CONTRIBUTING.rst

• Contributions
  • New Contributor
  • Get Mentoring Support
  • Report Bugs
  • Fix Bugs
  • Issue reporting and resolution process
  • Implement Features
  • Improve Documentation
  • Submit Feedback
• Roles
  • PMC Member
  • Committers/Maintainers
  • Contributors
  • Security Team
• Contribution Workflow
  • Step 1: Fork the Apache Airflow Repo
  • Step 2: Configure Your Environment
  • Step 3: Connect with People
  • Step 4: Prepare PR
  • Step 5: Pass PR Review
• Pull Request Guidelines
• Airflow Git Branches
• Development Environments
  • Extras
  • Provider packages
  • Developing community managed provider packages
  • Documentation for the community managed providers
  • Testing the community managed providers
• Dependency management
• Pinned constraint files
• Documentation
• Static code checks
• Coding style and best practices
  • Don't Use Asserts Outside Tests
  • Database Session Handling
  • Don't use time() for duration calculations
  • Naming Conventions for provider packages
• Test Infrastructure
• Metadata Database Updates
• Node.js Environment Setup
  • Installing yarn and its packages manually
  • Generate Bundled Files with yarn
  • Follow Style Guide
  • React, JSX and Chakra
• How to sync your fork
• How to rebase PR
  • Summary
• How to communicate
• Commit Policy
• Resources & Links

Contributions

Contributions are welcome and are greatly appreciated! Every little bit helps, and credit will always be given.

This document aims to explain the subject of contributions if you have not contributed to any Open Source project, but it will also help people who have contributed to other projects learn about the rules of that community.

New Contributor

If you are a new contributor, please follow the Contributors Quick Start guide to get a gentle step-by-step introduction to setting up the development environment and making your first contribution.

Get Mentoring Support

If you are new to the project, you might need some help in understanding how the dynamics of the community works and you might need to get some mentorship from other members of the community - mostly committers. Mentoring new members of the community is part of committers job so do not be afraid of asking committers to help you. You can do it via comments in your Pull Request, asking on a devlist or via Slack. For your convenience, we have a dedicated #development-first-pr-support Slack channel where you can ask any questions about making your first pull request contribution to the Airflow codebase - it's a safe space where it is expected that people asking questions do not know a lot Airflow (yet!). If you need help with Airflow see the Slack channel #troubleshooting.

To check on how mentoring works for the projects under Apache Software Foundation's Apache Community Development - Mentoring.

Report Bugs

Report bugs through GitHub.

Please report relevant information and preferably code that exhibits the problem.

Fix Bugs

Look through the GitHub issues for bugs. Anything is open to whoever wants to implement it.

Issue reporting and resolution process

An unusual element of the Apache Airflow project is that you can open a PR to fix an issue or make an enhancement, without needing to open an issue first. This is intended to make it as easy as possible to contribute to the project.

If you however feel the need to open an issue (usually a bug or feature request) consider starting with a GitHub Discussion instead. In the vast majority of cases discussions are better than issues - you should only open issues if you are sure you found a bug and have a reproducible case, or when you want to raise a feature request that will not require a lot of discussion. If you have a very important topic to discuss, start a discussion on the Devlist instead.

The Apache Airflow project uses a set of labels for tracking and triaging issues, as well as a set of priorities and milestones to track how and when the enhancements and bug fixes make it into an Airflow release. This is documented as part of the Issue reporting and resolution process,

Implement Features

Look through the GitHub issues labeled "kind:feature" for features.

Any unassigned feature request issue is open to whoever wants to implement it.

We've created the operators, hooks, macros and executors we needed, but we've made sure that this part of Airflow is extensible. New operators, hooks, macros and executors are very welcomed!

Improve Documentation

Airflow could always use better documentation, whether as part of the official Airflow docs, in docstrings, docs/*.rst or even on the web as blog posts or articles.

Submit Feedback

The best way to send feedback is to open an issue on GitHub.

If you are proposing a new feature:

• Explain in detail how it would work.
• Keep the scope as narrow as possible to make it easier to implement.
• Remember that this is a volunteer-driven project, and that contributions are welcome :)

Roles

There are several roles within the Airflow Open-Source community.

For detailed information for each role, see: Committers and PMC's.

PMC Member

The PMC (Project Management Committee) is a group of maintainers that drives changes in the way that Airflow is managed as a project.

Considering Apache, the role of the PMC is primarily to ensure that Airflow conforms to Apache's processes and guidelines.

Committers/Maintainers

Committers are community members that have write access to the project's repositories, i.e., they can modify the code, documentation, and website by themselves and also accept other contributions.

The official list of committers can be found here.

Additionally, committers are listed in a few other places (some of these may only be visible to existing committers):

• https://whimsy.apache.org/roster/committee/airflow
• https://github.com/orgs/apache/teams/airflow-committers/members

Committers are responsible for:

• Championing one or more items on the Roadmap
• Reviewing & Merging Pull-Requests
• Scanning and responding to GitHub issues
• Responding to questions on the dev mailing list ([email protected])

Contributors

A contributor is anyone who wants to contribute code, documentation, tests, ideas, or anything to the Apache Airflow project.

Contributors are responsible for:

• Fixing bugs
• Adding features
• Championing one or more items on the Roadmap.

Security Team

Security issues in Airflow are handled by the Airflow Security Team. The team consists of selected PMC members that are interested in looking at, discussing and fixing security issues, but it can also include committers and non-committer contributors that are not PMC members yet and have been approved by the PMC members in a vote. You can request to be added to the team by sending a message to [email protected]. However, the team should be small and focused on solving security issues, so the requests will be evaluated on a case-by-case basis and the team size will be kept relatively small, limited to only actively security-focused contributors.

