mirror of
https://github.com/jxxghp/MoviePilot.git
synced 2026-04-13 13:29:44 +08:00
Enhance memory analysis with detailed tracking, leak detection, and system insights
Co-authored-by: jxxghp <jxxghp@163.com>
This commit is contained in:
Cursor Agent
@@ -2,8 +2,10 @@ import gc
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import sys
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import threading
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import time
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import os
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import tracemalloc
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from datetime import datetime
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from typing import Optional
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from typing import Optional, Dict, List, Tuple
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import psutil
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from pympler import muppy, summary, asizeof
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@@ -30,6 +32,10 @@ class MemoryHelper(metaclass=Singleton):
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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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# 启用tracemalloc以获得更详细的内存信息
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if not tracemalloc.is_tracing():
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tracemalloc.start(25) # 保留25个帧
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@eventmanager.register(EventType.ConfigChanged)
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def handle_config_changed(self, event: Event):
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@@ -108,15 +114,21 @@ class MemoryHelper(metaclass=Singleton):
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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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# 第一步：写入基本信息和系统内存统计
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self._write_system_memory_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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# 第二步：写入Python对象类型统计
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self._write_python_objects_info(snapshot_file)
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# 第三步：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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# 第四步：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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# 第五步：分析内存泄漏和增长趋势
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self._append_memory_leak_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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@@ -125,30 +137,450 @@ class MemoryHelper(metaclass=Singleton):
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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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def _write_system_memory_info(self, 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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写入系统内存信息
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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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process = psutil.Process()
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memory_info = process.memory_info()
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memory_percent = process.memory_percent()
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# 获取系统总内存信息
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system_memory = psutil.virtual_memory()
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# 获取内存映射信息
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memory_maps = process.memory_maps()
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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("系统内存使用情况:\n")
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f.write("-" * 80 + "\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(f"进程内存使用率: {memory_percent:.2f}%\n")
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f.write(f"系统总内存: {system_memory.total / 1024 / 1024 / 1024:.2f} GB\n")
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f.write(f"系统可用内存: {system_memory.available / 1024 / 1024 / 1024:.2f} GB\n")
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f.write(f"系统内存使用率: {system_memory.percent:.2f}%\n")
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f.write(f"进程RSS内存: {memory_info.rss / 1024 / 1024:.2f} MB\n")
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f.write(f"进程VMS内存: {memory_info.vms / 1024 / 1024:.2f} MB\n")
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f.write(f"进程共享内存: {memory_info.shared / 1024 / 1024:.2f} MB\n")
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f.write(f"进程文本段: {memory_info.text / 1024 / 1024:.2f} MB\n")
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f.write(f"进程数据段: {memory_info.data / 1024 / 1024:.2f} MB\n")
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# 分析内存映射
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f.write("\n内存映射分析:\n")
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f.write("-" * 80 + "\n")
