Implementing distributed task scheduling using Golang's web framework Echo framework

With the development of the Internet and the advancement of information technology, the era of big data has arrived, and fields such as data analysis and machine learning have also been widely used. In these fields, task scheduling is an inevitable problem. How to achieve efficient task scheduling is crucial to improving efficiency. In this article, we will introduce how to use Golang's web framework Echo framework to implement distributed task scheduling.

1. Introduction to the Echo Framework

Echo is a high-performance, scalable, lightweight Go Web framework. It is based on the HTTP standard library and supports middleware, routing, simplified HTTP request and response processing and other functions. Echo has been greatly improved in performance and can easily handle high-concurrency scenarios. Echo is also very simple to install and use, and you can get started quickly.

2. Introduction to distributed task scheduling

The distributed task scheduling system is to divide a large task into several small tasks, and execute these small tasks on different nodes, and finally integrate them As a result, distributed execution of large tasks is achieved. Distributed task scheduling systems can improve task execution efficiency and optimize system resource utilization and other benefits.

A distributed task scheduling system generally includes three basic components: master, worker and memory. Master is responsible for managing workers and assigning tasks. Worker is responsible for executing tasks. The memory records task status, logs and other information and provides data storage services.

3. Use the Echo framework to implement distributed task scheduling

1. Install the Echo framework

Before using the Echo framework, you need to install the Echo framework first. You can use the go get command to install:

go get -u github.com/labstack/echo/v4

2. Create the main task scheduling program

In the main task scheduling program, you need to implement the following functions:

(1) Task adding interface

(2) Task deleting interface

(3) Task list interface

(4) Task execution interface

The following is A simplified version of the task scheduling main program:

package main

import (
    "github.com/labstack/echo/v4"
    "net/http"
)

type Task struct {
    Id      int
    Command string
}

var tasks []Task

func AddTask(c echo.Context) error {
    var task Task
    c.Bind(&task)
    task.Id = len(tasks) + 1
    tasks = append(tasks, task)
    return c.JSON(http.StatusOK, task)
}

func DeleteTask(c echo.Context) error {
    id := c.Param("id")
    for i, task := range tasks {
        if strconv.Itoa(task.Id) == id {
            tasks = append(tasks[:i], tasks[i+1:]...)
            return c.String(http.StatusOK, "Task has been deleted")
        }
    }
    return c.String(http.StatusNotFound, "Task not found")
}

func ListTasks(c echo.Context) error {
    return c.JSON(http.StatusOK, tasks)
}

func RunTask(c echo.Context) error {
    id := c.Param("id")
    for _, task := range tasks {
        if strconv.Itoa(task.Id) == id {
            exec.Command(task.Command).Start()
            return c.String(http.StatusOK, "Task has been started")
        }
    }
    return c.String(http.StatusNotFound, "Task not found")
}

func main() {
    e := echo.New()
    e.POST("/tasks", AddTask)
    e.DELETE("/tasks/:id", DeleteTask)
    e.GET("/tasks", ListTasks)
    e.POST("/tasks/:id/run", RunTask)
    e.Logger.Fatal(e.Start(":8080"))
}

3. Start the task scheduling main program

Use the go command to start the task scheduling main program:

go run main.go

4. Implementing the task executor

The task executor is a program that runs on the worker and is used to execute tasks. The task execution program needs to implement the following functions:

(1) Register worker

with Master (2) Receive task

(3) Execute task

( 4) Report task execution results

The following is a simplified version of the task execution program:

package main

import (
    "fmt"
    "github.com/labstack/echo/v4"
    "net/http"
    "strconv"
    "time"
)

type TaskResult struct {
    Id        int
    StartTime time.Time
    EndTime   time.Time
    Result    string
}

var taskResults []TaskResult

func AddWorker(c echo.Context) error {
    return c.String(http.StatusOK, "Worker registered")
}

func ReceiveTask(c echo.Context) error {
    id := c.Param("id")
    for _, task := range tasks {
        if strconv.Itoa(task.Id) == id {
            taskResult := TaskResult{
                Id:        task.Id,
                StartTime: time.Now(),
            }
            //Execute task here
            taskResult.Result = "Task finished"
            taskResult.EndTime = time.Now()
            taskResults = append(taskResults, taskResult)
            return c.String(http.StatusOK, "Task has been finished")
        }
    }
    return c.String(http.StatusNotFound, "Task not found")
}

func ReportTaskResult(c echo.Context) error {
    var taskResult TaskResult
    c.Bind(&taskResult)
    for i, tr := range taskResults {
        if tr.Id == taskResult.Id {
            taskResults[i] = taskResult
            return c.String(http.StatusOK, "Task result has been reported")
        }
    }
    return c.String(http.StatusNotFound, "Task result not found")
}

func main() {
    e := echo.New()
    e.POST("/workers", AddWorker)
    e.POST("/tasks/:id", ReceiveTask)
    e.POST("/results", ReportTaskResult)
    e.Logger.Fatal(e.Start(":8081"))
}

5. Start the task execution program

Use the go command to start the task Execution program:

go run worker.go

6. Test

Add a task in the main program and execute it through the run interface. After running, the task will be assigned to the worker node and executed on the worker.

7. Summary

Using the Echo framework, you can implement a simple distributed task scheduling system, extend its functionality, and implement a larger task scheduling system. The Echo framework has the advantages of high performance, scalability, lightweight, etc., and can handle high concurrency scenarios. In actual projects, issues such as data consistency, task retry mechanism, scalability, etc. need to be considered, and appropriate performance optimization should be performed.

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