cache altogether. This is the strategy used by the scheduled builds in CI - they will always rebuild all the images from scratch.

You can change the strategy by providing one of the --build-cache-local, --build-cache-pulled or even --build-cache-disabled flags when you run Breeze commands. For example:

./breeze build-image --python 3.7 --build-cache-local

Will build the CI image using local build cache (note that it will take quite a long time the first time you run it).

./breeze build-image --python 3.7 --production-image --build-cache-pulled

Will build the production image with pulled images as cache.

./breeze build-image --python 3.7 --production-image --build-cache-disabled

Will build the production image from the scratch.

You can also turn local docker caching by setting DOCKER_CACHE variable to "local", "pulled", "disabled" and exporting it.

export DOCKER_CACHE="local"

or

export DOCKER_CACHE="disabled"

Choosing image registry

By default images are pulled and pushed from and to DockerHub registry when you use Breeze's push-image or build commands.

But as described in CI Documentation, you can choose GitHub Container Registry.

Naming convention for DockerHub images.

Images used during CI builds:

apache/airflow-ci:<BRANCH>-pythonX.Y         - for production images
apache/airflow-ci:<BRANCH>-pythonX.Y-ci      - for CI images
apache/airflow-ci:<BRANCH>-pythonX.Y-build   - for production build stage
apache/airflow-ci:pythonX.Y-<BRANCH>         - for Python base image used for both CI and PROD image

For example:

apache/airflow-ci:main-python3.6                - production "main" image from current main
apache/airflow-ci:main-python3.6-ci             - CI "main" image from current main
apache/airflow-ci:v2-1-test-python3.6-ci          - CI "main" image from current v2-1-test branch
apache/airflow:python3.6-main                - base Python image for the main branch

You can see those CI DockerHub images at https://hub.docker.com/r/apache/airflow-ci

Released, production images

apache/airflow:<TAG>-pythonX.Y         - for tagged released production images
apache/airflow:<TAG>                   - for default Python version released production images
apache/airflow:latest-pythonX.Y        - for latest released production images
apache/airflow:latest                  - for default Python version of latest released production images

For example:

apache/airflow:2.1.0-python3.8         - for regular released 2.1.0 production image with Python 3.8
apache/airflow:2.1.0                   - for default Python version of 2.1.0 production image
apache/airflow:latest-python3.8        - for latest released Python 3.8 production image
apache/airflow:latest                  - for latest released default Python production image

You can see those CI DockerHub images at https://hub.docker.com/r/apache/airflow

Using GitHub Container Registry as build cache

We are using GitHub Container Registry as build cache.The images are all in organization wide "apache/" namespace. We are adding "airflow-" as prefix for image names of all Airflow images. The images are linked to the repository via org.opencontainers.image.source label in the image.

See https://docs.github.com/en/packages/learn-github-packages/connecting-a-repository-to-a-package

Naming convention for the GitHub packages.

Images with a commit SHA (built for pull requests and pushes)

ghcr.io/apache/airflow-<BRANCH>-pythonX.Y-ci-v2:<COMMIT_SHA>    - for CI images
ghcr.io/apache/airflow-<BRANCH>-pythonX.Y-v2:<COMMIT_SHA>       - for production images
ghcr.io/apache/airflow-<BRANCH>-pythonX.Y-build-v2:<COMMIT_SHA> - for production build stage
ghcr.io/apache/airflow-python-v2:X.Y-slim-buster-<COMMIT_SHA>   - for base Python images

Latest images (pushed when main merge succeeds):

ghcr.io/apache/airflow-<BRANCH>-pythonX.Y-ci-v2:latest    - for CI images
ghcr.io/apache/airflow-<BRANCH>-pythonX.Y-v2:latest       - for production images
ghcr.io/apache/airflow-<BRANCH>-pythonX.Y-build-v2:latest - for production build stage
ghcr.io/apache/airflow-python-v2:X.Y-slim-buster          - for base Python images

Note that we never push or pull "release" images to GitHub registry. Those are only pushed to DockerHub. You can see all the current GitHub images at https://github.com/apache/airflow/packages

In order to interact with the GitHub Container Registry you need to add --use-github-registry flag to the pull/push commands in Breeze. This way the images will be pulled/pushed from/to GitHub rather than from/to DockerHub. Images are build locally as apache/airflow images but then they are tagged with the right GitHub tags for you automatically.