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memory_regions = self._analyze_memory_maps(memory_maps)
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for region_type, size_mb in memory_regions.items():
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f.write(f"{region_type}: {size_mb:.2f} MB\n")
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f.flush()
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def _analyze_memory_maps(self, memory_maps) -> Dict[str, float]:
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"""
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分析内存映射，按类型分类统计
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"""
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regions = {}
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for mmap in memory_maps:
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size_mb = mmap.size / 1024 / 1024
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perms = mmap.perms
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if 'r' in perms and 'w' in perms:
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region_type = "读写内存"
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elif 'r' in perms and 'x' in perms:
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region_type = "代码段"
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elif 'r' in perms:
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region_type = "只读内存"
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else:
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region_type = "其他内存"
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if region_type in regions:
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regions[region_type] += size_mb
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else:
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regions[region_type] = size_mb
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return regions
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def _write_python_objects_info(self, snapshot_file):
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"""
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写入Python对象类型统计信息
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"""
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# 获取当前tracemalloc统计
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current, peak = tracemalloc.get_traced_memory()
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# 获取所有对象
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all_objects = muppy.get_objects()
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sum1 = summary.summarize(all_objects)
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# 计算Python对象总内存
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python_total_mb = 0
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for line in summary.format_(sum1):
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if '|' in line and line.strip() and not line.startswith('=') and not line.startswith('-'):
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parts = line.split('|')
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if len(parts) >= 3:
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try:
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size_str = parts[2].strip()
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if 'MB' in size_str:
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size_mb = float(size_str.replace('MB', '').strip())
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python_total_mb += size_mb
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except:
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pass
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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("Python内存使用情况:\n")
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f.write("-" * 80 + "\n")
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f.write(f"tracemalloc当前内存: {current / 1024 / 1024:.2f} MB\n")
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f.write(f"tracemalloc峰值内存: {peak / 1024 / 1024:.2f} MB\n")
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f.write(f"Python对象总内存: {python_total_mb:.2f} MB\n")
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f.write(f"未统计内存(可能为C扩展): {self._get_unaccounted_memory():.2f} MB\n")
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f.write("\n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 _get_unaccounted_memory(self) -> float:
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"""
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计算未统计的内存（可能是C扩展、系统缓存等）
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"""
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try:
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# 获取进程总内存
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process = psutil.Process()
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total_memory = process.memory_info().rss / 1024 / 1024 # MB
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# 获取Python对象总内存
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all_objects = muppy.get_objects()
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sum1 = summary.summarize(all_objects)
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python_total_mb = 0