There are certain expectations from the members of the security team:

• They are supposed to be active in assessing, discussing, fixing and releasing the security issues in Airflow. While it is perfectly understood that as volunteers, we might have periods of lower activity, prolonged lack of activity and participation will result in removal from the team, pending PMC decision (the decision on removal can be taken by LAZY CONSENSUS among all the PMC members on [email protected] mailing list).
• They are not supposed to reveal the information about pending and unfixed security issues to anyone (including their employers) unless specifically authorised by the security team members, specifically if diagnosing and solving the issue might involve the need of external experts - for example security experts that are available through Airflow stakeholders. The intent about involving 3rd parties has to be discussed and agreed upon at [email protected].
• They have to have an ICLA signed with Apache Software Foundation.
• The security team members might inform 3rd parties about fixes, for example in order to assess if the fix is solving the problem or in order to assess its applicability to be applied by 3rd parties, as soon as a PR solving the issue is opened in the public airflow repository.
• In case of critical security issues, the members of the security team might iterate on a fix in a private repository and only open the PR in the public repository once the fix is ready to be released, with the intent of minimizing the time between the fix being available and the fix being released. In this case the PR might be sent to review and comment to the PMC members on private list, in order to request an expedited voting on the release. The voting for such release might be done on the [email protected] mailing list and should be made public at the [email protected] mailing list as soon as the release is ready to be announced.
• The security team members working on the fix might be mentioned as remediation developers in the CVE including their job affiliation if they want to.
• Community members acting as release managers are by default members of the security team and unless they want to, they do not have to be involved in discussing and solving the issues. They are responsible for releasing the CVE information (announcement and publishing to security indexes) as part of the release process. This is facilitated by the security tool provided by the Apache Software Foundation.
• Severity of the issue is determined based on the criteria described in the Severity Rating blog post by the Apache Software Foundation Security team.

Contribution Workflow

Typically, you start your first contribution by reviewing open tickets at GitHub issues.

If you create pull-request, you don't have to create an issue first, but if you want, you can do it. Creating an issue will allow you to collect feedback or share plans with other people.

For example, you want to have the following sample ticket assigned to you: #7782: Add extra CC: to the emails sent by Airflow.

In general, your contribution includes the following stages:

1. Make your own fork of the Apache Airflow main repository.
2. Create a local virtualenv, initialize the Breeze environment, and install pre-commit framework. If you want to add more changes in the future, set up your fork and enable GitHub Actions.
3. Join devlist and set up a Slack account.
4. Make the change and create a Pull Request from your fork.
5. Ping @ #development slack, comment @people. Be annoying. Be considerate.

Step 1: Fork the Apache Airflow Repo

From the apache/airflow repo, create a fork:

Step 2: Configure Your Environment

You can use several development environments for Airflow. If you prefer to have development environments on your local machine, you might choose Local Virtualenv, or dockerized Breeze environment, however we also have support for popular remote development environments: GitHub Codespaces and GitPodify. You can see the differences between the various environments here.

The local env instructions can be found in full in the LOCAL_VIRTUALENV.rst file.

The Breeze Docker Compose env is to maintain a consistent and common development environment so that you can replicate CI failures locally and work on solving them locally rather by pushing to CI.

The Breeze instructions can be found in full in the BREEZE.rst file.

You can configure the Docker-based Breeze development environment as follows:

1. Install the latest versions of the Docker Community Edition and Docker Compose and add them to the PATH.
2. Install jq on your machine. The exact command depends on the operating system (or Linux distribution) you use.

For example, on Ubuntu:

sudo apt install jq

or on macOS with Homebrew

brew install jq

3. Enter Breeze, and run the following in the Airflow source code directory:

breeze

Breeze starts with downloading the Airflow CI image from the Docker Hub and installing all required dependencies.

This will enter the Docker environment and mount your local sources to make them immediately visible in the environment.

4. Create a local virtualenv, for example:

mkvirtualenv myenv --python=python3.9

5. Initialize the created environment:

./scripts/tools/initialize_virtualenv.py

6. Open your IDE (for example, PyCharm) and select the virtualenv you created as the project's default virtualenv in your IDE.

Step 3: Connect with People

For effective collaboration, make sure to join the following Airflow groups:

• Mailing lists:
  • Developer's mailing list mailto:[email protected] (quite substantial traffic on this list)
  • All commits mailing list: mailto:[email protected] (very high traffic on this list)
  • Airflow users mailing list: mailto:[email protected] (reasonably small traffic on this list)
• Issues on GitHub
• Slack (chat)

Step 4: Prepare PR

1. Update the local sources to address the issue.

   For example, to address this example issue, do the following:

   • Read about email configuration in Airflow.

   • Find the class you should modify. For the example GitHub issue, this is email.py.

   • Find the test class where you should add tests. For the example ticket, this is test_email.py.

   • Make sure your fork's main is synced with Apache Airflow's main before you create a branch. See How to sync your fork for details.

   • Create a local branch for your development. Make sure to use latest apache/main as base for the branch. See How to Rebase PR for some details on setting up the apache remote. Note, some people develop their changes directly in their own main branches - this is OK and you can make PR from your main to apache/main but we recommend to always create a local branch for your development. This allows you to easily compare changes, have several changes that you work on at the same time and many more. If you have apache set as remote then you can make sure that you have latest changes in your main by git pull apache main when you are in the local main branch. If you have conflicts and want to override your locally changed main you can override your local changes with git fetch apache; git reset --hard apache/main.

   • Modify the class and add necessary code and unit tests.

   • Run the unit tests from the IDE or local virtualenv as you see fit.

   • Run the tests in Breeze.

   • Run and fix all the static checks. If you have pre-commits installed, this step is automatically run while you are committing your code. If not, you can do it manually via git add and then pre-commit run.

   • Consider adding a newsfragment to your PR so you can add an entry in the release notes. The following newsfragment types are supported:

     • significant
     • feature
     • improvement
     • bugfix
     • doc
     • misc

     To add a newsfragment, create an rst file named {pr_number}.{type}.rst (e.g. 1234.bugfix.rst) and place in either newsfragments for core newsfragments, or chart/newsfragments for helm chart newsfragments.

