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Best practices for API performance testing Choose appropriate tools (HTTP client library, stress testing library, monitoring tools) Define clear test goals Use a real environment to simulate real scenarios for browser and server monitoring Practical cases Use Vegeta to simulate 100 concurrent requests Test/users API endpoint: for i := 0; i < 100; i { resp, _, err := attack.Do(target) }
Best Practices for Go Language API Performance Testing
API performance testing is essential to ensure that the application Program reliability and scalability are critical. This article introduces the best practices for API performance testing using Go language, as well as a practical case.
1. Choose the appropriate tool
http
or net/http
. pprof
or expvar
to analyze server performance. 2. Well-defined test goals
3. Use a real environment
4. Write real-world scenarios
5. Perform browser and server monitoring
pprof
and expvar
on the server to monitor CPU usage, memory usage, and request latency. Use Vegeta to simulate 100 concurrent request tests/users
API endpoint:
package main import ( "fmt" "log" "net/http" "os" "time" "github.com/tsenart/vegeta/v12/lib" ) func main() { // go run main.go "" target := vegeta.NewStaticTargeter(os.Args[1]) attack := vegeta.NewAttacker() var metrics vegeta.Metrics duration := 60 * time.Second for i := 0; i < 100; i++ { // 并发 100 个请求 resp, _, err := attack.Do(target) if err != nil { log.Fatal(err) } metrics.Add(resp) } rate := time.Duration(metrics.Requests) * time.Second / duration avgLatency := metrics.Latencies.Mean throughput := metrics.Throughput.Mean fmt.Printf("Rate: %.2f req/s\n", rate) fmt.Printf("Latency: %.2f ms\n", avgLatency/time.Millisecond) fmt.Printf("Throughput: %.2f kb/s\n", throughput) }
By following these best practices and using recommended tools, you can effectively perform Go language API performance testing. By conducting thorough testing, you can ensure that your application performs well in production.
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