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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:
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
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
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)
这种函数,需要结合实际场景和数据去使用和研究,只看说明的话,很难理解。
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