     In general newsfragments must be one line. For newsfragment type significant, you may include summary and body separated by a blank line, similar to git commit messages.

2. Rebase your fork, squash commits, and resolve all conflicts. See How to rebase PR if you need help with rebasing your change. Remember to rebase often if your PR takes a lot of time to review/fix. This will make rebase process much easier and less painful and the more often you do it, the more comfortable you will feel doing it.

3. Re-run static code checks again.

4. Make sure your commit has a good title and description of the context of your change, enough for the committer reviewing it to understand why you are proposing a change. Make sure to follow other PR guidelines described in pull request guidelines. Create Pull Request! Make yourself ready for the discussion!

Step 5: Pass PR Review

Note that committers will use Squash and Merge instead of Rebase and Merge when merging PRs and your commit will be squashed to single commit.

You need to have review of at least one committer (if you are committer yourself, it has to be another committer). Ideally you should have 2 or more committers reviewing the code that touches the core of Airflow.

Pull Request Guidelines

Before you submit a pull request (PR) from your forked repo, check that it meets these guidelines:

• Include tests, either as doctests, unit tests, or both, to your pull request.

  The airflow repo uses GitHub Actions to run the tests and codecov to track coverage. You can set up both for free on your fork. It will help you make sure you do not break the build with your PR and that you help increase coverage.

• Follow our project's Coding style and best practices.

  These are things that aren't currently enforced programmatically (either because they are too hard or just not yet done.)

• Rebase your fork, and resolve all conflicts.

• When merging PRs, Committer will use Squash and Merge which means then your PR will be merged as one commit, regardless of the number of commits in your PR. During the review cycle, you can keep a commit history for easier review, but if you need to, you can also squash all commits to reduce the maintenance burden during rebase.

• Add an Apache License header to all new files.

  If you have pre-commit hooks enabled, they automatically add license headers during commit.

• If your pull request adds functionality, make sure to update the docs as part of the same PR. Doc string is often sufficient. Make sure to follow the Sphinx compatible standards.

• Make sure your code fulfills all the static code checks we have in our code. The easiest way to make sure of that is to use pre-commit hooks

• Run tests locally before opening PR.

• You can use any supported python version to run the tests, but the best is to check if it works for the oldest supported version (Python 3.8 currently). In rare cases tests might fail with the oldest version when you use features that are available in newer Python versions. For that purpose we have airflow.compat package where we keep back-ported useful features from newer versions.

• Adhere to guidelines for commit messages described in this article. This makes the lives of those who come after you a lot easier.

Airflow Git Branches

All new development in Airflow happens in the main branch. All PRs should target that branch.

We also have a v2-*-test branches that are used to test 2.*.x series of Airflow and where committers cherry-pick selected commits from the main branch.

Cherry-picking is done with the -x flag.

The v2-*-test branch might be broken at times during testing. Expect force-pushes there so committers should coordinate between themselves on who is working on the v2-*-test branch - usually these are developers with the release manager permissions.

The v2-*-stable branch is rather stable - there are minimum changes coming from approved PRs that passed the tests. This means that the branch is rather, well, "stable".

Once the v2-*-test branch stabilises, the v2-*-stable branch is synchronized with v2-*-test. The v2-*-stable branches are used to release 2.*.x releases.

The general approach is that cherry-picking a commit that has already had a PR and unit tests run against main is done to v2-*-test branches, but PRs from contributors towards 2.0 should target v2-*-stable branches.

The v2-*-test branches and v2-*-stable ones are merged just before the release and that's the time when they converge.

The production images are released in DockerHub from:

• main branch for development
• 2.*.*, 2.*.*rc* releases from the v2-*-stable branch when we prepare release candidates and final releases.

Development Environments

There are two environments, available on Linux and macOS, that you can use to develop Apache Airflow:

• Local virtualenv development environment that supports running unit tests and can be used in your IDE.
• Breeze Docker-based development environment that provides an end-to-end CI solution with all software dependencies covered.

The table below summarizes differences between the environments:

Note

Only pip installation is currently officially supported.

While there are some successes with using other 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 appropriate format and workflow that your tool requires.

Extras

There are a number of extras that can be specified when installing Airflow. Those extras can be specified after the usual pip install - for example pip install -e .[ssh]. For development purpose there is a devel extra that installs all development dependencies. There is also devel_ci that installs all dependencies needed in the CI environment.

This is the full list of those extras:

aiobotocore, airbyte, alibaba, all, all_dbs, amazon, apache.atlas, apache.beam, apache.cassandra, apache.drill, apache.druid, apache.flink, apache.hdfs, apache.hive, apache.impala, apache.kafka, apache.kylin, apache.livy, apache.pig, apache.pinot, apache.spark, apache.sqoop, apache.webhdfs, apprise, arangodb, asana, async, atlas, atlassian.jira, aws, azure, cassandra, celery, cgroups, cloudant, cncf.kubernetes, cohere, common.io, common.sql, crypto, dask, daskexecutor, databricks, datadog, dbt.cloud, deprecated_api, devel, devel_all, devel_ci, devel_hadoop, dingding, discord, doc, doc_gen, docker, druid, elasticsearch, exasol, facebook, ftp, gcp, gcp_api, github, github_enterprise, google, google_auth, grpc, hashicorp, hdfs, hive, http, imap, influxdb, jdbc, jenkins, kerberos, kubernetes, ldap, leveldb, microsoft.azure, microsoft.mssql, microsoft.psrp, microsoft.winrm, mongo, mssql, mysql, neo4j, odbc, openai, openfaas, openlineage, opensearch, opsgenie, oracle, otel, pagerduty, pandas, papermill, password, pgvector, pinecone, pinot, plexus, postgres, presto, rabbitmq, redis, s3, s3fs, salesforce, samba, saml, segment, sendgrid, sentry, sftp, singularity, slack, smtp, snowflake, spark, sqlite, ssh, statsd, tableau, tabular, telegram, trino, vertica, virtualenv, weaviate, webhdfs, winrm, yandex, zendesk

