Large-scale task processing: using Golang WaitGroup and coroutine pool

Large-scale task processing: using Golang WaitGroup and coroutine pool

With the development of technology and the increasing popularity of Internet applications, large-scale task processing has become a popular choice for many software Challenges faced by developers. In this article, we will introduce how to use Golang's WaitGroup and coroutine pool to efficiently handle large-scale tasks, and give specific code examples.

First, let’s briefly introduce WaitGroup and coroutine pool in Golang.

WaitGroup is a thread synchronization tool provided in the Golang standard library, which can be used to wait for the end of a group of coroutines. WaitGroup has three methods: Add, Done and Wait. Set the number of waiting coroutines by calling the Add method. Each coroutine calls the Done method at the end, and the main coroutine waits for all coroutines to complete by calling the Wait method.

Coroutine pool is a technology used to manage coroutines. It avoids the problem of excessive occupation of system resources by limiting the number of coroutines executed simultaneously. A coroutine pool usually maintains a task queue and processes tasks by reusing already created coroutines.

The following is a code example that uses WaitGroup and coroutine pool to handle large-scale tasks:

package main

import (
    "fmt"
    "sync"
)

type Task struct {
    Id int
}

func processTask(task Task) {
    // 模拟处理任务的过程
    fmt.Printf("Processing task %d
", task.Id)
}

func worker(tasks <-chan Task, wg *sync.WaitGroup) {
    defer wg.Done()

    for task := range tasks {
        processTask(task)
    }
}

func main() {
    numWorkers := 5
    numTasks := 20

    var wg sync.WaitGroup
    tasks := make(chan Task)

    wg.Add(numWorkers)

    // 创建协程池
    for i := 0; i < numWorkers; i++ {
        go worker(tasks, &wg)
    }

    // 将任务添加到任务队列中
    for i := 0; i < numTasks; i++ {
        tasks <- Task{Id: i + 1}
    }

    close(tasks)

    wg.Wait()
}

In the above code, we define a Task structure to represent the task, which contains an Id field. The processTask function simulates the process of processing a task. Here it simply prints the ID of the task.

In the main function, we first set the size of the coroutine pool to 5 and created a Task type channel. Next, we set the number of waiting coroutines by calling the wg.Add method, and created 5 worker coroutines to handle the task.

Then, we add 20 tasks to the task queue through a loop and close the tasks channel to notify the coroutine pool that the task is completed.

Finally, we call the wg.Wait method to wait for all coroutines to complete their tasks.

Using the above code example, we can easily handle large-scale tasks. By using WaitGroup and coroutine pool, we can efficiently handle concurrent tasks, make full use of system resources, and avoid resource waste and performance problems caused by thread processing.

Summary:
In this article, we introduced how to use Golang's WaitGroup and coroutine pool to handle large-scale tasks. By using WaitGroup to wait for the end of a group of coroutines, and using a coroutine pool to control the amount of concurrency, we can handle a large number of tasks efficiently. By writing concurrency-safe code and reasonably managing the size of the coroutine pool and task allocation, we can make full use of system resources and improve task processing efficiency. I hope this article will help you understand and apply WaitGroup and coroutine pools.

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