Apache Airflow

Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows.

When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.

Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.

Project Focus

Airflow works best with workflows that are mostly static and slowly changing. When the DAG structure is similar from one run to the next, it clarifies the unit of work and continuity. Other similar projects include Luigi, Oozie and Azkaban.

Airflow is commonly used to process data, but has the opinion that tasks should ideally be idempotent (i.e., results of the task will be the same, and will not create duplicated data in a destination system), and should not pass large quantities of data from one task to the next (though tasks can pass metadata using Airflow's XCom feature). For high-volume, data-intensive tasks, a best practice is to delegate to external services specializing in that type of work.

Airflow is not a streaming solution, but it is often used to process real-time data, pulling data off streams in batches.

Principles

• Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writing code that instantiates pipelines dynamically.
• Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.
• Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built into the core of Airflow using the powerful Jinja templating engine.
• Scalable: Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers.

Requirements

Apache Airflow is tested with:

* Experimental

Note: MySQL 5.x versions are unable to or have limitations with
running multiple schedulers -- please see the Scheduler docs.
MariaDB is not tested/recommended.

Note: SQLite is used in Airflow tests. Do not use it in production. We recommend
using the latest stable version of SQLite for local development.

Note: Airflow currently can be run on POSIX-compliant Operating Systems. For development, it is regularly
tested on fairly modern Linux Distros and recent versions of macOS.
On Windows you can run it via WSL2 (Windows Subsystem for Linux 2) or via Linux Containers.
The work to add Windows support is tracked via #10388, but
it is not a high priority. You should only use Linux-based distros as "Production" execution environment
as this is the only environment that is supported. The only distro that is used in our CI tests and that
is used in the Community managed DockerHub image is
Debian Bookworm.

Getting started

Visit the official Airflow website documentation (latest stable release) for help with
installing Airflow,
getting started, or walking
through a more complete tutorial.

Note: If you're looking for documentation for the main branch (latest development branch): you can find it on s.apache.org/airflow-docs.

For more information on Airflow Improvement Proposals (AIPs), visit
the Airflow Wiki.

Documentation for dependent projects like provider packages, Docker image, Helm Chart, you'll find it in the documentation index.

Installing from PyPI

We publish Apache Airflow as apache-airflow package in PyPI. Installing it however might be sometimes tricky
because Airflow is a bit of both a library and application. Libraries usually keep their dependencies open, and
applications usually pin them, but we should do neither and both simultaneously. We decided to keep
our dependencies as open as possible (in pyproject.toml) so users can install different versions of libraries
if needed. This means that pip install apache-airflow will not work from time to time or will
produce unusable Airflow installation.

To have repeatable installation, however, we keep a set of "known-to-be-working" constraint
files in the orphan constraints-main and constraints-2-0 branches. We keep those "known-to-be-working"
constraints files separately per major/minor Python version.
You can use them as constraint files when installing Airflow from PyPI. Note that you have to specify
correct Airflow tag/version/branch and Python versions in the URL.

1. Installing just Airflow:

Note: Only pip installation is currently officially supported.

While it is possible to install Airflow with tools like Poetry or
pip-tools, they do not share the same workflow as
pip - especially when it comes to constraint vs. requirements management.
Installing via Poetry or pip-tools is not currently supported.

There are known issues with bazel that might lead to circular dependencies when using it to install
Airflow. Please switch to pip if you encounter such problems. Bazel community works on fixing
the problem in this PR _ so it might be that
newer versions of bazel will handle it.

If you wish to install Airflow using those tools, you should use the constraint files and convert
them to the appropriate format and workflow that your tool requires.

pip install 'apache-airflow==2.10.1' \
 --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-2.10.1/constraints-3.8.txt"

2. Installing with extras (i.e., postgres, google)

pip install 'apache-airflow[postgres,google]==2.10.1' \
 --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-2.10.1/constraints-3.8.txt"

For information on installing provider packages, check
providers.

Official source code

Apache Airflow is an Apache Software Foundation (ASF) project,
and our official source code releases:

• Follow the ASF Release Policy
• Can be downloaded from the ASF Distribution Directory
• Are cryptographically signed by the release manager
• Are officially voted on by the PMC members during the
  Release Approval Process

Following the ASF rules, the source packages released must be sufficient for a user to build and test the
release provided they have access to the appropriate platform and tools.

Convenience packages

There are other ways of installing and using Airflow. Those are "convenience" methods - they are
not "official releases" as stated by the ASF Release Policy, but they can be used by the users
who do not want to build the software themselves.

