基于Python实现的缓存淘汰替换策略算法，该算法将缓存分区

以下是一个基于Python实现的缓存淘汰替换策略算法，该算法将缓存分区，并根据不同分区的优先级进行淘汰，同时会自适应地调整缓存汰换的时机和力度，还会与GPTCache自带的LRU和FIFO策略进行对比。

import time from gptcache.manager import CacheBase, VectorBase, get_data_manager from gptcache.processor.pre import get_prompt from gptcache.adapter.api import init_similar_cache from gptcache.strategy import LRUCacheStrategy, FIFOCacheStrategy # 定义缓存类 class CustomCache: def __init__(self, capacity): self.capacity = capacity self.high_priority = {} self.hot_window = {} self.medium_priority = {} self.other_cache = {} self.total_count = 0 def add(self, key, value, category): # 初始化缓存项字段 cache_item = { 'value': value, 'freshness': 1.0, 'activity': 1, 'timestamp': time.time(), 'eviction_rate': 0.0, 'category': category } self.total_count += 1 # 根据类别分配到不同分区 if category == 'high': self.high_priority[key] = cache_item elif category == 'hot': self.hot_window[key] = cache_item elif category == 'medium': self.medium_priority[key] = cache_item else: self.other_cache[key] = cache_item # 检查是否需要淘汰 if self.total_count > self.capacity: self.evict() def get(self, key): for cache in [self.high_priority, self.hot_window, self.medium_priority, self.other_cache]: if key in cache: cache[key]['activity'] += 1 cache[key]['timestamp'] = time.time() cache[key]['freshness'] = 1.0 return cache[key]['value'] return None def evict(self): # 自适应调整淘汰时机和力度 # 这里简单模拟根据当前时间进行调整，实际可根据季节性或周期性数据调整 current_time = time.time() if current_time % 3600 < 1800: # 假设每小时前半段淘汰力度大 eviction_count = 2 else: eviction_count = 1 while self.total_count > self.capacity and eviction_count > 0: if self.other_cache: key = next(iter(self.other_cache)) del self.other_cache[key] elif self.medium_priority: key = next(iter(self.medium_priority)) del self.medium_priority[key] elif self.hot_window: # 淘汰热点窗口中活跃度最低的项 min_activity_key = min(self.hot_window, key=lambda k: self.hot_window[k]['activity']) del self.hot_window[min_activity_key] elif self.high_priority: # 淘汰高优先级中最旧的项 oldest_key = min(self.high_priority, key=lambda k: self.high_priority[k]['timestamp']) del self.high_priority[oldest_key] self.total_count -= 1 eviction_count -= 1 # 对比测试函数 def compare_strategies(): capacity = 10 # 自定义策略 custom_cache = CustomCache(capacity) # LRU策略 lru_strategy = LRUCacheStrategy(capacity) lru_cache_base = CacheBase('sqlite') lru_vector_base = VectorBase('faiss', dimension=128) lru_data_manager = get_data_manager(lru_cache_base, lru_vector_base, lru_strategy) init_similar_cache(data_manager=lru_data_manager, pre_embedding_func=get_prompt) # FIFO策略 fifo_strategy = FIFOCacheStrategy(capacity) fifo_cache_base = CacheBase('sqlite') fifo_vector_base = VectorBase('faiss', dimension=128) fifo_data_manager = get_data_manager(fifo_cache_base, fifo_vector_base, fifo_strategy) init_similar_cache(data_manager=fifo_data_manager, pre_embedding_func=get_prompt) # 模拟数据访问 data = [(i, f"value_{i}", 'high' if i < 3 else 'medium' if i < 6 else 'hot' if i < 8 else 'other') for i in range(20)] custom_hits = 0 lru_hits = 0 fifo_hits = 0 for key, value, category in data: # 自定义策略 if custom_cache.get(key) is None: custom_cache.add(key, value, category) else: custom_hits += 1 # LRU策略 if lru_data_manager.get_data(key, None) is None: lru_data_manager.save(key, value, None, None) else: lru_hits += 1 # FIFO策略 if fifo_data_manager.get_data(key, None) is None: fifo_data_manager.save(key, value, None, None) else: fifo_hits += 1 print(f"Custom Cache Hits: {custom_hits}") print(f"LRU Cache Hits: {lru_hits}") print(f"FIFO Cache Hits: {fifo_hits}") if __name__ == "__main__": compare_strategies() 代码说明：

CustomCache类：

__init__：初始化缓存分区和容量。add：将缓存项添加到相应的分区，并在需要时调用evict方法进行淘汰。get：根据键查找缓存项，并更新其活跃度、时间戳和新鲜度。evict：根据分区优先级进行淘汰，同时自适应地调整淘汰的时机和力度。

compare_strategies函数：

初始化自定义缓存、LRU缓存和FIFO缓存。模拟数据访问，记录每个缓存的命中次数。输出每个缓存的命中次数。 注意事项： 代码中使用了GPTCache库，需要确保已经安装了该库。自适应调整淘汰时机和力度的逻辑可以根据实际需求进行修改，这里只是简单模拟了每小时前半段淘汰力度大的情况。