Caching Strategies & In-Memory Optimization
Designing cache layers to cut redundant work and speed up reads, and the correctness costs that come with them. Covers cache placement across client, CDN, application, and in-memory store tiers, including platform-specific caching on mobile (in-memory and disk caches on iOS and Android, secure on-device token storage) and in the browser (localStorage, IndexedDB, Service Workers). Covers eviction policies, TTLs, write-through vs write-back, warming, and invalidation, plus cache stampede and thundering-herd mitigation when a hot key expires under load. Also covers using an in-memory store like Redis directly rather than only as a cache: distributed locks, counters, rate limiters, and leaderboards. Includes distributed and sharded cache architecture (replication, failover, sizing, migration) and the operational side of running one: health monitoring, alerting, and incident runbooks, plus the security and privacy risks caching introduces, such as cache poisoning and caching PII across shared tenants. Emphasizes hit-rate reasoning and the staleness/consistency trade-offs caching introduces.