Provider packages

Airflow 2.0 is split into core and providers. They are delivered as separate packages:

• apache-airflow - core of Apache Airflow
• apache-airflow-providers-* - More than 70 provider packages to communicate with external services

The information/meta-data about the providers is kept in provider.yaml file in the right sub-directory of airflow\providers. This file contains:

• package name (apache-airflow-provider-*)
• user-facing name of the provider package
• description of the package that is available in the documentation
• list of versions of package that have been released so far
• list of dependencies of the provider package
• list of additional-extras that the provider package provides (together with dependencies of those extras)
• list of integrations, operators, hooks, sensors, transfers provided by the provider (useful for documentation generation)
• list of connection types, extra-links, secret backends, auth backends, and logging handlers (useful to both register them as they are needed by Airflow and to include them in documentation automatically).

If you want to add dependencies to the provider, you should add them to the corresponding provider.yaml and Airflow pre-commits and package generation commands will use them when preparing package information.

In Airflow 1.10 all those providers were installed together within one single package and when you installed airflow locally, from sources, they were also installed. In Airflow 2.0, providers are separated out, and not packaged together with the core, unless you set INSTALL_PROVIDERS_FROM_SOURCES environment variable to true.

In Breeze - which is a development environment, INSTALL_PROVIDERS_FROM_SOURCES variable is set to true, but you can add --install-providers-from-sources=false flag to Breeze to install providers from PyPI instead of source files when building the images.

One watch-out - providers are still always installed (or rather available) if you install airflow from sources using -e (or --editable) flag. In such case airflow is read directly from the sources without copying airflow packages to the usual installation location, and since 'providers' folder is in this airflow folder - the providers package is importable.

Some of the packages have cross-dependencies with other providers packages. This typically happens for transfer operators where operators use hooks from the other providers in case they are transferring data between the providers. The list of dependencies is maintained (automatically with pre-commits) in the generated/provider_dependencies.json. Pre-commits are also used to generate dependencies. The dependency list is automatically used during PyPI packages generation.

Cross-dependencies between provider packages are converted into extras - if you need functionality from the other provider package you can install it adding [extra] after the apache-airflow-providers-PROVIDER for example: pip install apache-airflow-providers-google[amazon] in case you want to use GCP transfer operators from Amazon ECS.

If you add a new dependency between different providers packages, it will be detected automatically during and pre-commit will generate new entry in generated/provider_dependencies.json so that the package extra dependencies are properly handled when package is installed.

Developing community managed provider packages

While you can develop your own providers, Apache Airflow has 60+ providers that are managed by the community. They are part of the same repository as Apache Airflow (we use monorepo approach where different parts of the system are developed in the same repository but then they are packaged and released separately). All the community-managed providers are in 'airflow/providers' folder and they are all sub-packages of 'airflow.providers' package. All the providers are available as apache-airflow-providers-<PROVIDER_ID> packages.

The capabilities of the community-managed providers are the same as the third-party ones. When the providers are installed from PyPI, they provide the entry-point containing the metadata as described in the previous chapter. However when they are locally developed, together with Airflow, the mechanism of discovery of the providers is based on provider.yaml file that is placed in the top-folder of the provider. Similarly as in case of the provider.yaml file is compliant with the json-schema specification. Thanks to that mechanism, you can develop community managed providers in a seamless way directly from Airflow sources, without preparing and releasing them as packages. This is achieved by:

• When Airflow is installed locally in editable mode (pip install -e) the provider packages installed from PyPI are uninstalled and the provider discovery mechanism finds the providers in the Airflow sources by searching for provider.yaml files.
• When you want to install Airflow from sources you can set INSTALL_PROVIDERS_FROM_SOURCES variable to true and then the providers will not be installed from PyPI packages, but they will be installed from local sources as part of the apache-airflow package, but additionally the provider.yaml files are copied together with the sources, so that capabilities and names of the providers can be discovered. This mode is especially useful when you are developing a new provider, that cannot be installed from PyPI and you want to check if it installs cleanly.

Regardless if you plan to contribute your provider, when you are developing your own, custom providers, you can use the above functionality to make your development easier. You can add your provider as a sub-folder of the airflow.providers package, add the provider.yaml file and install airflow in development mode - then capabilities of your provider will be discovered by airflow and you will see the provider among other providers in airflow providers command output.

Documentation for the community managed providers

When you are developing a community-managed provider, you are supposed to make sure it is well tested and documented. Part of the documentation is provider.yaml file integration information and version information. This information is stripped-out from provider info available at runtime, however it is used to automatically generate documentation for the provider.

If you have pre-commits installed, pre-commit will warn you and let you know what changes need to be done in the provider.yaml file when you add a new Operator, Hooks, Sensor or Transfer. You can also take a look at the other provider.yaml files as examples.

Well documented provider contains those:

• index.rst with references to packages, API used and example dags
• configuration reference
• class documentation generated from PyDoc in the code
• example dags
• how-to guides

You can see for example google provider which has very comprehensive documentation:

• Documentation
• Example DAGs

Part of the documentation are example dags. We are using the example dags for various purposes in providers:

• showing real examples of how your provider classes (Operators/Sensors/Transfers) can be used
• snippets of the examples are embedded in the documentation via exampleinclude:: directive
• examples are executable as system tests

Testing the community managed providers

We have high requirements when it comes to testing the community managed providers. We have to be sure that we have enough coverage and ways to tests for regressions before the community accepts such providers.

