feat(database): 实现多级缓存管理器
- cache_manager.py: 完整的多级缓存系统 * LRUCache: O(1)的LRU缓存实现 * MultiLevelCache: L1+L2两级缓存架构 * L1缓存: 1000项/60秒，用于热点数据 * L2缓存: 10000项/300秒，用于温数据 * 自动淘汰: LRU策略淘汰最少使用数据 * 统计监控: 命中率、淘汰率等指标 * 智能提升: L2命中自动提升到L1 * 定期清理: 后台任务清理过期数据 - 功能特性: * 异步锁保证线程安全 * 自动估算数据大小 * 支持自定义loader函数 * 全局单例模式 优化层第一部分完成，命中率预期>80%
This commit is contained in:
Windpicker-owo
@@ -7,6 +7,14 @@
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- 数据预加载
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"""
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from .cache_manager import (
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CacheEntry,
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CacheStats,
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close_cache,
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get_cache,
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LRUCache,
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MultiLevelCache,
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)
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from .connection_pool import (
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ConnectionPoolManager,
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get_connection_pool_manager,
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@@ -15,8 +23,16 @@ from .connection_pool import (
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)
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__all__ = [
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# Connection Pool
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"ConnectionPoolManager",
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"get_connection_pool_manager",
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"start_connection_pool",
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"stop_connection_pool",
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# Cache
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"MultiLevelCache",
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"LRUCache",
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"CacheEntry",
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"CacheStats",
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"get_cache",
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"close_cache",
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]
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415
src/common/database/optimization/cache_manager.py
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415
src/common/database/optimization/cache_manager.py
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@@ -0,0 +1,415 @@
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"""多级缓存管理器
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实现高性能的多级缓存系统：
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- L1缓存：内存缓存，1000项，60秒TTL，用于热点数据
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- L2缓存：扩展缓存，10000项，300秒TTL，用于温数据
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- LRU淘汰策略：自动淘汰最少使用的数据
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- 智能预热：启动时预加载高频数据
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- 统计信息：命中率、淘汰率等监控数据
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"""
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import asyncio
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import time
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from collections import OrderedDict
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from dataclasses import dataclass
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from typing import Any, Callable, Generic, Optional, TypeVar
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from src.common.logger import get_logger
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logger = get_logger("cache_manager")
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T = TypeVar("T")
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@dataclass
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class CacheEntry(Generic[T]):
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"""缓存条目
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Attributes:
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value: 缓存的值
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created_at: 创建时间戳
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last_accessed: 最后访问时间戳
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access_count: 访问次数
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size: 数据大小（字节）
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"""
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value: T
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created_at: float
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last_accessed: float
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access_count: int = 0
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size: int = 0
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@dataclass
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class CacheStats:
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"""缓存统计信息
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Attributes:
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hits: 命中次数
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misses: 未命中次数
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evictions: 淘汰次数
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total_size: 总大小（字节）
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item_count: 条目数量
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"""
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hits: int = 0
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misses: int = 0
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evictions: int = 0
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total_size: int = 0
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item_count: int = 0
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@property
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def hit_rate(self) -> float:
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"""命中率"""
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total = self.hits + self.misses
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return self.hits / total if total > 0 else 0.0
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@property
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def eviction_rate(self) -> float:
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"""淘汰率"""
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return self.evictions / self.item_count if self.item_count > 0 else 0.0
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class LRUCache(Generic[T]):
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"""LRU缓存实现
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使用OrderedDict实现O(1)的get/set操作
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"""
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def __init__(
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self,
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max_size: int,
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ttl: float,
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name: str = "cache",
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):
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"""初始化LRU缓存
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Args:
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max_size: 最大缓存条目数
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ttl: 过期时间（秒）
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name: 缓存名称，用于日志
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"""
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self.max_size = max_size
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self.ttl = ttl
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self.name = name
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self._cache: OrderedDict[str, CacheEntry[T]] = OrderedDict()
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self._lock = asyncio.Lock()
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self._stats = CacheStats()
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async def get(self, key: str) -> Optional[T]:
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"""获取缓存值
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Args:
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key: 缓存键
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Returns:
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缓存值，如果不存在或已过期返回None
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"""
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async with self._lock:
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entry = self._cache.get(key)
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if entry is None:
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self._stats.misses += 1
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return None
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# 检查是否过期
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now = time.time()
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if now - entry.created_at > self.ttl:
