![]() trigger_dagrun import TriggerDagRunOperatorįrom pendulum import datetime, start_task ( task_type ) : decorators import dag, taskįrom airflow. Once the trigger_dagrun_dag task completes, the end_task will run.įrom airflow. Since both the wait_for_completion and the deferrable parameters of the trigger_dependent_dag task in the trigger_dagrun_dag are set to True, the task is deferred until the dependent_dag has finished its run. The following example DAG implements the TriggerDagRunOperator to trigger a DAG with the dag_id dependent_dag between two other tasks. Once the model is retrained and tested by the downstream DAG, the upstream DAG resumes and publishes the new model's results. In case of the model underperforming, the TriggerDagRunOperator is used to start a separate DAG that retrains the model while the upstream DAG waits. As of Airflow 2.6 this waiting process can be deferred to the triggerer by setting the parameter deferrable to True, turning the operator into a deferrable operator which increases Airflow's scalability and can reduce cost.Ī common use case for this implementation is when an upstream DAG fetches new testing data for a machine learning pipeline, runs and tests a model, and publishes the model's prediction. If you set the operator's wait_for_completion parameter to True, the upstream DAG will pause and resume only once the downstream DAG has finished running. You can trigger a downstream DAG with the TriggerDagRunOperator from any point in the upstream DAG. For more information about this operator, see TriggerDagRunOperator. This operator allows you to have a task in one DAG that triggers another DAG in the same Airflow environment. The TriggerDagRunOperator is a straightforward method of implementing cross-DAG dependencies from an upstream DAG. See Datasets and Data-Aware Scheduling in Airflow to learn more. One of those datasets has already been updated by an upstream DAG. The following image shows that the DAG dataset_dependent_example_dag runs only after two different datasets have been updated. In the Airflow UI, the Next Run column for the downstream DAG shows dataset dependencies for the DAG and how many dependencies have been updated since the last DAG run.
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