Airflow Breeze CI Environment
Prerequisites
Airflow Directory Structure inside Docker
Using the Airflow Breeze Environment
Using Your Host IDE
Breeze Command-Line Interface Reference
- Airflow Breeze Syntax
Troubleshooting
- Fixing File/Directory Ownership

Airflow Breeze CI Environment

Airflow Breeze is an easy-to-use development environment using Docker Compose. The environment is available for local use and is also used in Airflow's CI tests.

We called it Airflow Breeze as It's a Breeze to develop Airflow.

The advantages and disadvantages of using the Breeze environment vs. other ways of testing Airflow are described in CONTRIBUTING.rst.

Here is a short 10-minute video about Airflow Breeze (note that it shows an old version of Breeze. Some of the points in the video are not valid anymore. The video will be updated shortly with more up-to-date version):

Airflow Breeze Simplified Development Workflow

Prerequisites

Docker Community Edition

Version: Install the latest stable Docker Community Edition and add it to the PATH.
Permissions: Configure to run the docker commands directly and not only via root user. Your user should be in the docker group. See Docker installation guide for details.
Disk space: On macOS, increase your available disk space before starting to work with the environment. At least 128 GB of free disk space is recommended. You can also get by with a smaller space but make sure to clean up the Docker disk space periodically. See also Docker for Mac - Space for details on increasing disk space available for Docker on Mac.
Docker problems: Sometimes it is not obvious that space is an issue when you run into a problem with Docker. If you see a weird behaviour, try breeze cleanup-image command. Also see pruning instructions from Docker.

Here is an example configuration with more than 200GB disk space for Docker:

Docker Compose

Version: Install the latest stable Docker Compose and add it to the PATH. See Docker Compose Installation Guide for details.
Permissions: Configure to run the docker-compose command.

Docker in WSL 2

WSL 2 installation :

Install WSL 2 and a Linux Distro (e.g. Ubuntu) see WSL 2 Installation Guide for details.
Docker Desktop installation :

Install Docker Desktop for Windows. For Windows Home follow the Docker Windows Home Installation Guide. For Windows Pro, Enterprise, or Education follow the Docker Windows Installation Guide.
Docker setting :

WSL integration needs to be enabled

WSL 2 Filesystem Performance :

Accessing the host Windows filesystem incurs a performance penalty, it is therefore recommended to do development on the Linux filesystem. E.g. Run cd ~ and create a development folder in your Linux distro home and git pull the Airflow repo there.
WSL 2 Memory Usage :
WSL 2 can consume a lot of memory under the process name "Vmmem". To reclaim the memory after development you can:
- On the Linux distro clear cached memory: sudo sysctl -w vm.drop_caches=3
- If no longer using Docker you can quit Docker Desktop (right click system try icon and select "Quit Docker Desktop")
- If no longer using WSL you can shut it down on the Windows Host with the following command: wsl --shutdown
Developing in WSL 2 :

You can use all the standard Linux command line utilities to develop on WSL 2. Further VS Code supports developing in Windows but remotely executing in WSL. If VS Code is installed on the Windows host system then in the WSL Linux Distro you can run code . in the root directory of you Airflow repo to launch VS Code.

Docker Images Used by Breeze

For all development tasks, unit tests, integration tests, and static code checks, we use the CI image maintained on the DockerHub in the apache/airflow repository. This Docker image contains a lot of test-related packages (size of ~1GB). Its tag follows the pattern of <BRANCH>-python<PYTHON_MAJOR_MINOR_VERSION>-ci (for example, apache/airflow:master-python3.6-ci or apache/airflow:v1-10-test-python3.6-ci). The image is built using the Dockerfile.ci Dockerfile.

For testing production image, the Production image is used and maintained on the DockerHub in the `apache/airflow repository. This Docker image contains only size-optimised Airflow with selected extras and dependencies. Its tag follows the pattern of <BRANCH>-python<PYTHON_MAJOR_MINOR_VERSION> (for example, apache/airflow:master-python3.6 or apache/airflow:v1-10-test-python3.6).

More information about the images can be found in IMAGES.rst.

By default CI images are used unless --production-image flag is used.

Before you run tests, enter the environment or run local static checks, the necessary local images should be pulled and built from Docker Hub. This happens automatically for the test environment but you need to manually trigger it for static checks as described in Building the images and Pulling the latest images. The static checks will fail and inform what to do if the image is not yet built.

Building the image first time pulls a pre-built version of images from the Docker Hub, which may take some time. But for subsequent source code changes, no wait time is expected. However, changes to sensitive files like setup.py or Dockerfile.ci will trigger a rebuild that may take more time though it is highly optimized to only rebuild what is needed.

