概述
GROUPING SETS,GROUPING__ID,CUBE,ROLLUP
这几个分析函数通常用于OLAP中，不能累加，而且需要根据不同维度上钻和下钻的指标统计，比如，分小时、天、月的UV数。
数据准备
数据格式
2015-03,2015-03-10,cookie1 2015-03,2015-03-10,cookie5 2015-03,2015-03-12,cookie7 2015-04,2015-04-12,cookie3 2015-04,2015-04-13,cookie2 2015-04,2015-04-13,cookie4 2015-04,2015-04-16,cookie4 2015-03,2015-03-10,cookie2 2015-03,2015-03-10,cookie3 2015-04,2015-04-12,cookie5 2015-04,2015-04-13,cookie6 2015-04,2015-04-15,cookie3 2015-04,2015-04-15,cookie2 2015-04,2015-04-16,cookie1
创建表
use cookie; drop table if exists cookie5; create table cookie5(month string, day string, cookieid string) row format delimited fields terminated by ','; load data local inpath "/home/hadoop/cookie5.txt" into table cookie5; select * from cookie5;
玩一玩GROUPING SETS和GROUPING__ID
说明
在一个GROUP BY查询中，根据不同的维度组合进行聚合，等价于将不同维度的GROUP BY结果集进行UNION ALL
GROUPING__ID，表示结果属于哪一个分组集合。
查询语句
select month, day, count(distinct cookieid) as uv, GROUPING__ID from cookie.cookie5 group by month,day grouping sets (month,day) order by GROUPING__ID;
等价于
SELECT month,NULL,COUNT(DISTINCT cookieid) AS uv,1 AS GROUPING__ID FROM cookie5 GROUP BY month UNION ALL SELECT NULL,day,COUNT(DISTINCT cookieid) AS uv,2 AS GROUPING__ID FROM cookie5 GROUP BY day
查询结果
结果说明
第一列是按照month进行分组
第二列是按照day进行分组
第三列是按照month或day分组是，统计这一组有几个不同的cookieid
第四列grouping_id表示这一组结果属于哪个分组集合，根据grouping sets中的分组条件month，day，1是代表month，2是代表day
再比如
SELECT month, day, COUNT(DISTINCT cookieid) AS uv, GROUPING__ID FROM cookie5 GROUP BY month,day GROUPING SETS (month,day,(month,day)) ORDER BY GROUPING__ID;
等价于
SELECT month,NULL,COUNT(DISTINCT cookieid) AS uv,1 AS GROUPING__ID FROM cookie5 GROUP BY month UNION ALL SELECT NULL,day,COUNT(DISTINCT cookieid) AS uv,2 AS GROUPING__ID FROM cookie5 GROUP BY day UNION ALL SELECT month,day,COUNT(DISTINCT cookieid) AS uv,3 AS GROUPING__ID FROM cookie5 GROUP BY month,day
玩一玩CUBE
说明
根据GROUP BY的维度的所有组合进行聚合
查询语句
SELECT month, day, COUNT(DISTINCT cookieid) AS uv, GROUPING__ID FROM cookie5 GROUP BY month,day WITH CUBE ORDER BY GROUPING__ID;
等价于
SELECT NULL,NULL,COUNT(DISTINCT cookieid) AS uv,0 AS GROUPING__ID FROM cookie5 UNION ALL SELECT month,NULL,COUNT(DISTINCT cookieid) AS uv,1 AS GROUPING__ID FROM cookie5 GROUP BY month UNION ALL SELECT NULL,day,COUNT(DISTINCT cookieid) AS uv,2 AS GROUPING__ID FROM cookie5 GROUP BY day UNION ALL SELECT month,day,COUNT(DISTINCT cookieid) AS uv,3 AS GROUPING__ID FROM cookie5 GROUP BY month,day
查询结果
玩一玩ROLLUP
说明
是CUBE的子集，以最左侧的维度为主，从该维度进行层级聚合
查询语句
— 比如，以month维度进行层级聚合
SELECT month, day, COUNT(DISTINCT cookieid) AS uv, GROUPING__ID FROM cookie5 GROUP BY month,day WITH ROLLUP ORDER BY GROUPING__ID;
可以实现这样的上钻过程：
月天的UV->月的UV->总UV
–把month和day调换顺序，则以day维度进行层级聚合：
可以实现这样的上钻过程：
天月的UV->天的UV->总UV
（这里，根据天和月进行聚合，和根据天聚合结果一样，因为有父子关系，如果是其他维度组合的话，就会不一样）
原创文章，作者：ItWorker，如若转载，请注明出处：https://blog.ytso.com/tech/bigdata/8988.html