Understanding the Cost of Abstractions in .NET

Abstractions make our code elegant and modular — but they're not free. In this article, we explore how some common abstractions in .NET can hurt performance if used carelessly, and how to avoid the hidden costs.

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Clean code isn't always fast code. While abstractions like LINQ, interfaces, or dependency injection make our code cleaner and more maintainable, they can introduce runtime overhead. And in performance-critical paths, those costs add up quickly.

In this article, we'll walk through how common .NET abstractions can degrade performance, explore when to use them with care, and share techniques to avoid unnecessary overhead.

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🧱 1. LINQ and Deferred Execution

LINQ is incredibly expressive, but it's not always the most efficient — especially in tight loops or "hot paths."

❌ Common Issue: Unnecessary Materialisation and Allocations

var filtered = data.Where(x => x.IsActive).ToList();

`

While flexible, LINQ's deferred execution model can lead to hidden costs. The .ToList() call here forces immediate materialisation, introducing new allocations for the list and potentially temporary iterators. If this operation is repeated frequently on large datasets, the overhead can be significant.

✅ Prefer: Explicit Loops for Performance-Critical Code

csharp
var filtered = new List<MyItem>();
foreach (var item in data)
{
if (item.IsActive)
filtered.Add(item);
}


For large datasets or performance-critical code, avoiding LINQ can directly reduce allocations and CPU usage. LINQ is fantastic for readability and composability, but be mindful of its overhead when every millisecond counts.

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👻 2. Interfaces and Virtual Dispatch

Interfaces are core to good architecture, promoting loose coupling and testability. However, they introduce virtual dispatch, which can subtly impact performance:

• It can hinder inlining by the JIT compiler.
• It might increase CPU branch mispredictions.
• It can make profiling and debugging trickier.

❌ Hot-Path Abstraction:

`csharp
public interface IProcessor { void Process(); }

public class MyProcessor : IProcessor { /* ... */ }

IProcessor processor = new MyProcessor();
processor.Process(); // Virtual call
`

✅ Alternative: Use Concrete Types or readonly struct

`csharp
public readonly struct FastProcessor
{
public void Process() { /* fast logic */ }
}

var processor = new FastProcessor(); // Value type, no virtual dispatch
processor.Process();
`

Using a readonly struct for small, frequently used types enables the JIT to make more aggressive optimisations.

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⏳ 3. Async/Await Overhead

async/await simplifies asynchronous programming, but introduces state machines, heap allocations, and synchronisation context capturing.

❌ Overhead in Frequently Called Code:

csharp
public async Task<int> GetDataAsync()
{
await Task.Delay(100);
return 42;
}


✅ Use ValueTask<T> for Sync-Over-Async:

csharp
public ValueTask<int> GetDataAsync()
{
return new ValueTask<int>(42);
}


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💉 4. Overusing Dependency Injection

DI is powerful but can introduce resolution overhead, slow startup, and unnecessary service instantiation.

❌ Costly Service Registration:

csharp
services.AddScoped<IMyService, HeavyService>();


✅ Optimise with Lazy<T> or Factory Injection:

csharp
services.AddSingleton<Lazy<IHeavyService>>(provider =>
{
return new Lazy<IHeavyService>(() =>
new HeavyService(provider.GetRequiredService<ILogger>()));
});


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🔍 5. Benchmark and Profile — Don’t Guess

The most crucial rule in performance optimisation: measure before you optimise.

🛠️ Recommended Tools

• BenchmarkDotNet
• dotnet-trace
• PerfView
• Visual Studio Profiler

📊 Example Benchmark:

`csharp
[MemoryDiagnoser]
public class LinqVsLoopBenchmarks
{
private List _data;

[GlobalSetup]
public void Setup()
{
    _data = Enumerable.Range(0, 1000)
                      .Select(i => new MyItem { Id = i, Flag = i % 2 == 0 })
                      .ToList();
}

[Benchmark]
public List<MyItem> WithLINQ() =>
    _data.Where(x => x.Flag).ToList();

[Benchmark]
public List<MyItem> WithLoop()
{
    var result = new List<MyItem>();
    foreach (var item in _data)
    {
        if (item.Flag)
            result.Add(item);
    }
    return result;
}

}

public class MyItem { public int Id { get; set; } public bool Flag { get; set; } }
`

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📋 Summary Table

Abstraction	Common Risk	Recommended Optimisation
LINQ	Allocations, deferred execution	Use explicit loops in hot paths.
Interfaces	Virtual dispatch overhead	Prefer concrete types or readonly struct where possible.
Async/Await	GC pressure, state machine allocation	Use ValueTask<T> if results are often synchronously available.
Dependency Injection	Resolution overhead	Use Lazy<T>, factory methods, and correct lifetimes (Scoped/Singleton).

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✅ Performance Checklist

• Are you using LINQ in hot paths where performance is critical?
• Are virtual/interface calls affecting inlining in tight loops?
• Could you use ValueTask<T> to reduce async overhead?
• Are your DI services scoped correctly and not overused?
• Have you benchmarked and profiled with real workloads?

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🔚 Conclusion

Abstractions make code manageable — but they're not free. In performance-sensitive scenarios, the hidden costs of elegant design can be significant.

Optimisation is not about avoiding abstractions — it's about understanding their trade-offs and using them intentionally. Write clean code, measure its impact, and optimise only what truly matters.

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