Profiling and performance analysis of Golang functions

Question: How to optimize performance in Go language? Profiling: Use built-in tools to generate code execution information (CPU, memory, etc.). Analyze profiling results: Use the pprof tool to visually analyze the profiling file and find the performance bottleneck function. Benchmarking: Compare the performance of different implementations and understand the optimization effect. Practical case: Find server bottlenecks through profiling analysis and optimize loops to improve performance. Recommended tools: In addition to built-in tools, there are also third-party tools such as go-torch, pprof, go-perf, etc. to assist in performance optimization.

Profiling and performance analysis of Go functions

The profiling and performance analysis tools in the Go language are very powerful and can help you easily find out the problems in the Go code. Performance bottleneck. This article will introduce the use of profiling and performance analysis in the Go language.

Profiling

The Go language has a built-in profiling tool that can generate various information when the code is executed, including:

func main() {
    f := func() {
        // 占用 CPU 时间的代码
    }

    // 开始 profiling
    prof := pprof.StartCPUProfile(os.Stderr)
    defer prof.Stop()

    // 运行函数
    f()
}

You can use the following command to generate a CPU profiling file:

go run main.go > prof.out

Analyze profiling results

You can use the pprof tool to analyze the profiling file:

pprof -web prof.out

This will open an interactive interface in the browser , display profiling results. You can drill down to the function level to see which functions are taking up the most time.

Benchmarking

In addition to profiling, the Go language also provides benchmarking tools for comparing the performance of different implementations.

func BenchmarkMyFunction(b *testing.B) {
    for i := 0; i < b.N; i++ {
        f()
    }
}

You can run the benchmark using the following command:

go test -v -bench=.

Practical case

In the following example, we create a simple Go server that contains a performance bottleneck. Using the profiling tool, we can easily find out where the bottleneck is:

func main() {
    http.HandleFunc("/", handler)
    http.ListenAndServe(":8080", nil)
}

func handler(w http.ResponseWriter, r *http.Request) {
    for i := 0; i < 10000000; i++ {
        // 占用 CPU 时间的代码
    }

    w.Write([]byte("Hello, world!"))
}

Using the pprof tool to analyze the profiling file, we find that the loops in the handler function occupy most of the time. We can improve server performance by optimizing loops.

Performance Tool Recommendation

In addition to the built-in tools, there are many third-party tools that can help you profile and perform performance analysis of Go code, such as:

• [go -torch](https://github.com/uber-go/go-torch)
• [pprof](https://github.com/google/pprof)
• [go -perf](https://github.com/maruel/go-perf)

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