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for line in summary.format_(sum1):
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if '|' in line and line.strip() and not line.startswith('=') and not line.startswith('-'):
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parts = line.split('|')
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if len(parts) >= 3:
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try:
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size_str = parts[2].strip()
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if 'MB' in size_str:
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size_mb = float(size_str.replace('MB', '').strip())
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python_total_mb += size_mb
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except:
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pass
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return max(0, total_memory - python_total_mb)
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except:
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return 0.0
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def _append_memory_leak_analysis(self, snapshot_file):
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"""
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分析内存泄漏和增长趋势
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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("内存泄漏分析:\n")
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f.write("-" * 80 + "\n")
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# 获取tracemalloc统计
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current, peak = tracemalloc.get_traced_memory()
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f.write(f"当前tracemalloc内存: {current / 1024 / 1024:.2f} MB\n")
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f.write(f"tracemalloc峰值内存: {peak / 1024 / 1024:.2f} MB\n")
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# 获取内存分配统计
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try:
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stats = tracemalloc.get_traced_memory()
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f.write(f"内存分配统计: {stats}\n")
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# 获取前10个内存分配最多的位置
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snapshot = tracemalloc.take_snapshot()
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top_stats = snapshot.statistics('lineno')
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f.write("\n内存分配最多的位置 (前10个):\n")
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f.write("-" * 80 + "\n")
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for i, stat in enumerate(top_stats[:10], 1):
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f.write(f"{i:2d}. {stat.count:>8} 个对象, {stat.size / 1024 / 1024:>8.2f} MB\n")
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f.write(f" {stat.traceback.format()}\n")
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except Exception as e:
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f.write(f"获取内存分配统计失败: {e}\n")
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# 垃圾回收统计
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f.write("\n垃圾回收统计:\n")
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f.write("-" * 80 + "\n")
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for i in range(3):
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count = gc.get_count()[i]
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f.write(f"GC代 {i}: {count} 次\n")
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# 获取不可达对象数量
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unreachable = len(gc.garbage)
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f.write(f"不可达对象数量: {unreachable}\n")
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f.flush()
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logger.debug("内存泄漏分析已完成并写入")
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def create_detailed_memory_analysis(self):
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"""
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创建详细的内存分析报告，专门用于诊断内存问题
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"""
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try:
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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analysis_file = self._memory_snapshot_dir / f"detailed_memory_analysis_{timestamp}.txt"
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logger.info(f"开始创建详细内存分析: {analysis_file}")
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with open(analysis_file, 'w', encoding='utf-8') as f:
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f.write(f"详细内存分析报告 - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
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f.write("=" * 100 + "\n\n")
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# 1. 系统级内存分析
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self._write_detailed_system_analysis(f)
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# 2. Python对象深度分析
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self._write_detailed_python_analysis(f)
