Strategies for Ensuring Cache Consistency in Distributed Systems
The article reviews major cache‑consistency strategies—Cache‑Aside, Read‑Through, Write‑Through, and Write‑Behind—detailing their performance and consistency trade‑offs, discusses compensation techniques such as delayed double‑delete and MySQL binlog‑based incremental parsing (DTS) for reliable deletion and HA, and advises selecting the appropriate approach based on specific business requirements.
本文主要介绍了在不同场景下保证数据缓存一致性的相关策略，包括Cache-Aside、Read-Through、Write-Through和Write-Behind等策略。
Cache-Aside策略通过旁路缓存模式，在读请求中优先访问缓存，未命中时查询数据库并更新缓存；在写请求中更新数据库后删除缓存。该策略的优势在于性能和安全，但存在数据不一致的可能性。
Read-Through策略引入访问控制层，简化业务层逻辑，但同样存在并发场景下的不一致问题。
Write-Through策略在更新数据库后同时更新缓存，但存在并发冲突和性能问题。
Write-Behind策略异步回写缓存，减轻数据库压力，但一致性较弱。
针对不同场景，业界提出了多种补偿机制，如延时双删、基于数据库日志增量解析等，以解决缓存删除失败等问题。
此外，基于MySQL binlog增量解析的DTS服务提供了一种高效的解决方案，支持HA和有序消费。
在选择策略时，应根据业务场景和一致性要求进行权衡。
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