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Summary .NET 9 Performance Optimization Black Technology: The Complete Guide to From Memory Management to Web Performance

Popularity：191 ℃/2025-04-17 13:19:51

Introduction: The importance of performance optimization and performance improvement of .NET 9

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Performance optimization not only concerns code execution efficiency, but also directly affects user satisfaction and system scalability. For example, a slow-responsive web application can cause user churn, while an overly memory-consuming service can increase the cost of cloud deployment.

Performance optimization is key to ensuring applications run efficiently in high load and resource-constrained environments. Whether building web applications, microservices, or desktop programs, performance bottlenecks can lead to degraded user experience, waste of resources, and even system crashes.

.NET 9 brings developers a series of powerful performance optimization tools and improvements, covering multiple aspects such as memory management, asynchronous programming, code execution efficiency, and web application performance.

This article will dive into performance optimizations in .NET 9 to help you understand how to leverage these new features to improve your application's performance and provide practical advice and best practices. Whether you are a beginner or an experienced developer, this article will provide you with an effective reference.

.NET 9 has achieved breakthrough improvements in several areas, including:

  • Memory management: Introduce a garbage collection model that dynamically adapts to application size (DATAS) to optimize memory usage.
  • Asynchronous programming: Reduce startup overhead and enhance network performance, improving application responsiveness.
  • Code execution: Optimization of instant compiler (JIT) such as loop improvement and bounds check elimination to improve code efficiency.
  • Web Performance: Kestrel server performance improvement and HTTP/3 support to accelerate network transmission.

Memory management and garbage collection

Memory management is the basis of .NET application performance. The garbage collection (GC) mechanism reduces the memory management burden of developers by automatically recycling objects that are no longer in use. However, the behavior of GC directly affects the performance of the application, especially in scenarios where there are high concurrency or memory constrained. Frequent GC operations can cause an increase in pause time, while memory fragmentation can reduce the efficiency of available memory.

Dynamic adaptation to application size (DATAS)

.NET 9 introduces an important garbage collection improvement: Dynamic Adaptation Application Size (DATAS). This feature is enabled by default, aiming to dynamically adjust the heap size according to the actual memory requirements of the application, finding a balance between memory usage and performance. Compared with the traditional fixed heap size model, DATAS is better able to adapt to "burst" workloads, allocating more memory at peak loads and freeing up excess resources when loads are lowered.

How DATAS works

The core of DATAS is dynamic and adaptive, and its main mechanisms include:

  • Dynamically adjust the heap size: DATAS monitors the number of long-term surviving objects in the application and sets the maximum allocation before the next GC trigger based on this data.
  • Throughput and memory balance: It adjusts memory allocation according to the application's throughput requirements to ensure that performance does not significantly decline due to memory limitations.
  • Heap quantity management: Use a single heap initially and increase or decrease the number of heaps as needed.
  • Regular full heap compression: To prevent memory fragmentation, DATAS will perform full heap compression GC periodically.

Benchmark test data

The effectiveness of DATAS has been verified in the benchmark test. For example, in the JSON and Fortunes tests of TechEmpower:

  • Working set size: Improved by more than 80%, significantly reducing memory usage.
  • Throughput: Only 2-3% drop (requests per second, RPS), indicating a minimal performance impact.

Here is an example of the test data:

Benchmarking Machine Specifications Reduced throughput Work set improvement
TechEmpower JSON, Fortunes 48-core, Linux 2-3% (RPS) >80%

These data suggest that DATAS is particularly excellent in memory-constrained environments such as containerized applications, enabling significantly lower memory usage while maintaining high throughput.

Applicable scenarios

DATAS is designed to suit a variety of scenarios:

  • Containerized application: In platforms such as Kubernetes, DATAS helps applications utilize limited memory more efficiently.
  • Cloud Service: Dynamically adjust memory usage to reduce cloud costs.
  • High concurrency applications: Reduce GC pause time and improve response speed.

Configure DATAS

DATAS is enabled by default, but developers can adjust their behavior through runtime configuration. For example, you can disable DATAS by setting environment variables or configuration files, or adjust its parameters to meet specific needs. For more details, please refer to the official Microsoft documentation.

Best practices for memory management

In addition to leveraging DATAS, developers can also optimize memory usage through the following practices:

  1. Minimize object allocation

    • Reuse objects: Use object pool (e.g.MemoryPool<T>) Manage buffers to avoid frequent allocations. For example:
      var pool = MemoryPool<byte>.Shared;
using var memoryOwner = (1024);
var buffer = ;
    • Avoid unnecessary allocation:useCombinedSpan<T>Build strings to reduce intermediate objects:
      string result = (10, state, (span, state) => {
 ('a'); // Example fill logic
 });

  2. Use value types appropriately

    • For small, immutable data, the use of structs can reduce heap allocation. For example:
      public struct Point
{
    public int X { get; }
    public int Y { get; }
    public Point(int x, int y) => (X, Y) = (x, y);
}
    • Note: Avoid allocating too large structures on the stack to avoid causing performance problems.

  3. Leverage Span and Memory

    • These types allow for the operation of memory blocks without allocating additional memory. For example:
      int[] array = [1, 2, 3];
 Span<int> span = ();
 for (int i = 0; i < ; i++)
 {
 span[i]*= 2; // Modify the original array, no additional allocation
 }

Through these practices, developers can significantly reduce GC pressure and improve the memory efficiency and stability of their applications.