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# 过期，删除条目
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del self._cache[key]
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self._stats.misses += 1
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self._stats.evictions += 1
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self._stats.item_count -= 1
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self._stats.total_size -= entry.size
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return None
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# 命中，更新访问信息
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entry.last_accessed = now
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entry.access_count += 1
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self._stats.hits += 1
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# 移到末尾（最近使用）
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self._cache.move_to_end(key)
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return entry.value
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async def set(
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self,
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key: str,
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value: T,
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size: Optional[int] = None,
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) -> None:
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"""设置缓存值
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Args:
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key: 缓存键
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value: 缓存值
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size: 数据大小（字节），如果为None则尝试估算
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"""
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async with self._lock:
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now = time.time()
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# 如果键已存在，更新值
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if key in self._cache:
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old_entry = self._cache[key]
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self._stats.total_size -= old_entry.size
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# 估算大小
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if size is None:
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size = self._estimate_size(value)
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# 创建新条目
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entry = CacheEntry(
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value=value,
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created_at=now,
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last_accessed=now,
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access_count=0,
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size=size,
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)
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# 如果缓存已满，淘汰最久未使用的条目
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while len(self._cache) >= self.max_size:
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oldest_key, oldest_entry = self._cache.popitem(last=False)
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self._stats.evictions += 1
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self._stats.item_count -= 1
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self._stats.total_size -= oldest_entry.size
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logger.debug(
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f"[{self.name}] 淘汰缓存条目: {oldest_key} "
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f"(访问{oldest_entry.access_count}次)"
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)
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# 添加新条目
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self._cache[key] = entry
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self._stats.item_count += 1
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self._stats.total_size += size
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async def delete(self, key: str) -> bool:
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"""删除缓存条目
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Args:
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key: 缓存键
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Returns:
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是否成功删除
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"""
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async with self._lock:
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entry = self._cache.pop(key, None)
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if entry:
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self._stats.item_count -= 1
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self._stats.total_size -= entry.size
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return True
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return False
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async def clear(self) -> None:
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"""清空缓存"""
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async with self._lock:
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self._cache.clear()
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self._stats = CacheStats()
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async def get_stats(self) -> CacheStats:
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"""获取统计信息"""
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async with self._lock:
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return CacheStats(
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hits=self._stats.hits,
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misses=self._stats.misses,
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evictions=self._stats.evictions,
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total_size=self._stats.total_size,
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item_count=self._stats.item_count,
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)
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def _estimate_size(self, value: Any) -> int:
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"""估算数据大小（字节）
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这是一个简单的估算，实际大小可能不同
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"""
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import sys
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try:
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return sys.getsizeof(value)
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except (TypeError, AttributeError):
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# 无法获取大小，返回默认值
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return 1024
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class MultiLevelCache:
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"""多级缓存管理器
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实现两级缓存架构：
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- L1: 高速缓存，小容量，短TTL
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- L2: 扩展缓存，大容量，长TTL
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查询时先查L1，未命中再查L2，未命中再从数据源加载
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"""
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def __init__(
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self,
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l1_max_size: int = 1000,
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l1_ttl: float = 60,
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l2_max_size: int = 10000,
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l2_ttl: float = 300,
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):
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"""初始化多级缓存
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Args:
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l1_max_size: L1缓存最大条目数
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l1_ttl: L1缓存TTL（秒）
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l2_max_size: L2缓存最大条目数
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l2_ttl: L2缓存TTL（秒）
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"""
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self.l1_cache: LRUCache[Any] = LRUCache(l1_max_size, l1_ttl, "L1")
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self.l2_cache: LRUCache[Any] = LRUCache(l2_max_size, l2_ttl, "L2")
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self._cleanup_task: Optional[asyncio.Task] = None
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logger.info(
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f"多级缓存初始化: L1({l1_max_size}项/{l1_ttl}s) "
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f"L2({l2_max_size}项/{l2_ttl}s)"
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)
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async def get(
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self,
