Count Batch Loader Calls, Not Response Time, to Detect GraphQL N+1 Bugs

GraphQL N+1 problems often go undetected during development because fast local machines and small datasets make even inefficient queries appear responsive. The core issue arises when a resolver is called once per item rather than once for all items together — for example, fetching ten authors in ten separate batches instead of a single batch of ten. A practical detection method involves setting up a lightweight mock GraphQL server that records the size and frequency of each batch loader call, then asserting that the largest batch equals the number of unique keys requested. If the test reveals ten calls of size one instead of one call of size ten, the N+1 is caught before the code reaches production. This approach creates a reliable regression gate that is independent of network conditions, dataset size, or subjective performance intuition.