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Home Β» Optimizing Performance in .NET Core Applications: Tips and Tools
In today’s fast-growing, user-driven digital environment, performance optimization is no longer an option. This comprehensive guide is your guide to mastering .NET Core performance tuning using the latest tools, techniques, and strategies. We’ll look at everything from reducing response times to seamlessly scaling your applications to ensure your code is efficient, resilient, and future proof. What you’ll learn:Β
Best practices for performance optimization in .NET Core
Improving API performance in .NET CoreΒ
Step-by-step guide to optimizing .NET Core performanceΒ
Techniques for improving response timeΒ
Strategies for reducing CPU usageΒ
Improving scalability through optimizationΒ
Common mistakes and how to avoid themβ―
Performance optimization for maintaining relevance to the .NET Core ecosystem has several key points.β―Β
The βasyncβ and βawaitβ words both are important for writing asynchronous code, which allows for non-blocking operations. The word βasyncβ defines an asynchronous function and βawaitβ used to pause the execution of the method pauses until the awaited task completes.β―Β
Let’s consider a simple example where we are using asynchronous method with await is used for the pause response.β―Β
In most programming languages, string concatenation is a common operation, especially when constructing messages, preparing data for output, or manipulating text. The methods used for concatenation can vary depending on the language, and different techniques have different impacts on performance.Β Β
Repeated use of the ‘+’ operator, especially in a loop, can lead to poor performance. This is because strings in C# are immutable, meaning that once a string is created, it cannot be modified. Therefore, every time you concatenate strings using ‘+’, a new string is created, and the old string is copied to the new string. This can result in unnecessary memory allocations and have a significant performance impact.β―Β
Use String Builder when concatenating strings in a loop or large amounts.β―Β
β―In parallel programming, different parts of a task are run concurrently, rather than sequentially, potentially reducing execution time and making better use of available resources. This allows for faster computation by leveraging multi-core or multi-processor systems. The goal is to improve the performance and efficiency of computing tasks, especially those that are computation-heavy or require a large amount of data processing.β―Β
The Parallel class allows for concurrent execution of operations, which can lead to performance improvements, especially for CPU-bound tasks. Also itβs a non-blocking programming.β―Β
Below approach of using parallel is Data Parallelism, This is often used when you have large datasets and want to apply the same function (like summing elements or applying a transformation) to each data element simultaneously.β―Β
β―Optimizing database queries is important for enhancing the overall performance of applications, particularly because the quantity of statistics grows.Β
Β Use AsNoTracking() for read-handiest queries in Entity Framework CoreΒ to enhance the rate relying at the use.β―Β
Use Indexes, because it allows the database engine fast discover rows without scanning the whole table. Example like Primary key and Foreign key Indexed are routinely created for number one and overseas key dating among tables.Β
Note: Avoid Over-indexing as it can slow down the query operations so only create indexes that improve performance.β―Β
While interacting with database and return response data with specific fields to minimize data retrieval. Fetch only necessary fields using projections (e.g., .Select() in LINQ). This reduces the amount of data retrieved from the database.β―Β
Avoid Lazy Loading as lazy loading causes multiple queries to be sent to the database, which can lead to performance issues. Instead, use Eager Loading with Include to load related entities upfront in a single query.β―Β
CDN is used for serving static content material like java scripts, CSS, images, and so on to enhance the overall performance because it reduces area and decrease pace in fetching files. Azure CDN may be used to boost up the transport of your internet applications, media content material, APIs, and different static assets, along with images, videos, and scripts, even as lowering the burden in your foundation servers.β―Β
Each middleware in the pipeline adds overhead; keep it minimal and order properly. The best way to tackle with middleware is place the critical performed middleware at the start and remove middleware which are no longer used in the application.β―Β
Can add insights to monitor the application for check the application performance and exceptions.β―Β
Add Application Insights:β―Β
Compressing responses reduces payload size and improves response time.β―Β
Install NuGet Package ResponseCompressionβ―Β
Configure ResponseCompression in program.csβ―Β
builder.Services.AddResponseCompression(options =>β―Β
{β―Β
Β options.Providers.Add<GzipCompressionProvider>();β―Β
β―β―β―options.EnableForHttps = true;β―Β
});β―Β app.UseResponseCompression(); β―
app.UseRouting(); β―
app.UseEndpoints(endpoints => endpoints.MapControllers());β―
Β
Cachingβ―Use in-memory caching (IMemoryCache) or distributed caching (IDistributedCache) to store frequently accessed data in a temporary storage location, often referred to as a “cache,” to improve the speed and efficiency of applications. By retrieving data from the cache instead of recalculating or fetching it from the original source (e.g., database, API), you can significantly reduce latency and resource usage.β―Β
One of the types of Caching βIn-Memory Cachingβ can be done using interface IMemoryCache as shown in the below snippet.β―Β
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Reducing CPU usage in your .NET Core applicationsΒ leads to better scalability, responsiveness, and resource efficiency. Many strategies are already mentioned in the documentation itself, such as asynchronous programming, using string builders instead of string declarations, caching in process, working efficiently with database operations using. Select() .AsNotTracking() etc.β―Β
