feat(性能分析): 实现性能分析工具模块并添加相关测试
添加性能分析工具模块，包括时间测量、内存分析和性能统计功能 添加测试文件和示例配置，完善性能分析工具的使用场景 在工具模块中实现单例装饰器并导出到__init__.py
This commit is contained in:
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#!/usr/bin/env python3
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"""
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工具函数包
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"""
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# 导出核心工具
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from .logger import logger
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from .exceptions import *
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from .json_utils import *
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from .singleton import singleton
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from .executor import run_in_thread_pool, initialize_executor
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from .performance import (
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timeit,
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profile,
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aprofile,
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memory_profile,
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memory_profile_decorator,
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performance_monitor,
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PerformanceStats,
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performance_stats,
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global_stats
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)
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__all__ = [
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'logger',
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'timeit',
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'profile',
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'aprofile',
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'memory_profile',
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'memory_profile_decorator',
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'performance_monitor',
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'PerformanceStats',
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'performance_stats',
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'global_stats',
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'run_in_thread_pool',
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'initialize_executor',
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'singleton'
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]
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365
core/utils/performance.py
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365
core/utils/performance.py
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#!/usr/bin/env python3
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"""
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性能分析工具模块
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提供同步和异步函数的性能分析装饰器、上下文管理器和统计工具。
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主要功能：
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1. 函数执行时间分析（支持同步和异步）
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2. 内存使用分析
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3. 性能统计和报告生成
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4. 低开销的生产环境监控
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"""
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import time
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import asyncio
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import functools
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import logging
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from typing import Dict, Any, Callable, Optional
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import inspect
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# 尝试导入性能分析库
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try:
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from pyinstrument import Profiler
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from pyinstrument.renderers import HTMLRenderer
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PYINSTRUMENT_AVAILABLE = True
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except ImportError:
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PYINSTRUMENT_AVAILABLE = False
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# 尝试导入内存分析库
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try:
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from memory_profiler import memory_usage
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MEMORY_PROFILER_AVAILABLE = True
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except ImportError:
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MEMORY_PROFILER_AVAILABLE = False
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from .logger import logger
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class PerformanceStats:
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"""
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性能统计工具类
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用于收集和报告函数执行的性能指标
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"""
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def __init__(self):
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self.stats: Dict[str, Dict[str, Any]] = {}
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def record(self, func_name: str, duration: float, memory_used: Optional[float] = None):
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"""
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记录函数执行的性能数据
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Args:
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func_name: 函数名称
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duration: 执行时间（秒）
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memory_used: 使用的内存（MB），可选
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"""
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if func_name not in self.stats:
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self.stats[func_name] = {
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"count": 0,
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"total_time": 0.0,
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"avg_time": 0.0,
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"min_time": float('inf'),
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"max_time": 0.0,
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"total_memory": 0.0,
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"avg_memory": 0.0
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}
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stat = self.stats[func_name]
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stat["count"] += 1
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stat["total_time"] += duration
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stat["avg_time"] = stat["total_time"] / stat["count"]
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stat["min_time"] = min(stat["min_time"], duration)
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stat["max_time"] = max(stat["max_time"], duration)
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if memory_used is not None:
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stat["total_memory"] += memory_used
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stat["avg_memory"] = stat["total_memory"] / stat["count"]
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def report(self) -> str:
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"""
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生成性能统计报告
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Returns:
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格式化的性能统计报告字符串
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"""
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if not self.stats:
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return "暂无性能统计数据"
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report = ["\n=== 性能统计报告 ===\n"]
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report.append(f"{'函数名':<40} {'调用次数':<10} {'平均时间(ms)':<15} {'最长时间(ms)':<15} {'内存(MB)':<10}")
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report.append("-" * 100)
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for func_name, stat in sorted(self.stats.items(), key=lambda x: x[1]["total_time"], reverse=True):
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memory_str = f"{stat['avg_memory']:.2f}" if stat['avg_memory'] > 0 else "-"
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report.append(
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f"{func_name:<40} {stat['count']:<10} {stat['avg_time']*1000:<15.2f} "
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f"{stat['max_time']*1000:<15.2f} {memory_str:<10}"
