CI Environment
Running CI Builds locally
GitHub Registry Variables
Dockerhub Variables
CI Architecture
CI run types
Workflows
Naming conventions for stored images
Reproducing CI Runs locally
CI Sequence diagrams

CI Environment

Continuous Integration is important component of making Apache Airflow robust and stable. We are running a lot of tests for every pull request, for master and v1-10-test branches and regularly as CRON jobs.

Our execution environment for CI is GitHub Actions. GitHub Actions (GA) are very well integrated with GitHub code and Workflow and it has evolved fast in 2019/202 to become a fully-fledged CI environment, easy to use and develop for, so we decided to switch to it. Our previous CI system was Travis CI.

However part of the philosophy we have is that we are not tightly coupled with any of the CI environments we use. Most of our CI jobs are written as bash scripts which are executed as steps in the CI jobs. And we have a number of variables determine build behaviour.

GitHub Actions runs

Our builds on CI are highly optimized. They utilise some of the latest features provided by GitHub Actions environment that make it possible to reuse parts of the build process across different Jobs.

Big part of our CI runs use Container Images. Airflow has a lot of dependencies and in order to make sure that we are running tests in a well configured and repeatable environment, most of the tests, documentation building, and some more sophisticated static checks are run inside a docker container environment. This environment consist of two types of images: CI images and PROD images. CI Images are used for most of the tests and checks where PROD images are used in the Kubernetes tests.

In order to run the tests, we need to make sure tha the images are built using latest sources and that it is done quickly (full rebuild of such image from scratch might take ~15 minutes). Therefore optimisation techniques have been implemented that use efficiently cache from the GitHub Docker registry - in most cases this brings down the time needed to rebuild the image to ~4 minutes. In some cases (when dependencies change) it can be ~6-7 minutes and in case base image of Python releases new patch-level, it can be ~12 minutes.

Currently in master version of Airflow we run tests in 3 different versions of Python (3.6, 3.7, 3.8) which means that we have to build 6 images (3 CI ones and 3 PROD ones). Yet we run around 12 jobs with each of the CI images. That is a lot of time to just build the environment to run. Therefore we are utilising workflow_run feature of GitHub Actions. This feature allows to run a separate, independent workflow, when the main workflow is run - this separate workflow is different than the main one, because by default it runs using master version of the sources but also - and most of all - that it has WRITE access to the repository. This is especially important in our case where Pull Requests to Airflow might come from any repository, and it would be a huge security issue if anyone from outside could utilise the WRITE access to Apache Airflow repository via an external Pull Request.

Thanks to the WRITE access and fact that the 'workflow_run' by default uses the 'master' version of the sources, we can safely run some logic there will checkout the incoming Pull Request, build the container image from the sources from the incoming PR and push such image to an GitHub Docker Registry - so that this image can be built only once and used by all the jobs running tests. The image is tagged with unique RUN_ID of the incoming Pull Request and the tests run in the Pull Request can simply pull such image rather than build it from the scratch. Pulling such image takes ~ 1 minute, thanks to that we are saving a lot of precious time for jobs.

Local runs

The main goal of the CI philosophy we have that no matter how complex the test and integration infrastructure, as a developer you should be able to reproduce and re-run any of the failed checks locally. One part of it are pre-commit checks, that allow you to run the same static checks in CI and locally, but another part is the CI environment which is replicated locally with Breeze.

You can read more about Breeze in BREEZE.rst but in essence it is a script that allows you to re-create CI environment in your local development instance and interact with it. In its basic form, when you do development you can run all the same tests that will be run in CI - but locally, before you submit them as PR. Another use case where Breeze is useful is when tests fail on CI. You can take the RUN_ID of failed build pass it as --github-image-id parameter of Breeze and it will download the very same version of image that was used in CI and run it locally. This way, you can very easily reproduce any failed test that happens in CI - even if you do not check out the sources connected with the run.

You can read more about it in BREEZE.rst and TESTING.rst

Difference between local runs and GitHub Action workflows

Depending whether the scripts are run locally (most often via Breeze) or whether they are run in "CI Build" or "Build Image" workflows they can take different values.

