optimizing-query-performance

Slow Prisma queries typically stem from missing indexes, N+1 problems, or unoptimized field selection. Add indexes on frequently filtered and sorted fields, use batch operations (createMany, updateMany, deleteMany) instead of loops, and apply cursor pagination for large result sets. Validate improvements with EXPLAIN ANALYZE and latency metrics.

N+1 occurs when a query fetches one record, then triggers N additional queries for related data. Prevent it by selecting only needed fields, batching operations, and using cursor pagination. Monitor query logs to detect patterns where a single action generates cascading queries.

Add indexes on columns used frequently in WHERE clauses, sorting (ORDER BY), and joins. Index strategy focuses on fields accessed in filtered and sorted queries. Enable Prisma query logging to identify hot paths, then place indexes strategically to reduce scan time on large tables.

Yes, batch operations replace per-record loops with single database calls. Use createMany, updateMany, and deleteMany to process multiple records at once, significantly reducing network round trips and improving throughput on production workloads handling bulk data.

Cursor pagination fetches fixed-size result windows using keyset filtering instead of offset skipping. This approach scales better on large tables because it avoids scanning and discarding thousands of rows. Combine with field selection and indexes for optimal performance on sorted, filtered queries.

Use EXPLAIN ANALYZE to inspect query execution plans and identify missing indexes or inefficient scans. Track latency metrics before and after optimization to validate improvements. Prisma query logging reveals N+1 patterns and loop-based inefficiencies you can address with batching.