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MySQL索引优化

索引优化

1、单表索引优化

单表索引优化分析

范围值容易导致索引失效。

创建表

建表 SQL

CREATE TABLE IF NOT EXISTS article(
	id INT(10) UNSIGNED NOT NULL PRIMARY KEY AUTO_INCREMENT,
	author_id INT(10) UNSIGNED NOT NULL,
	category_id INT(10) UNSIGNED NOT NULL,
	views INT(10) UNSIGNED NOT NULL,
	comments INT(10) UNSIGNED NOT NULL,
	title VARCHAR(255) NOT NULL,
	content TEXT NOT NULL
);

INSERT INTO article(author_id,category_id,views,comments,title,content)
VALUES
(1,1,1,1,'1','1'),
(2,2,2,2,'2','2'),
(1,1,3,3,'3','3');

表中的测试数据

mysql> SELECT * FROM article;
+----+-----------+-------------+-------+----------+-------+---------+
| id | author_id | category_id | views | comments | title | content |
+----+-----------+-------------+-------+----------+-------+---------+
|  1 |         1 |           1 |     1 |        1 | 1     | 1       |
|  2 |         2 |           2 |     2 |        2 | 2     | 2       |
|  3 |         1 |           1 |     3 |        3 | 3     | 3       |
+----+-----------+-------------+-------+----------+-------+---------+
3 rows in set (0.00 sec)

查询案例

查询category_id为1且comments 大于1的情况下，views最多的article_id。

mysql> SELECT id, author_id FROM article WHERE category_id = 1 AND comments > 1 ORDER BY views DESC LIMIT 1;
+----+-----------+
| id | author_id |
+----+-----------+
|  3 |         1 |
+----+-----------+
1 row in set (0.00 sec)

此时 article 表中只有一个主键索引

mysql> SHOW INDEX FROM article;
+---------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| Table   | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment |
+---------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| article |          0 | PRIMARY  |            1 | id          | A         |           3 |     NULL | NULL   |      | BTREE      |         |               |
+---------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
1 row in set (0.00 sec)

使用 explain 分析 SQL 语句的执行效率：EXPLAIN SELECT id, author_id FROM article WHERE category_id = 1 AND comments > 1 ORDER BY views DESC LIMIT 1;

mysql> EXPLAIN SELECT id, author_id FROM article WHERE category_id = 1 AND comments > 1 ORDER BY views DESC LIMIT 1;
+----+-------------+---------+------+---------------+------+---------+------+------+-----------------------------+
| id | select_type | table   | type | possible_keys | key  | key_len | ref  | rows | Extra                       |
+----+-------------+---------+------+---------------+------+---------+------+------+-----------------------------+
|  1 | SIMPLE      | article | ALL  | NULL          | NULL | NULL    | NULL |    3 | Using where; Using filesort |
+----+-------------+---------+------+---------------+------+---------+------+------+-----------------------------+
1 row in set (0.00 sec)

结论：
- 很显然，type是ALL，即最坏的情况。
- Extra 里还出现了Using filesort，也是最坏的情况。
- 优化是必须的。

开始优化：新建索引

创建索引的 SQL 命令

# ALTER TABLE article ADD INDEX idx_article_ccv('category_id', 'comments', 'views'); 
create index idx_article_ccv on article(category_id, comments, views);

在 category_id 列、comments 列和 views 列上建立联合索引

mysql> create index idx_article_ccv on article(category_id, comments, views);
Query OK, 0 rows affected (0.01 sec)
Records: 0  Duplicates: 0  Warnings: 0

mysql> SHOW INDEX FROM article;
+---------+------------+-----------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| Table   | Non_unique | Key_name        | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment |
+---------+------------+-----------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| article |          0 | PRIMARY         |            1 | id          | A         |           3 |     NULL | NULL   |      | BTREE      |         |               |
| article |          1 | idx_article_ccv |            1 | category_id | A         |           3 |     NULL | NULL   |      | BTREE      |         |               |
| article |          1 | idx_article_ccv |            2 | comments    | A         |           3 |     NULL | NULL   |      | BTREE      |         |               |
| article |          1 | idx_article_ccv |            3 | views       | A         |           3 |     NULL | NULL   |      | BTREE      |         |               |
+---------+------------+-----------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
4 rows in set (0.00 sec)

