Production Deployment¶

It is time to deploy your DAG in production. To do this, first, you need to make sure that the Airflow is itself production-ready. Let's see what precautions you need to take.

Database backend¶

Airflow comes with an SQLite backend by default. This allows the user to run Airflow without any external database. However, such a setup is meant to be used for testing purposes only; running the default setup in production can lead to data loss in multiple scenarios. If you want to run production-grade Airflow, make sure you configure the backend to be an external database such as PostgreSQL or MySQL.

You can change the backend using the following config

[core]
sql_alchemy_conn = my_conn_string

Once you have changed the backend, airflow needs to create all the tables required for operation. Create an empty DB and give airflow's user the permission to CREATE/ALTER it. Once that is done, you can run -

airflow db upgrade

upgrade keeps track of migrations already applied, so it's safe to run as often as you need.

Note

Do not use airflow db init as it can create a lot of default connections, charts, etc. which are not required in production DB.

Multi-Node Cluster¶

Airflow uses airflow.executors.sequential_executor.SequentialExecutor by default. However, by its nature, the user is limited to executing at most one task at a time. Sequential Executor also pauses the scheduler when it runs a task, hence not recommended in a production setup. You should use the Local executor for a single machine. For a multi-node setup, you should use the Kubernetes executor or the Celery executor.

Once you have configured the executor, it is necessary to make sure that every node in the cluster contains the same configuration and dags. Airflow sends simple instructions such as "execute task X of dag Y", but does not send any dag files or configuration. You can use a simple cronjob or any other mechanism to sync DAGs and configs across your nodes, e.g., checkout DAGs from git repo every 5 minutes on all nodes.

Logging¶

If you are using disposable nodes in your cluster, configure the log storage to be a distributed file system (DFS) such as S3 and GCS, or external services such as Stackdriver Logging, Elasticsearch or Amazon CloudWatch. This way, the logs are available even after the node goes down or gets replaced. See Logging for Tasks for configurations.

Note

The logs only appear in your DFS after the task has finished. You can view the logs while the task is running in UI itself.

Configuration¶

Airflow comes bundled with a default airflow.cfg configuration file. You should use environment variables for configurations that change across deployments e.g. metadata DB, password, etc. You can accomplish this using the format AIRFLOW__{SECTION}__{KEY}

AIRFLOW__CORE__SQL_ALCHEMY_CONN=my_conn_id
AIRFLOW__WEBSERVER__BASE_URL=http://host:port

Some configurations such as the Airflow Backend connection URI can be derived from bash commands as well:

sql_alchemy_conn_cmd = bash_command_to_run

Scheduler Uptime¶

Airflow users have for a long time been affected by a core Airflow bug that causes the scheduler to hang without a trace.

Until fully resolved, you can mitigate this issue via a few short-term workarounds:

Set a reasonable run_duration setting in your airflow.cfg. Example config.
Add an exec style health check to your helm charts on the scheduler deployment to fail if the scheduler has not heartbeat in a while. Example health check definition.

Production Container Images¶

Customizing or extending the Production Image¶

Before you dive-deeply in the way how the Airflow Image is build, named and why we are doing it the way we do, you might want to know very quickly how you can extend or customize the existing image for Apache Airflow. This chapter gives you a short answer to those questions.

The docker image provided (as convenience binary package) in the Apache Airflow DockerHub is a bare image that has not many external dependencies and extras installed. Apache Airflow has many extras that can be installed alongside the "core" airflow image and they often require some additional dependencies. The Apache Airflow image provided as convenience package is optimized for size, so it provides just a bare minimal set of the extras and dependencies installed and in most cases you want to either extend or customize the image.

Airflow Summit 2020's Production Docker Image talk provides more details about the context, architecture and customization/extension methods for the Production Image.

Extending the image¶

Extending the image is easiest if you just need to add some dependencies that do not require compiling. The compilation framework of Linux (so called build-essential) is pretty big, and for the production images, size is really important factor to optimize for, so our Production Image does not contain build-essential. If you need compiler like gcc or g++ or make/cmake etc. - those are not found in the image and it is recommended that you follow the "customize" route instead.

How to extend the image - it is something you are most likely familiar with - simply build a new image using Dockerfile's FROM directive and add whatever you need. Then you can add your Debian dependencies with apt or PyPI dependencies with pip install or any other stuff you need.

You should be aware, about a few things:

The production image of airflow uses "airflow" user, so if you want to add some of the tools as root user, you need to switch to it with USER directive of the Dockerfile. Also you should remember about following the best practises of Dockerfiles to make sure your image is lean and small.

