Read-heavy endpoints hit the database on every request, even when the underlying data changes infrequently. At scale this creates unnecessary latency and DB load that a caching layer would eliminate.
The PRD hypothesis: Cache-Aside with TTL reduces read latency and DB load proportionally to cache hit rate.
Build a scenario that measures response times and query counts with no cache (baseline), then introduces Cache-Aside with Redis. Explore TTL tuning, cache invalidation on writes, and the thundering herd problem when popular keys expire simultaneously.
Integration tests should verify: cached reads skip the DB, writes invalidate stale entries, and TTL expiry triggers a fresh read. Benchmarks quantify latency reduction at various hit rates. Standard module structure, AC-1 through AC-5.
Read-heavy endpoints hit the database on every request, even when the underlying data changes infrequently. At scale this creates unnecessary latency and DB load that a caching layer would eliminate.
The PRD hypothesis: Cache-Aside with TTL reduces read latency and DB load proportionally to cache hit rate.
Build a scenario that measures response times and query counts with no cache (baseline), then introduces Cache-Aside with Redis. Explore TTL tuning, cache invalidation on writes, and the thundering herd problem when popular keys expire simultaneously.
Integration tests should verify: cached reads skip the DB, writes invalidate stale entries, and TTL expiry triggers a fresh read. Benchmarks quantify latency reduction at various hit rates. Standard module structure, AC-1 through AC-5.