标签：资料 log struct 开发 结合 partition pdo 第一个 进制
说明：Hive之cube、rollup，还有窗口函数，在传统关系型数据（Oracle、sqlserver）中都是有的，用法都很相似。
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
几个demo帮助大家了解：
| 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;
手工实现需要写的hql语句（写个程序自己生成的，手写累死）：
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
看着很蛋疼是不是，体会到cube的强大了吗！(低版本hive可以通过union all方式解决，算是没有办法的办法)
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;
等价以下sql语句：
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),());
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。（写一个排列组合的算法，就马上理解了，grouping_id其实就是所统计各列二进制和）
直接拿官方文档一个例子，O(∩_∩)O哈哈~
| Column1 (key) | Column2 (value) |
|---|---|
| 1 | NULL |
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
| 3 | NULL |
| 4 | 5 |
hql统计：
SELECT key, value, GROUPING__ID, count(*) from T1 GROUP BY key, value WITH ROLLUP
统计结果如下：
| 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。（通过类DataFilterNull.py 扫描，可以筛选过滤掉列中null、“”统计结果),
hive窗口函数，感觉大部分都是在模仿oracle，有对oracle熟悉的，应该看下就知道怎么用。
具体参见：http://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.0.0.2/ds_Hive/language_manual/ptf-window.html
主要围绕..over( partitoin by ..) ..
3g业务求新增激活时候，有的一部手机，可能注册多个渠道，这时候就要按时间顺序求第一个：
select f.udid,f.from_id,f.ins_date
from (select /* +MAPJOIN(u) */ u.device_id as udid ,g.device_id as gdid,u.from_id,u.ins_date,row_number() over (partition by u.device_id order by u.ins_date asc) as row_number
from user_device_info u
left outer join (select device_id from 3g_device_id where log_date<‘2013-07-25‘) g on ( u.device_id = g.device_id )
where u.log_date=‘2013-07-25‘ and u.device_id is not null and u.device_id <> ‘‘) f
where f.gdid is null and row_number=1
apache hive窗口函数官方介绍：http://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.0.0.2/ds_Hive/language_manual/ptf-window.html
apache hive官方：cube、rollup函数介绍：https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation,+Cube,+Grouping+and+Rollup
oracle窗口函数介绍：http://www.blogjava.net/pengpenglin/archive/2012/04/12/211334.html
标签：资料 log struct 开发 结合 partition pdo 第一个 进制
原文地址：http://www.cnblogs.com/xiohao/p/6404000.html
