Hive窗口分析函数

1、概念

窗口分析函数：窗口函数也称为OLAP（OnlineAnalytical Processing）函数，是对一组值进行操作，不需要使用Group by子句对数据进行分组，还能在同一行返回原来行的列和使用聚合函数得到的聚合列。
官网：
https://cwiki.apache.org/confluence/display/Hive/LanguageManual+WindowingAndAnalytics

2、sum, avg, max, min

数据准备：cookie.txt

cookie1,2015-04-10,1
cookie1,2015-04-11,5
cookie1,2015-04-12,7
cookie1,2015-04-13,3
cookie1,2015-04-14,2
cookie1,2015-04-15,4
cookie1,2015-04-16,4

建表准备相关：

create database if not exists myhive;
use myhive;
drop table if exists cookie;
create table cookie(cookieid string, createtime string, pv int) row format
delimited fields terminated by ',';
load data local inpath "/home/bigdata/cookie.txt" into table cookie;
select * from cookie;

sum

SELECT cookieid,createtime,pv,
SUM(pv) OVER(PARTITION BY cookieid ORDER BY createtime) AS pv1, -- 默认为从起点到
当前行
SUM(pv) OVER(PARTITION BY cookieid ORDER BY createtime ROWS BETWEEN UNBOUNDED
PRECEDING AND CURRENT ROW) AS pv2, --从起点到当前行，结果同pv1
SUM(pv) OVER(PARTITION BY cookieid) AS pv3, --分组内所有行
SUM(pv) OVER(PARTITION BY cookieid ORDER BY createtime ROWS BETWEEN 3 PRECEDING
AND CURRENT ROW) AS pv4, --当前行+往前3行
SUM(pv) OVER(PARTITION BY cookieid ORDER BY createtime ROWS BETWEEN 3 PRECEDING
AND 1 FOLLOWING) AS pv5, --当前行+往前3行+往后1行
SUM(pv) OVER(PARTITION BY cookieid ORDER BY createtime ROWS BETWEEN CURRENT ROW
AND UNBOUNDED FOLLOWING) AS pv6 --当前行+往后所有行
FROM cookie order by cookieid, createtime;

结果：

cookie1 2015-04-10 1 1 1 26 1 6 26
cookie1 2015-04-11 5 6 6 26 6 13 25
cookie1 2015-04-12 7 13 13 26 13 16 20
cookie1 2015-04-13 3 16 16 26 16 18 13
cookie1 2015-04-14 2 18 18 26 17 21 10
cookie1 2015-04-15 4 22 22 26 16 20 8
cookie1 2015-04-16 4 26 26 26 13 13 4

解释：

pv1: 分组内从起点到当前行的pv累积，如，11号的pv1=10号的pv+11号的pv, 12号=10号+11号+12号
pv2: 同pv1
pv3: 分组内(cookie1)所有的pv累加
pv4: 分组内当前行+往前3行，11号=10号+11号， 12号=10号+11号+12号， 13号=10号+11号+12号+13号， 14号=11号+12号+13号+14号
pv5: 分组内当前行+往前3行+往后1行，如，14号=11号+12号+13号+14号+15号=5+7+3+2+4=21
pv6: 分组内当前行+往后所有行，如，13号=13号+14号+15号+16号=3+2+4+4=13，14号=14号+15号+16号=2+4+4=10

扩展：

如果不指定ROWS BETWEEN,默认为从起点到当前行;
如果不指定ORDER BY，则将分组内所有值累加;
关键是理解ROWS BETWEEN含义,也叫做WINDOW子句：
PRECEDING：往前
FOLLOWING：往后
CURRENT ROW：当前行
UNBOUNDED：起点，UNBOUNDED PRECEDING 表示从前面的起点， UNBOUNDED FOLLOWING：表示到后面的终点