FROM apache/airflow:1.10.14
USER root
RUN apt-get update \
  && apt-get install -y --no-install-recommends \
         my-awesome-apt-dependency-to-add \
  && apt-get autoremove -yqq --purge \
  && apt-get clean \
  && rm -rf /var/lib/apt/lists/*
USER airflow

PyPI dependencies in Apache Airflow are installed in the user library, of the "airflow" user, so you need to install them with the --user flag and WITHOUT switching to airflow user. Note also that using --no-cache-dir is a good idea that can help to make your image smaller.

FROM apache/airflow:1.10.14
RUN pip install --no-cache-dir --user my-awesome-pip-dependency-to-add

If your apt, or PyPI dependencies require some of the build-essentials, then your best choice is to follow the "Customize the image" route. However it requires to checkout sources of Apache Airflow, so you might still want to choose to add build essentials to your image, even if your image will be significantly bigger.

FROM apache/airflow:1.10.14
USER root
RUN apt-get update \
  && apt-get install -y --no-install-recommends \
         build-essential my-awesome-apt-dependency-to-add \
  && apt-get autoremove -yqq --purge \
  && apt-get clean \
  && rm -rf /var/lib/apt/lists/*
USER airflow
RUN pip install --no-cache-dir --user my-awesome-pip-dependency-to-add

You can also embed your dags in the image by simply adding them with COPY directive of Airflow. The DAGs in production image are in /opt/airflow/dags folder.

Customizing the image¶

Customizing the image is an alternative way of adding your own dependencies to the image - better suited to prepare optimized production images.

The advantage of this method is that it produces optimized image even if you need some compile-time dependencies that are not needed in the final image. You need to use Airflow Sources to build such images from the official distribution folder of Apache Airflow for the released versions, or checked out from the GitHub project if you happen to do it from git sources.

The easiest way to build the image 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 #production-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 1.10.14 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_INSTALLATION_METHOD="apache-airflow" \
  --build-arg AIRFLOW_VERSION="1.10.14" \
  --build-arg AIRFLOW_INSTALL_VERSION="==1.10.14" \
  --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_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 \
    --production-image  --python 3.7 --install-airflow-version=1.10.14 \
    --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 \
  --build-arg PYTHON_BASE_IMAGE="python:3.7-slim-buster" \
  --build-arg PYTHON_MAJOR_MINOR_VERSION=3.7 \
  --build-arg AIRFLOW_INSTALLATION_METHOD="apache-airflow" \
  --build-arg AIRFLOW_VERSION="1.10.14" \
  --build-arg AIRFLOW_INSTALL_VERSION="==1.10.14" \
  --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="slack" \
  --build-arg ADDITIONAL_PYTHON_DEPS="apache-airflow-backport-providers-odbc \
      apache-airflow-backport-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

Customizing images in high security restricted environments¶

You can also make sure your image is only build using local constraint file and locally downloaded wheel files. This is often useful in Enterprise environments where the binary files are verified and vetted by the security teams.

This builds below builds the production image in version 3.7 with packages and constraints used from the local docker-context-files rather than installed from PyPI or GitHub. It also disables MySQL client installation as it is using external installation method.

Note that as a prerequisite - you need to have downloaded wheel files. In the example below we first download such constraint file locally and then use pip download to get the .whl files needed but in most likely scenario, those wheel files should be copied from an internal repository of such .whl files. Note that AIRFLOW_INSTALL_VERSION is only there for reference, the apache airflow .whl file in the right version is part of the .whl files downloaded.

Note that 'pip download' will only works on Linux host as some of the packages need to be compiled from sources and you cannot install them providing --platform switch. They also need to be downloaded using the same python version as the target image.

The pip download might happen in a separate environment. The files can be committed to a separate binary repository and vetted/verified by the security team and used subsequently to build images of Airflow when needed on an air-gaped system.