In most cases, rebuilding an image requires network connectivity (for example, to download new dependencies). If you work offline and do not want to rebuild the images when needed, you can set the FORCE_ANSWER_TO_QUESTIONS variable to no as described in the Default behaviour for user interaction section.

See the Troubleshooting section for steps you can make to clean the environment.

Getopt and gstat

For Linux, run apt install util-linux coreutils or an equivalent if your system is not Debian-based.
For macOS, install GNU getopt and gstat utilities to get Airflow Breeze running.

Run brew install gnu-getopt coreutils and then follow instructions to link the gnu-getopt version to become the first on the PATH. Make sure to re-login after you make the suggested changes.

Examples:

If you use bash, run this command and re-login:

echo 'export PATH="/usr/local/opt/gnu-getopt/bin:$PATH"' >> ~/.bash_profile
. ~/.bash_profile

If you use zsh, run this command and re-login:

echo 'export PATH="/usr/local/opt/gnu-getopt/bin:$PATH"' >> ~/.zprofile
. ~/.zprofile

Memory

Minimum 4GB RAM is required to run the full Breeze environment.

On macOS, 2GB of RAM are available for your Docker containers by default, but more memory is recommended (4GB should be comfortable). For details see Docker for Mac - Advanced tab.

On Windows WSL 2 expect the Linux Disto and Docker containers to use 7 - 8 GB of RAM.

Airflow Directory Structure inside Docker

When you are in the CI container, the following directories are used:

/opt/airflow - Contains sources of Airflow mounted from the host (AIRFLOW_SOURCES).
/root/airflow - Contains all the "dynamic" Airflow files (AIRFLOW_HOME), such as:
    airflow.db - sqlite database in case sqlite is used;
    dags - folder with non-test dags (test dags are in /opt/airflow/tests/dags);
    logs - logs from Airflow executions;
    unittest.cfg - unit test configuration generated when entering the environment;
    webserver_config.py - webserver configuration generated when running Airflow in the container.

Note that when running in your local environment, the /root/airflow/logs folder is actually mounted from your logs directory in the Airflow sources, so all logs created in the container are automatically visible in the host as well. Every time you enter the container, the logs directory is cleaned so that logs do not accumulate.

When you are in the production container, the following directories are used:

/opt/airflow - Contains sources of Airflow mounted from the host (AIRFLOW_SOURCES).
/root/airflow - Contains all the "dynamic" Airflow files (AIRFLOW_HOME), such as:
    airflow.db - sqlite database in case sqlite is used;
    dags - folder with non-test dags (test dags are in /opt/airflow/tests/dags);
    logs - logs from Airflow executions;
    unittest.cfg - unit test configuration generated when entering the environment;
    webserver_config.py - webserver configuration generated when running Airflow in the container.

Note that when running in your local environment, the /root/airflow/logs folder is actually mounted from your logs directory in the Airflow sources, so all logs created in the container are automatically visible in the host as well. Every time you enter the container, the logs directory is cleaned so that logs do not accumulate.

Using the Airflow Breeze Environment

Airflow Breeze is a bash script serving as a "swiss-army-knife" of Airflow testing. Under the hood it uses other scripts that you can also run manually if you have problem with running the Breeze environment.

Breeze script allows performing the following tasks:

Managing CI environment:

Build CI docker image with breeze build-image command

Enter interactive shell in CI container when shell (or no command) is specified

Join running interactive shell with breeze exec command

Stop running interactive environment with breeze stop command

Restart running interactive environment with breeze restart command

Run test specified with breeze tests command

Generate requirements with breeze generate-requirements command

Execute arbitrary command in the test environment with breeze shell command

Execute arbitrary docker-compose command with breeze docker-compose command

Push docker images with breeze push-image command (require committer's rights to push images)

You can optionally reset database if specified as extra --db-reset flag and for CI image you can also start integrations (separate Docker images) if specified as extra --integration flags. You can also chose which backend database should be used with --backend flag and python version with --python flag.

Managing Prod environment (with --production-image flag):

Build CI docker image with breeze build-image command

Enter interactive shell in PROD container when shell (or no command) is specified

Join running interactive shell with breeze exec command

Stop running interactive environment with breeze stop command

Restart running interactive environment with breeze restart command

Execute arbitrary command in the test environment with breeze shell command

Execute arbitrary docker-compose command with breeze docker-compose command

Push docker images with breeze push-image command (require committer's rights to push images)

You can optionally reset database if specified as extra --db-reset flag. You can also chose which backend database should be used with --backend flag and python version with --python flag.