Offloading long-running tasks is a critical technique in web application development, especially when building APIs that need to remain responsive to users. Long-running tasks are operations that take a significant amount of time to complete, such as sending emails, processing large files, performing complex calculations, or integrating with external services.β―Β
If these tasks are performed synchronously (directly in response to a request), they can block the API or web application, resulting in poor user experience, reduced scalability, and inefficient use of resources. Offloading these tasks to other systems or processes allows the web application to stay responsive and scale effectively.β―Β
How to offload long-running tasks in a web application or API, focusing on the key techniques, patterns, and technologies involved.β―β―
Long-running tasks can block the main application thread, preventing the server from processing other requests. Offloading tasks to another system or background worker frees up the API server to process more incoming requests, improving scalability. You can improve the user experience by not making users wait for long-running operations to complete, which is typically achieved through asynchronous processing or background job queues. Offloading tasks can optimize resource usage, for example by using a dedicated background processing server or a cloud-based service.β―Β
In .NET Core, you can create background services by implementing BackgroundService for long-running tasks like batch jobs, scheduled tasks, or real-time processing. Background services are a way to offload long-running tasks to dedicated processes or threads that run in the background, outside the scope of the API request/response cycle.β―Β
Email Sending Systemsβ―Sending large volumes of emails can strain system resources and cause performance issues if done synchronously.β―Β
Job scheduling systems like Hangfire or Quartz.NET allow for task scheduling and background processing. These tools let you run long-running tasks on a schedule or asynchronously. They provide features like retries, task chaining, and delayed executions.β―Β
Avoid Task.Wait() or Task.Result as they block the thread and increase CPU usage. Both Task.Wait() and Task.Result block the calling thread until the task completes. This defeats the purpose of asynchronous programming. Task.Result blocks the calling thread to retrieve the result of the task once it completes, again preventing any other work from being done.β―Β
Use Task.Run for CPU-Bound Operationsβ―
Although async and await are great for I/O-bound tasks, CPU-bound operations can also block the thread. In such cases, use Task.Run to offload CPU-bound tasks to a separate thread pool, ensuring that the main thread remains unblocked.β―
Reduce excessive logging, especially in production, as it can add unnecessary CPU overhead. It is necessary to use log in the application but better to use when in development mode.β―β―Β
Optimizing JSON serializationΒ is an important step in improving application performance, especially when working with large data sets or APIs that frequently process JSON (JavaScript Object Notation). JSON serialization converts objects or data structures (such as classes or dictionaries) into a JSON string. This typically occurs when sending data over a network, saving data to a file, or interacting with a web API.β―Β
Serialization and deserialization can be expensive, especially when dealing with large or complex objects. Optimizing the process can reduce latency and increase throughput. Inefficient serialization can lead to excessive memory consumption, particularly when dealing with large objects or large amounts of data. In API responses, the size of the JSON payload impacts the network bandwidth. Optimizing the payload can reduce bandwidth usage and improve response times. By optimizing serialization, you ensure that your application can handle increased traffic and load without performance degradation.β―
Use System.Text.Json offers the best performance, while Newtonsoft.Json can be considered for scenarios requiring advanced features (e.g., custom converters, LINQ to JSON).β―Β
Scalability refers to an applicationβs ability to handle increasing loads by efficiently utilizing resources and maintaining performance. Optimizing .NET Core applicationsΒ for scalability is essential for handling higher traffic and growing user demands.β―Β
Implementing Asynchronous Programming allows the application to handle multiple tasks concurrently without blocking threads which is already discussed above. Other points which are also discussed above are Caching, Optimizing database.β―Β
Enabling distributed caching is a common strategy to improve application performance and scalability, especially in large systems. Distributed caching is important when horizontally scaling an app where cached data needs to be shared across multiple instances of the app. To enable distributed caching in your .NET application, you can use a caching system such as Redis, Apache Ignite Memcached, or others.β―Β
Install Required NuGet PackagesWe will start by installing following NuGet packages:β―Β
Right click on Project and go to Manage Nuget Packages…β―
And Install above packagesβ―Β
In a .NET Core or .NET 6/7 application, you need to configure Redis as your distributed cache provider in the ConfigureServices method of the Startup.cs file (or Program.cs in .NET 6/7 or greater). This is an example for .NET 8.β―Β
Once Redis is configured, you can inject the IDistributedCache interface into your controllers or services to store and retrieve cached data.β―Β
By using the Microsoft.Extensions.Caching.StackExchangeRedis package, we can easily enable distributed caching in our .NET applications. Redis is a fast, scalable, and highly available caching solution suitable for large-scale systems. It is essential to configure the cache appropriately, handle cache failures gracefully, and monitor its performance to ensure optimal operation as your application scales.β―Β
Optimizing the performance of .NET Core applications is essential for ensuring they scale effectively and meet user demands. However, several common pitfalls can minimize performance.β―Β
