Interviewer: tell me about MySQL tuning?

MySQL, as a typical representative of relational databases, its popularity beyond any database, so in the Java interview, MySQL is an important knowledge point will be asked. There is an extremely common interview question in MySQL that we will look at systematically here, and this is the MySQL tuning question that we will discuss today.

The general idea behind MySQL tuning is as follows:

The specific tuning ideas are as follows.

1. Query statement optimization

1. Try to avoid using SELECT * and query only the columns you need.
2. Use JOIN instead of subqueries to reduce the level of nested queries.
3. Avoid using LIKE '%value%' in the WHERE clause, which will result in a full table scan.
4. Use the LIMIT clause wisely to limit the number of query results.

2. Index Optimization

1. Rationalizing the use of indexes: These include primary key indexes, unique indexes, general indexes, and union indexes. Make sure to create indexes on columns that are frequently used for query conditions.
2. Avoid over-indexing: Because each index takes up additional storage space and may affect the performance of write operations.

3. Table structure optimization

1. vertical scale: Separate infrequently used fields or large fields (e.g., TEXT, BLOB) in a table into a separate table, reducing the size and I/O overhead of the main table.
2. horizontal scale: Spread the data in a table into multiple tables, each containing a portion of the data, based on some rule (e.g., date, region, etc.). This improves query efficiency and reduces lock contention for a single table.
3. Archiving old data: Periodically archive old data that is not commonly used into the history table to reduce the amount of data in the main table and improve query performance.

4. Architecture optimization

1. read-write separation: Read-write separation is achieved through master-slave replication, which spreads read operations across multiple slave servers and reduces the load on the master server.
2. Database sharding (horizontal sharding/vertical sharding): Splits a large database into multiple smaller databases, each containing a portion of the data. Database scalability and query performance can be improved by sharding.
3. distributed database: e.g. TiDB, ES.
4. Using Cache: Use caching at the application level (e.g. Memcached, Redis, etc.) to reduce the number of direct accesses to the database.

5. Upgrade hardware

1. Selecting a high-performance CPU: to support complex query processing and large numbers of concurrent accesses.
2. Increase memory capacity: Because MySQL uses a lot of memory to cache data and indexes, which improves query efficiency.
3. Using high speed disks: such as SSDs (Solid State Drives) to reduce I/O wait times. Also, consider using RAID technology to improve disk read/write performance and reliability.
4. Optimize network configuration: Ensure efficient and stable data transfer between the database server and the client.

Post-lesson Reflections

MySQL Besides the above optimizations, what other optimizations do you know? Feel free to add to the list in the comments section.

This article has been included in my interview mini-site, which contains modules such as Redis, JVM, Concurrency, Concurrency, MySQL, Spring, Spring MVC, Spring Boot, Spring Cloud, MyBatis, Design Patterns, Message Queuing and more.