avg，min，max，和 sum
其他AVG，MIN，MAX，和SUM用法一样。只需要把sum函数，改成avg，min，max，sum等就可以。

SELECT cookieid, createtime, pv,
round(AVG(pv) OVER(PARTITION BY cookieid ORDER BY createtime), 2) AS pv1, --默
认为从起点到当前行
round(AVG(pv) OVER(PARTITION BY cookieid ORDER BY createtime ROWS BETWEEN
UNBOUNDED PRECEDING AND CURRENT ROW), 2) AS pv2, --从起点到当前行，结果同pv1
round(AVG(pv) OVER(PARTITION BY cookieid), 2) AS pv3, --分组内所有行
round(AVG(pv) OVER(PARTITION BY cookieid ORDER BY createtime ROWS BETWEEN 3
PRECEDING AND CURRENT ROW), 2) AS pv4, --当前行+往前3行
round(AVG(pv) OVER(PARTITION BY cookieid ORDER BY createtime ROWS BETWEEN 3
PRECEDING AND 1 FOLLOWING), 2) AS pv5, --当前行+往前3行+往后1行
round(AVG(pv) OVER(PARTITION BY cookieid ORDER BY createtime ROWS BETWEEN
CURRENT ROW AND UNBOUNDED FOLLOWING), 2) AS pv6 --当前行+往后所有行
FROM cookie;

执行结果：

cookie1 2015-04-16 4 3.71 3.71 3.71 3.25 3.25 4.0
cookie1 2015-04-15 4 3.67 3.67 3.71 4.0 4.0 4.0
cookie1 2015-04-14 2 3.6 3.6 3.71 4.25 4.2 3.33
cookie1 2015-04-13 3 4.0 4.0 3.71 4.0 3.6 3.25
cookie1 2015-04-12 7 4.33 4.33 3.71 4.33 4.0 4.0
cookie1 2015-04-11 5 3.0 3.0 3.71 3.0 4.33 4.17
cookie1 2015-04-10 1 1.0 1.0 3.71 1.0 3.0 3.71

3、ntile, row_number, rank, dense_rank

准备数据：cookie2.txt

cookie1,2015-04-10,1
cookie1,2015-04-11,5
cookie1,2015-04-12,7
cookie1,2015-04-13,3
cookie1,2015-04-14,2
cookie1,2015-04-15,4
cookie1,2015-04-16,4
cookie2,2015-04-10,2
cookie2,2015-04-11,3
cookie2,2015-04-12,5
cookie2,2015-04-13,6
cookie2,2015-04-14,3
cookie2,2015-04-15,9
cookie2,2015-04-16,7

建表导入数据相关操作：

create database if not exists myhive;
use myhive;
drop table if exists cookie2;
create table cookie2(cookieid string, createtime string, pv int) row format
delimited fields terminated by ',';
load data local inpath "/home/bigdata/cookie2.txt" into table cookie2;
select * from cookie2;

ntile

NTILE(n)，用于将分组数据按照顺序切分成n片，返回当前切片值
NTILE不支持ROWS BETWEEN，比如 NTILE(2) OVER(PARTITION BY cookieid ORDER BY
createtime ROWS BETWEEN 3 PRECEDING AND CURRENT ROW)
如果切片不均匀，默认增加第一个切片的分布

SQL语句实例：

SELECT cookieid,createtime,pv,
NTILE(2) OVER(PARTITION BY cookieid ORDER BY createtime) AS rn1, --分组内将数据
分成2片
NTILE(3) OVER(PARTITION BY cookieid ORDER BY createtime) AS rn2, --分组内将数据
分成3片
NTILE(4) OVER(ORDER BY createtime) AS rn3 --将所有数据分成4片
FROM cookie2
ORDER BY cookieid,createtime;

结果：

cookie1 2015-04-10 1 1 1 1
cookie1 2015-04-11 5 1 1 1
cookie1 2015-04-12 7 1 1 2
cookie1 2015-04-13 3 1 2 2
cookie1 2015-04-14 2 2 2 3
cookie1 2015-04-15 4 2 3 4
cookie1 2015-04-16 4 2 3 4
cookie2 2015-04-10 2 1 1 1
cookie2 2015-04-11 3 1 1 1
cookie2 2015-04-12 5 1 1 2
cookie2 2015-04-13 6 1 2 2
cookie2 2015-04-14 3 2 2 3
cookie2 2015-04-15 9 2 3 3
cookie2 2015-04-16 7 2 3 4

