SQL 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。
1. GROUP BY 语法
SELECT column_name, aggregate_function(column_name)
FROM table_name
WHERE column_name operator value
GROUP BY column_name;
FROM table_name
WHERE column_name operator value
GROUP BY column_name;
样本数据库
在本教程中,我们将使用 CodeBaoku 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 | 编程宝库 | http://www.codebaoku.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:
+-----+---------+-------+------------+ | 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 | +-----+---------+-------+------------+
2. GROUP BY 范例
统计 access_log 各个 site_id 的访问量:
SELECT site_id, SUM(access_log.count) AS nums FROM access_log GROUP BY site_id; 执行结果: +---------+----------+ | site_id | nums | +---------+----------+ | 1 | 275 | | 2 | 10 | | 3 | 521 | | 4 | 13 | | 5 | 750 | +---------+----------+
3. GROUP BY 多表连接范例
下面的 SQL 语句统计有记录的网站的记录数量:
SELECT Websites.name,COUNT(access_log.aid) AS nums FROM access_log LEFT JOIN Websites ON access_log.site_id=Websites.id GROUP BY Websites.name; 执行结果: +-----------+----------+ | name | nums | +-----------+----------+ | Facebook | 2 | | Google | 2 | | 微博 | 1 | | 淘宝 | 1 | | 编程宝库 | 3 | +-----------+----------+
HAVING 子句可以筛选分组后的各组数据,而 WHERE 关键字无法与聚合函数一起使用。SQL HAVING 语法:SELECT column_name, aggregate_function(column_name) FROM table_na...