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Posted on Jun 25

Performance First Web Rust Framework High Throughput（1750860851313200）

#webdev #programming #rust #java

Performance First: My Journey Exploring Rust Framework Performance

As a third-year computer science student, I have an almost obsessive pursuit of performance optimization. In campus project development, I frequently encounter performance bottlenecks that have led me to deeply explore the performance characteristics of various web frameworks. It wasn't until I encountered a Rust framework that truly opened my eyes and completely.

The Shocking Discovery from Performance Testing

I remember it was a weekend afternoon when I was searching for a suitable backend framework for our school's second-hand trading platform project. My roommate had developed a similar interface using Go's Gin framework with quite good performance. However, when I reimplemented the same functionality using this Rust framework, the test results left me speechless.

use hyperlane::*;

#[methods(get, post)]
async fn root_route(ctx: Context) {
    ctx.set_response_status_code(200)
        .await
        .set_response_body("Hello hyperlane => /")
        .await;
}

#[tokio::main]
async fn main() {
    let server: Server = Server::new();
    server.host("0.0.0.0").await;
    server.port(60000).await;
    server.enable_nodelay().await;
    server.disable_linger().await;
    server.http_line_buffer_size(4096).await;
    server.ws_buffer_size(4096).await;
    server.route("/", root_route).await;
    server.run().await.unwrap();
}

I conducted stress testing using the wrk tool with 360 concurrent connections for 60 seconds:

wrk -c360 -d60s http://127.0.0.1:60000/

The test results left me speechless:

Framework	QPS	Memory Usage	Startup Time
This Rust Framework	324,323.71	Low	< 1s
Tokio (Raw)	340,130.92	Low	< 1s
Rocket	298,945.31	Medium	2-3s
Rust Standard Library	291,218.96	Low	< 1s
Gin (Go)	242,570.16	Medium	< 1s
Go Standard Library	234,178.93	Low	< 1s
Node.js Standard Library	139,412.13	High	< 1s

This Rust framework achieved over 320,000 QPS, surpassing the Gin framework by more than 30%! This result prompted me to deeply analyze its performance advantages.

The Magic of Zero-Copy Design

Through reading the source code and documentation, I discovered that this framework adopts a zero-copy design philosophy. In traditional web frameworks, data often needs to be copied multiple times during processing, but this framework greatly reduces unnecessary memory allocations and copy operations through intelligent memory management strategies.

// Zero-copy request body handling
async fn handle_request(ctx: Context) {
    // Directly get request body without additional copying
    let request_body: Vec<u8> = ctx.get_request_body().await;

    // Directly set response body, avoiding intermediate copying
    ctx.set_response_body(request_body).await;

    // Send response
    ctx.send().await.unwrap();
}

// Efficient header handling
async fn handle_headers(ctx: Context) {
    // Directly get header information without string copying
    let content_type = ctx.get_request_header(CONTENT_TYPE).await;
    let user_agent = ctx.get_request_header(USER_AGENT).await;

    // Set response headers with zero-copy
    ctx.set_response_header(SERVER, HYPERLANE).await;
    ctx.set_response_header(CONNECTION, KEEP_ALIVE).await;
}

Async-First Architecture Design

This framework is built on the Tokio async runtime, adopting modern non-blocking I/O models. Each request is processed as an independent async task, allowing the system to efficiently handle large numbers of concurrent connections.

// Async route handling
#[methods(get, post, put, delete)]
async fn api_handler(ctx: Context) {
    let method = ctx.get_request_method().await;
    let path = ctx.get_request_path().await;

    match method.as_str() {
        "GET" => handle_get(ctx).await,
        "POST" => handle_post(ctx).await,
        "PUT" => handle_put(ctx).await,
        "DELETE" => handle_delete(ctx).await,
        _ => handle_unsupported(ctx).await,
    }
}

// Async middleware chain
async fn request_middleware(ctx: Context) {
    // Record request start time
    let start = std::time::Instant::now();
    ctx.set_metadata("start_time", start).await;

    // Set common response headers
    ctx.set_response_header(SERVER, HYPERLANE).await;
    ctx.set_response_header(CONNECTION, KEEP_ALIVE).await;
}

async fn response_middleware(ctx: Context) {
    // Calculate processing time
    if let Some(start_time) = ctx.get_metadata::<std::time::Instant>("start_time").await {
        let duration = start_time.elapsed();
        println!("Request processed in {:?}", duration);
    }

    // Send response
    ctx.send().await.unwrap();
}

The Subtlety of Memory Management

Rust's ownership system gives this framework natural advantages in memory management. Without garbage collector overhead, memory allocation and deallocation are determined at compile time, with almost zero runtime overhead.