再次执行查询：type变成了range，这是可以忍受的。但是extra里使用Using filesort仍是无法接受的。

mysql> EXPLAIN SELECT id, author_id FROM article WHERE category_id = 1 AND comments > 1 ORDER BY views DESC LIMIT 1;
+----+-------------+---------+-------+-----------------+-----------------+---------+------+------+---------------------------------------+
| id | select_type | table   | type  | possible_keys   | key             | key_len | ref  | rows | Extra                                 |
+----+-------------+---------+-------+-----------------+-----------------+---------+------+------+---------------------------------------+
|  1 | SIMPLE      | article | range | idx_article_ccv | idx_article_ccv | 8       | NULL |    1 | Using index condition; Using filesort |
+----+-------------+---------+-------+-----------------+-----------------+---------+------+------+---------------------------------------+
1 row in set (0.00 sec)

分析：
- 但是我们已经建立了索引，为啥没用呢？
- 这是因为按照B+Tree索引的工作原理，先排序 category_id，如果遇到相同的 category_id 则再排序comments，如果遇到相同的 comments 则再排序 views。
- 当comments字段在联合索引里处于中间位置时，因为comments>1条件是一个范围值（所谓 range），MySQL 无法利用索引再对后面的views部分进行检索，即 range 类型查询字段后面的索引无效。
- 将查询条件中的 comments > 1 改为 comments = 1 ，发现 Use filesort 神奇地消失了，从这点可以验证：范围后的索引会导致索引失效

mysql> EXPLAIN SELECT id, author_id FROM article WHERE category_id = 1 AND comments = 1 ORDER BY views DESC LIMIT 1;
+----+-------------+---------+------+-----------------+-----------------+---------+-------------+------+-------------+
| id | select_type | table   | type | possible_keys   | key             | key_len | ref         | rows | Extra       |
+----+-------------+---------+------+-----------------+-----------------+---------+-------------+------+-------------+
|  1 | SIMPLE      | article | ref  | idx_article_ccv | idx_article_ccv | 8       | const,const |    1 | Using where |
+----+-------------+---------+------+-----------------+-----------------+---------+-------------+------+-------------+
1 row in set (0.00 sec)

删除索引

删除索引的 SQL 指令

DROP INDEX idx_article_ccv ON article;

删除刚才创建的 idx_article_ccv 索引

mysql> DROP INDEX idx_article_ccv ON article;
Query OK, 0 rows affected (0.00 sec)
Records: 0  Duplicates: 0  Warnings: 0

mysql> SHOW INDEX FROM article;
+---------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| Table   | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment |
+---------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| article |          0 | PRIMARY  |            1 | id          | A         |           3 |     NULL | NULL   |      | BTREE      |         |               |
+---------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
1 row in set (0.00 sec)

再次创建索引

创建索引的 SQL 指令

# ALTER TABLE article ADD INDEX idx_article_ccv('category_id',  'views'); 
create index idx_article_ccv on article(category_id, views);

由于 range 后（comments > 1）的索引会失效，这次我们建立索引时，直接抛弃 comments 列，先利用 category_id 和 views 的联合索引查询所需要的数据，再从其中取出 comments > 1 的数据（我觉着应该是这样的）

mysql> create index idx_article_ccv on article(category_id, views);
Query OK, 0 rows affected (0.30 sec)
Records: 0  Duplicates: 0  Warnings: 0

mysql> SHOW INDEX FROM article;
+---------+------------+-----------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| Table   | Non_unique | Key_name        | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment |
+---------+------------+-----------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| article |          0 | PRIMARY         |            1 | id          | A         |           3 |     NULL | NULL   |      | BTREE      |         |               |
| article |          1 | idx_article_ccv |            1 | category_id | A         |           3 |     NULL | NULL   |      | BTREE      |         |               |
| article |          1 | idx_article_ccv |            2 | views       | A         |           3 |     NULL | NULL   |      | BTREE      |         |               |
+---------+------------+-----------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
3 rows in set (0.00 sec)

