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
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,1.10.10Themasterandv1-10-testlabels are built from branches so they change over time. The1.10.*and in the future2.*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
-cisuffix is added for CI images - The
-manifestis added for manifest images (see below for explanation of manifest 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-imageYou can build production image using this command:
./breeze build-image --production-imageBy 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-imageThe 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=1.10.12This will build the image using command similar to:
pip install apache-airflow[sendgrid]==1.10.12 \
--constraint "https://raw.githubusercontent.com/apache/airflow/constraints-1.10.12/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"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-localWill 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-pulledWill build the production image with pulled images as cache.
./breeze build-image --python 3.7 --production-image --build-cache-disabledWill 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"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 stageFor 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 appliedYou 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 stateNote 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.
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).
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.
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 |
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=3.7This 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=gcpThis 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"
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_REPO |
apache/airflow |
the repository from which PIP dependencies are pre-installed |
AIRFLOW_BRANCH |
master |
the branch from which PIP dependencies are pre-installed initially |
AIRFLOW_CONSTRAINTS_REFERENCE |
constraints-master |
reference (branch or tag) from Github
repository from which constraints are
used. By default it is set to
constraints-master but can be
constraints-1-10 for 1.10.* versions
or it could point to specific version
for example constraints-1.10.12 |
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. Note that most files
created on behalf of airflow user belong
to the root group (0) to keep
OpenShift Guidelines compatibility |
AIRFLOW_USER_HOME_DIR |
/home/airflow |
Home directory of the Airflow user |
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" |
AIRFLOW_CONSTRAINTS_REFERENCE |
reference (branch or tag) from
Github where constraints file
is taken from. By default it is
constraints-master but can be
constraints-1-10 for 1.10.*
constraint or if you want to
point to specific varsion
``constraints-1.10.12 |
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 1.10.12 tag and constraints taken from constraints-1-10-12 branch in Github.
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/1.10.12.tar.gz#egg=apache-airflow" \
--build-arg AIRFLOW_CONSTRAINTS_REFERENCE="constraints-1-10" \
--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 1.10.12 Pypi package and constraints taken from 1.10.12 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="==1.10.12" \
--build-arg AIRFLOW_BRANCH="v1-10-test" \
--build-arg AIRFLOW_CONSTRAINTS_REFERENCE="constraints-1.10.12" \
--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 1.10.10 tagged constraints.
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 AIRFLOW_CONSTRAINTS_REFERENCE="constraints-1.10.10" \
--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="==1.10.12" \ --build-arg AIRFLOW_CONSTRAINTS_REFERENCE="constraints-1-10" \ --build-arg AIRFLOW_SOURCES_FROM="empty" \ --build-arg AIRFLOW_SOURCES_TO="/empty" \ --build-arg ADDITIONAL_AIRFLOW_EXTRAS="jdbc" --build-arg ADDITIONAL_DEV_DEPS="gcc g++" --build-arg ADDITIONAL_RUNTIME_DEPS="default-jre-headless"
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.
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-imageFor 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- 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
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_VERSIONvariable. - 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.envby 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
The entrypoint in the PROD image contains all the initialisation needed for tests to be immediately executed.
It is copied from scripts/in_container/entrypoint_prod.sh.
The PROD image entrypoint works as follows:
- In case the user is not "airflow" (with undefined user id) and the group id of the user is set to 0 (root), then the user is dynamically added to /etc/passwd at entry using USER_NAME variable to define the user name. This is in order to accommodate the OpenShift Guidelines
- If
AIRFLOW__CORE__SQL_ALCHEMY_CONNvariable 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_CONNvariable is set or if it is set to sqlite SQL alchemy connection then db reset is executed. - If
AIRFLOW__CELERY__BROKER_URLvariable 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 exitThe 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.