MySql SQL statement execution plan: How to optimize the MySQL query process

With the rapid development of the Internet, data storage and processing are becoming more and more important. Therefore, relational databases are an integral part of modern software platforms. MySQL database has become one of the most popular relational databases because it is simple to use, easy to deploy and manage. However, MySQL database performance issues often become an issue when dealing with large amounts of data. In this article, we will delve into MySQL's SQL statement execution plan and introduce how to improve the performance of the MySQL database by optimizing the query process.

What is a SQL statement execution plan?

SQL statement execution plan is an important part of MySQL database optimization. Simply put, it is MySQL's way of estimating the computational cost of executing a query. The execution plan tells you how MySQL will execute the query, which indexes to use, and which join algorithms and strategies to use. You can use the EXPLAIN command to generate a SQL execution plan.

Before executing the query, MySQL will parse, optimize and execute the query. During the query process, MySQL uses the query optimizer to analyze the query statement and determine the most efficient execution plan to minimize the use of I/O and CPU resources. The optimizer considers many factors such as available indexes, JOIN order, data size and join type when choosing the best execution plan.

How to read SQL statement execution plan

The easiest way to generate a query execution plan is to use the EXPLAIN keyword. EXPLAIN analyzes the query and returns the query execution plan. A query execution plan contains the individual steps of the query, the order of joins between steps, and the indexes and types used by MySQL to find the data.

To generate a query execution plan, use the keyword EXPLAIN before the query, and then follow the query SQL statement. The following is an example:

EXPLAIN SELECT * FROM employees WHERE salary > 50000;

Execute this query and you will get the following query execution plan:

+----+-------------+-----------+-------+---------------+------+---------+------+-------+-------------+
| id | select_type | table     | type  | possible_keys | key  | key_len | ref  | rows  | Extra       |
+----+-------------+-----------+-------+---------------+------+---------+------+-------+-------------+
| 1  | SIMPLE      | employees | range | salary        | salary | 4       | NULL | 18404 | Using where |
+----+-------------+-----------+-------+---------------+------+---------+------+-------+-------------+

The execution plan provides a lot of useful information. Let us analyze one by one:

• id: the identifier of the query step;
• select_type: query type;
• table: which data table is accessed by the query;
• type: The way to find data in the data table, including ALL, index, range, ref and eq_ref;
• possible_keys: Query available indexes;
• key: MySQL actually uses Index;
• key_len: the length used by the index;
• ref: the column in the index used by MySQL to compare with the query;
• rows: MySQL estimates that the data table needs to be scanned Number of rows;
• Extra: additional query information.

How to optimize the query process of MySQL

The method of optimizing the query process of the MySQL database depends on the query type, data table size and required result set size. The following are the most commonly used MySQL query optimization techniques:

1. Use appropriate indexes

Using appropriate indexes can significantly improve query performance. Indexes help MySQL find data in data tables. The choice of index depends on the specific requirements of the query. You can consider the following aspects when selecting an index:

• For large data tables, using composite indexes can improve query performance. Composite indexes involve multiple columns and are very useful in multi-condition queries.
• When a query involves selecting fewer rows from a data table, it is best to use a covering index. Otherwise, MySQL will scan all data rows, which will greatly reduce query performance.
• Avoid using indexes on BLOB, TEXT, and CHAR type columns because columns of these data types are large and indexes are slow.
• Do not use indexes on small data tables because indexes may increase query time.

2. Caching query results

In order to improve performance, MySQL caches query results. If you query the same data two or more times, MySQL will read the results from the cache rather than from the data table. To enable query result caching, set the query_cache_type and query_cache_size parameters.

3. Reduce the data involved in the query

Although in most cases all data for the query results is required, in many cases it is possible to query only the required data columns . This can be achieved by using the column names in the SELECT ... FROM statement.

4. Avoid using subqueries

Subqueries will increase query time. If possible, you should avoid using subqueries. An alternative is to use INNER JOIN to merge the two tables.

5. Using optimized queries

You can use query optimization tools such as the EXPLAIN and OPTIMIZE TABLE commands to optimize MySQL queries to make them faster. The query optimizer can provide helpful hints on how to optimize queries.

Conclusion

MySQL’s SQL statement execution plan is an important part of MySQL database optimization. You can improve the performance of your MySQL database by using appropriate indexes, caching query results, reducing the data involved, avoiding subqueries and using optimized queries. When it comes to processing large data tables, it is very important to optimize the SQL execution plan as this will significantly improve query performance.

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