This operator takes a Python function that returns True or False based on logic implemented for your use case. (ShortCircuitOperator) Īnother option for implementing conditional logic in your DAGs is the decorator, which is a decorated version of the ShortCircuitOperator. More examples using the decorator, can be found on the Astronomer Registry. Trigger_rule = "none_failed_min_one_success" ,īranching > Label ( option ) > t > empty_follow > join python import BranchPythonOperatorĭag_id = 'branch_python_operator_example' , """Example DAG demonstrating the usage of the BranchPythonOperator."""įrom airflow. Random_choice_instance > Label ( option ) > t > empty_follow > joinīranch_python_operator_decorator_example ( ) # Label is optional here, but it can help identify more complex branches Trigger_rule = "none_failed_min_one_success" Random_choice_instance = random_choice ( choices = options )
Run_this_first = EmptyOperator ( task_id = "run_this_first" ) Schedule = branch_python_operator_decorator_example ( ) : decorators import dag, taskįrom airflow. """Example DAG demonstrating the usage of the API decorator."""įrom airflow. In the following example we use a choose_branch function that returns one set of task IDs if the result is greater than 0.5 and a different set if the result is less than or equal to 0.5: accepts any Python function as an input as long as the function returns a list of valid IDs for Airflow tasks that the DAG should run after the function completes. One of the simplest ways to implement branching in Airflow is to use the decorator, which is a decorated version of the BranchPythonOperator. See Introduction to Airflow (BranchPythonOperator)
See Managing Dependencies in Apache Airflow. To get the most out of this guide, you should have an understanding of: In this guide, you'll learn how you can use (BranchPythonOperator) and (ShortCircuitOperator), other available branching operators, and additional resources to implement conditional logic in your Airflow DAGs. Fortunately, Airflow has multiple options for building conditional logic and/or branching into your DAGs. Or you may have a case where part of your pipeline should only run under certain external conditions. For example, you may have a use case where you need to decide between multiple tasks to execute based on the results of an upstream task. When designing your data pipelines, you may encounter use cases that require more complex task flows than "Task A > Task B > Task C".
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