介绍MySQL大表优化方案

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Introduction to MySQL Large Table Optimization Scheme

As the website editor for UTHEME, we will be discussing the optimization of large tables in MySQL. With the increasing amount of data being added to the business tables on a monthly basis, slow queries on large tables have become a problem during peak business hours. In this article, we will discuss some optimization strategies for large tables in MySQL.

Database Design and Index Optimization

MySQL databases have a high level of flexibility and can cause performance issues. Performance is highly dependent on the table design and index optimization capabilities of developers. Here are some optimization suggestions:

1. Convert time types to timestamp format and use int type to store them. This will increase query efficiency and optimize search performance.

2. Use notnull field definitions; null values are difficult to optimize and take up additional index space.

3. Use TINYINT type instead of ENUM to store accurate floating point numbers. DECIMAL should be used instead of FLOAT and DOUBLE.

4. The length of the field should be based on the business needs; do not set it too large.

5. Avoid using TEXT types as much as possible. If necessary, split infrequently used large fields into other tables.

6. MySQL has a limit on index column length. The length of each index column in the Innodb engine is limited to 767 bytes(bytes), and the length of all columns that make up the index cannot exceed 3072 bytes (in MySQL 8.0, a single index can create 1024 characters).

7. Contact the DBA for large table DDL requirements.

8. Leftmost Index Matching Rule: This refers to the leftmost preference when creating a composite index. Place the most frequently used column in the where clause on the leftmost side.

Parallel Query in PolarDB MySQL8.0

After switching to PolarDB, the volume of data has decreased, but the slow-query issue persists. We need PolarDB's parallel-query optimization:

1. PolarDB MySQL8.0 has introduced a parallel query framework. When the amount of data reaches a certain threshold, the framework will automatically start the parallel query, reducing the query time exponentially.

2. The ParallelQuery utilizes multiple-core CPU parallel processing capabilities with 8-core, 32GB configuration being an example.

3. Parallel Query can also be customized to specific SQL to achieve optimization.

Interactive Analysis with Hologres

For real-time dashboard reporting, Hologres (https://help.aliyun.com/product/113622.html) can be used.

Conclusion

Optimizing large tables with millions of data points is often done based on the cost. It's not always necessary to split a large database horizontally immediately, which can be a challenge for maintenance and an additional cost for the business. Ensure that the database design, index optimization, and table partitioning strategy are tailored to the business requirements.

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