ferruzzi commented on code in PR #62645: URL: https://github.com/apache/airflow/pull/62645#discussion_r3076434768
########## task-sdk/src/airflow/sdk/execution_time/callback_supervisor.py: ########## @@ -0,0 +1,317 @@ +# 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. +"""Supervised execution of callback workloads.""" + +from __future__ import annotations + +import sys +import time +from importlib import import_module +from typing import TYPE_CHECKING, BinaryIO, ClassVar, Protocol +from uuid import UUID + +import attrs +import structlog +from pydantic import TypeAdapter + +from airflow.sdk._shared.module_loading import accepts_context, accepts_keyword_args +from airflow.sdk.execution_time.supervisor import ( + MIN_HEARTBEAT_INTERVAL, + SOCKET_CLEANUP_TIMEOUT, + WatchedSubprocess, + _make_process_nondumpable, +) + +if TYPE_CHECKING: + from structlog.typing import FilteringBoundLogger + from typing_extensions import Self + + # Core (airflow.executors.workloads.base.BundleInfo) and SDK (airflow.sdk.api.datamodels._generated.BundleInfo) + # are structurally identical, but MyPy treats them as different types. This Protocol makes MyPy happy. + class _BundleInfoLike(Protocol): + name: str + version: str | None + + +__all__ = ["CallbackSubprocess", "supervise_callback"] + +log: FilteringBoundLogger = structlog.get_logger(logger_name="callback_supervisor") + + +def execute_callback( + callback_path: str, + callback_kwargs: dict, + log, +) -> tuple[bool, str | None]: + """ + Execute a callback function by importing and calling it, returning the success state. + + Supports two patterns: + 1. Functions - called directly with kwargs + 2. Classes that return callable instances (like BaseNotifier) - instantiated then called with context + + Example: + # Function callback + execute_callback("my_module.alert_func", {"msg": "Alert!", "context": {...}}, log) + + # Notifier callback + execute_callback("airflow.providers.slack...SlackWebhookNotifier", {"text": "Alert!"}, log) + + :param callback_path: Dot-separated import path to the callback function or class. + :param callback_kwargs: Keyword arguments to pass to the callback. + :param log: Logger instance for recording execution. + :return: Tuple of (success: bool, error_message: str | None) + """ + if not callback_path: + return False, "Callback path not found." + + try: + # Import the callback callable + # Expected format: "module.path.to.function_or_class" + module_path, function_name = callback_path.rsplit(".", 1) + module = import_module(module_path) + callback_callable = getattr(module, function_name) + + log.debug("Executing callback", callback_path=callback_path, callback_kwargs=callback_kwargs) + + kwargs_without_context = {k: v for k, v in callback_kwargs.items() if k != "context"} + + # Call the callable with all kwargs if it accepts context, otherwise strip context. + if accepts_context(callback_callable): + result = callback_callable(**callback_kwargs) + else: + result = callback_callable(**kwargs_without_context) + + # If the callback was a class then it is now instantiated and callable, call it. + # The constructor already received the full kwargs above; the __call__ method + # typically only needs context (e.g. BaseNotifier.__call__(self, *args)). + # Some callables (like BaseNotifier.__call__) only accept positional args, + # so check the signature first rather than catching a broad TypeError. + if callable(result): + context = callback_kwargs.get("context", {}) + if accepts_keyword_args(result): + result = result(context=context) if accepts_context(result) else result() + else: + # Positional-only callable (e.g. BaseNotifier.__call__(self, *args)) + result = result(context) + + log.info("Callback executed successfully", callback_path=callback_path) + return True, None + + except Exception as e: + error_msg = f"Callback execution failed: {type(e).__name__}: {str(e)}" + log.exception( + "Callback execution failed", + callback_path=callback_path, + callback_kwargs=callback_kwargs, + error_msg=error_msg, + ) + return False, error_msg + + +# An empty message set; the callback subprocess doesn't currently communicate back to the +# supervisor. This means callback code cannot access runtime services like Connection.get() +# or Variable.get() which require the supervisor to pass requests to the API server. +# To enable this, add the needed message types here and implement _handle_request accordingly. +# See ActivitySubprocess.decoder in supervisor.py for the full task message set and examples. +_EmptyMessage: TypeAdapter[None] = TypeAdapter(None) + + [email protected](kw_only=True) +class CallbackSubprocess(WatchedSubprocess): + """ + Supervised subprocess for executing callbacks. + + Uses the WatchedSubprocess infrastructure for fork/monitor/signal handling + while keeping a simple lifecycle: start, run callback, exit. + """ + + decoder: ClassVar[TypeAdapter] = _EmptyMessage + + @classmethod + def start( # type: ignore[override] + cls, + *, + id: str, + callback_path: str, + callback_kwargs: dict, + bundle_info: _BundleInfoLike | None = None, + logger: FilteringBoundLogger | None = None, + **kwargs, + ) -> Self: + """Fork and start a new subprocess to execute the given callback.""" + + # Use a closure to pass callback data to the child process. Note that this + # ONLY works because WatchedSubprocess.start() uses os.fork(), so the child + # inherits the parent's memory space and the variables are available directly. + def _target(): + _log = structlog.get_logger(logger_name="callback_runner") + + # If bundle info is provided, initialize the bundle and ensure its path is importable. + # This is needed for user-defined callbacks that live inside a DAG bundle rather than + # in an installed package or the plugins directory. + if bundle_info and bundle_info.name: + try: + from airflow.dag_processing.bundles.manager import DagBundlesManager + + bundle = DagBundlesManager().get_bundle( + name=bundle_info.name, + version=bundle_info.version, + ) + bundle.initialize() + if (bundle_path := str(bundle.path)) not in sys.path: + sys.path.append(bundle_path) + log.debug( + "Added bundle path to sys.path", bundle_name=bundle_info.name, path=bundle_path + ) + except Exception: + log.warning( + "Failed to initialize DAG bundle for callback", + bundle_name=bundle_info.name, + exc_info=True, + ) Review Comment: sure, changed. -- This is an automated message from the Apache Git Service. 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