Source code for airflow.timetables.simple

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from __future__ import annotations

import operator
from typing import TYPE_CHECKING, Any, Collection

from airflow.timetables.base import DagRunInfo, DataInterval, TimeRestriction, Timetable

    from pendulum import DateTime
    from sqlalchemy import Session

    from airflow.models.dataset import DatasetEvent
    from airflow.utils.types import DagRunType

class _TrivialTimetable(Timetable):
    """Some code reuse for "trivial" timetables that has nothing complex."""

    periodic = False
    can_run = False
    run_ordering = ("execution_date",)

    def deserialize(cls, data: dict[str, Any]) -> Timetable:
        return cls()

    def __eq__(self, other: Any) -> bool:
        """As long as *other* is of the same type.

        This is only for testing purposes and should not be relied on otherwise.
        if not isinstance(other, type(self)):
            return NotImplemented
        return True

    def serialize(self) -> dict[str, Any]:
        return {}

    def infer_manual_data_interval(self, *, run_after: DateTime) -> DataInterval:
        return DataInterval.exact(run_after)

[docs]class NullTimetable(_TrivialTimetable): """Timetable that never schedules anything. This corresponds to ``schedule=None``. """
[docs] description: str = "Never, external triggers only"
[docs] def summary(self) -> str: return "None"
[docs] def next_dagrun_info( self, *, last_automated_data_interval: DataInterval | None, restriction: TimeRestriction, ) -> DagRunInfo | None: return None
[docs]class OnceTimetable(_TrivialTimetable): """Timetable that schedules the execution once as soon as possible. This corresponds to ``schedule="@once"``. """
[docs] description: str = "Once, as soon as possible"
[docs] def summary(self) -> str: return "@once"
[docs] def next_dagrun_info( self, *, last_automated_data_interval: DataInterval | None, restriction: TimeRestriction, ) -> DagRunInfo | None: if last_automated_data_interval is not None: return None # Already run, no more scheduling. if restriction.earliest is None: # No start date, won't run. return None # "@once" always schedule to the start_date determined by the DAG and # tasks, regardless of catchup or not. This has been the case since 1.10 # and we're inheriting it. See AIRFLOW-1928. run_after = restriction.earliest if restriction.latest is not None and run_after > restriction.latest: return None return DagRunInfo.exact(run_after)
[docs]class DatasetTriggeredTimetable(NullTimetable): """Timetable that never schedules anything. This should not be directly used anywhere, but only set if a DAG is triggered by datasets. :meta private: """
[docs] description: str = "Triggered by datasets"
[docs] def summary(self) -> str: return "Dataset"
[docs] def generate_run_id( self, *, run_type: DagRunType, logical_date: DateTime, data_interval: DataInterval | None, session: Session | None = None, events: Collection[DatasetEvent] | None = None, **extra, ) -> str: from airflow.models.dagrun import DagRun return DagRun.generate_run_id(run_type, logical_date)
[docs] def data_interval_for_events( self, logical_date: DateTime, events: Collection[DatasetEvent], ) -> DataInterval: if not events: return DataInterval(logical_date, logical_date) start = min( events, key=operator.attrgetter('source_dag_run.data_interval_start') ).source_dag_run.data_interval_start end = max( events, key=operator.attrgetter('source_dag_run.data_interval_end') ).source_dag_run.data_interval_end return DataInterval(start, end)

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