You can use those variables when you try to reproduce the build locally.

Variable	Local development	Build Image CI workflow	Main CI Workflow	Comment
Basic variables
`PYTHON_MAJOR_MINOR_VERSION`				Major/Minor version of python used.
`DB_RESET`	false	true	true	Determines whether database should be reset at the container entry. By default locally the database is not reset, which allows to keep the database content between runs in case of Postgres or MySQL. However, it requires to perform manual init/reset if you stop the environment.
Dockerhub variables
`DOCKERHUB_USER`	apache			Name of the DockerHub user to use
`DOCKERHUB_REPO`	airflow			Name of the DockerHub repository to use
Mount variables
`MOUNT_LOCAL_SOURCES`	true	false	false	Determines whether local sources are mounted to inside the container. Useful for local development, as changes you make locally can be immediately tested in the container. We mount only selected, important folders. We do not mount the whole project folder in order to avoid accidental use of artifacts (such as `egg-info` directories) generated locally on the host during development.
Force variables
`FORCE_PULL_IMAGES`	true	true	true	Determines if images are force-pulled, no matter if they are already present locally. This includes not only the CI/PROD images but also the python base images. Note that if python base images change, also the CI and PROD images need to be fully rebuild unless they were already built with that base python image. This is false for local development to avoid often pulling and rebuilding the image. It is true for CI workflow in case waiting from images is enabled as the images needs to be force-pulled from GitHub Registry, but it is set to false when waiting for images is disabled.
`FORCE_BUILD_IMAGES`	false	false	false	Forces building images. This is generally not very useful in CI as in CI environment image is built or pulled only once, so there is no need to set the variable to true. For local builds it forces rebuild, regardless if it is determined to be needed.
`FORCE_ANSWER_TO_QUESTIONS`		yes	yes	This variable determines if answer to questions during the build process should be automatically given. For local development, the user is occasionally asked to provide answers to questions such as - whether the image should be rebuilt. By default the user has to answer but in the CI environment, we force "yes" answer.
`SKIP_CHECK_REMOTE_IMAGE`	false	true	true	Determines whether we check if remote image is "fresher" than the current image. When doing local breeze runs we try to determine if it will be faster to rebuild the image or whether the image should be pulled first from the cache because it has been rebuilt. This is slightly experimental feature and will be improved in the future as the current mechanism does not always work properly.
Host variables
`HOST_USER_ID`				User id of the host user.
`HOST_GROUP_ID`				Group id of the host user.
`HOST_OS`		Linux	Linux	OS of the Host (Darwin/Linux).
`HOST_HOME`				Home directory on the host.
`HOST_AIRFLOW_SOURCES`				Directory where airflow sources are located on the host.
Image variables
`INSTALL_AIRFLOW_VERSION`				Installs Airflow version from PyPI when building image. Can be "none" to skip airflow installation so that it can be installed from locally prepared packages.
`INSTALL_AIRFLOW_REFERENCE`				Installs Airflow version from GitHub branch or tag.
Version suffix variables
`VERSION_SUFFIX_FOR_PYPI`				Version suffix used during backport package preparation for PyPI builds.
`VERSION_SUFFIX_FOR_SVN`				Version suffix used during backport package preparation for SVN builds.
Git variables
COMMIT_SHA		GITHUB_SHA	GITHUB_SHA	SHA of the commit of the build is run
Verbosity variables
`PRINT_INFO_FROM_SCRIPTS`	true	true	true	Allows to print output to terminal from running scripts. It prints some extra outputs if true including what the commands do, results of some operations, summary of variable values, exit status from the scripts, outputs of failing commands. If verbose is on it also prints the commands executed by docker, kind, helm, kubectl. Disabled in pre-commit checks. set to false in pre-commits
`VERBOSE`	false	true	true	Determines whether docker, helm, kind, kubectl commands should be printed before execution. This is useful to determine what exact commands were executed for debugging purpose as well as allows to replicate those commands easily by copy&pasting them from the output. requires `PRINT_INFO_FROM_SCRIPTS` set to true.
`VERBOSE_COMMANDS`	false	false	false	Determines whether every command executed in bash should also be printed before execution. This is a low-level debugging feature of bash (set -x) and it should only be used if you are lost at where the script failed.
Image build variables
`UPGRADE_TO_LATEST_CONSTRAINTS`	false	false	false	Determines whether the build should attempt to upgrade all PIP dependencies to latest ones matching `setup.py` limits. This tries to replicate the situation of "fresh" user who just installs airflow and uses latest version of matching dependencies. By default we are using a tested set of dependency constraints stored in separated "orphan" branches of the airflow repository ("constraints-master, "constraints-1-10") but when this flag is set to anything but false (for example commit SHA), they are not used used and "eager" upgrade strategy is used when installing dependencies. We set it to true in case of direct pushes (merges) to master and scheduled builds so that the constraints are tested. In those builds, in case we determine that the tests pass we automatically push latest set of "tested" constraints to the repository. Setting the value to commit SHA is best way to assure that constraints are upgraded even if there is no change to setup.py This way our constraints are automatically tested and updated whenever new versions of libraries are released. true in case of direct pushes and scheduled builds
`CHECK_IMAGE_FOR_REBUILD`	true	true	true	Determines whether attempt should be made to rebuild the CI image with latest sources. It is true by default for local builds, however it is set to true in case we know that the image we pulled or built already contains the right sources. In such case we should set it to false, especially in case our local sources are not the ones we intend to use (for example when `--github-image-id` is used in Breeze. In CI builds it is set to true in case of the "Build Image" workflow or when waiting for images is disabled in the CI workflow. if waiting for images the variable is set to false automatically.
`SKIP_BUILDING_PROD_IMAGE`	false	false	false	Determines whether we should skip building the PROD image with latest sources. It is set to false, but in deploy app for kubernetes step it is set to "true", because at this stage we know we have good image build or pulled. set to true in "Deploy App to Kubernetes" to false automatically.