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# 3. 内存映射详细分析
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self._write_detailed_memory_maps(f)
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# 4. 大对象分析
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self._write_detailed_large_objects(f)
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# 5. 内存泄漏检测
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self._write_memory_leak_detection(f)
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logger.info(f"详细内存分析已保存: {analysis_file}")
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return analysis_file
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except Exception as e:
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logger.error(f"创建详细内存分析失败: {e}")
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return None
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def _write_detailed_system_analysis(self, f):
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"""
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写入详细的系统内存分析
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"""
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f.write("1. 系统级内存分析\n")
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f.write("-" * 50 + "\n")
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process = psutil.Process()
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memory_info = process.memory_info()
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f.write(f"进程ID: {process.pid}\n")
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f.write(f"进程名称: {process.name()}\n")
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f.write(f"进程命令行: {' '.join(process.cmdline())}\n\n")
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f.write("内存使用详情:\n")
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f.write(f" RSS (物理内存): {memory_info.rss / 1024 / 1024:.2f} MB\n")
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f.write(f" VMS (虚拟内存): {memory_info.vms / 1024 / 1024:.2f} MB\n")
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f.write(f" 共享内存: {memory_info.shared / 1024 / 1024:.2f} MB\n")
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f.write(f" 文本段: {memory_info.text / 1024 / 1024:.2f} MB\n")
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f.write(f" 数据段: {memory_info.data / 1024 / 1024:.2f} MB\n")
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f.write(f" 库内存: {memory_info.lib / 1024 / 1024:.2f} MB\n")
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f.write(f" 脏页: {memory_info.dirty / 1024 / 1024:.2f} MB\n")
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# 系统内存信息
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system_memory = psutil.virtual_memory()
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f.write(f"\n系统内存:\n")
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f.write(f" 总内存: {system_memory.total / 1024 / 1024 / 1024:.2f} GB\n")
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f.write(f" 可用内存: {system_memory.available / 1024 / 1024 / 1024:.2f} GB\n")
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f.write(f" 使用率: {system_memory.percent:.2f}%\n")
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f.write(f" 缓存: {system_memory.cached / 1024 / 1024 / 1024:.2f} GB\n")
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f.write(f" 缓冲区: {system_memory.buffers / 1024 / 1024 / 1024:.2f} GB\n")
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f.write("\n" + "=" * 100 + "\n\n")
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def _write_detailed_python_analysis(self, f):
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"""
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写入详细的Python对象分析
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"""
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f.write("2. Python对象深度分析\n")
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f.write("-" * 50 + "\n")
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# 强制垃圾回收
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collected = gc.collect()
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f.write(f"垃圾回收清理对象数: {collected}\n\n")
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# 获取所有对象
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all_objects = muppy.get_objects()
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f.write(f"总对象数: {len(all_objects):,}\n")
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# 按类型统计
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type_stats = {}
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for obj in all_objects:
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obj_type = type(obj).__name__
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if obj_type not in type_stats:
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type_stats[obj_type] = {'count': 0, 'size': 0}
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type_stats[obj_type]['count'] += 1
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type_stats[obj_type]['size'] += sys.getsizeof(obj)
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# 按大小排序
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sorted_types = sorted(type_stats.items(), key=lambda x: x[1]['size'], reverse=True)
