Source code for airflow.decorators.sensor

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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

[docs]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. """
[docs] template_fields: Sequence[str] = ("op_args", "op_kwargs")
[docs] template_fields_renderers: dict[str, str] = {"op_args": "py", "op_kwargs": "py"}
[docs] 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).
[docs] 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)
[docs]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, )

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