caching

Caching stores frequently accessed data close to the consumer, dramatically reducing database queries and latency. Cache-aside, write-through, and read-through patterns let you choose when and how to load data, with TTL policies and invalidation strategies keeping data fresh under high-traffic workloads.

Cache-aside loads data on miss; write-through updates cache and database together for consistency; write-behind batches writes asynchronously for speed. Choose based on consistency needs: cache-aside suits read-heavy workloads, write-through ensures immediate consistency, write-behind maximizes throughput with eventual consistency.

Use TTL expiration to auto-evict old data, implement versioning for invalidation on updates, and employ cross-system consistency checks. Invalidation strategies ensure cache reflects database changes; read-through patterns auto-load fresh data on miss.

Both Redis and Memcached work as distributed caches across services, reducing per-instance memory overhead. Redis offers richer data structures and persistence; Memcached provides simpler, faster key-value storage. Choose based on consistency requirements and data complexity.

Lazy loading (cache-aside) fetches data on first access, reducing startup overhead but causing initial request delays. Immediate population (read-through or batch preload) eliminates cache misses and warm starts but requires upfront compute. Use lazy loading for sparse access patterns and immediate population for predictable, high-frequency data.

Latency-sensitive, high-read workloads with skewed access patterns gain the most: APIs serving product details, user profiles, or configuration under peak traffic. Write-behind suits batch-heavy systems; cache-aside fits read-dominated services. Distributed caches handle cross-service consistency at scale.