golang pprof性能调试：寻找cpu瓶颈

1、引入pprof进行性能调试

在代码中加入：

import _ "net/http/pprof"

go func() {
	  http.ListenAndServe("0.0.0.0:8899", nil)
}()

示例：为冒泡排序加入pprof debug

package main

import (
	"math/rand"
	"net/http"
	_ "net/http/pprof"
	"time"
)

func generate(n int) []int {
	rand.Seed(time.Now().UnixNano())
	nums := make([]int, 0)
	for i := 0; i < n; i++ {
		nums = append(nums, rand.Int())
	}
	return nums
}
func bubbleSort(nums []int) {
	for i := 0; i < len(nums); i++ {
		for j := 1; j < len(nums)-i; j++ {
			if nums[j] < nums[j-1] {
				nums[j], nums[j-1] = nums[j-1], nums[j]
			}
		}
	}
}

func main() {
	go func() {
		http.ListenAndServe("0.0.0.0:8899", nil)
	}()

	for {
		n := 10
		for i := 0; i < 5; i++ {
			nums := generate(n)
			bubbleSort(nums)
			n *= 10
		}
	}
}

2、pprof命令行交互式查看程序最消耗cpu的地方

1、启动上述程序：

go run main.go

2、访问pprof暴露的cpu debug api接口，收集30s内的cpu数据：

go tool pprof http://localhost:8899/debug/pprof/profile\?seconds\=30

3、在交互命令中，使用top，list来查看最消耗cpu的地方

go tool pprof http://localhost:8899/debug/pprof/profile\?seconds\=30
Fetching profile over HTTP from http://localhost:8899/debug/pprof/profile?seconds=30
Saved profile in /Users/zejia.lu/pprof/pprof.samples.cpu.003.pb.gz
Type: cpu
Time: Nov 27, 2022 at 12:09pm (CST)
Duration: 30.19s, Total samples = 26.13s (86.56%)
Entering interactive mode (type "help" for commands, "o" for options)
(pprof) top
Showing nodes accounting for 26.06s, 99.73% of 26.13s total
Dropped 18 nodes (cum <= 0.13s)
      flat  flat%   sum%        cum   cum%
    24.57s 94.03% 94.03%     26.06s 99.73%  main.bubbleSort (inline)
     1.49s  5.70% 99.73%      1.49s  5.70%  runtime.asyncPreempt
         0     0% 99.73%     26.06s 99.73%  main.main
         0     0% 99.73%     26.06s 99.73%  runtime.main
(pprof) list main.bubbleSort
Total: 26.13s
ROUTINE ======================== main.bubbleSort in /Users/zejia.lu/go_pprof/main.go
    24.57s     26.06s (flat, cum) 99.73% of Total
         .          .     15:	}
         .          .     16:	return nums
         .          .     17:}
         .          .     18:func bubbleSort(nums []int) {
         .          .     19:	for i := 0; i < len(nums); i++ {
    12.41s     13.31s     20:		for j := 1; j < len(nums)-i; j++ {
     4.33s      4.92s     21:			if nums[j] < nums[j-1] {
     7.83s      7.83s     22:				nums[j], nums[j-1] = nums[j-1], nums[j]
         .          .     23:			}
         .          .     24:		}
         .          .     25:	}
         .          .     26:}
         .          .     27:

4、输出列解析

flat	当前函数占用 cpu 耗时
flat %	当前函数占用 cpu 耗时百分比
sum%	函数占用 cpu 时间累积占比，从小到大一直累积到 100%
cum	    当前函数加上调用当前函数的函数占用 cpu 的总耗时
%cum	当前函数加上调用当前函数的函数占用 cpu 的总耗时占比

5、结果分析

我们可以看到最消耗cpu的函数：
24.57s 94.03% 94.03%     26.06s 99.73%  main.bubbleSort (inline)

我们可以看到这个函数里cpu消耗在哪里：
12.41s     13.31s     20:		for j := 1; j < len(nums)-i; j++ {
4.33s      4.92s     21:			if nums[j] < nums[j-1] {
7.83s      7.83s     22:				nums[j], nums[j-1] = nums[j-1], nums[j]

3、pprof ui方式查看程序最消耗cpu的地方

1、调试时指定通过ui方式查看，ui地址为8888端口，要调试的服务暴露的pprof端口为8899：

go tool pprof -http=":8888" http://localhost:8899/debug/pprof/profile

2、访问8888端口查看ui界面进行可视化调试：

查看最消耗cpu的地方：

4、pprof ui方式查看cpu消耗拓扑图

5、pprof ui方式查看cpu消耗火焰图

生成追踪图和火焰图需要安装graphviz

brew install graphviz

常用命令

// 查看cpu消耗前20的函数调用
top 20

// 查看累计cpu消耗前20的函数调用
top -cum 20

// 查看代码符号附近的cpu占用情况。其中代码符号类似于c++的函数符号表，可以简单理解成函数名
list "代码符号"