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In the MySQL database, indexing is a very important means of performance optimization. When the amount of data in the table increases, inappropriate indexes can cause queries to slow down or even cause database crashes. In order to improve database performance, indexes need to be used rationally when designing table structures and query statements.
The composite index is a more advanced indexing technology that improves query efficiency by combining multiple fields as indexes. In this article, we will detail how to improve MySQL performance by using composite indexes.
A composite index, also called a joint index, creates multiple fields as one index and can provide indexes for multiple columns. Improve query efficiency. A composite index can be a primary key index, a unique index, or an ordinary index.
For example, a user table has fields such as id, username, age, sex, email, etc. If you need to query based on the age and sex fields at the same time, you can create a composite index:
CREATE INDEX age_sex_index ON user(age,sex);
In this way, when querying the data of age=18 and sex='male', the database will first arrange it according to the age field, and then arrange it according to the sex field in this group, and finally get the data we need.
When selecting compound index fields, you need to consider the actual situation of the application and the fields involved in the query statement. Here are a few points to note:
(1) Select important fields
The composite index should contain the most important fields, which are often used in queries and sorting.
(2) A small number of fields
The number of fields in a composite index should be as small as possible, generally no more than 3, to avoid being too complex and affecting query efficiency.
(3) Leftmost matching principle
In composite indexes, the leftmost matching principle refers to the fields involved in the query statement, which must be the leftmost part of the index before the index can be used . For example, if there is a composite index (age, sex), the age field must be used as a filtering condition in the query statement, and then the sex field can be used to filter.
(4) Field type
The field type in the composite index needs to consider the collation rules and string length. For example, if you need to sort on a varchar field, the index will use string sorting, which will be slower than numeric sorting.
When creating a composite index, you need to specify multiple field names in the CREATE INDEX statement, and determine the sorting rules of the index according to the query requirements. For example:
CREATE INDEX age_sex_index ON user(age,sex);
When querying compound index, you need to pay special attention to the following points:
(1) Avoid using too many Too many indexes
If there are too many indexes on the table, the time to query the database will increase significantly.
(2) Avoid using the LIKE statement
When using the LIKE statement, the query speed will slow down because MySQL must traverse the entire table for pattern matching.
(3) Avoid using ORDER BY and GROUP BY
ORDER BY and GROUP BY will slow down the query, especially in large tables.
(1) How to optimize the SQL statement for establishing a composite index?
Some SQL statements use complex WHERE clauses, resulting in reduced index usage efficiency. At this time, you need to optimize the WHERE clause and put the index at the front as much as possible to make it match first.
(2) How to avoid full table scan when using compound index query in the query statement?
Use the SHOW INDEXES statement or EXPLAIN statement to optimize the query statement, and use the FORCE INDEX statement to ensure that the composite index is used during the query.
(3) How to modify or delete compound index?
You can use the ALTER TABLE statement to add, modify, or delete composite indexes. For example:
ALTER TABLE user ADD INDEX age_sex_index(age,sex); ALTER TABLE user DROP INDEX age_sex_index;
Compound index is an effective means to improve MySQL performance and can greatly improve query efficiency, but you need to pay attention to index selection and query when using it optimization problem. Proper use of composite indexes can avoid inefficiencies when using multiple single-column indexes and improve data query efficiency.
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