Caching

Caching is about the latency-consistency trade-off. The mental model: every caching decision is choosing where on the spectrum from 'always fresh' to 'maximally fast' you want to be. Different data has different requirements — profile pictures can be stale for hours, bank balances cannot be stale for milliseconds. Why Caching Matters Caching is storing frequently accessed data in a fast-access layer so you don't have to fetch it from the slow source every time. Without caching: Every request hits the database → database becomes bottleneck → slow responses With caching: Most requests served from memory → database has less load → fast responses The Numbers That Matter Access Time 0.5 ns 100 ns 100,000 ns 10,000,000 ns Caching puts hot data in RAM (100ns) instead of disk (100,000ns) — that's a 1000x speedup. Real-World Impact Facebook caches 90% of their reads in Memcached. Without caching, they'd need 10x more database servers. Interview Tip 'The first thing I'd add to any system under read pressure is a caching layer. It's the highest ROI optimization in most architectures.' Java Implementation — LRU Cache Python Implementation JavaScript/TypeScript Implementation