performance-patterns

N+1 queries occur when a single query triggers multiple additional queries in a loop, drastically slowing performance. Use eager loading, batching, or DataLoader to fetch related data in one or two queries instead of N+1 separate calls.

Over-fetching returns excess data; under-fetching requires multiple requests. Use field selection, DTOs (Data Transfer Objects), and pagination to return precisely the data clients need in a single efficient request.

Performance anti-patterns span database queries, API design, and frontend rendering. Scan for N+1 queries, missing pagination, inefficient algorithms, unnecessary re-renders, and sequential async operations to pinpoint slowdowns across all layers.

Memoization prevents redundant re-renders by caching component results. Combine with pagination and efficient API request patterns to minimize data transfer and rendering overhead on the client side.

Pagination is essential when datasets grow large or APIs serve many clients. It reduces memory usage, speeds up response times, and prevents timeouts by retrieving data in manageable chunks rather than all at once.

Sequential async operations waste time waiting unnecessarily. Parallelization executes independent operations concurrently, reducing overall execution time and improving throughput across backend services and API layers.