This article explains why caching is a performance trade‑off, outlines essential cache features such as size limits, eviction policies, TTL, configuration, integration APIs, and distributed versus local modes, and provides a comprehensive checklist for evaluating cache providers.
This article explains the fundamentals of data consistency in distributed systems, compares strong, weak, and eventual consistency, describes three classic cache patterns (Cache‑Aside, Read‑Through/Write‑Through, Write‑Behind), and presents practical techniques such as delayed double delete, retry mechanisms, and binlog‑based asynchronous deletion to keep Redis caches and MySQL databases in sync.
This article explains how large‑scale internet applications can use various caching patterns—such as Cache‑Aside, Read‑Through, Write‑Through, and Write‑Behind—to alleviate database pressure, maintain data consistency, and avoid pitfalls like cache penetration, avalanche, and thundering under extreme traffic loads.
This article explains how large‑scale internet applications can use various caching patterns—such as Cache‑Aside, Read‑Through, Write‑Through, and Write‑Behind—to reduce database load, avoid consistency problems, and mitigate high‑concurrency issues like cache avalanche and penetration.
This article explains why massive traffic spikes can cripple database performance, introduces cache layers and common caching patterns, and offers practical solutions to consistency, concurrency, penetration, and avalanche problems for large‑scale internet applications.
Cache leverages the space‑time trade‑off to boost performance, but introduces complexity; this guide explains its fundamental idea, local vs distributed solutions, popular Java caching libraries, and key read/write patterns such as Cache‑Aside, Read‑Through, and Write‑Behind, highlighting benefits, drawbacks, and best‑practice considerations.
This article explains what cache avalanche, cache breakdown, and cache penetration are, why they threaten database stability, and presents practical solutions such as mutex locking, pre‑warming, double‑cache, timed updates, empty‑value caching, Bloom filters, as well as common cache patterns like Cache‑Aside, Read/Write‑Through, and Write‑Behind.
This article explores common caching read/write patterns—including Cache‑Aside, Read‑Through/Write‑Through, Write‑Back, and Write‑Around—detailing their mechanisms, advantages, disadvantages, and suitable application scenarios, while also addressing consistency and thread‑safety concerns. It also provides practical guidance on handling cache misses and ensuring data integrity across distributed systems.
This article explains various cache eviction strategies, compares heap, off‑heap, disk and distributed cache types, and provides concrete Java implementations using Guava Cache, EhCache 3.x and MapDB with code examples and usage patterns such as Cache‑Aside and Cache‑As‑SoR.
This article explains why caching is essential for backend services, outlines common cache problems such as penetration, concurrency, and avalanche, and compares cache-aside, read/write‑through, and write‑back patterns with practical guidance on choosing and updating caches.