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)
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report.append("=" * 100)
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return "\n".join(report)
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def reset(self):
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"""
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重置性能统计数据
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"""
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self.stats.clear()
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# 创建全局性能统计实例
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performance_stats = PerformanceStats()
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def timeit(func: Callable = None, *, log_level: int = logging.INFO, collect_stats: bool = True):
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"""
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函数执行时间分析装饰器（支持同步和异步）
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Args:
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func: 要装饰的函数
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log_level: 日志级别
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collect_stats: 是否收集到全局统计中
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Returns:
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装饰后的函数
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"""
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def decorator(func: Callable) -> Callable:
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func_name = func.__qualname__
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is_coroutine = inspect.iscoroutinefunction(func)
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if is_coroutine:
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@functools.wraps(func)
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async def async_wrapper(*args, **kwargs):
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start_time = time.perf_counter()
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try:
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result = await func(*args, **kwargs)
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finally:
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end_time = time.perf_counter()
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duration = end_time - start_time
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if collect_stats:
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performance_stats.record(func_name, duration)
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logger.log(log_level, f"[性能] {func_name} 执行时间: {duration*1000:.2f} ms")
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return result
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return async_wrapper
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else:
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@functools.wraps(func)
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def sync_wrapper(*args, **kwargs):
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start_time = time.perf_counter()
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try:
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result = func(*args, **kwargs)
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finally:
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end_time = time.perf_counter()
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duration = end_time - start_time
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if collect_stats:
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performance_stats.record(func_name, duration)
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logger.log(log_level, f"[性能] {func_name} 执行时间: {duration*1000:.2f} ms")
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return result
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return sync_wrapper
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if func is None:
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return decorator
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return decorator(func)
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class profile:
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"""
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性能分析上下文管理器
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使用 pyinstrument 进行详细的性能分析
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"""
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def __init__(self, enabled: bool = True, output_file: Optional[str] = None):
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"""
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Args:
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enabled: 是否启用分析
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output_file: 分析结果输出文件路径（HTML格式）
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"""
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self.enabled = enabled
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self.output_file = output_file
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self.profiler = None
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def __enter__(self):
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if self.enabled and PYINSTRUMENT_AVAILABLE:
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self.profiler = Profiler()
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self.profiler.start()
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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if self.enabled and PYINSTRUMENT_AVAILABLE and self.profiler:
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self.profiler.stop()
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# 输出到日志
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logger.info(f"[性能分析] {self.profiler.print()}")
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# 如果指定了输出文件，保存为HTML
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if self.output_file:
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try:
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html = self.profiler.render(HTMLRenderer())
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with open(self.output_file, 'w', encoding='utf-8') as f:
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f.write(html)
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logger.info(f"[性能分析] 报告已保存到: {self.output_file}")
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except Exception as e:
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logger.error(f"[性能分析] 保存报告失败: {e}")
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async def aprofile(func: Callable, *args, **kwargs):
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"""
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异步函数性能分析
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Args:
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func: 要分析的异步函数
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*args: 函数参数
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**kwargs: 函数关键字参数
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Returns:
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函数执行结果
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"""
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if not PYINSTRUMENT_AVAILABLE:
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logger.warning("[性能分析] pyinstrument 未安装，无法进行详细分析")
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return await func(*args, **kwargs)
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profiler = Profiler()
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profiler.start()
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try:
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result = await func(*args, **kwargs)
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finally:
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profiler.stop()
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logger.info(f"[性能分析] {profiler.print()}")
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return result
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class memory_profile:
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"""
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内存分析上下文管理器
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"""
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def __init__(self, interval: float = 0.1, enabled: bool = True):
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"""
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Args:
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interval: 内存采样间隔（秒）
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enabled: 是否启用内存分析
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"""
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self.interval = interval
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self.enabled = enabled