再次执行查询：可以看到，type变为了ref，Extra中的Using filesort也消失了，结果非常理想

ysql> EXPLAIN SELECT id, author_id FROM article WHERE category_id = 1 AND comments > 1 ORDER BY views DESC LIMIT 1;
+----+-------------+---------+------+-----------------+-----------------+---------+-------+------+-------------+
| id | select_type | table   | type | possible_keys   | key             | key_len | ref   | rows | Extra       |
+----+-------------+---------+------+-----------------+-----------------+---------+-------+------+-------------+
|  1 | SIMPLE      | article | ref  | idx_article_ccv | idx_article_ccv | 4       | const |    2 | Using where |
+----+-------------+---------+------+-----------------+-----------------+---------+-------+------+-------------+
1 row in set (0.00 sec)

为了不影响之后的测试，删除该表的 idx_article_ccv 索引

mysql> DROP INDEX idx_article_ccv ON article;
Query OK, 0 rows affected (0.05 sec)
Records: 0  Duplicates: 0  Warnings: 0

mysql> SHOW INDEX FROM article;
+---------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| Table   | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment |
+---------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| article |          0 | PRIMARY  |            1 | id          | A         |           3 |     NULL | NULL   |      | BTREE      |         |               |
+---------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
1 row in set (0.01 sec)

2、两表索引优化

两表索引优化分析：主外键

主要是两表连接查询：用小表驱动大表，大表索引优化越大，越快。

创建表

建表 SQL

CREATE TABLE IF NOT EXISTS class(
	id INT(10) UNSIGNED NOT NULL AUTO_INCREMENT,
	card INT(10) UNSIGNED NOT NULL,
	PRIMARY KEY(id)
);

CREATE TABLE IF NOT EXISTS book(
	bookid INT(10) UNSIGNED NOT NULL AUTO_INCREMENT,
	card INT(10) UNSIGNED NOT NULL,
	PRIMARY KEY(bookid)
);

INSERT INTO class(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO class(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO class(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO class(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO class(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO class(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO class(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO class(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO class(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO class(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO class(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO class(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO class(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO class(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO class(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO class(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO class(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO class(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO class(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO class(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO class(card) VALUES(FLOOR(1+(RAND()*20)));

INSERT INTO book(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO book(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO book(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO book(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO book(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO book(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO book(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO book(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO book(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO book(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO book(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO book(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO book(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO book(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO book(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO book(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO book(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO book(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO book(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO book(card) VALUES(FLOOR(1+(RAND()*20)));

class 表中的测试数据

mysql> select * from class;
+----+------+
| id | card |
+----+------+
|  1 |   12 |
|  2 |   13 |
|  3 |   12 |
|  4 |   17 |
|  5 |   11 |
|  6 |    3 |
|  7 |    1 |
|  8 |   16 |
|  9 |   17 |
| 10 |   16 |
| 11 |    9 |
| 12 |   17 |
| 13 |   18 |
| 14 |   16 |
| 15 |    7 |
| 16 |    8 |
| 17 |   19 |
| 18 |    9 |
| 19 |    6 |
| 20 |    5 |
| 21 |    6 |
+----+------+
21 rows in set (0.00 sec)

book 表中的测试数据

mysql> select * from book;
+--------+------+
| bookid | card |
+--------+------+
|      1 |   16 |
|      2 |    1 |
|      3 |   17 |
|      4 |    3 |
|      5 |   20 |
|      6 |   12 |
|      7 |   18 |
|      8 |   13 |
|      9 |   13 |
|     10 |    4 |
|     11 |    1 |
|     12 |   13 |
|     13 |   20 |
|     14 |   20 |
|     15 |    1 |
|     16 |    2 |
|     17 |    9 |
|     18 |   16 |
|     19 |   14 |
|     20 |    2 |
+--------+------+
20 rows in set (0.00 sec)

