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

With the continuous development of Internet applications, the amount of data is increasing, and how to effectively process massive data has become an important issue. Among them, data horizontal sharding (Sharding) is a widely used solution. In this article, we will introduce how to perform horizontal segmentation of data in MySQL database and Go language.

1. What is data horizontal segmentation?

Data horizontal sharding is a method used to horizontally split data into multiple database nodes. It can help applications share load and improve application scalability and availability. In horizontal sharding of data, data is divided into segments, with each segment stored in a different database node.

2. Why is data horizontally segmented?

As the amount of data continues to increase, it is difficult for a single database to carry such a large amount of data. In order to improve the performance and scalability of the application, it is necessary to use multiple database nodes for horizontal sharding of data. Horizontal data sharding can spread data to multiple nodes, with each node responsible for processing a portion of the data. In addition, horizontal sharding of data can also improve system availability. If a node fails, it will only affect the data managed by the node and will not affect the operation of the entire system.

3. Horizontal sharding of data in MySQL database

1. Vertical Sharding

Vertical sharding refers to different tables or data fields Split data into different database nodes. This method is suitable for situations where the degree of coupling between data tables is low or there are large differences between data fields.

2. Horizontal Sharding

Horizontal Sharding refers to splitting according to the rows of data, and splitting different data rows into different nodes. This method is suitable for situations where the coupling degree between data tables is high or there are small differences between data fields. In horizontal splitting, it is often necessary to use Sharding Key to divide data.

3. Consistent Hashing

Consistent hashing is a sharding strategy commonly used in database clusters. It disperses data among different nodes, and each node is responsible for processing a part of the data. In consistent hashing, a hash function is used to map data onto a ring, with each node occupying a certain range on the ring. When new data arrives, the hash function is used to calculate the corresponding position, and then the corresponding node is found based on the position for processing.

4. Horizontal segmentation processing in Go language

Go language is a fast, simple, and safe programming language. The Go language provides a rich network library and concurrency model, giving it a strong advantage in processing high concurrency and massive data. In the Go language, you can use the following libraries for horizontal data segmentation processing:

1. go-mysql: A library for processing MySQL databases that supports horizontal data segmentation and load balancing.
2. go-xmysql: A lightweight MySQL database client that supports horizontal sharding and read-write separation.
3. Vitess: An open source MySQL database cluster management system that supports horizontal sharding and load balancing.

The above three libraries all provide data horizontal segmentation functions and load balancing functions, which can be selected and configured according to the needs of the application. When using these libraries for horizontal data segmentation processing, you need to pay attention to the following points:

1. Installation and configuration: You need to install the corresponding libraries and plug-ins, and configure them accordingly.
2. Selection of sharding key: You need to select an appropriate sharding key (Sharding Key) in order to divide the data. The choice of shard key should be based on the actual situation of the application.
3. Fault handling: Fault handling is required to detect and handle node failures in a timely manner.
4. Cache processing: You can use cache (such as Redis) to store hotspot data to improve data access speed.

5. Summary

Horizontal sharding of data is an important method to improve application performance and scalability. It can help us process massive amounts of data effectively. In the MySQL database and Go language, we can use the corresponding libraries for horizontal data segmentation processing. However, when using horizontal sharding of data, you need to pay attention to issues such as selecting appropriate sharding keys, fault handling, and caching. Only by fully considering the needs of the application and the actual situation can data horizontal segmentation be efficiently performed.

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