Preparing the constraint files and wheel files:

rm docker-context-files/*.whl docker-context-files/*.txt

curl -Lo "docker-context-files/constraints-1-10.txt" \
  https://raw.githubusercontent.com/apache/airflow/constraints-1-10/constraints-3.7.txt

pip download --dest docker-context-files \
  --constraint docker-context-files/constraints-1-10.txt  \
  apache-airflow[async,aws,azure,celery,dask,elasticsearch,gcp,kubernetes,mysql,postgres,redis,slack,ssh,statsd,virtualenv]==1.10.14

Building the image (after copying the files downloaded to the "docker-context-files" directory:

./breeze build-image \
    --production-image --python 3.7 --install-airflow-version=1.10.14 \
    --disable-mysql-client-installation --disable-pip-cache --install-from-local-files-when-building \
    --constraints-location="/docker-context-files/constraints-1-10.txt"

or

docker build . \
  --build-arg PYTHON_BASE_IMAGE="python:3.7-slim-buster" \
  --build-arg PYTHON_MAJOR_MINOR_VERSION=3.7 \
  --build-arg AIRFLOW_INSTALLATION_METHOD="apache-airflow" \
  --build-arg AIRFLOW_VERSION="1.10.14" \
  --build-arg AIRFLOW_INSTALL_VERSION="==1.10.14" \
  --build-arg AIRFLOW_CONSTRAINTS_REFERENCE="constraints-1-10" \
  --build-arg AIRFLOW_SOURCES_FROM="empty" \
  --build-arg AIRFLOW_SOURCES_TO="/empty" \
  --build-arg INSTALL_MYSQL_CLIENT="false" \
  --build-arg AIRFLOW_PRE_CACHED_PIP_PACKAGES="false" \
  --build-arg INSTALL_FROM_DOCKER_CONTEXT_FILES="true" \
  --build-arg AIRFLOW_CONSTRAINTS_LOCATION="/docker-context-files/constraints-1-10.txt"

Customizing & extending the image together¶

You can combine both - customizing & extending the image. You can build the image first using customize method (either with docker command or with breeze and then you can extend the resulting image using FROM any dependencies you want.

Customizing PYPI installation¶

You can customize PYPI sources used during image build by adding a docker-context-files/.pypirc file This .pypirc will never be committed to the repository and will not be present in the final production image. It is added and used only in the build segment of the image so it is never copied to the final image.

External sources for dependencies¶

In corporate environments, there is often the need to build your Container images using other than default sources of dependencies. The docker file uses standard sources (such as Debian apt repositories or PyPI repository. However, in corporate environments, the dependencies are often only possible to be installed from internal, vetted repositories that are reviewed and approved by the internal security teams. In those cases, you might need to use those different sources.

This is rather easy if you extend the image - you simply write your extension commands using the right sources - either by adding/replacing the sources in apt configuration or specifying the source repository in pip install command.

It's a bit more involved in the case of customizing the image. We do not have yet (but we are working on it) a capability of changing the sources via build args. However, since the builds use Dockerfile that is a source file, you can rather easily simply modify the file manually and specify different sources to be used by either of the commands.

Comparing extending and customizing the image¶

Here is the comparison of the two types of building images.

	Extending the image	Customizing the image
Produces optimized image	No	Yes
Use Airflow Dockerfile sources to build the image	No	Yes
Requires Airflow sources	No	Yes
You can build it with Breeze	No	Yes
Allows to use non-default sources for dependencies	Yes	No [1]

[1] When you combine customizing and extending the image, you can use external sources: in the "extend" part. There are plans to add functionality to add external sources option to image customization. You can also modify Dockerfile manually if you want to use non-default sources for dependencies.

Using the production image¶

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
The AIRFLOW_HOME is set by default to /opt/airflow/ - this means that DAGs are in default in the /opt/airflow/dags folder and logs are in the /opt/airflow/logs
The working directory is /opt/airflow by default.
If 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 AIRFLOW__CORE__SQL_ALCHEMY_CONN_CMD variable is passed to the container, it is evaluated as a command to execute and result of this evaluation is used as AIRFLOW__CORE__SQL_ALCHEMY_CONN. The _CMD variable takes precedence over the AIRFLOW__CORE__SQL_ALCHEMY_CONN variable.
If no AIRFLOW__CORE__SQL_ALCHEMY_CONN variable is set then SQLite database is created in ${AIRFLOW_HOME}/airflow.db and db reset is executed.
If first argument equals to "bash" - you are dropped to a bash shell or you can 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 first argument equals to "airflow" - the rest of the arguments is treated as an airflow command to execute. Example:

docker run -it apache/airflow:master-python3.6 airflow webserver

If there are any other arguments - they are simply passed to the "airflow" command

> docker run -it apache/airflow:master-python3.6 version
2.0.0.dev0

If AIRFLOW__CELERY__BROKER_URL variable is passed and airflow command with scheduler, worker of flower command is used, then the script checks the broker connection and waits until the Celery broker database is reachable. If AIRFLOW__CELERY__BROKER_URL_CMD variable is passed to the container, it is evaluated as a command to execute and result of this evaluation is used as AIRFLOW__CELERY__BROKER_URL. The _CMD variable takes precedence over the AIRFLOW__CELERY__BROKER_URL variable.

