MySQL advanced part 1: index

Indexes

Index Overview

Indexes （index） Help MySQL Data structure for efficient data acquisition （ Orderly ）. Indexes are added to the fields of the database table , It is a mechanism to improve query efficiency . Out of data , The database system also maintains a data structure that satisfies a specific search algorithm , These data structures are referenced in some way （ Point to ） data , In this way, advanced search algorithms can be implemented on these data structures , This data structure is the index . Like the one below Sketch Map Shown :

On the left is the data table , There are two columns and seven records , On the far left is the physical address of the data record （ Note that logically adjacent records are not physically adjacent to each other on disk ）. In order to speed up Col2 Lookup , You can maintain a binary search tree as shown on the right , Each node contains an index key value and a pointer to the physical address of the corresponding data record , In this way, we can use binary search to get the corresponding data quickly .

Generally speaking, the index itself is very large , It's impossible to store everything in memory , So indexes are often stored on disk as index files . Index is the most commonly used tool in database to improve performance .

Index strengths and weaknesses

advantage

1） It's like a catalog index of books , Improve the efficiency of data retrieval , Reduce the IO cost .

2） Sort data through index columns , Reduce the cost of sorting data , Reduce CPU Consumption of .

Inferiority

1） The index is actually a table , The table holds the primary key and index fields , And point to the record of the entity class , So index columns also need to occupy space .

2） Although indexing greatly improves query efficiency , At the same time, it also reduces the speed of updating tables , Such as on the table INSERT、UPDATE、DELETE. Because when updating tables ,MySQL Not only to save data , And save the index file . Each update adds the field of the index column , Will adjust the index information after the key value changes due to the update .

Index structure

The index is in MySQL In the storage engine layer , Not at the server level . So the indexes of each storage engine are not necessarily the same , Not all storage engines support all index types .MySQL The following are currently available 4 Species index ：

• BTREE Indexes ： The most common type of index , Most indexes support B Tree index .
• HASH Indexes ： Only Memory Engine support , The use scenario is simple .
• R-tree Indexes （ Spatial index ）： The spatial index is MyISAM A special index type of the engine , Mainly used for geospatial data types , Usually used less , No special introduction .
• Full-text （ Full-text index ） ： Full text index is also MyISAM A special index type of , Mainly used for full-text indexing ,InnoDB from Mysql5.6 Full text indexing is now supported .

MyISAM、InnoDB、Memory Three storage engines support various index types

Indexes	InnoDB engine	MyISAM engine	Memory engine
BTREE Indexes	Support	Support	Support
HASH Indexes	I won't support it	I won't support it	Support
R-tree Indexes	I won't support it	Support	I won't support it
Full-text	5.6 Support for	Support	I won't support it

What we usually call index , If not specified , All refer to B+ Trees （ Multiple search trees , It doesn't have to be a binary ） Index of structural organization . Where the clustered index 、 Composite index 、 Prefix index 、 The only index defaults to B+tree Indexes , Collectively referred to as Indexes .

BTREE structure

BTree It's also called multiple balanced search tree , One m Forked BTree Characteristics are as follows ：

• Each node in the tree contains at most m A child .
• Except root node and leaf node , Each node has at least [ceil(m/2)] A child .
• If the root node is not a leaf node , There are at least two children .
• All the leaf nodes are on the same layer .
• Each non leaf node is composed of n individual key And n+1 Pointer composition , among [ceil(m/2)-1] <= n <= m-1

With 5 fork BTree For example ,key The number of ： Formula derivation [ceil(m/2)-1] <= n <= m-1. therefore 2 <= n <=4 . When n>4 when , The intermediate node splits into the parent node , Split nodes on both sides .

Insert C N G A H E K Q M F W L T Z D P R X Y S Take the data .

The evolution process is as follows ：

1). Before insertion 4 Letters C N G A

2). Insert H,n>4, The middle element G Letters split up to new nodes

3). Insert E,K,Q There is no need to split

4). Insert M, The middle element M The letter splits up to the parent node G

5). Insert F,W,L,T There is no need to split

6). Insert Z, The middle element T Split up into parent nodes

7). Insert D, The middle element D Split up into parent nodes . Then insert P,R,X,Y There is no need to split

8). Finally insert S,NPQR node n>5, Intermediate nodes Q Split up , But split the parent node DGMT Of n>5, Intermediate nodes M Split up

Here we are , The BTREE The tree has been built , BTREE Trees and Binary tree comparison , Query data more efficiently , Because for the same amount of data ,BTREE The hierarchical structure of a tree is smaller than that of a binary tree , So search speed is fast .

B+TREE structure

B+Tree by BTree Variants ,B+Tree And BTree The difference is ：

1). n fork B+Tree Up to n individual key, and BTree Up to n-1 individual key.

2). B+Tree The leaf node in the key Information , In accordance with the key In order of size .