You can read more about the CI configuration and how CI builds are using DockerHub/GitHub images in CI.rst.

Note that you need to be committer and have the right to push to DockerHub and GitHub and you need to be logged in to the registry. Only committers can push images directly. You need to login with your Personal Access Token with "packages" write scope to be able to push to those repositories or pull from them in case of GitHub Packages.

GitHub Container Registry

docker login ghcr.io

Since there are different naming conventions used for Airflow images and there are multiple images used, Breeze provides easy to use management interface for the images. The CI system of ours is designed in the way that it should automatically refresh caches, rebuild the images periodically and update them whenever new version of base Python is released. However, occasionally, you might need to rebuild images locally and push them directly to the registries to refresh them.

This can be done with Breeze command line which has easy-to-use tool to manage those images. For example:

Force building Python 3.6 CI image using local cache and pushing it container registry:

./breeze build-image --python 3.6 --force-build-images --force-pull-base-python-image --build-cache-local
./breeze push-image --python 3.6 --use-github-registry

Building Python 3.8 CI image using cache pulled from DockerHub and pushing it back:

./breeze build-image --python 3.8
./breeze push-image --python 3.8

You can also pull and run images being result of a specific CI run in GitHub Actions. This is a powerful tool that allows to reproduce CI failures locally, enter the images and fix them much faster. It is enough to pass --github-image-id and the registry and Breeze will download and execute commands using the same image that was used during the CI build.

For example this command will run the same Python 3.8 image as was used in build identified with 9a621eaa394c0a0a336f8e1b31b35eff4e4ee86e commit SHA with enabled Kerberos integration.

./breeze --github-image-id 9a621eaa394c0a0a336f8e1b31b35eff4e4ee86e \
  --use-github-registry \
  --python 3.8 --integration kerberos

You can see more details and examples in Breeze

Customizing the CI image

Customizing the CI image allows to add your own dependencies to the image.

The easiest way to build the customized image is to use breeze script, but you can also build suc customized image by running appropriately crafted docker build in which you specify all the build-args that you need to add to customize it. You can read about all the args and ways you can build the image in the #ci-image-build-arguments chapter below.

Here just a few examples are presented which should give you general understanding of what you can customize.

This builds the production image in version 3.7 with additional airflow extras from 2.0.0 PyPI package and additional apt dev and runtime dependencies.

docker build . -f Dockerfile.ci \
  --build-arg PYTHON_BASE_IMAGE="python:3.7-slim-buster" \
  --build-arg ADDITIONAL_AIRFLOW_EXTRAS="jdbc"
  --build-arg ADDITIONAL_PYTHON_DEPS="pandas"
  --build-arg ADDITIONAL_DEV_APT_DEPS="gcc g++"
  --build-arg ADDITIONAL_RUNTIME_APT_DEPS="default-jre-headless"
  --tag my-image:0.0.1

the same image can be built using breeze (it supports auto-completion of the options):

./breeze build-image -f Dockerfile.ci \
    --production-image  --python 3.7 \
    --additional-extras=jdbc --additional-python-deps="pandas" \
    --additional-dev-apt-deps="gcc g++" --additional-runtime-apt-deps="default-jre-headless"

