feat: 新增日志系统与执行追踪装饰器
- 基于 Loguru 重新封装,支持异步写入和多线程安全。 - 实现 @trace_step 装饰器,自动记录步骤名、参数及执行耗时。 - 引入 source 标签区分框架系统(System)与业务任务(task)日志。 - 新增 logger 模块测试用例 test_logger.py
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124
utils/logger.py
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124
utils/logger.py
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#!/usr/bin/env python
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# coding=utf-8
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"""
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@author: CNWei,ChenWei
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@Software: PyCharm
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@contact: t6g888@163.com,chenwei@zygj.com
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@file: logger
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@date: 2026/1/15 11:30
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@desc:
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"""
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import sys
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import time
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import functools
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from pathlib import Path
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import inspect
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from loguru import logger
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# 1. 确定日志存储路径
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LOG_DIR = Path(__file__).parent.parent / "logs"
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LOG_DIR.mkdir(exist_ok=True)
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# 2. 统一定义日志格式 (美化版)
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# <green> 等标签是控制台颜色,文件日志中会自动剥离颜色代码
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LOG_FORMAT = (
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"<green>{time:YYYY-MM-DD HH:mm:ss.SSS}</green> | "
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"<level>{level: <8}</level> | "
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"<magenta>{extra[source]: <8}</magenta> | "
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"<cyan>{module}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> - "
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"<level>{message}</level>"
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)
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def setup_logger():
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"""
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只需在项目入口调用一次。
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如果是简单的自动化脚本,甚至可以直接在模块内执行。
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"""
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# 移除 Loguru 默认的控制台处理器(避免重复打印)
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logger.remove()
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# 添加自定义控制台输出
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logger.add(
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sys.stdout,
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format=LOG_FORMAT,
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level="INFO",
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colorize=True,
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# 默认给一个 'Global' 的 device 标签
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filter=lambda record: record["extra"].setdefault("source", "System")
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)
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# 添加按天滚动的日志文件
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logger.add(
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str(LOG_DIR / "appium_{time:YYYY-MM-DD}.log"),
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format=LOG_FORMAT,
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level="DEBUG",
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rotation="00:00", # 每天午夜滚动
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retention="10 days", # 保留最近10天
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compression="zip", # 旧日志自动压缩
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encoding="utf-8",
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enqueue=True # 开启队列模式,确保多线程下日志不串行
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)
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# --- 核心特性 1:装饰器集成 ---
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def trace_step(step_desc="", source: str = 'task'):
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"""
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通用执行追踪装饰器:
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1. 智能识别并过滤 self/cls 参数
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2. 记录入参、出参、耗时
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3. 异常自动捕获并记录
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"""
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def decorator(func):
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@functools.wraps(func)
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def wrapper(*args, **kwargs):
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# --- 智能参数解析 ---
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# 获取函数的签名
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sig = inspect.signature(func)
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params = list(sig.parameters.values())
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# 检查第一个参数名是否为 'self' 或 'cls'
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# 这样既兼容了 PageObject 的实例方法,也兼容了纯函数
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if params and params[0].name in ('self', 'cls'):
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display_args = args[1:]
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else:
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display_args = args
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# 格式化参数显示,方便阅读
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args_repr = [repr(a) for a in display_args]
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kwargs_repr = [f"{k}={v!r}" for k, v in kwargs.items()]
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all_params = ", ".join(args_repr + kwargs_repr)
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func_name = f"{func.__module__}.{func.__name__}"
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# 使用 bind 临时改变这一步的 source 标签
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_logger = logger.bind(source=source)
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# 使用关联的上下文 logger
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# logger.info(f"🚀 [步骤开始] {step_desc} | 执行方法: {func_name} | 参数: {display_args} {kwargs}")
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_logger.info(f"🚀 [步骤开始] {step_desc} | 方法: {func_name}({all_params})")
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start_t = time.perf_counter()
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try:
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result = func(*args, **kwargs)
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duration = time.perf_counter() - start_t
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_logger.success(f"✅ [步骤成功] {step_desc} | 耗时: {duration:.2f}s | 返回: {result!r}")
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return result
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except Exception as e:
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duration = time.perf_counter() - start_t
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_logger.error(
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f"❌ [步骤失败] {step_desc} | 耗时: {duration:.2f}s | 异常: {type(e).__name__}: {e}")
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raise e
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return wrapper
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return decorator
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# 初始化
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setup_logger()
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# 导出供外部使用
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__all__ = ["logger", "trace_step"]
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