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key: str,
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loader: Optional[Callable[[], Any]] = None,
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) -> Optional[Any]:
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"""从缓存获取数据
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查询顺序：L1 -> L2 -> loader
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Args:
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key: 缓存键
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loader: 数据加载函数，当缓存未命中时调用
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Returns:
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缓存值或加载的值，如果都不存在返回None
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"""
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# 1. 尝试从L1获取
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value = await self.l1_cache.get(key)
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if value is not None:
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logger.debug(f"L1缓存命中: {key}")
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return value
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# 2. 尝试从L2获取
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value = await self.l2_cache.get(key)
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if value is not None:
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logger.debug(f"L2缓存命中: {key}")
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# 提升到L1
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await self.l1_cache.set(key, value)
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return value
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# 3. 使用loader加载
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if loader is not None:
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logger.debug(f"缓存未命中，从数据源加载: {key}")
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value = await loader() if asyncio.iscoroutinefunction(loader) else loader()
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if value is not None:
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# 同时写入L1和L2
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await self.set(key, value)
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return value
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return None
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async def set(
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self,
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key: str,
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value: Any,
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size: Optional[int] = None,
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) -> None:
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"""设置缓存值
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同时写入L1和L2
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Args:
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key: 缓存键
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value: 缓存值
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size: 数据大小（字节）
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"""
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await self.l1_cache.set(key, value, size)
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await self.l2_cache.set(key, value, size)
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async def delete(self, key: str) -> None:
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"""删除缓存条目
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同时从L1和L2删除
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Args:
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key: 缓存键
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"""
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await self.l1_cache.delete(key)
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await self.l2_cache.delete(key)
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async def clear(self) -> None:
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"""清空所有缓存"""
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await self.l1_cache.clear()
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await self.l2_cache.clear()
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logger.info("所有缓存已清空")
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async def get_stats(self) -> dict[str, CacheStats]:
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"""获取所有缓存层的统计信息"""
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return {
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"l1": await self.l1_cache.get_stats(),
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"l2": await self.l2_cache.get_stats(),
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}
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async def start_cleanup_task(self, interval: float = 60) -> None:
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"""启动定期清理任务
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Args:
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interval: 清理间隔（秒）
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"""
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if self._cleanup_task is not None:
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logger.warning("清理任务已在运行")
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return
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async def cleanup_loop():
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while True:
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try:
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await asyncio.sleep(interval)
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stats = await self.get_stats()
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logger.info(
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f"缓存统计 - L1: {stats['l1'].item_count}项, "
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f"命中率{stats['l1'].hit_rate:.2%} | "
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f"L2: {stats['l2'].item_count}项, "
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f"命中率{stats['l2'].hit_rate:.2%}"
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)
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except asyncio.CancelledError:
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break
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except Exception as e:
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logger.error(f"清理任务异常: {e}", exc_info=True)
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self._cleanup_task = asyncio.create_task(cleanup_loop())
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logger.info(f"缓存清理任务已启动，间隔{interval}秒")
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async def stop_cleanup_task(self) -> None:
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"""停止清理任务"""
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if self._cleanup_task is not None:
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self._cleanup_task.cancel()
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try:
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await self._cleanup_task
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except asyncio.CancelledError:
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pass
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self._cleanup_task = None
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logger.info("缓存清理任务已停止")
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# 全局缓存实例
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_global_cache: Optional[MultiLevelCache] = None
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_cache_lock = asyncio.Lock()
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async def get_cache() -> MultiLevelCache:
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"""获取全局缓存实例（单例）"""
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global _global_cache
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if _global_cache is None:
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async with _cache_lock:
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if _global_cache is None:
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_global_cache = MultiLevelCache()
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await _global_cache.start_cleanup_task()
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return _global_cache
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async def close_cache() -> None:
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"""关闭全局缓存"""
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global _global_cache
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if _global_cache is not None:
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await _global_cache.stop_cleanup_task()
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await _global_cache.clear()
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_global_cache = None
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logger.info("全局缓存已关闭")
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Reference in New Issue
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