Home Backend Development Golang Golang concurrent scheduler: optimized implementation of Go WaitGroup

Golang concurrent scheduler: optimized implementation of Go WaitGroup

Sep 28, 2023 am 11:48 AM
golang concurrent scheduler

Golang并发调度器:Go WaitGroup的优化实现

Golang concurrent scheduler: optimized implementation of Go WaitGroup

Introduction:
Go language uses the concurrency model of goroutine, and the WaitGroup type in the built-in sync package , providing a convenient way to write concurrent programs. However, as the size of the program increases, the use of a large number of goroutines and WaitGroups may cause performance bottlenecks. In order to optimize these problems, this article will introduce a method that can manage concurrent tasks more efficiently.

1. Design ideas of concurrent scheduler:
In order to better manage concurrent tasks, we need to design a concurrent scheduler. The concurrent scheduler mainly includes the following components: task queue, goroutine pool, task execution function and semaphore. The design ideas of the scheduler are as follows:
1. Task queue: used to store tasks to be executed. In the form of a queue, tasks are taken out for execution when the process is idle;
2. Goroutine pool: used to manage goroutine Quantity, by limiting the number of goroutines, avoids performance problems caused by the creation and destruction of a large number of goroutines;
3. Task execution function: defined by the user, representing specific task execution logic;
4. Semaphore: used to control the running status of the scheduler to ensure that all tasks are executed and exited.

2. Specific implementation of concurrent scheduler:
The following is a code example based on the optimized implementation of Golang concurrent scheduler:

package main

import (
    "fmt"
    "sync"
    "time"
)

// 定义任务结构体
type Task struct {
    TaskID int // 任务ID
}

func main() {
    var (
        tasksNumber = 100 // 待执行任务数量
        goroutineNum = 10 // goroutine数量
        wg sync.WaitGroup
        taskQueue = make(chan Task, tasksNumber) // 任务队列
    )
    // 初始化任务队列
    for i := 0; i < tasksNumber; i++ {
        task := Task{
            TaskID: i,
        }
        taskQueue <- task
    }
    close(taskQueue)

    // 启动goroutine
    for i := 0; i < goroutineNum; i++ {
        wg.Add(1)
        go func(id int) {
            defer wg.Done()
            for task := range taskQueue {
                execute(task)
            }
        }(i)
    }
    wg.Wait()
}

// 任务的具体执行函数
func execute(task Task) {
    fmt.Printf("TaskID: %d, Now: %s
", task.TaskID, time.Now().Format("2006-01-02 15:04:05"))
    time.Sleep(1 * time.Second)
    fmt.Printf("TaskID: %d, Finished
", task.TaskID)
}
Copy after login

In the above code, we first create a task queue (taskQueue) and put the tasks to be executed into it. Subsequently, we started the goroutine pool, and each goroutine obtained tasks from the task queue and executed them. Finally, wait for all tasks to be completed through the WaitGroup object.

3. Summary:
Through the above optimization code implementation, we can better manage concurrent tasks and avoid performance bottlenecks caused by the use of a large number of goroutines and WaitGroup. The design of the concurrent scheduler enables us to handle concurrent tasks more efficiently and improve the concurrency capability and overall performance of the program.

In addition, for more complex concurrency scenarios, we can further improve the performance and flexibility of the concurrent scheduler by introducing connection pools, task priorities, scheduling strategies, etc. I hope this article can provide readers with some reference and help when writing efficient concurrent programs.

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