Parallel programming of Golang functions in distributed systems

In distributed systems, Go functions implement parallel programming through Goroutine and Channel, significantly improving system performance. Goroutine is a lightweight thread started by the go keyword and can be executed concurrently on different CPU cores. Channel is a pipeline for communication between Goroutines, created using the make function. In practical cases, the concurrent crawler example shows how to use Goroutine and Channel for parallel crawling. Parallel programming benefits include improved performance, scalability, and reduced resource usage, but there are caveats such as synchronization issues, race conditions, and deadlocks.

Parallel programming of Go functions in distributed systems

In distributed systems, parallel programming can significantly improve the system performance. The built-in concurrency features of the Go language allow developers to easily write parallel code. This article will explore how to use Go functions for parallel programming and provide practical cases as a reference.

Goroutine

Goroutine is a lightweight thread in the Go language. It can be executed concurrently on different CPU cores without creating separate processes. Goroutine is started by go keyword as follows:

package main

func main() {
    go func() {
        // 并行执行的任务
    }()
}

Channel

Channel is used in Go language for communication between Goroutines of pipelines. It allows Goroutines to safely pass data across different threads, enabling parallel processing. Channel is created using the make function, as shown below:

ch := make(chan int)

Practical case: Concurrent crawler

In order to better understand the parallel programming of Go functions , we create a simple example of a concurrent crawler:

package main

import (
    "fmt"
    "net/http"
)

func main() {
    urls := []string{
        "https://example.com",
        "https://example2.com",
        "https://example3.com",
    }

    ch := make(chan string)

    // 创建 Goroutine 进行并行爬取
    for _, url := range urls {
        go crawl(url, ch)
    }

    // 从 Channel 中接收爬取结果
    for i := 0; i < len(urls); i++ {
        fmt.Println(<-ch)
    }
}

func crawl(url string, ch chan string) {
    resp, err := http.Get(url)
    if err != nil {
        return
    }
    defer resp.Body.Close()

    ch <- resp.Status
}

Advantages

Using Go functions for parallel programming has the following advantages:

• Improvement Performance
• Improve scalability
• Reduce resource usage

Notes

Writing parallelism in a distributed system When coding, you need to pay attention to the following:

• Synchronization issues
• Competition conditions
• Deadlock

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