查询案例

实现两表的连接，连接条件是 class.card = book.card

mysql> SELECT * FROM class LEFT JOIN book ON class.card = book.card;
+----+------+--------+------+
| id | card | bookid | card |
+----+------+--------+------+
|  1 |   12 |      6 |   12 |
|  2 |   13 |      8 |   13 |
|  2 |   13 |      9 |   13 |
|  2 |   13 |     12 |   13 |
|  3 |   12 |      6 |   12 |
|  4 |   17 |      3 |   17 |
|  5 |   11 |   NULL | NULL |
|  6 |    3 |      4 |    3 |
|  7 |    1 |      2 |    1 |
|  7 |    1 |     11 |    1 |
|  7 |    1 |     15 |    1 |
|  8 |   16 |      1 |   16 |
|  8 |   16 |     18 |   16 |
|  9 |   17 |      3 |   17 |
| 10 |   16 |      1 |   16 |
| 10 |   16 |     18 |   16 |
| 11 |    9 |     17 |    9 |
| 12 |   17 |      3 |   17 |
| 13 |   18 |      7 |   18 |
| 14 |   16 |      1 |   16 |
| 14 |   16 |     18 |   16 |
| 15 |    7 |   NULL | NULL |
| 16 |    8 |   NULL | NULL |
| 17 |   19 |   NULL | NULL |
| 18 |    9 |     17 |    9 |
| 19 |    6 |   NULL | NULL |
| 20 |    5 |   NULL | NULL |
| 21 |    6 |   NULL | NULL |
+----+------+--------+------+
28 rows in set (0.00 sec)

使用 explain 分析 SQL 语句的性能，可以看到：驱动表是左表 class 表

mysql> EXPLAIN SELECT * FROM class LEFT JOIN book ON class.card = book.card;
+----+-------------+-------+------+---------------+------+---------+------+------+----------------------------------------------------+
| id | select_type | table | type | possible_keys | key  | key_len | ref  | rows | Extra                                              |
+----+-------------+-------+------+---------------+------+---------+------+------+----------------------------------------------------+
|  1 | SIMPLE      | class | ALL  | NULL          | NULL | NULL    | NULL |   21 | NULL                                               |
|  1 | SIMPLE      | book  | ALL  | NULL          | NULL | NULL    | NULL |   20 | Using where; Using join buffer (Block Nested Loop) |
+----+-------------+-------+------+---------------+------+---------+------+------+----------------------------------------------------+
2 rows in set (0.00 sec)

结论：
- type 有 All ，rows 为表中数据总行数，说明 class 和 book 进行了全表检索
- 即每次 class 表对 book 表进行左外连接时，都需要在 book 表中进行一次全表检索

添加索引：在右表添加索引

添加索引的 SQL 指令

ALTER TABLE 'book' ADD INDEX Y ('card');

在 book 的 card 字段上添加索引

mysql> ALTER TABLE book ADD INDEX Y (card);
Query OK, 0 rows affected (0.30 sec)
Records: 0  Duplicates: 0  Warnings: 0

mysql> SHOW INDEX FROM book;
+-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment |
+-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| book  |          0 | PRIMARY  |            1 | bookid      | A         |          20 |     NULL | NULL   |      | BTREE      |         |               |
| book  |          1 | Y        |            1 | card        | A         |          20 |     NULL | NULL   |      | BTREE      |         |               |
+-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
2 rows in set (0.00 sec)

