MySQL database and Go language: How to perform vertical data segmentation?

As the amount of data continues to grow, the traditional single database architecture gradually exposes its bottlenecks and performance limitations. In order to solve these problems, large applications usually use vertical data segmentation, splitting a single database into multiple logical databases, and dispersing the data to different servers according to different rules, thereby improving the scalability and performance of the system. . This article will introduce how to use MySQL database and Go language to perform vertical data segmentation processing.

Advantages of MySQL database and Go language

MySQL is a free open source relational database. After years of development and optimization, it has now become one of the most popular databases. It supports large-scale data processing and analysis, is highly reliable and secure, and can be easily expanded horizontally and vertically.
Go language is a simple and efficient programming language with powerful features of coroutines and concurrent programming. It supports Web programming and system programming, and is very suitable for high-load distributed applications.

How to implement vertical data segmentation

The most common way is to shard the data according to the business logic and distribute the data to different servers according to different rules. This method has the following steps:

1. Define the sharding key

Data sharding is divided according to the sharding key. The sharding key means that the data can be identified and allocated to the correct A unique identifier on the shard. Sharding keys can be defined according to different business requirements, such as geographical location, user ID, timestamp or other business-related attributes.

2. Select the sharding algorithm

The sharding algorithm is an algorithm that maps the sharding key to a specific sharding ID. Depending on the sharding algorithm, you can choose a hash algorithm or a range algorithm. The hash algorithm performs hash calculations based on the shard key and maps the result to the shard ID. The range algorithm assigns the corresponding fragment ID to the data according to the specified fragmentation range.

3. Implement data access logic

Implement the data access logic in the code, access different shard servers through the database connection pool, query data according to the shard key and perform data read and write operations .

Use MySQL and Go language to implement vertical data segmentation

In MySQL, vertical segmentation is achieved by using sub-databases and tables. According to business needs, different tables can be allocated to different databases, or the same table can be divided and stored in different databases. When querying using shard keys, you need to query all related databases and tables and merge the results.

In the Go language, you can use the ORM framework for database access and data operations. Using an ORM framework can simplify database interaction, avoid security issues such as SQL injection, and improve code readability and maintainability.

Summary

Vertical segmentation of data is an effective way to solve the bottleneck of large-scale data processing and analysis. By combining the MySQL database and Go language for implementation, distributed applications with high performance, high reliability and high scalability can be realized. In practical applications, reasonable sharding strategies and algorithms need to be selected based on specific business needs and data access patterns. At the same time, technical solutions such as data consistency and concurrency issues also need to be considered.

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