Deep Dive into Caffeine Cache: Unraveling Window TinyLFU and Efficient Implementations

This article delves into the inner workings of the high-performance caching library Caffeine, focusing on its unique Window TinyLFU eviction policy. It explains how Window TinyLFU combines frequency and recency information, utilizing a CountMinSketch data structure for efficient frequency estimation. Furthermore, the article analyzes Caffeine's expiration mechanisms based on ordered queues and a hierarchical timer wheel, and how its adaptive caching policy dynamically adjusts cache configurations using a hill-climbing algorithm to achieve high-performance cache management.