测试结果：可以看到第二行的type变为了ref，rows也变成了优化比较明显。

mysql> EXPLAIN SELECT * FROM class LEFT JOIN book ON class.card = book.card;
+----+-------------+-------+------+---------------+------+---------+-----------------+------+-------------+
| id | select_type | table | type | possible_keys | key  | key_len | ref             | rows | Extra       |
+----+-------------+-------+------+---------------+------+---------+-----------------+------+-------------+
|  1 | SIMPLE      | class | ALL  | NULL          | NULL | NULL    | NULL            |   21 | NULL        |
|  1 | SIMPLE      | book  | ref  | Y             | Y    | 4       | db01.class.card |    1 | Using index |
+----+-------------+-------+------+---------------+------+---------+-----------------+------+-------------+
2 rows in set (0.00 sec)

分析：
- 这是由左连接特性决定的。LEFT JOIN条件用于确定如何从右表搜索行，左边一定都有，所以右边是我们的关键点，一定需要建立索引。
- 左表连接右表，则需要拿着左表的数据去右表里面查，索引需要在右表中建立索引

添加索引：在右表添加索引

删除之前 book 表中的索引

DROP INDEX Y ON book;

在 class 表的 card 字段上建立索引

ALTER TABLE class ADD INDEX X(card);

再次执行左连接，凉凉~~~

mysql> EXPLAIN SELECT * FROM class LEFT JOIN book ON class.card = book.card;
+----+-------------+-------+-------+---------------+------+---------+------+------+----------------------------------------------------+
| id | select_type | table | type  | possible_keys | key  | key_len | ref  | rows | Extra                                              |
+----+-------------+-------+-------+---------------+------+---------+------+------+----------------------------------------------------+
|  1 | SIMPLE      | class | index | NULL          | X    | 4       | NULL |   21 | Using index                                        |
|  1 | SIMPLE      | book  | ALL   | NULL          | NULL | NULL    | NULL |   20 | Using where; Using join buffer (Block Nested Loop) |
+----+-------------+-------+-------+---------------+------+---------+------+------+----------------------------------------------------+
2 rows in set (0.00 sec)

别怕，我们来执行右连接：可以看到第二行的type变为了ref，rows也变成了优化比较明显。

mysql> EXPLAIN SELECT * FROM class RIGHT JOIN book ON class.card = book.card;
+----+-------------+-------+------+---------------+------+---------+----------------+------+-------------+
| id | select_type | table | type | possible_keys | key  | key_len | ref            | rows | Extra       |
+----+-------------+-------+------+---------------+------+---------+----------------+------+-------------+
|  1 | SIMPLE      | book  | ALL  | NULL          | NULL | NULL    | NULL           |   20 | NULL        |
|  1 | SIMPLE      | class | ref  | X             | X    | 4       | db01.book.card |    1 | Using index |
+----+-------------+-------+------+---------------+------+---------+----------------+------+-------------+
2 rows in set (0.00 sec)

分析：
这是因为RIGHT JOIN条件用于确定如何从左表搜索行，右边一定都有，所以左边是我们的关键点，一定需要建立索引。
class RIGHT JOIN book ：book 里面的数据一定存在于结果集中，我们需要拿着 book 表中的数据，去 class 表中搜索，所以索引需要建立在 class 表中
为了不影响之后的测试，删除该表的 idx_article_ccv 索引

mysql> DROP INDEX X ON class;
Query OK, 0 rows affected (0.04 sec)
Records: 0  Duplicates: 0  Warnings: 0

mysql> SHOW INDEX FROM class;
+-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment |
+-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| class |          0 | PRIMARY  |            1 | id          | A         |          21 |     NULL | NULL   |      | BTREE      |         |               |
+-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
1 row in set (0.00 sec)

3、三表索引优化

三表索引优化分析

创建表

建表 SQL

CREATE TABLE IF NOT EXISTS phone(
	phoneid INT(10) UNSIGNED NOT NULL AUTO_INCREMENT,
	card INT(10) UNSIGNED NOT NULL,
	PRIMARY KEY(phoneid)
)ENGINE=INNODB;

