The concept of index

A MySQL index is a data structure used to speed up database queries. It is similar to the catalog of books and can quickly guide us to find the information we need. MySQL indexes can be sorted and stored according to certain algorithms and data structures, so as to achieve efficient data search and access. In a database, indexes can speed up data query and update operations and improve system performance.

MySQL supports multiple index types, common ones include B-tree index, hash index, and full-text index. Among them, the B-tree index is the most commonly used one. It is a balanced tree structure that can sort data according to certain rules, so that queries can quickly locate the required data. B-tree indexes include primary key indexes, unique indexes, and ordinary indexes.

The primary key index is a special unique index, which enforces that each record in the table must have a unique primary key, which can be used to quickly locate the specified record. A unique index is an index that enforces that each index value must be unique, which can be used to avoid duplicate data in the table. Ordinary index is the most basic index type, it can speed up the query, but it does not enforce that the index value must be unique.

In addition to B-tree indexes, MySQL also supports hash indexes and full-text indexes. The hash index uses the hash algorithm to sort the index, and can quickly locate the specified data. However, hash indexes only support equivalent queries, not range queries, so there are limitations in some scenarios. A full-text index is a type of index that can be used to quickly search text content. It supports operations such as fuzzy search and full-text search, which can be used to quickly search text content.

To sum up, the MySQL index is a data structure used to speed up database queries. Different types of indexes are suitable for different scenarios. Developers need to select and optimize according to the actual situation. When designing indexes, you need to pay attention to avoiding excessive use of indexes, combined indexes, selection of data types, and regular maintenance to improve system performance and stability.

The principle of indexing

The principle of MySQL indexing can be briefly summarized as follows: sort and store the data in the table according to a certain algorithm and data structure to form an index table, and quickly locate the target data through the index table. Specifically, MySQL indexes are implemented using B-tree or B+tree data structures.

B-tree is a balanced tree structure, which sorts node data according to certain rules. Each node contains multiple keywords and pointers, which can support fast search, insertion and deletion operations. In B-tree, each node has a minimum and maximum key value, all nodes whose key value is less than the minimum key value of the node are on the left side of the node, and all key values greater than the maximum key value of the node The nodes are all to the right of this node. Therefore, fast range query and equivalent query can be performed through B-tree.

B+tree is a variant of B-tree. In B+tree, internal nodes do not store data, but only keywords and child node pointers, while data is only stored in leaf nodes. Leaf nodes are connected by pointers, which can support fast range query and equivalent query. Compared with B-tree, B+tree can make better use of memory space and reduce disk I/O operations, so it is more commonly used in practical applications.

There are many types of indexes in MySQL, including primary key indexes, unique indexes, ordinary indexes, full-text indexes, etc. Each type of index has its applicable scenarios and advantages and disadvantages. For example, the primary key index can be used to quickly locate the specified record, the unique index can avoid duplicate data in the table, the common index can speed up the query speed, and the full-text index can be used to quickly search for text content, etc.

When designing indexes, you need to pay attention to avoiding excessive use of indexes, combined indexes, selection of data types, and regular maintenance to improve system performance and stability. At the same time, MySQL also provides an optimizer, which can select the optimal execution plan according to query conditions and indexes, thereby further improving query efficiency.

type of index

Commonly used index types in MySQL include:
1. Primary Key Index (Primary Key Index): The primary key index is a special unique index, which requires the value of the index column to be unique and not empty, and is used to quickly locate a row of data in the table. Primary key indexes can be created automatically or specified manually.
2. Unique Index (Unique Index): The unique index requires the value of the index column to be unique, but allows null values to avoid duplicate data in the table. A table can have multiple unique indexes.
3. Normal Index : Normal Index is the most basic index type without any restrictions, and is used to speed up the query. A table can have multiple ordinary indexes.
4. Fulltext Index : Fulltext index is used to quickly search text content, such as articles or logs, and can support full-text search, word segmentation, keyword matching and other functions.
5. Composite Index : Composite index uses multiple columns as part of the index to optimize the performance of composite queries. The order of composite indexes is very important and should be determined according to the frequency of queries and the efficiency of filtering.
6. Spatial Index : Spatial index is used to store and query spatial data, such as geographic location and 3D model, and can support spatial range query, nearest neighbor query, distance query and other functions.
7. Prefix Index (Prefix Index): A prefix index is a special index type that only indexes a part of column values, which can be used to optimize query performance and save storage space. However, using a prefix index may lead to problems that the index is not unique and the query results are inaccurate.
In practical applications, the appropriate index type should be selected according to specific business needs and query characteristics, and excessive use of indexes and creation of redundant indexes should be avoided to improve system performance and stability.

