search
HomeBackend DevelopmentGolangLarge-scale task processing: Concurrency optimization method using Go WaitGroup

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

Sep 27, 2023 pm 02:19 PM
go languageConcurrency optimizationLarge scale task processing

大规模任务处理:使用Go WaitGroup的并发优化方法

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.

The above is the detailed content of Large-scale task processing: Concurrency optimization method using Go WaitGroup. For more information, please follow other related articles on the PHP Chinese website!

Statement
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn
Golang and Python: Understanding the DifferencesGolang and Python: Understanding the DifferencesApr 18, 2025 am 12:21 AM

The main differences between Golang and Python are concurrency models, type systems, performance and execution speed. 1. Golang uses the CSP model, which is suitable for high concurrent tasks; Python relies on multi-threading and GIL, which is suitable for I/O-intensive tasks. 2. Golang is a static type, and Python is a dynamic type. 3. Golang compiled language execution speed is fast, and Python interpreted language development is fast.

Golang vs. C  : Assessing the Speed DifferenceGolang vs. C : Assessing the Speed DifferenceApr 18, 2025 am 12:20 AM

Golang is usually slower than C, but Golang has more advantages in concurrent programming and development efficiency: 1) Golang's garbage collection and concurrency model makes it perform well in high concurrency scenarios; 2) C obtains higher performance through manual memory management and hardware optimization, but has higher development complexity.

Golang: A Key Language for Cloud Computing and DevOpsGolang: A Key Language for Cloud Computing and DevOpsApr 18, 2025 am 12:18 AM

Golang is widely used in cloud computing and DevOps, and its advantages lie in simplicity, efficiency and concurrent programming capabilities. 1) In cloud computing, Golang efficiently handles concurrent requests through goroutine and channel mechanisms. 2) In DevOps, Golang's fast compilation and cross-platform features make it the first choice for automation tools.

Golang and C  : Understanding Execution EfficiencyGolang and C : Understanding Execution EfficiencyApr 18, 2025 am 12:16 AM

Golang and C each have their own advantages in performance efficiency. 1) Golang improves efficiency through goroutine and garbage collection, but may introduce pause time. 2) C realizes high performance through manual memory management and optimization, but developers need to deal with memory leaks and other issues. When choosing, you need to consider project requirements and team technology stack.

Golang vs. Python: Concurrency and MultithreadingGolang vs. Python: Concurrency and MultithreadingApr 17, 2025 am 12:20 AM

Golang is more suitable for high concurrency tasks, while Python has more advantages in flexibility. 1.Golang efficiently handles concurrency through goroutine and channel. 2. Python relies on threading and asyncio, which is affected by GIL, but provides multiple concurrency methods. The choice should be based on specific needs.

Golang and C  : The Trade-offs in PerformanceGolang and C : The Trade-offs in PerformanceApr 17, 2025 am 12:18 AM

The performance differences between Golang and C are mainly reflected in memory management, compilation optimization and runtime efficiency. 1) Golang's garbage collection mechanism is convenient but may affect performance, 2) C's manual memory management and compiler optimization are more efficient in recursive computing.

Golang vs. Python: Applications and Use CasesGolang vs. Python: Applications and Use CasesApr 17, 2025 am 12:17 AM

ChooseGolangforhighperformanceandconcurrency,idealforbackendservicesandnetworkprogramming;selectPythonforrapiddevelopment,datascience,andmachinelearningduetoitsversatilityandextensivelibraries.

Golang vs. Python: Key Differences and SimilaritiesGolang vs. Python: Key Differences and SimilaritiesApr 17, 2025 am 12:15 AM

Golang and Python each have their own advantages: Golang is suitable for high performance and concurrent programming, while Python is suitable for data science and web development. Golang is known for its concurrency model and efficient performance, while Python is known for its concise syntax and rich library ecosystem.

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Article

R.E.P.O. Energy Crystals Explained and What They Do (Yellow Crystal)
1 months agoBy尊渡假赌尊渡假赌尊渡假赌
R.E.P.O. Best Graphic Settings
1 months agoBy尊渡假赌尊渡假赌尊渡假赌
Will R.E.P.O. Have Crossplay?
1 months agoBy尊渡假赌尊渡假赌尊渡假赌

Hot Tools

Safe Exam Browser

Safe Exam Browser

Safe Exam Browser is a secure browser environment for taking online exams securely. This software turns any computer into a secure workstation. It controls access to any utility and prevents students from using unauthorized resources.

WebStorm Mac version

WebStorm Mac version

Useful JavaScript development tools

SAP NetWeaver Server Adapter for Eclipse

SAP NetWeaver Server Adapter for Eclipse

Integrate Eclipse with SAP NetWeaver application server.

MinGW - Minimalist GNU for Windows

MinGW - Minimalist GNU for Windows

This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.

Atom editor mac version download

Atom editor mac version download

The most popular open source editor