Asynchronous Programming Enhancement

Asynchronous programming is especially important when handling I/O-intensive operations (such as network requests, file read and write). passasyncandawait, developers can write non-blocking code to improve application responsiveness and throughput. .NET 9 has made several optimizations in asynchronous programming, including reducing startup overhead, improving type checking performance, and enhancing asynchronous support for networking and JSON serialization.

Improvements in asynchronous programming

  1. Reduce startup overhead

    • .NET 9 optimizedMethod, removes boxing operations in instant compilation (tier 0), reducing the performance overhead of asynchronous method startup.
    • In high-frequency calls scenarios, this improvement significantly improves performance.

  2. Type Check Optimization

    • Type checking method (e.g.typeof(T).IsGenericType) is optimized to intrinsics, with significantly improved performance.
    • For example,The type check code size is reduced from 250 bytes in .NET 8 to 6 bytes in .NET 9, which significantly improves execution efficiency.

  3. Network performance improvement

    • TLS Handshake: Allocation dropped from 5.03 KB to 3.3 KB, and average time dropped from 2.652 ms to 2.581 ms.
    • HTTP GET Request: Average time dropped from 92.42 us to 77.13 us and allocation dropped from 1.98 KB to 1.8 KB.
    • These improvements directly improve the efficiency of asynchronous I/O operations.

  4. JSON serialization enhancement

    • .NET 9 added to JSON serializerPipeWriterAsynchronous overloading improves the performance of streaming JSON serialization. For example:
      await (pipeWriter, data);

Best practices for asynchronous programming

To take advantage of the asynchronous improvements of .NET 9, developers should follow the following practices:

  1. Priority useasyncandawait

    • Avoid synchronous blocking operations. For example, useawait (1000)Instead(1000)： 
      async Task DelayAsync()
 {
 await (1000);
 ("Delayed completion");
 }

  2. accomplishIAsyncDisposable

    • For classes that require asynchronous cleaning of resources, useIAsyncDisposable： 
      public class MyResource : IAsyncDisposable
 {
 public ValueTask DisposeAsync()
     {
 // Asynchronously release resources
 return ;
     }
 }

  3. avoidasync void

    • In addition to event handlers, useasync TaskAlternativeasync void, in order to catch exceptions and wait for completion.

  4. Reasonable configurationConfigureAwait

    • In the library code, useConfigureAwait(false)Avoid context switching:
      await (() => { /* Work */ }).ConfigureAwait(false);

These practices can help developers write efficient asynchronous code to take advantage of the performance improvements of .NET 9.

Code optimization

Code optimization is the key to improving application performance, especially in compute-intensive tasks. .NET 9's on-time compiler (JIT) has introduced several improvements, including loop optimization, inline enhancement, and bounds check elimination, significantly improving code execution efficiency.

Circular optimization

Loops are a common structure for performance-sensitive code, and the JIT of .NET 9 optimizes it:

  1. Count down loop

    • Willfor (int i = 0; i < n; i++)Optimized tofor (int i = n-1; i >= 0; i--), reduce the comparison instructions using the CPU's zero flag.

  2. Inductive variable optimization

    • Identify and simplify inductive variables in the loop and reduce repeated calculations. For example, pre-calculate array addresses.

  3. Complex cyclic recognition

    • Enhanced recognition of complex loops and generate more efficient machine code.

Inline improvements

Inline reduces call overhead by embedding small methods into call points, .NET 9 improves inline capabilities:

  • Generic Methods: Improved inline support for small generic methods.
  • Effect: Reduce code size and execution time, for example, the performance of the property getter is significantly improved after being inlined.

Boundary check elimination

Although bounds checking of array access ensures security, it increases overhead. NET 9's JIT eliminates these checks in security situations. For example:

int sum = 0;
for (int i = 0; i < ; i++)
{
    sum += array[i];
}

JIT IdentifiesiWithin the safe range, eliminate boundary checks and speed up loop execution.

These optimizations are automatically applied by JIT, and developers can benefit without modifying their code.

Web application performance

The performance of web applications directly affects user experience and server load. .NET 9 improves the efficiency of web applications by optimizing Kestrel servers and supporting HTTP/3.

Kestrel Server Optimization

  1. Network performance
    • TLS handshake allocation is reduced and HTTP GET request time is shortened.
  2. HTTP/3 support
    • HTTP/3 based on QUIC protocol reduces latency through 0-RTT handshake and congestion control.

Web Performance Best Practices

  1. Response compression

    • Enable Gzip or Brotli:
      (options =>
{
    <GzipCompressionProvider>();
});
();

  2. Bundle and compress static resources

    • Use tools to compress JS and CSS files.

  3. Cache Policy

    • useIMemoryCacheCache data:
      if (!(key, out var data))
{
    data = await GetDataAsync();
    (key, data, (10));
}

  4. Enable HTTP/2 and HTTP/3

    • Configure Kestrel:
      (options =>
{
    (5000, o =>  = HttpProtocols.Http1AndHttp2AndHttp3);
});

Performance measurement and analysis

Performance optimization requires scientific measurement tools such as BenchmarkDotNet and Visual Studio Profiler.

BenchmarkDotNet

For microbenchmarking:

[MemoryDiagnoser]
 public class Benchmarks
 {
 [Benchmark]
 public void TestMethod()
     {
 // Test code
     }
 }

Visual Studio Profiler

For application-level analysis:

  1. Open Performance Profile.
  2. Select the analysis type.
  3. Run and analyze the results.

Conclusion

.NET 9 provides developers with powerful performance optimization tools through DATAS, asynchronous optimization, JIT improvements and web performance improvements. Combined with the summary of this article, we can build more efficient .NET applications, improve user experience and reduce resource consumption.

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