比如，统计一个cookie，pv数最多的前1/3的天

SELECT cookieid, createtime, pv,
NTILE(3) OVER(PARTITION BY cookieid ORDER BY pv DESC) AS rn
FROM cookie2;

结果：

cookie1 2015-04-12 7 1
cookie1 2015-04-11 5 1
cookie1 2015-04-16 4 1
cookie1 2015-04-15 4 2
cookie1 2015-04-13 3 2
cookie1 2015-04-14 2 3
cookie1 2015-04-10 1 3
cookie2 2015-04-15 9 1
cookie2 2015-04-16 7 1
cookie2 2015-04-13 6 1
cookie2 2015-04-12 5 2
cookie2 2015-04-11 3 2
cookie2 2015-04-14 3 3
cookie2 2015-04-10 2 3

其中：rn = 1 的记录，就是我们想要的结果
row_number
ROW_NUMBER() – 从1开始，按照顺序，生成分组内记录的序列 –比如，按照pv降序排列，生成分组内每天的pv名次，ROW_NUMBER() 的应用场景非常多，再比如，获取分组内排序第一的记录;获取一个session中的第一条refer等。
// 分组排序

SELECT cookieid, createtime, pv,
ROW_NUMBER() OVER(PARTITION BY cookieid ORDER BY pv desc) AS rn
FROM cookie2;

结果：

cookie1 2015-04-12 7 1
cookie1 2015-04-11 5 2
cookie1 2015-04-16 4 3
cookie1 2015-04-15 4 4
cookie1 2015-04-13 3 5
cookie1 2015-04-14 2 6
cookie1 2015-04-10 1 7
cookie2 2015-04-15 9 1
cookie2 2015-04-16 7 2
cookie2 2015-04-13 6 3
cookie2 2015-04-12 5 4
cookie2 2015-04-11 3 5
cookie2 2015-04-14 3 6
cookie2 2015-04-10 2 7

所以如果需要取每一组的前3名，只需要rn<=3即可
rank 和 dense_rank
RANK() 生成数据项在分组中的排名，排名相等会在名次中留下空位
DENSE_RANK() 生成数据项在分组中的排名，排名相等会在名次中不会留下空位
SQL语句实例：

SELECT cookieid, createtime, pv,
RANK() OVER(PARTITION BY cookieid ORDER BY pv desc) AS rn1,
DENSE_RANK() OVER(PARTITION BY cookieid ORDER BY pv desc) AS rn2,
ROW_NUMBER() OVER(PARTITION BY cookieid ORDER BY pv DESC) AS rn3
FROM cookie2
WHERE cookieid = 'cookie1';

结果：

cookie1 2015-04-12 7 1 1 1
cookie1 2015-04-11 5 2 2 2
cookie1 2015-04-16 4 3 3 3
cookie1 2015-04-15 4 3 3 4
cookie1 2015-04-13 3 5 4 5
cookie1 2015-04-14 2 6 5 6
cookie1 2015-04-10 1 7 6 7

三者对比总结：

row_number 按顺序编号，不留空位
rank 按顺序编号，相同的值编相同号，留空位
dense_rank 按顺序编号，相同的值编相同的号，不留空位

4、cume_dist, percent_rank

数据准备：cookie3.txt

d1,user1,1000
d1,user2,2000
d1,user3,3000
d2,user4,4000
d2,user5,5000

建表导入数据相关操作：

create database if not exists myhive;
use myhive;
drop table if exists cookie3;
create table cookie3(dept string, userid string, sal int) row format delimited
fields terminated by ',';
load data local inpath "/home/bigdata/cookie3.txt" into table cookie3;
select * from cookie3;

cume_dist
–CUME_DIST 小于等于当前值的行数/分组内总行数
比如，统计小于等于当前薪水的人数，所占总人数的比例

SELECT dept, userid, sal,
round(CUME_DIST() OVER(ORDER BY sal), 2) AS rn1,
round(CUME_DIST() OVER(PARTITION BY dept ORDER BY sal), 2) AS rn2
FROM cookie3;

