database-performance

EF Core query optimization uses SQL-based joins and precise column selection to eliminate N+1 queries and Cartesian explosions. This approach fetches associated data efficiently through a single database call, preventing redundant queries and improving loading speed.

Applying the AsNoTracking pattern to EF Core read queries bypasses change tracking overhead, directly speeding up database reads. Combining this with row limits and precise column selection ensures efficient data retrieval without unnecessary processing.

CQRS improves database optimization by implementing read/write model separation for distinct operations. This pattern allows specific query strategies for reads and writes, ensuring scalable data access layers that avoid generic repository anti-patterns.

Dapper is ideal for query optimization when you need direct SQL-based joins and precise control over data retrieval. It complements EF Core by handling performance-critical read operations, avoiding the generic repository anti-pattern.

The generic repository pattern causes performance bottlenecks by enforcing uniform data access that ignores specific query needs. This leads to inefficient data retrieval and prevents read/write separation, causing N+1 queries and application-side joins.