How to analyze the reasons why MySQL queries are slow

MySQL query is slow, which is a problem encountered by many MySQL users. Slow query not only affects the performance of the database, but also affects the user experience. In this article, we will learn how to analyze the reasons for slow MySQL queries and provide some solutions.

1. Monitor the slow query log

First, we need to enable the slow query log function of MySQL in order to analyze the cause of slow query. The slow query log records the time spent on the query, SQL statements, client addresses and other related information, which is of great help to us in analyzing slow queries.

To enable the slow query log, we need to modify the MySQL configuration file "my.cnf" or "my.ini" and add the following content under the "[mysqld]" node:

slow_query_log = 1
slow_query_log_file = /var/log/mysql/slow-query.log
long_query_time = 2

Among them, "slow_query_log" means turning on the slow query log, and a value of 1 means turning it on. "slow_query_log_file" specifies the saving path of the slow query log, which can be modified according to the actual situation. "long_query_time" means how many seconds the query time exceeds before it is considered a slow query. The default It is 10 seconds and can be set according to the actual situation. After modifying the configuration file, restart the MySQL service to make the configuration file take effect.

2. Use slow query log analysis tools

After we have the slow query log, we need to use some tools to analyze the log and find out the reason for the slow query. The following introduces two commonly used slow query log analysis tools:

2.1 mysqldumpslow

mysqldumpslow is MySQL’s own slow query log analysis tool. It can analyze the slow query log according to different dimensions, such as time and time. Analyze slow query logs according to query statements, client addresses, etc., and provide corresponding statistical results.

Before using mysqldumpslow for analysis, we need to understand some parameters of mysqldumpslow:

• -a: Output the results in reverse order of query time
• -s: Specify Dimension of sorting, commonly used parameters are: t (sorted by time), al (sorted by query statement), ar (sorted by client address), etc.
• -t: Number of output results

Using the mysqldumpslow command requires the slow query log as input. Generally, the default path of the slow query log is "/var/log/mysql/slow-query.log". The following is an example:

mysqldumpslow -s t -t 10 /var/log/mysql/slow-query.log

The above command will sort by time and output the first 10 pieces of data.

2.2 pt-query-digest

pt-query-digest is a slow query log analysis tool in the Percona tool suite. It can not only analyze MySQL's slow query logs, but also analyze other Database slow query log. Compared with mysqldumpslow, pt-query-digest supports more dimensions and more precise analysis results.

Before using pt-query-digest, you need to install the Percona tool suite, and then run the following command to perform analysis:

pt-query-digest /var/log/mysql/slow-query.log

After the operation is completed, pt-query-digest will give the corresponding The statistical results can be sorted according to different dimensions.

3. Optimize the query statement

Analyzing the slow query log can find out the reason for the slow query, but to truly solve the problem, you need to optimize the query statement. Here are some ways to optimize query statements.

3.1 Determine appropriate indexes

Indexes are the key to speeding up queries. Indexes allow MySQL to locate rows of data faster. When designing the table, set appropriate indexes according to the query requirements. Generally, we should add indexes for columns that frequently appear in WHERE clauses, while avoiding too many indexes because indexes take up disk space and affect performance when writing data.

If we cannot determine which columns need to be indexed, we can use the EXPLAIN command to view the execution plan of the query and find out where optimization is needed. For example, the following SQL query statement:

SELECT * FROM table WHERE name = 'Tom' and age > 18

Execute the EXPLAIN command:

EXPLAIN SELECT * FROM table WHERE name = 'Tom' and age > 18

and get the following results:

id  select_type table  type  possible_keys  key  key_len  ref  rows  Extra
1   SIMPLE      table  ref   idx_name_age   idx_name_age 123 const      10    Using where

Among them, "type" represents the type of query, common types There are: ALL (full table scan), index (index scan), etc.; "possible_keys" indicates the indexes that may be used; "key" indicates the actually used index; "Extra" indicates other information, such as whether a temporary table is used, etc. If the query uses a full table scan, it means that the appropriate index is not used.

3.2 Avoid using unnecessary subqueries

A subquery is a nested query statement that selects data, and it can be nested in other query statements. Although subqueries can easily query complex data, in some cases, subqueries have low performance and can easily cause problems. In order to avoid subquery performance problems, we can use associated queries or temporary tables to replace subqueries, or optimize subqueries.

3.3 Fetching data on demand

When we execute the SELECT query statement, sometimes we do not need to query all columns and rows, but only some columns and rows. At this time, we should try to fetch data on demand and obtain the required number of rows through the LIMIT clause to reduce the workload of the database and the amount of data transmission. For example, the following SQL query statement:

SELECT * FROM table WHERE id > 100 ORDER BY id DESC

Only needs to query records with ID greater than 100, and sort them in descending order by ID. If there are many records in the table, we can use the LIMIT clause to limit the result set of the query:

SELECT * FROM table WHERE id > 100 ORDER BY id DESC LIMIT 50

以上就是几种优化查询语句的方法，在实际的应用中，我们需要根据具体的情况选择合适的方法。

总结

MySQL 查询慢不仅影响了数据库的性能，还会影响到用户的体验。为了解决查询慢的问题，我们可以使用慢查询日志分析工具，找出问题所在，然后对查询语句进行优化。通过合理地使用索引、避免使用不必要的子查询和按需取数据等方法，可以提高查询的效率，减少查询所花费的时间，让用户获得更好的体验。

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