How to build fast data visualization applications using Go language and Vue.js

In today's era of information explosion, data has become the core resource of enterprise and organizational management, and visual processing of data has become a very important skill. Go language and Vue.js are both one of the most popular programming languages ​​and frameworks. The combination of the two can quickly build efficient data visualization applications.

This article will introduce how to use Go language and Vue.js to build fast data visualization applications, including how to establish data sources, how to use Go language to manage data on the backend, and how to use Vue.js to build a data visualization front-end. Hopefully this guide will help readers master data visualization skills.

Step One: Establish a Data Source

Before building any data visualization application, you must first establish a reliable data source. This is a critical step to ensure your visualization application is correct, fast and reliable. The following are some common types of data sources:

1. Data comes from database

For many applications, the database is a very common data source. Most databases can establish connections using the Go language. For example, for MySQL database, developers can use github.com/go-sql-driver/mysql to connect to MySQL:

import (
  "database/sql"
  _ "github.com/go-sql-driver/mysql"
)

db, err := sql.Open("mysql", "user:password@/dbname")

2. Restful API

Restful API is a modern web application common data source method. If you want to connect to a data source from Restful API, you can use the http standard library of Go language to connect:

resp, err := http.Get("https://api.example.com/data")
if err != nil {
  // handle error
}
defer resp.Body.Close()
body, err := ioutil.ReadAll(resp.Body)
if err != nil {
  // handle error
}

Step 2: Use Go language to manage data on the backend

Before building a data visualization application, you first need to figure out how to manage the data. The Go language provides very powerful data management tools such as structures, maps, slices, etc. We can use these tools to manage data.

The following is an example of using Go language to manage data:

type Data struct{
  City string
  Population int
}

dataList:= []Data{{"New York", 8623000}, {"Los Angeles", 3991000}}

// 添加数据
dataList = append(dataList, Data{"Houston", 2300000})

// 删除数据
dataList = append(dataList[:2], dataList[3:]...)

// 修改数据
dataList[0].Population = 8800000

The above code creates a Data structure representing the city and population. Then we added several city data to a slice named dataList and demonstrated how to add, modify and delete data.

Step 3: Use Vue.js to build the data visualization front-end

Vue.js is an extremely lightweight JavaScript framework developed by Evan You. Vue.js has a very efficient two-way data binding mechanism, which allows developers to implement more functions with less code. The following is an example of implementing a histogram:

1. Add the following code in the HTML file

<div id="app">
  <canvas :id="chartId"></canvas>
</div>

2. Add the following code in the js file

new Chart(document.getElementById(this.chartId), {
    type: 'bar',
    data: {
        labels: chartData.labels,
        datasets: [{
            label: this.title,
            data: chartData.values,
            backgroundColor: 'rgb(0, 99, 132)',
        }]
    },
    options: {
        legend: { display: false },
        title: { display: true, text: this.title }
    }
})

The above code uses Vue.js to create a component named Chart, which uses the Chart.js library to draw histograms. We can use the <div id="app"> tag in the HTML file to specify the mounting location of Vue.js.

Conclusion

In this article, we introduced how to use Go language and Vue.js to build fast data visualization applications. We first introduced how to establish a reliable data source, then explained how to use the Go language to manage data, and finally demonstrated the use of Vue.js to realize the drawing of a histogram. Hopefully this guide will help readers master data visualization skills.

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