database-performance

To fix N+1 query issues in EF Core, you should refactor your data access layer to use batched queries and SQL-based joins instead of retrieving related records in application code. This prevents excessive database round trips and improves load times.

The best way to optimize read queries for speed in .NET is to apply the AsNoTracking method to your EF Core queries. This prevents the change tracker from storing entity state, reducing memory overhead and improving query execution speed for read-only data.

Read/write model separation improves database performance by implementing CQRS patterns to optimize data access. This approach splits read and write operations into distinct models, allowing read queries to use projections and AsNoTracking while write operations handle state mutations.

You should choose Dapper over EF Core for database optimization when you need to execute raw SQL-based joins and require fine-grained control over query execution. Dapper is often preferred for read-heavy scenarios where EF Core's change tracking adds unnecessary overhead.

Dashboard load times are slow when displaying many related records due to retrieving excessive data and performing joins in application code. You can resolve this by using batched queries, applying row limits, and utilizing projections to fetch only the required fields.

A limitation of relying strictly on SQL-based joins for data access optimization is the increased complexity of mapping flat result sets to complex object graphs. While it prevents N+1 queries, you must manually handle projections and ensure row limits are applied to avoid retrieving excessive data.