Running CI Builds locally

The following variables are automatically determined based on CI environment variables. You can locally by setting CI="true" and run the ci scripts from the scripts/ci folder:

provider_packages - scripts to build and test provider packages
constraints - scripts to build and publish latest set of valid constraints
docs - scripts to build documentation
images - scripts to build and push CI and PROD images
kubernetes - scripts to setup kubernetes cluster, deploy airflow and run kubernetes tests with it
testing - scripts that run unit and integration tests
tools - scripts that perform various clean-up and preparation tasks

Common libraries of functions for all the scripts can be found in libraries folder.

For detailed use of those scripts you can refer to .github/workflows/ - those scripts are used by the CI workflows of ours.

The default values are "sane" you can change them to interact with your own repositories or registries. Note that you need to set "CI" variable to true in order to get the same results as in CI.

Variable	Default	Comment
CI	`false`	If set to "true", we simulate behaviour of all scripts as if they are in CI environment
CI_TARGET_REPO	`apache/airflow`	Target repository for the CI build. Used to compare incoming changes from PR with the target.
CI_TARGET_BRANCH	`master`	Target branch where the PR should land. Used to compare incoming changes from PR with the target.
CI_BUILD_ID	`0`	Unique id of the build that is kept across re runs (for GitHub actions it is `GITHUB_RUN_ID`)
CI_JOB_ID	`0`	Unique id of the job - used to produce unique artifact names.
CI_EVENT_TYPE	`pull_request`	Type of the event. It can be one of [`pull_request`, `pull_request_target`, `schedule`, `push`]
CI_REF	`refs/head/master`	Branch in the source repository that is used to make the pull request.

GitHub Registry Variables

Our CI uses GitHub Registry to pull and push images to/from by default. You can however make it interact with DockerHub registry or change the GitHub registry to interact with and use your own repo by changing GITHUB_REPOSITORY and providing your own GitHub Username and Token.

