Supported versions¶
Version Life Cycle¶
Apache Airflow® version life cycle:
Version |
Current Patch/Minor |
State |
First Release |
Limited Support |
EOL/Terminated |
|---|---|---|---|---|---|
3 |
3.0.0 |
Supported |
Apr 22, 2025 |
TBD |
TBD |
2 |
2.10.5 |
Supported |
Dec 17, 2020 |
TBD |
TBD |
1.10 |
1.10.15 |
EOL |
Aug 27, 2018 |
Dec 17, 2020 |
June 17, 2021 |
1.9 |
1.9.0 |
EOL |
Jan 03, 2018 |
Aug 27, 2018 |
Aug 27, 2018 |
1.8 |
1.8.2 |
EOL |
Mar 19, 2017 |
Jan 03, 2018 |
Jan 03, 2018 |
1.7 |
1.7.1.2 |
EOL |
Mar 28, 2016 |
Mar 19, 2017 |
Mar 19, 2017 |
Limited support versions will be supported with security and critical bug fixes only. EOL versions will not get any fixes or support. We highly recommend installing the latest Airflow release which has richer features.
Support for Python and Kubernetes versions¶
For Airflow 2.0+ versions, 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.
We drop support for Python and Kubernetes versions when they reach EOL. We drop support for those EOL versions in main right after the 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 3.6 it means that we drop support in main right after 23.12.2021, and the first MAJOR or MINOR version of Airflow released after will not have it.
The “oldest” supported version of Python/Kubernetes is the default one. “Default” is only meaningful in terms of “smoke tests” in CI PRs which are run using this default version and default reference image available in Docker Hub. Currently the
apache/airflow:latestandapache/airflow:2.10.2images are Python 3.8 images, however, in the first MINOR/MAJOR release of Airflow released after 2024-10-14, they will become Python 3.9 images.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 a new images/support in Airflow based on the working CI setup.