Production image build arguments¶

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_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.
`AIRFLOW_CONSTRAINTS_REFERENCE`	`constraints-master`	Reference (branch or tag) from GitHub where constraints file is taken from It can be `constraints-master` but also can be `constraints-1-10` for 1.10.* installation. In case of building specific version you want to point it to specific tag, for example `constraints-1.10.14`.
`INSTALL_PROVIDERS_FROM_SOURCES`	`false`	If set to `true` and image is built from sources, all provider packages are installed from sources rather than from packages. It has no effect when installing from PyPI or GitHub repo.
`AIRFLOW_EXTRAS`	(see Dockerfile)	Default extras with which airflow is installed.
`INSTALL_FROM_PYPI`	`true`	If set to true, Airflow is installed from PyPI. if you want to install Airflow from self-build package you can set it to false, put package in `docker-context-files` and set `INSTALL_FROM_DOCKER_CONTEXT_FILES` to `true`. For this you have to also keep `AIRFLOW_PRE_CACHED_PIP_PACKAGES` flag set to `false`.
`AIRFLOW_PRE_CACHED_PIP_PACKAGES`	`false`	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.
`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` to false.
`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.
`DEV_APT_COMMAND`	(see Dockerfile)	Dev apt command executed before dev deps are installed in the Build image.
`ADDITIONAL_DEV_APT_COMMAND`		Additional Dev apt command executed before dev dep are installed in the Build image. Should start with `&&`.
`DEV_APT_DEPS`	(see Dockerfile)	Dev APT dependencies installed in the Build image.
`ADDITIONAL_DEV_APT_DEPS`		Additional apt dev dependencies installed in the Build 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 the Main image.
`ADDITIONAL_RUNTIME_APT_COMMAND`		Additional Runtime apt command executed before runtime dep are installed in the Main image. Should start with `&&`.
`RUNTIME_APT_DEPS`	(see Dockerfile)	Runtime APT dependencies installed in the Main image.
`ADDITIONAL_RUNTIME_APT_DEPS`		Additional apt runtime dependencies installed in the Main image.
`ADDITIONAL_RUNTIME_APT_ENV`		Additional env variables defined when installing runtime deps.
`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).
`INSTALL_MYSQL_CLIENT`	`true`	Whether MySQL client should be installed The mysql extra is removed from extras if the client is not installed.

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	Default	What to specify
`AIRFLOW_INSTALLATION_METHOD`	`apache-airflow`	Should point to the installation method of Apache Airflow. It can be `apache-airflow` for installation from packages and URL to installation from GitHub repository tag or branch or "." to install from sources. Note that installing from local sources requires appropriate values of the `AIRFLOW_SOURCES_FROM` and `AIRFLOW_SOURCES_TO` variables as described below. Only used when `INSTALL_FROM_PYPI` is set to `true`.
`AIRFLOW_INSTALL_VERSION`		Optional - might be used for package installation of different Airflow version for example"==1.10.14". For consistency, you should also set``AIRFLOW_VERSION`` to the same value AIRFLOW_VERSION is embedded as label in the image created.
`AIRFLOW_CONSTRAINTS_REFERENCE`	`constraints-master`	Reference (branch or tag) from GitHub where constraints file is taken from. It can be `constraints-master` but also can be``constraints-1-10`` for 1.10.* installations. In case of building specific version you want to point it to specific tag, for example `constraints-1.10.14`
`AIRFLOW_WWW`	`www`	In case of Airflow 2.0 it should be "www", in case of Airflow 1.10 series it should be "www_rbac".
`AIRFLOW_SOURCES_FROM`	`empty`	Sources of Airflow. Set it to "." when you install airflow from local sources.
`AIRFLOW_SOURCES_TO`	`/empty`	Target for Airflow sources. Set to "/opt/airflow" when you want to install airflow from local sources.