Variable	Default	Comment
USE_GITHUB_REGISTRY	true	If set to "true", we interact with GitHub Registry registry not the DockerHub one.
GITHUB_REGISTRY	`docker.pkg.github.com`	DNS name of the GitHub registry to use.
GITHUB_REPOSITORY	`apache/airflow`	Prefix of the image. It indicates which. registry from GitHub to use
GITHUB_USERNAME		Username to use to login to GitHub
GITHUB_TOKEN		Personal token to use to login to GitHub
GITHUB_REGISTRY_WAIT_FOR_IMAGE	`false`	Wait for the image to be available. This is useful if commit SHA is used as pull tag
GITHUB_REGISTRY_PULL_IMAGE_TAG	`latest`	Pull this image tag. This is "latest" by default, can be commit SHA or RUN_ID.
GITHUB_REGISTRY_PUSH_IMAGE_TAG	`latest`	Pull this image tag. This is "latest" by default, can be commit SHA or RUN_ID.

Dockerhub Variables

If USE_GITHUB_REGISTRY is set to "false" you can interact directly with DockerHub. By default you pull from/push to "apache/airflow" DockerHub repository, but you can change that to your own repository by setting those environment variables:

Variable	Default	Comment
DOCKERHUB_USER	`apache`	Name of the DockerHub user to use
DOCKERHUB_REPO	`airflow`	Name of the DockerHub repo to use

CI Architecture

The following components are part of the CI infrastructure

Apache Airflow Code Repository - our code repository at https://github.com/apache/airflow
Apache Airflow Forks - forks of the Apache Airflow Code Repository from which contributors make Pull Requests
GitHub Actions - (GA) UI + execution engine for our jobs
GA CRON trigger - GitHub Actions CRON triggering our jobs
GA Workers - virtual machines running our jobs at GitHub Actions (max 20 in parallel)
GitHub Private Image Registry- image registry used as build cache for CI jobs. It is at https://docker.pkg.github.com/apache/airflow/airflow
DockerHub Public Image Registry - publicly available image registry at DockerHub. It is at https://hub.docker.com/repository/docker/apache/airflow
DockerHub Build Workers - virtual machines running build jibs at DockerHub
Official Images (future) - these are official images that are prominently visible in DockerHub. We aim our images to become official images so that you will be able to pull them with docker pull apache-airflow

CI run types

The following CI Job run types are currently run for Apache Airflow (run by ci.yaml workflow) and each of the run types has different purpose and context.

Pull request run

Those runs are results of PR from the forks made by contributors. Most builds for Apache Airflow fall into this category. They are executed in the context of the "Fork", not main Airflow Code Repository which means that they have only "read" permission to all the GitHub resources (container registry, code repository). This is necessary as the code in those PRs (including CI job definition) might be modified by people who are not committers for the Apache Airflow Code Repository.

The main purpose of those jobs is to check if PR builds cleanly, if the test run properly and if the PR is ready to review and merge. The runs are using cached images from the Private GitHub registry - CI, Production Images as well as base Python images that are also cached in the Private GitHub registry. Also for those builds we only execute Python tests if important files changed (so for example if it is "no-code" change, no tests will be executed.

The workflow involved in Pull Requests review and approval is a bit more complex than simple workflows in most of other projects because we've implemented some optimizations related to efficient use of queue slots we share with other Apache Software Foundation projects. More details about it can be found in PULL_REQUEST_WORKFLOW.rst.

Direct Push/Merge Run

Those runs are results of direct pushes done by the committers or as result of merge of a Pull Request by the committers. Those runs execute in the context of the Apache Airflow Code Repository and have also write permission for GitHub resources (container registry, code repository). The main purpose for the run is to check if the code after merge still holds all the assertions - like whether it still builds, all tests are green.

