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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76
performance_config_example.py
Normal file
76
performance_config_example.py
Normal file
@@ -0,0 +1,76 @@
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#!/usr/bin/env python3
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"""
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性能分析配置示例
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展示如何在项目中配置和使用性能分析功能。
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"""
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# 配置性能分析的使用方式
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PERFORMANCE_CONFIG = {
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# 全局性能分析开关
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'enabled': True,
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# 详细性能分析开关（使用 pyinstrument）
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'detailed': False,
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# 内存分析开关
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'memory': False,
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# 性能监控阈值（秒）
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'threshold': 0.5,
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# 性能报告输出文件
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'output_file': 'performance_report.html',
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# 要监控的核心组件列表
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'monitored_components': [
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'core.ws.WS',
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'core.managers.plugin_manager',
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'core.managers.browser_manager',
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'core.utils.executor.CodeExecutor',
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'core.handlers.event_handler',
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]
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}
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def get_performance_config():
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"""
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获取性能分析配置
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Returns:
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dict: 性能分析配置
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"""
|
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import os
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import json
|
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|
||||
# 从环境变量加载配置
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||||
config = PERFORMANCE_CONFIG.copy()
|
||||
|
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if os.environ.get('PERFORMANCE_PROFILE'):
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config['detailed'] = os.environ['PERFORMANCE_PROFILE'] == '1'
|
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|
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if os.environ.get('PERFORMANCE_MEMORY'):
|
||||
config['memory'] = os.environ['PERFORMANCE_MEMORY'] == '1'
|
||||
|
||||
if os.environ.get('PERFORMANCE_THRESHOLD'):
|
||||
try:
|
||||
config['threshold'] = float(os.environ['PERFORMANCE_THRESHOLD'])
|
||||
except ValueError:
|
||||
pass
|
||||
|
||||
if os.environ.get('PERFORMANCE_OUTPUT'):
|
||||
config['output_file'] = os.environ['PERFORMANCE_OUTPUT']
|
||||
|
||||
if os.environ.get('PERFORMANCE_STATS'):
|
||||
config['enabled'] = os.environ['PERFORMANCE_STATS'] == '1'
|
||||
|
||||
return config
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 打印当前配置
|
||||
print("当前性能分析配置:")
|
||||
print("=" * 50)
|
||||
config = get_performance_config()
|
||||
for key, value in config.items():
|
||||
print(f"{key}: {value}")
|
||||
94
profile_main.py
Normal file
94
profile_main.py
Normal file
@@ -0,0 +1,94 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
性能分析入口文件
|
||||
|
||||
用于启动带有性能分析功能的应用程序。
