This article outlines a multi‑level cache system for handling over a million QPS, detailing the architecture from client to database, key components like Caffeine and Redis Cluster, and providing concrete code examples for read‑through and write‑through flows.
This article explains why CPU caches are needed, describes the hierarchy and internal structure of L1, L2, and L3 caches, compares direct‑mapped, set‑associative and fully‑associative designs, and shows how cache‑aware coding (row‑major vs column‑major loops) dramatically improves execution speed.
This article explains the purpose of CPU caches, describes the three cache levels and their internal structures—including direct‑mapped, set‑associative, and fully‑associative designs—and shows how cache‑aware programming can dramatically improve code performance.
This article explains what caching is, outlines various cache types, analyzes common high‑traffic cache issues such as penetration, breakdown, avalanche, and hot‑key handling, and presents real‑world architectures from Weibo and Zhihu to illustrate effective cache design and operation.
MySQL 8.0 replaces duplicated server‑level and engine‑level metadata with a unified data dictionary stored in InnoDB, introduces a two‑level cache (local and shared) built on templated hash maps, and provides atomic DDL operations, dramatically improving metadata consistency, performance, and management simplicity.
This article provides a comprehensive overview of caching in large distributed systems, covering CDN, reverse‑proxy, local and distributed caches, popular implementations such as Ehcache, Guava, Memcached and Redis, common pitfalls like consistency and avalanche, and practical solutions with real‑world case studies.
In this interview, Sina Weibo platform architect Chen Bo shares the history, design principles, and scaling techniques of Weibo's feed caching system, covering early LAMP setups, hybrid push‑pull models, service‑oriented migration, cloud integration, and practical advice for engineers.
This article summarizes Yao Yu's "Scaling Redis at Twitter" talk, detailing why Twitter chose Redis, the massive memory and QPS requirements, custom data models, Hybrid List and BTree extensions, cluster management, and operational lessons for building a high‑performance caching service.
This article explains why CPU caches are needed, the evolution to multi‑level caches, the concept of cache lines, practical experiments demonstrating their impact, and how different cache organization strategies such as fully associative, direct‑mapped, and N‑way set‑associative affect performance and eviction policies.