Index use

How to use the index

1. Use indexes in the WHERE clause : Using indexes in the WHERE clause can speed up queries, such as using index columns in query statements for filter conditions. For example, to query the information of students whose age is greater than 20 in the students table, you can use the following SQL statement:

SELECT * FROM students WHERE age > 20;

2. Use indexes in the O RDER BY clause : Using indexes in the ORDER BY clause can speed up sorting operations, such as sorting the result set in ascending or descending order by a certain column. For example, to query the information of students whose age is greater than 20 in the students table and sort them in ascending order by id, you can use the following SQL statement:

SELECT * FROM students WHERE age > 20 ORDER BY id ASC;

3. Use indexes in JOIN operations : Using indexes in JOIN operations can speed up the associated operations between tables, such as connecting tables through a certain column. For example, to query information about the students' classes in the students table and classes table, you can use the following SQL statement:

SELECT * FROM students JOIN classes ON students.class_id = classes.id;

4. Use indexes in the GROUP BY clause : Using indexes in the GROUP BY clause can speed up the aggregation of the result set, such as counting the total, average, maximum, minimum, etc. of a column. For example, to query the number of students in each class in the students table, you can use the following SQL statement:

SELECT class_id, COUNT(*) FROM students GROUP BY class_id;

5. Using indexes in UNION operations : Using indexes in UNION operations can speed up the merging of multiple result sets, for example, merging the result sets of multiple SELECT statements into one result set. For example, to query the information of students whose age is greater than 20 and less than 20 in the students table, you can use the following SQL statement:

SELECT * FROM students WHERE age > 20 UNION SELECT * FROM students WHERE age < 20;

Precautions

Do not overuse indexes and avoid creating redundant indexes, otherwise performance degradation and storage space waste will result.

For frequently updated tables, consider reducing the index usage to improve update performance.

For large tables and complex queries, you can use the performance analysis tools provided by MySQL, such as the EXPLAIN command, MySQL Workbench, and Percona Toolkit, to optimize query performance.

Index Optimization Tips

1. Determine the columns that need to be indexed : Generally, indexes should be created on columns that are often used for querying, joining, sorting, or grouping. Indexes should not be used on columns that are seldom queried or used, as this wastes space and degrades performance.

2. Avoid creating redundant indexes : Redundant indexes refer to creating multiple indexes on the same column or a subset of columns. Redundant indexes waste storage space, reduce write performance, and increase redundant index scans during query, resulting in degraded query performance.

3. Use a prefix index : a prefix index refers to creating an index on only a part of the column. The prefix index can reduce the size of the index, improve query performance and storage space utilization.

4. Consider using a joint index : A joint index refers to creating an index on multiple columns at the same time. Joint indexes can improve query performance and the efficiency of covering index queries. However, the joint index may also have some limitations, such as not being able to use part of the index, or needing to query in the order of the index.

5. Make sure the order of the index columns is correct : When creating a joint index, you need to ensure that the order of the index columns is correct. If the index columns are not in the correct order, the index may not be used, or query performance may degrade.

6. Make sure the data type of the index column matches : the data type of the index column should match the data type of the query condition. If the data types do not match, the index may not be used or the query performance may degrade.

7. Avoid performing function operations on index columns : performing function operations on index columns will make it impossible to use the index. If you need to perform functional operations on indexed columns, you can consider using computed columns instead of functional operations when querying, or use other types of indexes such as full-text indexes.

8. Regularly optimize indexes : Regularly optimize indexes can improve query performance and reduce storage space usage. For example, you can use the OPTIMIZE TABLE command to optimize the table, or use the performance analysis tools provided by MySQL to identify and optimize indexes.

In-depth analysis of the principle and optimization strategy of MySQL index