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f.write("对象类型统计 (按内存大小排序):\n")
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f.write(f"{'类型':<20} {'数量':<10} {'总大小(MB)':<12} {'平均大小(B)':<12}\n")
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f.write("-" * 60 + "\n")
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total_python_memory = 0
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for obj_type, stats in sorted_types[:20]: # 只显示前20个
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size_mb = stats['size'] / 1024 / 1024
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avg_size = stats['size'] / stats['count'] if stats['count'] > 0 else 0
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total_python_memory += size_mb
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f.write(f"{obj_type:<20} {stats['count']:<10,} {size_mb:<12.2f} {avg_size:<12.1f}\n")
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f.write(f"\nPython对象总内存: {total_python_memory:.2f} MB\n")
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# 计算未统计内存
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process = psutil.Process()
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total_memory = process.memory_info().rss / 1024 / 1024
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unaccounted = total_memory - total_python_memory
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f.write(f"未统计内存: {unaccounted:.2f} MB ({unaccounted/total_memory*100:.1f}%)\n")
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f.write("\n" + "=" * 100 + "\n\n")
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def _write_detailed_memory_maps(self, f):
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"""
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写入详细的内存映射分析
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"""
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f.write("3. 内存映射详细分析\n")
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f.write("-" * 50 + "\n")
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process = psutil.Process()
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memory_maps = process.memory_maps()
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# 按权限分类
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perm_stats = {}
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file_stats = {}
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for mmap in memory_maps:
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size_mb = mmap.size / 1024 / 1024
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perms = mmap.perms
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# 按权限统计
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if perms not in perm_stats:
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perm_stats[perms] = {'count': 0, 'size': 0}
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perm_stats[perms]['count'] += 1
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perm_stats[perms]['size'] += size_mb
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# 按文件统计
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if mmap.path:
|
||||
if mmap.path not in file_stats:
|
||||
file_stats[mmap.path] = {'count': 0, 'size': 0}
|
||||
file_stats[mmap.path]['count'] += 1
|
||||
file_stats[mmap.path]['size'] += size_mb
|
||||
|
||||
f.write("按权限分类的内存映射:\n")
|
||||
f.write(f"{'权限':<10} {'数量':<8} {'大小(MB)':<12}\n")
|
||||
f.write("-" * 35 + "\n")
|
||||
for perms, stats in sorted(perm_stats.items(), key=lambda x: x[1]['size'], reverse=True):
|
||||
f.write(f"{perms:<10} {stats['count']:<8} {stats['size']:<12.2f}\n")
|
||||
|
||||
f.write(f"\n按文件分类的内存映射 (前10个):\n")
|
||||
f.write(f"{'文件路径':<50} {'大小(MB)':<12}\n")
|
||||
f.write("-" * 70 + "\n")
|
||||
for path, stats in sorted(file_stats.items(), key=lambda x: x[1]['size'], reverse=True)[:10]:
|
||||
if len(path) > 47:
|
||||
path = path[:44] + "..."
|
||||
f.write(f"{path:<50} {stats['size']:<12.2f}\n")
|
||||
|
||||
f.write("\n" + "=" * 100 + "\n\n")
|
||||
|
||||
def _write_detailed_large_objects(self, f):
|
||||
"""
|
||||
写入大对象详细分析
|
||||
"""
|
||||
f.write("4. 大对象详细分析\n")
|
||||
f.write("-" * 50 + "\n")
|
||||
|
||||
all_objects = muppy.get_objects()
|
||||
large_objects = []
|
||||
|
||||
for obj in all_objects:
|
||||
try:
|
||||
size = asizeof.asizeof(obj)
|
||||
if size > 1024 * 1024: # 大于1MB的对象
|
||||
large_objects.append((obj, size))
|
||||
except:
|
||||
continue
|
||||
|
||||
# 按大小排序
|
||||
large_objects.sort(key=lambda x: x[1], reverse=True)
|
||||
|
||||
f.write(f"大对象 (>1MB) 数量: {len(large_objects)}\n\n")
|
||||
|
||||
for i, (obj, size) in enumerate(large_objects[:20], 1): # 只显示前20个
|
||||
size_mb = size / 1024 / 1024
|
||||
obj_type = type(obj).__name__
|
||||
|
||||
f.write(f"{i:2d}. {obj_type} - {size_mb:.2f} MB\n")
|
||||
|
||||
# 尝试获取更多信息
|
||||
try:
|
||||
if isinstance(obj, dict):
|
||||
f.write(f" 字典项数: {len(obj)}\n")
|
||||
if obj:
|
||||
sample_keys = list(obj.keys())[:3]
|
||||
f.write(f" 示例键: {sample_keys}\n")
|
||||
elif isinstance(obj, (list, tuple)):
|
||||
f.write(f" 元素数量: {len(obj)}\n")
|
||||
elif isinstance(obj, str):
|
||||
f.write(f" 字符串长度: {len(obj)}\n")
|
||||
if len(obj) > 100:
|
||||
f.write(f" 内容预览: {obj[:100]}...\n")
|
||||
else:
