Caching Strategies & In-Memory Optimization Questions

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.