# -*- coding: utf-8 -*- """ 性能监控工具 监控系统性能，识别瓶颈，提供优化建议 """ import time import logging import threading from typing import Dict, List, Any, Optional from datetime import datetime, timedelta from collections import defaultdict, deque import psutil from .performance_config import PerformanceConfig logger = logging.getLogger(__name__) class PerformanceMonitor: """性能监控器""" def __init__(self): self.query_times = deque(maxlen=1000) self.api_response_times = deque(maxlen=1000) self.cache_hit_rates = defaultdict(list) self.system_metrics = deque(maxlen=100) self.monitoring_enabled = True self.monitor_thread = None self.start_monitoring() def start_monitoring(self): """启动性能监控""" if self.monitor_thread and self.monitor_thread.is_alive(): return self.monitoring_enabled = True self.monitor_thread = threading.Thread(target=self._monitor_loop, daemon=True) self.monitor_thread.start() logger.info("性能监控已启动") def stop_monitoring(self): """停止性能监控""" self.monitoring_enabled = False if self.monitor_thread: self.monitor_thread.join(timeout=5) logger.info("性能监控已停止") def _monitor_loop(self): """监控循环""" while self.monitoring_enabled: try: self._collect_system_metrics() self._analyze_performance() time.sleep(PerformanceConfig.MONITORING_INTERVAL) except Exception as e: logger.error(f"性能监控异常: {e}") time.sleep(10) def _collect_system_metrics(self): """收集系统指标""" try: metrics = { 'timestamp': datetime.now(), 'cpu_percent': psutil.cpu_percent(interval=1), 'memory_percent': psutil.virtual_memory().percent, 'disk_percent': psutil.disk_usage('/').percent, 'active_connections': len(psutil.net_connections()), 'query_count': len(self.query_times), 'api_count': len(self.api_response_times) } self.system_metrics.append(metrics) except Exception as e: logger.error(f"收集系统指标失败: {e}") def _analyze_performance(self): """分析性能""" try: # 分析查询性能 if self.query_times: avg_query_time = sum(self.query_times) / len(self.query_times) slow_queries = [t for t in self.query_times if t > PerformanceConfig.SLOW_QUERY_THRESHOLD] if len(slow_queries) > len(self.query_times) * 0.1: # 超过10%的查询是慢查询 logger.warning(f"检测到慢查询: 平均{avg_query_time:.2f}s, 慢查询比例{len(slow_queries)/len(self.query_times)*100:.1f}%") # 分析API性能 if self.api_response_times: avg_api_time = sum(self.api_response_times) / len(self.api_response_times) if avg_api_time > 2.0: # 平均API响应时间超过2秒 logger.warning(f"API响应时间较慢: 平均{avg_api_time:.2f}s") # 分析缓存性能 for cache_name, hit_rates in self.cache_hit_rates.items(): if hit_rates: avg_hit_rate = sum(hit_rates) / len(hit_rates) if avg_hit_rate < 0.5: # 缓存命中率低于50% logger.warning(f"缓存命中率较低: {cache_name} {avg_hit_rate*100:.1f}%") except Exception as e: logger.error(f"性能分析失败: {e}") def record_query_time(self, query_name: str, duration: float): """记录查询时间""" self.query_times.append(duration) if PerformanceConfig.PERFORMANCE_LOG_ENABLED: if duration > PerformanceConfig.SLOW_QUERY_THRESHOLD: logger.warning(f"慢查询: {query_name} 耗时 {duration:.2f}s") else: logger.debug(f"查询: {query_name} 耗时 {duration:.2f}s") def record_api_response_time(self, api_name: str, duration: float): """记录API响应时间""" self.api_response_times.append(duration) if PerformanceConfig.PERFORMANCE_LOG_ENABLED: if duration > 2.0: logger.warning(f"慢API: {api_name} 耗时 {duration:.2f}s") else: logger.debug(f"API: {api_name} 耗时 {duration:.2f}s") def record_cache_hit(self, cache_name: str, hit: bool): """记录缓存命中""" hit_rate = 1.0 if hit