Manage and Interact with Kubernetes tests environment:

Manage KinD Kubernetes cluster and deploy Airflow to KinD cluster breeze kind-cluster commands

Run Kubernetes tests specified with breeze kind-cluster tests command

Enter the interactive kubernetes test environment with breeze kind-cluster shell command

Run static checks:

Run static checks - either for currently staged change or for all files with breeze static-check command

Build documentation:

Build documentation with breeze build-docs command

Set up local development environment:

Setup local virtualenv with breeze setup-virtualenv command

Setup autocomplete for itself with breeze setup-autocomplete command

Interactive Breeze Environment

Entering Breeze environment

You enter the Breeze test environment by running the ./breeze script. You can run it with the help command to see the list of available options. See Breeze Command-Line Interface Reference for details.

./breeze

The First time you run Breeze, it pulls and builds a local version of Docker images. It pulls the latest Airflow CI images from Airflow DockerHub and uses them to build your local Docker images. Note that the first run (per python) might take up to 10 minutes on a fast connection to start. Subsequent runs should be much faster.

Once you enter the environment, you are dropped into bash shell of the Airflow container and you can run tests immediately.

You can set up autocomplete for commands and add the checked-out Airflow repository to your PATH to run Breeze without the ./ and from any directory.

When you enter the Breeze environment, automatically an environment file is sourced from files/airflow-breeze-config/variables.env. The files folder from your local sources is automatically mounted to the container under /files path and you can put there any files you want to make available for the Breeze container.

Running tests in the CI interactive environment

Breeze helps with running tests in the same environment/way as CI tests are run. You can run various types of tests while you enter Breeze CI interactive environment - this is described in detail in TESTING.rst

Launching multiple terminals

Often if you want to run full airflow in the Breeze environment you need to launch multiple terminals and run airflow webserver, airflow scheduler, airflow worker in separate terminals.

This can be achieved either via tmux or via exec-ing into the running container from the host. Tmux is installed inside the container and you can launch it with tmux command. Tmux provides you with the capability of creating multiple virtual terminals and multiplex between them. More about tmux can be found at tmux github wiki page . Tmux has several useful shortcuts that allow you to split the terminals, open new tabs etc - it's pretty useful to learn it.

Another - slightly easier - way is to exec into Breeze terminal from the host's terminal. Often you can have multiple terminals in the host (Linux/MacOS/WSL2 on Windows) and you can simply use those terminals to enter the running container. It's as easy as launching breeze exec while you already started the Breeze environment. You will be dropped into bash and environment variables will be read in the same way as when you enter the environment. You can do it multiple times and open as many terminals as you need.

CLIs for cloud providers

For development convenience we installed simple wrappers for the most common cloud providers CLIs. Those CLIs are not installed when you build or pull the image - they will be downloaded as docker images the first time you attempt to use them. It is downloaded and executed in your host's docker engine so once it is downloaded, it will stay until you remove the downloaded images from your host container.

For each of those CLI credentials are taken (automatically) from the credentials you have defined in your ${HOME} directory on host.

Those tools also have host Airflow source directory mounted in /opt/airflow path so you can directly transfer files to/from your airflow host sources.

Those are currently installed CLIs (they are available as aliases to the docker commands):

Cloud Provider	CLI tool	Docker image	Configuration dir
Amazon Web Services	aws	amazon/aws-cli:latest	.aws
Microsoft Azure	az	mcr.microsoft.com/azure-cli:latest	.azure
Google Cloud Platform	bq	gcr.io/google.com/cloudsdktool/cloud-sdk:latest	.config/gcloud
	gcloud	gcr.io/google.com/cloudsdktool/cloud-sdk:latest	.config/gcloud
	gsutil	gcr.io/google.com/cloudsdktool/cloud-sdk:latest	.config/gcloud

For each of the CLIs we have also an accompanying *-update alias (for example aws-update) which will pull the latest image for the tool. Note that all Google Cloud Platform tools are served by one image and they are updated together.

Also - in case you run several different Breeze containers in parallel (from different directories, with different versions) - they docker images for CLI Cloud Providers tools are shared so if you update it for one Breeze container, they will also get updated for all the other containers.

Stopping Interactive environment

After starting up, the environment runs in the background and takes precious memory. You can always stop it via:

./breeze stop

Restarting Breeze environment

You can also restart the environment and enter it via:

./breeze restart

Choosing different Breeze Environment configuration

You can use additional breeze flags to customize your environment. For example, you can specify a Python version to use, backend and a container environment for testing. With Breeze, you can recreate the same environments as we have in matrix builds in the CI.

For example, you can choose to run Python 3.6 tests with MySQL as backend and in the Docker environment as follows:

./breeze --python 3.6 --backend mysql

The choices you make are persisted in the ./.build/ cache directory so that next time when you use the breeze script, it could use the values that were used previously. This way you do not have to specify them when you run the script. You can delete the .build/ directory in case you want to restore the default settings.

The defaults when you run the Breeze environment are Python 3.6, Sqlite, and Docker.

