Application of Golang function concurrent programming in large projects

In large Go projects, concurrent programming can improve performance and scalability. 1. Concurrency original value: goroutine is a lightweight thread, and channel is a buffer for safely transferring data. 2. Concurrency mode: Pipeline concurrency is used in the producer-consumer model; the work pool maintains a fixed number of goroutines, waiting to execute work. 3. Practical case: The e-commerce back-end service uses pipelines to process orders concurrently, and uses work pools to optimize database connections.

Application of Go functional concurrent programming in large-scale projects

Overview

In large-scale Go projects, making full use of concurrent programming can significantly to improve performance and scalability. Go's built-in concurrency mechanism provides powerful tools for writing efficient parallel code.

Concurrency primitives

goroutines are lightweight threads in Go that can execute code without locking the entire process. To create a goroutine, use the go keyword:

go func() {
    // 并发执行的代码
}

channel is a buffer used to safely pass data between goroutines. Channels have types to ensure data type safety:

var dataChannel chan int
func main() {
    dataChannel = make(chan int)
    go sendData(dataChannel)
    receivedData := <-dataChannel
    fmt.Println("Received data:", receivedData)
}

func sendData(ch chan int) {
    ch <- 42 // 发送数据
}

Concurrency Mode

Pipeline Concurrency Use pipes to pass data from one goroutine to another, thereby inside the pipe Implement the producer and consumer model:

func pipeExample() {
    numJobs := 1000

    input := make(chan int)
    processed := make(chan int)

    // 启动一个 goroutine 表示消费者
    go func() {
        for {
            select {
            case job := <-input:
                processedData := process(job)
                processed <- processedData
            }
        }
    }()

    // 启动多个 goroutine 表示生产者
    for i := 0; i < numJobs; i++ {
        go func(i int) {
            input <- i
        }(i)
    }

    close(input) // 当所有工作都完成时关闭输入通道

    // 等待所有工作处理完成
    for i := 0; i < numJobs; i++ {
        _ = <-processed
    }
}

Work pool Maintain a fixed number of goroutines, these goroutines are waiting for work to be executed:

func workerPoolExample() {
    jobs := make(chan int)
    results := make(chan int)

    // 启动一个 goroutine 表示工作池中的每一个 worker
    for w := 1; w <= numWorkers; w++ {
        go worker(jobs, results)
    }

    for j := 0; j < numJobs; j++ {
        jobs <- j
    }
    close(jobs)

    for a := 1; a <= numJobs; a++ {
        _ = <-results // 等待接收所有结果
    }
}

func worker(jobs <-chan int, results chan<- int) {
    for j := range jobs {
        result := process(j)
        results <- result
    }
}

Practical case

A large e-commerce website developed a backend service using Go to process online orders. The service needs to process hundreds of incoming orders in parallel and uses a MySQL database to store order details.

Using pipeline concurrency

The service uses pipeline concurrency to implement the order processing pipeline:

• Get the order's from the REST API Producer goroutine.
• A set of Consumer goroutines Get orders from the pipeline, validate the orders, and store them in the database.

Using Work Pools

The service also uses work pools to optimize database connections:

• The work pool maintains a group of idle databases connect.
• Every time a database connection is needed, the service gets a connection from the worker pool and returns it to the consumer goroutine.
• After use is completed, the consumer goroutine returns the connection to the worker pool.

By combining pipeline concurrency and worker pools, the service is able to efficiently process multiple incoming orders simultaneously and optimize the use of database resources.

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