https://4f0a9a3b.wangpeng.pages.dev/en/tags/high-availability/Recent content in High Availability on Zhuifeng NotesHugoenTue, 25 Aug 2026 00:00:00 +0000https://4f0a9a3b.wangpeng.pages.dev/en/tech/redis/cache-design/Tue, 25 Aug 2026 00:00:00 +0000https://4f0a9a3b.wangpeng.pages.dev/en/tech/redis/cache-design/<p>Introducing a cache dramatically reduces database pressure, but poor design introduces the classic problems of penetration, breakdown and avalanche. We break down the causes and countermeasures below.</p> <h2 id="the-three-classic-problems">The Three Classic Problems </h2> <ul> <li><strong>Cache penetration</strong>: querying data that does not exist, so neither cache nor DB returns it and every request hits the DB. Mitigate with caching empty values (short TTL) or a Bloom filter to block illegal keys.</li> <li><strong>Cache breakdown</strong>: the moment a hot key expires, many concurrent requests all fall back to the DB at once. Mitigate with a mutex (only one thread rebuilds), logical expiration (async refresh), or never-expire hot keys.</li> <li><strong>Cache avalanche</strong>: a large number of keys expire simultaneously, or Redis becomes entirely unavailable, overwhelming the DB. Mitigate with random jitter on expiration times, multi-level caching, and Redis high availability (Sentinel/Cluster).</li> </ul> <h2 id="cache-aside-and-consistency">Cache-Aside and Consistency </h2> <p>The most common pattern is <strong>Cache-Aside (旁路缓存)</strong>:</p>