josh-fell commented on a change in pull request #20530: URL: https://github.com/apache/airflow/pull/20530#discussion_r776404095
########## File path: airflow/decorators/sensor.py ########## @@ -0,0 +1,93 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +from inspect import signature +from typing import Any, Callable, Dict, Optional, Tuple + +from airflow.decorators.base import get_unique_task_id, task_decorator_factory +from airflow.sensors.base import BaseSensorOperator + + +class DecoratedSensorOperator(BaseSensorOperator): + """ + Wraps a Python callable and captures args/kwargs when called for execution. + + :param python_callable: A reference to an object that is callable + :type python_callable: python callable + :param task_id: task Id + :type task_id: str + :param op_kwargs: a dictionary of keyword arguments that will get unpacked + in your function (templated) + :type op_kwargs: dict + :param op_args: a list of positional arguments that will get unpacked when + calling your callable (templated) + :type op_args: list + :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. + :type kwargs_to_upstream: dict + """ + + template_fields = ('op_args', 'op_kwargs') + template_fields_renderers = {"op_args": "py", "op_kwargs": "py"} + + # 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 = ('python_callable',) + + def __init__( + self, + *, + python_callable: Callable, + task_id: str, + op_args: Tuple[Any], + op_kwargs: Dict[str, Any], + **kwargs, + ) -> None: + kwargs.pop('multiple_outputs') + kwargs['task_id'] = get_unique_task_id(task_id, kwargs.get('dag'), kwargs.get('task_group')) + self.python_callable = python_callable + # Check that arguments can be binded + signature(python_callable).bind(*op_args, **op_kwargs) + self.op_args = op_args + self.op_kwargs = op_kwargs + super().__init__(**kwargs) + + def poke(self, context: Dict) -> bool: + return self.python_callable(*self.op_args, **self.op_kwargs) + + +def sensor(python_callable: Optional[Callable] = None, multiple_outputs: Optional[bool] = None, **kwargs): + """ + 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 + :type python_callable: Optional[Callable] + :param multiple_outputs: if set, function return value will be + unrolled to multiple XCom values. List/Tuples will unroll to xcom values + with index as key. Dict will unroll to xcom values with keys as XCom keys. + Defaults to False. Review comment: > @josh-fell re > > Tuples and Lists are not currently supported for multiple_outputs. > > are you sure? how come it indicates it is supported in the docstring e.g. for _PythonDecoratedOperator? @dstandish Yeah, originally as part of AIP-31 there was proposed support for unrolling Tuples and Lists. However, there seemed to be a consensus that handling these datatypes added more complexity than value (see [this comment](https://github.com/apache/airflow/pull/8962/files#r428948073) and [this one](https://github.com/apache/airflow/pull/10349#issuecomment-680918458) when `multiple_outputs` via type inference was added). The original docstrings still contained the verbiage about Lists/Tuples and has remained. When handling outputs in `DecoratedOperator`, there is an [explicit check](https://github.com/apache/airflow/blob/8a03a505e1df0f9de276038c5509135ac569a667/airflow/decorators/base.py#L137-L160) on the return value confirming it is a dict type. Also the type-hint inference only handles dict types. Despite this, #15813 was logged earlier this year to at least add Tuples to type-hint inference of `multiple_outputs`. Perhaps this should be revisited? The docstrings are planned to be corrected as part of #19608. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
