Home Backend Development Golang How to use context to implement concurrent task control in Go

How to use context to implement concurrent task control in Go

Jul 22, 2023 pm 03:01 PM
go context Concurrent task control

Go language is a high-performance programming language that supports concurrent programming. It is particularly powerful when handling concurrent tasks. In Go, there is a very important mechanism that can help us better control concurrent tasks, and that is Context.

Context is a standard package in Go that provides a simple way to pass request-scoped data and manage the life cycle of the corresponding goroutine. Use context to share data between multiple goroutines and control their execution time.

This article will introduce in detail how to use context to implement concurrent task control, and provide relevant code examples. Before starting, we need to install the Go language development environment.

First of all, we need to understand several core types and functions in the context package:

  1. context.Context: represents the context of a request and contains basic information about the request, such as timeout. time, cancellation signal, etc. We can create and pass Context objects to pass and share data between multiple goroutines.
  2. context.WithCancel(parent context.Context) (context.Context, context.CancelFunc): Creates a new Context and returns a function that can be used to cancel this Context. When we call this CancelFunc, Context will send a cancellation signal to all child Contexts.
  3. context.WithTimeout(parent context.Context, timeout time.Duration) (context.Context, context.CancelFunc): Create a new Context and return a function that can be used to cancel this Context. When the specified timeout is exceeded, the Context will automatically send a cancellation signal to all sub-Contexts.

With the above basic concepts, we can start writing code. Suppose we need to implement a task to download files concurrently, where each download task should have a timeout and the entire download task can be manually aborted as needed.

We first define a Download function to simulate the file download process:

func Download(ctx context.Context, url string) {
    // 模拟文件下载过程
    time.Sleep(2 * time.Second)
    fmt.Printf("Download %s success
", url)
}
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Next, we define a DownloadTask function to create a download task and start a goroutine to execute the download Operation:

func DownloadTask(ctx context.Context, url string) {
    go func() {
        // 创建一个带有超时时间的Context
        ctx, cancel := context.WithTimeout(ctx, 3*time.Second)
        defer cancel()

        // 执行文件下载
        Download(ctx, url)
    }()
}
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In the main function, we can create a top-level Context and pass it to functions that need to perform concurrent tasks as needed:

func main() {
    // 创建一个顶层Context
    ctx := context.Background()

    // 创建一个带有取消函数的Context
    ctx, cancel := context.WithCancel(ctx)
    defer cancel()

    // 创建一个带有超时时间的Context
    ctx, timeoutCancel := context.WithTimeout(ctx, 10*time.Second)
    defer timeoutCancel()

    // 启动并发下载任务
    DownloadTask(ctx, "https://example.com/file1")
    DownloadTask(ctx, "https://example.com/file2")
    DownloadTask(ctx, "https://example.com/file3")

    // 等待任务完成
    time.Sleep(5 * time.Second)
}
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In the above code, we create a The top-level Context and two sub-Contexts are created through WithCancel and WithTimeout. Then, we started three download tasks and set the timeout for each task to 3 seconds. Finally, we use time.Sleep to wait for the task to complete.

Through the above sample code, we can see that it is very simple to use context for concurrent task control in Go. We can use the WithCancel method to create a manually cancelable Context, use the WithTimeout method to create an automatically cancelable Context, and pass the Context to the relevant goroutine where needed.

Using context can better control the life cycle of concurrent tasks and avoid the entire program crashing or being unable to exit due to an exception in a certain task. By using context properly, we can develop high-performance concurrent programs more efficiently.

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