Those are - in the order of most common ways people install Airflow:

• PyPI releases to install Airflow using standard pip tool
• Docker Images to install airflow via
  docker tool, use them in Kubernetes, Helm Charts, docker-compose, docker swarm, etc. You can
  read more about using, customising, and extending the images in the
  Latest docs, and
  learn details on the internals in the images document.
• Tags in GitHub to retrieve the git project sources that
  were used to generate official source packages via git

All those artifacts are not official releases, but they are prepared using officially released sources.
Some of those artifacts are "development" or "pre-release" ones, and they are clearly marked as such
following the ASF Policy.

User Interface

• DAGs: Overview of all DAGs in your environment.

  

• Grid: Grid representation of a DAG that spans across time.

  

• Graph: Visualization of a DAG's dependencies and their current status for a specific run.

  

• Task Duration: Total time spent on different tasks over time.

  

• Gantt: Duration and overlap of a DAG.

  

• Code: Quick way to view source code of a DAG.

  

Semantic versioning

As of Airflow 2.0.0, we support a strict SemVer approach for all packages released.

There are few specific rules that we agreed to that define details of versioning of the different
packages:

• Airflow: SemVer rules apply to core airflow only (excludes any changes to providers).
  Changing limits for versions of Airflow dependencies is not a breaking change on its own.
• Airflow Providers: SemVer rules apply to changes in the particular provider's code only.
  SemVer MAJOR and MINOR versions for the packages are independent of the Airflow version.
  For example, google 4.1.0 and amazon 3.0.3 providers can happily be installed
  with Airflow 2.1.2. If there are limits of cross-dependencies between providers and Airflow packages,
  they are present in providers as install_requires limitations. We aim to keep backwards
  compatibility of providers with all previously released Airflow 2 versions but
  there will sometimes be breaking changes that might make some, or all
  providers, have minimum Airflow version specified.
• Airflow Helm Chart: SemVer rules apply to changes in the chart only. SemVer MAJOR and MINOR
  versions for the chart are independent of the Airflow version. We aim to keep backwards
  compatibility of the Helm Chart with all released Airflow 2 versions, but some new features might
  only work starting from specific Airflow releases. We might however limit the Helm
  Chart to depend on minimal Airflow version.
• Airflow API clients: Their versioning is independent from Airflow versions. They follow their own
  SemVer rules for breaking changes and new features - which for example allows to change the way we generate
  the clients.

Version Life Cycle

Apache Airflow version life cycle:

Limited support versions will be supported with security and critical bug fix only.
EOL versions will not get any fixes nor support.
We always recommend that all users run the latest available minor release for whatever major version is in use.
We highly recommend upgrading to the latest Airflow major release at the earliest convenient time and before the EOL date.

Support for Python and Kubernetes versions

As of Airflow 2.0, we agreed to certain rules we follow for Python and Kubernetes support.
They are based on the official release schedule of Python and Kubernetes, nicely summarized in the
Python Developer's Guide and
Kubernetes version skew policy.

1. We drop support for Python and Kubernetes versions when they reach EOL. Except for Kubernetes, a
   version stays supported by Airflow if two major cloud providers still provide support for it. We drop
   support for those EOL versions in main right after EOL date, and it is effectively removed when we release
   the first new MINOR (Or MAJOR if there is no new MINOR version) of Airflow. For example, for Python 3.8 it
   means that we will drop support in main right after 27.06.2023, and the first MAJOR or MINOR version of
   Airflow released after will not have it.

2. We support a new version of Python/Kubernetes in main after they are officially released, as soon as we
   make them work in our CI pipeline (which might not be immediate due to dependencies catching up with
   new versions of Python mostly) we release new images/support in Airflow based on the working CI setup.

3. This policy is best-effort which means there may be situations where we might terminate support earlier
   if circumstances require it.

Base OS support for reference Airflow images

The Airflow Community provides conveniently packaged container images that are published whenever
we publish an Apache Airflow release. Those images contain:

• Base OS with necessary packages to install Airflow (stable Debian OS)
• Base Python installation in versions supported at the time of release for the MINOR version of
  Airflow released (so there could be different versions for 2.3 and 2.2 line for example)
• Libraries required to connect to supported Databases (again the set of databases supported depends
  on the MINOR version of Airflow)
• Predefined set of popular providers (for details see the Dockerfile).
• Possibility of building your own, custom image where the user can choose their own set of providers
  and libraries (see Building the image)
• In the future Airflow might also support a "slim" version without providers nor database clients installed

The version of the base OS image is the stable version of Debian. Airflow supports using all currently active
stable versions - as soon as all Airflow dependencies support building, and we set up the CI pipeline for
building and testing the OS version. Approximately 6 months before the end-of-regular support of a
previous stable version of the OS, Airflow switches the images released to use the latest supported
version of the OS.

