MySQL's index is a data structure that can speed up queries. It is similar to the table of contents of a book and can help us quickly find what we want. required information. By using specific algorithms and data structures, MySQL indexes can efficiently sort and store data, allowing for fast data lookup and access. In the database, indexes can speed up data query and update operations and improve system performance.
Common MySQL index types include B-tree index, hash index and full-text index, etc., supporting multiple index types. Among them, 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 forces each record in the table to have a unique primary key and can be used to quickly locate the specified record. A unique index is an index that enforces that each index value must be unique and can be used to avoid duplicate data in the table. Ordinary index is the most basic index type, which can improve query speed, but does not require that the index value must be unique.
In addition to B-tree indexes, MySQL also supports hash indexes and full-text indexes. By using a hash algorithm to sort the index, a hash index can quickly and accurately locate the required data. In some cases, the limitation of hash index is that it cannot support range queries and can only support equality queries. Full-text indexing 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, and can be used to quickly search text content.
To sum up, MySQL index is a data structure used to speed up database queries. Different types of indexes are suitable for different scenarios. Developers need to choose and optimize based on the actual situation. When designing indexes, attention needs to be paid to avoiding issues such as excessive use of indexes, combined indexes, selection of data types, and regular maintenance to improve system performance and stability.
The principle of MySQL index can be simply summarized as follows: the data in the table is sorted and stored according to a certain algorithm and data structure to form an index table. Quickly locate target data. MySQL indexes are implemented using B-tree or B tree data structures.
B-tree is a balanced tree structure that 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 with a key value less than the minimum key value of the node are on the left side of the node. All nodes with a key value greater than the maximum key value of the node are on the left side of the node. The nodes are all to the right of this node. Therefore, fast range queries and equivalent queries can be performed through B-tree.
B tree is a variant of B-tree. In B tree, internal nodes do not store data, only keywords and child node pointers are stored, and data is only stored in leaf nodes. Leaf nodes are connected through pointers, which can support fast range queries and equivalent queries. Compared with B-tree, B-tree uses memory space more efficiently and can reduce disk I/O operations, so it is more commonly used in practice.
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, advantages and disadvantages. For example, primary key indexes can be used to quickly locate specified records, unique indexes can avoid duplicate data in the table, ordinary indexes can speed up queries, and full-text indexes can be used to quickly search text content, etc.
When designing indexes, you need to pay attention to avoid issues such as excessive use of indexes, combined indexes, selection of data types, and regular maintenance to improve system performance and stability. MySQL is also equipped with an optimizer, which can select the best execution plan based on query conditions and indexes, thereby further improving query efficiency.
Commonly used index types in MySQL include:
1, Primary Key Index (Primary Key Index): The primary key index is a special unique index. It requires that the value of the index column is unique and not empty, and is used to quickly locate a certain row of data in the table. . Primary key indexes can be created automatically or manually specified.
2, Unique Index (Unique Index): A 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): Normal index is the most basic index type without any restrictions and is used to speed up query speed. A table can have multiple ordinary indexes.
4, Fulltext Index (Fulltext Index): Full-text index is used to quickly search text content, such as articles or logs, and can support full-text retrieval, word segmentation, keyword matching and other functions.
5, Composite Index (Composite Index): A composite index uses multiple columns as part of the index to optimize the performance of compound queries. The order of combined indexes is important and should be determined based on the frequency of queries and the efficiency of filtering.
6, Spatial Index (Spatial Index): Spatial index is used to store and query spatial data, such as geographical location and three-dimensional model, and can support spatial range query, nearest neighbor query, distance query and other functions.
7, Prefix Index (Prefix Index): Prefix index is a special index type that only indexes part of the column value and can be used to optimize query performance and save storage space. However, using prefix indexes may lead to non-unique indexes and inaccurate query results.
In actual applications, appropriate index types should be selected based on 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.
1. Use index in WHERE clause: Using index in WHERE clause can speed up query , such as using index columns to filter conditions in query statements. 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, OUse index in the RDER BY clause: use it in the ORDER BY clause Indexes can speed up sorting operations, such as sorting a 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 index in the JOIN operation: In the JOIN operation Using indexes can speed up association operations between tables, such as joining tables through a certain column. For example, to query the 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 index in the GROUP BY clause: Use it in the GROUP BY clause Indexes can speed up aggregation operations on result sets, such as counting the total number, average, maximum value, minimum value of a certain column, etc. 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 multiple Result set merging operations, such as 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;
Do not overuse indexes and avoid creating redundant indexes, otherwise it will Resulting in performance degradation and wasted storage space.
For frequently updated tables, you can consider reducing 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, Percona Toolkit, etc., to optimize query performance.
1. Determine the columns that need to be indexed: Generally, indexes should be created on columns that are frequently used for queries, joins, sorting, or grouping. . Indexes should not be used on columns that are rarely queried or used, otherwise they will waste space and reduce 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 queries, resulting in reduced query performance.
3, Use prefix index: Prefix index means creating an index for only part of the column. Prefix indexes can reduce index size, 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. Union indexes can improve query performance and efficiency of covering index queries. However, joint indexes may also have some limitations, such as being unable to use part of the index, or requiring queries in the order of the indexes.
5, Ensure that 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 order of index columns is incorrect, the index may not be usable or query performance may be degraded.
6, Ensure that 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 query performance may decrease.
7, Avoid performing function operations on index columns: Performing function operations on index columns will result in the inability to use the index. If you need to perform functional operations on indexed columns, you can consider using calculated columns instead of functional operations during queries, or use other types of indexes such as full-text indexes.
8, Optimize the index regularly: Regularly optimizing the index 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.
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