TIANHE
233 lines
6.2 KiB
Python
233 lines
6.2 KiB
Python
# -*- coding: utf-8 -*-
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"""
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===================================
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数据缓存管理模块
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===================================
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参考 daily_stock_analysis 项目实现
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用于缓存实时行情和K线数据，减少重复请求
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特性：
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1. TTL (Time To Live) 过期机制
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2. LRU (Least Recently Used) 淘汰策略
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3. 按数据类型分区管理
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"""
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import time
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import logging
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from typing import Dict, Any, Optional, List
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from collections import OrderedDict
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from dataclasses import dataclass, field
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from datetime import datetime
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import threading
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logger = logging.getLogger(__name__)
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@dataclass
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class CacheEntry:
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"""缓存条目"""
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data: Any
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timestamp: float
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ttl: float
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hit_count: int = 0
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def is_expired(self) -> bool:
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"""检查是否过期"""
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return time.time() - self.timestamp > self.ttl
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def age(self) -> float:
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"""返回缓存年龄（秒）"""
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return time.time() - self.timestamp
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class DataCache:
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"""
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数据缓存管理器
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特性：
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- TTL 过期机制
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- 最大容量限制
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- LRU 淘汰策略
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- 线程安全
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"""
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def __init__(
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self,
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name: str = "default",
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default_ttl: float = 600.0, # 默认10分钟
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max_size: int = 1000 # 最大缓存条目数
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):
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self.name = name
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self.default_ttl = default_ttl
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self.max_size = max_size
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self._cache: OrderedDict[str, CacheEntry] = OrderedDict()
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self._lock = threading.RLock()
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# 统计信息
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self._hits = 0
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self._misses = 0
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def get(self, key: str) -> Optional[Any]:
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"""
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获取缓存数据
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Returns:
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缓存的数据，不存在或过期返回 None
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"""
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with self._lock:
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if key not in self._cache:
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self._misses += 1
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return None
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entry = self._cache[key]
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# 检查是否过期
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if entry.is_expired():
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del self._cache[key]
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self._misses += 1
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logger.debug(f"[缓存] {self.name}:{key} 已过期，删除")
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return None
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# 更新访问顺序（LRU）
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self._cache.move_to_end(key)
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entry.hit_count += 1
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self._hits += 1
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logger.debug(f"[缓存命中] {self.name}:{key} (年龄: {entry.age():.0f}s/{entry.ttl:.0f}s)")
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return entry.data
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def set(
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self,
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key: str,
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data: Any,
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ttl: Optional[float] = None
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) -> None:
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"""
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设置缓存数据
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Args:
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key: 缓存键
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data: 缓存数据
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ttl: 过期时间（秒），None 使用默认值
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"""
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with self._lock:
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# 检查容量，执行 LRU 淘汰
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while len(self._cache) >= self.max_size:
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oldest_key, _ = self._cache.popitem(last=False)
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logger.debug(f"[缓存] {self.name} 容量已满，淘汰: {oldest_key}")
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actual_ttl = ttl if ttl is not None else self.default_ttl
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self._cache[key] = CacheEntry(
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data=data,
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timestamp=time.time(),
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ttl=actual_ttl
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)
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logger.debug(f"[缓存更新] {self.name}:{key} TTL={actual_ttl}s")
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def delete(self, key: str) -> bool:
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"""删除缓存条目"""
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with self._lock:
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if key in self._cache:
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del self._cache[key]
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logger.debug(f"[缓存] {self.name}:{key} 已删除")
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return True
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return False
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def clear(self) -> int:
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"""清空缓存"""
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with self._lock:
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count = len(self._cache)
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self._cache.clear()
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logger.info(f"[缓存] {self.name} 已清空 {count} 条记录")
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return count
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def cleanup_expired(self) -> int:
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"""清理过期条目"""
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with self._lock:
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expired_keys = [
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key for key, entry in self._cache.items()
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if entry.is_expired()
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]
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for key in expired_keys:
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del self._cache[key]
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if expired_keys:
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logger.debug(f"[缓存] {self.name} 清理 {len(expired_keys)} 条过期记录")
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return len(expired_keys)
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def stats(self) -> Dict[str, Any]:
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"""获取缓存统计信息"""
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with self._lock:
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total_requests = self._hits + self._misses
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hit_rate = self._hits / total_requests if total_requests > 0 else 0
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return {
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'name': self.name,
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'size': len(self._cache),
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'max_size': self.max_size,
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'hits': self._hits,
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'misses': self._misses,
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'hit_rate': f"{hit_rate:.1%}",
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'default_ttl': self.default_ttl
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}
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# ============================================
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# 全局缓存实例
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# ============================================
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# A股实时行情缓存（20分钟TTL，全市场数据量大）
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_ashare_realtime_cache = DataCache(
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name="ashare_realtime",
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default_ttl=1200.0, # 20分钟
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max_size=6000 # 约5000+股票
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)
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# K线数据缓存（5分钟TTL，按需缓存）
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_kline_cache = DataCache(
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name="kline",
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default_ttl=300.0, # 5分钟
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max_size=500 # 最多500个交易对
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)
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# 股票基本信息缓存（1天TTL）
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_stock_info_cache = DataCache(
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name="stock_info",
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default_ttl=86400.0, # 24小时
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max_size=6000
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)
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def get_realtime_cache() -> DataCache:
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"""获取实时行情缓存"""
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return _ashare_realtime_cache
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def get_kline_cache() -> DataCache:
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"""获取K线数据缓存"""
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return _kline_cache
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def get_stock_info_cache() -> DataCache:
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"""获取股票信息缓存"""
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return _stock_info_cache
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def generate_kline_cache_key(
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symbol: str,
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timeframe: str,
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limit: int,
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before_time: Optional[int] = None
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) -> str:
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"""
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生成K线缓存键
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格式: symbol:timeframe:limit[:before_time]
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"""
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key = f"{symbol}:{timeframe}:{limit}"
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if before_time:
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key += f":{before_time}"
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return key
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