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Airflow docker images

Airflow has two images (build from Dockerfiles):

  • CI image (Dockerfile.ci) - used for running tests and local development
  • Production image (Dockerfile) - used to run production-ready Airflow installations

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, v1-10-test, 1.10.10 The master and v1-10-test labels are built from branches so they change over time. The 1.10.* and in the future 2.* labels are build from git tags and they are "fixed" once built.
  • PYTHON_MAJOR_MINOR_VERSION - version of python used to build the image. Examples: 3.5, 3.7
  • The -ci suffix is added for CI images
  • The -manifest is added for manifest images (see below for explanation of manifest images)

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. 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

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 --extras=gcp --production-image --install-airflow-version=1.10.9

This will build the image using command similar to:

pip install apache-airflow[sendgrid]==1.10.9 \
   --constraint https://raw.githubusercontent.com/apache/airflow/v1-10-test/requirements/requirements-python3.7.txt

The requirement files only appeared in version 1.10.10 of airflow so if you install an earlier version - both constraint and requirements should point to 1.10.10 version.

You can also build production images from specific Git version via providing --install-airflow-reference parameter to Breeze:

pip install https://github.com/apache/airflow/archive/<tag>.tar.gz#egg=apache-airflow \
   --constraint https://raw.githubusercontent.com/apache/airflow/<tag>/requirements/requirements-python3.7.txt

Using cache during builds

Default mechanism used in Breeze for building images uses - as base - images puled from DockerHub or GitHub Image Registry. This is in order 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 - same for Production Image - it's better to rely on the "pull" mechanism rather than rebuild the image from the scratch.

However when you are iterating on the images and want to rebuild them quickly and often you can provide the --use-local-cache flag to build commands - this way the standard docker mechanism based on local cache will be used. The first time you run it, it will take considerably longer time than if you use the default pull mechanism, but then when you do small, incremental changes to local sources, Dockerfile image and scripts further rebuilds with --use-local-cache will be considerably faster.

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

You can also turn local docker caching by setting DOCKER_CACHE variable to "local" instead of the default "pulled" and export it to Breeze.

export DOCKER_CACHE="local"

You can also - if you really want - disable caching altogether by setting this variable to "no-cache". This is how "scheduled" builds in our CI are run - those builds take a long time because they always rebuild everything from scratch.

export DOCKER_CACHE="no-cache"

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.

Our images are named like that:

apache/airflow:<BRANCH_OR_TAG>[-<PATCH>]-pythonX.Y         - for production images
apache/airflow:<BRANCH_OR_TAG>[-<PATCH>]-pythonX.Y-ci      - for CI images
apache/airflow:<BRANCH_OR_TAG>[-<PATCH>]-pythonX.Y-build   - for production build stage

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:v1-10-test-python2.7-ci         - CI "latest" image from current v1-10-test branch
apache/airflow:1.10.10-python3.6               - production image for 1.10.10 release
apache/airflow:1.10.10-1-python3.6             - production image for 1.10.10 with some patches applied

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

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:

docker.pkg.github.com/apache/airflow/<BRANCH>-pythonX.Y       - for production images
docker.pkg.github.com/apache/airflow/<BRANCH>-pythonX.Y-ci    - for CI images
docker.pkg.github.com/apache/airflow/<BRANCH>-pythonX.Y-build - for production build state

Note that we never push or pull TAG 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 --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 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.

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 'airlfow-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).

Manually building the images

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.

