Caching Strategies

Caching is the most commonly misapplied performance technique. The failure mode is not "cache too little" — it is "cache without an invalidation strategy and then discover the problem in production six months later when users complain about stale data that you cannot explain."

When to Use

✅ Use for:

• Choosing which caching pattern fits a use case (cache-aside, write-through, write-behind)
• Designing TTL values for different data freshness requirements
• Implementing Redis caching patterns: sorted sets, pub/sub invalidation, Lua scripts
• Configuring Cache-Control headers, ETags, and CDN behavior
• Preventing cache stampedes via locking, probabilistic early expiry, or background refresh
• Cache warming strategies for cold-start scenarios
• Multi-tier cache design (in-memory L1, Redis L2, CDN L3)

❌ NOT for:

• Database-internal query plan caching (handled by the database)
• Python functools.lru_cache / JavaScript memoize utilities (pure function memoization)
• CPU branch prediction or hardware cache tuning