INSERT INTO phone(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO phone(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO phone(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO phone(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO phone(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO phone(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO phone(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO phone(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO phone(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO phone(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO phone(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO phone(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO phone(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO phone(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO phone(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO phone(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO phone(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO phone(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO phone(card) VALUES(FLOOR(1+(RAND()*20)));
INSERT INTO phone(card) VALUES(FLOOR(1+(RAND()*20)));

phone 表中的测试数据

mysql> select * from phone;
+---------+------+
| phoneid | card |
+---------+------+
|       1 |    7 |
|       2 |    7 |
|       3 |   13 |
|       4 |    6 |
|       5 |    8 |
|       6 |    4 |
|       7 |   16 |
|       8 |    4 |
|       9 |   15 |
|      10 |    1 |
|      11 |   20 |
|      12 |   18 |
|      13 |    9 |
|      14 |    9 |
|      15 |   20 |
|      16 |   11 |
|      17 |   15 |
|      18 |    3 |
|      19 |    8 |
|      20 |   10 |
+---------+------+
20 rows in set (0.00 sec)

查询案例

实现三表的连接查询：

mysql> SELECT * FROM class LEFT JOIN book ON class.card = book.card LEFT JOIN phone ON book.card = phone.card;
+----+------+--------+------+---------+------+
| id | card | bookid | card | phoneid | card |
+----+------+--------+------+---------+------+
|  2 |   13 |      8 |   13 |       3 |   13 |
|  2 |   13 |      9 |   13 |       3 |   13 |
|  2 |   13 |     12 |   13 |       3 |   13 |
|  8 |   16 |      1 |   16 |       7 |   16 |
| 10 |   16 |      1 |   16 |       7 |   16 |
| 14 |   16 |      1 |   16 |       7 |   16 |
|  8 |   16 |     18 |   16 |       7 |   16 |
| 10 |   16 |     18 |   16 |       7 |   16 |
| 14 |   16 |     18 |   16 |       7 |   16 |
|  7 |    1 |      2 |    1 |      10 |    1 |
|  7 |    1 |     11 |    1 |      10 |    1 |
|  7 |    1 |     15 |    1 |      10 |    1 |
| 13 |   18 |      7 |   18 |      12 |   18 |
| 11 |    9 |     17 |    9 |      13 |    9 |
| 18 |    9 |     17 |    9 |      13 |    9 |
| 11 |    9 |     17 |    9 |      14 |    9 |
| 18 |    9 |     17 |    9 |      14 |    9 |
|  6 |    3 |      4 |    3 |      18 |    3 |
|  4 |   17 |      3 |   17 |    NULL | NULL |
|  9 |   17 |      3 |   17 |    NULL | NULL |
| 12 |   17 |      3 |   17 |    NULL | NULL |
|  1 |   12 |      6 |   12 |    NULL | NULL |
|  3 |   12 |      6 |   12 |    NULL | NULL |
|  5 |   11 |   NULL | NULL |    NULL | NULL |
| 15 |    7 |   NULL | NULL |    NULL | NULL |
| 16 |    8 |   NULL | NULL |    NULL | NULL |
| 17 |   19 |   NULL | NULL |    NULL | NULL |
| 19 |    6 |   NULL | NULL |    NULL | NULL |
| 20 |    5 |   NULL | NULL |    NULL | NULL |
| 21 |    6 |   NULL | NULL |    NULL | NULL |
+----+------+--------+------+---------+------+
30 rows in set (0.00 sec)

使用 explain 分析 SQL 指令：

mysql> EXPLAIN SELECT * FROM class LEFT JOIN book ON class.card = book.card LEFT JOIN phone ON book.card = phone.card;
+----+-------------+-------+------+---------------+------+---------+------+------+----------------------------------------------------+
| id | select_type | table | type | possible_keys | key  | key_len | ref  | rows | Extra                                              |
+----+-------------+-------+------+---------------+------+---------+------+------+----------------------------------------------------+
|  1 | SIMPLE      | class | ALL  | NULL          | NULL | NULL    | NULL |   21 | NULL                                               |
|  1 | SIMPLE      | book  | ALL  | NULL          | NULL | NULL    | NULL |   20 | Using where; Using join buffer (Block Nested Loop) |
|  1 | SIMPLE      | phone | ALL  | NULL          | NULL | NULL    | NULL |   20 | Using where; Using join buffer (Block Nested Loop) |
+----+-------------+-------+------+---------------+------+---------+------+------+----------------------------------------------------+
3 rows in set (0.00 sec)

