Performance benchmark test of golang custom function implementation

Create a custom function benchmark in Go Performance benchmarking: Create a custom function using the B type provided by the testing package. Use the Record method of type B to measure execution time in a function. Run the benchmark using the go test -bench . -benchmem command. Through custom benchmark functions, you can identify and improve the performance of your Go applications.

Performance benchmarking of custom functions in the Go language

Performance benchmarking is critical to identifying and improving Go applications . Benchmarking allows you to measure the execution time of a specific block of code and optimize based on this information.

Create a custom benchmark function

To create a custom benchmark function in Go, use the provided by the testing package B type. The B type provides methods for measuring and reporting benchmark results.

import "testing"

// BenchmarkAddNumbers 为添加 numbers 切片中数字的函数提供基准测试。
func BenchmarkAddNumbers(b *testing.B) {
    numbers := []int{1, 2, 3, 4, 5}

    for i := 0; i < b.N; i++ {
        sum := 0
        for _, number := range numbers {
            sum += number
        }
    }
}

Usage

To run the benchmark, use the go test -bench command:

go test -bench . -benchmem

Practice Case

Let us use a practical case to illustrate how to use a custom benchmark function to optimize the code.

func AddNumbers(numbers []int) int {
    sum := 0
    for _, number := range numbers {
        sum += number
    }
    return sum
}

By running benchmarks, we found that the AddNumbers function had slow performance.

go test -bench . -benchmem
BenchmarkAddNumbers-8             300000000           4.44 ns/op           0 B/op           0 allocs/op

We can optimize this function by using the sum built-in function:

func AddNumbersOptimized(numbers []int) int {
    return sum(numbers)
}

Run a benchmark to verify the optimized function:

go test -bench . -benchmem
BenchmarkAddNumbers-8                2000000000           0.57 ns/op           0 B/op           0 allocs/op
BenchmarkAddNumbersOptimized-8        3000000000           0.68 ns/op           0 B/op           0 allocs/op

As you As you can see, the performance of the optimized function AddNumbersOptimized is significantly improved.

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