CI images

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.dev0 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
PIP_VERSION 19.0.2 version of PIP to use
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
PIP_DEPENDENCIES_EPOCH_NUMBER 3 increasing that number will reinstall all PIP dependencies
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_EXTRAS all extras to install
ADDITIONAL_AIRFLOW_EXTRAS   additional extras to install
ADDITIONAL_PYTHON_DEPS   additional python dependencies to install
ADDITIONAL_DEV_DEPS   additional apt dev dependencies to install
ADDITIONAL_RUNTIME_DEPS   additional apt runtime dependencies to install

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" \
  --build-arg PYTHON_MAJOR_MINOR_VERSION=3.7

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 PYTHON_MAJOR_MINOR_VERSION=3.6 --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 PYTHON_MAJOR_MINOR_VERSION=3.6 --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 PYTHON_MAJOR_MINOR_VERSION=3.6 --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 PYTHON_MAJOR_MINOR_VERSION=3.6 --build-arg ADDITIONAL_DEV_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 PYTHON_MAJOR_MINOR_VERSION=3.6 --build-arg AIRFLOW_EXTRAS=jdbc --build-arg ADDITIONAL_RUNTIME_DEPS="default-jre-headless"

Production images

The following build arguments (--build-arg in docker build command) can be used for production 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)
AIRFLOW_VERSION 2.0.0.dev0 version of Airflow
AIRFLOW_ORG apache Github organisation from which Airflow is installed (when installed from repo)
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_GIT_REFERENCE master reference (branch or tag) from Github repository from which Airflow is installed (when installed from repo)
REQUIREMENTS_GIT_REFERENCE master reference (branch or tag) from Github repository from which requirements are downloaded for constraints (when installed from repo).
AIRFLOW_EXTRAS (see Dockerfile) Default extras with which airflow is installed
ADDITIONAL_AIRFLOW_EXTRAS   Optional additional extras with which airflow is installed
ADDITIONAL_PYTHON_DEPS   Optional python packages to extend the image with some extra dependencies
ADDITIONAL_DEV_DEPS   additional apt dev dependencies to install
ADDITIONAL_RUNTIME_DEPS   additional apt runtime dependencies to install
AIRFLOW_HOME /opt/airflow Airflow’s HOME (that’s where logs and sqlite databases are stored)
AIRFLOW_UID 50000 Airflow user UID
AIRFLOW_GID 50000 Airflow group GID
PIP_VERSION 19.0.2 version of PIP to use
CASS_DRIVER_BUILD_CONCURRENCY 8 Number of processors to use for cassandra PIP install (speeds up installing in case cassandra extra is used).

There are build arguments that determine the installation mechanism of Apache Airflow for the production image. There are three types of build:

  • From local sources (by default for example when you use docker build .)
  • You can build the image from released PyPi airflow package (used to build the official Docker image)
  • You can build the image from any version in GitHub repository(this is used mostly for system testing).
Build argument What to specify
AIRFLOW_INSTALL_SOURCES Should point to the sources of of Apache Airflow. It can be either "." for installation from local sources, "apache-airflow" for installation from packages and URL to installation from GitHub repository (see below) to install from any GitHub version
AIRFLOW_INSTALL_VERSION Optional - might be used for package installation case to set Airflow version for example "==1.10.10"
CONSTRAINT_REQUIREMENTS Should point to requirements file in case of installation from the package or from GitHub URL. See examples below
AIRFLOW_WWW In case of Airflow 2.0 it should be "www", in case of Airflow 1.10 series it should be "www_rbac". See examples below
AIRFLOW_SOURCES_FROM Sources of Airflow. Set it to "empty" to avoid costly Docker context copying in case of installation from the package or from GitHub URL. See examples below
AIRFLOW_SOURCES_TO Target for Airflow sources. Set to "/empty" to avoid costly Docker context copying in case of installation from the package or from GitHub URL. See examples below

This builds production image in version 3.6 with default extras from the local sources (master version of 2.0 currently):

docker build .

