With DAG(dag_id="bi_dashboard_refresher", start_date=datetime(2022, 11, 4)) as bi_dashboard_refresher_dag:ĭata_generator_sensor = ExternalTaskSensor(Įxternal_task_id='dashboard_refresher_trigger',įailed_states=['failed', refresh_dashboard():ĭata_generator_sensor > refresh_dashboard() With DAG(dag_id="data_generator", start_date=datetime(2022, 11, 4)) as generate_data():Įxternal_dag_id="bi_dashboard_refresher",Įxternal_task_id="data_generator_sensor", If it's present in the DAG and gets cleared, the scheduler will automatically trigger the computation of the dependent parts! For example, if I clean the generate_data task with its downstream dependencies, it'll automatically not only clean the ExternalTaskMarker but also bi_dashboard_refresher.data_generator_sensor: The ExternalTaskMarker added in Apache Airflow 2 automatizes this reprocessing dependency. Although this building pattern is valid, it becomes cumbersome at daily life mostly due to the reprocessing complexity. It's not rare to find DAGs with ExternalTaskSensor where a downstream DAG waits for some important job to be done by the upstream DAG. The second feature simplifies inter-DAG dependencies management. Otherwise, it'll take the real-time, so the value that changes when the DAGs get backfilled. If it's true, Airflow will use the execution time (so immutable). ![]() It's there to resolve the reference date the boundaries are evaluated against. Target_upper=pendulum.datetime(2022, 11, 7),īesides the time and task parameters you've certainly noticed the use_task_logical_date attribute. Processing_version_dispatcher = BranchDateTimeOperator(įollow_task_ids_if_true=,įollow_task_ids_if_false=, For example, to define a time-based branch for BranchDateTimeOperator you simply need to define the boundary for one side of the branch and, of course, the DAG branches to follow after evaluating these boundaries: The new operators integrate this time dimension natively as parameters. They address relatively common scenarios where a DAG can behave differently and the behavior is conditioned by a time (e.g. Instead, the new release completes the scope with 2 time-sensitive branch operators called BranchDateTimeOperator and BranchDayOfWeekOperator. No, Apache Airflow 2 doesn't get rid of the BranchPythonOperator. The first feature concerns the branch operators.
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