Part 3: Caching Strategies, Redis/Valkey & Stampede Prevention
← Previous Chapter: Part 2: L4/L7 Load Balancing & API Gateways | Series Hub: System Design Masterclass | Next Chapter: Part 4: Database Scaling, Sharding & Distributed SQL → Prerequisite: Read Part 2: L4/L7 Load Balancing, API Gateways & eBPF Routing to understand edge ingress distribution before designing the cache hierarchy. Answer-first: Production caching in Go couples in-memory L1 caches with distributed Redis or Valkey clusters to shield relational databases. Employing the XFetch probabilistic early expiration algorithm alongside Go Singleflight deduplication completely eliminates thundering herd stampedes, while scalable Bloom filters prevent cache penetration, maintaining sub-millisecond P99 response times under 200,000 requests per second. ...