atris-backend

N+1 queries occur when fetching a parent record triggers separate queries for each child. Prevent this by planning batch operations upfront, loading related data in a single query, and reviewing your data access patterns before implementation to catch inefficient loops.

Consistent API design means using boring, uniform patterns rather than custom abstractions for each endpoint. Define naming conventions, error response shapes, and pagination formats once, then apply them uniformly across all service endpoints to reduce cognitive load and maintenance burden.

Propagate errors with full context—include the operation, input values, and system state—rather than generic messages. This contextual error handling speeds debugging and helps distinguish between user input issues, service failures, and data anomalies in your error handling strategy.

Write three concrete, working examples of a pattern before abstracting it into a shared component. This rule prevents premature generalization, ensures the abstraction actually solves a real problem, and surfaces edge cases that generic code often misses.

Backend over-engineering—vague naming, inconsistent patterns, N+1 queries, and poor error handling—accumulates as services scale. Simplicity and consistency from the start prevent this drift by making patterns obvious to new maintainers and reducing surprise complexity.

Yes, this policy applies to API design, data access patterns, service components, and backend workflows regardless of your current project structure. It focuses on planning, implementation, and review practices that fit into typical backend projects without requiring infrastructure changes.