hive 中的grouping set，cube，roll up函数

最新推荐文章于 2023-04-18 10:28:57 发布

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最新推荐文章于 2023-04-18 10:28:57 发布

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文章标签：大数据数据库 python

原文链接：https://my.oschina.net/u/2000675/blog/2050360

本文详细介绍了Hive SQL中的GROUPINGSETS、CUBE和ROLLUP三种分组函数的使用方法，并通过具体实例展示了如何利用这些函数进行复杂的数据统计与分析。

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GROUPING SETS

GROUPING SETS作为GROUP BY的子句，允许开发人员在GROUP BY语句后面指定多个统计选项，可以简单理解为多条group by语句通过union all把查询结果聚合起来结合起来，下面是几个实例可以帮助我们了解，

以acorn_3g.test_xinyan_reg为例：

[dp@YZSJHL19-87 xjob]$ hive -e "use acorn_3g;desc test_xinyan_reg;"
user_id                 bigint                  None                 
device_id               int                     None   手机，平板             
os_id                   int                     None   操作系统类型             
app_id                  int                     None   手机app_id             
client_version          string                  None   客户端版本             
from_id                 int                     None   四级渠道

grouping sets语句	等价hive语句
select device_id,os_id,app_id,count(user_id) from test_xinyan_reg group by device_id,os_id,app_id grouping sets((device_id))	SELECT device_id,null,null,count(user_id) FROM test_xinyan_reg group by device_id
select device_id,os_id,app_id,count(user_id) from test_xinyan_reg group by device_id,os_id,app_id grouping sets((device_id,os_id))	SELECT device_id,os_id,null,count(user_id) FROM test_xinyan_reg group by device_id,os_id
select device_id,os_id,app_id,count(user_id) from test_xinyan_reg group by device_id,os_id,app_id grouping sets((device_id,os_id),(device_id))	SELECT device_id,os_id,null,count(user_id) FROM test_xinyan_reg group by device_id,os_id UNION ALL SELECT device_id,null,null,count(user_id) FROM test_xinyan_reg group by device_id
select device_id,os_id,app_id,count(user_id) from test_xinyan_reg group by device_id,os_id,app_id grouping sets((device_id),(os_id),(device_id,os_id),())	SELECT device_id,null,null,count(user_id) FROM test_xinyan_reg group by device_id UNION ALL SELECT null,os_id,null,count(user_id) FROM test_xinyan_reg group by os_id UNION ALL SELECT device_id,os_id,null,count(user_id) FROM test_xinyan_reg group by device_id,os_id UNION ALL SELECT null,null,null,count(user_id) FROM test_xinyan_reg

CUBE

cube简称数据魔方，可以实现hive多个任意维度的查询，cube(a,b,c)则首先会对(a,b,c)进行group by，然后依次是(a,b),(a,c),(a),(b,c),(b),(c),最后在对全表进行group by，他会统计所选列中值的所有组合的聚合

select device_id,os_id,app_id,client_version,from_id,count(user_id) 
from test_xinyan_reg 
group by device_id,os_id,app_id,client_version,from_id with cube;