This is needed because some of the conflicting changes from multiple PRs might cause build and test failures after merge even if they do not fail in isolation. Also those runs are already reviewed and confirmed by the committers so they can be used to do some housekeeping: - pushing most recent image build in the PR to the GitHub Private Registry (for caching) - upgrading to latest constraints and pushing those constraints if all tests succeed - refresh latest Python base images in case new patch-level is released

The housekeeping is important - Python base images are refreshed with varying frequency (once every few months usually but sometimes several times per week) with the latest security and bug fixes. Those patch level images releases can occasionally break Airflow builds (specifically Docker image builds based on those images) therefore in PRs we only use latest "good" python image that we store in the private GitHub cache. The direct push/master builds are not using registry cache to pull the python images - they are directly pulling the images from DockerHub, therefore they will try the latest images after they are released and in case they are fine, CI Docker image is build and tests are passing - those jobs will push the base images to the private GitHub Registry so that they be used by subsequent PR runs.

Scheduled runs

Those runs are results of (nightly) triggered job - only for master branch. The main purpose of the job is to check if there was no impact of external dependency changes on the Apache Airflow code (for example transitive dependencies released that fail the build). It also checks if the Docker images can be build from the scratch (again - to see if some dependencies have not changed - for example downloaded package releases etc. Another reason for the nightly build is that the builds tags most recent master with nightly-master tag so that DockerHub build can pick up the moved tag and prepare a nightly public master build in the DockerHub registry. The v1-10-test branch images are build in DockerHub when pushing v1-10-stable manually.

All runs consist of the same jobs, but the jobs behave slightly differently or they are skipped in different run categories. Here is a summary of the run categories with regards of the jobs they are running. Those jobs often have matrix run strategy which runs several different variations of the jobs (with different Backend type / Python version, type of the tests to run for example). The following chapter describes the workflows that execute for each run.

Those runs and their corresponding Build Images runs are only executed in main apache/airflow repository, they are not executed in forks - we want to be nice to the contributors and not use their free build minutes on GitHub Actions.

Workflows

Build Images Workflow

This workflow has two purposes - it builds images for the CI Workflow but also it cancels duplicate or failed builds in order to save job time in GitHub Actions and allow for faster feedback for developers.

It's a special type of workflow: workflow_run which means that it is triggered by other workflows (in our case it is triggered by the CI Build workflow). This also means that the workflow has Write permission to the Airflow repository and it can - for example - push to the GitHub registry the images used by CI Builds which means that the images can be built only once and reused by all the CI jobs (including the matrix jobs). We've implemented it in the way that the CI Build running will wait until the images are built by the "Build Images" workflow.

It's possible to disable this feature and go back to the previous behaviour via GITHUB_REGISTRY_WAIT_FOR_IMAGE flag in the "Build Workflow image". Setting it to "false" switches back to the behaviour that each job builds own image.

You can also switch back to jobs building the images on its own on the fork level by setting AIRFLOW_GITHUB_REGISTRY_WAIT_FOR_IMAGE secret to false. This will disable pushing the "RUN_ID" images to GitHub Registry and all the images will be built locally by each job. It is about 20% slower for the whole build on average, but it does not require to have access to push images to GitHub, which sometimes might be not available (depending on the account status).

The write permission also allows to cancel duplicate workflows. It is not possible for the Pull Request CI Builds run from the forks as they have no Write permission allowing them to cancels running workflows. In our case we perform several different cancellations:

we cancel duplicate "CI Build" workflow runs s (i.e. workflows from the same repository and branch that were started in quick succession - this allows to save workers that would have been busy running older version of the same Pull Request (usually with fix-ups) and free them for other runs.
we cancel duplicate "Build Images" workflow runs for the same reasons. The "Build Images" builds run image builds which takes quite some time, so pushing a fixup quickly on the same branch will also cancel the past "Build Images" workflows.
last, but not least - we cancel any of the "CI Build" workflow runs that failed in some important jobs. This is another optimisations - GitHub does not have "fail-fast" on the whole run and this cancelling effectively implements "fail-fast" of runs for some important jobs. Note that it only works when you submit new PRs or push new changes. In case the jobs failed and no new PR is pushed after that, the whole run will run to completion.

The workflow has the following jobs:

Job	Description
Cancel workflow runs	Cancels duplicated and failed workflows
Build Info	Prints detailed information about the build
Build CI/PROD images	Builds all configured CI and PROD images

The images are stored in the GitHub Registry and the names of those images follow the patterns described in Naming conventions for stored images

Image building is configured in "fail-fast" mode. When any of the images fails to build, it cancels other builds and the source "CI Build" workflow run that triggered it.