|
||||
f.write(f" 内容: {obj}\n")
|
||||
elif hasattr(obj, '__dict__'):
|
||||
f.write(f" 属性数量: {len(obj.__dict__)}\n")
|
||||
if hasattr(obj, '__class__'):
|
||||
f.write(f" 类名: {obj.__class__.__name__}\n")
|
||||
except:
|
||||
pass
|
||||
|
||||
f.write("\n")
|
||||
|
||||
f.write("=" * 100 + "\n\n")
|
||||
|
||||
def _write_memory_leak_detection(self, f):
|
||||
"""
|
||||
写入内存泄漏检测
|
||||
"""
|
||||
f.write("5. 内存泄漏检测\n")
|
||||
f.write("-" * 50 + "\n")
|
||||
|
||||
# tracemalloc分析
|
||||
current, peak = tracemalloc.get_traced_memory()
|
||||
f.write(f"tracemalloc当前内存: {current / 1024 / 1024:.2f} MB\n")
|
||||
f.write(f"tracemalloc峰值内存: {peak / 1024 / 1024:.2f} MB\n")
|
||||
|
||||
try:
|
||||
snapshot = tracemalloc.take_snapshot()
|
||||
top_stats = snapshot.statistics('lineno')
|
||||
|
||||
f.write(f"\n内存分配最多的位置 (前15个):\n")
|
||||
f.write("-" * 50 + "\n")
|
||||
for i, stat in enumerate(top_stats[:15], 1):
|
||||
f.write(f"{i:2d}. {stat.count:>8} 个对象, {stat.size / 1024 / 1024:>8.2f} MB\n")
|
||||
for line in stat.traceback.format():
|
||||
f.write(f" {line}\n")
|
||||
f.write("\n")
|
||||
except Exception as e:
|
||||
f.write(f"获取tracemalloc统计失败: {e}\n")
|
||||
|
||||
# 垃圾回收分析
|
||||
f.write("垃圾回收分析:\n")
|
||||
f.write("-" * 50 + "\n")
|
||||
gc_counts = gc.get_count()
|
||||
f.write(f"GC计数: {gc_counts}\n")
|
||||
|
||||
# 检查不可达对象
|
||||
unreachable = len(gc.garbage)
|
||||
f.write(f"不可达对象数量: {unreachable}\n")
|
||||
if unreachable > 0:
|
||||
f.write("不可达对象详情:\n")
|
||||
for i, obj in enumerate(gc.garbage[:5], 1): # 只显示前5个
|
||||
f.write(f" {i}. {type(obj).__name__} - {id(obj)}\n")
|
||||
|
||||
f.write("\n" + "=" * 100 + "\n\n")
|
||||
|
||||
def _append_class_analysis(self, snapshot_file):
|
||||
"""
|
||||
@@ -455,3 +887,110 @@ class MemoryHelper(metaclass=Singleton):
|
||||
except Exception as e:
|
||||
logger.debug(f"获取变量名失败: {e}")
|
||||
return f"{type(obj).__name__}_{id(obj)}"
|
||||
|
||||
def get_memory_summary(self) -> Dict[str, float]:
|
||||
"""
|
||||
获取内存使用摘要
|
||||
"""
|
||||
try:
|
||||
process = psutil.Process()
|
||||
memory_info = process.memory_info()
|
||||
|
||||
# 获取Python对象总内存
|
||||
all_objects = muppy.get_objects()
|
||||
sum1 = summary.summarize(all_objects)
|
||||
|
||||
python_total_mb = 0
|
||||
for line in summary.format_(sum1):
|
||||
if '|' in line and line.strip() and not line.startswith('=') and not line.startswith('-'):
|
||||
parts = line.split('|')
|
||||
if len(parts) >= 3:
|
||||
try:
|
||||
size_str = parts[2].strip()
|
||||
if 'MB' in size_str:
|
||||
size_mb = float(size_str.replace('MB', '').strip())
|
||||
python_total_mb += size_mb
|
||||
except:
|
||||
pass
|
||||
|
||||
total_memory = memory_info.rss / 1024 / 1024
|
||||
unaccounted = total_memory - python_total_mb
|
||||
|
||||
return {
|
||||
'total_memory_mb': total_memory,
|
||||
'python_objects_mb': python_total_mb,
|
||||
'unaccounted_mb': unaccounted,
|
||||
'unaccounted_percent': (unaccounted / total_memory * 100) if total_memory > 0 else 0
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"获取内存摘要失败: {e}")
|
||||
return {}
|
||||
|
||||
def force_garbage_collection(self):
|
||||
"""
|
||||
强制垃圾回收并返回清理的对象数量
|
||||
"""
|
||||
try:
|
||||
collected = gc.collect()
|
||||
logger.info(f"强制垃圾回收完成，清理了 {collected} 个对象")
|
||||
return collected
|
||||
except Exception as e:
|
||||
logger.error(f"强制垃圾回收失败: {e}")
|
||||
return 0
|
||||
|
||||
def analyze_memory_growth(self, interval_seconds: int = 300) -> Dict[str, float]:
|
||||
"""
|
||||
分析内存增长趋势
|
||||
:param interval_seconds: 分析间隔（秒）
|
||||
:return: 内存增长信息
|
||||
"""
|
||||
try:
|
||||
# 获取当前内存使用
|
||||
current_summary = self.get_memory_summary()
|
||||
|
||||
# 等待指定时间
|
||||
time.sleep(interval_seconds)
|
||||
|
||||
# 获取新的内存使用
|
||||
new_summary = self.get_memory_summary()
|
||||
|
||||
if current_summary and new_summary:
|
||||
growth_info = {
|
||||
'total_growth_mb': new_summary['total_memory_mb'] - current_summary['total_memory_mb'],
|
||||
'python_growth_mb': new_summary['python_objects_mb'] - current_summary['python_objects_mb'],
|
||||
'unaccounted_growth_mb': new_summary['unaccounted_mb'] - current_summary['unaccounted_mb'],
|
||||
'growth_rate_mb_per_hour': (new_summary['total_memory_mb'] - current_summary['total_memory_mb']) * 3600 / interval_seconds
|
||||
}
|
||||
|
||||
logger.info(f"内存增长分析: 总增长 {growth_info['total_growth_mb']:.2f} MB, "
|
||||
f"Python对象增长 {growth_info['python_growth_mb']:.2f} MB, "
|
||||
f"未统计增长 {growth_info['unaccounted_growth_mb']:.2f} MB")
|
||||
|
||||
return growth_info
|
||||
|
||||
return {}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"分析内存增长失败: {e}")
|
||||
return {}
|
||||
|
||||
|
||||
# 使用示例
|
||||
if __name__ == "__main__":
|
||||
# 创建内存分析器实例
|
||||
memory_helper = MemoryHelper()
|
||||
|
||||
# 获取内存摘要
|
||||
summary = memory_helper.get_memory_summary()
|
||||
print("内存使用摘要:")
|
||||
for key, value in summary.items():
|
||||
print(f" {key}: {value:.2f}")
|
||||
|
||||
# 创建详细分析报告
|
||||
analysis_file = memory_helper.create_detailed_memory_analysis()
|
||||
if analysis_file:
|
||||
print(f"详细分析报告已保存到: {analysis_file}")
|
||||
|
||||
# 强制垃圾回收
|
||||
collected = memory_helper.force_garbage_collection()
|
||||
print(f"垃圾回收清理了 {collected} 个对象")
|
||||
|
||||
Reference in New Issue
Block a user