![]() ![]() The Flask-AppBuilder (FAB) based UI allows Role-based Access Control and has more advanced features compared to We strongly recommend users to use Python >= 3.6 Use Airflow RBAC UIĪirflow 1.10.10 ships with 2 UIs, the default is non-RBAC Flask-admin based UI and Flask-appbuilder based UI. Airflow Master no longer supports Python 2.Īirflow 1.10.* would be the last series to support Python 2. Python 2 has reached end of its life on Jan 2020. When a task is marked as success by a user in Airflow UI, function defined in on_success_callback will be called. The above code returned None previously, now it will return ‘’. ![]() Setting empty string to a Airflow Variable will now return an empty string, it previously returned None. The documentation, hence if you need the old behavior use none_failed_or_skipped. We have changed the implementation to match This was not in-line with what was documented about that trigger rule. If you have used none_failed trigger rule in your DAG, change it to use the new none_failed_or_skipped trigger rule.Īs previously implemented, the actual behavior of none_failed trigger rule would skip the current task if all parents of the task Run airflow upgradedb after pip install -U apache-airflow=1.10.10 as 1.10.10 contains 3 database migrations. If you are updating Apache Airflow from a previous version to 1.10.10, please take a note of the following: When triggering a DAG from the CLI or the REST API, it s possible to pass configuration for the DAG run as a JSON blob.įrom Airflow 1.10.10, when a user clicks on Trigger Dag button, a new screen confirming the trigger request, and allowing the user to pass a JSON configuration Allow passing DagRun conf when triggering dags via UI ![]() This should significantly improve execution time and resource usage. With Airflow 1.10.10 tasks using Dummy Operators would be scheduled & evaluated by the Scheduler but not sent to theĮxecutor. Previously, when using Kubernetes Executor, the executor would spin up a whole worker pod to execute a dummy task. The Dummy operators does not actually do any work and are mostly used for organizing/grouping tasks along when a particular DAG isĭetails: Tasks using Dummy Operators are no longer sent to executor With DAG Serialization, an empty DagBag is created andĭags are loaded from DB only when needed (i.e. Without DAG Serialization all the DAGs are loaded in the DagBag during the The main advantage of this would be reduction in Webserver startup time for large number of DAGs. The Webserver can now run without access to DAG Files when DAG Serialization is turned on. backend = _vault.VaultBackend backend_kwargs = Stateless Webserver using DAG Serialization If no backend is defined, Airflow falls-back to Environment VariablesĪs of 1.10.10, Airflow supports the following Secret Backends:Įxample configuration to use Hashicorp Vault as the backend:
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |