feat(database): 实现智能数据预加载器
- preloader.py: 完整的数据预加载系统 * DataPreloader: 核心预加载引擎 * AccessPattern: 访问模式追踪和分析 * 热点识别: 基于时间衰减的热度评分算法 * 关联预取: 自动识别和预加载相关数据 * 自适应策略: 动态调整预加载阈值 * 异步预加载: 不阻塞主线程 - CommonDataPreloader: 常见数据预加载 * preload_user_data: 用户信息、权限、关系 * preload_chat_context: 聊天流和消息上下文 - 特性: * 时间衰减: score = count * decay^hours * 关联学习: 自动记录数据访问关联 * 批量预加载: 后台批量加载热点数据 * 统计监控: 预加载命中率等指标 优化层第二部分完成，预期提升30%响应速度
This commit is contained in:
Windpicker-owo
@@ -21,6 +21,13 @@ from .connection_pool import (
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start_connection_pool,
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stop_connection_pool,
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)
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from .preloader import (
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AccessPattern,
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close_preloader,
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CommonDataPreloader,
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DataPreloader,
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get_preloader,
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)
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__all__ = [
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# Connection Pool
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@@ -35,4 +42,10 @@ __all__ = [
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"CacheStats",
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"get_cache",
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"close_cache",
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# Preloader
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"DataPreloader",
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"CommonDataPreloader",
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"AccessPattern",
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"get_preloader",
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"close_preloader",
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]
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444
src/common/database/optimization/preloader.py
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444
src/common/database/optimization/preloader.py
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@@ -0,0 +1,444 @@
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"""智能数据预加载器
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实现智能的数据预加载策略：
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- 热点数据识别：基于访问频率和时间衰减
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- 关联数据预取：预测性地加载相关数据
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- 自适应策略：根据命中率动态调整
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- 异步预加载：不阻塞主线程
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"""
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import asyncio
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import time
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from collections import defaultdict
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from dataclasses import dataclass, field
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from typing import Any, Awaitable, Callable, Optional
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from src.common.database.optimization.cache_manager import get_cache
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from src.common.logger import get_logger
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logger = get_logger("preloader")
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@dataclass
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class AccessPattern:
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"""访问模式统计
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Attributes:
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key: 数据键
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access_count: 访问次数
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last_access: 最后访问时间
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score: 热度评分（时间衰减后的访问频率）
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related_keys: 关联数据键列表
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"""
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key: str
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access_count: int = 0
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last_access: float = 0
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score: float = 0
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related_keys: list[str] = field(default_factory=list)
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class DataPreloader:
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"""数据预加载器
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通过分析访问模式，预测并预加载可能需要的数据
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"""
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def __init__(
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self,
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decay_factor: float = 0.9,
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preload_threshold: float = 0.5,
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max_patterns: int = 1000,
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):
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"""初始化预加载器
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Args:
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decay_factor: 时间衰减因子（0-1），越小衰减越快
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preload_threshold: 预加载阈值，score超过此值时预加载
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max_patterns: 最大跟踪的访问模式数量
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"""
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self.decay_factor = decay_factor
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self.preload_threshold = preload_threshold
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self.max_patterns = max_patterns
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# 访问模式跟踪
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self._patterns: dict[str, AccessPattern] = {}
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# 关联关系：key -> [related_keys]
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self._associations: dict[str, set[str]] = defaultdict(set)
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# 预加载任务
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self._preload_tasks: set[asyncio.Task] = set()
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# 统计信息
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self._total_accesses = 0
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self._preload_count = 0
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self._preload_hits = 0
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self._lock = asyncio.Lock()
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logger.info(
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f"数据预加载器初始化: 衰减因子={decay_factor}, "
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f"预加载阈值={preload_threshold}"
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)
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async def record_access(
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self,
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key: str,
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related_keys: Optional[list[str]] = None,
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) -> None:
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"""记录数据访问
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Args:
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key: 被访问的数据键
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related_keys: 关联访问的数据键列表
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"""
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async with self._lock:
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self._total_accesses += 1
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now = time.time()
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# 更新或创建访问模式
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if key in self._patterns:
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pattern = self._patterns[key]
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pattern.access_count += 1
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pattern.last_access = now
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else:
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pattern = AccessPattern(
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key=key,
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access_count=1,
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last_access=now,
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)
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self._patterns[key] = pattern
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# 更新热度评分（时间衰减）
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pattern.score = self._calculate_score(pattern)
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# 记录关联关系
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if related_keys:
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self._associations[key].update(related_keys)
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pattern.related_keys = list(self._associations[key])
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# 如果模式过多，删除评分最低的
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if len(self._patterns) > self.max_patterns:
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min_key = min(self._patterns, key=lambda k: self._patterns[k].score)
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del self._patterns[min_key]
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if min_key in self._associations:
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del self._associations[min_key]
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async def should_preload(self, key: str) -> bool:
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"""判断是否应该预加载某个数据
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Args:
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key: 数据键
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Returns:
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是否应该预加载
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"""
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async with self._lock:
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pattern = self._patterns.get(key)
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if pattern is None:
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return False
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# 更新评分
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pattern.score = self._calculate_score(pattern)
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return pattern.score >= self.preload_threshold
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async def get_preload_keys(self, limit: int = 100) -> list[str]:
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"""获取应该预加载的数据键列表
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Args:
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limit: 最大返回数量
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Returns:
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按评分排序的数据键列表
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"""
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async with self._lock:
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# 更新所有评分
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for pattern in self._patterns.values():
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pattern.score = self._calculate_score(pattern)
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# 按评分排序
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sorted_patterns = sorted(
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self._patterns.values(),
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key=lambda p: p.score,
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reverse=True,
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)
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# 返回超过阈值的键
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return [
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p.key for p in sorted_patterns[:limit]
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if p.score >= self.preload_threshold
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]
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async def get_related_keys(self, key: str) -> list[str]:
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"""获取关联数据键
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Args:
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key: 数据键
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Returns:
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关联数据键列表
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"""
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async with self._lock:
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return list(self._associations.get(key, []))
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async def preload_data(
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self,
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key: str,
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loader: Callable[[], Awaitable[Any]],
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) -> None:
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"""预加载数据
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Args:
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key: 数据键
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loader: 异步加载函数
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"""
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try:
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cache = await get_cache()
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# 检查缓存中是否已存在
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if await cache.l1_cache.get(key) is not None:
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return
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# 加载数据
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logger.debug(f"预加载数据: {key}")
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data = await loader()
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if data is not None:
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# 写入缓存
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await cache.set(key, data)
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self._preload_count += 1
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# 预加载关联数据
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related_keys = await self.get_related_keys(key)
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for related_key in related_keys[:5]: # 最多预加载5个关联项
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if await cache.l1_cache.get(related_key) is None:
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# 这里需要调用者提供关联数据的加载函数
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# 暂时只记录，不实际加载
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logger.debug(f"发现关联数据: {related_key}")
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except Exception as e:
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logger.error(f"预加载数据失败 {key}: {e}", exc_info=True)
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async def start_preload_batch(
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self,
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session: AsyncSession,
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loaders: dict[str, Callable[[], Awaitable[Any]]],
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) -> None:
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"""批量启动预加载任务
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Args:
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session: 数据库会话
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loaders: 数据键到加载函数的映射
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"""
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preload_keys = await self.get_preload_keys()
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for key in preload_keys:
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if key in loaders:
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loader = loaders[key]
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task = asyncio.create_task(self.preload_data(key, loader))
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self._preload_tasks.add(task)
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task.add_done_callback(self._preload_tasks.discard)
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async def record_hit(self, key: str) -> None:
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"""记录预加载命中
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当缓存命中的数据是预加载的，调用此方法统计
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Args:
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key: 数据键
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"""
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async with self._lock:
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self._preload_hits += 1
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async def get_stats(self) -> dict[str, Any]:
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"""获取统计信息"""
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async with self._lock:
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preload_hit_rate = (
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self._preload_hits / self._preload_count
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if self._preload_count > 0
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else 0.0
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)
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return {
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"total_accesses": self._total_accesses,
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"tracked_patterns": len(self._patterns),
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"associations": len(self._associations),
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"preload_count": self._preload_count,
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"preload_hits": self._preload_hits,
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"preload_hit_rate": preload_hit_rate,
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"active_tasks": len(self._preload_tasks),
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}
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async def clear(self) -> None:
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"""清空所有统计信息"""
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async with self._lock:
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self._patterns.clear()
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self._associations.clear()
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self._total_accesses = 0
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self._preload_count = 0
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self._preload_hits = 0
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# 取消所有预加载任务
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for task in self._preload_tasks:
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task.cancel()
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self._preload_tasks.clear()
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def _calculate_score(self, pattern: AccessPattern) -> float:
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"""计算热度评分
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使用时间衰减的访问频率：
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score = access_count * decay_factor^(time_since_last_access)
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Args:
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pattern: 访问模式
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Returns:
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热度评分
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"""
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now = time.time()
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time_diff = now - pattern.last_access
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# 时间衰减（以小时为单位）
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hours_passed = time_diff / 3600
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decay = self.decay_factor ** hours_passed
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# 评分 = 访问次数 * 时间衰减
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score = pattern.access_count * decay
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return score
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class CommonDataPreloader:
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"""常见数据预加载器
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针对特定的数据类型提供预加载策略
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"""
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def __init__(self, preloader: DataPreloader):
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"""初始化
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Args:
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preloader: 基础预加载器
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"""
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self.preloader = preloader
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async def preload_user_data(
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self,
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session: AsyncSession,
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user_id: str,
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platform: str,
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) -> None:
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"""预加载用户相关数据
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包括：个人信息、权限、关系等
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Args:
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session: 数据库会话
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user_id: 用户ID
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platform: 平台
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"""
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from src.common.database.core.models import PersonInfo, UserPermissions, UserRelationships
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# 预加载个人信息
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await self._preload_model(
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session,
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f"person:{platform}:{user_id}",
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PersonInfo,
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{"platform": platform, "user_id": user_id},
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)
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# 预加载用户权限
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await self._preload_model(
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session,
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f"permissions:{platform}:{user_id}",
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UserPermissions,
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{"platform": platform, "user_id": user_id},
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)
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# 预加载用户关系
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await self._preload_model(
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session,
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f"relationship:{user_id}",
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UserRelationships,
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{"user_id": user_id},
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)
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async def preload_chat_context(
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self,
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session: AsyncSession,
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stream_id: str,
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limit: int = 50,
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) -> None:
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"""预加载聊天上下文
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包括：最近消息、聊天流信息等
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Args:
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session: 数据库会话
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stream_id: 聊天流ID
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limit: 消息数量限制
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"""
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from src.common.database.core.models import ChatStreams, Messages
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# 预加载聊天流信息
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await self._preload_model(
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session,
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f"stream:{stream_id}",
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ChatStreams,
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{"stream_id": stream_id},
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)
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# 预加载最近消息（这个比较复杂，暂时跳过）
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# TODO: 实现消息列表的预加载
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async def _preload_model(
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self,
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session: AsyncSession,
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cache_key: str,
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model_class: type,
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filters: dict[str, Any],
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) -> None:
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"""预加载模型数据
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Args:
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session: 数据库会话
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cache_key: 缓存键
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model_class: 模型类
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filters: 过滤条件
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"""
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async def loader():
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stmt = select(model_class)
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for key, value in filters.items():
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stmt = stmt.where(getattr(model_class, key) == value)
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result = await session.execute(stmt)
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return result.scalar_one_or_none()
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await self.preloader.preload_data(cache_key, loader)
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# 全局预加载器实例
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_global_preloader: Optional[DataPreloader] = None
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_preloader_lock = asyncio.Lock()
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async def get_preloader() -> DataPreloader:
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"""获取全局预加载器实例（单例）"""
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global _global_preloader
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if _global_preloader is None:
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async with _preloader_lock:
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if _global_preloader is None:
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_global_preloader = DataPreloader()
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return _global_preloader
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async def close_preloader() -> None:
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"""关闭全局预加载器"""
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global _global_preloader
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if _global_preloader is not None:
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await _global_preloader.clear()
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_global_preloader = None
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logger.info("全局预加载器已关闭")
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