mirror of
https://github.com/jxxghp/MoviePilot.git
synced 2026-02-13 15:37:33 +08:00
458 lines
17 KiB
Python
458 lines
17 KiB
Python
import gc
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import sys
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import threading
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import time
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from datetime import datetime
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from typing import Optional
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import psutil
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from pympler import muppy, summary, asizeof
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from app.core.config import settings
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from app.core.event import eventmanager, Event
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from app.log import logger
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from app.schemas import ConfigChangeEventData
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from app.schemas.types import EventType
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from app.utils.singleton import Singleton
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class MemoryHelper(metaclass=Singleton):
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"""
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内存管理工具类，用于监控和优化内存使用
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"""
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def __init__(self):
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# 检查间隔(秒) - 从配置获取，默认5分钟
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self._check_interval = settings.MEMORY_SNAPSHOT_INTERVAL * 60
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self._monitoring = False
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self._monitor_thread: Optional[threading.Thread] = None
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# 内存快照保存目录
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self._memory_snapshot_dir = settings.LOG_PATH / "memory_snapshots"
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# 保留的快照文件数量
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self._keep_count = settings.MEMORY_SNAPSHOT_KEEP_COUNT
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@eventmanager.register(EventType.ConfigChanged)
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def handle_config_changed(self, event: Event):
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"""
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处理配置变更事件，更新内存监控设置
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:param event: 事件对象
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"""
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if not event:
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return
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event_data: ConfigChangeEventData = event.event_data
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if event_data.key not in ['MEMORY_ANALYSIS', 'MEMORY_SNAPSHOT_INTERVAL', 'MEMORY_SNAPSHOT_KEEP_COUNT']:
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return
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# 更新配置
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if event_data.key == 'MEMORY_SNAPSHOT_INTERVAL':
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self._check_interval = settings.MEMORY_SNAPSHOT_INTERVAL * 60
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elif event_data.key == 'MEMORY_SNAPSHOT_KEEP_COUNT':
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self._keep_count = settings.MEMORY_SNAPSHOT_KEEP_COUNT
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self.stop_monitoring()
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self.start_monitoring()
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def start_monitoring(self):
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"""
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开始内存监控
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"""
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if not settings.MEMORY_ANALYSIS:
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return
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if self._monitoring:
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return
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# 创建内存快照目录
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self._memory_snapshot_dir.mkdir(parents=True, exist_ok=True)
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# 初始化内存分析器
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self._monitoring = True
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self._monitor_thread = threading.Thread(target=self._monitor_loop, daemon=True)
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self._monitor_thread.start()
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logger.info("内存监控已启动")
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def stop_monitoring(self):
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"""
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停止内存监控
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"""
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self._monitoring = False
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if self._monitor_thread:
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self._monitor_thread.join(timeout=5)
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logger.info("内存监控已停止")
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def _monitor_loop(self):
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"""
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内存监控循环
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"""
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logger.info("内存监控循环开始")
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while self._monitoring:
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try:
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# 生成内存快照
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self._create_memory_snapshot()
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time.sleep(self._check_interval)
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except Exception as e:
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logger.error(f"内存监控出错: {e}")
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# 出错后等待1分钟再继续
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time.sleep(60)
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logger.info("内存监控循环结束")
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def _create_memory_snapshot(self):
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"""
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创建内存快照并保存到文件
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"""
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try:
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# 获取当前时间戳
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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snapshot_file = self._memory_snapshot_dir / f"memory_snapshot_{timestamp}.txt"
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# 获取系统内存使用情况
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memory_usage = psutil.Process().memory_info().rss
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logger.info(f"开始创建内存快照: {snapshot_file}")
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# 第一步：写入基本信息和对象类型统计
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self._write_basic_info(snapshot_file, memory_usage)
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# 第二步：分析并写入类实例内存使用情况
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self._append_class_analysis(snapshot_file)
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# 第三步：分析并写入大内存变量详情
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self._append_variable_analysis(snapshot_file)
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logger.info(f"内存快照已保存: {snapshot_file}, 当前内存使用: {memory_usage / 1024 / 1024:.2f} MB")
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# 清理过期的快照文件（保留最近30个）
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self._cleanup_old_snapshots()
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except Exception as e:
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logger.error(f"创建内存快照失败: {e}")
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@staticmethod
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def _write_basic_info(snapshot_file, memory_usage):
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"""
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写入基本信息和对象类型统计
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"""
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# 获取当前进程的内存使用情况
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all_objects = muppy.get_objects()
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sum1 = summary.summarize(all_objects)
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with open(snapshot_file, 'w', encoding='utf-8') as f:
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f.write(f"内存快照时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
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f.write(f"当前进程内存使用: {memory_usage / 1024 / 1024:.2f} MB\n")
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f.write("=" * 80 + "\n")
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f.write("对象类型统计:\n")
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f.write("-" * 80 + "\n")
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# 写入对象统计信息
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for line in summary.format_(sum1):
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f.write(line + "\n")
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# 立即刷新到磁盘
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f.flush()
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logger.debug("基本信息已写入快照文件")
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def _append_class_analysis(self, snapshot_file):
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"""
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分析并追加类实例内存使用情况
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"""
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with open(snapshot_file, 'a', encoding='utf-8') as f:
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f.write("\n" + "=" * 80 + "\n")
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f.write("类实例内存使用情况 (按内存大小排序):\n")
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f.write("-" * 80 + "\n")
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f.write("正在分析中...\n")
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# 立即刷新，让用户知道这部分开始了
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f.flush()
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try:
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logger.debug("开始分析类实例内存使用情况")
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class_objects = self._get_class_memory_usage()
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# 重新打开文件，移除"正在分析中..."并写入实际结果
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with open(snapshot_file, 'r', encoding='utf-8') as f:
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content = f.read()
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# 替换"正在分析中..."
