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Airflow docker images

Airflow has two 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

Image naming conventions

The images are named as follows:

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

where:

  • BRANCH_OR_TAG - branch or tag used when creating the image. Examples: master, v2-0-test, v1-10-test, 2.0.0. The master, v1-10-test v2-0-test labels are built from branches so they change over time. The 1.10.* and 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
  • 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: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

The easiest way to build those images is to use BREEZE.rst.

Note! Breeze by default builds production image from local sources. You can change it's behaviour by providing --install-airflow-version parameter, where you can specify the tag/branch used to download Airflow package from in GitHub repository. You can also change the repository itself by adding --dockerhub-user and --dockerhub-repo flag values.

You can build the CI image using this command:

./breeze build-image

You can build production image using 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.

In Breeze by default, the airflow is installed using local sources of Apache Airflow.

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=presto \
    --production-image --install-airflow-version=2.0.0

Note

On November 2020, new version of PIP (20.3) has been released with a new, 2020 resolver. This resolver might work with Apache Airflow as of 20.3.3, but it might lead to errors in installation. It might depend on your choice of extras. In order to install Airflow you might need to either downgrade pip to version 20.2.4 pip install --upgrade pip==20.2.4 or, in case you use Pip 20.3, you need to add option --use-deprecated legacy-resolver to your pip install command.

While pip 20.3.3 solved most of the teething problems of 20.3, this note will remain here until we set pip 20.3 as official version in our CI pipeline where we are testing the installation as well. Due to those constraints, 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.

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"

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-master 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-master/constraints-3.6.txt"

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

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

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 different image registry by setting GITHUB_REGISTRY to docker.pkg.github.com for GitHub Package Registry or ghcr.io for GitHub Container Registry.

Default is the GitHub Package Registry one. The Pull Request forks have no access to the secret but they auto-detect the registry used when they wait for the images.

Our images are named like that:

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

For example:

apache/airflow:master-python3.6                - production "latest" image from current master
apache/airflow:master-python3.6-ci             - CI "latest" image from current master
apache/airflow:v2-0-test-python3.6-ci          - CI "latest" image from current v2-0-test branch
apache/airflow:2.0.0-python3.6                 - production image for 2.0.0 release
apache/airflow:python3.6-master                - base Python image for the master branch

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

Using GitHub registries as build cache

By default DockerHub registry is used when you push or pull such images. However for CI builds we keep the images in GitHub registry as well - this way we can easily push the images automatically after merge requests and use such images for Pull Requests as cache - which makes it much it much faster for CI builds (images are available in cache right after merged request in master finishes it's build), The difference is visible especially if significant changes are done in the Dockerfile.CI.

The images are named differently (in Docker definition of image names - registry URL is part of the image name if DockerHub is not used as registry). Also GitHub has its own structure for registries each project has its own registry naming convention that should be followed. The name of images for GitHub registry are different as they must follow limitation of the registry used.

We are still using GitHub Packages as registry, but we are in the process of testing and switching to GitHub Container Registry, and the naming conventions are slightly different (GitHub Packages required all packages to have "organization/repository/" URL prefix ("apache/airflow/", where in GitHub Container Registry, all images are in "organization" not in "repository" and they are all in organization wide "apache/" namespace rather than in "apache/airflow/" one). We are adding "airflow-" as prefix for image names of all Airflow images instead. The images are linked to the repository via org.opencontainers.image.source label in the image.

Naming convention for GitHub Packages

Images built as "Run ID snapshot":

docker.pkg.github.com.io/apache-airflow/<BRANCH>-pythonX.Y-ci-v2:<RUNID>    - for CI images
docker.pkg.github.com/apache-airflow/<BRANCH>-pythonX.Y-v2:<RUNID>       - for production images
docker.pkg.github.com/apache-airflow/<BRANCH>-pythonX.Y-build-v2:<RUNID> - for production build stage
docker.pkg.github.com/apache-airflow/pythonX.Y-<BRANCH>-v2:X.Y-slim-buster-<RUN_ID>  - for base Python images

Latest images (pushed when master merge succeeds):

docker.pkg.github.com/apache/airflow/<BRANCH>-pythonX.Y-ci-v2:latest    - for CI images
docker.pkg.github.com/apache/airflow/<BRANCH>-pythonX.Y-v2:latest       - for production images
docker.pkg.github.com/apache/airflow/<BRANCH>-pythonX.Y-build-v2:latest - for production build stage
docker.pkg.github.com/apache/airflow/python-<BRANCH>-v1:X.Y-slim-buster - for base Python images

Naming convention for GitHub Container Registry

Images built as "Run ID snapshot":

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

Latest images (pushed when master 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-<BRANCH>-v2:X.Y-slim-buster - for base Python images

Note that we never push or pull "release" images to GitHub registry. It is only used for CI builds

You can see all the current GitHub images at https://github.com/apache/airflow/packages

In order to interact with the GitHub images 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. You can also specify --github-registry option and choose which of the GitHub registries are used (docker.pkg.github.com chooses GitHub Packages and ghcr.io chooses GitHub Container Registry).

