Enhancing .NET Core Performance: Caching, Lazy Loading, Indexing, and Profiling

4 min readMay 2, 2024

In this guide, we’ll explore various techniques for enhancing the performance of .NET Core applications. We’ll focus on caching, lazy loading, indexing, and profiling strategies to optimize resource utilization and improve overall efficiency.

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Caching:

What: Caching involves storing frequently accessed data in memory to reduce the need for repeated expensive computations or database queries.
Why: Caching improves application performance by providing quick access to data, reducing response times, and alleviating load on backend systems.
How: Implement caching using libraries like MemoryCache or distributed caching solutions like Redis. Store data in memory with a defined expiration or eviction policy.
Problems Solved: Caching reduces latency, improves scalability, and enhances user experience by serving cached data instead of fetching it from slower data sources repeatedly.
Alternatives: Other alternatives include database query optimization, precomputed results, or using in-memory databases like SQLite for data storage.

using Microsoft.Extensions.Caching.Memory;
using System;

public class CacheService
{
    private readonly IMemoryCache _cache;

    public CacheService(IMemoryCache cache)
    {
        _cache = cache;
    }

    public string GetCachedData(string key)
    {
        if (_cache.TryGetValue(key, out string cachedData))
        {
            Console.WriteLine("Data retrieved from cache.");
            return cachedData;
        }

        // If data not found in cache, fetch it from database or other source
        // For demonstration purposes, simulating data retrieval
        string data = FetchDataFromDatabase();

        // Cache the data with a sliding expiration of 10 minutes
        _cache.Set(key, data, TimeSpan.FromMinutes(10));

        Console.WriteLine("Data retrieved from database and cached.");
        return data;
    }

    private string FetchDataFromDatabase()
    {
        // Simulated method to fetch data from database
        return "Sample data from database";
    }
}

Lazy Loading:

What: Lazy loading delays the initialization of objects until the point where they are actually needed, improving memory efficiency and startup time.
Why: Lazy loading conserves resources by loading objects on-demand, reducing memory consumption and startup time.
How: Implement lazy loading using the Lazy<T> class or custom lazy loading patterns. Wrap resource-intensive object creation logic inside a lazy initialization block.
Problems Solved: Lazy loading prevents unnecessary resource allocation, optimizes memory usage, and improves application responsiveness by deferring object creation until required.
Alternatives: Eager loading, where objects are initialized upfront, can be an alternative but may lead to higher memory consumption and slower startup times.

using System;

public class LazyLoader<T> where T : class
{
    private readonly Lazy<T> _lazyLoader;

    public LazyLoader(Func<T> initializeFunc)
    {
        _lazyLoader = new Lazy<T>(initializeFunc);
    }

    public T Instance => _lazyLoader.Value;
}

Usage:

var lazyService = new LazyLoader<HeavyService>(() => new HeavyService());
// HeavyService instance is not created until it's accessed for the first time
HeavyService heavyService = lazyService.Instance;

Indexing:

What: Indexing involves creating indexes on database columns to accelerate data retrieval operations, particularly for frequently queried fields.
Why: Indexing improves query performance by reducing the time required to locate specific data rows within a database table.
How: Define indexes on database columns using database management tools or entity framework configurations. Choose appropriate indexing strategies based on query patterns and data access patterns.
Problems Solved: Indexing enhances database query performance, reduces query execution time, and boosts overall application responsiveness.
Alternatives: Alternative approaches include query optimization techniques, denormalization, or using NoSQL databases optimized for specific query patterns.

Let’s consider a scenario where we have an Orders table with a CustomerId column frequently queried for order history. We can create an index on the CustomerId column:

public class OrderContext : DbContext
{
    public DbSet<Order> Orders { get; set; }

    protected override void OnModelCreating(ModelBuilder modelBuilder)
    {
        modelBuilder.Entity<Order>()
            .HasIndex(o => o.CustomerId);
    }
}

Profiling:

What: Profiling involves analyzing the execution of a .NET Core application to identify performance bottlenecks, memory leaks, or inefficient code segments.
Why: Profiling helps developers identify and resolve performance issues, ensuring optimal application performance and resource utilization.
How: Utilize profiling tools like PerfView, dotMemory, or Visual Studio Performance Profiler to capture and analyze performance data. Identify areas of improvement based on profiling results and optimize critical code paths.
Problems Solved: Profiling enables developers to diagnose and address performance bottlenecks, memory leaks, and other issues impacting application performance.
Alternatives: Manual code review, performance testing, or utilization of monitoring tools can complement profiling for identifying and resolving performance issues.

For profiling, we’ll use Visual Studio Performance Profiler to analyze the execution of a sample .NET Core application and identify performance bottlenecks.

Open Visual Studio and load the .NET Core project.
Go to Debug > Performance Profiler.
Start profiling the application by selecting appropriate profiling options.
Analyze the profiling report to identify hot paths, memory usage, and performance bottlenecks.

Conclusion:

By leveraging caching, lazy loading, indexing, and profiling techniques, .NET Core developers can significantly improve the performance and responsiveness of their applications. These strategies enable efficient resource utilization, reduced response times, and enhanced scalability, resulting in better user experience and higher application throughput.