CI Build Workflow

This workflow is a regular workflow that performs all checks of Airflow code.

Job	Description	PR	Push Merge	CRON (1)
Build info	Prints detailed information about the build	Yes	Yes	Yes
Helm tests	Runs tests for the Helm chart	Yes	Yes	Yes
Test OpenAPI client gen	Tests if OpenAPIClient continues to generate	Yes	Yes	Yes
CI Images	Waits for CI Images (3)	Yes	Yes	Yes
Static checks	Performs static checks without pylint	Yes	Yes	Yes
Static checks: pylint	Performs pylint static checks	Yes	Yes	Yes
Build docs	Builds documentation	Yes	Yes	Yes
Spell check docs	Spell check for documentation	Yes	Yes	Yes
Backport packages	Prepares Backport Packages for 1.10 Airflow	Yes	Yes	Yes
Trigger tests	Checks if tests should be triggered	Yes	Yes	Yes
Tests [Pg/Msql/Sqlite]	Run all the Pytest tests for Python code	Yes(2)	Yes	Yes
Quarantined tests	Flaky tests that we need to fix (5)	Yes(2)	Yes	Yes
Upload coverage	Uploads test coverage from all the tests	Yes	Yes	Yes
PROD Images	Waits for CI Images (3)	Yes	Yes	Yes
Tests Kubernetes	Run Kubernetes test	Yes(2)	Yes	Yes
Push PROD images	Pushes PROD images to GitHub Registry (4)		Yes
Push CI images	Pushes CI images to GitHub Registry (4)		Yes
Constraints	Upgrade constraints to latest ones (4)		Yes	Yes
Constraints push	Pushes all upgraded constraints (4)		Yes	Yes
Tag Repo nightly	Tags the repository with nightly tag (6)			Yes

Comments:

CRON jobs builds images from scratch - to test if everything works properly for clean builds

The tests are run when the Trigger Tests job determine that important files change (this allows for example "no-code" changes to build much faster)

The jobs wait for CI images if GITHUB_REGISTRY_WAIT_FOR_IMAGE variable is set to "true". You can set it to "false" to disable using shared images - this is slower though as the images are rebuilt in every job that needs them. You can also set your own fork's secret AIRFLOW_GITHUB_REGISTRY_WAIT_FOR_IMAGE to false to trigger the same behaviour.

PROD and CI images are pushed as "latest" to DockerHub registry and constraints are upgraded only if all tests are successful. Note that images are not pushed in CRON jobs because they are rebuilt from scratch and we want to push incremental changes to the DockerHub registry.

Flaky tests never fail in regular builds. See the next chapter where our approach to flaky tests is explained.

Nightly tag is pushed to the repository only in CRON job and only if all tests pass. This causes the DockerHub images are built automatically and made available to developers.

Scheduled quarantined builds

This workflow runs only quarantined tests. Those tests do not fail the build even if some tests fail (only if the whole pytest execution fails). Instead this workflow updates one of the issues where we keep status of quarantined tests. Once the test succeeds in NUM_RUNS subsequent runs, it is marked as stable and can be removed from quarantine. You can read more about quarantine in TESTING.rst

The issues are only updated if the test is run as direct push or scheduled run and only in the apache/airflow repository - so that the issues are not updated in forks.

The issues that gets updated are different for different branches:

master: Quarantine tests master
v1-10-stable: Quarantine tests v1-10-stable
v1-10-test: Quarantine tests v1-10-test

Those runs and their corresponding Build Images runs are only executed in main apache/airflow repository, they are not executed in forks - we want to be nice to the contributors and not use their free build minutes on GitHub Actions.

Force sync master from apache/airflow

This is manually triggered workflow (via GitHub UI manual run) that should only be run in GitHub forks. When triggered, it will force-push the "apache/airflow" master to the fork's master. It's the easiest way to sync your fork master to the Apache Airflow's one.