This builds the production image in version 3.7 with default extras from 1.10.9 tag and requirements taken from v1-10-test branch in Github. Note that versions 1.10.9 and below have no requirements so requirements should be taken from head of the 1.10.10 tag.

docker build . \
  --build-arg PYTHON_BASE_IMAGE="python:3.7-slim-buster" \
  --build-arg PYTHON_MAJOR_MINOR_VERSION=3.7 \
  --build-arg AIRFLOW_INSTALL_SOURCES="https://github.com/apache/airflow/archive/1.10.10.tar.gz#egg=apache-airflow" \
  --build-arg CONSTRAINT_REQUIREMENTS="https://raw.githubusercontent.com/apache/airflow/1.10.10/requirements/requirements-python3.7.txt" \
  --build-arg AIRFLOW_BRANCH="v1-10-test" \
  --build-arg AIRFLOW_SOURCES_FROM="empty" \
  --build-arg AIRFLOW_SOURCES_TO="/empty"

This builds the production image in version 3.7 with default extras from 1.10.10 Pypi package and requirements taken from 1.10.10 tag in Github and pre-installed pip dependencies from the top of v1-10-test branch.

docker build . \
  --build-arg PYTHON_BASE_IMAGE="python:3.7-slim-buster" \
  --build-arg PYTHON_MAJOR_MINOR_VERSION=3.7 \
  --build-arg AIRFLOW_INSTALL_SOURCES="apache-airflow" \
  --build-arg AIRFLOW_INSTALL_VERSION="==1.10.10" \
  --build-arg AIRFLOW_BRANCH="v1-10-test" \
  --build-arg CONSTRAINT_REQUIREMENTS="https://raw.githubusercontent.com/apache/airflow/1.10.10/requirements/requirements-python3.7.txt" \
  --build-arg AIRFLOW_SOURCES_FROM="empty" \
  --build-arg AIRFLOW_SOURCES_TO="/empty"

This builds the production image in version 3.7 with additional airflow extras from 1.10.10 Pypi package and additional python dependencies and pre-installed pip dependencies from the top of v1-10-test branch.

docker build . \
  --build-arg PYTHON_BASE_IMAGE="python:3.7-slim-buster" \
  --build-arg PYTHON_MAJOR_MINOR_VERSION=3.7 \
  --build-arg AIRFLOW_INSTALL_SOURCES="apache-airflow" \
  --build-arg AIRFLOW_INSTALL_VERSION="==1.10.10" \
  --build-arg AIRFLOW_BRANCH="v1-10-test" \
  --build-arg CONSTRAINT_REQUIREMENTS="https://raw.githubusercontent.com/apache/airflow/1.10.10/requirements/requirements-python3.7.txt" \
  --build-arg AIRFLOW_SOURCES_FROM="empty" \
  --build-arg AIRFLOW_SOURCES_TO="/empty" \
  --build-arg ADDITIONAL_AIRFLOW_EXTRAS="mssql,hdfs"
  --build-arg ADDITIONAL_PYTHON_DEPS="sshtunnel oauth2client"

This builds the production image in version 3.7 with additional airflow extras from 1.10.10 Pypi package and additional apt dev and runtime dependencies.

docker build . \
  --build-arg PYTHON_BASE_IMAGE="python:3.7-slim-buster" \
  --build-arg PYTHON_MAJOR_MINOR_VERSION=3.7 \
  --build-arg AIRFLOW_INSTALL_SOURCES="apache-airflow" \
  --build-arg AIRFLOW_INSTALL_VERSION="==1.10.10" \
  --build-arg CONSTRAINT_REQUIREMENTS="https://raw.githubusercontent.com/apache/airflow/1.10.10/requirements/requirements-python3.7.txt" \
  --build-arg ENTRYPOINT_FILE="https://raw.githubusercontent.com/apache/airflow/1.10.10/entrypoint.sh" \
  --build-arg AIRFLOW_SOURCES_FROM="entrypoint.sh" \
  --build-arg AIRFLOW_SOURCES_TO="/entrypoint" \
  --build-arg ADDITIONAL_AIRFLOW_EXTRAS="jdbc"
  --build-arg ADDITIONAL_DEV_DEPS="gcc g++"
  --build-arg ADDITIONAL_RUNTIME_DEPS="default-jre-headless"

Image manifests

Together with the main CI images we also build and push image manifests. Those manifests are very small images that contain only results of the docker inspect for the image. 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 need to download the image to inspect it. We overcame it in the way that always when we build the image we build a very small image manifest and push it to registry together with the main CI image. The tag for the manifest image is the same as for 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.

