Hidden Performance Killers in Your .NET Core Application: Can We Mute Them with Our Skills?

Unmasking Common Bottlenecks and Empowering Developers to Eliminate Them

.NET Core offers high performance, scalability, and flexibility. Yet, even the best frameworks can falter when hidden performance killers go unnoticed. These “silent assassins” can degrade your application’s efficiency, increase response times, and inflate resource usage.

But here’s the good news: With the right skills and tools, we can identify and mute them!

In this blog, we’ll uncover some of the most common performance issues in .NET Core applications and explore actionable solutions to tackle them.

1. Inefficient Database Queries

The Problem:

• Over-reliance on lazy loading.
• N+1 query problem in ORM tools like Entity Framework.
• Lack of proper indexes on frequently queried fields.

Impact:

• Increased query execution time.
• Unnecessary database calls leading to higher latency.

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Solution:

• Eager Loading: Use .Include() in Entity Framework to load related data in a single query

var orders = context.Orders.Include(o => o.Customer).ToList();

• Optimize Queries: Avoid fetching entire tables. Use .Select() to fetch only required fields

var orders = context.Orders.Select(o => new { o.Id, o.TotalAmount }).ToList();

• Indexing: Ensure database fields used in filters or joins are indexed.

2. Inefficient JSON Serialization

The Problem:

• Default serializers may not always be optimal for large payloads.
• Unnecessary serialization and deserialization cycles.

Impact:

• Increased CPU usage and memory consumption.

Solution:

• Use System.Text.Json, which is faster and more efficient than older libraries like Newtonsoft.Json.

var options = new JsonSerializerOptions { WriteIndented = false };
var json = JsonSerializer.Serialize(data, options);

• Avoid Overhead: Use DTOs (Data Transfer Objects) to send only necessary data.

3. Overlooked Asynchronous Programming

The Problem:

• Blocking synchronous calls in a predominantly asynchronous application.
• Forgetting to use async and await for I/O-bound operations.

Impact:

• Thread starvation in high-concurrency scenarios.
• Increased response times under load.

Solution:

• Ensure all I/O operations are asynchronous.

var data = await httpClient.GetStringAsync("https://api.example.com");

• Use tools like Async Fixer to identify non-optimal synchronous code.

4. Misconfigured Dependency Injection (DI)

The Problem:

• Registering services with the wrong lifetime (e.g., using Scoped instead of Singleton).
• Injecting unnecessary dependencies.

Impact:

• Memory leaks due to lingering scoped or transient objects.
• Increased CPU usage from redundant object creation.

Solution:

• Use appropriate lifetimes:
• Singleton for services reused across the app’s lifetime.
• Scoped for request-specific services.
• Transient for lightweight, short-lived services.

services.AddSingleton<IMyService, MyService>();

• Audit and clean up unnecessary dependency injections.

5. Ignoring Response Compression

The Problem:

• Large API responses sent uncompressed.
• Bandwidth wasted on transmitting redundant data.

Impact:

• Slower response times for clients, especially over mobile networks.

Solution:

• Enable response compression middleware.

builder.Services.AddResponseCompression();
app.UseResponseCompression();

6. Poor Caching Strategy

The Problem:

• Missing or ineffective caching mechanisms.
• Overuse of memory-based caching without eviction policies.

Impact:

• Repeated processing of identical requests.
• Memory pressure and potential crashes.

Solution:

• Implement in-memory caching for frequently accessed data.

var cachedData = memoryCache.GetOrCreate("key", entry =>
{
    entry.AbsoluteExpirationRelativeToNow = TimeSpan.FromMinutes(5);
    return FetchData();
});

• Use Distributed Caching for scaling across multiple servers (e.g., Redis).

7. Logging Overhead

The Problem:

• Verbose logging in production.
• Synchronous logging, which blocks request processing.

Impact:

• Increased I/O latency and storage costs.

Solution:

• Use structured logging tools like Serilog or ElasticSearch.
• Log only what’s necessary in production, and switch to async logging.

Log.Logger = new LoggerConfiguration()
    .WriteTo.Async(a => a.File("logs/log.txt"))
    .CreateLogger();

8. Memory Leaks and High GC Pressure

The Problem:

• Unreleased unmanaged resources.
• Unintentional references to objects, preventing garbage collection.

Impact:

• Increased memory usage and degraded performance.

Solution:

• Use the using statement to ensure resources are disposed of.

using (var connection = new SqlConnection(connectionString))
{
    connection.Open();
    // Work with the connection
}

• Monitor garbage collection using dotnet-counters:

dotnet-counters monitor System.Runtime

9. Overloaded Middleware Pipeline

The Problem:

• Too many middleware components.
• Middleware doing unnecessary work for every request.

Impact:

• Increased request latency.

Solution:

• Optimize middleware order. Place commonly used middleware (e.g., authentication) earlier.
• Avoid redundant middleware by consolidating functionality where possible.

10. Skipping Profiling and Performance Testing

The Problem:

• Lack of insights into performance bottlenecks.
• Performance issues going unnoticed until production.

Impact:

• Unpredictable application behavior under load.

Solution:

• Use tools like dotTrace, PerfView, or Application Insights to profile your application.
• Perform load testing using Apache JMeter or k6.

Conclusion

Hidden performance killers can creep into even the most well-designed .NET Core applications. But with the right skills and practices, you can identify, mute, and eliminate them effectively. By focusing on database optimization, caching strategies, asynchronous programming, and efficient middleware, you can ensure your application runs smoothly and scales seamlessly.

Key Takeaways:

• Proactively monitor and profile your application.
• Adopt efficient design patterns and best practices.
• Continuously test and optimize your codebase.

Are you facing a specific performance issue? Share your challenges in the comments, and let’s tackle them together!