Launching Breeze integrations

When Breeze starts, it can start additional integrations. Those are additional docker containers that are started in the same docker-compose command. Those are required by some of the tests as described in TESTING.rst.

By default Breeze starts only airflow container without any integration enabled. If you selected postgres or mysql backend, the container for the selected backend is also started (but only the one that is selected). You can start the additional integrations by passing --integration flag with appropriate integration name when starting Breeze. You can specify several --integration flags to start more than one integration at a time. Finally you can specify --integration all to start all integrations.

Once integration is started, it will continue to run until the environment is stopped with breeze stop command. or restarted via breeze restart command

Note that running integrations uses significant resources - CPU and memory.

Mounting Local Sources to Breeze

Important sources of Airflow are mounted inside the airflow container that you enter. This means that you can continue editing your changes on the host in your favourite IDE and have them visible in the Docker immediately and ready to test without rebuilding images. You can disable mounting by specifying --skip-mounting-local-sources flag when running Breeze. In this case you will have sources embedded in the container and changes to these sources will not be persistent.

After you run Breeze for the first time, you will have empty directory files in your source code, which will be mapped to /files in your Docker container. You can pass there any files you need to configure and run Docker. They will not be removed between Docker runs.

By default /files/dags folder is mounted from your local <AIRFLOW_SOURCES>/files/dags and this is the directory used by airflow scheduler and webserver to scan dags for. You can use it to test your dags from local sources in Airflow. If you wish to add local DAGs that can be run by Breeze.

Port Forwarding

When you run Airflow Breeze, the following ports are automatically forwarded:

28080 -> forwarded to Airflow webserver -> airflow:8080
25433 -> forwarded to Postgres database -> postgres:5432
23306 -> forwarded to MySQL database -> mysql:3306

You can connect to these ports/databases using:

Webserver: http://127.0.0.1:28080
Postgres: jdbc:postgresql://127.0.0.1:25433/airflow?user=postgres&password=airflow
Mysql: jdbc:mysql://localhost:23306/airflow?user=root

Start the webserver manually with the airflow webserver command if you want to connect to the webserver. You can use tmux to multiply terminals. You may need to create a user prior to running the webserver in order to log in. This can be done with the following command:

airflow users create --role Admin --username admin --password admin --email admin@example.com --firstname foo --lastname bar

For databases, you need to run airflow db reset at least once (or run some tests) after you started Airflow Breeze to get the database/tables created. You can connect to databases with IDE or any other database client:

You can change the used host port numbers by setting appropriate environment variables:

WEBSERVER_HOST_PORT
POSTGRES_HOST_PORT
MYSQL_HOST_PORT

If you set these variables, next time when you enter the environment the new ports should be in effect.

Cleaning the Environment

You may need to clean up your Docker environment occasionally. The images are quite big (1.5GB for both images needed for static code analysis and CI tests) and, if you often rebuild/update them, you may end up with some unused image data.

To clean up the Docker environment:

Stop Breeze with ./breeze stop.
Run the docker system prune command.
Run docker images --all and docker ps --all to verify that your Docker is clean.

Both commands should return an empty list of images and containers respectively.

If you run into disk space errors, consider pruning your Docker images with the docker system prune --all command. You may need to restart the Docker Engine before running this command.

In case of disk space errors on macOS, increase the disk space available for Docker. See Prerequisites for details.

Running static checks

You can run static checks via Breeze. You can also run them via pre-commit command but with auto-completion Breeze makes it easier to run selective static checks. If you press <TAB> after the static-check and if you have auto-complete setup you should see auto-completable list of all checks available.

./breeze static-check mypy

The above will run mypy check for currently staged files.

You can also add arbitrary pre-commit flag after --

./breeze static-check mypy -- --all-files

The above will run mypy check for all files.

Running Kubernetes tests in virtual environment

Breeze helps with running Kubernetes tests in the same environment/way as CI tests are run. Breeze helps to setup KinD cluster for testing, setting up virtualenv and downloads the right tools automatically to run the tests.

This is described in Testing Kubernetes in detail.

Building the Documentation

To build documentation in Breeze, use the build-docs command:

./breeze build-docs

Results of the build can be found in the docs/_build folder.

Often errors during documentation generation come from the docstrings of auto-api generated classes. During the docs building auto-api generated files are stored in the docs/_api folder. This helps you easily identify the location the problems with documentation originated from.

Running Arbitrary Commands in the Breeze Environment

To run other commands/executables inside the Breeze Docker-based environment, use the ./breeze shell command. You should add your command as -c "command" after -- as extra arguments.

breeze shell -- -c "ls -la"

Running Docker Compose Commands

To run Docker Compose commands (such as help, pull, etc), use the docker-compose command. To add extra arguments, specify them after -- as extra arguments.