|
||||
|
||||
使用方法：
|
||||
python profile_main.py [options]
|
||||
|
||||
选项：
|
||||
-h, --help 显示帮助信息
|
||||
--profile, -p 启用详细性能分析（使用 pyinstrument）
|
||||
--memory, -m 启用内存使用分析
|
||||
--output, -o FILE 性能分析报告输出文件（HTML格式）
|
||||
--threshold, -t SEC 设置性能监控阈值（秒）
|
||||
--stats, -s 在程序结束时输出性能统计报告
|
||||
"""
|
||||
|
||||
import sys
|
||||
import argparse
|
||||
import os
|
||||
|
||||
# 将项目根目录添加到 sys.path
|
||||
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
|
||||
sys.path.insert(0, ROOT_DIR)
|
||||
|
||||
# 解析命令行参数
|
||||
parser = argparse.ArgumentParser(description='性能分析入口文件')
|
||||
parser.add_argument('--profile', '-p', action='store_true', help='启用详细性能分析（使用 pyinstrument）')
|
||||
parser.add_argument('--memory', '-m', action='store_true', help='启用内存使用分析')
|
||||
parser.add_argument('--output', '-o', type=str, default='performance_report.html', help='性能分析报告输出文件（HTML格式）')
|
||||
parser.add_argument('--threshold', '-t', type=float, default=0.5, help='设置性能监控阈值（秒）')
|
||||
parser.add_argument('--stats', '-s', action='store_true', help='在程序结束时输出性能统计报告')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# 设置全局性能分析配置
|
||||
os.environ['PERFORMANCE_PROFILE'] = '1' if args.profile else '0'
|
||||
os.environ['PERFORMANCE_MEMORY'] = '1' if args.memory else '0'
|
||||
os.environ['PERFORMANCE_OUTPUT'] = args.output
|
||||
os.environ['PERFORMANCE_THRESHOLD'] = str(args.threshold)
|
||||
os.environ['PERFORMANCE_STATS'] = '1' if args.stats else '0'
|
||||
|
||||
# 导入并运行主程序
|
||||
from core.utils.performance import profile, aprofile
|
||||
from main import main
|
||||
import asyncio
|
||||
|
||||
async def main_with_profile():
|
||||
"""
|
||||
带有性能分析的主函数入口
|
||||
"""
|
||||
if args.profile:
|
||||
# 使用 pyinstrument 进行详细性能分析
|
||||
from pyinstrument import Profiler
|
||||
from pyinstrument.renderers import HTMLRenderer
|
||||
|
||||
profiler = Profiler()
|
||||
profiler.start()
|
||||
|
||||
try:
|
||||
await main()
|
||||
finally:
|
||||
profiler.stop()
|
||||
|
||||
# 输出分析结果到控制台
|
||||
print("\n" + "=" * 80)
|
||||
print("性能分析结果")
|
||||
print("=" * 80)
|
||||
print(profiler.print())
|
||||
|
||||
# 保存HTML报告
|
||||
try:
|
||||
html = profiler.render(HTMLRenderer())
|
||||
with open(args.output, 'w', encoding='utf-8') as f:
|
||||
f.write(html)
|
||||
print(f"\n性能分析报告已保存到: {args.output}")
|
||||
except Exception as e:
|
||||
print(f"\n保存性能分析报告失败: {e}")
|
||||
else:
|
||||
# 不使用详细分析，直接运行
|
||||
await main()
|
||||
|
||||
if __name__ == "__main__":
|
||||
try:
|
||||
asyncio.run(main_with_profile())
|
||||
finally:
|
||||
# 输出性能统计报告
|
||||
if args.stats:
|
||||
from core.utils.performance import performance_stats
|
||||
print("\n" + "=" * 80)
|
||||
print("性能统计报告")
|
||||
print("=" * 80)
|
||||
print(performance_stats.report())
|
||||
4
requirements-dev.txt
Normal file
4
requirements-dev.txt
Normal file
@@ -0,0 +1,4 @@
|
||||
# 开发依赖
|
||||
pyinstrument>=4.5.0 # 性能分析工具，支持异步代码
|
||||
memory-profiler>=0.61.0 # 内存分析工具
|
||||
psutil>=5.9.8 # 系统资源监控
|
||||
81
test_performance_simple.py
Normal file
81
test_performance_simple.py
Normal file
@@ -0,0 +1,81 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
简单的性能分析功能测试脚本
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import time
|
||||
from core.utils.performance import (
|
||||
timeit,
|
||||
profile,
|
||||
aprofile,
|
||||
PerformanceStats,
|
||||
performance_stats
|
||||
)
|
||||
|
||||
|
||||
print("=" * 80)
|
||||
print("性能分析功能测试")
|
||||
print("=" * 80)
|
||||
|
||||
# 重置全局性能统计
|
||||
performance_stats.reset()
|
||||
|
||||
# 测试1: 同步函数的时间测量
|
||||
@timeit
|
||||
def sync_test():
|
||||
"""同步测试函数"""
|
||||
time.sleep(0.1)
|
||||
return "sync done"
|
||||
|
||||
# 测试2: 异步函数的时间测量
|
||||
@timeit
|
||||
async def async_test():
|
||||
"""异步测试函数"""
|
||||
await asyncio.sleep(0.1)
|
||||
return "async done"
|
||||
|
||||
# 异步主函数
|
||||
async def main():
|
||||
# 同步函数测试
|
||||
print("执行同步函数...")
|
||||
sync_result = sync_test()
|
||||
print(f"同步函数结果: {sync_result}")
|
||||
|
||||
# 异步函数测试
|
||||
print("\n执行异步函数...")
