Best practices for golang function pipeline communication

The best practice is: use buffered pipes to avoid coroutine blocking. Limit pipeline concurrency to prevent deadlock. Close the sender end of the pipe and notify the receiver. Use one-way pipes to prevent unsafe access. Pipe multiple receivers to implement fan-out operations.

Best Practices for Pipeline Communication in Go

Pipelines are used in Go for secure communication between concurrent program components. A kind of channel. Pipes provide a lock-free mechanism that allows coroutines to send and receive values ​​without locking.

Best Practices:

• Use buffered pipes: Buffered pipes allow multiple values ​​to be stored simultaneously, thus Avoid coroutine blocking.

  // 创建一个有缓冲大小为 10 的管道
  bufferedChan := make(chan int, 10)

• Limit pipe concurrency: Using non-buffered pipes or limiting the buffer size can prevent coroutines from over-consuming pipes, leading to deadlocks.

  // 创建一个非缓冲管道
  unbufferedChan := make(chan int)

• Close the sender end of the pipe: After the sender has finished sending values ​​to the pipe, the sender end of the pipe should be closed to notify the receiver.

  close(chan)

• Use one-way pipes: One-way pipes can only be used to send or receive values, which prevents unsafe concurrent access.

  input := make(chan<- int)  // 只发送管道
  output := make(<-chan int)  // 只接收管道

• Use pipes for multiple receivers: Pipes can be received by multiple receivers at the same time, which can achieve fan-out operations.

  // 从 c1 和 c2 合并数据，分别使用两个协程接收数据
  func merge(c1, c2 <-chan int) <-chan int {
    out := make(chan int)
    go func() {
        for v := range c1 {
            out <- v
        }
        close(out)
    }()
    go func() {
        for v := range c2 {
            out <- v
        }
        close(out)
    }()
    return out
  }

Practical case:

In a scenario that requires processing a large amount of data, pipelines can be used to process data in parallel.

// 并行处理数据
func processData(data []int) []int {
    result := make(chan int)  // 用于收集结果

    // 创建多个协程并行处理数据
    for _, num := range data {
        go func(num int) {
            result <- processSingle(num)  // 单个协程处理数据
        }(num)
    }

    // 从管道中收集结果
    processedData := make([]int, 0, len(data))
    for i := 0; i < len(data); i++ {
        processedData = append(processedData, <-result)
    }
    return processedData
}

By using pipelines, the processing tasks of large amounts of data can be distributed to multiple coroutines, thereby improving the efficiency of the program.

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