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 1.10.14 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_INSTALLATION_METHOD="https://github.com/apache/airflow/archive/1.10.14.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 1.10.14 PyPI package and constraints taken from 1.10.14 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_INSTALLATION_METHOD="apache-airflow" \
  --build-arg AIRFLOW_VERSION="1.10.14" \
  --build-arg AIRFLOW_INSTALL_VERSION="==1.10.14" \
  --build-arg AIRFLOW_BRANCH="v1-10-test" \
  --build-arg AIRFLOW_CONSTRAINTS_REFERENCE="constraints-1.10.14" \
  --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 1.10.14 PyPI package and additional python dependencies and pre-installed pip dependencies from 1.10.14 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_INSTALLATION_METHOD="apache-airflow" \
  --build-arg AIRFLOW_VERSION="1.10.14" \
  --build-arg AIRFLOW_INSTALL_VERSION="==1.10.14" \
  --build-arg AIRFLOW_BRANCH="v1-10-test" \
  --build-arg AIRFLOW_CONSTRAINTS_REFERENCE="constraints-1.10.14" \
  --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 1.10.14 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_INSTALLATION_METHOD="apache-airflow" \
  --build-arg AIRFLOW_VERSION="1.10.14" \
  --build-arg AIRFLOW_INSTALL_VERSION="==1.10.14" \
  --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_APT_DEPS="gcc g++" \
  --build-arg ADDITIONAL_RUNTIME_APT_DEPS="default-jre-headless"

More details about the images¶

You can read more details about the images - the context, their parameters and internal structure in the IMAGES.rst document.

Kerberos-authenticated workers¶

Apache Airflow has a built-in mechanism for authenticating the operation with a KDC (Key Distribution Center). Airflow has a separate command airflow kerberos that acts as token refresher. It uses the pre-configured Kerberos Keytab to authenticate in the KDC to obtain a valid token, and then refreshing valid token at regular intervals within the current token expiry window.

Each request for refresh uses a configured principal, and only keytab valid for the principal specified is capable of retrieving the authentication token.

The best practice to implement proper security mechanism in this case is to make sure that worker workloads have no access to the Keytab but only have access to the periodically refreshed, temporary authentication tokens. This can be achieved in docker environment by running the airflow kerberos command and the worker command in separate containers - where only the airflow kerberos token has access to the Keytab file (preferably configured as secret resource). Those two containers should share a volume where the temporary token should be written by the airflow kerberos and read by the workers.

In the Kubernetes environment, this can be realized by the concept of side-car, where both Kerberos token refresher and worker are part of the same Pod. Only the Kerberos side-car has access to Keytab secret and both containers in the same Pod share the volume, where temporary token is written by the side-care container and read by the worker container.

This concept is implemented in the development version of the Helm Chart that is part of Airflow source code.

Secured Server and Service Access on Google Cloud¶

This section describes techniques and solutions for securely accessing servers and services when your Airflow environment is deployed on Google Cloud, or you connect to Google services, or you are connecting to the Google API.

IAM and Service Accounts¶

You should do not rely on internal network segmentation or firewalling as our primary security mechanisms. To protect your organization's data, every request you make should contain sender identity. In the case of Google Cloud, the identity is provided by the IAM and Service account. Each Compute Engine instance has an associated service account identity. It provides cryptographic credentials that your workload can use to prove its identity when making calls to Google APIs or third-party services. Each instance has access only to short-lived credentials. If you use Google-managed service account keys, then the private key is always held in escrow and is never directly accessible.

If you are using Kubernetes Engine, you can use Workload Identity to assign an identity to individual pods.

For more information about service accounts in the Airflow, see Google Cloud Connection

Impersonate Service Accounts¶

If you need access to other service accounts, you can impersonate other service accounts to exchange the token with the default identity to another service account. Thus, the account keys are still managed by Google and cannot be read by your workload.

It is not recommended to generate service account keys and store them in the metadata database or the secrets backend. Even with the use of the backend secret, the service account key is available for your workload.

Access to Compute Engine Instance¶

If you want to establish an SSH connection to the Compute Engine instance, you must have the network address of this instance and credentials to access it. To simplify this task, you can use ComputeEngineHook instead of SSHHook

The ComputeEngineHook support authorization with Google OS Login service. It is an extremely robust way to manage Linux access properly as it stores short-lived ssh keys in the metadata service, offers PAM modules for access and sudo privilege checking and offers nsswitch user lookup into the metadata service as well.

It also solves the discovery problem that arises as your infrastructure grows. You can use the instance name instead of the network address.

Access to Amazon Web Service¶

Thanks to Web Identity Federation, you can exchange the Google Cloud Platform identity to the Amazon Web Service identity, which effectively means access to Amazon Web Service platform. For more information, see: Google Cloud to AWS authentication using Web Identity Federation