When traffic grows, performance issues are often not caused by weak hardware—they are caused by repeated work. The same expensive queries, API calls, and rendered payloads happen again and again.
Caching reduces that repeated work. Done well, it lowers latency, cuts infrastructure cost, and improves user experience during peak traffic.
Use the Right Cache at the Right Layer
- CDN Cache: Static assets and cacheable page responses close to users
- Application Cache (Redis): Frequently requested computed data
- Database Query Cache: Heavy reads with stable result windows
Start with Read Patterns, Not Tools
Before selecting a cache technology, profile read traffic by endpoint and query cost. This reveals which paths produce the highest latency and compute waste.
TTL and Invalidation Principles
- Use short TTL for volatile data
- Use event-driven invalidation for business-critical updates
- Avoid global cache flushes unless absolutely necessary
- Include tenant/user context in cache keys where required
Prevent Cache-Related Failures
- Guard against cache stampede with request coalescing
- Implement stale-while-revalidate for resilience
- Set fallback behavior when cache is unavailable
- Monitor hit ratio, eviction rate, and keyspace growth
Performance Metrics to Track After Rollout
- p95 and p99 latency by endpoint
- Database CPU and query volume reduction
- Cache hit ratio and miss penalty
- Infrastructure cost per 1,000 requests
Caching is most effective when treated as architecture, not a patch. A layered strategy across CDN, application, and data access can unlock major performance gains while keeping correctness intact.
