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This article explains the fundamentals of caching, enumerates various client‑side, server‑side and distributed cache types, describes common caching patterns and expiration policies, and highlights practical considerations to effectively improve web application performance and reliability.
This article provides a comprehensive guide to cache usage, covering its performance benefits and costs, various update strategies, granularity control, and detailed optimizations for issues such as penetration, avalanche, hot key reconstruction, and distributed batch operations.
This comprehensive guide explains caching fundamentals, motivations, and hardware/software perspectives, details client, network, and server cache types, addresses common problems like breakdown, avalanche, and penetration with solutions, and covers strategies, consistency, expiration policies, distributed architectures, design best practices, and implementation examples.
This article comprehensively explains cache benefits and costs, usage scenarios, update policies, and advanced optimizations such as penetration protection, bottom‑hole mitigation, avalanche prevention, hot‑key rebuilding, and efficient batch operations, providing practical guidance for building high‑performance backend systems.
This article reviews various cache system types, compares popular distributed caches such as Memcache, Tair, and Redis, explains Redis cluster high‑availability mechanisms, discusses sharding strategies, and outlines common cache problems and solutions, providing practical configuration examples for Java backend developers.
This article explains Redis’s memory eviction policies, expiration strategies, common cache problems such as penetration, breakdown, and avalanche, outlines deployment modes, compares RDB and AOF persistence, and lists typical business scenarios for using Redis as a high‑performance key‑value store.
To achieve low‑latency, high‑throughput data access in systems like Gaode’s navigation service, the article advises evaluating CPU and I/O bottlenecks, choosing between local (HashMap/ConcurrentHashMap or Caffeine) and distributed caches (Redis preferred), applying appropriate eviction, TTL, and consistency patterns, and mitigating cache penetration, breakdown, and avalanche risks.
This article explains when caching is needed, how to choose appropriate in‑process and distributed caches, design multi‑level cache architectures, handle cache updates, avoid common pitfalls such as penetration, breakdown and avalanche, and provides practical advice on serialization, GC tuning, monitoring, and framework selection.
This article explains why traditional H5 pages perform poorly compared to native apps, introduces Progressive Web Apps (PWA) as a solution, details how service workers enable offline caching, presents real‑world performance comparisons, and reviews common cache strategies such as cache‑first, network‑first, stale‑while‑revalidate, cache‑only, and network‑only.