仅仅专注于特定查询，并在下面加载示例数据。这确实解决了其他一些查询，例如count(distinct ...)其他人提到的查询。

的alias in the HAVING出现要么略微优于或相当多的优于其替代（取决于查询）。

这使用一个预先存在的表，其中大约有500万行是通过我的回答快速创建的，耗时3至5分钟。

结果结构：

CREATE TABLE `ratings` (
  `id` int(11) NOT NULL AUTO_INCREMENT,
  `thing` int(11) NOT NULL,
  PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=5046214 DEFAULT CHARSET=utf8;

但是使用INNODB代替。由于范围保留插入而创建预期的INNODB间隙异常。只是说而已，没有区别。470万行。

修改表以接近Tim的假定架构。

rename table ratings to students; -- not exactly instanteous (a COPY)
alter table students add column camId int; -- get it near Tim's schema
-- don't add the `camId` index yet

以下将花费一些时间。一次又一次地大块运行它，否则您的连接可能会超时。超时是由于update语句中有500万行没有LIMIT子句。注意，我们 确实
有一个LIMIT子句。

因此，我们将进行半百万次的行迭代。将列设置为1到20之间的随机数

update students set camId=floor(rand()*20+1) where camId is null limit 500000; -- well that took a while (no surprise)

继续运行以上内容，直到no camId为null。

我跑了10次（整个过程需要7到10分钟）

select camId,count(*) from students
group by camId order by 1 ;

1   235641
2   236060
3   236249
4   235736
5   236333
6   235540
7   235870
8   236815
9   235950
10  235594
11  236504
12  236483
13  235656
14  236264
15  236050
16  236176
17  236097
18  235239
19  235556
20  234779

select count(*) from students;
-- 4.7 Million rows

创建一个有用的索引（当然是在插入之后）。

create index `ix_stu_cam` on students(camId); -- takes 45 seconds

ANALYZE TABLE students; -- update the stats: http://dev.mysql.com/doc/refman/5.7/en/analyze-table.html
-- the above is fine, takes 1 second

创建校园表。

create table campus
(   camID int auto_increment primary key,
    camName varchar(100) not null
);
insert campus(camName) values
('one'),('2'),('3'),('4'),('5'),
('6'),('7'),('8'),('9'),('ten'),
('etc'),('etc'),('etc'),('etc'),('etc'),
('etc'),('etc'),('etc'),('etc'),('twenty');
-- ok 20 of them

运行两个查询：

SELECT students.camID, campus.camName, COUNT(students.id) as studentCount 
FROM students 
JOIN campus 
    ON campus.camID = students.camID 
GROUP BY students.camID, campus.camName 
HAVING COUNT(students.id) > 3 
ORDER BY studentCount; 
-- run it many many times, back to back, 5.50 seconds, 20 rows of output

和

SELECT students.camID, campus.camName, COUNT(students.id) as studentCount 
FROM students 
JOIN campus 
    ON campus.camID = students.camID 
GROUP BY students.camID, campus.camName 
HAVING studentCount > 3 
ORDER BY studentCount; 
-- run it many many times, back to back, 5.50 seconds, 20 rows of output

所以时代是一样的。每跑十遍。

两者的EXPLAIN输出相同

+----+-------------+----------+------+---------------+------------+---------+----------------------+--------+---------------------------------+
| id | select_type | table    | type | possible_keys | key        | key_len | ref                  | rows   | Extra                           |
+----+-------------+----------+------+---------------+------------+---------+----------------------+--------+---------------------------------+
|  1 | SIMPLE      | campus   | ALL  | PRIMARY       | NULL       | NULL    | NULL                 |     20 | Using temporary; Using filesort |
|  1 | SIMPLE      | students | ref  | ix_stu_cam    | ix_stu_cam | 5       | bigtest.campus.camID | 123766 | Using index                     |
+----+-------------+----------+------+---------------+------------+---------+----------------------+--------+---------------------------------+

使用AVG（）函数，通过以下两个查询having（具有相同的EXPLAIN输出）中的别名，我的性能提高了约12％。

SELECT students.camID, campus.camName, avg(students.id) as studentAvg 
FROM students 
JOIN campus 
    ON campus.camID = students.camID 
GROUP BY students.camID, campus.camName 
HAVING avg(students.id) > 2200000 
ORDER BY students.camID; 
-- avg time 7.5

explain

SELECT students.camID, campus.camName, avg(students.id) as studentAvg 
FROM students 
JOIN campus 
    ON campus.camID = students.camID 
GROUP BY students.camID, campus.camName 
HAVING studentAvg > 2200000
ORDER BY students.camID;
-- avg time 6.5

最后是DISTINCT：

SELECT students.camID, count(distinct students.id) as studentDistinct 
FROM students 
JOIN campus 
    ON campus.camID = students.camID 
GROUP BY students.camID 
HAVING count(distinct students.id) > 1000000 
ORDER BY students.camID; -- 10.6   10.84   12.1   11.49   10.1   9.97   10.27   11.53   9.84 9.98
-- 9.9

 SELECT students.camID, count(distinct students.id) as studentDistinct 
 FROM students 
 JOIN campus 
    ON campus.camID = students.camID 
 GROUP BY students.camID 
 HAVING studentDistinct > 1000000 
 ORDER BY students.camID; -- 6.81    6.55   6.75   6.31   7.11 6.36   6.55
-- 6.45

具有相同输出的别名持续运行
速度提高35％EXPLAIN。见下文。因此，相同的Explain输出已显示两次，但并不能得出相同的性能，但这只是一般的提示。

+----+-------------+----------+-------+---------------+------------+---------+----------------------+--------+----------------------------------------------+
| id | select_type | table    | type  | possible_keys | key        | key_len | ref                  | rows   | Extra                                        |
+----+-------------+----------+-------+---------------+------------+---------+----------------------+--------+----------------------------------------------+
|  1 | SIMPLE      | campus   | index | PRIMARY       | PRIMARY    | 4       | NULL                 |     20 | Using index; Using temporary; Using filesort |
|  1 | SIMPLE      | students | ref   | ix_stu_cam    | ix_stu_cam | 5       | bigtest.campus.camID | 123766 | Using index                                  |
+----+-------------+----------+-------+---------------+------------+---------+----------------------+--------+----------------------------------------------+

目前，Optimizer似乎更喜欢别名，尤其是对于DISTINCT.