Processing when changing the shape of your DAG, by say adding in new Scheduler would have much more work to do in order to figure out what tasks Without the metadata at the DAG run level, the Airflow Informs the scheduler on which set of schedules should be evaluated for Note: Use schedule_interval=None and not schedule_interval='None' whenįor each schedule, while creating a DAG Run entry for each schedule.ĭAG runs have a state associated to them (running, failed, success) and Alternatively, you can alsoĭon’t schedule, use for exclusively “externally once and only once an hour at the beginning of the hourĠ 0 * * once a week at midnight on Sunday morningĠ 0 * * once a month at midnight of the first day of the monthĠ 0 1 * once a year at midnight of January 1 schedule_interval is defined as a DAG arguments, and receivesĪ str, or a datetime.timedelta object. To start a scheduler, simply run the command:Ī DAG Run is an object representing an instantiation of the DAG in time.Įach DAG may or may not have a schedule, which informs how DAG Runs areĬreated. If it happens to be the _executor.LocalExecutor, tasks will beĮxecuted as subprocesses in the case of _executor.CeleryExecutor, _executor.DaskExecutor`, andĪ_executor.MesosExecutor, tasks are executed remotely. The scheduler starts an instance of the executor specified in the yourĪirflow.cfg. ![]() Let’s Repeat That The scheduler runs your job one schedule_interval AFTER the In other words, the job instance is started once the period it covers The run stamped will be trigger soon after T23:59. Note that if you run a DAG on a schedule_interval of one day, It will use the configuration specified in To kick it off, all you need to do isĮxecute airflow scheduler. The Airflow scheduler is designed to run as a persistent service in anĪirflow production environment. It spins up a subprocess, which monitors and stays in sync with a folderįor all DAG objects it may contain, and periodically (every minute or so)Ĭollects DAG parsing results and inspects active tasks to see whether ![]() Task instances whose dependencies have been met. The Airflow scheduler monitors all tasks and all DAGs, and triggers the
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