database-performance

To avoid N+1 queries in EF Core, apply eager loading, batch queries, and proper projections. This ensures related data is fetched efficiently in a single database round trip rather than executing separate queries for each entity.

Separating read and write models optimizes query performance by establishing clear boundaries. Use AsNoTracking for reads to skip change tracking overhead, and direct write operations through dedicated modification paths to enforce per-query row limits.

Using Dapper alongside EF Core improves query optimization by allowing precise SQL-side joins and specialized read operations. This combination enforces safe querying practices across data access layers while maintaining strict per-query row limits.

Enforcing per-query row limits prevents unbounded result sets, while SQL-side joins outperform application-side joins by leveraging database engine optimizations. This approach avoids loading excessive data into memory and reduces network latency.

Configure a data access layer using frontmatter-driven entries with required names and descriptions. This setup enforces per-query limits, applies AsNoTracking for reads, and processes joins directly on the SQL server side.