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. Only committers can push images directly. You need to login with your Personal Access Token with "packages" scope to be able to push to those repositories or pull from them in case of GitHub Packages.

GitHub Packages:

docker login docker.pkg.github.com

GitHub Container Registry

docker login ghcr.io

Interacting with container registries

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 --build-cache-local
./breeze push-image --python 3.6 --github-registry ghcr.io

Building Python 3.7 PROD images (both build and final image) using cache pulled from docker.pkg.github.com and pushing it back:

./breeze build-image --production-image --python 3.7 --github-registry docker.pkg.github.com
./breeze push-image --production-image --python 3.7 --github-registry docker.pkg.github.com

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 210056909 run with enabled Kerberos integration (assuming docker.pkg.github.com was used as build cache).

./breeze --github-image-id 210056909 \
  --github-registry docker.pkg.github.com \
  --python 3.8 --integration kerberos

You can see more details and examples in Breeze

Technical details of Airflow images

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.

The production 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).

Customizing the image

Customizing the image is an alternative way of adding your own dependencies to the image.

The easiest way to build the image is to use breeze script, but you can also build such 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 AIRFLOW_INSTALLATION_METHOD="apache-airflow" \
  --build-arg AIRFLOW_VERSION="2.0.0" \
  --build-arg AIRFLOW_VERSION_SPECIFICATION="==2.0.0" \
  --build-arg AIRFLOW_SOURCES_FROM="empty" \
  --build-arg AIRFLOW_SOURCES_TO="/empty" \
  --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

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 --install-airflow-version=2.0.0 \
    --additional-extras=jdbc --additional-python-deps="pandas" \
    --additional-dev-apt-deps="gcc g++" --additional-runtime-apt-deps="default-jre-headless"

You can build the default production image with standard docker build command but they will only build default versions of the image and will not use the dockerhub versions of images as cache.

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 AIRFLOW_VERSION="2.0.0" \
  --build-arg AIRFLOW_VERSION_SPECIFICATION="==2.0.0" \
  --build-arg AIRFLOW_SOURCES_FROM="empty" \
  --build-arg AIRFLOW_SOURCES_TO="/empty" \
  --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

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
AIRFLOW_VERSION 2.0.0 version of Airflow
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
CASS_DRIVER_NO_CYTHON 1 if set to 1 no CYTHON compilation is done for cassandra driver (much faster)
AIRFLOW_REPO apache/airflow the repository from which PIP dependencies are pre-installed
AIRFLOW_BRANCH master 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-master but can be constraints-2-0 for 2.0.* versions constraints-1-10 for 1.10.* versions or it could point to specific version for example constraints-2.0.0 is empty, it is auto-detected
INSTALL_PROVIDERS_FROM_SOURCES true If set to false and image is built from sources, all provider packages are not installed. By default when building from sources, all provider packages are also installed together with the core airflow package. It has no effect when installing from PyPI or GitHub repo.
INSTALL_FROM_DOCKER_CONTEXT_FILES false If set to true, Airflow, providers and all dependencies are installed from from locally built/downloaded .whl and .tar.gz files placed in the docker-context-files. In certain corporate environments, this is required to install airflow from such pre-vetted packages rather than from PyPI. For this to work, also set INSTALL_FROM_PYPI. Note that packages starting with apache?airflow glob are treated differently than other packages. All apache?airflow packages are installed with dependencies limited by airflow constraints. All other packages are installed without dependencies 'as-is'. If you wish to install airflow via 'pip download' with all dependencies downloaded, you have to rename the apache airflow and provider packages to not start with apache?airflow glob.
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.
INSTALL_FROM_PYPI true If set to true, Airflow is installed from PyPI. If you want to install Airflow from externally provided binary package you can set it to false, place the package in docker-context-files and set INSTALL_FROM_DOCKER_CONTEXT_FILES to true. For this you have to also set the AIRFLOW_PRE_CACHED_PIP_PACKAGES flag to false
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 20.2.4 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"

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

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"

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"

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++"

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"

Production images

You can find details about using, building, extending and customising the production images in the Latest documentation

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:master-python3.6-ci is named apache/airflow:master-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 master 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.

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 --github-registry ghcr.io
        ./breeze push-image --github-registry docker.pkg.github.com
        ./breeze push-image --production-image
        ./breeze push-image --github-registry ghcr.io --production-image
        ./breeze push-image --github-registry docker.pkg.github.com --production-image
done

Embedded image scripts

Both images have a set of scripts that can be used in the image. Those are:
  • /entrypoint - entrypoint script used when entering the image
  • /clean-logs - script for periodic log cleaning

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
  • installs older version of Airflow (if older version of Airflow is requested to be installed via INSTALL_AIRFLOW_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

Using, customising, and extending the production image

You can read more about using, customising, and extending the production image in the documentation.