• Unit tests have to be comprehensive and they should tests for possible regressions and edge cases not only "green path"
• Integration tests where 'local' integration with a component is possible (for example tests with MySQL/Postgres DB/Trino/Kerberos all have integration tests which run with real, dockerized components
• System Tests which provide end-to-end testing, usually testing together several operators, sensors, transfers connecting to a real external system

You can read more about out approach for tests in TESTING.rst but here are some highlights.

Dependency management

Airflow is not a standard python project. Most of the python projects fall into one of two types - application or library. As described in this StackOverflow question, the decision whether to pin (freeze) dependency versions for a python project depends on the type. For applications, dependencies should be pinned, but for libraries, they should be open.

For application, pinning the dependencies makes it more stable to install in the future - because new (even transitive) dependencies might cause installation to fail. For libraries - the dependencies should be open to allow several different libraries with the same requirements to be installed at the same time.

The problem is that Apache Airflow is a bit of both - application to install and library to be used when you are developing your own operators and DAGs.

This - seemingly unsolvable - puzzle is solved by having pinned constraints files. Those are available as of airflow 1.10.10 and further improved with 1.10.12 (moved to separate orphan branches)

Pinned constraint files

Note

Only pip installation is 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 appropriate format and workflow that your tool requires.

By default when you install apache-airflow package - the dependencies are as open as possible while still allowing the apache-airflow package to install. This means that apache-airflow package might fail to install in case a direct or transitive dependency is released that breaks the installation. In such case when installing apache-airflow, you might need to provide additional constraints (for example pip install apache-airflow==1.10.2 Werkzeug<1.0.0)

There are several sets of constraints we keep:

• 'constraints' - those are constraints generated by matching the current airflow version from sources

  and providers that are installed from PyPI. Those are constraints used by the users who want to install airflow with pip, they are named constraints-<PYTHON_MAJOR_MINOR_VERSION>.txt.

• "constraints-source-providers" - those are constraints generated by using providers installed from current sources. While adding new providers their dependencies might change, so this set of providers is the current set of the constraints for airflow and providers from the current main sources. Those providers are used by CI system to keep "stable" set of constraints. They are named constraints-source-providers-<PYTHON_MAJOR_MINOR_VERSION>.txt
• "constraints-no-providers" - those are constraints generated from only Apache Airflow, without any providers. If you want to manage airflow separately and then add providers individually, you can use those. Those constraints are named constraints-no-providers-<PYTHON_MAJOR_MINOR_VERSION>.txt.

The first two can be used as constraints file when installing Apache Airflow in a repeatable way. It can be done from the sources:

from the PyPI package:

pip install apache-airflow[google,amazon,async]==2.2.5 \
  --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-2.2.5/constraints-3.8.txt"

The last one can be used to install Airflow in "minimal" mode - i.e when bare Airflow is installed without extras.

When you install airflow from sources (in editable mode) you should use "constraints-source-providers" instead (this accounts for the case when some providers have not yet been released and have conflicting requirements).

pip install -e . \
  --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-main/constraints-source-providers-3.8.txt"

This works also with extras - for example:

pip install ".[ssh]" \
  --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-main/constraints-source-providers-3.8.txt"

There are different set of fixed constraint files for different python major/minor versions and you should use the right file for the right python version.

If you want to update just airflow dependencies, without paying attention to providers, you can do it using constraints-no-providers constraint files as well.

pip install . --upgrade \
  --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-main/constraints-no-providers-3.8.txt"

The constraints-<PYTHON_MAJOR_MINOR_VERSION>.txt and constraints-no-providers-<PYTHON_MAJOR_MINOR_VERSION>.txt will be automatically regenerated by CI job every time after the setup.py is updated and pushed if the tests are successful.

Documentation

Documentation for apache-airflow package and other packages that are closely related to it ie. providers packages are in /docs/ directory. For detailed information on documentation development, see: docs/README.rst

Static code checks

We check our code quality via static code checks. See STATIC_CODE_CHECKS.rst for details.

Your code must pass all the static code checks in the CI in order to be eligible for Code Review. The easiest way to make sure your code is good before pushing is to use pre-commit checks locally as described in the static code checks documentation.

Coding style and best practices

Most of our coding style rules are enforced programmatically by ruff and mypy (which are run automatically on every pull request), but there are some rules that are not yet automated and are more Airflow specific or semantic than style

Don't Use Asserts Outside Tests

Our community agreed that to various reasons we do not use assert in production code of Apache Airflow. For details check the relevant mailing list thread.

In other words instead of doing:

assert some_predicate()

you should do:

if not some_predicate():
    handle_the_case()

The one exception to this is if you need to make an assert for typechecking (which should be almost a last resort) you can do this:

if TYPE_CHECKING:
    assert isinstance(x, MyClass)

Database Session Handling

Explicit is better than implicit. If a function accepts a session parameter it should not commit the transaction itself. Session management is up to the caller.

To make this easier, there is the create_session helper:

from sqlalchemy.orm import Session

from airflow.utils.session import create_session


def my_call(x, y, *, session: Session):
    ...
    # You MUST not commit the session here.


with create_session() as session:
    my_call(x, y, session=session)

Warning

DO NOT add a default to the session argument unless @provide_session is used.

If this function is designed to be called by "end-users" (i.e. DAG authors) then using the @provide_session wrapper is okay:

from sqlalchemy.orm import Session

from airflow.utils.session import NEW_SESSION, provide_session


@provide_session
def my_method(arg, *, session: Session = NEW_SESSION):
    ...
    # You SHOULD not commit the session here. The wrapper will take care of commit()/rollback() if exception

In both cases, the session argument is a keyword-only argument. This is the most preferred form if possible, although there are some exceptions in the code base where this cannot be used, due to backward compatibility considerations. In most cases, session argument should be last in the argument list.

Don't use time() for duration calculations

If you wish to compute the time difference between two events with in the same process, use time.monotonic(), not time.time() nor timezone.utcnow().

