MySQL database and Go language: how to segment data?

MySQL database and Go language: How to segment data?

As the amount of data increases, the read and write performance problems of the database become more and more serious. Therefore, database data segmentation has become a common solution to solve the bottleneck of data read and write performance. This article will introduce how to use Go language and MySQL database for data segmentation.

1. Data segmentation scheme of MySQL database

In the MySQL database, the commonly used data segmentation schemes mainly include horizontal segmentation and vertical segmentation.

1. Horizontal segmentation

Horizontal segmentation is to split the data of a single table into multiple smaller tables, and different data is distributed in different tables. This method is suitable for data reading and writing performance bottlenecks in large tables.

Horizontal segmentation can be based on the range of data rows (Range), data hashing (Hash), segmentation according to specific rules (List), and segmentation based on the primary key distribution of the table (Round-Robin ) etc. For example, using the Range sharding method, you can divide a table according to row intervals, so that data in different ranges can be dispersed to different data nodes, reducing the load pressure on a single node; using the Hash sharding method, you can use a fixed Hash The function value cuts the data, and each Hash value corresponds to a set of data, which can also improve the dispersion of the data.

However, the cost of horizontal sharding is high, it requires modification of the application, and it also increases the issues of data security and consistency. When performing data sharding, distributed transactions and fault tolerance must also be considered. And other issues.

2. Vertical Segmentation

Vertical Segmentation is to split a large table into multiple different tables based on its business attributes, data access frequency and other characteristics. Each table has different data fields and data structures, which are usually divided into two methods: vertical partitioning and vertical sharding.

Vertical partitioning means cutting data based on business or data attributes. For example, redundant fields that are not frequently used in the user table are split into a separate table to reduce IO operations and improve query performance.

Vertical table partitioning is to divide a large table into multiple small tables according to the different amounts of data access. For example, the order table is dispersed into different sub-tables according to different order statuses to avoid access bottlenecks caused by excessive data volume in a single table. However, this method will increase the number of associated query operations between tables, which will have a greater impact on application and database performance.

In practical applications, vertical partitioning is often used in combination with horizontal partitioning. For example, for an order table with millions of levels, the data is divided into different sub-tables according to the order status, and Range or Hash is used to divide the data. The sharding method divides each subtable into different partitions.

2. The connection between Go language and MySQL database

As a high-concurrency programming language, Go language has attracted more and more attention. When writing an application using Go language, how to perform data segmentation with MySQL database?

Go language provides a commonly used MySQL driver library: Go-MySQL-Driver. Through this driver library, we can easily use Go language to operate MySQL database.

When using Go language for data sharding, you need to consider whether the implementation method matches the sharding strategy of the MySQL database. The interface-based programming idea of ​​Go language supports plug-in design. Therefore, we can design interfaces so that applications can choose data access methods according to different data sharding strategies.

For example, we can design a set of interfaces:

type DB interface {
    Select(table string, params map[string]interface{}, result interface{}) error
    Insert(table string, data interface{}) error
    Update(table string, where map[string]interface{}, update map[string]interface{}) error
    Delete(table string, where map[string]interface{}) error
}

Through this interface, we can define different data access methods in the application. For example: the data access method using Hash method is as follows:

type HashDB struct {
    nodes []*sql.DB
}

func (db *HashDB) Select(table string, params map[string]interface{}, result interface{}) error {
    // 计算shard key，并选择对应分区进行操作
    node := db.nodes[hash(params["shard_key"].(string)) % len(db.nodes)]
    // 执行查询语句
    return node.Select(table, params, result)
}

func (db *HashDB) Insert(table string, data interface{}) error {
    // 计算shard key，并选择对应分区进行操作
    node := db.nodes[hash(data.GetShardKey().(string)) % len(db.nodes)]
    // 执行插入语句
    return node.Insert(table, data)
}

// 其他方法省略

When using Go language for data sharding, you also need to consider the issue of synchronous updates. The ultimate goal of data segmentation is to improve read and write performance, but it also needs to ensure data consistency. In a multi-shard environment, we need to consider cross-partition concurrency issues. For example, data update operations in multiple partitions need to be performed in the same transaction to ensure data consistency.

3. Summary

This article introduces the data segmentation scheme of the MySQL database and how to use the Go language and the database for data segmentation. The implementation of data segmentation needs to consider many aspects, such as performance, cost, consistency, etc., and needs to be selected according to different scenarios and business needs. At the same time, using Go language for data segmentation also needs to consider issues such as synchronous updates to avoid data consistency issues caused by concurrency in data access.

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