Large-scale task processing: Concurrency optimization method using Go WaitGroup

Large-scale task processing: Concurrency optimization method using Go WaitGroup

Overview:
In modern software development, the concurrency of task processing is to improve system performance and responsiveness are key. However, when faced with large-scale task processing, traditional concurrent processing methods may lead to resource waste and performance degradation. This article will introduce how to use WaitGroup in Go language to optimize concurrent processing of large-scale tasks.

1. Challenges of concurrent processing
When a large number of tasks need to be processed at the same time, a common processing method is to use goroutine and channel. Each task will be packaged into a goroutine and executed in a separate thread. This can make full use of CPU resources, switch between different tasks, and improve concurrency.

However, when the workload is very large, simply creating a large number of goroutines may lead to excessive consumption of system resources and performance degradation. At the same time, excessive competition and switching will also increase the overall overhead.

2. Introduction to WaitGroup
WaitGroup in Go language is a synchronization primitive used to wait for multiple concurrent operations to complete. It can be used to ensure that all goroutine executions are completed before continuing to perform other operations.

The basic usage is as follows:

1. Create a WaitGroup object: var wg sync.WaitGroup
2. Increase the count: wg.Add(1)
3. Execute goroutine: go func() { // Execute task wg.Done() // Task completed, reduce count}()
4. Wait for all tasks to complete: wg.Wait()

3. Methods for optimizing large-scale task processing
By combining WaitGroup and limiting the number of concurrencies, we can optimize the concurrent processing of large-scale tasks. The following are the specific steps:

1. Group tasks: Divide large-scale tasks into multiple smaller task groups. For example, divide 1000 tasks into 10 groups, each group contains 100 tasks.
2. Create WaitGroup: Create a WaitGroup object for each task group.
3. Set concurrency limit: In order to avoid excessive consumption of system resources, you can set a concurrency limit, such as only executing 10 task groups at the same time.
4. Processing task groups: For each task group, increment the WaitGroup count, execute each task in the task group, and decrement the count when the task completes. This ensures that the main thread waits until the task group completes execution.
5. Control the number of concurrency: During the processing of task groups, through appropriate control, ensure that the number of task groups executed at the same time does not exceed the set concurrency limit.
6. Wait for the task group to complete: After all task groups are processed, use the Wait() method of WaitGroup to wait for all task groups to be executed.

The following is a code example that applies the above method:

package main

import (
    "sync"
    "fmt"
)

func main() {
    taskGroups := [][]int{ // 假设有10个任务组
        {1, 2, 3, 4, 5},
        {6, 7, 8, 9, 10},
        //...
        {46, 47, 48, 49, 50},
    }

    concurrencyLimit := 5 // 并发限制为5

    var wg sync.WaitGroup

    for _, taskGroup := range taskGroups {
        // 增加计数
        wg.Add(1)

        go func(tasks []int) {
            // 任务组处理
            defer wg.Done() // 任务组完成时减少计数

            for _, task := range tasks {
                // 执行任务
                fmt.Printf("Processing task %d
", task)
            }
        }(taskGroup)

        // 控制并发数
        if wg.Count()%concurrencyLimit == 0 {
            // 等待当前并发数达到限制时，等待所有任务组处理完成
            wg.Wait()
        }
    }

    // 等待所有任务组处理完成
    wg.Wait()
}

Through the above code example, we can see that using WaitGroup and the concurrency limit method can handle large-scale tasks , Make full use of system resources and improve the efficiency of concurrent processing.

Conclusion:
When processing large-scale tasks, reasonable utilization of concurrency is the key to improving system performance and responsiveness. Using the WaitGroup and concurrency limiting methods in the Go language can provide an effective solution to the problem of resource waste and performance degradation during large-scale task processing.

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