MySQL database and Go language: How to perform internal segmentation processing of data?

With the continuous development of Internet applications, databases have become the core component of various Internet applications. As one of the most popular relational databases today, MySQL is widely used in various Internet applications. For large amounts of data processing, it is often necessary to divide the data into internal segments to improve the operating efficiency of the program and reduce the pressure on the database. This article will introduce how to process internal segmentation of data in MySQL database and Go language.

1. Partitioning of MySQL database

Partitioning of MySQL database is a method of splitting a large table into multiple small tables. Each small table becomes a partition, and each partition stores a different range. The data. The partitioning of the MySQL database improves the query efficiency of the database and also reduces the burden on the database. System performance can be improved by horizontally expanding the database server, and the partition range can also be reduced to ensure data security and improve query efficiency.

MySQL database supports multiple partitioning methods:

1. Hash partitioning: The data is partitioned through the hash algorithm, ensuring that the data stored in each partition is basically the same.
2. Range partitioning: Partition based on the range or value range of the data.
3. Column partitioning: Partition the data according to the value of the column.
4. Column list partitioning: The values ​​of multiple columns jointly partition the data.
5. Game partition: Distribute the table equally to each server according to the primary key range of each partition, so that the amount of data on each server is roughly the same.

2. Grouping of Go language

In Go language, data grouping can be achieved through slice and map. Among them, slice is an ordered collection type that can be read and written based on the index of the data; map is an unordered key-value pair collection type that can be read and written based on the key.

1. slice grouping

Slice grouping needs to be traversed using a for loop, grouped by finding the remainder of the number of each element, and then saving the grouped data in a new slice. The specific implementation is as follows:

func sliceGrouping(n int, sliceData []int) [][]int {
    grouping := make([][]int, n)   // 新建n个[]int切片，用于存放分组后的数据
    for _, v := range sliceData {  // 遍历切片数据
        index := v % n             // 对每个元素编号求余数
        grouping[index] = append(grouping[index], v)  // 将元素添加到对应切片中
    }
    return grouping
}

2. map grouping

Map grouping also needs to be traversed through a for loop, but since map is a key-value pair collection type, the elements can be directly Add to the corresponding map. The specific implementation is as follows:

func mapGrouping(n int, mapData map[string]int) map[string][]int {
    grouping := make(map[string][]int)   // 新建一个map，用于存放分组后的数据
    for k, v := range mapData {          // 遍历map数据
        index := v % n                   // 对每个元素编号求余数
        grouping[string(index)] = append(grouping[string(index)], v)  // 将元素添加到对应map中
    }
    return grouping
}

3. Internal segmentation processing of data

In practical applications, data often needs to be divided and processed to improve the operating efficiency of the program. For example, in a large table containing 10,000 records, when performing query operations, the data can be divided into 10 partitions, each containing 1,000 records. This can effectively improve query efficiency and reduce the pressure on the database. In the MySQL database, this function can be achieved through partition operations; in the Go language, data can be grouped through slice and map.

The following is a comprehensive example. First, create a table named test in the MySQL database, then divide the table into three partitions through hash partitioning, and finally query and process the partitioned data in the Go language.

1. Create test table and partition:

CREATE TABLE test (
    id INT NOT NULL AUTO_INCREMENT,
    name VARCHAR(50) NOT NULL,
    age INT NOT NULL,
    PRIMARY KEY (id)
)
PARTITION BY HASH (id) PARTITIONS 3;  -- 将表分为3个分区

2. Query partition data and process in Go language:

func main() {
    db, err := sql.Open("mysql", "root:123456@tcp(127.0.0.1:3306)/test")  // 连接数据库
    if err != nil {
        panic(err.Error())
    }
    defer db.Close()

    rows, err := db.Query("SELECT * FROM test")  // 查询数据
    if err != nil {
        panic(err.Error())
    }
    defer rows.Close()

    data := make(map[string][]int)  // 新建一个map，用于存放分区数据
    for rows.Next() {    // 遍历查询结果
        var id, age int
        var name string
        err = rows.Scan(&id, &name, &age)
        if err != nil {
            panic(err.Error())
        }
        index := id % 3    // 对每条记录的id编号求余数
        data[string(index)] = append(data[string(index)], id)   // 将记录添加到对应的map中
    }

    fmt.Println(data)   // 输出分区数据
}

above In the example, we first created a test table and divided it into three partitions. Then we queried all the records in the test table in Go language and divided the records into three partitions based on the remainder of the id number. Finally, the partition data is output. Through the above examples, we can see that it is very convenient to perform data segmentation processing in the MySQL database and Go language.

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