|
||||
async_result = await async_test()
|
||||
print(f"异步函数结果: {async_result}")
|
||||
|
||||
# 测试3: 详细性能分析
|
||||
print("\n2. 测试性能分析上下文管理器:")
|
||||
print("=" * 80)
|
||||
|
||||
with profile(enabled=False): # 禁用实际分析以避免输出太多
|
||||
time.sleep(0.05)
|
||||
print("性能分析上下文管理器测试完成")
|
||||
|
||||
# 测试4: 性能统计报告
|
||||
print("\n3. 测试性能统计报告:")
|
||||
print("=" * 80)
|
||||
|
||||
# 执行多次函数调用
|
||||
for i in range(3):
|
||||
sync_test()
|
||||
await async_test()
|
||||
|
||||
# 生成并打印性能报告
|
||||
print("\n性能统计报告:")
|
||||
print(performance_stats.report())
|
||||
|
||||
|
||||
# 执行测试
|
||||
print("\n1. 测试时间测量装饰器:")
|
||||
print("=" * 80)
|
||||
|
||||
# 使用 asyncio.run() 执行异步主函数
|
||||
asyncio.run(main())
|
||||
|
||||
print("\n" + "=" * 80)
|
||||
print("所有测试完成！")
|
||||
print("=" * 80)
|
||||
266
tests/test_performance.py
Normal file
266
tests/test_performance.py
Normal file
@@ -0,0 +1,266 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
性能分析工具测试
|
||||
|
||||
测试各种性能分析功能的正确性和可用性。
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import time
|
||||
import pytest
|
||||
from typing import Optional
|
||||
|
||||
# 导入性能分析工具
|
||||
from core.utils.performance import (
|
||||
timeit,
|
||||
profile,
|
||||
aprofile,
|
||||
memory_profile,
|
||||
memory_profile_decorator,
|
||||
performance_monitor,
|
||||
PerformanceStats,
|
||||
performance_stats
|
||||
)
|
||||
|
||||
|
||||
# 重置全局性能统计
|
||||
def setup_module():
|
||||
performance_stats.reset()
|
||||
|
||||
|
||||
def teardown_module():
|
||||
performance_stats.reset()
|
||||
|
||||
|
||||
class TestTimeitDecorator:
|
||||
"""测试 timeit 装饰器"""
|
||||
|
||||
@timeit(log_level=20) # 使用 INFO 级别
|
||||
def test_sync_function(self):
|
||||
"""测试同步函数的时间测量"""
|
||||
time.sleep(0.1)
|
||||
return "done"
|
||||
|
||||
@timeit(log_level=20)
|
||||
async def test_async_function(self):
|
||||
"""测试异步函数的时间测量"""
|
||||
await asyncio.sleep(0.1)
|
||||
return "done"
|
||||
|
||||
def test_sync_function_works(self):
|
||||
"""验证同步函数能正常执行"""
|
||||
result = self.test_sync_function()
|
||||
assert result == "done"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_async_function_works(self):
|
||||
"""验证异步函数能正常执行"""
|
||||
result = await self.test_async_function()
|
||||
assert result == "done"
|
||||
|
||||
|
||||
class TestProfileContextManager:
|
||||
"""测试 profile 上下文管理器"""
|
||||
|
||||
def test_profile_sync_code(self):
|
||||
"""测试同步代码的性能分析"""
|
||||
# 捕获标准输出
|
||||
import io
|
||||
import sys
|
||||
from contextlib import redirect_stdout
|
||||
|
||||
f = io.StringIO()
|
||||
with redirect_stdout(f):
|
||||
with profile(enabled=False): # 禁用实际分析以提高测试速度
|
||||
time.sleep(0.01)
|
||||
|
||||
output = f.getvalue()
|
||||
# 应该没有输出（因为 enabled=False）
|
||||
assert "性能分析" not in output
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_aprofile_async_function(self):
|
||||
"""测试异步函数的性能分析"""
|
||||
async def async_test():
|
||||
await asyncio.sleep(0.01)
|
||||
return "test"
|
||||
|
||||
result = await aprofile(async_test)
|
||||
assert result == "test"
|
||||
|
||||
|
||||
class TestPerformanceMonitor:
|
||||
"""测试 performance_monitor 装饰器"""
|
||||
|
||||
@performance_monitor(threshold=0.05)
|
||||
def test_slow_sync_function(self):
|
||||
"""测试慢速同步函数的监控"""
|
||||
time.sleep(0.1) # 超过阈值
|
||||
return "slow"
|
||||
|
||||
@performance_monitor(threshold=0.05)
|
||||
def test_fast_sync_function(self):
|
||||
"""测试快速同步函数的监控"""
|
||||
time.sleep(0.01) # 低于阈值
|
||||
return "fast"
|
||||
|
||||
@performance_monitor(threshold=0.05)
|
||||
async def test_slow_async_function(self):
|
||||
"""测试慢速异步函数的监控"""
|
||||
await asyncio.sleep(0.1)
|
||||
return "slow_async"
|
||||
|
||||
def test_slow_function_triggers_warning(self):
|
||||
"""验证慢速函数会触发警告"""
|
||||
result = self.test_slow_sync_function()
|
||||
assert result == "slow"
|
||||
|
||||
def test_fast_function_no_warning(self):
|
||||
"""验证快速函数不会触发警告"""
|
||||
result = self.test_fast_sync_function()
|
||||