./breeze docker-compose pull -- --ignore-pull-failures

Managing Dependencies

If you need to change apt dependencies in the Dockerfile.ci, add Python packages in setup.py or add javascript dependencies in package.json, you can either add dependencies temporarily for a single Breeze session or permanently in setup.py, Dockerfile.ci, or package.json files.

Installing Dependencies for a Single Breeze Session

You can install dependencies inside the container using sudo apt install, pip install or yarn install (in airflow/www folder) respectively. This is useful if you want to test something quickly while you are in the container. However, these changes are not retained: they disappear once you exit the container (except for the node.js dependencies if your sources are mounted to the container). Therefore, if you want to retain a new dependency, follow the second option described below.

Adding Dependencies Permanently

You can add dependencies to the Dockerfile.ci, setup.py or package.json and rebuild the image. This should happen automatically if you modify any of these files. After you exit the container and re-run breeze, Breeze detects changes in dependencies, asks you to confirm rebuilding the image and proceeds with rebuilding if you confirm (or skip it if you do not confirm). After rebuilding is done, Breeze drops you to shell. You may also use the build-image command to only rebuild CI image and not to go into shell.

Generating requirements

Whenever you modify and commit setup.py, you need to re-generate requirement files. Those requirement files ara stored separately for each python version in the requirements folder. Those are constraints rather than requirements as described in detail in the CONTRIBUTING.rst contributing documentation.

In case you modify setup.py you need to update the requirements - for every python version supported.

breeze generate-requirements --python 3.6

breeze generate-requirements --python 3.7

breeze generate-requirements --python 3.8

This bumps requirements to latest versions and stores hash of setup.py so that we are automatically upgrading the requirements as we add new ones.

Incremental apt Dependencies in the Dockerfile.ci during development

During development, changing dependencies in apt-get closer to the top of the Dockerfile.ci invalidates cache for most of the image. It takes long time for Breeze to rebuild the image. So, it is a recommended practice to add new dependencies initially closer to the end of the Dockerfile.ci. This way dependencies will be added incrementally.

Before merge, these dependencies should be moved to the appropriate apt-get install command, which is already in the Dockerfile.ci.

Setting Up the auto-completion

The breeze command comes with a built-in bash/zsh autocomplete option for its options. When you start typing the command, you can use <TAB> to show all the available switches and get autocompletion on typical values of parameters that you can use.

You can set up the autocomplete option automatically by running:

./breeze setup-autocomplete

You get the autocompletion working when you re-enter the shell.

Zsh autocompletion is currently limited to only autocomplete options. Bash autocompletion also completes options values (for example, Python version or static check name).

Setting default answers for User Interaction

Sometimes during the build, you are asked whether to perform an action, skip it, or quit. This happens when rebuilding or removing an image - actions that take a lot of time and could be potentially destructive.

For automation scripts, you can export one of the three variables to control the default interaction behaviour:

export FORCE_ANSWER_TO_QUESTIONS="yes"

If FORCE_ANSWER_TO_QUESTIONS is set to yes, the images are automatically rebuilt when needed. Images are deleted without asking.

export FORCE_ANSWER_TO_QUESTIONS="no"

If FORCE_ANSWER_TO_QUESTIONS is set to no, the old images are used even if rebuilding is needed. This is useful when you work offline. Deleting images is aborted.

export FORCE_ANSWER_TO_QUESTIONS="quit"

If FORCE_ANSWER_TO_QUESTIONS is set to quit, the whole script is aborted. Deleting images is aborted.

If more than one variable is set, yes takes precedence over no, which takes precedence over quit.

Using Your Host IDE

You can set up your host IDE (for example, IntelliJ's PyCharm/Idea) to work with Breeze and benefit from all the features provided by your IDE, such as local and remote debugging, autocompletion, documentation support, etc.

To use your host IDE with Breeze:

Create a local virtual environment as follows:

mkvirtualenv <ENV_NAME> --python=python<VERSION>

You can use any of the following wrappers to create and manage your virtual environemnts: pyenv, pyenv-virtualenv, or virtualenvwrapper.

Ideally, you should have virtualenvs for all Python versions supported by Airflow (3.5, 3.6, 3.7) and switch between them with the workon command.
Use the workon command to enter the Breeze environment.
Initialize the created local virtualenv:

./breeze initialize-local-virtualenv
Select the virtualenv you created as the project's default virtualenv in your IDE.

Note that you can also use the local virtualenv for Airflow development without Breeze. This is a lightweight solution that has its own limitations.

More details on using the local virtualenv are available in the LOCAL_VIRTUALENV.rst.

