This article examines how popular caching systems such as Redis, Memcached, Guava Cache, and Ehcache implement eviction policies like LRU, LFU, and FIFO, comparing their algorithmic approaches, code implementations, and expiration handling mechanisms.
This article examines common NoSQL key‑value stores such as Memcached and Redis, compares their strengths and limitations, and proposes a distributed architecture with routing, storage, management, and migration nodes that achieves high availability, automatic fault‑tolerance, load balancing, and elastic scaling.
This article explains the security risks surrounding Redis, covering access control, authentication, command renaming, network exposure, and code‑execution vulnerabilities, and provides practical configuration steps to protect a Redis deployment from common attacks.
The author recounts a real‑world incident where consolidating many Redis GET calls into a single MGET reduced network latency but unexpectedly increased overall response time, leading to timeout errors; they detail the investigation using Redis slowlog, code analysis, and proper pagination techniques to resolve the hidden performance bottleneck.
This guide presents a comprehensive collection of Redis configuration directives, covering daemonization, networking, persistence, replication, security, memory management, AOF, Lua scripting, slow log, event notifications, and advanced tuning options, enabling administrators to optimize performance, reliability, and safety of their Redis deployments.
This article compares Redis with MySQL, outlines their similarities and differences, examines Redis alongside Memcached, EhCache, and OSCache, and proposes a simple high‑availability architecture for Redis, highlighting performance, data model, scalability, and operational considerations.
This article reviews the five core Redis data structures—String, Hash, List, Set, and Sorted Set—detailing practical use‑cases such as caching, user profile storage, timelines, message queues, social graphs, and weighted rankings, plus a look at Pub/Sub and transaction features.
At Baidu’s 49th Technical Salon, Cheng Yishi explained how the company revamped its MooseFS and Redis systems by adding a Shadow Master to split reads from writes, introducing Slave nodes for failover, and deploying a Redis proxy middleware, thereby dramatically improving performance, scalability, and high‑availability for critical services.
