How do you implement caching strategies for high-performance custom software?

Caching strategy operates at multiple levels for compounding performance gains. Client-side: HTTP cache headers (Cache-Control, ETag) for static assets and API responses, browser storage (localStorage for user preferences, sessionStorage for temporary state), and Service Workers for offline-capable applications. CDN caching: cache static assets and API responses at edge locations (Cloudflare, CloudFront), implement cache invalidation via purge APIs or surrogate keys, and use stale-while-revalidate for always-fast responses. Application-level: Redis for session storage, API response caching, and computed results. Caching patterns: cache-aside (application checks cache, fetches from DB on miss), write-through (write to cache and DB simultaneously), write-behind (write to cache, async write to DB), and read-through (cache automatically fetches on miss). Database-level: materialized views for complex aggregations, query result caching with pgstatstatements for identifying cache candidates, and connection pooling to reduce overhead. Cache invalidation: time-based (TTL expiration), event-based (invalidate on data change using pub/sub), and version-based (cache key includes data version). Implementation details: use consistent hashing for distributed cache, implement cache warming for cold starts, set appropriate TTLs per data type (static content: hours/days, user data: minutes, real-time data: seconds), and monitor cache hit rates (target 90%+). Anti-patterns: caching mutable data without invalidation, caching personal data in shared caches, and treating cache as a source of truth.