You can customize more aspects of the image - such as additional commands executed before apt dependencies are installed, or adding extra sources to install your dependencies from. You can see all the arguments described below but here is an example of rather complex command to customize the image based on example in this comment:

docker build . -f Dockerfile.ci \
  --build-arg PYTHON_BASE_IMAGE="python:3.7-slim-buster" \
  --build-arg AIRFLOW_INSTALLATION_METHOD="apache-airflow" \
  --build-arg ADDITIONAL_AIRFLOW_EXTRAS="slack" \
  --build-arg ADDITIONAL_PYTHON_DEPS="apache-airflow-providers-odbc \
      azure-storage-blob \
      sshtunnel \
      google-api-python-client \
      oauth2client \
      beautifulsoup4 \
      dateparser \
      rocketchat_API \
      typeform" \
  --build-arg ADDITIONAL_DEV_APT_DEPS="msodbcsql17 unixodbc-dev g++" \
  --build-arg ADDITIONAL_DEV_APT_COMMAND="curl https://packages.microsoft.com/keys/microsoft.asc | apt-key add --no-tty - && curl https://packages.microsoft.com/config/debian/10/prod.list > /etc/apt/sources.list.d/mssql-release.list" \
  --build-arg ADDITIONAL_DEV_ENV_VARS="ACCEPT_EULA=Y" \
  --build-arg ADDITIONAL_RUNTIME_APT_COMMAND="curl https://packages.microsoft.com/keys/microsoft.asc | apt-key add --no-tty - && curl https://packages.microsoft.com/config/debian/10/prod.list > /etc/apt/sources.list.d/mssql-release.list" \
  --build-arg ADDITIONAL_RUNTIME_APT_DEPS="msodbcsql17 unixodbc git procps vim" \
  --build-arg ADDITIONAL_RUNTIME_ENV_VARS="ACCEPT_EULA=Y" \
  --tag my-image:0.0.1

CI image build arguments

The following build arguments (--build-arg in docker build command) can be used for CI images:

Build argument	Default value	Description
`PYTHON_BASE_IMAGE`	`python:3.6-slim-buster`	Base Python image
`PYTHON_MAJOR_MINOR_VERSION`	`3.6`	major/minor version of Python (should match base image)
`DEPENDENCIES_EPOCH_NUMBER`	`2`	increasing this number will reinstall all apt dependencies
`PIP_NO_CACHE_DIR`	`true`	if true, then no pip cache will be stored
`HOME`	`/root`	Home directory of the root user (CI image has root user as default)
`AIRFLOW_HOME`	`/root/airflow`	Airflow’s HOME (that’s where logs and sqlite databases are stored)
`AIRFLOW_SOURCES`	`/opt/airflow`	Mounted sources of Airflow
`AIRFLOW_REPO`	`apache/airflow`	the repository from which PIP dependencies are pre-installed
`AIRFLOW_BRANCH`	`main`	the branch from which PIP dependencies are pre-installed
`AIRFLOW_CI_BUILD_EPOCH`	`1`	increasing this value will reinstall PIP dependencies from the repository from scratch
`AIRFLOW_CONSTRAINTS_LOCATION`		If not empty, it will override the source of the constraints with the specified URL or file. Note that the file has to be in docker context so it's best to place such file in one of the folders included in .dockerignore. for example in the 'docker-context-files'. Note that the location does not work for the first stage of installation when the stage of installation when the `AIRFLOW_PRE_CACHED_PIP_PACKAGES` is set to true. Default location from GitHub is used in this case.
`AIRFLOW_CONSTRAINTS_REFERENCE`		reference (branch or tag) from GitHub repository from which constraints are used. By default it is set to `constraints-main` but can be `constraints-2-0` for 2.0.* versions or it could point to specific version for example `constraints-2.0.0` is empty, it is auto-detected
`AIRFLOW_EXTRAS`	`all`	extras to install
`UPGRADE_TO_NEWER_DEPENDENCIES`	`false`	If set to true, the dependencies are upgraded to newer versions matching setup.py before installation.
`CONTINUE_ON_PIP_CHECK_FAILURE`	`false`	By default the image will fail if pip check fails for it. This is good for interactive building but on CI the image should be built regardless - we have a separate step to verify image.
`AIRFLOW_PRE_CACHED_PIP_PACKAGES`	`true`	Allows to pre-cache airflow PIP packages from the GitHub of Apache Airflow This allows to optimize iterations for Image builds and speeds up CI builds But in some corporate environments it might be forbidden to download anything from public repositories.
`ADDITIONAL_AIRFLOW_EXTRAS`		additional extras to install
`ADDITIONAL_PYTHON_DEPS`		additional Python dependencies to install
`DEV_APT_COMMAND`	(see Dockerfile)	Dev apt command executed before dev deps are installed in the first part of image
`ADDITIONAL_DEV_APT_COMMAND`		Additional Dev apt command executed before dev dep are installed in the first part of the image
`DEV_APT_DEPS`	(see Dockerfile)	Dev APT dependencies installed in the first part of the image
`ADDITIONAL_DEV_APT_DEPS`		Additional apt dev dependencies installed in the first part of the image
`ADDITIONAL_DEV_APT_ENV`		Additional env variables defined when installing dev deps
`RUNTIME_APT_COMMAND`	(see Dockerfile)	Runtime apt command executed before deps are installed in first part of the image
`ADDITIONAL_RUNTIME_APT_COMMAND`		Additional Runtime apt command executed before runtime dep are installed in the second part of the image
`RUNTIME_APT_DEPS`	(see Dockerfile)	Runtime APT dependencies installed in the second part of the image
`ADDITIONAL_RUNTIME_APT_DEPS`		Additional apt runtime dependencies installed in second part of the image
`ADDITIONAL_RUNTIME_APT_ENV`		Additional env variables defined when installing runtime deps
`AIRFLOW_PIP_VERSION`	`21.1`	PIP version used.
`PIP_PROGRESS_BAR`	`on`	Progress bar for PIP installation

