Golang uses Channels to implement task distribution and result aggregation

Golang uses Channels to implement task distribution and result aggregation

Introduction:
In concurrent programming, task distribution and result aggregation are common requirements. Channels in Golang provide a concise and effective way to implement this pattern of task distribution and result aggregation. This article will introduce how to use Channels to implement task distribution and result aggregation, and provide corresponding code examples.

1. Task distribution:
   Task distribution refers to distributing tasks to concurrent work coroutines for processing. In Golang, one or more Channels can be used to distribute tasks. A typical scenario is to obtain tasks from an external data source and send the tasks to a task channel for processing by the worker coroutine.

The following is a simple code example that implements the process of obtaining tasks from an external data source and sending the tasks to a task channel:

package main

import (
    "fmt"
)

func main() {
    tasks := make(chan string)

    go func() {
        for i := 0; i < 10; i++ {
            task := fmt.Sprintf("Task %d", i)
            tasks <- task
        }
        close(tasks)
    }()

    for task := range tasks {
        fmt.Println(task)
    }
}

In the above example, We created a string type channel tasks and sent 10 tasks to the channel through an anonymous function. Finally, the tasks in the channel are traversed through the range keyword and the contents of the tasks are output.

2. Result aggregation:
   Result aggregation refers to aggregating the task results completed by the work coroutine. In Golang, you can use one or more Channels to receive the results of tasks completed by the worker coroutine and aggregate the results. A typical scenario is to send the results processed by each worker coroutine to a result channel, and then aggregate the results by traversing the result channel.

The following is a simple code example that implements the process of sending the task results completed by the work coroutine to a result channel, and aggregating the results by traversing the result channel:

package main

import (
    "fmt"
)

func worker(id int, tasks <-chan string, results chan<- string) {
    for task := range tasks {
        result := fmt.Sprintf("Worker %d processed %s", id, task)
        results <- result
    }
}

func main() {
    tasks := make(chan string)
    results := make(chan string)

    // 创建 3 个工作协程
    for i := 1; i <= 3; i++ {
        go worker(i, tasks, results)
    }

    // 向任务通道发送 10 个任务
    for i := 1; i <= 10; i++ {
        task := fmt.Sprintf("Task %d", i)
        tasks <- task
    }
    close(tasks)

    // 遍历结果通道，输出每个任务的处理结果
    for result := range results {
        fmt.Println(result)
    }
}

In the above example, we created two channels, tasks and results, to achieve task distribution and result aggregation by sending tasks to the tasks channel and receiving results through the results channel. At the same time, 3 work coroutines are created to process tasks and the processing results are sent to the results channel. Finally, the results are aggregated by traversing the result channels and the processing results of each task are output.

Summary:
Channels in Golang provide a concise and effective way to implement task distribution and result aggregation patterns. Through task distribution, tasks are sent to work coroutines for concurrent processing; through result aggregation, task results completed by work coroutines are aggregated. This pattern makes concurrent programming simpler and more efficient.

Using Channels in Golang, we can better utilize system resources and improve task processing efficiency. In the code example, we simply send the task to the channel. In actual applications, it may be necessary to buffer the channel, use buffered channels, etc. to further optimize the process of task distribution and result aggregation.

Through the introduction and code examples of this article, I believe readers can understand how to use Channels to achieve task distribution and result aggregation, so as to better apply concurrent programming technology.

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