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content = content.replace("正在分析中...\n", "")
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with open(snapshot_file, 'w', encoding='utf-8') as f:
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f.write(content)
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if class_objects:
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# 只显示前100个类
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for i, class_info in enumerate(class_objects[:100], 1):
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f.write(f"{i:3d}. {class_info['name']:<50} "
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f"{class_info['size_mb']:>8.2f} MB ({class_info['count']} 个实例)\n")
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else:
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f.write("未找到有效的类实例信息\n")
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f.flush()
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except Exception as e:
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logger.error(f"获取类实例信息失败: {e}")
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# 即使出错也要更新文件
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with open(snapshot_file, 'r', encoding='utf-8') as f:
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content = f.read()
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content = content.replace("正在分析中...\n", f"获取类实例信息失败: {e}\n")
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with open(snapshot_file, 'w', encoding='utf-8') as f:
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f.write(content)
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f.flush()
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logger.debug("类实例分析已完成并写入")
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def _append_variable_analysis(self, snapshot_file):
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"""
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分析并追加大内存变量详情
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"""
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with open(snapshot_file, 'a', encoding='utf-8') as f:
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f.write("\n" + "=" * 80 + "\n")
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f.write("大内存变量详情 (前100个):\n")
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f.write("-" * 80 + "\n")
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f.write("正在分析中...\n")
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# 立即刷新，让用户知道这部分开始了
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f.flush()
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try:
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logger.debug("开始分析大内存变量")
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large_variables = self._get_large_variables(100)
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# 重新打开文件，移除"正在分析中..."并写入实际结果
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with open(snapshot_file, 'r', encoding='utf-8') as f:
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content = f.read()
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# 替换最后的"正在分析中..."
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content = content.replace("正在分析中...\n", "")
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with open(snapshot_file, 'w', encoding='utf-8') as f:
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f.write(content)
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if large_variables:
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for i, var_info in enumerate(large_variables, 1):
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f.write(
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f"{i:3d}. {var_info['name']:<30} {var_info['type']:<15} {var_info['size_mb']:>8.2f} MB\n")
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else:
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f.write("未找到大内存变量\n")
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f.flush()
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except Exception as e:
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logger.error(f"获取大内存变量信息失败: {e}")
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# 即使出错也要更新文件
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with open(snapshot_file, 'r', encoding='utf-8') as f:
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content = f.read()
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content = content.replace("正在分析中...\n", f"获取变量信息失败: {e}\n")
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with open(snapshot_file, 'w', encoding='utf-8') as f:
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f.write(content)
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f.flush()
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logger.debug("大内存变量分析已完成并写入")
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def _cleanup_old_snapshots(self):
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"""
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清理过期的内存快照文件，只保留最近的指定数量文件
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"""
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try:
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snapshot_files = list(self._memory_snapshot_dir.glob("memory_snapshot_*.txt"))
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if len(snapshot_files) > self._keep_count:
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# 按修改时间排序，删除最旧的文件
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snapshot_files.sort(key=lambda x: x.stat().st_mtime)
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for old_file in snapshot_files[:-self._keep_count]:
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old_file.unlink()
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logger.debug(f"已删除过期内存快照: {old_file}")
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except Exception as e:
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logger.error(f"清理过期快照失败: {e}")
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@staticmethod
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def _get_class_memory_usage():
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"""
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获取所有类实例的内存使用情况，按内存大小排序
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"""
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class_info = {}
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processed_count = 0
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error_count = 0
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# 获取所有对象
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all_objects = muppy.get_objects()
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logger.debug(f"开始分析 {len(all_objects)} 个对象的类实例内存使用情况")
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for obj in all_objects:
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try:
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# 跳过类对象本身，统计类的实例
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if isinstance(obj, type):
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continue
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# 获取对象的类名 - 这里可能会出错
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obj_class = type(obj)
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# 安全地获取类名
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try:
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if hasattr(obj_class, '__module__') and hasattr(obj_class, '__name__'):
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class_name = f"{obj_class.__module__}.{obj_class.__name__}"
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else:
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class_name = str(obj_class)
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except Exception as e:
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# 如果获取类名失败，使用简单的类型描述
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class_name = f"<unknown_class_{id(obj_class)}>"
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logger.debug(f"获取类名失败: {e}")
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# 计算对象本身的内存使用（不包括引用对象，避免重复计算）
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size_bytes = sys.getsizeof(obj)
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if size_bytes < 100: # 跳过太小的对象
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continue
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size_mb = size_bytes / 1024 / 1024
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processed_count += 1
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if class_name in class_info:
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class_info[class_name]['size_mb'] += size_mb
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class_info[class_name]['count'] += 1
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else:
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class_info[class_name] = {
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'name': class_name,
