Vertical Scaling vs. Horizontal Scaling: A Comparison of MySQL and TiDB

Vertical expansion and horizontal expansion: Comparison of MySQL and TiDB

在不断发展的数据处理场景中，数据库的可扩展性成为了一项重要的考虑指标。数据库的扩展主要分为垂直扩展和水平扩展。本文将以MySQL和TiDB为例，探讨垂直扩展和水平扩展的概念、原理以及对比优劣。

Vertical expansion is to improve the scalability of the database by increasing the processing power of a single server. This expansion method mainly relies on hardware upgrades of the database server, such as increasing CPU cores, memory capacity, disk speed, etc. The benefits of vertical expansion are relatively simple and low-cost, and highly transparent to applications. However, there are some limitations to vertical scaling. First, the physical limitations of the hardware determine the maximum expansion limit. Secondly, vertical scaling does not have high availability. If the server fails, the entire database service will be unavailable.

Horizontal expansion is to improve the scalability of the database by adding server nodes. This scaling method distributes data across different servers, with each server processing a portion of the data. The advantage of horizontal expansion is that it can be expanded infinitely, and concurrent processing capabilities and system throughput can be improved by adding nodes. In addition, horizontal scaling also provides high availability. If one node fails, services can continue to be provided from other nodes. However, horizontal expansion also brings some new challenges, such as data sharding, data consistency and load balancing.

MySQL is a widely used open source relational database that supports vertical expansion and horizontal expansion. When vertical expansion is required, processing capabilities can be increased by increasing the hardware configuration of the server. The following is a code example that uses Python to connect to a MySQL database and perform query operations:

import mysql.connector

# 连接数据库
cnx = mysql.connector.connect(user='username', password='password', host='localhost', database='database')

# 创建游标
cursor = cnx.cursor()

# 执行查询
query = "SELECT * FROM table"
cursor.execute(query)

# 获取结果
for row in cursor:
    print(row)

# 关闭游标和数据库连接
cursor.close()
cnx.close()

TiDB is a distributed NewSQL database that focuses on horizontal expansion. TiDB stores data shards on multiple nodes and uses the Raft consistency algorithm to ensure data consistency. The following is a code example, using Golang to connect to the TiDB database and perform query operations:

package main

import (
    "database/sql"
    "fmt"

    _ "github.com/pingcap/tidb/types/parser_driver"
    _ "github.com/pingcap/tidb/types/parser_driver/parser"
)

func main() {
    // 连接数据库
    db, err := sql.Open("tidb", "username:password@tcp(127.0.0.1:4000)/database")
    if err != nil {
        fmt.Println(err)
        return
    }
    defer db.Close()

    // 执行查询
    rows, err := db.Query("SELECT * FROM table")
    if err != nil {
        fmt.Println(err)
        return
    }
    defer rows.Close()

    // 获取结果
    for rows.Next() {
        var data string
        err := rows.Scan(&data)
        if err != nil {
            fmt.Println(err)
            return
        }
        fmt.Println(data)
    }
}

Through the above example, we can see the similarities between MySQL and TiDB in connection and query operations. However, when horizontal expansion is required, TiDB provides a simpler and more powerful solution. Just add new nodes, TiDB can automatically distribute data and load balance, providing higher disaster recovery and scalability.

In summary, vertical expansion is suitable for processing small-scale data and low concurrency scenarios, while horizontal expansion is suitable for large-scale data and high-concurrency scenarios. Both MySQL and TiDB are good choices. Which method to use needs to be decided based on actual needs and scenarios. Whether it is vertical expansion or horizontal expansion, factors such as hardware cost, performance requirements, availability, and operation and maintenance complexity need to be comprehensively considered to arrive at the most appropriate database expansion solution.

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