Using Golang’s web framework Echo framework to implement distributed task scheduling and monitoring

With the continuous development of the Internet, the application of distributed systems is becoming more and more widespread. Distributed systems have been widely used in enterprise-level application development due to their advantages such as high reliability, high concurrency, and high scalability. Distributed task scheduling and monitoring is a very important issue. The solution to this problem is very critical for the reliable operation of the entire distributed system. Therefore, this article will introduce a solution for implementing distributed task scheduling and monitoring using Golang's Web framework Echo framework. .

1. What is the Echo framework

Echo is a lightweight Go language-based Web framework. Its design goal is to provide a high-performance, easy-to-use Web framework while retaining the efficient execution and powerful capabilities of the Go language. The features of the Echo framework include the following:

• High performance: The performance of the Echo framework is very high, supporting Gzip, automatic API documentation, custom middleware and other features;
• Easy to use : The Echo framework provides a series of simple and easy-to-use APIs, which can easily create routes, render templates, process requests, etc.;
• Powerful scalability: The Echo framework also supports plug-in mechanisms, which can easily modify functions Go for extensions and deep customization.

2. Distributed task scheduling and monitoring solution

In a distributed system, task scheduling and monitoring are essential functions. Properly scheduling tasks and monitoring the status of machines can effectively ensure the reliability of the entire system. Therefore, we need a task scheduling and monitoring solution with high reliability and high concurrency. Here is how to use the Echo framework to implement it.

2.1 Task Scheduling

Task scheduling is a very important part of the distributed system. Different scheduling algorithms will directly affect the stability and performance of the system. In this article, we use the simplest task scheduling algorithm-Polling Scheduling Algorithm. Each worker (worker node) periodically polls the task queue from the master (central node). If there is a task in the task queue, it will take the task out of the queue for execution, otherwise it will continue to wait.

2.1.1 Define task type

In order to implement task scheduling, we need to define the data structure of the task. The task contains at least the following attributes:

• Task ID: a number used to uniquely identify the task;
• Task name: task name, used to identify the type of task;
• Task status: divided into completed (Completed), in progress (Running), not started (Idle) and other states;
• Task description information: Detailed description of the relevant information of the task;
• Task Creation time and update time: Record the task creation time and latest update time respectively.

We can define the following structure to represent tasks:

type Task struct {

ID          int64     `json:"id"`
Name        string    `json:"name"`
Status      string    `json:"status"`
Description string    `json:"description"`
CreatedAt   time.Time `json:"created_at"`
UpdatedAt   time.Time `json:"updated_at"`

}

2.1.2 Define task queue

After defining the task type, we also need to define the task queue. Task queues are usually implemented using queue data structures and follow the first-in-first-out (FIFO) principle to ensure the order of task execution. We can use the queue data structure in Golang's standard library - a doubly linked list (List) to achieve this. The code is as follows:

type TaskQueue struct {

queue *list.List
lock  sync.Mutex

}

func NewTaskQueue() *TaskQueue {

return &TaskQueue{
    queue: list.New(),
}

}

func ( q TaskQueue) Push(task Task) {

q.lock.Lock()
q.queue.PushBack(task)
q.lock.Unlock()

}

func (q TaskQueue) Pop() Task {

q.lock.Lock()
task := q.queue.Front().Value.(*Task)
q.queue.Remove(q.queue.Front())
q.lock.Unlock()
return task

}

2.1.3 Define worker nodes

In a distributed task scheduling system, worker nodes take tasks out of the task queue and execute them. The worker node needs to periodically request tasks from the master node. If there are still unfinished tasks, continue to execute the tasks. Here we define a worker structure to represent the working node:

type Worker struct {

ID          int64
Address     string
ActiveTime  time.Time
IsAvailable bool

}

where ID represents the ID of the working node and Address represents the working node The address of the service, ActiveTime indicates the last active time of the working node, and IsAvailable indicates whether the current working node is available.

