How to optimize MySQL statements? Simple and easy to use optimization method

A database commonly used by MySQL is also related to our data storage, so it is also critical. Then MySQL statement optimization is an important task, which can improve the performance and response speed of the database. Sometimes you don't know what optimization methods are available. Here are some simple and practical optimization methods for MySQL statements. I hope they can help you.

Principle of MySQL architecture

Before doing so, we need to understand the working structure of various components of MySQL so that we can understand the MySQL server. As shown below:

The MySQL working architecture can be roughly divided into three layers. The top layer is the client. Functions such as connection processing, authorization and authentication, and security are all handled in this layer.

The core services of MySQL are in the middle layer, including query parsing, analysis, optimization, caching, and built-in functions. All cross storage engine functions are also implemented in this layer: stored procedures, triggers, views, etc.

At the lowest level, the storage engine is responsible for data storage and extraction in MySQL. Similar to the file system under Linux, each storage engine has its advantages and disadvantages. The middle service layer communicates with the storage engine through APIs. These API interfaces mask the differences between different storage engines.

MySQL optimization method

Use index

Indexes can speed up queries and reduce the amount of computation required for queries. Adding indexes to columns used in queries can significantly improve query performance. To add an index, you can use the ALTER TABLE statement or the CREATE INDEX statement. For example, to add an index on the age column of the table named users, you can execute the following statement:

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ALTER TABLE users ADD INDEX idx_age (age);

Avoid using SELECT *: In the query, only the required columns are selected instead of using SELECT * to retrieve all columns. This can reduce the amount of data transmission and improve query efficiency. For example, to retrieve the value of the age column in the users table, but do not need the value of other columns, you can execute the following query:

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Use EXPLAIN to analyze query plans: EXPLAIN statements can be used to analyze query plans and understand how queries are executed. Through the analysis of the results, you can find the bottleneck in the query and make targeted optimization. For example, to analyze the query plan of a SELECT statement, you can execute the following statement:

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SELECT age FROM users;

Optimize data type: Selecting an appropriate data type can significantly improve query performance. For example, if a column is used to store strings, you should use the VARCHAR type instead of the TEXT type. The VARCHAR type only stores the string length and the actual string, while the TEXT type stores the complete string.

Limit result set: In a query, limit the size of the result set as much as possible to reduce the amount of data transmission and calculation. For example, you can use the LIMIT statement to limit the number of result sets. For example, to retrieve the first 10 rows in the users table with age greater than 18, you can execute the following query:

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SELECT * FROM users WHERE age > 18 LIMIT 10;

Avoid using the LIKE operator: The LIKE operator may cause a full table scan in the query, reducing the query efficiency. If you must use the LIKE operator for fuzzy matching, you can consider adding an appropriate index to speed up the query.

Avoid using subqueries: Subqueries may cause performance degradation because they require additional computation and memory. If sub queries must be used, they should be converted to JOIN operations to improve query efficiency. For example, to convert a subquery into a JOIN operation, you can execute the following statement:

The IN in the SQL statement should not contain too many values

MySQL optimizes IN accordingly, that is, all constants in IN are stored in an array, and the array is ordered. However, if there are many values, the consumption will be

It is also relatively large. Another example: select id from t where num in (1,2,3) For continuous values, use between instead of in; Or replace it with a connection.

The SELECT statement must indicate the field name

SELECT * increases unnecessary consumption (CPU, 10, memory, network bandwidth); It increases the possibility of using overlay indexes; When the table structure changes, the forward break also needs to be updated. Therefore, it is required to directly add the field name after the select.

In conclusion, MySQL statement optimization is an important task, which can improve the performance and response speed of the database. By using indexes, avoiding SELECT *, using EXPLAIN to analyze query plans, optimizing data types, limiting result sets, avoiding LIKE operators and sub queries, and regularly optimizing the database, you can significantly improve the query efficiency of MySQL databases.