The cooperation of coroutines and Channels can realize concurrent programming and improve program performance and throughput. Through Channels, coroutines can communicate and exchange data safely and efficiently. The main steps are as follows: Create a Channel receiving task. Start multiple coroutines to receive and process tasks from Channel. Create a task on the main thread and send it to the Channel. Closing the Channel tells the coroutine that there are no more tasks. Use sync.WaitGroup to wait for all coroutines to complete.
Coroutine is a user-mode lightweight thread, which is related to the process or Compared with threads, the creation and destruction of coroutines consumes fewer resources. Channel is a mechanism in the Go language for communication between goroutines. The combination of coroutines and channels enables concurrent programming, thereby improving program performance and throughput.
Let us use a practical case to demonstrate the cooperation between coroutine and channel. This case will show how to process a set of tasks in parallel.
// 任务定义 type Task struct { ID int Data []int } // 任务处理函数 func processTask(task *Task) { // 耗时处理 fmt.Printf("Processing task %d\n", task.ID) time.Sleep(time.Second * 2) } func main() { // 创建一个 channel 用于接收任务 tasks := make(chan *Task, 10) // 启动 4 个协程来处理任务 for i := 0; i < 4; i++ { go func() { for { // 从 channel 中接收任务 task := <-tasks // 处理任务 processTask(task) } }() } // 创建任务并将其发送到 channel for i := 0; i < 10; i++ { task := &Task{ ID: i, Data: []int{i, i + 1, i + 2}, } tasks <- task } // 关闭 channel 告知协程没有更多任务 close(tasks) // 等待所有协程完成 var wg sync.WaitGroup wg.Add(4) for i := 0; i < 4; i++ { go func() { // 协程退出时通知WaitGroup wg.Done() }() } wg.Wait() }
Code running process:
tasks
for receiving tasks. sync.WaitGroup
to wait for all coroutines to complete. The combination of coroutines and channels can achieve concurrent programming, thereby improving program performance and throughput. By using channels, coroutines can communicate and exchange data safely and efficiently. This is useful for handling large amounts of tasks or scenarios that require parallel processing.
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