结论：
- type 有All ，rows 为表数据总行数，说明 class、 book 和 phone 表都进行了全表检索
- Extra 中 Using join buffer ，表明连接过程中使用了 join 缓冲区

创建索引

创建索引的 SQL 语句

ALTER TABLE book ADD INDEX Y (card);
ALTER TABLE phone ADD INDEX Z (card);

进行 LEFT JOIN ，永远都在右表的字段上建立索引

mysql> ALTER TABLE book ADD INDEX Y (card);
Query OK, 0 rows affected (0.06 sec)
Records: 0  Duplicates: 0  Warnings: 0

mysql> SHOW INDEX FROM book;
+-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment |
+-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| book  |          0 | PRIMARY  |            1 | bookid      | A         |          20 |     NULL | NULL   |      | BTREE      |         |               |
| book  |          1 | Y        |            1 | card        | A         |          20 |     NULL | NULL   |      | BTREE      |         |               |
+-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
2 rows in set (0.00 sec)

mysql> ALTER TABLE phone ADD INDEX Z (card);
Query OK, 0 rows affected (0.05 sec)
Records: 0  Duplicates: 0  Warnings: 0

mysql> SHOW INDEX FROM phone;
+-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| Table | Non_unique | Key_name | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment |
+-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| phone |          0 | PRIMARY  |            1 | phoneid     | A         |          20 |     NULL | NULL   |      | BTREE      |         |               |
| phone |          1 | Z        |            1 | card        | A         |          20 |     NULL | NULL   |      | BTREE      |         |               |
+-------+------------+----------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
2 rows in set (0.00 sec)

执行查询：后2行的type都是ref，且总rows优化很好，效果不错。因此索引最好设置在需要经常查询的字段中。

mysql> EXPLAIN SELECT * FROM class LEFT JOIN book ON class.card=book.card LEFT JOIN phone ON book.card = phone.card;
+----+-------------+-------+------+---------------+------+---------+-----------------+------+-------------+
| id | select_type | table | type | possible_keys | key  | key_len | ref             | rows | Extra       |
+----+-------------+-------+------+---------------+------+---------+-----------------+------+-------------+
|  1 | SIMPLE      | class | ALL  | NULL          | NULL | NULL    | NULL            |   21 | NULL        |
|  1 | SIMPLE      | book  | ref  | Y             | Y    | 4       | db01.class.card |    1 | Using index |
|  1 | SIMPLE      | phone | ref  | Z             | Z    | 4       | db01.book.card  |    1 | Using index |
+----+-------------+-------+------+---------------+------+---------+-----------------+------+-------------+
3 rows in set (0.00 sec)

Join 语句优化的结论

将 left join 看作是两层嵌套 for 循环

尽可能减少Join语句中的NestedLoop的循环总次数；
永远用小结果集驱动大的结果集（在大结果集中建立索引，在小结果集中遍历全表）；
优先优化NestedLoop的内层循环；
保证Join语句中被驱动表上Join条件字段已经被索引；
当无法保证被驱动表的Join条件字段被索引且内存资源充足的前提下，不要太吝惜JoinBuffer的设置；

我的理解

使用小表驱动大表，这就相当于外层 for 循环的次数少，内层 for 循环的次数多。
然后我们在大表中建立了索引，这样内层 for 循环的效率明显提高
综上，使用小表驱动大表，在大表中建立了索引

小表的每条都得遍历，用小表的id在大表中用索引查，所以快。

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原文地址：https://www.cnblogs.com/friend-c/p/15395198.html