2.1.4 Define the Master node

The Master node is the control node of the entire distributed scheduling system. It is responsible for task scheduling and monitoring. The Master needs to maintain a task queue and a list of working nodes, process requests from each working node, and allocate tasks to specific working nodes. The code is as follows:

type Master struct {

TaskQueue  *TaskQueue
Workers    []*Worker
isStop     bool
taskChan   chan *Task
register   chan *Worker
report     chan *Worker
disconnect chan *Worker
lock       sync.Mutex

}

func NewMaster() *Master {

return &Master{
    TaskQueue:  NewTaskQueue(),
    Workers:    make([]*Worker, 0),
    isStop:     false,
    taskChan:   make(chan *Task),
    register:   make(chan *Worker),
    report:     make(chan *Worker),
    disconnect: make(chan *Worker),
}

}

func (m *Master) Run() {

go func() {
    for {
        select {
        case worker := <-m.register:
            m.registerWorker(worker)
        case worker := <-m.report:
            m.updateWorker(worker)
        case worker := <-m.disconnect:
            m.removeWorker(worker)
        case task := <-m.taskChan:
            m.dispatchTask(task)
        default:
            time.Sleep(time.Second * time.Duration(1))
        }

        if m.isStop {
            break
        }
    }
}()

}

2.1.5 Implementing task scheduling algorithm

Task scheduling requires a scheduling algorithm, and the polling scheduling algorithm is used here , distribute tasks evenly to nodes. This algorithm is simple to implement, but there may be "large tasks" in the task queue, causing the execution time of some node tasks to be too long, resulting in a decrease in the performance of the entire system. Therefore, we need to implement a dynamic load balancing algorithm to ensure the stability and reliability of the system. A load balancing algorithm based on resource utilization can be used here. For details, please refer to "Research Review of Load Balancing Algorithms".

2.2 Task Monitoring

Task monitoring is also a very important part of the distributed system. We need to obtain the status of working nodes, task execution and other information in real time to ensure the reliability of the entire system. In order to achieve task monitoring, we can use the WebSocket feature of the Echo framework to push monitoring data to the front-end for display in real time.

2.2.1 Define WebSocket routing

为了实现任务监控，我们需要定义WebSocket路由。WebSocket是一种基于TCP协议的全双工通信协议，允许服务器主动向客户端推送数据，实现实时通信。我们可以通过Echo框架提供的WebSocket API来实现WebSocket通信，代码如下所示：

func (s *Server) WebSocketHandler(c echo.Context) error {

ws, err := upgrader.Upgrade(c.Response(), c.Request(), nil)
if err != nil {
    return err
}

client := NewClient(ws)

s.clients[client] = true

go client.ReadPump()
go client.WritePump()

return nil

}

其中，upgrader是Echo框架中提供的WebSocket升级器，用于将HTTP连接升级为WebSocket连接。NewClient是一个封装了WebSocket连接的客户端结构体。这样就可以轻松地实现从服务器向客户端推送实时监控数据了。

2.2.2 实现数据推送逻辑

推送数据的逻辑比较简单，我们只需要将需要推送的数据通过WebSocket发送到客户端即可。推送的数据可以是工作节点的一些统计信息，如：CPU利用率、内存利用率等，也可以是任务的执行状态、进度等信息。代码如下：

func (c *Client) WritePump() {

ticker := time.NewTicker(pingPeriod)
defer func() {
    ticker.Stop()
    c.ws.Close()
}()

for {
    select {
    case message, ok := <-c.send:
        c.ws.SetWriteDeadline(time.Now().Add(writeWait))
        if !ok {
            c.write(websocket.CloseMessage, []byte{})
            return
        }

        w, err := c.ws.NextWriter(websocket.TextMessage)
        if err != nil {
            return
        }
        w.Write(message)

        n := len(c.send)
        for i := 0; i < n; i++ {
            w.Write(newline)
            w.Write(<-c.send)
        }

        if err := w.Close(); err != nil {
            return
        }
    }
}

}

3. 总结

本文主要介绍了使用Golang的Web框架Echo框架实现分布式任务调度与监控的方案。通过使用Echo框架，我们可以非常方便地创建路由、处理请求等，实现了分布式任务调度和监控的功能。本文只是简单地介绍了任务调度和监控的实现方式，实际应用中还需要考虑更多的问题，如：任务失败重试机制、工作节点故障处理策略等。

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