Excessive or improper use of asynchronous programming can degrade performance rather than improve it. For example, performing I/O bound operations synchronously or blocking on asynchronous code using Result or. Wait() can cause deadlocks or unnecessary thread pool usage.β―Β
By using ConfigureAwait(false), you tell the runtime not to capture the synchronization context and resume execution on the original thread. This is beneficial when you donβt need to update the UI.β―Β
β―Memory leaks arise whilst items are held in reminiscence longer than necessary, or if unmanaged sources aren`t launched properly. This can motive excessive reminiscence utilization and, eventually, software crashes or slowdowns. `Β
βusingβ announcement in C# is a critical device for stopping reminiscence leaks with the aid of using making sure that unmanaged sources are disposed of properly. It is mainly critical whilst running with sources like report handles, database connections, community connections, and different items that enforce the IDisposable interface.β―Β
Using IDisposable correctly to release unmanaged resources. For example, in database connections, file streams, etc. Minimize allocations in frequently called code paths and prefer value types (like structs) where appropriate over reference types.β―Β
Overusing awaits in Loopsβ―Β Using await inside a loop can result in blocking each iteration until the previous one is completed, which can lead to significant delays when processing large datasets.β―Β
When multiple async operations are independent, you should initiate them all and then wait them together (using Task.WhenAll), instead of awaiting each one sequentially.β―Β
Using inefficient caching strategies can either lead to cache misses or cause memory bloat.β―Β
Using distributed caching like Redis or Memcached for data is a better solution for insufficient caching and improve performance. Don’t cache large objects or results that change frequently, unless the cache is explicitly invalidated.β―Β
Over-injecting services that are expensive to create (e.g., DbContext) or not properly managing service lifetimes can cause performance issues.β―Β
Use the correct service lifetimes in Dependency Injection (Transient, Scoped, Singleton) based on the nature of the service.β―β―Β
Use Scoped for services that need to be created per request (e.g., DbContext in Entity Framework), and Singleton for services that can be reused across requests without state changes.β―
Avoiding these common performance pitfalls requires a mix of good programming practices, appropriate use of asynchronous programming, proper resource management, and leveraging the right tools for monitoring and profiling.β―Β
In today’s fast-growing digital landscape, optimizing the performance of .NET Core applications is not just best practice, it’s a necessity. Throughout this blog, we explored various strategies and tools that can significantly enhance the efficiency and responsiveness of your applications. We discussed the importance of efficient memory management, the benefits of asynchronous programming, and the use of built-in performance profiling tools. Performance optimization is not a one-time task but an ongoing process. By implementing monitoring tools like Application Insights, you can continuously assess your application’s performance. The benefits of performance optimization extend beyond speed. By applying the techniques discussed, you can create applications that not only meet but exceed user expectations.β―Β
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Microservices have changed how we build modern software by breaking applications into smaller, independent parts. Instead of one large system, you create smaller services that focus on specific tasks. This makes it easier to update, scale, and develop faster. Microservices and .NETΒ can resolve scalability issues and provide a strong foundation for your system during peak traffic.Β
Power Automate can do these tasks automatically, so you donβt have to. Itβs like teaching a robot to do your chores while you focus on more important things! It works with many apps and tools, making your life easier and saving you time.
Key focus areas include:Β
I/O operations: Use asynchronous methods to minimize thread blocking.Β
Memory management: Reduce unnecessary allocations and avoid memory leaks.Β
Dependency injection: Use efficient lifetimes like scoped and singleton appropriately.Β
Caching: Implement in-memory or distributed caching for frequently accessed data.Β
Database optimization: Use efficient queries and avoid retrieving excessive data.Β
Use asynchronous methods (async/await) for I/O-bound operations to free up threads for other tasks. Avoid synchronous file or network operations, and buffer data to minimize memory usage.Β
Asynchronous programming allows tasks to run concurrently without blocking the main thread. This improves responsiveness and throughput by handling multiple requests or tasks simultaneously.Β
Use the async and await keywords to define and consume asynchronous methods. Prefer APIs like HttpClient.GetAsync() for network calls and Stream.ReadAsync() for file operations.Β
These types enable efficient memory usage by working directly with slices of arrays, avoiding heap allocations, and reducing garbage collection overhead. Use them in performance-critical scenarios like parsing or data processing.Β
Dependency injection (DI) is a design pattern that provides dependencies to classes. Efficient DI usage improves code maintainability and reduces overhead by reusing services instead of creating new instances.Β
Use appropriate lifetimes:Β
Singleton: For objects shared across the application lifecycle.Β
Scoped: For per-request instances in web applications.Β
Transient: For lightweight objects with short lifetimes.Β
Caching stores frequently accessed data in memory to avoid repetitive and time-consuming operations, such as database queries. It improves response times and reduces load on underlying systems.Β
Use built-in mechanisms like MemoryCache for in-memory caching or DistributedCache for shared caching. Cache expensive-to-retrieve data and define expiration policies to keep data fresh.Β
Use the Lazy<T> class to defer initialization. For databases, enable lazy loading in Entity Framework Core to load related data only when accessed.Β
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