Cache Invalidation Strategies for Scalable Systems
Issue #86: System Design Interview Roadmap
🎯 What We'll Master Today
Four Production-Tested Invalidation Strategies: TTL-based, event-driven, lazy, and hybrid patterns with real-world insights
Enterprise Failure Patterns: Clock skew disasters, thundering herd scenarios, and memory pressure complications
FAANG-Scale Case Studies: Netflix's dynamic TTL, Facebook's TAO lazy invalidation, Instagram's multi-tier architecture
Hands-On Implementation: Complete working system with real-time monitoring and performance benchmarking
Production Deployment: Battle-tested patterns for cache avalanche prevention and graceful degradation
The Silent Killer of User Experience
Picture this: You're debugging why users see their old profile pictures for 30 minutes after updating them, while your database shows the correct data immediately. Meanwhile, your colleague struggles with a cache that invalidates too aggressively, causing database overload during peak traffic. Both of you are facing the same fundamental challenge: cache invalidation timing.
This scenario plays out daily across millions of applications because cache invalidation represents one of the hardest problems in distributed systems. As Phil Karlton famously said, "There are only two hard things in Computer Science: cache invalidation and naming things." Today, we'll master the invalidation part.