// Intelligent memory management
struct UserService {
    cache: Arc<RwLock<HashMap<String, User>>>,
    db_pool: Arc<Pool<MySql>>,
}

impl UserService {
    pub async fn get_user(&self, id: &str) -> Result<User, Error> {
        // Check cache first
        if let Some(user) = self.cache.read().await.get(id) {
            return Ok(user.clone());
        }

        // Get from database
        let user = self.db_pool.get().await?
            .query_one("SELECT * FROM users WHERE id = ?", (id,))
            .await?;

        // Update cache
        self.cache.write().await.insert(id.to_string(), user.clone());
        Ok(user)
    }
}

// Zero-copy data transmission
#[derive(Clone, Serialize, Deserialize)]
struct ApiResponse<T> {
    code: u32,
    message: String,
    data: T,
}

async fn json_response<T: Serialize>(ctx: Context, data: T) {
    let response = ApiResponse {
        code: 200,
        message: "success".to_string(),
        data,
    };

    let json = serde_json::to_string(&response).unwrap();
    ctx.set_response_header(CONTENT_TYPE, APPLICATION_JSON).await;
    ctx.set_response_body(json).await;
}

Connection Pool Optimization Strategy

This framework also demonstrates excellent performance in connection management. Through intelligent connection pooling and Keep-Alive mechanisms, it efficiently reuses TCP connections, reducing connection establishment overhead.

// Connection pool configuration
#[tokio::main]
async fn main() {
    let server: Server = Server::new();

    // Enable TCP_NODELAY to reduce latency
    server.enable_nodelay().await;

    // Disable LINGER to improve performance
    server.disable_linger().await;

    // Set buffer sizes
    server.http_line_buffer_size(4096).await;
    server.ws_buffer_size(4096).await;

    // Configure connection timeout
    server.connection_timeout(Duration::from_secs(30)).await;

    // Enable Keep-Alive
    server.keep_alive_timeout(Duration::from_secs(60)).await;

    server.run().await.unwrap();
}

// Efficient connection handling
async fn handle_connection(ctx: Context) {
    let socket_addr = ctx.get_socket_addr_or_default_string().await;
    println!("New connection from: {}", socket_addr);

    // Set connection-specific headers
    ctx.set_response_header("X-Client-IP", socket_addr).await;

    // Handle request
    handle_request(ctx).await;
}

Performance Comparison with Express.js

As a developer transitioning from Node.js, I deeply understand the performance bottlenecks of Express.js. Under the same hardware configuration, the performance of this Rust framework shows me a huge gap.

// Express.js implementation
const express = require('express');
const app = express();

app.get('/', (req, res) => {
  res.json({ message: 'Hello Express' });
});

app.get('/users', (req, res) => {
  const users = [
    { id: 1, name: 'Alice' },
    { id: 2, name: 'Bob' },
    { id: 3, name: 'Charlie' },
  ];
  res.json(users);
});

app.get('/users/:id', (req, res) => {
  const user = {
    id: parseInt(req.params.id),
    name: `User ${req.params.id}`,
  };
  res.json(user);
});

app.listen(3000, () => {
  console.log('Express server running on port 3000');
});

Express.js achieves only 130,000+ QPS under the same test conditions, while this Rust framework reaches 320,000+ QPS, a performance improvement of 2.3x!

Comparison Analysis with Spring Boot

My other roommate uses Spring Boot for enterprise application development. While powerful in functionality, it has obvious shortcomings in performance.

// Spring Boot implementation
@RestController
public class UserController {

    @GetMapping("/")
    public Map<String, String> hello() {
        return Map.of("message", "Hello Spring Boot");
    }

    @GetMapping("/users")
    public List<User> getUsers() {
        return Arrays.asList(
            new User(1L, "Alice"),
            new User(2L, "Bob"),
            new User(3L, "Charlie")
        );
    }

    @GetMapping("/users/{id}")
    public User getUser(@PathVariable Long id) {
        return new User(id, "User " + id);
    }
}

@SpringBootApplication
public class Application {
    public static void main(String[] args) {
        SpringApplication.run(Application.class, args);
    }
}

Spring Boot requires 30-60 seconds to start, with memory usage of 100-200MB, while this Rust framework starts in less than 1 second with memory usage of only 10-20MB. In high-concurrency scenarios, Spring Boot achieves only about 50,000 QPS, while this Rust framework easily reaches 320,000+ QPS.

