Caching

Previous Chapter: Chapter 1 — Microservices Foundation | Series Hub | Next Chapter: Chapter 3 — Traffic Shield: Kafka Peak Shaving Answer-first: Shopee eliminates flash-sale inventory overselling and hot-key contention by combining client purchase tokens, local memory short-circuiting, and Redis Lua atomic stock deduction with sub-key sharding. Splitting high-demand SKU inventory across randomized sub-keys prevents single Redis master saturation, guaranteeing sub-two-millisecond reservation latencies and mathematically verified zero overselling across millions of concurrent checkout requests. ...

Answer-first: Mitigating caching vulnerabilities at scale requires a multi-layered defense against three fatal failure modes: cache penetration is eliminated using Bloom filters and null-object caching; cache avalanche is prevented by injecting randomized TTL jitter and asynchronous background warming; and cache breakdown is solved using Go singleflight to coalesce thousands of duplicate concurrent requests into a single database query. Prerequisite: Advanced understanding of memory caching hierarchies (L1 in-process vs L2 distributed clusters), probabilistic data structures (Bloom and Cuckoo filters), Go synchronization primitives, and database connection pool behavior is assumed for this chapter. ...