Hey fellow Airflowers, I am thrilled to announce the availability of Apache Airflow 3.0.0rc1 & *Task SDK 1.0.0rc1* for testing! Airflow 3.0 marks a significant milestone as the first major release in over four years, introducing improvements that enhance user experience, task execution, and system scalability.
This email is calling for a vote on the release, which will last at least 7 days until 10th April. and until 3 binding +1 votes have been received. Consider this my (non-binding) +1. Airflow 3.0.0rc1 is available at: https://dist.apache.org/repos/dist/dev/airflow/3.0.0rc1/ "apache-airflow" Meta package: - *apache-airflow-3.0.0-source.tar.gz* is a source release that comes with INSTALL instructions. - *apache-airflow-3.0.0.tar.gz* is the binary Python "sdist" release. - *apache_airflow-3.0.0-py3-none-any.whl* is the binary Python wheel "binary" release. "apache-airflow-core" package - *apache_airflow_core-3.0.0.tar.gz* is the binary Python "sdist" release. - *apache_airflow_3.0.0-py3-none-any.whl* is the binary Python wheel "binary" release. Task SDK 1.0.0rc1 is available at: https://dist.apache.org/repos/dist/dev/airflow/task-sdk/1.0.0rc1/ "apache-airflow-task-sdk" package - *apache-airflow-task-sdk-1.0.0-source.tar.gz* is a source release - *apache_airflow_task_sdk-1.0.0.tar.gz* is the binary Python "sdist" release. - *apache_airflow_task_sdk-1.0.0-py3-none-any.whl* is the binary Python wheel "binary" release. Public keys are available at: https://dist.apache.org/repos/dist/release/airflow/KEYS Please vote accordingly: [ ] +1 approve [ ] +0 no opinion [ ] -1 disapprove with the reason Only votes from PMC members are binding, but all members of the community are encouraged to test the release and vote with "(non-binding)". The test procedure for PMC members is described in: https://github.com/apache/airflow/blob/main/dev/README_RELEASE_AIRFLOW.md\#verify-the-release-candidate-by-pmc-members The test procedure for contributors and members of the community who would like to test this RC is described in: https://github.com/apache/airflow/blob/main/dev/README_RELEASE_AIRFLOW.md\#verify-the-release-candidate-by-contributors Please note that the version number excludes the 'rcX' string, so it's now simply 3.0.0 for Airflow package and 1.0.0 for Task SDK. This will allow us to rename the artifact without modifying the artifact checksums when we actually release. Release Notes: https://github.com/apache/airflow/blob/3.0.0rc1/RELEASE_NOTES.rst *Testing Instructions using PyPI*: You can build a virtualenv that installs this, and other required packages (e.g. task sdk), like this: ``` uv venv uv pip install apache-airflow apache-airflow-providers-standard==0.3.0rc1 --pre ``` Get Involved We encourage the community to test this release and report any issues or feedback. Your contributions help us ensure a stable and reliable Airflow 3.0.0 release. Please report issues using Github at https://github.com/apache/airflow/issues and mark that this is an issue in 3.0.0. For an updated list of all known issues in the beta can also be found in the above link with the label “affected_version:3.0.0rc” A huge thank you to all the contributors who have worked on this milestone release! Best, Kaxil --- What's new in 3.0.0? Notable Features DAG versioning & Bundles Airflow now tracks DAG versions, offering better visibility into historical DAG changes and execution states. The introduction of DAG Bundles ensures tasks run with the correct code version, even as DAGs evolve. Modern Web Application The UI has been rebuilt using React and a complete API-driven structure, improving maintainability and extensibility. It includes a new component-based design system and an enhanced information architecture. A new React-based plugin system supports custom widgets, improved workflow visibility, and integration with external tools. Task Execution Interface Airflow 3.0 adopts a client / server architecture, decoupling task execution from the internal meta-database via API-based interaction. This allows for remote execution across networks, multi-language support, enhanced security, and better dependency management. The Edge Executor further enables seamless remote task execution without direct database connections. Data Assets & Asset-Centric Syntax Airflow 3.0 enhances dataset management by introducing Data Assets, expanding beyond tables and files to include ML models and more. Assets can be explicitly defined using the @asset decorator, simplifying tracking and dependencies. External Event-Driven Scheduling Airflow now supports event-driven DAG triggers from external sources like message queues and blob stores. This builds upon dataset scheduling and enhances integration with the external data ecosystem.