Breeze Command-Line Interface Reference

Airflow Breeze Syntax

This is the current syntax for ./breeze:

####################################################################################################

Usage: breeze [FLAGS] [COMMAND] -- <EXTRA_ARGS>

By default the script enters IT environment and drops you to bash shell, but you can choose one
of the commands to run specific actions instead. Add --help after each command to see details:

Commands without arguments:

shell [Default] Enters interactive shell in the container
build-docs Builds documentation in the container
build-image Builds CI or Production docker image
cleanup-image Cleans up the container image created
exec Execs into running breeze container in new terminal
generate-requirements Generates pinned requirements for pip dependencies
push-image Pushes images to registry
initialize-local-virtualenv Initializes local virtualenv
setup-autocomplete Sets up autocomplete for breeze
stop Stops the docker-compose environment
restart Stops the docker-compose environment including DB cleanup
toggle-suppress-cheatsheet Toggles on/off cheatsheet
toggle-suppress-asciiart Toggles on/off asciiart

Commands with arguments:

docker-compose <ARG> Executes specified docker-compose command
kind-cluster <ARG> Manages KinD cluster on the host
prepare-backport-readme <ARG> Prepares backport packages readme files
prepare-backport-packages <ARG> Prepares backport packages
static-check <ARG> Performs selected static check for changed files
tests <ARG> Runs selected tests in the container

Help commands:

flags Shows all breeze's flags
help Shows this help message
help-all Shows detailed help for all commands and flags

####################################################################################################

Detailed usage

####################################################################################################

Detailed usage for command: shell

breeze shell [FLAGS] [-- <EXTRA_ARGS>]

This is default subcommand if no subcommand is used.

Enters interactive shell where you can run all tests, start Airflow webserver, scheduler,
workers, interact with the database, run DAGs etc. It is the default command if no command
is selected. The shell is executed in the container and in case integrations are chosen,
the integrations will be started as separated docker containers - under the docker-compose
supervision. Local sources are by default mounted to within the container so you can edit
them locally and run tests immediately in the container. Several folders ('files', 'dist')
are also mounted so that you can exchange files between the host and container.

The 'files/airflow-breeze-config/variables.env' file can contain additional variables
and setup. This file is automatically sourced when you enter the container. Database
and webserver ports are forwarded to appropriate database/webserver so that you can
connect to it from your host environment.

You can also pass <EXTRA_ARGS> after -- they will be passed as bash parameters, this is
especially useful to pass bash options, for example -c to execute command:

'breeze shell -- -c "ls -la"'

Flags:

Run 'breeze flags' to see all applicable flags.

####################################################################################################

Detailed usage for command: build-docs

breeze build-docs

Builds Airflow documentation. The documentation is build inside docker container - to
maintain the same build environment for everyone. Appropriate sources are mapped from
the host to the container so that latest sources are used. The folders where documentation
is generated ('docs/build') are also mounted to the container - this way results of
the documentation build is available in the host.

####################################################################################################

Detailed usage for command: build-image

breeze build-image [FLAGS]

Builds docker image (CI or production) without entering the container. You can pass
additional options to this command, such as '--force-build-image',
'--force-pull-image', '--python', '--build-cache-local' or '-build-cache-pulled'
in order to modify build behaviour.

You can also pass '--production-image' flag to build production image rather than CI image.

Flags:

-p, --python <PYTHON_MAJOR_MINOR_VERSION>
Python version used for the image. This is always major/minor version.

Note that versions 2.7 and 3.5 are only valid when installing Airflow 1.10 with
--install-airflow-version or --install-airflow-reference flags.

One of:

2.7 3.5 3.6 3.7 3.8

-a, --install-airflow-version <INSTALL_AIRFLOW_VERSION>
If specified, installs Airflow directly from PIP released version. This happens at
image building time in production image and at container entering time for CI image. One of:

1.10.10 1.10.9 1.10.8 1.10.7 1.10.6 1.10.5 1.10.4 1.10.3 1.10.2 master v1-10-test

-t, --install-airflow-reference <INSTALL_AIRFLOW_REFERENCE>
If specified, installs Airflow directly from reference in GitHub. This happens at
image building time in production image and at container entering time for CI image.

-I, --production-image
Use production image for entering the environment and builds (not for tests).

-F, --force-build-images
Forces building of the local docker images. The images are rebuilt
automatically for the first time or when changes are detected in
package-related files, but you can force it using this flag.

-P, --force-pull-images
Forces pulling of images from DockerHub before building to populate cache. The
images are pulled by default only for the first time you run the
environment, later the locally build images are used as cache.

-E, --extras
Extras to pass to build images The default are different for CI and production images:

CI image:
devel_ci

Production image:
async,aws,azure,celery,dask,elasticsearch,gcp,kubernetes,mysql,postgres,redis,slack,
ssh,statsd,virtualenv

--additional-extras
Additional extras to pass to build images The default is no additional extras.

