#AIRFLOW SCHEDULER AUTO RESTART FULL#

Restriction.latest, we must respect it and not schedule a run by returning Finally, if our calculated data interval is later than Restriction.catchup also needs to be considered-if it’s False, weĬan’t schedule before the current time, even if start_date values are in the However, we pick the next non-holiday workday’s midnight after

If there was not a previous scheduled run, Non-holiday weekday by looping through subsequent days to find one that is notĪ Saturday, Sunday, or US holiday. If there was a run scheduled previously, we should now schedule for the next (usually after the end of the data interval). (the start of the data interval), not when the run will be scheduled Scheduled, calculated from end_date arguments.Ĭatchup: A boolean reflecting the DAG’s catchup argument.īoth earliest and latest apply to the DAG run’s logical date Latest: Similar to earliest, this is the latest time the DAG may be The DAG and its tasks, or None if there are no start_date arguments Pendulum.DateTime calculated from all the start_date arguments from How the DAG and its tasks specify the schedule, and contains three attributes:Įarliest: The earliest time the DAG may be scheduled. Is the first time ever the DAG is being scheduled. Interval of this DAG’s previous non-manually-triggered run, or None if this last_automated_data_interval is aĭataInterval instance indicating the data interval ( start = next_start, end = ( next_start + timedelta ( days = 1 ))) latest : return None # Over the DAG's scheduled end don't schedule. latest is not None and next_start > restriction. get_next_workday ( next_start ) if restriction. replace ( tzinfo = UTC ) # Skip weekends and holidays next_start = self. min : # If earliest does not fall on midnight, skip to the next day. replace ( tzinfo = UTC )) elif next_start. next_start = max ( next_start, DateTime. catchup : # If the DAG has catchup=False, today is the earliest to consider. earliest if next_start is None : # No start_date. replace ( tzinfo = UTC ) else : # This is the first ever run on the regular schedule. combine (( last_start + timedelta ( days = 1 )). last_start = last_automated_data_interval. Next is the implementation of next_dagrun_info:ĭef next_dagrun_info ( self, *, last_automated_data_interval : DataInterval | None, restriction : TimeRestriction, ) -> DagRunInfo | None : if last_automated_data_interval is not None : # There was a previous run on the regular schedule. We thenĬreate a DataInterval object to describe this

#AIRFLOW SCHEDULER AUTO RESTART FULL#

The start of the interval, the end is simply one full day after it. Sunday), it should be pushed further back to the previous Friday. Run_after falls on a Sunday or Monday (i.e. Should usually start at the midnight one day prior to run_after, but if Since our timetable createsĪ data interval for each complete work day, the data interval inferred here That indicates when the DAG is externally triggered.

The method accepts one argument run_after, a pendulum.DateTime object get_next_workday ( start, incr =- 1 ) return DataInterval ( start = start, end = ( start + timedelta ( days = 1 ))) replace ( tzinfo = UTC ) # Skip backwards over weekends and holidays to find last run start = self. combine (( run_after - timedelta ( days = 1 )). No runs happen on midnights Sunday and Monday.ĭo not schedule a run on defined holidays.įor simplicity, we will only deal with UTC datetimes in this example.ĭef infer_manual_data_interval ( self, run_after : DateTime ) -> DataInterval : start = DateTime. Monday happens on midnight Tuesday and so on. Run’s data interval would cover from midnight of each day, to midnight of theĮach run would be created right after the data interval ends. Schedule a run for each Monday, Tuesday, Wednesday, Thursday, and Friday. Further, the above schedule string cannot skip This means data collected on Friday will not be processed right after FridayĮnds, but on the next Monday, and that run’s interval would be from midnightįriday to midnight Monday. Would be schedule='0 0 * * 1-5' (midnight on Monday to Friday), but

Process data collected during the work day.

Export dynamic environment variables available for operators to useĬustomizing DAG Scheduling with Timetables ¶įor our example, let’s say a company wants to run a job after each weekday to.

(Optional) Adding IDE auto-completion support.

Customize view of Apache from Airflow web UI.

Customizing DAG Scheduling with Timetables.

Configuring Flask Application for Airflow Webserver.

Add tags to DAGs and use it for filtering in the UI.

Airflow scheduler auto restart