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# to you under the Apache License, Version 2.0 (the
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# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
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# software distributed under the License is distributed on an
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from __future__ import annotations
import operator
from typing import TYPE_CHECKING, Any, Collection
from pendulum import DateTime
from airflow.timetables.base import DagRunInfo, DataInterval, Timetable
if TYPE_CHECKING:
from sqlalchemy import Session
from airflow.models.dataset import DatasetEvent
from airflow.timetables.base import TimeRestriction
from airflow.utils.types import DagRunType
class _TrivialTimetable(Timetable):
"""Some code reuse for "trivial" timetables that has nothing complex."""
periodic = False
run_ordering = ("execution_date",)
@classmethod
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)
class DatasetTriggeredTimetable(_TrivialTimetable):
"""Timetable that never schedules anything.
This should not be directly used anywhere, but only set if a DAG is triggered by datasets.
:meta private:
"""
description: str = "Triggered by datasets"
@property
def summary(self) -> str:
return "Dataset"
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)
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)
def next_dagrun_info(
self,
*,
last_automated_data_interval: DataInterval | None,
restriction: TimeRestriction,
) -> DagRunInfo | None:
return None