--additional-python-deps
Additional python dependencies to use when building the images.

--additional-dev-deps
Additional apt dev dependencies to use when building the images.

--additional-runtime-deps
Additional apt runtime dependencies to use when building the images.

-C, --force-clean-images
Force build images with cache disabled. This will remove the pulled or build images
and start building images from scratch. This might take a long time.

-L, --build-cache-local
Uses local cache to build images. No pulled images will be used, but results of local
builds in the Docker cache are used instead. This will take longer than when the pulled
cache is used for the first time, but subsequent '--build-cache-local' builds will be
faster as they will use mostly the locally build cache.

This is default strategy used by the Production image builds.

-U, --build-cache-pulled
Uses images pulled from registry (either DockerHub or GitHub depending on
--github-registry flag) to build images. The pulled images will be used as cache.
Those builds are usually faster than when ''--build-cache-local'' with the exception if
the registry images are not yet updated. The DockerHub images are updated nightly and the
GitHub images are updated after merges to master so it might be that the images are still
outdated vs. the latest version of the Dockerfiles you are using. In this case, the
''--build-cache-local'' might be faster, especially if you iterate and change the
Dockerfiles yourself.

This is default strategy used by the CI image builds.

-X, --build-cache-disabled
Disables cache during docker builds. This is useful if you want to make sure you want to
rebuild everything from scratch.

This strategy is used by default for both Production and CI images for the scheduled
(nightly) builds in CI.

-D, --dockerhub-user
DockerHub user used to pull, push and build images. Default: apache.

-H, --dockerhub-repo
DockerHub repository used to pull, push, build images. Default: airflow.

-c, --github-registry
If GitHub registry is enabled, pulls and pushes are done from the GitHub registry not
DockerHub. You need to be logged in to the registry in order to be able to pull/push from it
and you need to be committer to push to Apache Airflow' GitHub registry.

-G, --github-organisation
GitHub organisation used to pull, push images when cache is used. Default: apache.

-g, --github-repo
GitHub repository used to pull, push images when cache is used. Default: airflow.

-v, --verbose
Show verbose information about executed commands (enabled by default for running test).
Note that you can further increase verbosity and see all the commands executed by breeze
by running 'export VERBOSE_COMMANDS="true"' before running breeze.

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Detailed usage for command: cleanup-image

breeze cleanup-image [FLAGS]

Removes the breeze-related images created in your local docker image cache. This will
not reclaim space in docker cache. You need to 'docker system prune' (optionally
with --all) to reclaim that space.

Flags:

-p, --python <PYTHON_MAJOR_MINOR_VERSION>
Python version used for the image. This is always major/minor version.

Note that versions 2.7 and 3.5 are only valid when installing Airflow 1.10 with
--install-airflow-version or --install-airflow-reference flags.

One of:

2.7 3.5 3.6 3.7 3.8

-I, --production-image
Use production image for entering the environment and builds (not for tests).

-v, --verbose
Show verbose information about executed commands (enabled by default for running test).
Note that you can further increase verbosity and see all the commands executed by breeze
by running 'export VERBOSE_COMMANDS="true"' before running breeze.

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Detailed usage for command: exec

breeze exec [-- <EXTRA_ARGS>]

Execs into interactive shell to an already running container. The container mus be started
already by breeze shell command. If you are not familiar with tmux, this is the best
way to run multiple processes in the same container at the same time for example scheduler,
webserver, workers, database console and interactive terminal.

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Detailed usage for command: generate-requirements

breeze generate-requirements [FLAGS]

Generates pinned requirements from setup.py. Those requirements are generated in requirements
directory - separately for different python version. Those requirements are used to run
CI builds as well as run repeatable production image builds. You can use those requirements
to predictably install released Airflow versions. You should run it always after you update
setup.py.

Flags:

-p, --python <PYTHON_MAJOR_MINOR_VERSION>
Python version used for the image. This is always major/minor version.

Note that versions 2.7 and 3.5 are only valid when installing Airflow 1.10 with
--install-airflow-version or --install-airflow-reference flags.

One of:

2.7 3.5 3.6 3.7 3.8

-v, --verbose
Show verbose information about executed commands (enabled by default for running test).
Note that you can further increase verbosity and see all the commands executed by breeze
by running 'export VERBOSE_COMMANDS="true"' before running breeze.

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Detailed usage for command: push-image

breeze push_image [FLAGS]

Pushes images to docker registry. You can push the images to DockerHub registry (default)
or to the GitHub registry (if --github-registry flag is used).

For DockerHub pushes --dockerhub-user and --dockerhub-repo flags can be used to specify
the repository to push to. For GitHub repository --github-organisation and --github-repo
flags can be used for the same purpose.

