Real benchmark results comparing IAsyncEnumerable and Task.WhenAll. Learn when to choose speed vs responsiveness, memory efficiency, and user experience in C# async operations.
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. ...
Async/await is powerful but overused. This guide breaks down async misconceptions, shows real enterprise use cases, and gives you a practical decision framework for async in C#.
Learn why preferring interfaces over abstract classes in C# improves code flexibility, testability, and maintainability for robust .NET applications.
Master IEquatable in C# to optimize equality checks, improve collection performance, and eliminate boxing overhead. Essential for value types and collections.
A practical guide to C# Default Interface Methods: how to use them, when to avoid them, and how they help you build future-proof APIs.
Discover the best practices for encapsulation in C#. Learn when to use auto-properties, when to switch to backing fields, and how to keep your property setters clean, focused, and maintainable with practical examples and actionable guidelines.
Discover why structs as dictionary keys can cause hidden allocations in C#. Learn how to implement IEquatable, use readonly and record structs, and write allocation-free, high-performance code for hot paths.
Discover how guard clauses in C# simplify validation and error handling. Learn to write fail-fast code, avoid nested conditionals, and keep business logic clean with modern language features and reusable helpers.
TL;DR: Efficient C# File Processing Strategies Prevent system crashes: Avoid naive parallel processing that launches thousands of concurrent file operations. Optimize throughput: Use SemaphoreSlim to control concurrency (start with 2x CPU cores) for balanced performance. Reduce memory consumption: Implement true async I/O with useAsync: true and process files line-by-line instead of loading them entirely. Minimize database overhead: Batch related records from multiple files before making database calls. Maximize system resources: For production systems, implement adaptive throttling that responds to CPU/memory conditions. Leverage modern C# features: Use IAsyncEnumerable for efficient streaming and TPL Dataflow for complex processing pipelines. Ever tried to build an import tool that needs to process thousands of CSV files at once? I have, and I learned the hard way that simply starting a thousand file operations simultaneously is a recipe for disaster. ...