Delete old artifacts

This workflow is introduced, to delete old artifacts from the GitHub Actions build. We set it to delete old artifacts that are > 7 days old. It only runs for the 'apache/airflow' repository.

We also have a script that can help to clean-up the old artifacts: remove_artifacts.sh

CodeQL scan

The CodeQL security scan uses GitHub security scan framework to scan our code for security violations. It is run for JavaScript and python code.

Publishing documentation

Documentation from the master branch is automatically published on Amazon S3.

To make this possible, GitHub Action has secrets set up with credentials for an Amazon Web Service account - DOCS_AWS_ACCESS_KEY_ID and DOCS_AWS_SECRET_ACCESS_KEY.

This account has permission to write/list/put objects to bucket apache-airflow-docs. This bucket has public access configured, which means it is accessible through the website endpoint. For more information, see: `Hosting a static website on Amazon S3: <https://docs.aws.amazon.com/AmazonS3/latest/dev/WebsiteHosting.html>`_

Website endpoint: http://apache-airflow-docs.s3-website.eu-central-1.amazonaws.com/

Naming conventions for stored images

The images produced during the CI builds are stored in the GitHub Registry

The images are stored with both "latest" tag (for last master push image that passes all the tests as well with the tags indicating the origin of the image.

The image names follow the patterns:

Image	Name pattern	Tag for format	Comment
Python image	python	<X.Y>-slim-buster-<RUN_ID> <X.Y>-slim-buster-<COMMIT_SHA>	Base python image used by both production and CI image. Python maintainer release new versions of those image with security fixes every few weeks.
CI image	<BRANCH>-python<X.Y>-ci	<RUN_ID> <COMMIT_SHA>	CI image - this is the image used for most of the tests.
PROD Build image	<BRANCH>-python<X.Y>-build	<RUN_ID> <COMMIT_SHA>	Production Build image - this is the "build" segment of production image. It contains build-essentials and all necessary packages to install PIP packages.
PROD image	<BRANCH>-python<X.Y>	<RUN_ID> <COMMIT_SHA>	Production image. This is the actual production image - optimized for size. It contains only compiled libraries and minimal set of dependencies to run Airflow.

<BRANCH> might be either "master" or "v1-10-test" or "v2-0-test"
<X.Y> - Python version (Major + Minor). For "master" and "v2-0-test" should be in ["3.6", "3.7", "3.8"]. For v1-10-test it should be in ["2.7", "3.5", "3.6". "3.7", "3.8"].
<RUN_ID> - GitHub Actions RUN_ID. You can get it from CI action job outputs (run id is printed in logs and displayed as part of the step name. All PRs belong to some RUN_ID and this way you can pull the very exact version of image used in that RUN_ID
<COMMIT_SHA> - for images that get merged to "master", "v2-0-test" of "v1-10-test" the images are also tagged with the commit SHA of that particular commit. This way you can easily find the image that was used for testing for that "master", "v2-0-test" or "v1-10-test" test run.

Reproducing CI Runs locally

Since we store images from every CI run, you should be able easily reproduce any of the CI build problems locally. You can do it by pulling and using the right image and running it with the right docker command, For example knowing that the CI build had 210056909 RUN_ID (you can find it from GitHub CI logs):

docker pull docker.pkg.github.com/apache/airflow/master-python3.6-ci:210056909

docker run -it docker.pkg.github.com/apache/airflow/master-python3.6-ci:210056909

But you usually need to pass more variables amd complex setup if you want to connect to a database or enable some integrations. Therefore it is easiest to use Breeze for that. For example if you need to reproduce a MySQL environment with kerberos integration enabled for run 210056909, in python 3.8 environment you can run:

./breeze --github-image-id 210056909 --python 3.8 --integration kerberos

You will be dropped into a shell with the exact version that was used during the CI run and you will be able to run pytest tests manually, easily reproducing the environment that was used in CI. Note that in this case, you do not need to checkout the sources that were used for that run - they are already part of the image - but remember that any changes you make in those sources are lost when you leave the image as the sources are not mapped from your host machine.

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