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from __future__ import annotations
from typing import Callable, Sequence
from airflow.decorators.base import TaskDecorator, get_unique_task_id, task_decorator_factory
from airflow.sensors.python import PythonSensor
class DecoratedSensorOperator(PythonSensor):
"""
Wraps a Python callable and captures args/kwargs when called for execution.
:param python_callable: A reference to an object that is callable
:param task_id: task Id
:param op_args: a list of positional arguments that will get unpacked when
calling your callable (templated)
:param op_kwargs: a dictionary of keyword arguments that will get unpacked
in your function (templated)
:param kwargs_to_upstream: For certain operators, we might need to upstream certain arguments
that would otherwise be absorbed by the DecoratedOperator (for example python_callable for the
PythonOperator). This gives a user the option to upstream kwargs as needed.
"""
template_fields: Sequence[str] = ("op_args", "op_kwargs")
template_fields_renderers: dict[str, str] = {"op_args": "py", "op_kwargs": "py"}
custom_operator_name = "@task.sensor"
# since we won't mutate the arguments, we should just do the shallow copy
# there are some cases we can't deepcopy the objects (e.g protobuf).
shallow_copy_attrs: Sequence[str] = ("python_callable",)
def __init__(
self,
*,
task_id: str,
**kwargs,
) -> None:
kwargs.pop("multiple_outputs")
kwargs["task_id"] = get_unique_task_id(task_id, kwargs.get("dag"), kwargs.get("task_group"))
super().__init__(**kwargs)
def sensor_task(python_callable: Callable | None = None, **kwargs) -> TaskDecorator:
"""
Wraps a function into an Airflow operator.
Accepts kwargs for operator kwarg. Can be reused in a single DAG.
:param python_callable: Function to decorate
"""
return task_decorator_factory(
python_callable=python_callable,
multiple_outputs=False,
decorated_operator_class=DecoratedSensorOperator,
**kwargs,
)