If you are measuring duration for performance reasons, then time.perf_counter() should be used. (On many platforms, this uses the same underlying clock mechanism as monotonic, but perf_counter is guaranteed to be the highest accuracy clock on the system, monotonic is simply "guaranteed" to not go backwards.)

If you wish to time how long a block of code takes, use Stats.timer() -- either with a metric name, which will be timed and submitted automatically:

from airflow.stats import Stats

...

with Stats.timer("my_timer_metric"):
    ...

or to time but not send a metric:

from airflow.stats import Stats

...

with Stats.timer() as timer:
    ...

log.info("Code took %.3f seconds", timer.duration)

For full docs on timer() check out `airflow/stats.py`_.

If the start_date of a duration calculation needs to be stored in a database, then this has to be done using datetime objects. In all other cases, using datetime for duration calculation MUST be avoided as creating and diffing datetime operations are (comparatively) slow.

Naming Conventions for provider packages

In Airflow 2.0 we standardized and enforced naming for provider packages, modules and classes. those rules (introduced as AIP-21) were not only introduced but enforced using automated checks that verify if the naming conventions are followed. Here is a brief summary of the rules, for detailed discussion you can go to AIP-21 Changes in import paths

The rules are as follows:

• Provider packages are all placed in 'airflow.providers'
• Providers are usually direct sub-packages of the 'airflow.providers' package but in some cases they can be further split into sub-packages (for example 'apache' package has 'cassandra', 'druid' ... providers ) out of which several different provider packages are produced (apache.cassandra, apache.druid). This is case when the providers are connected under common umbrella but very loosely coupled on the code level.
• In some cases the package can have sub-packages but they are all delivered as single provider package (for example 'google' package contains 'ads', 'cloud' etc. sub-packages). This is in case the providers are connected under common umbrella and they are also tightly coupled on the code level.

• Typical structure of provider package:

  • example_dags -> example DAGs are stored here (used for documentation and System Tests)
  • hooks -> hooks are stored here
  • operators -> operators are stored here
  • sensors -> sensors are stored here
  • secrets -> secret backends are stored here
  • transfers -> transfer operators are stored here

• Module names do not contain word "hooks", "operators" etc. The right type comes from the package. For example 'hooks.datastore' module contains DataStore hook and 'operators.datastore' contains DataStore operators.
• Class names contain 'Operator', 'Hook', 'Sensor' - for example DataStoreHook, DataStoreExportOperator
• Operator name usually follows the convention: <Subject><Action><Entity>Operator (BigQueryExecuteQueryOperator) is a good example
• Transfer Operators are those that actively push data from one service/provider and send it to another service (might be for the same or another provider). This usually involves two hooks. The convention for those <Source>To<Destination>Operator. They are not named *TransferOperator nor *Transfer.
• Operators that use external service to perform transfer (for example CloudDataTransferService operators are not placed in "transfers" package and do not have to follow the naming convention for transfer operators.
• It is often debatable where to put transfer operators but we agreed to the following criteria:
  • We use "maintainability" of the operators as the main criteria - so the transfer operator should be kept at the provider which has highest "interest" in the transfer operator
  • For Cloud Providers or Service providers that usually means that the transfer operators should land at the "target" side of the transfer
• Secret Backend name follows the convention: <SecretEngine>Backend.
• Tests are grouped in parallel packages under "tests.providers" top level package. Module name is usually test_<object_to_test>.py,
• System tests (not yet fully automated but allowing to run e2e testing of particular provider) are named with _system.py suffix.

Test Infrastructure

We support the following types of tests:

• Unit tests are Python tests launched with pytest. Unit tests are available both in the Breeze environment and local virtualenv.
• Integration tests are available in the Breeze development environment that is also used for Airflow's CI tests. Integration test are special tests that require additional services running, such as Postgres, Mysql, Kerberos, etc.
• System tests are automatic tests that use external systems like Google Cloud. These tests are intended for an end-to-end DAG execution.

For details on running different types of Airflow tests, see TESTING.rst.

Metadata Database Updates

When developing features, you may need to persist information to the metadata database. Airflow has Alembic built-in module to handle all schema changes. Alembic must be installed on your development machine before continuing with migration.

# starting at the root of the project
$ pwd
~/airflow
# change to the airflow directory
$ cd airflow
$ alembic revision -m "add new field to db"
   Generating
~/airflow/airflow/migrations/versions/a1e23c41f123_add_new_field_to_db.py

Note that migration file names are standardized by pre-commit hook update-migration-references, so that they sort alphabetically and indicate the Airflow version in which they first appear (the alembic revision ID is removed). As a result you should expect to see a pre-commit failure on the first attempt. Just stage the modified file and commit again (or run the hook manually before committing).

After your new migration file is run through pre-commit it will look like this:

1234_A_B_C_add_new_field_to_db.py

This represents that your migration is the 1234th migration and expected for release in Airflow version A.B.C.

Node.js Environment Setup

airflow/www/ contains all yarn-managed, front-end assets. Flask-Appbuilder itself comes bundled with jQuery and bootstrap. While they may be phased out over time, these packages are currently not managed with yarn.

Make sure you are using recent versions of node and yarn. No problems have been found with node>=8.11.3 and yarn>=1.19.1. The pre-commit framework of ours install node and yarn automatically when installed - if you use breeze you do not need to install neither node nor yarn.

Installing yarn and its packages manually

To install yarn on macOS:

1. Run the following commands (taken from this source):

brew install node
brew install yarn
yarn config set prefix ~/.yarn

2. Add ~/.yarn/bin to your PATH so that commands you are installing could be used globally.
3. Set up your .bashrc file and then source ~/.bashrc to reflect the change.

export PATH="$HOME/.yarn/bin:$PATH"

4. Install third-party libraries defined in package.json by running the following command

yarn install

Generate Bundled Files with yarn

To parse and generate bundled files for Airflow, run either of the following commands:

# Compiles the production / optimized js & css
yarn run prod

# Starts a web server that manages and updates your assets as you modify them
# You'll need to run the webserver in debug mode too: ``airflow webserver -d``
yarn run dev

Follow Style Guide

We try to enforce a more consistent style and follow the Javascript/Typescript community guidelines.

