Source code for airflow.timetables.assets
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from __future__ import annotations
import typing
from airflow.assets import AssetAll, BaseAsset
from airflow.exceptions import AirflowTimetableInvalid
from airflow.timetables.simple import AssetTriggeredTimetable
from airflow.utils.types import DagRunType
if typing.TYPE_CHECKING:
from collections.abc import Collection
import pendulum
from airflow.assets import Asset
from airflow.timetables.base import DagRunInfo, DataInterval, TimeRestriction, Timetable
[docs]class AssetOrTimeSchedule(AssetTriggeredTimetable):
"""Combine time-based scheduling with event-based scheduling."""
def __init__(
self,
*,
timetable: Timetable,
assets: Collection[Asset] | BaseAsset,
) -> None:
self.timetable = timetable
if isinstance(assets, BaseAsset):
self.asset_condition = assets
else:
self.asset_condition = AssetAll(*assets)
self.description = f"Triggered by assets or {timetable.description}"
self.periodic = timetable.periodic
self.can_be_scheduled = timetable.can_be_scheduled
self.active_runs_limit = timetable.active_runs_limit
@classmethod
[docs] def deserialize(cls, data: dict[str, typing.Any]) -> Timetable:
from airflow.serialization.serialized_objects import decode_asset_condition, decode_timetable
return cls(
assets=decode_asset_condition(data["asset_condition"]),
timetable=decode_timetable(data["timetable"]),
)
[docs] def serialize(self) -> dict[str, typing.Any]:
from airflow.serialization.serialized_objects import encode_asset_condition, encode_timetable
return {
"asset_condition": encode_asset_condition(self.asset_condition),
"timetable": encode_timetable(self.timetable),
}
[docs] def validate(self) -> None:
if isinstance(self.timetable, AssetTriggeredTimetable):
raise AirflowTimetableInvalid("cannot nest asset timetables")
if not isinstance(self.asset_condition, BaseAsset):
raise AirflowTimetableInvalid("all elements in 'assets' must be assets")
@property
[docs] def summary(self) -> str:
return f"Asset or {self.timetable.summary}"
[docs] def infer_manual_data_interval(self, *, run_after: pendulum.DateTime) -> DataInterval:
return self.timetable.infer_manual_data_interval(run_after=run_after)
[docs] def next_dagrun_info(
self, *, last_automated_data_interval: DataInterval | None, restriction: TimeRestriction
) -> DagRunInfo | None:
return self.timetable.next_dagrun_info(
last_automated_data_interval=last_automated_data_interval,
restriction=restriction,
)
[docs] def generate_run_id(self, *, run_type: DagRunType, **kwargs: typing.Any) -> str:
if run_type != DagRunType.ASSET_TRIGGERED:
return self.timetable.generate_run_id(run_type=run_type, **kwargs)
return super().generate_run_id(run_type=run_type, **kwargs)