What are the slow query logs and optimization suggestions for learning MySQL?

What are the slow query logs and optimization suggestions for learning MySQL?

Slow queries refer to query statements that take a long time to execute in the MySQL database, which may cause system performance to decrease. In order to discover and solve slow query problems in a timely manner, MySQL provides a slow query log tool to help developers optimize performance by recording slow query statements, execution time and other related information.

1. Enable slow query log
To use the slow query log function, you first need to make the corresponding settings in the MySQL configuration file. In the MySQL configuration file my.cnf or my.ini, find the [mysqld] section, add or modify the following parameters:

slow_query_log = 1 // Enable slow query log, the default value is 0
slow_query_log_file = /var/log/mysql/slow-query.log // Slow query log file path
long_query_time = 1 // Define a query that takes more than a few seconds to execute as a slow query. The default value is 10 seconds

Save the configuration file and restart the MySQL service. The slow query log will start recording.

2. Analyze slow query log
The slow query log records detailed information of query statements whose execution time exceeds the definition of long_query_time. We can find out which statements execute slowly by analyzing the slow query log. The following is an example of a slow query log:

Time: 2020-01-01T00:00:05.123456Z

User@Host: root[root] @ localhost [] Id: 1

Query_time: 3.001145 Lock_time: 0.000000 Rows_sent: 1 Rows_examined: 1000

SET timestamp=1577840405;
SELECT * FROM user WHERE age > ; 30;

In the log, Query_time represents the query execution time, Lock_time represents the query lock time, Rows_sent represents the number of rows sent to the client, and Rows_examined represents the number of queried rows.

3. Optimization Suggestions Tips
After we analyze the slow query log, we can make corresponding optimizations based on the specific query statements and performance bottlenecks. Here are some common optimization suggestions:

1. Ensure correct indexes: Speed ​​up query operations by creating appropriate indexes. You can use the EXPLAIN statement to view the query execution plan and determine whether an index is used.

Example:
EXPLAIN SELECT * FROM user WHERE age > 30;

2. Avoid full table scan: Reduce full table scan operations as much as possible, especially In the big table. You can reduce the query result set by adding indexes, optimizing query conditions, or using paging.
3. Use appropriate data types: Choose appropriate data types and avoid using data types that are too large or too small. Data types that are too large waste storage space, while data types that are too small may cause data overflow or truncation.
4. Reduce the amount of data transmission: Try to reduce the amount of data transmission in query results, select only the required fields, and avoid returning unnecessary data.
5. Batch operation: Combine multiple single operations into batch operations to reduce the number of database connections and transaction submission times.

Example:
INSERT INTO user (name, age) VALUES ('Tom', 20), ('John', 30), ('Alice', 40);

6. Use prepared statements: Using prepared statements can improve query performance and security.

Example:
PREPARE stmt FROM 'SELECT * FROM user WHERE age > ?';
EXECUTE stmt USING 30;

7. Partition table: for Large tables can be partitioned according to a certain field to improve query efficiency.

Example:
CREATE TABLE user (id INT, name VARCHAR(50), age INT) PARTITION BY RANGE (age) (
PARTITION p0 VALUES LESS THAN (20),
PARTITION p1 VALUES LESS THAN (40),
PARTITION p2 VALUES LESS THAN MAXVALUE
);

Summary
Learning MySQL’s slow query log and optimization suggestions are essential for improving database performance. It's important. By enabling the slow query log, we can record statements whose query execution time exceeds the preset threshold, and perform corresponding performance optimization by analyzing the slow query log. Reasonable use of indexes, reduction of data transfer volume, use of preprocessing statements, partition tables and other optimization techniques can significantly improve database query efficiency and system performance.

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