You can also add --production-image flag to switch to production image (default is CI one)

Examples:

'breeze push-image' or
'breeze push-image --dockerhub-user user' to push to your private registry or
'breeze push-image --production-image' - to push production image or
'breeze push-image --github-registry' - to push to GitHub image registry or
'breeze push-image --github-registry --github-organisation org' - for other organisation

Flags:

-D, --dockerhub-user
DockerHub user used to pull, push and build images. Default: apache.

-H, --dockerhub-repo
DockerHub repository used to pull, push, build images. Default: airflow.

-c, --github-registry
If GitHub registry is enabled, pulls and pushes are done from the GitHub registry not
DockerHub. You need to be logged in to the registry in order to be able to pull/push from it
and you need to be committer to push to Apache Airflow' GitHub registry.

-G, --github-organisation
GitHub organisation used to pull, push images when cache is used. Default: apache.

-g, --github-repo
GitHub repository used to pull, push images when cache is used. Default: airflow.

-v, --verbose
Show verbose information about executed commands (enabled by default for running test).
Note that you can further increase verbosity and see all the commands executed by breeze
by running 'export VERBOSE_COMMANDS="true"' before running breeze.

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Detailed usage for command: initialize-local-virtualenv

breeze initialize-local-virtualenv [FLAGS]

Initializes locally created virtualenv installing all dependencies of Airflow
taking into account the frozen requirements from requirements folder.
This local virtualenv can be used to aid autocompletion and IDE support as
well as run unit tests directly from the IDE. You need to have virtualenv
activated before running this command.

Flags:

-p, --python <PYTHON_MAJOR_MINOR_VERSION>
Python version used for the image. This is always major/minor version.

Note that versions 2.7 and 3.5 are only valid when installing Airflow 1.10 with
--install-airflow-version or --install-airflow-reference flags.

One of:

2.7 3.5 3.6 3.7 3.8

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Detailed usage for command: setup-autocomplete

breeze setup-autocomplete

Sets up autocomplete for breeze commands. Once you do it you need to re-enter the bash
shell and when typing breeze command <TAB> will provide autocomplete for
parameters and values.

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Detailed usage for command: stop

breeze stop

Brings down running docker compose environment. When you start the environment, the docker
containers will continue running so that startup time is shorter. But they take quite a lot of
memory and CPU. This command stops all running containers from the environment.

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Detailed usage for command: restart

breeze restart [FLAGS]

Restarts running docker compose environment. When you restart the environment, the docker
containers will be restarted. That includes cleaning up the databases. This is
especially useful if you switch between different versions of Airflow.

Flags:

Run 'breeze flags' to see all applicable flags.

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Detailed usage for command: toggle-suppress-cheatsheet

breeze toggle-suppress-cheatsheet

Toggles on/off cheatsheet displayed before starting bash shell.

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Detailed usage for command: toggle-suppress-asciiart

breeze toggle-suppress-asciiart

Toggles on/off asciiart displayed before starting bash shell.

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Detailed usage for command: docker-compose

breeze docker-compose [FLAGS] COMMAND [-- <EXTRA_ARGS>]

Run docker-compose command instead of entering the environment. Use 'help' as command
to see available commands. The <EXTRA_ARGS> passed after -- are treated
as additional options passed to docker-compose. For example

'breeze docker-compose pull -- --ignore-pull-failures'

Flags:

-p, --python <PYTHON_MAJOR_MINOR_VERSION>
Python version used for the image. This is always major/minor version.

Note that versions 2.7 and 3.5 are only valid when installing Airflow 1.10 with
--install-airflow-version or --install-airflow-reference flags.

One of:

2.7 3.5 3.6 3.7 3.8

-b, --backend <BACKEND>
Backend to use for tests - it determines which database is used.
One of:

sqlite mysql postgres

Default: sqlite

--postgres-version <POSTGRES_VERSION>
Postgres version used. One of:

9.6 10

--mysql-version <MYSQL_VERSION>
Mysql version used. One of:

5.7 8

-v, --verbose
Show verbose information about executed commands (enabled by default for running test).
Note that you can further increase verbosity and see all the commands executed by breeze
by running 'export VERBOSE_COMMANDS="true"' before running breeze.

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Detailed usage for command: kind-cluster

breeze kind-cluster [FLAGS] OPERATION

Manages host-side Kind Kubernetes cluster that is used to run Kubernetes integration tests.
It allows to start/stop/restart/status the Kind Kubernetes cluster and deploy Airflow to it.
This enables you to run tests inside the breeze environment with latest airflow images loaded.
Note that in case of deploying airflow, the first step is to rebuild the image and loading it
to the cluster so you can also pass appropriate build image flags that will influence
rebuilding the production image. Operation is one of:

start stop restart status deploy test shell