Practical implementation of Spring Cloud microservice sub-library and sub-table

With the rapid development of the Internet and the continuous expansion of business scale, more and more companies are beginning to adopt microservice architecture. As an excellent microservice framework, Spring Cloud not only implements service splitting and management, but also provides a series of efficient solutions. Database and table sharding is one of the important means to ensure system stability and scalability in times of high concurrency. This article will introduce how to implement sharding of databases and tables under the Spring Cloud microservice architecture.

1. The principle of sub-database and sub-table

Before performing sub-database and sub-table, let’s first understand its principle. The so-called sharding of databases and tables means that the data originally stored in one table is dispersed and stored in multiple databases or multiple tables. This can disperse data into different databases or tables, avoid excessive pressure on a single database or table, and improve the scalability and performance of the system.

Let’s take a look at how to implement sub-database and sub-table.

1. Sub-database

Spreading data into multiple libraries can reduce the burden on a single database. When dividing the database, we need to disperse different data into different databases, and at the same time ensure the consistency of the data, that is, all data are synchronized in some way.

2. Table splitting

Dispersing data into multiple tables can achieve distributed storage of the same data and reduce the pressure on a single table. When dividing tables, we need to disperse the same data into different tables while ensuring the relevance of the data.

2. Implementing sub-databases and tables

In the Spring Cloud microservice framework, we can implement sub-databases and tables through database routing, data middleware and Sharding-JDBC.

1. Database routing

Database routing usually routes data to different databases based on business rules. This implementation method is relatively simple, but it cannot achieve dynamic scaling of data. In addition, due to the need for data routing at the application layer, certain performance losses will occur.

2. Data middleware

Data middleware generally implements data routing and load balancing on the upper layer of the database. In addition, data middleware can also achieve distributed storage and high availability of data. However, data middleware requires additional hardware and software support, and scalability and cost issues need to be considered.

3. Sharding-JDBC

Sharding-JDBC is a lightweight Java framework designed to provide transparent sharding of databases and tables. It implements database and table sharding by providing a set of data source agents and some rule configurations. When using Sharding-JDBC, we only need to modify the original data source configuration, and there is no need to change the part of the code that operates the database. In this way, transparent sharding of databases and tables can be achieved.

3. Use Sharding-JDBC to implement sharding of databases and tables

Let’s take a simple microservice architecture as an example to introduce how to use Sharding-JDBC to implement sharding of databases and tables.

1. Create database

First we need to create two databases db1 and db2, and create two tables user and order in these two databases.

2. Integrating Sharding-JDBC

Integrating Sharding-JDBC in a Spring Boot project is very simple, just add the dependency of Sharding-JDBC in pom.xml. At the same time, we also need to add Sharding-JDBC related configuration in the configuration file.

3. Configuring sharding rules and sharding rules

In Sharding-JDBC, we can realize the sharding function by configuring sharding rules and table sharding rules .

• Sub-database rules

Sub-database rules mainly disperse data into different databases based on business rules. When implementing sharding rules, we need to pay attention to the following points:

(1) Sharding-JDBC can support multiple data sources, and we need to configure a data source for each data source.

(2) We need to define a standard sub-database strategy class to determine the data source selection strategy.

(3) We need to define a sharding rule class to determine the key attributes of the data source and the number of shards.

• Table sharding rules

The table sharding rules mainly distribute the same data into different tables. When implementing table partitioning rules, we need to pay attention to the following points:

(1) Multiple physical tables need to be configured for each logical table.

(2) We need to define a standard table partitioning strategy class to determine the data table selection strategy.

(3) We need to define a table rule class to determine the key attributes of the data table.

4. Summary

Sub-database and sub-table are one of the important means to ensure system stability and scalability in times of high concurrency. Under the Spring Cloud microservice architecture, we can implement database and table sharding through database routing, data middleware and Sharding-JDBC. Among them, Sharding-JDBC is a lightweight Java framework designed to provide transparent sharding of databases and tables. Sharding-JDBC can be used to implement sharding of databases and tables simply and efficiently, improving the scalability and performance of the system.

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