Can My Laptop Handle One Million Concurrent HTTP Requests Using Go?

Can Your Laptop Handle a Million Concurrent HTTP Requests?

Imagine sending 1,000,000 simultaneous HTTP requests to a REST API service, pushing your computer's limits to maximize concurrency. While tools exist for this task, let's dive into how to do it in Go using goroutines.

package main

import (
    "fmt"
    "net/http"
    "runtime"
    "time"
)

func main() {
    runtime.GOMAXPROCS(runtime.NumCPU()) // Use all available CPU cores
    transport := &http.Transport{}        // Create an HTTP transport

    for i := 0; i < 1000000; i++ {
        go func() { // Start a goroutine for each HTTP request
            req, _ := http.NewRequest("GET", "http://myapi.com", nil)
            req.Header.Set("User-Agent", "custom-agent")
            req.SetBasicAuth("xxx", "xxx")

            resp, err := transport.RoundTrip(req)
            if err != nil {
                panic("HTTP request failed.")
            }
            defer resp.Body.Close()

            if resp.StatusCode != 302 {
                panic("Unexpected response returned.")
            }

            location := resp.Header.Get("Location")
            if location == "" {
                panic("No location header returned.")
            }

            fmt.Println("Location Header Value:", location)
        }()
    }

    time.Sleep(60 * time.Second) // Sleep for 60 seconds
}

But Wait, There's a Problem!

Running this script results in errors due to file descriptor limits. The system simply can't handle so many simultaneous connections.

An Improved Solution

To overcome these limitations, we need to use a more sophisticated approach.

Using a Dispatcher and Worker Pool

This solution involves creating a dispatcher goroutine that pushes requests onto a channel. A worker pool of goroutines pulls requests from the channel, processes them, and sends them to a response channel. A consumer goroutine then processes the responses.

// Dispatcher function
func dispatcher(reqChan chan *http.Request, reqs int) {
    defer close(reqChan)
    for i := 0; i < reqs; i++ {
        req, err := http.NewRequest("GET", "http://localhost/", nil)
        if err != nil {
            log.Println(err)
        }
        reqChan <- req
    }
}

// Worker function
func worker(t *http.Transport, reqChan chan *http.Request, respChan chan Response) {
    for req := range reqChan {
        resp, err := t.RoundTrip(req)
        r := Response{resp, err}
        respChan <- r
    }
}

// Consumer function
func consumer(respChan chan Response, reqs int) (int64, int64) {
    var (
        conns int64
        size  int64
    )
    for conns < int64(reqs) {
        select {
        case r, ok := <-respChan:
            if ok {
                if r.err != nil {
                    log.Println(r.err)
                } else {
                    size += r.ContentLength
                    if err := r.Body.Close(); err != nil {
                        log.Println(r.err)
                    }
                }
                conns++
            }
        }
    }
    return conns, size
}

Results

Running this improved script generates impressive results, such as:

Connections: 1000000

Concurrent: 200

Total size: 15000000 bytes

Total time: 38m20.3012317s

Average time: 2.280131ms

Optimizing for Performance

However, tweaking the number of concurrent requests and total requests will help you push your system's limits and stress-test its capabilities. Remember, this is an extreme test and can rapidly consume system resources.

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