overunderfetching

Overunderfetching is a term used to describe a pattern of inefficiency in data retrieval where systems experience both overfetching and underfetching across their data access paths. It arises when there is a mismatch between the granularity of data that clients request and what servers or data stores provide, leading to either unnecessarily large responses or multiple round-trips to assemble the needed information.

In RESTful architectures, fixed resource representations may include more fields than a client needs (overfetching) or require additional requests to fetch related resources (underfetching). In database querying, wide rows with many columns can pull in irrelevant data, while queries designed for a single need may omit related data, forcing additional queries.

Consequences include increased bandwidth consumption, higher latency, greater CPU and memory usage, and more complex client logic. These issues can degrade performance for mobile or low-bandwidth clients and complicate caching and scalability strategies.

Mitigation strategies focus on aligning data models with client needs and improving data shaping capabilities. Approaches include providing sparse fieldsets or partial responses, adopting queryable interfaces such as GraphQL, or designing REST endpoints with configurable projections. Server-side data shaping through DTOs or views, backend-for-frontend patterns, batching and caching, and thorough monitoring of data access paths can also reduce overunderfetching by allowing clients to retrieve exactly the data they require with minimal round-trips.