3). All non leaf nodes can be regarded as key The index part of .

because B+Tree Only leaf nodes are saved key Information , Query any key All from root Go to the leaves . therefore B+Tree The query efficiency is more stable .

MySQL Medium B+Tree

MySql Index data structure for classic B+Tree optimized . In the original B+Tree On the basis of , Add a pointer to the linked list of adjacent leaf nodes , So we have a sequence pointer B+Tree, Improve the performance of interval access .

MySQL Medium B+Tree Index structure diagram :

Index classification

1） Single value index ： That is, an index contains only a single column , A table can have multiple single-column indexes

2） unique index ： The value of the index column must be unique , But you can have an empty value

3） Composite index ： That is, an index contains multiple columns

Index Syntax

When the index creates the table , You can create at the same time , You can also add new indexes at any time .

Prepare the environment :

create database demo_01 default charset=utf8mb4;

use demo_01;

CREATE TABLE `city` (
  `city_id` int(11) NOT NULL AUTO_INCREMENT,
  `city_name` varchar(50) NOT NULL,
  `country_id` int(11) NOT NULL,
  PRIMARY KEY (`city_id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;

CREATE TABLE `country` (
  `country_id` int(11) NOT NULL AUTO_INCREMENT,
  `country_name` varchar(100) NOT NULL,
  PRIMARY KEY (`country_id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;


insert into `city` (`city_id`, `city_name`, `country_id`) values(1,' Xi'an ',1);
insert into `city` (`city_id`, `city_name`, `country_id`) values(2,'NewYork',2);
insert into `city` (`city_id`, `city_name`, `country_id`) values(3,' Beijing ',1);
insert into `city` (`city_id`, `city_name`, `country_id`) values(4,' Shanghai ',1);

insert into `country` (`country_id`, `country_name`) values(1,'China');
insert into `country` (`country_id`, `country_name`) values(2,'America');
insert into `country` (`country_id`, `country_name`) values(3,'Japan');
insert into `country` (`country_id`, `country_name`) values(4,'UK');

Create index

grammar ：

CREATE 	[UNIQUE|FULLTEXT|SPATIAL]  INDEX index_name 
[USING  index_type]
ON tbl_name(index_col_name,...)

index_col_name : column_name[(length)][ASC | DESC]

Example ： by city In the table city_name Field creation index :

Look at the index

grammar

show index  from  table_name;

Example ： see city Index information in the table ：

Delete index

grammar

DROP  INDEX  index_name  ON  tbl_name;

Example ： Want to delete city The index on the table idx_city_name, It can be operated as follows ：

ALERT command

1). alter  table  tb_name  add  primary  key(column_list); 
	 This statement adds a primary key , This means that the index value must be unique , And cannot be NULL	
	
2). alter  table  tb_name  add  unique index_name(column_list);
	 The value of the index created by this statement must be unique （ except NULL Outside ,NULL There may be many times ）
	
3). alter  table  tb_name  add  index index_name(column_list);
	 Add a normal index ,  Index values can appear multiple times .
	
4). alter  table  tb_name  add  fulltext  index_name(column_list);
	 The statement specifies that the index is FULLTEXT,  For full-text indexing 

Index design principles

Index design can follow some existing principles , When creating an index, try to conform to these principles , It is easy to improve the efficiency of the index , More efficient use of indexes .

• The query frequency is high , And a table with a large amount of data is indexed .

• Selection of index fields , The best candidate column should be from where Clause is extracted from the condition of clause , If where There are more combinations in clauses , Then choose the most commonly used 、 The combination of the best filtering Columns .

• Use unique index , The more distinguishable , The more efficient the index is .

• Index can effectively improve the efficiency of query data , But the number of indexes is not the better , More indexes , The cost of maintaining the index naturally goes up . For inserting 、 to update 、 Delete etc. DML For tables that operate more frequently , Too many indexes , It's going to introduce quite a high maintenance cost , Reduce DML Efficiency of operation , Increase the time consumption of the corresponding operation . In addition, if there are too many indexes ,MySQL It's also a choice problem , Although you'll still find a usable index in the end , But it certainly raises the cost of choice .

• Use short index , After the index is created, it is also stored on the hard disk , Therefore, the index access is improved I/O efficiency , It can also improve the overall access efficiency . If the total length of the fields that make up the index is short , Then more index values can be stored in a given size of memory block , Correspondingly, it can effectively improve MySQL Access to the index I/O efficiency .

• Use the leftmost prefix ,N A composite index of columns , So it's equivalent to creating N An index , If you query where Clause uses the first few fields that make up the index , So this query SQL We can use composite index to improve query efficiency .

Create composite index :
CREATE INDEX idx_name_email_status ON tb_seller(NAME,email,STATUS);

Equivalent to
Yes name Create index ;
Yes name , email Created index ;
Yes name , email, status Created index ;