else 0.0 self.cache_hit_rates[cache_name].append(hit_rate) # 保持最近100次记录 if len(self.cache_hit_rates[cache_name]) > 100: self.cache_hit_rates[cache_name] = self.cache_hit_rates[cache_name][-100:] def get_performance_report(self) -> Dict[str, Any]: """获取性能报告""" try: report = { 'timestamp': datetime.now().isoformat(), 'query_performance': self._get_query_performance(), 'api_performance': self._get_api_performance(), 'cache_performance': self._get_cache_performance(), 'system_performance': self._get_system_performance(), 'recommendations': self._get_optimization_recommendations() } return report except Exception as e: logger.error(f"生成性能报告失败: {e}") return {'error': str(e)} def _get_query_performance(self) -> Dict[str, Any]: """获取查询性能""" if not self.query_times: return {'status': 'no_data'} avg_time = sum(self.query_times) / len(self.query_times) max_time = max(self.query_times) slow_queries = len([t for t in self.query_times if t > PerformanceConfig.SLOW_QUERY_THRESHOLD]) return { 'total_queries': len(self.query_times), 'avg_time': round(avg_time, 3), 'max_time': round(max_time, 3), 'slow_queries': slow_queries, 'slow_query_rate': round(slow_queries / len(self.query_times) * 100, 1) } def _get_api_performance(self) -> Dict[str, Any]: """获取API性能""" if not self.api_response_times: return {'status': 'no_data'} avg_time = sum(self.api_response_times) / len(self.api_response_times) max_time = max(self.api_response_times) slow_apis = len([t for t in self.api_response_times if t > 2.0]) return { 'total_requests': len(self.api_response_times), 'avg_time': round(avg_time, 3), 'max_time': round(max_time, 3), 'slow_requests': slow_apis, 'slow_request_rate': round(slow_apis / len(self.api_response_times) * 100, 1) } def _get_cache_performance(self) -> Dict[str, Any]: """获取缓存性能""" cache_stats = {} for cache_name, hit_rates in self.cache_hit_rates.items(): if hit_rates: avg_hit_rate = sum(hit_rates) / len(hit_rates) cache_stats[cache_name] = { 'hit_rate': round(avg_hit_rate * 100, 1), 'total_requests': len(hit_rates) } return cache_stats if cache_stats else {'status': 'no_data'} def _get_system_performance(self) -> Dict[str, Any]: """获取系统性能""" if not self.system_metrics: return {'status': 'no_data'} latest = self.system_metrics[-1] return { 'cpu_percent': latest['cpu_percent'], 'memory_percent': latest['memory_percent'], 'disk_percent': latest['disk_percent'], 'active_connections': latest['active_connections'] } def _get_optimization_recommendations(self) -> List[str]: """获取优化建议""" recommendations = [] # 查询性能建议 if self.query_times: avg_query_time = sum(self.query_times) / len(self.query_times) if avg_query_time > 1.0: recommendations.append("考虑优化数据库查询，添加索引或使用缓存") # API性能建议 if self.api_response_times: avg_api_time = sum(self.api_response_times) / len(self.api_response_times) if avg_api_time > 2.0: recommendations.append("考虑优化API响应时间，使用异步处理或缓存") # 缓存性能建议 for cache_name, hit_rates in self.cache_hit_rates.items(): if hit_rates: avg_hit_rate = sum(hit_rates) / len(hit_rates) if avg_hit_rate < 0.5: recommendations.append(f"优化{cache_name}缓存策略，提高命中率") # 系统资源建议 if self.system_metrics: latest = self.system_metrics[-1] if latest['cpu_percent'] > 80: recommendations.append("CPU使用率过高，考虑优化计算密集型操作") if latest['memory_percent'] > 80: recommendations.append("内存使用率过高，考虑清理缓存或优化内存使用") return recommendations # 全局性能监控器实例 performance_monitor = PerformanceMonitor()