SubDAGs were commonly used for grouping tasks in the UI, but they had many drawbacks in their execution behaviour (primarily that they only executed a single task in parallel!) To improve this experience, we’ve introduced “Task Groups”: a method for organizing tasks which provides the same grouping behaviour as a subdag without any of the execution-time drawbacks. There’s no config or other set up required to run more than one scheduler-just start up a scheduler somewhere else (ensuring it has access to the DAG files) and it will cooperate with your existing schedulers through the database.įor more information, read the Scheduler HA documentation. To fully use this feature you need Postgres 9.6+ or MySQL 8+ (MySQL 5, and MariaDB won’t work with more than one scheduler I’m afraid). This is super useful for both resiliency (in case a scheduler goes down) and scheduling performance. It’s now possible and supported to run more than a single scheduler instance. Over at Astronomer.io we’ve benchmarked the scheduler-it’s fast (we had to triple check the numbers as we don’t quite believe them at first!) Scheduler is now HA compatible (AIP-15) Massive Scheduler performance improvementsĪs part of AIP-15 (Scheduler HA+performance) and other work Kamil did, we significantly improved the performance of the Airflow Scheduler.

We now have a fully supported, no-longer-experimental API with a comprehensive OpenAPI specification Which are used to populate the run schedule with task instances from this dag.From corators import dag, task from import days_ago ( default_args = () def load ( total_order_value : float ): print ( 'Total order value is: %.2f ' % total_order_value ) order_data = extract () order_summary = transform ( order_data ) load ( order_summary ) tutorial_etl_dag = tutorial_taskflow_api_etl () Fully specified REST API (AIP-32) The date range in this context is a start_date and optionally an end_date, To also wait for all task instances immediately downstream of the previous Of its previous task_instance, wait_for_downstream=True will cause a task instance

While depends_on_past=True causes a task instance to depend on the success You may also want to consider wait_for_downstream=True when using depends_on_past=True. Will disregard this dependency because there would be no Task instances with execution_date=start_date Will depend on the success of their previous task instance (that is, previousĪccording to execution_date). Note that if you use depends_on_past=True, individual task instances airflow webserver will start a web server if youĪre interested in tracking the progress visually as your backfill progresses. If you do have a webserver up, you will be able

From datetime import timedelta # The DAG object we'll need this to instantiate a DAG from airflow import DAG # Operators we need this to operate! from import BashOperator from import days_ago # These args will get passed on to each operator # You can override them on a per-task basis during operator initialization default_args =, dag = dag, ) t1 > Įverything looks like it's running fine so let's run a backfill.īackfill will respect your dependencies, emit logs into files and talk to

Airflow 2.0 tutorial