Scalability design tips in MySQL

MySQL is a popular open source relational database management system that has continued to evolve and develop since its development in the early 1980s. Today, MySQL has become one of the most popular databases for web applications and is the basis for many companies' core business systems. In order to meet the growing data processing needs, MySQL must ensure its scalability and continue to expand outward without affecting performance.

In this article, we will discuss MySQL’s scalability design techniques, including vertical expansion, horizontal expansion, sharding, etc.

Vertical expansion (vertical split)

Vertical expansion is one of the most commonly used scalability techniques in MySQL, which is achieved by adding more hardware resources (such as CPU, memory, storage) to improve system performance. Vertical scaling allows the database to handle larger data volumes faster and to better support concurrent access.

The disadvantages of vertical expansion are mainly cost and performance limitations. Because the cost of hardware is typically higher than another scaling technique (horizontal scaling), this option may not be practical for many small or medium-sized businesses. In addition, due to the architectural limitations of computer hardware, there is always a bottleneck in the performance improvement of vertical expansion.

Horizontal expansion (horizontal splitting)

Horizontal expansion (also known as horizontal slicing or partitioning) is to increase the availability of the database system by distributing data across multiple computer nodes. A method of scalability. Sharding can significantly improve the throughput and availability of the system, allowing the database to handle a large number of queries and requests without reducing performance.

The main methods of horizontal expansion are replication and partitioning. Replication refers to copying data to different servers to ensure that specific services are accessible on multiple nodes and improve system availability and performance. Partitioning distributes data in different databases according to certain rules, and queries different databases according to different rules to improve system performance.

However, there are some problems with horizontal scaling, such as the cost and inefficiency of cross-node data access. In addition, horizontal expansion requires consideration of some issues at the application level, such as how to allocate data, how to merge query results, etc.

Data Partitioning

Before partitioning data, you need to understand the partitioning methods supported by MySQL so that you can choose the partitioning method that suits your business needs. The main partitioning methods supported by MySQL are as follows:

1. RANGE (range partitioning)

This method is based on the range partitioning of a certain column. Expression to divide data into different partitions.

2. LIST (list partitioning)

This method is based on the partitioning of a certain column value list, which divides the data in the table according to the value list of the specified column.

3. HASH (Hash Partition)

Hash partitioning is to allocate the data in the table to a specified number of partitions. The size of each partition is basically the same. You can Avoid uneven distribution of data.

4. KEY (key partition)

This method is to partition based on a unique key or primary key. MySQL maps key values ​​into specific partitions, thereby dividing the table into multiple logical partitions.

Database Architecture

When designing a MySQL database for horizontal expansion, its architecture needs to have the ability to expand horizontally. Therefore, it is recommended to design the database architecture as centralized. This means that there can be one or more master nodes that support multiple partitions by replicating from nodes to other nodes.

In this architecture, the master node handles write requests and queries, distributes data to slave nodes, and maintains metadata for the entire system. The slave node is only responsible for responding to read requests and will not affect the verification of whether the data has been successfully modified. Using this centralized architecture, horizontal scalability can be achieved through third-party tools such as MySQL Cluster or Galera Cluster.

Optimizing queries

In MySQL, queries are very important operations, and they account for most of the load on the database server. Therefore, optimizing queries is an important step in improving database performance. The following are some tips for query optimization:

1. Index table

Using index tables can significantly improve query efficiency. An index table is usually a table sorted based on a certain column (or columns) so that queries have fast access to the query columns.

2. Avoid using SELECT *

Avoid using SELECT * in queries, because it will obtain all columns and rows in the table, resulting in performance degradation.

3. Merge query

Combining multiple queries into one query statement can significantly improve the execution effect. Multiple queries can be combined into a single query using JOIN, UNION, or other query operators.

Summary

When designing a MySQL database, scalability is crucial. MySQL provides a variety of scalability design techniques, including vertical expansion, horizontal expansion, data partitioning, etc. It’s important to choose a technique that suits your needs. At the same time, the design of the database architecture is also very important. By implementing some query optimization techniques, you can maximize the performance of your MySQL database.

The above is the detailed content of Scalability design tips in MySQL. For more information, please follow other related articles on the PHP Chinese website!