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'size_mb': size_mb,
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'count': 1
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}
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except Exception as e:
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# 捕获所有可能的异常，包括SQLAlchemy、ORM等框架的异常
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error_count += 1
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if error_count <= 5: # 只记录前5个错误，避免日志过多
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logger.debug(f"分析对象时出错: {e}")
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continue
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logger.debug(f"类实例分析完成: 处理了 {processed_count} 个对象, 遇到 {error_count} 个错误")
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# 按内存大小排序
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sorted_classes = sorted(class_info.values(), key=lambda x: x['size_mb'], reverse=True)
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return sorted_classes
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def _get_large_variables(self, limit=100):
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"""
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获取大内存变量信息，按内存大小排序
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使用已计算对象集合避免重复计算
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"""
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large_vars = []
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processed_count = 0
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calculated_objects = set() # 避免重复计算
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# 获取所有对象
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all_objects = muppy.get_objects()
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logger.debug(f"开始分析 {len(all_objects)} 个对象的内存使用情况")
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for obj in all_objects:
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# 跳过类对象
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if isinstance(obj, type):
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continue
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# 跳过已经计算过的对象
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obj_id = id(obj)
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if obj_id in calculated_objects:
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continue
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try:
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# 首先使用 sys.getsizeof 快速筛选
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shallow_size = sys.getsizeof(obj)
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if shallow_size < 1024: # 只处理大于1KB的对象
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continue
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# 对于较大的对象，使用 asizeof 进行深度计算
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size_bytes = asizeof.asizeof(obj)
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# 只处理大于10KB的对象，提高分析效率
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if size_bytes < 10240:
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continue
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size_mb = size_bytes / 1024 / 1024
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processed_count += 1
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calculated_objects.add(obj_id)
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# 获取对象信息
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var_info = self._get_variable_info(obj, size_mb)
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if var_info:
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large_vars.append(var_info)
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# 如果已经找到足够多的大对象，可以提前结束
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if len(large_vars) >= limit * 2: # 多收集一些，后面排序筛选
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break
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except Exception as e:
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# 更广泛的异常捕获
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logger.debug(f"分析对象失败: {e}")
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continue
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logger.debug(f"处理了 {processed_count} 个大对象，找到 {len(large_vars)} 个有效变量")
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# 按内存大小排序并返回前N个
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large_vars.sort(key=lambda x: x['size_mb'], reverse=True)
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return large_vars[:limit]
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def _get_variable_info(self, obj, size_mb):
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"""
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获取变量的描述信息
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"""
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try:
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obj_type = type(obj).__name__
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# 尝试获取变量名
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var_name = self._get_variable_name(obj)
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# 生成描述性信息
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if isinstance(obj, dict):
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key_count = len(obj)
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if key_count > 0:
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sample_keys = list(obj.keys())[:3]
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var_name += f" ({key_count}项, 键: {sample_keys})"
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elif isinstance(obj, (list, tuple, set)):
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var_name += f" ({len(obj)}个元素)"
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elif isinstance(obj, str):
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if len(obj) > 50:
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var_name += f" (长度: {len(obj)}, 内容: '{obj[:50]}...')"
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else:
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var_name += f" ('{obj}')"
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elif hasattr(obj, '__class__') and hasattr(obj.__class__, '__name__'):
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if hasattr(obj, '__dict__'):
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attr_count = len(obj.__dict__)
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var_name += f" ({attr_count}个属性)"
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return {
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'name': var_name,
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'type': obj_type,
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'size_mb': size_mb
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}
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except Exception as e:
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logger.debug(f"获取变量信息失败: {e}")
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return None
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@staticmethod
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def _get_variable_name(obj):
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"""
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尝试获取变量名
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"""
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try:
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# 尝试通过gc获取引用该对象的变量名
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referrers = gc.get_referrers(obj)
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for referrer in referrers:
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if isinstance(referrer, dict):
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# 检查是否在某个模块的全局变量中
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for name, value in referrer.items():
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if value is obj and isinstance(name, str):
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return name
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elif hasattr(referrer, '__dict__'):
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# 检查是否在某个实例的属性中
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for name, value in referrer.__dict__.items():
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if value is obj and isinstance(name, str):
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return f"{type(referrer).__name__}.{name}"
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# 如果找不到变量名，返回对象类型和id
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return f"{type(obj).__name__}_{id(obj)}"
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except Exception as e:
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logger.debug(f"获取变量名失败: {e}")
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return f"{type(obj).__name__}_{id(obj)}"
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