[GitHub] [airflow] jhtimmins commented on a change in pull request #14709: Refactor Taskflow decorator for extensibility

[GitBox <gi...@apache.org>]

jhtimmins commented on a change in pull request #14709:
URL: https://github.com/apache/airflow/pull/14709#discussion_r594810779



##########
File path: airflow/decorators/base.py
##########
@@ -0,0 +1,194 @@
+# 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.
+
+import functools
+import inspect
+import re
+from inspect import signature
+from typing import Any, Callable, Dict, Optional, Tuple, TypeVar, cast
+
+from airflow.exceptions import AirflowException
+from airflow.models import BaseOperator
+from airflow.models.dag import DAG, DagContext
+from airflow.models.xcom_arg import XComArg
+from airflow.utils.decorators import apply_defaults
+from airflow.utils.task_group import TaskGroup, TaskGroupContext
+
+
+class BaseDecoratedOperator(BaseOperator):
+    """
+    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 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 multiple_outputs: if set, function return value will be
+        unrolled to multiple XCom values. Dict will unroll to xcom values with keys as keys.
+        Defaults to False.
+    :type multiple_outputs: bool
+    """
+
+    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',)
+
+    @apply_defaults
+    def __init__(
+        self,
+        *,
+        python_callable: Callable,
+        task_id: str,
+        op_args: Tuple[Any],
+        op_kwargs: Dict[str, Any],
+        multiple_outputs: bool = False,
+        **kwargs,
+    ) -> None:
+        kwargs['task_id'] = self._get_unique_task_id(task_id, kwargs.get('dag'), kwargs.get('task_group'))
+        super().__init__(**kwargs)
+        self.python_callable = python_callable
+
+        # Check that arguments can be binded
+        signature(python_callable).bind(*op_args, **op_kwargs)
+        self.multiple_outputs = multiple_outputs
+        self.op_args = op_args
+        self.op_kwargs = op_kwargs
+
+    @staticmethod
+    def _get_unique_task_id(
+        task_id: str, dag: Optional[DAG] = None, task_group: Optional[TaskGroup] = None
+    ) -> str:
+        """
+        Generate unique task id given a DAG (or if run in a DAG context)
+        Ids are generated by appending a unique number to the end of
+        the original task id.
+
+        Example:
+          task_id
+          task_id__1
+          task_id__2
+          ...
+          task_id__20
+        """
+        dag = dag or DagContext.get_current_dag()
+        if not dag:
+            return task_id
+
+        # We need to check if we are in the context of TaskGroup as the task_id may
+        # already be altered
+        task_group = task_group or TaskGroupContext.get_current_task_group(dag)
+        tg_task_id = task_group.child_id(task_id) if task_group else task_id
+
+        if tg_task_id not in dag.task_ids:
+            return task_id
+        core = re.split(r'__\d+$', task_id)[0]
+        suffixes = sorted(
+            [
+                int(re.split(r'^.+__', task_id)[1])
+                for task_id in dag.task_ids
+                if re.match(rf'^{core}__\d+$', task_id)
+            ]
+        )
+        if not suffixes:
+            return f'{core}__1'
+        return f'{core}__{suffixes[-1] + 1}'
+
+    @staticmethod
+    def validate_python_callable(python_callable):
+        """
+        Validate that python callable can be wrapped by operator.
+        Raises exception if invalid.
+
+        :param python_callable: Python object to be validated
+        :raises: TypeError, AirflowException
+        """
+        if not callable(python_callable):
+            raise TypeError('`python_callable` param must be callable')
+        if 'self' in signature(python_callable).parameters.keys():
+            raise AirflowException('@task does not support methods')
+
+    def execute(self, context: Dict):
+        raise NotImplementedError()
+
+
+T = TypeVar("T", bound=Callable)  # pylint: disable=invalid-name
+
+
+def task_decorator_factory(
+    python_callable: Optional[Callable] = None,
+    multiple_outputs: Optional[bool] = None,
+    decorated_operator_class: BaseDecoratedOperator = None,
+    **kwargs,
+) -> Callable[[T], T]:
+    """
+    A factory that generates a wrapper that raps 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.
+    :type multiple_outputs: bool
+    :param decorated_operator_class: The operator that executes the logic needed to run the python function in
+        the correct environment
+    :type decorated_operator_class: BaseDecoratedOperator
+
+    """
+    # try to infer from  type annotation
+    if python_callable and multiple_outputs is None:
+        sig = signature(python_callable).return_annotation
+        ttype = getattr(sig, "__origin__", None)
+
+        multiple_outputs = sig != inspect.Signature.empty and ttype in (dict, Dict)
+
+    def wrapper(f: T):
+        """
+        Python wrapper to generate PythonDecoratedOperator out of simple python functions.
+        Used for Airflow Decorated interface
+        """
+        BaseDecoratedOperator.validate_python_callable(f)
+        kwargs.setdefault('task_id', f.__name__)
+
+        @functools.wraps(f)
+        def factory(*args, **f_kwargs):
+            op = decorated_operator_class(
+                python_callable=f,
+                op_args=args,
+                op_kwargs=f_kwargs,
+                multiple_outputs=multiple_outputs,
+                **kwargs,
+            )
+            if f.__doc__:
+                op.doc_md = f.__doc__
+            return XComArg(op)
+
+        return cast(T, factory)
+
+    if callable(python_callable):
+        return wrapper(python_callable)
+    elif python_callable is not None:
+        raise AirflowException('No args allowed while using @task, use kwargs instead')
+    return wrapper

Review comment:
       How is it possible to get to this final `return wrapper`? What's the scenario where `python_callable` is valid but not a callable?




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