This article explains common cache replacement algorithms such as FIFO, LRU, LFU, and W‑TinyLFU, discusses their drawbacks, and provides a detailed SpringBoot example showing how to integrate the Caffeine caching library with code snippets and configuration instructions.
This article provides a comprehensive guide to caching technologies, covering HTTP caching, CDN caching, load‑balancer caching, in‑process caching, and distributed caching, while explaining strategies, algorithms, and common pitfalls such as cache avalanche, penetration, and breakdown.
This article traces iQIYI’s five‑stage evolution of Java caching—from simple database lookups and Redis synchronization to Guava Cache enhancements and finally to Caffeine’s W‑TinyLFU implementation—explaining each approach, its advantages, drawbacks, and the underlying algorithms that drive modern cache performance.
This article explains the fundamentals of caching, the role of distributed caches like memcached in high‑concurrency environments, and details the algorithms—including remainder hashing and consistent hashing with virtual nodes—that enable memcached to distribute data across multiple servers efficiently.
Under high‑concurrency loads, disk I/O becomes a bottleneck, prompting the use of caches; this article explains the fundamentals of caching, the role of memcached, and details its distributed implementation, covering simple modulo hashing, consistent hashing, and optimized virtual‑node techniques.
The article blends a humorous interview scenario with a thorough explanation of caching concepts, including definitions, hit/miss mechanics, eviction strategies, and detailed Java implementations of algorithms such as LFU, LRU, FIFO, ARC, and Random Cache, illustrating how to choose and code cache policies.
