Distributed Computing: Using Go WaitGroup to develop a distributed task scheduling system

Distributed computing: Using Go WaitGroup to develop a distributed task scheduling system

Introduction:
In today's computing environment, distributed computing is an efficient The computing method is widely used in large-scale data processing and complex task solving. The distributed task scheduling system is one of the core components of distributed computing and is responsible for scheduling and coordinating the work of each task node. This article will introduce how to use WaitGroup in Go language to implement a simple distributed task scheduling system, and provide specific code examples.

1. Principle of distributed task scheduling system
The distributed task scheduling system mainly consists of the following modules:

1. Task manager: responsible for receiving and managing tasks Submit, divide the task into multiple subtasks, and assign the subtasks to available nodes for execution according to the scheduling policy.
2. Node Manager: Responsible for registering and managing the status of nodes, receiving and executing tasks.
3. Scheduler: Decide when to send tasks to nodes based on task priority, resource status and other information.
4. Communication protocol: used for communication between task manager, node manager and scheduler, transmitting task and node status information.

2. Use Go WaitGroup to implement a distributed task scheduling system
The Go language provides the WaitGroup type, which can effectively manage the execution of a group of goroutines. We can use WaitGroup to implement the task manager and node manager in the distributed task scheduling system.

1. Implementation of task manager
   The task manager is responsible for receiving and managing task submissions and dividing tasks into multiple subtasks. Each subtask is executed through a goroutine.

The specific code examples are as follows:

package main

import (
    "sync"
    "fmt"
)

func worker(id int, wg *sync.WaitGroup) {
    defer wg.Done()

    fmt.Printf("Worker %d started
", id)
    // TODO: 执行任务逻辑
    fmt.Printf("Worker %d finished
", id)
}

func main() {
    var wg sync.WaitGroup
    totalTasks := 10
    for i := 0; i < totalTasks; i++ {
        wg.Add(1)
        go worker(i, &wg)
    }
    wg.Wait()
    fmt.Println("All tasks finished")
}

2. Implementation of node manager
   The node manager is responsible for registering and managing the status of nodes, and receiving and executing tasks. Each node listens to the task queue through a goroutine and executes corresponding tasks.

The specific code examples are as follows:

package main

import (
    "sync"
    "fmt"
)

type Task struct {
    ID int
}

func worker(id int, tasks <-chan Task, wg *sync.WaitGroup) {
    defer wg.Done()

    fmt.Printf("Worker %d started
", id)
    for task := range tasks {
        fmt.Printf("Worker %d processing task %d
", id, task.ID)
        // TODO: 执行任务逻辑
    }
    fmt.Printf("Worker %d finished
", id)
}

func main() {
    var wg sync.WaitGroup
    totalTasks := 10
    totalWorkers := 3
    tasks := make(chan Task, totalTasks)

    for i := 0; i < totalWorkers; i++ {
        wg.Add(1)
        go worker(i, tasks, &wg)
    }

    for i := 0; i < totalTasks; i++ {
        tasks <- Task{ID: i}
    }
    close(tasks)

    wg.Wait()
    fmt.Println("All tasks finished")
}

3. Summary
This article introduces how to use WaitGroup in the Go language to implement a simple distributed task scheduling system. By using WaitGroup, we can effectively manage the execution sequence of a group of goroutines and achieve parallel execution of tasks. Of course, this is just a simple example. The actual distributed task scheduling system also needs to consider more details and complex issues, such as task priority scheduling, node status monitoring, etc. I hope this article can help readers understand distributed computing and use Go language to develop distributed task scheduling systems.

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