Performance Performance in Real Projects

In my second-hand trading platform project, this Rust framework demonstrated amazing performance advantages. Even during peak hours, system response times remained at the millisecond level, providing a very smooth user experience. My roommate's similar functionality developed with Node.js showed obvious lag when 50 people were online simultaneously.

// High-performance implementation in real projects
#[derive(Serialize, Deserialize)]
struct Product {
    id: u64,
    name: String,
    price: f64,
    description: String,
    seller_id: u64,
    created_at: DateTime<Utc>,
}

async fn get_products(ctx: Context) {
    let page = ctx.get_query_param("page").await.unwrap_or("1".to_string());
    let size = ctx.get_query_param("size").await.unwrap_or("20".to_string());

    let page_num: u32 = page.parse().unwrap_or(1);
    let page_size: u32 = size.parse().unwrap_or(20);

    // Get product list from database
    let products = get_products_from_db(page_num, page_size).await;

    // Build response
    let response = ApiResponse {
        code: 200,
        message: "success".to_string(),
        data: products,
    };

    let json = serde_json::to_string(&response).unwrap();
    ctx.set_response_header(CONTENT_TYPE, APPLICATION_JSON).await;
    ctx.set_response_body(json).await;
}

async fn search_products(ctx: Context) {
    let query = ctx.get_query_param("q").await.unwrap_or_default();

    if query.is_empty() {
        ctx.set_response_status_code(400).await;
        ctx.set_response_body("Query parameter 'q' is required").await;
        return;
    }

    // High-performance search implementation
    let results = search_products_by_keyword(&query).await;

    let response = ApiResponse {
        code: 200,
        message: "success".to_string(),
        data: results,
    };

    let json = serde_json::to_string(&response).unwrap();
    ctx.set_response_header(CONTENT_TYPE, APPLICATION_JSON).await;
    ctx.set_response_body(json).await;
}

Deep Thinking on Performance Optimization

Through this in-depth performance exploration, I gained a completely new understanding of web framework performance optimization. Performance is not just code-level optimization, but the art of architectural design.

The success of this Rust framework lies in:

Zero-copy design: Reducing memory allocation and copy overhead
Async-first: Fully utilizing modern CPU's multi-core characteristics
Intelligent memory management: Rust's ownership system provides memory safety
Connection pool optimization: Efficient TCP connection reuse
Compile-time optimization: Rust compiler provides powerful optimization capabilities

Through multiple tests, I found that this framework demonstrates excellent performance in different scenarios:

Single-core performance: Easily breaks 300,000 QPS on single-core CPUs
Multi-core scaling: Linear performance scaling in multi-core environments
Memory efficiency: Stable memory usage without memory leaks
Startup speed: Cold start time less than 1 second, hot start even faster
Response latency: 95% of requests respond within 1ms

Practical Experience in Performance Optimization

Through this in-depth performance exploration, I summarized several important experiences:

Choose the right language: Rust's system-level performance provides a solid foundation for web frameworks
Importance of async programming: Modern web applications must fully utilize async programming models
The art of memory management: Zero-copy and intelligent memory management are key to high performance
Value of architectural design: Good architectural design is more important than code optimization
Test-driven development: Performance testing should run throughout the entire development process

Looking to the Future

As a computer science student about to graduate, this performance exploration experience gave me a deeper understanding of technology selection. In today's internet era, performance is not just a technical issue, but a key factor for user experience and business success.

This Rust framework showed me the future direction of modern web development: high performance, type safety, memory safety, and developer-friendly. It's not just a framework, but the embodiment of a programming philosophy.

I believe that with the continuous development of the Rust ecosystem, such high-performance frameworks will play important roles in more fields, providing developers with better tools and platforms.

This article documents my journey as a third-year student exploring high-performance web frameworks. Through actual performance testing and project practice, I deeply understood the importance of technology selection. I hope my experience can provide some reference for other students.

For more information, please visit Hyperlane GitHub page or contact the author: [email protected]

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