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A practical guide to structuring ASP.NET Core APIs for reliable integration and unit testing using clean architecture principles and minimal, production-ready C# code.

EF Core’s default optimistic concurrency model is a great starting point, but it’s not a silver bullet. When write contention heats up, its limitations can lead to performance bottlenecks and data integrity challenges. Understanding the trade-offs between optimistic and pessimistic concurrency is crucial for building robust, scalable applications. This article explores the conceptual costs and benefits of each strategy, helping you decide when to stick with the default and when to reach for explicit locking. ...

This guide covers 22 clean code principles in C# that go beyond SOLID, naming, error handling, layering, and API design for real-world maintainable systems.

Cut payload size by 60–80% in ASP.NET Core with built-in Brotli and Gzip compression. Step-by-step code, production tweaks, and real benchmarks.

Your app performs well with MemoryCache for 1K users. But when traffic scales to 10K users across three load-balanced servers, cache misses explode and response times spike to 800ms. I’ve seen production APIs crash under load because the team relied solely on MemoryCache. Here’s how we fixed it with hybrid caching strategies that combine the speed of local memory with the consistency of distributed cache. TL;DR MemoryCache works for single servers but fails in distributed environments Hybrid caching uses MemoryCache (L1) + Redis (L2) for best performance Multi-tenant apps need tenant-scoped cache keys to prevent data leakage System.Text.Json provides the best balance of performance and debuggability Monitor cache hit ratios per tenant and cache level for optimal tuning MemoryCache works great for single-server applications, but it hits hard limits in distributed environments. This guide covers hybrid caching strategies that keep your multi-tenant ASP.NET Core apps fast and scalable, with real benchmarks and production-ready code. ...

Real benchmark results comparing IAsyncEnumerable and Task.WhenAll. Learn when to choose speed vs responsiveness, memory efficiency, and user experience in C# async operations.

A practical guide to SQL indexing strategies every developer should know. Includes real-world scenarios, code examples, and performance tuning insights.

Copy-paste coding may seem convenient, but it often causes hidden bugs. Learn practical C# patterns to replace duplication and maintain clean, maintainable code.

TL;DR In multi-tenant SaaS, generic audit logging can easily leak data between tenants. This is a security and compliance nightmare. Overriding DbContext.SaveChanges() is a common but clunky solution that tightly couples auditing logic to your data context. EF Core Interceptors provide a clean, decoupled way to hook into the save process and add per-tenant audit logs automatically. The solution involves creating a SaveChangesInterceptor, grabbing the current TenantId from a scoped service, and logging entity changes before they hit the database. This pattern is perfect for auditable, compliant SaaS applications but might be overkill for simple, single-server projects. I once got a panicked call about a critical bug. An admin from “Company A” could see user creation events from “Company B” in their audit trail. It was a classic multi-tenant data bleed, but not in the main application data—it was in the logs. This is one of those sneaky bugs that passes all unit tests but can absolutely destroy trust with your customers and fail a compliance audit. ...

Most projects I review have a Utils or Helpers class packed with static methods. At first glance, static helpers look like the fastest way to solve problems. You don’t need to new up objects or wire dependencies. Just call Helper.DoSomething() and move on. That convenience is exactly why they sneak into codebases. But over time, static helpers turn into a source of pain, especially in production systems that need to evolve. ...