How to create high-performance MySQL data aggregation charts using Go language

As the amount of data continues to grow, how to aggregate and display data quickly and efficiently has become a challenge faced by data scientists and engineers. As a mature and stable relational database, MySQL has high performance and reliability in storing and processing data. In this article, we will explore how to use the Go language to create high-performance MySQL data aggregation charts.

First of all, you need to understand some basic principles of Go language and MySQL database. Go language is a fast, efficient, concurrent programming-oriented programming language that has many advantages when dealing with concurrent and parallel programming. MySQL is an open source relational database that is widely used and has stable performance.

Next, we will introduce how to use Go language to connect and operate MySQL database, and use aggregate functions to achieve data aggregation and display.

1. Connecting to MySQL database
Using Go language to connect to MySQL database requires the help of a third-party library. Here we use the go-sql-driver/mysql library. You can download it through the following command:

go get -u github.com/go-sql-driver/mysql

To connect to the MySQL database, you need to know the user name, password, host address and other information of the database. , you can connect through the following code:

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

func main() {
db, err := sql.Open("mysql", "user:password@tcp(host:port)/dbname")
if err != nil {

panic(err.Error())

}
defer db.Close()
}

2. Use aggregate functions for data aggregation
For a large amount of data, we usually need to perform aggregation operations, such as sum, Average, maximum, minimum, etc. MySQL provides a variety of aggregate functions, including SUM, AVG, MAX, MIN, COUNT, etc.

The following takes average value as an example to demonstrate how to use Go language to connect to the MySQL database to achieve data aggregation and display.

First, you need to complete the data import, which can be stored in a table in the MySQL database. Suppose we have the following data table:

CREATE TABLE mytable (
id INT PRIMARY KEY AUTO_INCREMENT,
name VARCHAR(50),
value INT
);

Next, we execute the following code to insert 100,000 pieces of data into the table:

func insertData(db *sql.DB) {
for i := 0; i

name := fmt.Sprintf("item%d", i)
value := rand.Intn(100)
_, err := db.Exec("INSERT INTO mytable (name, value) VALUES (?, ?)", name, value)
if err != nil {
  panic(err.Error())
}

}
}

Then, you can use the following code to find the average of all data:

func getAvgValue(db *sql.DB) {
var avgValue float64
err := db.QueryRow("SELECT AVG(value) FROM mytable").Scan(&avgValue)
if err != nil {

panic(err.Error())

}
fmt.Println("The average value is:", avgValue)
}

Through experiments, it can be found that when the amount of data reaches 100,000, the Go language is very efficient in connecting and operating the MySQL database. Querying averages is also very fast.

3. Use charts to display data
Next, we will use the Go language and the web development framework gin to build a web application and display the aggregated data in charts.

First, you need to install gin and related dependency packages:

go get -u github.com/gin-gonic/gin
go get github.com/gin-gonic/contrib/ static
go get -u github.com/go-sql-driver/mysql

Then, you can use the following code to create a web application to display the aggregated data on a histogram:

package main

import (
"database/sql"
"fmt"
"net/http"

"github.com/gin-gonic /contrib/static"
"github.com/gin-gonic/gin"
_ "github.com/go-sql-driver/mysql"
)

var db *sql .DB

func main() {
initDB()
defer db.Close()

// Initialize gin framework
r := gin.Default()

//Set the static file directory
r.Use(static.Serve("/", static.LocalFile("./static", true)))

// Add Route
r.GET("/data", getChartData)

// Listening port
r.Run(":8080")
}

func initDB( ) {
var err error
db, err = sql.Open("mysql", "user:password@tcp(host:port)/dbname")
if err != nil {

panic(err.Error())

}
}

func getChartData(c *gin.Context) {
var data []struct {

Name  string  `json:"name"`
Value float64 `json:"value"`

}

// Query aggregated data
rows, err := db.Query("SELECT name, AVG(value) AS value FROM mytable GROUP BY name")
if err != nil {

panic(err.Error())

}

//Construct data format
for rows.Next() {

var name string
var value float64
err := rows.Scan(&name, &value)
if err != nil {
  panic(err.Error())
}
data = append(data, struct {
  Name  string  `json:"name"`
  Value float64 `json:"value"`
}{Name: name, Value: value})

}

// Return json data
c.JSON(http.StatusOK, data)
}

In the web application, we use the gin framework and static file directory to query the aggregated data in the MySQL database through the getChartData function and return it in json format. In the front-end page, third-party JavaScript chart libraries (such as ECharts, HighCharts, etc.) can be used to easily convert data into chart display.

Conclusion
Through the above introduction, I believe that readers have a deeper understanding of how to use Go language to create high-performance MySQL data aggregation charts. As the amount of data continues to grow, learning to use advanced programming tools and techniques to process data will become an increasingly important skill.

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