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Image build arguments reference

The following build arguments (--build-arg in docker build command) can be used for production images. Those arguments are used when you want to customize the image. You can see some examples of it in :ref:`Building from PyPI packages<image-build-pypi>`.

Basic arguments

Those are the most common arguments that you use when you want to build a custom image.

Build argument Default value Description
PYTHON_BASE_IMAGE python:3.6-slim-buster Base python image.
AIRFLOW_VERSION :subst-code:`|airflow-version|` version of Airflow.
AIRFLOW_EXTRAS (see below the table) Default extras with which airflow is installed.
ADDITIONAL_AIRFLOW_EXTRAS   Optional additional extras with which airflow is installed.
AIRFLOW_HOME /opt/airflow Airflow’s HOME (that’s where logs and SQLite databases are stored).
AIRFLOW_USER_HOME_DIR /home/airflow Home directory of the Airflow user.
AIRFLOW_PIP_VERSION 21.2.4 | PIP version used.
PIP_PROGRESS_BAR on Progress bar for PIP installation
AIRFLOW_UID 50000 Airflow user UID.
AIRFLOW_CONSTRAINTS_REFERENCE   Reference (branch or tag) from GitHub where constraints file is taken from It can be constraints-main or constraints-2-0 for 2.0.* installation. In case of building specific version you want to point it to specific tag, for example :subst-code:`constraints-|airflow-version|`. Auto-detected if empty.

Note

Before Airflow 2.2, the image also had AIRFLOW_GID parameter, but it did not provide any additional functionality - only added confusion - so it has been removed.

List of default extras in the production Dockerfile:

  • amazon
  • async
  • celery
  • cncf.kubernetes
  • dask
  • docker
  • elasticsearch
  • ftp
  • google
  • google_auth
  • grpc
  • hashicorp
  • http
  • ldap
  • microsoft.azure
  • mysql
  • odbc
  • pandas
  • postgres
  • redis
  • sendgrid
  • sftp
  • slack
  • ssh
  • statsd
  • virtualenv

Image optimization options

The main advantage of Customization method of building Airflow image, is that it allows to build highly optimized image because the final image (RUNTIME) might not contain all the dependencies that are needed to build and install all other dependencies (DEV). Those arguments allow to control what is installed in the DEV image and what is installed in RUNTIME one, thus allowing to produce much more optimized images. See :ref:`Building optimized images<image-build-optimized>`. for examples of using those arguments.

Build argument Default value Description
UPGRADE_TO_NEWER_DEPENDENCIES false If set to true, the dependencies are upgraded to newer versions matching setup.py before installation.
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.
INSTALL_MYSQL_CLIENT true Whether MySQL client should be installed The mysql extra is removed from extras if the client is not installed.
INSTALL_MSSQL_CLIENT true Whether MsSQL client should be installed

Installing Airflow using different methods

Those parameters are useful only if you want to install Airflow using different installation methods than the default (installing from PyPI packages).

This is usually only useful if you have your own fork of Airflow and want to build the images locally from those sources - either locally or directly from GitHub sources. This way you do not need to release your Airflow and Providers via PyPI - they can be installed directly from sources or from GitHub repository. Another option of installation is to build Airflow from previously prepared binary Python packages which might be useful if you need to build Airflow in environments that require high levels of security.

You can see some examples of those in:
Build argument Default value Description
AIRFLOW_INSTALLATION_METHOD apache-airflow Installation method of Apache Airflow. apache-airflow for installation from PyPI. It can be GitHub repository URL including branch or tag to install from that repository or "." to install from local sources. Installing from sources requires appropriate values of the AIRFLOW_SOURCES_FROM and AIRFLOW_SOURCES_TO variables (see below)
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 install Airflow from local sources.
AIRFLOW_VERSION_SPECIFICATION   Optional - might be used for using limit for Airflow version installation - for example <2.0.2 for automated builds.
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_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 file.
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.

Pre-caching PIP dependencies

When image is build from PIP, by default pre-caching of PIP dependencies is used. This is in order to speed-up incremental builds during development. When pre-cached PIP dependencies are used and setup.py or setup.cfg changes, the PIP dependencies are already pre-installed, thus resulting in much faster image rebuild. This is purely an optimization of time needed to build the images and should be disabled if you want to install Airflow from docker context files.

Build argument Default value Description
AIRFLOW_BRANCH main the branch from which PIP dependencies are pre-installed initially.
AIRFLOW_REPO apache/airflow the repository from which PIP dependencies are pre-installed.
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.