在golang中，经常使用协程做高并发，本文列举了几种常见并发模型。
package main
import (
"fmt"
"math/rand"
"os"
"runtime"
"sync"
"sync/atomic"
"time"
)
type Scenario struct {
Name string
Description []string
Examples []string
RunExample func()
}
var s1 = &Scenario{
Name: "s1",
Description: []string{
"简单并发执行任务",
},
Examples: []string{
"比如并发的请求后端某个接口",
},
RunExample: RunScenario1,
}
var s2 = &Scenario{
Name: "s2",
Description: []string{
"持续一定时间的高并发模型",
},
Examples: []string{
"在规定时间内，持续的高并发请求后端服务， 防止服务死循环",
},
RunExample: RunScenario2,
}
var s3 = &Scenario{
Name: "s3",
Description: []string{
"基于大数据量的并发任务模型, goroutine worker pool",
},
Examples: []string{
"比如技术支持要给某个客户删除几个TB/GB的文件",
},
RunExample: RunScenario3,
}
var s4 = &Scenario{
Name: "s4",
Description: []string{
"等待异步任务执行结果(goroutine+select+channel)",
},
Examples: []string{
"",
},
RunExample: RunScenario4,
}
var s5 = &Scenario{
Name: "s5",
Description: []string{
"定时的反馈结果(Ticker)",
},
Examples: []string{
"比如测试上传接口的性能，要实时给出指标: 吞吐率，IOPS,成功率等",
},
RunExample: RunScenario5,
}
var Scenarios []*Scenario
func init() {
Scenarios = append(Scenarios, s1)
Scenarios = append(Scenarios, s2)
Scenarios = append(Scenarios, s3)
Scenarios = append(Scenarios, s4)
Scenarios = append(Scenarios, s5)
}
// 常用的并发与同步场景
func main() {
if len(os.Args) == 1 {
fmt.Println("请选择使用场景 ==> ")
for _, sc := range Scenarios {
fmt.Printf("场景: %s ,", sc.Name)
printDescription(sc.Description)
}
return
}
for _, arg := range os.Args[1:] {
sc := matchScenario(arg)
if sc != nil {
printDescription(sc.Description)
printExamples(sc.Examples)
sc.RunExample()
}
}
}
func printDescription(str []string) {
fmt.Printf("场景描述: %s \n", str)
}
func printExamples(str []string) {
fmt.Printf("场景举例: %s \n", str)
}
func matchScenario(name string) *Scenario {
for _, sc := range Scenarios {
if sc.Name == name {
return sc
}
}
return nil
}
var doSomething = func(i int) string {
time.Sleep(time.Millisecond * time.Duration(10))
fmt.Printf("Goroutine %d do things .... \n", i)
return fmt.Sprintf("Goroutine %d", i)
}
var takeSomthing = func(res string) string {
time.Sleep(time.Millisecond * time.Duration(10))
tmp := fmt.Sprintf("Take result from %s.... \n", res)
fmt.Println(tmp)
return tmp
}
// 场景1: 简单并发任务
func RunScenario1() {
count := 10
var wg sync.WaitGroup
for i := 0; i < count; i++ {
wg.Add(1)
go func(index int) {
defer wg.Done()
doSomething(index)
}(i)
}
wg.Wait()
}
// 场景2: 按时间来持续并发
func RunScenario2() {
timeout := time.Now().Add(time.Second * time.Duration(10))
n := runtime.NumCPU()
waitForAll := make(chan struct{})
done := make(chan struct{})
concurrentCount := make(chan struct{}, n)
for i := 0; i < n; i++ {
concurrentCount <- struct{}{}
}
go func() {
for time.Now().Before(timeout) {
<-done
concurrentCount <- struct{}{}
}
waitForAll <- struct{}{}
}()
go func() {
for {
<-concurrentCount
go func() {
doSomething(rand.Intn(n))
done <- struct{}{}
}()
}
}()
<-waitForAll
}
// 场景3：以 worker pool 方式 并发做事/发送请求
func RunScenario3() {
numOfConcurrency := runtime.NumCPU()
taskTool := 10
jobs := make(chan int, taskTool)
results := make(chan int, taskTool)
var wg sync.WaitGroup
// workExample
workExampleFunc := func(id int, jobs <-chan int, results chan<- int, wg *sync.WaitGroup) {
defer wg.Done()
for job := range jobs {
res := job * 2
fmt.Printf("Worker %d do things, produce result %d \n", id, res)
time.Sleep(time.Millisecond * time.Duration(100))
results <- res
}
}
for i := 0; i < numOfConcurrency; i++ {
wg.Add(1)
go workExampleFunc(i, jobs, results, &wg)
}
totalTasks := 100
wg.Add(1)
go func() {
defer wg.Done()
for i := 0; i < totalTasks; i++ {
n := <-results
fmt.Printf("Got results %d \n", n)
}
close(results)
}()
for i := 0; i < totalTasks; i++ {
jobs <- i
}
close(jobs)
wg.Wait()
}
// 场景4: 等待异步任务执行结果(goroutine+select+channel)
func RunScenario4() {
sth := make(chan string)
result := make(chan string)
go func() {
id := rand.Intn(100)
for {
sth <- doSomething(id)
}
}()
go func() {
for {
result <- takeSomthing(<-sth)
}
}()
select {
case c := <-result:
fmt.Printf("Got result %s ", c)
case <-time.After(time.Duration(30 * time.Second)):
fmt.Errorf("指定时间内都没有得到结果")
}
}
var doUploadMock = func() bool {
time.Sleep(time.Millisecond * time.Duration(100))
n := rand.Intn(100)
if n > 50 {
return true
} else {
return false
}
}
// 场景5: 定时的反馈结果(Ticker)
// 测试上传接口的性能，要实时给出指标: 吞吐率，成功率等
func RunScenario5() {
totalSize := int64(0)
totalCount := int64(0)
totalErr := int64(0)
concurrencyCount := runtime.NumCPU()
stop := make(chan struct{})
fileSizeExample := int64(10)
timeout := 10 // seconds to stop
go func() {
for i := 0; i < concurrencyCount; i++ {
go func(index int) {
for {
select {
case <-stop:
return
default:
break
}
res := doUploadMock()
if res {
atomic.AddInt64(&totalCount, 1)
atomic.AddInt64(&totalSize, fileSizeExample)
} else {
atomic.AddInt64(&totalErr, 1)
}
}
}(i)
}
}()
t := time.NewTicker(time.Second)
index := 0
for {
select {
case <-t.C:
index++
tmpCount := atomic.LoadInt64(&totalCount)
tmpSize := atomic.LoadInt64(&totalSize)
tmpErr := atomic.LoadInt64(&totalErr)
fmt.Printf("吞吐率: %d，成功率: %d \n", tmpSize/int64(index), tmpCount*100/(tmpCount+tmpErr))
if index > timeout {
t.Stop()
close(stop)
return
}
}
}
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