![]() ![]() 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. Instead, it updates max_tries to 0 and sets the current task instance state to None, which causes the task to re-run.Ĭlick on the failed task in the Tree or Graph views and then click on Clear.For more information on schedule values, see DAG Run. Clearing a task instance doesn’t delete the task instance record. The errors after going through the logs, you can re-run the tasks by clearing them for the Some of the tasks can fail during the scheduled run. ![]() This behavior is great for atomic datasets that can easily be split into periods. If the dag.catchup value had been True instead, the scheduler would have created a DAG Runįor each completed interval between -02 (but not yet one for ,Īs that interval hasn’t completed) and the scheduler will execute them sequentially.Ĭatchup is also triggered when you turn off a DAG for a specified period and then re-enable it. Just after midnight on the morning of with a data interval between With a data between -02, and the next one will be created at 6 AM, (or from the command line), a single DAG Run will be created In the example above, if the DAG is picked up by the scheduler daemon on datetime ( 2015, 12, 1, tz = "UTC" ), description = "A simple tutorial DAG", schedule =, catchup = False, ) """ Code that goes along with the Airflow tutorial located at: """ from import DAG from import BashOperator import datetime import pendulum dag = DAG ( "tutorial", default_args =, start_date = pendulum. When tasks in the DAG will start running. The same logical date, it marks the start of the DAG’s first data interval, not Similarly, since the start_date argument for the DAG and its tasks points to Of a DAG run, for example, denotes the start of the data interval, not when the “logical date” (also called execution_date in Airflow versions prior to 2.2) after 00:00:00.Īll dates in Airflow are tied to the data interval concept in some way. Other words, a run covering the data period of generally does not To ensure the run is able to collect all the data within the time period. Its data interval would start each day at midnight (00:00) and end at midnightĪ DAG run is usually scheduled after its associated data interval has ended, ![]() For a DAG scheduled with for example, each of Each DAG run in Airflow has an assigned “data interval” that represents the time
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