Diagnose N+1 Queries and Design a Correct Service Cache

Explain the database N+1 query problem and how service-level caching can improve or complicate a read path. Use an explicitly hypothetical API example and discuss how you would validate a proposed fix. ### Part 1 — N+1 Queries Describe how one request can issue a query for a collection and then one additional query per item. Explain how to detect this pattern and compare ways to avoid it. #### What This Part Should Cover Request-scoped query counts, joins or batch fetching, pagination, and tradeoffs involving result size or duplicate rows. ### Part 2 — Service Caching Explain what data you would cache, how keys and freshness are defined, and how cache failures or invalidation affect correctness. #### What This Part Should Cover A declared consistency policy, invalidation or expiration, stampede control, and evidence that caching addresses the actual bottleneck. ### Constraints No database engine, ORM, cache product, or latency target is supplied. Do not assume caching is a substitute for fixing an inefficient query pattern. Any concrete resource schema is illustrative. ### Clarifying Questions - Does query count grow with page size, and what data does the response actually need? - How stale may the data be, and what writes can invalidate it? - Are cache keys scoped by user or tenant permissions? ```hint Measure one request end to end A faster individual query can still leave a request slow if it is executed once per returned item. ``` ### What a Strong Answer Covers - Correct N+1 diagnosis and an appropriate bounded-fetch strategy. - Cache keys, freshness, invalidation, and failure behavior. - Measurement of query count, latency, load, and correctness after the change. ### Follow-up Questions - How can joining several one-to-many relationships create a new performance problem? - How would you stop many concurrent cache misses from overwhelming the database?