等价于以下sql

SELECT device_id,null,null,null,null ,count(user_id) FROM test_xinyan_reg group by device_id
UNION ALL
SELECT null,os_id,null,null,null ,count(user_id) FROM test_xinyan_reg group by os_id
UNION ALL
SELECT device_id,os_id,null,null,null ,count(user_id) FROM test_xinyan_reg group by device_id,os_id
UNION ALL
SELECT null,null,app_id,null,null ,count(user_id) FROM test_xinyan_reg group by app_id
UNION ALL
SELECT device_id,null,app_id,null,null ,count(user_id) FROM test_xinyan_reg group by device_id,app_id
UNION ALL
SELECT null,os_id,app_id,null,null ,count(user_id) FROM test_xinyan_reg group by os_id,app_id
UNION ALL
SELECT device_id,os_id,app_id,null,null ,count(user_id) FROM test_xinyan_reg group by device_id,os_id,app_id
UNION ALL
SELECT null,null,null,client_version,null ,count(user_id) FROM test_xinyan_reg group by client_version
UNION ALL
SELECT device_id,null,null,client_version,null ,count(user_id) FROM test_xinyan_reg group by device_id,client_version
UNION ALL
SELECT null,os_id,null,client_version,null ,count(user_id) FROM test_xinyan_reg group by os_id,client_version
UNION ALL
SELECT device_id,os_id,null,client_version,null ,count(user_id) FROM test_xinyan_reg group by device_id,os_id,client_version
UNION ALL
SELECT null,null,app_id,client_version,null ,count(user_id) FROM test_xinyan_reg group by app_id,client_version
UNION ALL
SELECT device_id,null,app_id,client_version,null ,count(user_id) FROM test_xinyan_reg group by device_id,app_id,client_version
UNION ALL
SELECT null,os_id,app_id,client_version,null ,count(user_id) FROM test_xinyan_reg group by os_id,app_id,client_version
UNION ALL
SELECT device_id,os_id,app_id,client_version,null ,count(user_id) FROM test_xinyan_reg group by device_id,os_id,app_id,client_version
UNION ALL
SELECT null,null,null,null,from_id ,count(user_id) FROM test_xinyan_reg group by from_id
UNION ALL
SELECT device_id,null,null,null,from_id ,count(user_id) FROM test_xinyan_reg group by device_id,from_id
UNION ALL
SELECT null,os_id,null,null,from_id ,count(user_id) FROM test_xinyan_reg group by os_id,from_id
UNION ALL
SELECT device_id,os_id,null,null,from_id ,count(user_id) FROM test_xinyan_reg group by device_id,os_id,from_id
UNION ALL
SELECT null,null,app_id,null,from_id ,count(user_id) FROM test_xinyan_reg group by app_id,from_id
UNION ALL
SELECT device_id,null,app_id,null,from_id ,count(user_id) FROM test_xinyan_reg group by device_id,app_id,from_id
UNION ALL
SELECT null,os_id,app_id,null,from_id ,count(user_id) FROM test_xinyan_reg group by os_id,app_id,from_id
UNION ALL
SELECT device_id,os_id,app_id,null,from_id ,count(user_id) FROM test_xinyan_reg group by device_id,os_id,app_id,from_id
UNION ALL
SELECT null,null,null,client_version,from_id ,count(user_id) FROM test_xinyan_reg group by client_version,from_id
UNION ALL
SELECT device_id,null,null,client_version,from_id ,count(user_id) FROM test_xinyan_reg group by device_id,client_version,from_id
UNION ALL
SELECT null,os_id,null,client_version,from_id ,count(user_id) FROM test_xinyan_reg group by os_id,client_version,from_id
UNION ALL
SELECT device_id,os_id,null,client_version,from_id ,count(user_id) FROM test_xinyan_reg group by device_id,os_id,client_version,from_id
UNION ALL
SELECT null,null,app_id,client_version,from_id ,count(user_id) FROM test_xinyan_reg group by app_id,client_version,from_id
UNION ALL
SELECT device_id,null,app_id,client_version,from_id ,count(user_id) FROM test_xinyan_reg group by device_id,app_id,client_version,from_id
UNION ALL
SELECT null,os_id,app_id,client_version,from_id ,count(user_id) FROM test_xinyan_reg group by os_id,app_id,client_version,from_id
UNION ALL
SELECT device_id,os_id,app_id,client_version,from_id ,count(user_id) FROM test_xinyan_reg group by device_id,os_id,app_id,client_version,from_id
UNION ALL
SELECT null,null,null,null,null ,count(user_id) FROM test_xinyan_reg

ROLL UP

rollup可以实现从右到做递减多级的统计，显示统计某一层次结构的聚合。

 select device_id,os_id,app_id,client_version,from_id,count(user_id) 
from test_xinyan_reg 
group by device_id,os_id,app_id,client_version,from_id with rollup;

等价于以下sql

 select device_id,os_id,app_id,client_version,from_id,count(user_id) 
from test_xinyan_reg 
group by device_id,os_id,app_id,client_version,from_id 
grouping sets ((device_id,os_id,app_id,client_version,from_id),(device_id,os_id,app_id,client_version),(device_id,os_id,app_id),(device_id,os_id),(device_id),());

Grouping_ID

当我们没有统计某一列时，它的值显示为null，这可能与列本身就有null值冲突，这就需要一种方法区分是没有统计还是值本来就是null。（grouping_id其实就是所统计各列二进制和）

Column1 (key)	Column2 (value)
1	NULL
1	1
2	2
3	3
3	NULL
4	5

hsql：

  SELECT key, value, GROUPING__ID, count(*) from T1 GROUP BY key, value WITH ROLLUP

结果：

NULL	NULL	0 00	6
1	NULL	1 10	2
1	NULL	3 11	1
1	1	3 11	1
2	NULL	1 10	1
2	2	3 11	1
3	NULL	1 10	2
3	NULL	3 11	1
3	3	3 11	1
4	NULL	1 10	1
4	5	3 11	1

GROUPING__ID转变为二进制，如果对应位上有值为null，说明这列本身值就是null。

转载于:https://my.oschina.net/u/2000675/blog/2050360