Data processing pipeline: High concurrency practice of Go WaitGroup

Data processing pipeline: High concurrency practice of Go WaitGroup

Introduction:
In today's era of data explosion, processing large-scale data has become the key to many systems need. In order to improve efficiency and reduce response time, we need to use high concurrency technology to process this data. As an efficient language with excellent concurrency performance, Go language has become the first choice of many developers. This article will introduce how to use WaitGroup in the Go language to implement a highly concurrent data processing pipeline, and give specific code examples.

1. What is a data processing pipeline?
The data processing pipeline is a way to process data concurrently. It decomposes the data processing process into multiple steps, and each step can be executed independently and concurrently. In this way, the performance of multi-core CPUs can be fully utilized and the efficiency of data processing can be improved.

2. WaitGroup in Go language
WaitGroup is a concurrency primitive in Go language. It provides a mechanism to coordinate the parallel execution of multiple goroutines. WaitGroup has three main methods: Add, Done and Wait. The Add method is used to increase the value of the counter, the Done method is used to decrement the value of the counter, and the Wait method is used to block the current goroutine until the counter returns to zero.

3. Use WaitGroup to implement data processing pipeline
The following is a sample code that uses WaitGroup to implement data processing pipeline:

package main

import (
    "fmt"
    "sync"
)

func main() {
    // 创建WaitGroup
    var wg sync.WaitGroup

    // 设置数据处理流水线的阶段数
    phases := 3

    // 创建数据通道
    dataCh := make(chan int)

    // 启动数据处理流水线
    wg.Add(phases)
    go produce(dataCh, &wg)
    go process(dataCh, &wg)
    go consume(dataCh, &wg)

    // 等待数据处理流水线的完成
    wg.Wait()
}

// 数据生产阶段
func produce(dataCh chan<- int, wg *sync.WaitGroup) {
    defer wg.Done()

    for i := 1; i <= 10; i++ {
        dataCh <- i
    }

    close(dataCh)
}

// 数据处理阶段
func process(dataCh <-chan int, wg *sync.WaitGroup) {
    defer wg.Done()

    for data := range dataCh {
        // 模拟数据处理过程
        result := data * 2

        fmt.Println(result)
    }
}

// 数据消费阶段
func consume(dataCh <-chan int, wg *sync.WaitGroup) {
    defer wg.Done()

    for range dataCh {
        // 模拟数据消费过程
        // ...
    }
}

In the above code, a WaitGroup is first created and set The number of stages of the data pipeline that need to be processed. Then, a data channel dataCh is created for transferring data between various stages. Then, three goroutines are started to represent the production, processing and consumption stages of data. At the end of each phase, the WaitGroup's counter value is decremented by calling the Done method. Finally, the Wait method is called to block the main goroutine until all stages are completed.

4. Summary
By using WaitGroup in the Go language, we can easily implement a high-concurrency data processing pipeline. By decomposing the data processing process into multiple stages and using WaitGroup to coordinate the execution of each stage, we can make full use of the performance of multi-core CPUs and improve the efficiency of data processing. I hope the content of this article will be helpful to developers who want to understand and apply concurrent programming.

Reference documentation:

• Go language official documentation: https://golang.org/pkg/sync/
• Go by Example: https://gobyexample .com/waitgroups

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