Source code for airflow.timetables.trigger

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from __future__ import annotations

import datetime
from typing import TYPE_CHECKING, Any

from airflow.timetables._cron import CronMixin
from airflow.timetables.base import DagRunInfo, DataInterval, Timetable
from airflow.utils.timezone import coerce_datetime, utcnow

if TYPE_CHECKING:
    from dateutil.relativedelta import relativedelta
    from pendulum import DateTime
    from pendulum.tz.timezone import FixedTimezone, Timezone

    from airflow.timetables.base import TimeRestriction


[docs]class CronTriggerTimetable(CronMixin, Timetable): """ Timetable that triggers DAG runs according to a cron expression. This is different from ``CronDataIntervalTimetable``, where the cron expression specifies the *data interval* of a DAG run. With this timetable, the data intervals are specified independently from the cron expression. Also for the same reason, this timetable kicks off a DAG run immediately at the start of the period (similar to POSIX cron), instead of needing to wait for one data interval to pass. Don't pass ``@once`` in here; use ``OnceTimetable`` instead. :param cron: cron string that defines when to run :param timezone: Which timezone to use to interpret the cron string :param interval: timedelta that defines the data interval start. Default 0. *run_immediately* controls, if no *start_time* is given to the DAG, when the first run of the DAG should be scheduled. It has no effect if there already exist runs for this DAG. * If *True*, always run immediately the most recent possible DAG run. * If *False*, wait to run until the next scheduled time in the future. * If passed a ``timedelta``, will run the most recent possible DAG run if that run's ``data_interval_end`` is within timedelta of now. * If *None*, the timedelta is calculated as 10% of the time between the most recent past scheduled time and the next scheduled time. E.g. if running every hour, this would run the previous time if less than 6 minutes had past since the previous run time, otherwise it would wait until the next hour. """ def __init__( self, cron: str, *, timezone: str | Timezone | FixedTimezone, interval: datetime.timedelta | relativedelta = datetime.timedelta(), run_immediately: bool | datetime.timedelta = False, ) -> None: super().__init__(cron, timezone) self._interval = interval self.run_immediately = run_immediately @classmethod
[docs] def deserialize(cls, data: dict[str, Any]) -> Timetable: from airflow.serialization.serialized_objects import decode_relativedelta, decode_timezone interval: datetime.timedelta | relativedelta if isinstance(data["interval"], dict): interval = decode_relativedelta(data["interval"]) else: interval = datetime.timedelta(seconds=data["interval"]) immediate: bool | datetime.timedelta if "immediate" not in data: immediate = False elif isinstance(data["immediate"], float): immediate = datetime.timedelta(seconds=data["interval"]) else: immediate = data["immediate"] return cls( data["expression"], timezone=decode_timezone(data["timezone"]), interval=interval, run_immediately=immediate, )
[docs] def serialize(self) -> dict[str, Any]: from airflow.serialization.serialized_objects import encode_relativedelta, encode_timezone interval: float | dict[str, Any] if isinstance(self._interval, datetime.timedelta): interval = self._interval.total_seconds() else: interval = encode_relativedelta(self._interval) timezone = encode_timezone(self._timezone) immediate: bool | float if isinstance(self.run_immediately, datetime.timedelta): immediate = self.run_immediately.total_seconds() else: immediate = self.run_immediately return { "expression": self._expression, "timezone": timezone, "interval": interval, "run_immediately": immediate, }
[docs] def infer_manual_data_interval(self, *, run_after: DateTime) -> DataInterval: return DataInterval( # pendulum.Datetime ± timedelta should return pendulum.Datetime # however mypy decide that output would be datetime.datetime run_after - self._interval, # type: ignore[arg-type] run_after, )
[docs] def next_dagrun_info( self, *, last_automated_data_interval: DataInterval | None, restriction: TimeRestriction, ) -> DagRunInfo | None: if restriction.catchup: if last_automated_data_interval is not None: next_start_time = self._get_next(last_automated_data_interval.end) elif restriction.earliest is None: next_start_time = self._calc_first_run() else: next_start_time = self._align_to_next(restriction.earliest) else: start_time_candidates = [self._align_to_prev(coerce_datetime(utcnow()))] if last_automated_data_interval is not None: start_time_candidates.append(self._get_next(last_automated_data_interval.end)) elif restriction.earliest is None: # Run immediately has no effect if there is restriction on earliest start_time_candidates.append(self._calc_first_run()) if restriction.earliest is not None: start_time_candidates.append(self._align_to_next(restriction.earliest)) next_start_time = max(start_time_candidates) if restriction.latest is not None and restriction.latest < next_start_time: return None return DagRunInfo.interval( # pendulum.Datetime ± timedelta should return pendulum.Datetime # however mypy decide that output would be datetime.datetime next_start_time - self._interval, # type: ignore[arg-type] next_start_time, )
def _calc_first_run(self): """ If no start_time is set, determine the start. If True, always prefer past run, if False, never. If None, if within 10% of next run, if timedelta, if within that timedelta from past run. """ now = coerce_datetime(utcnow()) past_run_time = self._align_to_prev(now) next_run_time = self._align_to_next(now) if self.run_immediately is True: # not truthy, actually set to True return past_run_time gap_between_runs = next_run_time - past_run_time gap_to_past = now - past_run_time if isinstance(self.run_immediately, datetime.timedelta): buffer_between_runs = self.run_immediately else: buffer_between_runs = max(gap_between_runs / 10, datetime.timedelta(minutes=5)) if gap_to_past <= buffer_between_runs: return past_run_time else: return next_run_time

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