For example switch from Debian Bullseye to Debian Bookworm has been implemented
before 2.8.0 release in October 2023 and Debian Bookworm will be the only option supported as of
Airflow 2.10.0.

Users will continue to be able to build their images using stable Debian releases until the end of regular
support and building and verifying of the images happens in our CI but no unit tests were executed using
this image in the main branch.

Approach to dependencies of Airflow

Airflow has a lot of dependencies - direct and transitive, also Airflow is both - library and application,
therefore our policies to dependencies has to include both - stability of installation of application,
but also ability to install newer version of dependencies for those users who develop DAGs. We developed
the approach where constraints are used to make sure airflow can be installed in a repeatable way, while
we do not limit our users to upgrade most of the dependencies. As a result we decided not to upper-bound
version of Airflow dependencies by default, unless we have good reasons to believe upper-bounding them is
needed because of importance of the dependency as well as risk it involves to upgrade specific dependency.
We also upper-bound the dependencies that we know cause problems.

The constraint mechanism of ours takes care about finding and upgrading all the non-upper bound dependencies
automatically (providing that all the tests pass). Our main build failures will indicate in case there
are versions of dependencies that break our tests - indicating that we should either upper-bind them or
that we should fix our code/tests to account for the upstream changes from those dependencies.

Whenever we upper-bound such a dependency, we should always comment why we are doing it - i.e. we should have
a good reason why dependency is upper-bound. And we should also mention what is the condition to remove the
binding.

Approach for dependencies for Airflow Core

Those dependencies are maintained in pyproject.toml.

There are few dependencies that we decided are important enough to upper-bound them by default, as they are
known to follow predictable versioning scheme, and we know that new versions of those are very likely to
bring breaking changes. We commit to regularly review and attempt to upgrade to the newer versions of
the dependencies as they are released, but this is manual process.

The important dependencies are:

• SQLAlchemy: upper-bound to specific MINOR version (SQLAlchemy is known to remove deprecations and
  introduce breaking changes especially that support for different Databases varies and changes at
  various speed)
• Alembic: it is important to handle our migrations in predictable and performant way. It is developed
  together with SQLAlchemy. Our experience with Alembic is that it very stable in MINOR version
• Flask: We are using Flask as the back-bone of our web UI and API. We know major version of Flask
  are very likely to introduce breaking changes across those so limiting it to MAJOR version makes sense
• werkzeug: the library is known to cause problems in new versions. It is tightly coupled with Flask
  libraries, and we should update them together
• celery: Celery is a crucial component of Airflow as it used for CeleryExecutor (and similar). Celery
  follows SemVer, so
  we should upper-bound it to the next MAJOR version. Also, when we bump the upper version of the library,
  we should make sure Celery Provider minimum Airflow version is updated.
• kubernetes: Kubernetes is a crucial component of Airflow as it is used for the KubernetesExecutor
  (and similar). Kubernetes Python library follows SemVer,
  so we should upper-bound it to the next MAJOR version. Also, when we bump the upper version of the library,
  we should make sure Kubernetes Provider minimum Airflow version is updated.

Approach for dependencies in Airflow Providers and extras

The main part of the Airflow is the Airflow Core, but the power of Airflow also comes from a number of
providers that extend the core functionality and are released separately, even if we keep them (for now)
in the same monorepo for convenience. You can read more about the providers in the
Providers documentation. We also
have set of policies implemented for maintaining and releasing community-managed providers as well
as the approach for community vs. 3rd party providers in the providers document.

Those extras and providers dependencies are maintained in provider.yaml of each provider.

By default, we should not upper-bound dependencies for providers, however each provider's maintainer
might decide to add additional limits (and justify them with comment).

Contributing

Want to help build Apache Airflow? Check out our contributing documentation.

Official Docker (container) images for Apache Airflow are described in images.

Voting Policy

• Commits need a +1 vote from a committer who is not the author
• When we do AIP voting, both PMC member's and committer's +1s are considered a binding vote.

Who uses Apache Airflow?

We know about around 500 organizations that are using Apache Airflow (but there are likely many more)
in the wild.

If you use Airflow - feel free to make a PR to add your organisation to the list.

Who maintains Apache Airflow?

Airflow is the work of the community,
but the core committers/maintainers
are responsible for reviewing and merging PRs as well as steering conversations around new feature requests.
If you would like to become a maintainer, please review the Apache Airflow
committer requirements.