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
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
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。
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
2015-04 NULL 6 1 1
2015-03 NULL 5 1 1
NULL 2015-04-16 2 2
NULL 2015-04-15 2 2
NULL 2015-04-13 3 2
NULL 2015-04-12 2 2
NULL 2015-03-12 1 2
NULL 2015-03-10 4 2
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
hive> select
> month,
> day,
> count(distinct cookieid) as uv,
> GROUPING__ID
> from cookie5
> group by month,day
> with cube
> order by grouping__id;
Total MapReduce CPU Time Spent: 7 seconds 500 msec
显示结果
OK
NULL NULL 7 0 0
2015-03 NULL 5 1 1
2015-04 NULL 6 1 1
NULL 2015-04-16 2 2
NULL 2015-04-15 2 2
NULL 2015-04-13 3 2
NULL 2015-04-12 2 2
NULL 2015-03-12 1 2
NULL 2015-03-10 4 2
2015-04 2015-04-12 2 3
2015-04 2015-04-16 2 3
2015-03 2015-03-12 1 3
2015-03 2015-03-10 4 3
2015-04 2015-04-15 2 3
2015-04 2015-04-13 3 3
Time taken: 44.156 seconds, Fetched: 15 row(s)
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;
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)
Total MapReduce CPU Time Spent: 7 seconds 500 msec
OK
2015-04 NULL 6 1
2015-03 NULL 5 1
NULL 2015-03-10 4 2
NULL 2015-04-16 2 2
NULL 2015-04-15 2 2
NULL 2015-04-13 3 2
NULL 2015-04-12 2 2
NULL 2015-03-12 1 2
2015-04 2015-04-16 2 3
2015-04 2015-04-12 2 3
2015-04 2015-04-13 3 3
2015-03 2015-03-12 1 3
2015-03 2015-03-10 4 3
2015-04 2015-04-15 2 3
Time taken: 44.196 seconds, Fetched: 14 row(s)