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self.memory_start = 0.0
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self.memory_end = 0.0
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def __enter__(self):
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if self.enabled and MEMORY_PROFILER_AVAILABLE:
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self.memory_start = memory_usage()[0]
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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if self.enabled and MEMORY_PROFILER_AVAILABLE:
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self.memory_end = memory_usage()[0]
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memory_used = self.memory_end - self.memory_start
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logger.info(f"[内存分析] 使用内存: {memory_used:.2f} MB")
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def memory_profile_decorator(func: Callable = None, *, interval: float = 0.1):
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"""
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内存分析装饰器（支持同步函数）
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Args:
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func: 要装饰的函数
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interval: 内存采样间隔
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Returns:
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装饰后的函数
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"""
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def decorator(func: Callable) -> Callable:
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@functools.wraps(func)
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def wrapper(*args, **kwargs):
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if not MEMORY_PROFILER_AVAILABLE:
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return func(*args, **kwargs)
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mem_usage = memory_usage(
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(func, args, kwargs),
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interval=interval,
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timeout=None,
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include_children=False
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)
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max_memory = max(mem_usage)
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logger.info(f"[内存分析] {func.__qualname__} 最大内存使用: {max_memory:.2f} MB")
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return func(*args, **kwargs)
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return wrapper
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if func is None:
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return decorator
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return decorator(func)
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def performance_monitor(func: Callable = None, *, threshold: float = 1.0):
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"""
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性能监控装饰器
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仅当函数执行时间超过阈值时记录日志
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适合生产环境使用
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Args:
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func: 要装饰的函数
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threshold: 时间阈值（秒）
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Returns:
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装饰后的函数
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"""
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def decorator(func: Callable) -> Callable:
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func_name = func.__qualname__
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is_coroutine = inspect.iscoroutinefunction(func)
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if is_coroutine:
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@functools.wraps(func)
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async def async_wrapper(*args, **kwargs):
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start_time = time.perf_counter()
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result = await func(*args, **kwargs)
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end_time = time.perf_counter()
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duration = end_time - start_time
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if duration > threshold:
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logger.warning(f"[性能监控] {func_name} 执行时间过长: {duration*1000:.2f} ms (阈值: {threshold*1000:.2f} ms)")
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return result
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return async_wrapper
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else:
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@functools.wraps(func)
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def sync_wrapper(*args, **kwargs):
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start_time = time.perf_counter()
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result = func(*args, **kwargs)
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end_time = time.perf_counter()
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duration = end_time - start_time
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if duration > threshold:
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logger.warning(f"[性能监控] {func_name} 执行时间过长: {duration*1000:.2f} ms (阈值: {threshold*1000:.2f} ms)")
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return result
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return sync_wrapper
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if func is None:
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return decorator
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return decorator(func)
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# 全局实例
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global_stats = PerformanceStats()
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__all__ = [
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'timeit',
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'profile',
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'aprofile',
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'memory_profile',
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'memory_profile_decorator',
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'performance_monitor',
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'PerformanceStats',
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'performance_stats',
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'global_stats'
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]
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@@ -2,6 +2,7 @@
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通用单例模式基类
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"""
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from typing import Any, Optional, Type, TypeVar
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import functools
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T = TypeVar('T')
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@@ -38,3 +39,29 @@ class Singleton:
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if self._initialized:
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return
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self._initialized = True
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def singleton(cls: Type[T]) -> Type[T]:
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"""
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单例装饰器
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将普通类转换为单例类，确保整个应用程序中只有一个实例。
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Args:
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cls: 要转换为单例的类
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Returns:
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Type[T]: 单例类
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"""
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_instance: Optional[T] = None
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_initialized: bool = False
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@functools.wraps(cls)
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def wrapper(*args: Any, **kwargs: Any) -> T:
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nonlocal _instance, _initialized
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if _instance is None:
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_instance = cls(*args, **kwargs)
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return _instance
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return wrapper
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Reference in New Issue
Block a user