The Astro CLI includes two commands to run DAG validation tests. astro dev pytest: will run all pytest test suites in the test directory of your current Airflow project.Įvery new Astro project will be initialized with a test/dags folder in your Astro project directory.astro dev parse: will quickly parse your DAGs to find any Python syntax or DAG import errors.Airflow does not need to be running to use these commands. This folder contains the test_dag_integrity.py script defining several examples of using pytest with Airflow. airflow dags test: Given a DAG ID and execution date, this command writes the results of a single DAG run to the metadata database.The Airflow CLI offers two commands related to local testing: For more information on the Astro CLI's testing capabilities, see Test and troubleshoot locally. airflow tasks test: This command tests one specific task instance without checking for dependencies or recording the outcome in the metadata database.This command is useful for testing full DAGs by creating manual DAG runs from the command line. With the Astro CLI, you can run all Airflow CLI commands using astro dev run. For example, to run airflow dags test on the DAG my_dag for the execution date of run:įrom airflow. """Tests that the EvenNumberCheckOperator returns True for 10.""" dag = DAG ( 'test_dag', default_args = ) models import TaskInstanceĬlass EvenNumberCheckOperator ( unittest. Task = EvenNumberCheckOperator ( my_operator_param = self. datetime ( 2022, 1, 1 ), schedule =, tags =, ) as dag : start = EmptyOperator ( task_id = "start", ) section_1 = SubDagOperator ( task_id = "section-1", subdag = subdag ( DAG_NAME, "section-1", dag. Defaults to """ get_ip = GetRequestOperator ( task_id = "get_ip", url = "" ) ( multiple_outputs = True ) def prepare_email ( raw_json : dict ) -> dict : external_ip = raw_json return, start_date = datetime. datetime ( 2021, 1, 1, tz = "UTC" ), catchup = False, tags =, ) def example_dag_decorator ( email : str = ): """ DAG to send server IP to email. Schedule interval put in place, the logical date is going to indicate the timeĪt which it marks the start of the data interval, where the DAG run’s startĭate would then be the logical date + scheduled ( schedule = None, start_date = pendulum. However, when the DAG is being automatically scheduled, with certain Logical is because of the abstract nature of it having multiple meanings,ĭepending on the context of the DAG run itself.įor example, if a DAG run is manually triggered by the user, its logical date would be theĭate and time of which the DAG run was triggered, and the value should be equal (formally known as execution date), which describes the intended time aĭAG run is scheduled or triggered. Run’s start and end date, there is another date called logical date This period describes the time when the DAG actually ‘ran.’ Aside from the DAG Tasks specified inside a DAG are also instantiated intoĪ DAG run will have a start date when it starts, and end date when it ends. In much the same way a DAG instantiates into a DAG Run every time it’s run, Run will have one data interval covering a single day in that 3 month period,Īnd that data interval is all the tasks, operators and sensors inside the DAG Those DAG Runs will all have been started on the same actual day, but each DAG The previous 3 months of data-no problem, since Airflow can backfill the DAGĪnd run copies of it for every day in those previous 3 months, all at once. It’s been rewritten, and you want to run it on Same DAG, and each has a defined data interval, which identifies the period ofĪs an example of why this is useful, consider writing a DAG that processes aĭaily set of experimental data. If schedule is not enough to express the DAG’s schedule, see Timetables.įor more information on logical date, see Data Interval andĮvery time you run a DAG, you are creating a new instance of that DAG whichĪirflow calls a DAG Run. For more information on schedule values, see DAG Run.
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