MySQL中的WITH ROLLUP

MySQL的扩展SQL中有一个非常有意思的应用WITH ROLLUP，在分组的统计数据的基础上再进行相同的统计（SUM,AVG,COUNT…），非常类似于Oracle中统计函数的功能，Oracle的统计函数更多更强大。下面演示单个司机以及所有司机的总行驶里程数和平均行驶里程数：mysql> select name,sum（miles） as "miles/driver" -> from driver_log group by name with rollup;+-------+--------------+| name | miles/driver |+-------+--------------+| Ben | 362 || Henry | 911 || Suzi | 893 || NULL | 2166 |+-------+--------------+4 rows in set （0.00 sec） mysql> select name,avg（miles） as driver_avg -> from driver_log group by name with rollup;+-------+------------+| name | driver_avg |+-------+------------+| Ben | 120.6667 || Henry | 182.2000 || Suzi | 446.5000 || NULL | 216.6000 |+-------+------------+4 rows in set （0.00 sec） mysql> select name,sum（miles） as "miles/driver",avg（miles） as driver_avg -> from driver_log group by name with rollup;+-------+--------------+------------+| name | miles/driver | driver_avg |+-------+--------------+------------+| Ben | 362 | 120.6667 || Henry | 911 | 182.2000 || Suzi | 893 | 446.5000 || NULL | 2166 | 216.6000 |+-------+--------------+------------+4 rows in set （0.00 sec）在多个分组下WITH ROLLUP同样有效：mysql> select srcuser,dstuser,count（*） from mail group by srcuser,dstuser;+---------+---------+----------+| srcuser | dstuser | count（*） |+---------+---------+----------+| barb | barb | 1 || barb | tricia | 2 || gene | barb | 2 || gene | gene | 3 || gene | tricia | 1 || phil | barb | 1 || phil | phil | 2 || phil | tricia | 2 || tricia | gene | 1 || tricia | phil | 1 |+---------+---------+----------+10 rows in set （0.05 sec） mysql> select srcuser,dstuser,count（*） from mail group by srcuser,dstuser with rollup;+---------+---------+----------+| srcuser | dstuser | count（*） |+---------+---------+----------+| barb | barb | 1 || barb | tricia | 2 || barb | NULL | 3 || gene | barb | 2 || gene | gene | 3 || gene | tricia | 1 || gene | NULL | 6 || phil | barb | 1 || phil | phil | 2 || phil | tricia | 2 || phil | NULL | 5 || tricia | gene | 1 || tricia | phil | 1 || tricia | NULL | 2 || NULL | NULL | 16 |+---------+---------+----------+15 rows in set （0.00 sec）本文永久更新链接地址