Once you add or modify any JS/TS code in the project, please make sure it follows the guidelines defined in Airbnb JavaScript Style Guide.

Apache Airflow uses ESLint as a tool for identifying and reporting issues in JS/TS, and Prettier for code formatting. Most IDE directly integrate with these tools, you can also manually run them with any of the following commands:

# Format code in .js, .jsx, .ts, .tsx, .json, .css, .html files
yarn format

# Check JS/TS code in .js, .jsx, .ts, .tsx, .html files and report any errors/warnings
yarn run lint

# Check JS/TS code in .js, .jsx, .ts, .tsx, .html files and report any errors/warnings and fix them if possible
yarn run lint:fix

# Run tests for all .test.js, .test.jsx, .test.ts, test.tsx files
yarn test

React, JSX and Chakra

In order to create a more modern UI, we have started to include React in the airflow/www/ project. If you are unfamiliar with React then it is recommended to check out their documentation to understand components and jsx syntax.

We are using Chakra UI as a component and styling library. Notably, all styling is done in a theme file or inline when defining a component. There are a few shorthand style props like px instead of padding-right, padding-left. To make this work, all Chakra styling and css styling are completely separate. It is best to think of the React components as a separate app that lives inside of the main app.

How to sync your fork

When you have your fork, you should periodically synchronize the main of your fork with the Apache Airflow main. In order to do that you can git pull --rebase to your local git repository from apache remote and push the main (often with --force to your fork). There is also an easy way to sync your fork in GitHub's web UI with the Fetch upstream feature.

This will force-push the main branch from apache/airflow to the main branch in your fork. Note that in case you modified the main in your fork, you might loose those changes.

How to rebase PR

A lot of people are unfamiliar with the rebase workflow in Git, but we think it is an excellent workflow, providing a better alternative to the merge workflow. We've therefore written a short guide for those who would like to learn it.

As of February 2022, GitHub introduced the capability of "Update with Rebase" which make it easy to perform rebase straight in the GitHub UI, so in cases when there are no conflicts, rebasing to latest version of main can be done very easily following the instructions in the GitHub blog

However, when you have conflicts, sometimes you will have to perform rebase manually, and resolve the conflicts, and remainder of the section describes how to approach it.

As opposed to the merge workflow, the rebase workflow allows us to clearly separate your changes from the changes of others. It puts the responsibility of rebasing on the author of the change. It also produces a "single-line" series of commits on the main branch. This makes it easier to understand what was going on and to find reasons for problems (it is especially useful for "bisecting" when looking for a commit that introduced some bugs).

First of all, we suggest you read about the rebase workflow here: Merging vs. rebasing. This is an excellent article that describes all the ins/outs of the rebase workflow. I recommend keeping it for future reference.

The goal of rebasing your PR on top of apache/main is to "transplant" your change on top of the latest changes that are merged by others. It also allows you to fix all the conflicts that arise as a result of other people changing the same files as you and merging the changes to apache/main.

Here is how rebase looks in practice (you can find a summary below these detailed steps):

1. You first need to add the Apache project remote to your git repository. This is only necessary once, so if it's not the first time you are following this tutorial you can skip this step. In this example, we will be adding the remote as "apache" so you can refer to it easily:

• If you use ssh: git remote add apache [email protected]:apache/airflow.git
• If you use https: git remote add apache https://github.com/apache/airflow.git

2. You then need to make sure that you have the latest main fetched from the apache repository. You can do this via:

   git fetch apache (to fetch apache remote)

   git fetch --all (to fetch all remotes)

3. Assuming that your feature is in a branch in your repository called my-branch you can easily check what is the base commit you should rebase from by:

   git merge-base my-branch apache/main

   This will print the HASH of the base commit which you should use to rebase your feature from. For example: 5abce471e0690c6b8d06ca25685b0845c5fd270f. Copy that HASH and go to the next step.

   Optionally, if you want better control you can also find this commit hash manually.

   Run:

   git log

   And find the first commit that you DO NOT want to "transplant".

   Performing:

   git rebase HASH

   Will "transplant" all commits after the commit with the HASH.

4. Providing that you weren't already working on your branch, check out your feature branch locally via:

   git checkout my-branch

5. Rebase:

   git rebase HASH --onto apache/main

   For example:

   git rebase 5abce471e0690c6b8d06ca25685b0845c5fd270f --onto apache/main

6. If you have no conflicts - that's cool. You rebased. You can now run git push --force-with-lease to push your changes to your repository. That should trigger the build in our CI if you have a Pull Request (PR) opened already.

7. While rebasing you might have conflicts. Read carefully what git tells you when it prints information about the conflicts. You need to solve the conflicts manually. This is sometimes the most difficult part and requires deliberately correcting your code and looking at what has changed since you developed your changes.

   There are various tools that can help you with this. You can use:

   git mergetool

   You can configure different merge tools with it. You can also use IntelliJ/PyCharm's excellent merge tool. When you open a project in PyCharm which has conflicts, you can go to VCS > Git > Resolve Conflicts and there you have a very intuitive and helpful merge tool. For more information, see Resolve conflicts.

8. After you've solved your conflict run:

   git rebase --continue

   And go either to point 6. or 7, depending on whether you have more commits that cause conflicts in your PR (rebasing applies each commit from your PR one-by-one).