assert result == "fast"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_slow_async_function_triggers_warning(self):
|
||||
"""验证慢速异步函数会触发警告"""
|
||||
result = await self.test_slow_async_function()
|
||||
assert result == "slow_async"
|
||||
|
||||
|
||||
class TestMemoryAnalysis:
|
||||
"""测试内存分析功能"""
|
||||
|
||||
def test_memory_profile_context_manager(self):
|
||||
"""测试内存分析上下文管理器"""
|
||||
# 禁用内存分析以提高测试速度
|
||||
with memory_profile(enabled=False):
|
||||
data = [i for i in range(1000)]
|
||||
sum(data)
|
||||
|
||||
@memory_profile_decorator
|
||||
def test_memory_intensive_function(self):
|
||||
"""测试内存密集型函数"""
|
||||
# 小数据集，避免测试耗时过长
|
||||
data = [i for i in range(1000)]
|
||||
return sum(data)
|
||||
|
||||
def test_memory_function_works(self):
|
||||
"""验证内存分析函数能正常执行"""
|
||||
result = self.test_memory_intensive_function()
|
||||
assert result == 499500
|
||||
|
||||
|
||||
class TestPerformanceStats:
|
||||
"""测试性能统计功能"""
|
||||
|
||||
def test_stats_initialization(self):
|
||||
"""测试性能统计对象初始化"""
|
||||
stats = PerformanceStats()
|
||||
assert isinstance(stats, PerformanceStats)
|
||||
assert stats.stats == {}
|
||||
|
||||
def test_stats_record(self):
|
||||
"""测试记录性能数据"""
|
||||
stats = PerformanceStats()
|
||||
stats.record("test_func", 0.1)
|
||||
|
||||
assert "test_func" in stats.stats
|
||||
assert stats.stats["test_func"]["count"] == 1
|
||||
assert stats.stats["test_func"]["total_time"] == 0.1
|
||||
assert stats.stats["test_func"]["avg_time"] == 0.1
|
||||
|
||||
def test_stats_report(self):
|
||||
"""测试生成性能报告"""
|
||||
stats = PerformanceStats()
|
||||
stats.record("func1", 0.1)
|
||||
stats.record("func2", 0.2)
|
||||
|
||||
report = stats.report()
|
||||
assert isinstance(report, str)
|
||||
assert "func1" in report
|
||||
assert "func2" in report
|
||||
|
||||
def test_stats_reset(self):
|
||||
"""测试重置性能统计"""
|
||||
stats = PerformanceStats()
|
||||
stats.record("test_func", 0.1)
|
||||
stats.reset()
|
||||
|
||||
assert stats.stats == {}
|
||||
|
||||
def test_global_stats_recording(self):
|
||||
"""测试全局性能统计记录"""
|
||||
# 先重置全局统计
|
||||
performance_stats.reset()
|
||||
|
||||
@timeit(collect_stats=True)
|
||||
def test_func():
|
||||
time.sleep(0.01)
|
||||
|
||||
test_func()
|
||||
|
||||
# 验证是否记录了性能数据
|
||||
assert "test_func" in performance_stats.stats
|
||||
assert performance_stats.stats["test_func"]["count"] == 1
|
||||
|
||||
|
||||
class TestIntegration:
|
||||
"""综合测试"""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_combined_features(self):
|
||||
"""测试多种性能分析功能的组合使用"""
|
||||
# 重置全局统计
|
||||
performance_stats.reset()
|
||||
|
||||
@timeit(collect_stats=True)
|
||||
@performance_monitor(threshold=0.05)
|
||||
async def test_async_func():
|
||||
await asyncio.sleep(0.06) # 超过阈值
|
||||
return "combined"
|
||||
|
||||
result = await test_async_func()
|
||||
assert result == "combined"
|
||||
|
||||
# 验证性能统计
|
||||
assert "test_async_func" in performance_stats.stats
|
||||
assert performance_stats.stats["test_async_func"]["count"] == 1
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 运行基本测试
|
||||
print("开始性能分析功能测试...")
|
||||
|
||||
# 测试同步函数
|
||||
@timeit
|
||||
def test_sync():
|
||||
time.sleep(0.1)
|
||||
return "sync"
|
||||
|
||||
# 测试异步函数
|
||||
@timeit
|
||||
async def test_async():
|
||||
await asyncio.sleep(0.1)
|
||||
return "async"
|
||||
|
||||
# 测试性能监控
|
||||
@performance_monitor(threshold=0.05)
|
||||
def slow_func():
|
||||
time.sleep(0.1)
|
||||
return "slow"
|
||||
|
||||
# 运行测试
|
||||
sync_result = test_sync()
|
||||
async_result = asyncio.run(test_async())
|
||||
slow_result = slow_func()
|
||||
|
||||
print(f"\n测试结果:")
|
||||
print(f"sync_result: {sync_result}")
|
||||
print(f"async_result: {async_result}")
|
||||
print(f"slow_result: {slow_result}")
|
||||
|
||||
# 输出性能统计报告
|
||||
print("\n性能统计报告:")
|
||||
print(performance_stats.report())
|
||||
|
||||
print("\n性能分析功能测试完成！")
|
||||
Reference in New Issue
Block a user