Adding a cache layer without proper design can cause data inconsistency, cache breakdown, and performance volatility; this article breaks down cache patterns, eviction strategies, concurrency controls, and distributed consistency models, showing when and how to apply each for reliable high‑performance systems.
The article explains that overwhelming request rates exceed database capacity, causing exponential latency, and presents six proven cache‑design techniques—including in‑process, Redis, multi‑level, cache‑aside, write‑through/write‑behind, and edge caching—to keep Spring Boot APIs stable, fast, and cost‑effective under high load.
This article explains the relationship between MySQL and Redis, discusses why cache consistency is challenging, and details four cache update design patterns—delete‑then‑update, update‑then‑invalidate, read/write‑through, and write‑behind—along with their advantages, drawbacks, and typical execution flows.
This article explains common cache update patterns—Cache Aside, Read‑Through, Write‑Through, and Write‑Behind—illustrates their read/write flows, highlights typical pitfalls such as dirty data caused by wrong update order, and offers practical safeguards for maintaining data consistency in large‑scale systems.
This article explains why cache‑database inconsistencies occur in large systems, details the cache‑aside, read‑through, write‑through and write‑behind strategies, highlights three common pitfalls with concrete examples, and offers practical recommendations such as proper update ordering and cache expiration to ensure data freshness.
This article explains five common caching strategies—Cache‑Aside, Read‑Through, Write‑Through, Write‑Around, and Write‑Back—detailing how they work, their advantages and drawbacks, and how to choose the best fit based on data access patterns and performance goals.
This article explains cache read and write mechanisms, the impact of operation order on data consistency, and presents optimization techniques such as delayed double deletion and binlog subscription to mitigate inconsistency in high‑concurrency backend systems.
