dotnet-performance

Optimize EF Core queries by applying AsNoTracking for read-only data, AsSplitQuery to avoid cartesian explosions, and compiled queries to reduce query compilation overhead. Projections and safe bulk operations further minimize data transfer and database roundtrips.

Implement caching in .NET using local IMemoryCache for in-process storage or distributed Redis cache for shared environments. Apply cache invalidation patterns to ensure data consistency when underlying data changes, preventing stale reads across your ASP.NET Core services.

Configure HttpClient resilience in ASP.NET Core by using HttpClientFactory with Polly policies. Apply retry policies for transient failures and circuit breaker policies to prevent cascading failures, ensuring robust external HTTP calls during service outages.

Use AsNoTracking in EF Core for read-only scenarios to skip change tracking overhead, reducing memory and processing time. Use compiled queries when the same LINQ query executes repeatedly, caching the compiled query tree to eliminate per-request compilation costs.

Yes, AsSplitQuery fixes EF Core cartesian explosion issues by splitting a single query into multiple SQL queries. This approach loads related collections separately, preventing massive row duplication and reducing memory consumption during multi-join data access.

Yes, this .NET performance optimization approach works with both in-memory and distributed caching. It supports local IMemoryCache for single-instance scenarios and Redis distributed cache for multi-instance ASP.NET Core deployments, applying invalidation patterns across both.