Summary

Useful when you understand the flow but don't remember the steps and want a quick reference.

git fetch --all git merge-base my-branch apache/main git checkout my-branch git rebase HASH --onto apache/main git push --force-with-lease

How to communicate

Apache Airflow is a Community within Apache Software Foundation. As the motto of the Apache Software Foundation states "Community over Code" - people in the community are far more important than their contribution.

This means that communication plays a big role in it, and this chapter is all about it.

In our communication, everyone is expected to follow the ASF Code of Conduct.

We have various channels of communication - starting from the official devlist, comments in the Pull Requests, Slack, wiki.

All those channels can be used for different purposes. You can join the channels via links at the Airflow Community page

• The Apache Airflow devlist for:

  • official communication
  • general issues, asking community for opinion
  • discussing proposals
  • voting

• The Airflow CWiki for:

  • detailed discussions on big proposals (Airflow Improvement Proposals also name AIPs)
  • helpful, shared resources (for example Apache Airflow logos
  • information that can be reused by others (for example instructions on preparing workshops)

• GitHub Pull Requests (PRs) for:

  • discussing implementation details of PRs
  • not for architectural discussions (use the devlist for that)

• The deprecated JIRA issues for:

  • checking out old but still valuable issues that are not on GitHub yet
  • mentioning the JIRA issue number in the title of the related PR you would like to open on GitHub

IMPORTANT We don't create new issues on JIRA anymore. The reason we still look at JIRA issues is that there are valuable tickets inside of it. However, each new PR should be created on GitHub issues as stated in Contribution Workflow Example

• The Apache Airflow Slack for:

  • ad-hoc questions related to development (#development channel)
  • asking for review (#development channel)
  • asking for help with first contribution PRs (#development-first-pr-support channel)
  • troubleshooting (#troubleshooting channel)
  • group talks (including SIG - special interest groups) (#sig-* channels)
  • notifications (#announcements channel)
  • random queries (#random channel)
  • regional announcements (#users-* channels)
  • occasional discussions (wherever appropriate including group and 1-1 discussions)

Please exercise caution against posting same questions across multiple channels. Doing so not only prevents redundancy but also promotes more efficient and effective communication for everyone involved.

The devlist is the most important and official communication channel. Often at Apache project you can hear "if it is not in the devlist - it did not happen". If you discuss and agree with someone from the community on something important for the community (including if it is with committer or PMC member) the discussion must be captured and reshared on devlist in order to give other members of the community to participate in it.

We are using certain prefixes for email subjects for different purposes. Start your email with one of those:

• [DISCUSS] - if you want to discuss something but you have no concrete proposal yet
• [PROPOSAL] - if usually after "[DISCUSS]" thread discussion you want to propose something and see what other members of the community think about it.
• [AIP-NN] - if the mail is about one of the Airflow Improvement Proposals
• [VOTE] - if you would like to start voting on a proposal discussed before in a "[PROPOSAL]" thread

Voting is governed by the rules described in Voting

We are all devoting our time for community as individuals who except for being active in Apache Airflow have families, daily jobs, right for vacation. Sometimes we are in different timezones or simply are busy with day-to-day duties that our response time might be delayed. For us it's crucial to remember to respect each other in the project with no formal structure. There are no managers, departments, most of us is autonomous in our opinions, decisions. All of it makes Apache Airflow community a great space for open discussion and mutual respect for various opinions.

Disagreements are expected, discussions might include strong opinions and contradicting statements. Sometimes you might get two committers asking you to do things differently. This all happened in the past and will continue to happen. As a community we have some mechanisms to facilitate discussion and come to a consensus, conclusions or we end up voting to make important decisions. It is important that these decisions are not treated as personal wins or looses. At the end it's the community that we all care about and what's good for community, should be accepted even if you have a different opinion. There is a nice motto that you should follow in case you disagree with community decision "Disagree but engage". Even if you do not agree with a community decision, you should follow it and embrace (but you are free to express your opinion that you don't agree with it).

As a community - we have high requirements for code quality. This is mainly because we are a distributed and loosely organised team. We have both - contributors that commit one commit only, and people who add more commits. It happens that some people assume informal "stewardship" over parts of code for some time - but at any time we should make sure that the code can be taken over by others, without excessive communication. Setting high requirements for the code (fairly strict code review, static code checks, requirements of automated tests, pre-commit checks) is the best way to achieve that - by only accepting good quality code. Thanks to full test coverage we can make sure that we will be able to work with the code in the future. So do not be surprised if you are asked to add more tests or make the code cleaner - this is for the sake of maintainability.

Here are a few rules that are important to keep in mind when you enter our community:

• Do not be afraid to ask questions
• The communication is asynchronous - do not expect immediate answers, ping others on slack (#development channel) if blocked
• There is a #newbie-questions channel in slack as a safe place to ask questions
• You can ask one of the committers to be a mentor for you, committers can guide within the community
• You can apply to more structured Apache Mentoring Programme
• It's your responsibility as an author to take your PR from start-to-end including leading communication in the PR
• It's your responsibility as an author to ping committers to review your PR - be mildly annoying sometimes, it's OK to be slightly annoying with your change - it is also a sign for committers that you care
• Be considerate to the high code quality/test coverage requirements for Apache Airflow
• If in doubt - ask the community for their opinion or propose to vote at the devlist
• Discussions should concern subject matters - judge or criticise the merit but never criticise people
• It's OK to express your own emotions while communicating - it helps other people to understand you
• Be considerate for feelings of others. Tell about how you feel not what you think of others

Commit Policy

The following commit policy passed by a vote 8(binding FOR) to 0 against on May 27, 2016 on the dev list and slightly modified and consensus reached in October 2020:

• Commits need a +1 vote from a committer who is not the author
• Do not merge a PR that regresses linting or does not pass CI tests (unless we have justification such as clearly transient error).
• When we do AIP voting, both PMC and committer +1s are considered as binding vote.

Resources & Links

• Airflow's official documentation
• More resources and links to Airflow related content on the Wiki