SQL GROUP BY 语句
SQL GROUP BY 语句
FROM table_name
WHERE column_name operator value
GROUP BY column_name;
演示数据库
GROUP BY 简单应用
FROM access_log GROUP BY site_id;
SQL GROUP BY 多表连接
LEFT JOIN Websites
ON access_log.site_id=Websites.id
GROUP BY Websites.name;
GROUP BY 语句可结合一些聚合函数来使用
GROUP BY 语句GROUP BY 语句用于结合聚合函数，根据一个或多个列对结果集进行分组。
SQL GROUP BY 语法 SELECT column_name, aggregate_function(column_name)FROM table_name
WHERE column_name operator value
GROUP BY column_name;
演示数据库
在本教程中，我们将使用 RUNOOB 样本数据库。
下面是选自 "Websites" 表的数据：
+----+--------------+---------------------------+-------+---------+
| id | name | url | alexa | country |
+----+--------------+---------------------------+-------+---------+
| 1 | Google | https://www.google.cm/ | 1 | USA |
| 2 | 淘宝 | https://www.taobao.com/ | 13 | CN |
| 3 | 软件开发网 | http://www.mscto.com/ | 4689 | CN |
| 4 | 微博 | http://weibo.com/ | 20 | CN |
| 5 | Facebook | https://www.facebook.com/ | 3 | USA |
| 7 | stackoverflow | http://stackoverflow.com/ | 0 | IND |
+----+---------------+---------------------------+-------+---------+
下面是 "access_log" 网站访问记录表的数据：
mysql> SELECT * FROM access_log;
+-----+---------+-------+------------+
| aid | site_id | count | date |
+-----+---------+-------+------------+
| 1 | 1 | 45 | 2016-05-10 |
| 2 | 3 | 100 | 2016-05-13 |
| 3 | 1 | 230 | 2016-05-14 |
| 4 | 2 | 10 | 2016-05-14 |
| 5 | 5 | 205 | 2016-05-14 |
| 6 | 4 | 13 | 2016-05-15 |
| 7 | 3 | 220 | 2016-05-15 |
| 8 | 5 | 545 | 2016-05-16 |
| 9 | 3 | 201 | 2016-05-17 |
+-----+---------+-------+------------+
9 rows in set (0.00 sec)
GROUP BY 简单应用
统计 access_log 各个 site_id 的访问量：
实例 SELECT site_id, SUM(access_log.count) AS numsFROM access_log GROUP BY site_id;
执行以上 SQL 输出结果如下：
SQL GROUP BY 多表连接
下面的 SQL 语句统计有记录的网站的记录数量：
实例 SELECT Websites.name,COUNT(access_log.aid) AS nums FROM access_logLEFT JOIN Websites
ON access_log.site_id=Websites.id
GROUP BY Websites.name;
执行以上 SQL 输出结果如下：
相关文章
Natalia
2021-02-20
Damara
2020-08-12
Psyche
2020-08-11
Meta
2023-07-20
Catherine
2023-07-20
Rhoda
2023-07-20
Psyche
2023-07-20
Cytheria
2023-07-20
Gamila
2023-07-20
Alanni
2023-07-20
Petunia
2023-07-20
Hellens
2023-07-20
Belle
2023-07-20
Nyako
2023-07-20
Dagny
2023-07-20
Clementine
2023-07-20
Isleta
2023-07-20
Isis
2023-07-20
Aggie
2023-07-20