Here are some examples of how CI images can built manually. CI is always built from local sources.

This builds the CI image in version 3.7 with default extras ("all").

docker build . -f Dockerfile.ci --build-arg PYTHON_BASE_IMAGE="python:3.7-slim-buster" --tag my-image:0.0.1

This builds the CI image in version 3.6 with "gcp" extra only.

docker build . -f Dockerfile.ci --build-arg PYTHON_BASE_IMAGE="python:3.7-slim-buster" \
  --build-arg AIRFLOW_EXTRAS=gcp --tag my-image:0.0.1

This builds the CI image in version 3.6 with "apache-beam" extra added.

docker build . -f Dockerfile.ci --build-arg PYTHON_BASE_IMAGE="python:3.7-slim-buster" \
  --build-arg ADDITIONAL_AIRFLOW_EXTRAS="apache-beam" --tag my-image:0.0.1

This builds the CI image in version 3.6 with "mssql" additional package added.

docker build . -f Dockerfile.ci --build-arg PYTHON_BASE_IMAGE="python:3.7-slim-buster" \
  --build-arg ADDITIONAL_PYTHON_DEPS="mssql" --tag my-image:0.0.1

This builds the CI image in version 3.6 with "gcc" and "g++" additional apt dev dependencies added.

docker build . -f Dockerfile.ci --build-arg PYTHON_BASE_IMAGE="python:3.7-slim-buster" \
  --build-arg ADDITIONAL_DEV_APT_DEPS="gcc g++" --tag my-image:0.0.1

This builds the CI image in version 3.6 with "jdbc" extra and "default-jre-headless" additional apt runtime dependencies added.

docker build . -f Dockerfile.ci --build-arg PYTHON_BASE_IMAGE="python:3.7-slim-buster" \
  --build-arg AIRFLOW_EXTRAS=jdbc --build-arg ADDITIONAL_RUNTIME_DEPS="default-jre-headless" \
  --tag my-image:0.0.1

CI Image manifests

Together with the main CI images we also build and push image manifests. Those manifests are very small images that contain only content of randomly generated file at the 'crucial' part of the CI image building. This is in order to be able to determine very quickly if the image in the docker registry has changed a lot since the last time. Unfortunately docker registry (specifically DockerHub registry) has no anonymous way of querying image details via API. You really need to download the image to inspect it. We workaround it in the way that always when we build the image we build a very small image manifest containing randomly generated UUID and push it to registry together with the main CI image. The tag for the manifest image reflects the image it refers to with added -manifest suffix. The manifest image for apache/airflow:main-python3.6-ci is named apache/airflow:main-python3.6-ci-manifest.

