Hive分析窗口函数(五) 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。
Hive版本为 apache-hive-0.13.1

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
 
CREATE EXTERNAL TABLE lxw1234 (
month STRING,
day STRING, 
cookieid STRING 
) ROW FORMAT DELIMITED 
FIELDS TERMINATED BY ',' 
stored as textfile location '/tmp/lxw11/';
 
hive> select * from lxw1234;
OK
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

--GROUPING SETS

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
SELECT 
month,
day,
COUNT(DISTINCT cookieid) AS uv,
GROUPING__ID 
FROM lxw1234 
GROUP BY month,day 
GROUPING SETS (month,day) 
ORDER BY GROUPING__ID;
 
month      day            uv      GROUPING__ID
------------------------------------------------
2015-03    NULL            5       1
2015-04    NULL            6       1
NULL       2015-03-10      4       2
NULL       2015-03-12      1       2
NULL       2015-04-12      2       2
NULL       2015-04-13      3       2
NULL       2015-04-15      2       2
NULL       2015-04-16      2       2
 
等价于 
SELECT month,NULL,COUNT(DISTINCT cookieid) AS uv,1 AS GROUPING__ID FROM lxw1234 GROUP BY month 
UNION ALL 
SELECT NULL,day,COUNT(DISTINCT cookieid) AS uv,2 AS GROUPING__ID FROM lxw1234 GROUP BY day
再如：
SELECT 
month,
day,
COUNT(DISTINCT cookieid) AS uv,
GROUPING__ID 
FROM lxw1234 
GROUP BY month,day 
GROUPING SETS (month,day,(month,day)) 
ORDER BY GROUPING__ID;
 
month         day             uv      GROUPING__ID
------------------------------------------------
2015-03       NULL            5       1
2015-04       NULL            6       1
NULL          2015-03-10      4       2
NULL          2015-03-12      1       2
NULL          2015-04-12      2       2
NULL          2015-04-13      3       2
NULL          2015-04-15      2       2
NULL          2015-04-16      2       2
2015-03       2015-03-10      4       3
2015-03       2015-03-12      1       3
2015-04       2015-04-12      2       3
2015-04       2015-04-13      3       3
2015-04       2015-04-15      2       3
2015-04       2015-04-16      2       3
 
等价于
SELECT month,NULL,COUNT(DISTINCT cookieid) AS uv,1 AS GROUPING__ID FROM lxw1234 GROUP BY month 
UNION ALL 
SELECT NULL,day,COUNT(DISTINCT cookieid) AS uv,2 AS GROUPING__ID FROM lxw1234 GROUP BY day
UNION ALL 
SELECT month,day,COUNT(DISTINCT cookieid) AS uv,3 AS GROUPING__ID FROM lxw1234 GROUP BY month,day
其中的 GROUPING__ID，表示结果属于哪一个分组集合。

--CUBE

根据GROUP BY的维度的所有组合进行聚合。
SELECT 
month,
day,
COUNT(DISTINCT cookieid) AS uv,
GROUPING__ID 
FROM lxw1234 
GROUP BY month,day 
WITH CUBE 
ORDER BY GROUPING__ID;
 
month                  day             uv     GROUPING__ID
--------------------------------------------
NULL            NULL            7       0
2015-03         NULL            5       1
2015-04         NULL            6       1
NULL            2015-04-12      2       2
NULL            2015-04-13      3       2
NULL            2015-04-15      2       2
NULL            2015-04-16      2       2
NULL            2015-03-10      4       2
NULL            2015-03-12      1       2
2015-03         2015-03-10      4       3
2015-03         2015-03-12      1       3
2015-04         2015-04-16      2       3
2015-04         2015-04-12      2       3
2015-04         2015-04-13      3       3
2015-04         2015-04-15      2       3
 
等价于
SELECT NULL,NULL,COUNT(DISTINCT cookieid) AS uv,0 AS GROUPING__ID FROM lxw1234
UNION ALL 
SELECT month,NULL,COUNT(DISTINCT cookieid) AS uv,1 AS GROUPING__ID FROM lxw1234 GROUP BY month 
UNION ALL 
SELECT NULL,day,COUNT(DISTINCT cookieid) AS uv,2 AS GROUPING__ID FROM lxw1234 GROUP BY day
UNION ALL 
SELECT month,day,COUNT(DISTINCT cookieid) AS uv,3 AS GROUPING__ID FROM lxw1234 GROUP BY month,day

--ROLLUP

是CUBE的子集，以最左侧的维度为主，从该维度进行层级聚合。
比如，以month维度进行层级聚合：
SELECT 
month,
day,
COUNT(DISTINCT cookieid) AS uv,
GROUPING__ID  
FROM lxw1234 
GROUP BY month,day
WITH ROLLUP 
ORDER BY GROUPING__ID;
 
month                  day             uv     GROUPING__ID
---------------------------------------------------
NULL             NULL            7       0
2015-03          NULL            5       1
2015-04          NULL            6       1
2015-03          2015-03-10      4       3
2015-03          2015-03-12      1       3
2015-04          2015-04-12      2       3
2015-04          2015-04-13      3       3
2015-04          2015-04-15      2       3
2015-04          2015-04-16      2       3
 
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：
 
SELECT 
day,
month,
COUNT(DISTINCT cookieid) AS uv,
GROUPING__ID  
FROM lxw1234 
GROUP BY day,month 
WITH ROLLUP 
ORDER BY GROUPING__ID;

day                    month              uv     GROUPING__ID
-------------------------------------------------------
NULL            NULL               7       0
2015-04-13      NULL               3       1
2015-03-12      NULL               1       1
2015-04-15      NULL               2       1
2015-03-10      NULL               4       1
2015-04-16      NULL               2       1
2015-04-12      NULL               2       1
2015-04-12      2015-04            2       3
2015-03-10      2015-03            4       3
2015-03-12      2015-03            1       3
2015-04-13      2015-04            3       3
2015-04-15      2015-04            2       3
2015-04-16      2015-04            2       3
 
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）