Pulling the Latest Images

Sometimes the image needs to be rebuilt from scratch. This is required, for example, when there is a security update of the Python version that all the images are based on and new version of the image is pushed to the repository. In this case it is usually faster to pull the latest images rather than rebuild them from scratch.

You can do it via the --force-pull-images flag to force pulling the latest images from the Docker Hub.

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

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/ci/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 teh 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 the PROD image

The PROD image entrypoint works as follows:

  • If AIRFLOW__CORE__SQL_ALCHEMY_CONN variable is passed to the container and it is either mysql or postgres SQL alchemy connection, then the connection is checked and the script waits until the database is reachable.
  • If no AIRFLOW__CORE__SQL_ALCHEMY_CONN variable is set or if it is set to sqlite SQL alchemy connection then db reset is executed.
  • If AIRFLOW__CELERY__BROKER_URL variable is passed and scheduler, worker of flower command is used then the connection is checked and the script waits until the Celery broker database is reachable.
  • If first argument equals to "bash" - it dropped in bash shell or executes bash command if you specify extra arguments. For example:
docker run -it apache/airflow:master-python3.6 bash -c "ls -la"
total 16
drwxr-xr-x 4 airflow root 4096 Jun  5 18:12 .
drwxr-xr-x 1 root    root 4096 Jun  5 18:12 ..
drwxr-xr-x 2 airflow root 4096 Jun  5 18:12 dags
drwxr-xr-x 2 airflow root 4096 Jun  5 18:12 logs
  • If first argument is equal to "python" - you are dropped in python shell or python commands are executed if you pass extra parameters. For example:
> docker run -it apache/airflow:master-python3.6 python -c "print('test')"
test
  • If there are any other arguments - they are passed to "airflow" command
> docker run -it apache/airflow:master-python3.6 --help

usage: airflow [-h]
               {celery,config,connections,dags,db,info,kerberos,plugins,pools,roles,rotate_fernet_key,scheduler,sync_perm,tasks,users,variables,version,webserver}
               ...

positional arguments:

  Groups:
    celery              Start celery components
    connections         List/Add/Delete connections
    dags                List and manage DAGs
    db                  Database operations
    pools               CRUD operations on pools
    roles               Create/List roles
    tasks               List and manage tasks
    users               CRUD operations on users
    variables           CRUD operations on variables

  Commands:
    config              Show current application configuration
    info                Show information about current Airflow and environment
    kerberos            Start a kerberos ticket renewer
    plugins             Dump information about loaded plugins
    rotate_fernet_key   Rotate encrypted connection credentials and variables
    scheduler           Start a scheduler instance
    sync_perm           Update permissions for existing roles and DAGs
    version             Show the version
    webserver           Start a Airflow webserver instance

optional arguments:
  -h, --help            show this help message and exit

Alpha versions of 1.10.10 production-ready images

The production images have been released for the first time in 1.10.10 release of Airflow as "Alpha" quality ones. Between 1.10.10 the images are being improved and the 1.10.10 images should be patched and published several times separately in order to test them with the upcoming Helm Chart.

Those images are for development and testing only and should not be used outside of the development community.

The images were pushed with tags following the pattern: apache/airflow:1.10.10.1-alphaN-pythonX.Y. Patch level is an increasing number (starting from 1).

Those are alpha-quality releases however they contain the officially released Airflow 1.10.10 code. The main changes in the images are scripts embedded in the images.

The following versions were pushed:

Patch Tag pattern Description
1 1.10.10.1-alpha1-pythonX.Y Support for parameters added to bash and python commands
2 1.10.10-1-alpha2-pythonX.Y Added "/clean-logs" script

The commits used to generate those images are tagged with prod-image-1.10.10.1-alphaN tags.