The image is quickly pulled (it is really, really small) when important files change and the content of the randomly generated UUID is compared with the one in our image. If the contents are different this means that the user should rebase to latest main and rebuild the image with pulling the image from the repo as this will likely be faster than rebuilding the image locally.

The random UUID is generated right after pre-cached pip install is run - and usually it means that significant changes have been made to apt packages or even the base Python image has changed.

Working with the images

Pulling the Latest Images

Sometimes the image needs to be refreshed from the registry in DockerHub - because you have an outdated version. You can do it via the --force-pull-images flag to force pulling the latest images from the DockerHub.

For production image:

./breeze build-image --force-pull-images --production-image

For CI image Breeze automatically uses force pulling in case it determines that your image is very outdated, however uou can also force it with the same flag.

./breeze build-image --force-pull-images

Refreshing Base Python images

Python base images are updated from time-to-time, usually as a result of implementing security fixes. When you build your image locally using docker build you use the version of image that you have locally. For the CI builds using breeze we use the image that is stored in our repository in order to use cache efficiently. However we can refresh the image to latest available by specifying --force-pull-base-python-image and running it manually (you need to have access to DockerHub and our GitHub Registies in order to be able to do that.

#/bin/bash
export DOCKERHUB_USER="apache"
export GITHUB_REPOSITORY="apache/airflow"
export FORCE_ANSWER_TO_QUESTIONS="true"
export CI="true"

for python_version in "3.6" "3.7" "3.8"
do
        ./breeze build-image --python ${python_version} --build-cache-local \
                --force-pull-base-python-image --verbose
        ./breeze build-image --python ${python_version} --build-cache-local \
                --production-image --verbose
        ./breeze push-image
        ./breeze push-image --use-github-registry
        ./breeze push-image --production-image
        ./breeze push-image --production-image --use-github-registry
done

Running the CI image

The entrypoint in the CI image contains all the initialisation needed for tests to be immediately executed. It is copied from scripts/in_container/entrypoint_ci.sh.

The default behaviour is that you are dropped into bash shell. However if RUN_TESTS variable is set to "true", then tests passed as arguments are executed

The entrypoint performs those operations:

checks if the environment is ready to test (including database and all integrations). It waits until all the components are ready to work
removes and re-installs another version of Airflow (if another version of Airflow is requested to be reinstalled via USE_AIRFLOW_PYPI_VERSION variable.
Sets up Kerberos if Kerberos integration is enabled (generates and configures Kerberos token)
Sets up ssh keys for ssh tests and restarts the SSH server
Sets all variables and configurations needed for unit tests to run
Reads additional variables set in files/airflow-breeze-config/variables.env by sourcing that file
In case of CI run sets parallelism to 2 to avoid excessive number of processes to run
In case of CI run sets default parameters for pytest
In case of running integration/long_running/quarantined tests - it sets the right pytest flags
Sets default "tests" target in case the target is not explicitly set as additional argument
Runs system tests if RUN_SYSTEM_TESTS flag is specified, otherwise runs regular unit and integration tests

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IMAGES.rst

IMAGES.rst

Airflow Docker images

PROD image

CI image

Image naming conventions

Building docker images from current sources

Building PROD docker images from released PIP packages

Using cache during builds

Choosing image registry

Using GitHub Container Registry as build cache

Customizing the CI image

CI image build arguments

CI Image manifests

Working with the images

Pulling the Latest Images

Refreshing Base Python images

Running the CI image

Files

IMAGES.rst

Latest commit

History

IMAGES.rst

File metadata and controls