Golang pipeline's support mechanism for concurrent function communication

Pipeline is a special type in Golang, used for safe and efficient communication between Goroutines, especially suitable for parallel processing and data exchange. Create a pipe using make(chan T), where T is the pass data type; data is sent and received via the

Golang Pipeline: A mechanism for parallel processing of function communication

Pipeline is a special type in Golang that allows Goroutine ( Communicate safely and efficiently between concurrently executing functions). This is useful in parallel processing and data exchange scenarios.

Pipe syntax

Create a pipeline using the make(chan T) syntax, where T is the type of data passed in the pipe. For example:

myChannel := make(chan int)

Send data to the pipe

Use the operator to receive data from the pipe. For example:

data := <-myChannel

Receive data from the pipe

Use the operator to send data to the pipe. For example:

myChannel <- 42

Practical Example: Parallel Sum

The following example demonstrates how to use a pipeline to calculate the sum of slices in parallel:

package main

import "fmt"

func main() {
    numbers := []int{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
    numWorkers := 2

    // 创建管道
    results := make(chan int)

    // 创建 Goroutine 并在管道上发送部分和
    for i := 0; i < numWorkers; i++ {
        go func(start, end int) {
            partialSum := 0
            for i := start; i <= end; i++ {
                partialSum += numbers[i]
            }
            results <- partialSum
        }(i * len(numbers) / numWorkers, (i+1) * len(numbers) / numWorkers - 1)
    }

    // 读取管道并计算总和
    totalSum := 0
    for i := 0; i < numWorkers; i++ {
        totalSum += <-results
    }

    fmt.Println("Total sum:", totalSum)
}

In this example, results Pipes are used to pass partial sums between individual Goroutines and the main Goroutine. The main Goroutine reads the results from the pipe and calculates the final sum. This implementation effectively decomposes the summation task into parts that are executed in parallel, significantly improving performance.

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