ferruzzi commented on code in PR #61153: URL: https://github.com/apache/airflow/pull/61153#discussion_r2791211467
########## airflow-core/src/airflow/executors/workloads/callback.py: ########## @@ -0,0 +1,158 @@ +# 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. +"""Callback workload schemas for executor communication.""" + +from __future__ import annotations + +from enum import Enum +from importlib import import_module +from pathlib import Path +from typing import TYPE_CHECKING, Literal +from uuid import UUID + +import structlog +from pydantic import BaseModel, Field, field_validator + +from airflow.executors.workloads.base import BaseDagBundleWorkload, BundleInfo + +if TYPE_CHECKING: + from airflow.api_fastapi.auth.tokens import JWTGenerator + from airflow.models import DagRun + from airflow.models.callback import Callback as CallbackModel, CallbackKey + +log = structlog.get_logger(__name__) + + +class CallbackFetchMethod(str, Enum): + """Methods used to fetch callback at runtime.""" + + # For future use once Dag Processor callbacks (on_success_callback/on_failure_callback) get moved to executors + DAG_ATTRIBUTE = "dag_attribute" + + # For deadline callbacks since they import callbacks through the import path + IMPORT_PATH = "import_path" + + +class CallbackDTO(BaseModel): + """Schema for Callback with minimal required fields needed for Executors and Task SDK.""" + + id: str # A uuid.UUID stored as a string + fetch_method: CallbackFetchMethod + data: dict + + @field_validator("id", mode="before") + @classmethod + def validate_id(cls, v): + """Convert UUID to str if needed.""" + if isinstance(v, UUID): + return str(v) + return v + + @property + def key(self) -> CallbackKey: + """Return callback ID as key (CallbackKey = str).""" + return self.id + + +class ExecuteCallback(BaseDagBundleWorkload): + """Execute the given Callback.""" + + callback: CallbackDTO + + type: Literal["ExecuteCallback"] = Field(init=False, default="ExecuteCallback") + + @classmethod + def make( + cls, + callback: CallbackModel, + dag_run: DagRun, + dag_rel_path: Path | None = None, + generator: JWTGenerator | None = None, + bundle_info: BundleInfo | None = None, + ) -> ExecuteCallback: + """Create an ExecuteCallback workload from a Callback ORM model.""" + if not bundle_info: + bundle_info = BundleInfo( + name=dag_run.dag_model.bundle_name, + version=dag_run.bundle_version, + ) + fname = f"executor_callbacks/{callback.id}" # TODO: better log file template + + return cls( + callback=CallbackDTO.model_validate(callback, from_attributes=True), + dag_rel_path=dag_rel_path or Path(dag_run.dag_model.relative_fileloc or ""), + token=cls.generate_token(str(callback.id), generator), + log_path=fname, + bundle_info=bundle_info, + ) + + +def execute_callback_workload( + callback: CallbackDTO, + 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 + callback.data = {"path": "my_module.alert_func", "kwargs": {"msg": "Alert!", "context": {...}}} + execute_callback_workload(callback, log) # Calls alert_func(msg="Alert!", context={...}) + + # Notifier callback + callback.data = {"path": "airflow.providers.slack...SlackWebhookNotifier", "kwargs": {"text": "Alert!", "context": {...}}} + execute_callback_workload(callback, log) # SlackWebhookNotifier(text=..., context=...) then calls instance(context) + + :param callback: The Callback schema containing path and kwargs + :param log: Logger instance for recording execution + :return: Tuple of (success: bool, error_message: str | None) + """ + callback_path = callback.data.get("path") + callback_kwargs = callback.data.get("kwargs", {}) + + if not callback_path: + return False, "Callback path not found in data." + + 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 %s(%s)...", callback_path, callback_kwargs) + + # If the callback is a callabale, call it. If it is a class, instantiate it. + result = callback_callable(**callback_kwargs) + + # If the callback is a class then it is now instantiated and callable, call it. + if callable(result): + context = callback_kwargs.get("context", {}) + log.debug("Calling result with context for %s", callback_path) + result = result(context) + + log.info("Callback %s executed successfully.", callback_path) + return True, None Review Comment: I modelled it after the existing pattern for the task workload. The existing task workload pattern was that supervise() returned an `int` exit_code which the calling method bubbles up and _run_worker() catches. This returns a true/false bool for success or fail, and the calling method (in this case _execute_callback() in local_executor) has an `if not success: raise`. which _run_worker() catches see _run_worker() in local_executor.py around line 103 for where that happens. -- This is an automated message from the Apache Git Service. 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