结果：

d1 user1 1000 0.2 0.33
d1 user2 2000 0.4 0.67
d1 user3 3000 0.6 1.0
d2 user4 4000 0.8 0.5
d2 user5 5000 1.0 1.0

SQL语句实例：

SELECT dept, userid, sal,
round(CUME_DIST() OVER(ORDER BY sal), 2) AS rn1,
round(CUME_DIST() OVER(PARTITION BY dept ORDER BY sal desc), 2) AS rn2
FROM cookie3;

结果：

d1 user3 3000 0.6 0.33
d1 user2 2000 0.4 0.67
d1 user1 1000 0.2 1.0
d2 user5 5000 1.0 0.5
d2 user4 4000 0.8 1.0

percent_rank
PERCENT_RANK 分组内当前行的RANK值-1/分组内总行数-1
SQL语句实例：

SELECT dept, userid, sal,
PERCENT_RANK() OVER(ORDER BY sal) AS rn1, --分组内
RANK() OVER(ORDER BY sal) AS rn11, --分组内RANK值
SUM(1) OVER(PARTITION BY NULL) AS rn12, --分组内总行数
PERCENT_RANK() OVER(PARTITION BY dept ORDER BY sal) AS rn2
FROM cookie3;

结果：

d1 user1 1000 0.0 1 5 0.0
d1 user2 2000 0.25 2 5 0.5
d1 user3 3000 0.5 3 5 1.0
d2 user4 4000 0.75 4 5 0.0
d2 user5 5000 1.0 5 5 1.0

SQL语句实例：

SELECT dept, userid, sal,
PERCENT_RANK() OVER(PARTITION BY dept ORDER BY sal) AS rn1, --分组内
RANK() OVER(ORDER BY sal) AS rn11, --分组内RANK值
SUM(1) OVER(PARTITION BY NULL) AS rn12, --分组内总行数
PERCENT_RANK() OVER(PARTITION BY dept ORDER BY sal) AS rn2
FROM cookie3;

执行结果：

d2 user5 5000 1.0 5 5 1.0
d2 user4 4000 0.0 4 5 0.0
d1 user3 3000 1.0 3 5 1.0
d1 user2 2000 0.5 2 5 0.5
d1 user1 1000 0.0 1 5 0.0

5、lag, lead, frist_value, last_value

数据准备：cookie4.txt

cookie1,2015-04-10 10:00:02,url2
cookie1,2015-04-10 10:00:00,url1
cookie1,2015-04-10 10:03:04,1url3
cookie1,2015-04-10 10:50:05,url6
cookie1,2015-04-10 11:00:00,url7
cookie1,2015-04-10 10:10:00,url4
cookie1,2015-04-10 10:50:01,url5
cookie2,2015-04-10 10:00:02,url22
cookie2,2015-04-10 10:00:00,url11
cookie2,2015-04-10 10:03:04,1url33
cookie2,2015-04-10 10:50:05,url66
cookie2,2015-04-10 11:00:00,url77
cookie2,2015-04-10 10:10:00,url44
cookie2,2015-04-10 10:50:01,url55

建表导入数据相关操作：

create database if not exists myhive;
use myhive;
drop table if exists cookie4;
create table cookie4(cookieid string, createtime string, url string) row format
delimited fields terminated by ',';
load data local inpath "/home/bigdata/cookie4.txt" into table cookie4;
select * from cookie4;

lag
LAG(col,n,DEFAULT) 用于统计窗口内往上第n行值
第一个参数为列名，
第二个参数为往上第n行（可选，默认为1），
第三个参数为默认值（当往上第n行为NULL时候，取默认值，如不指定，则为NULL）
SQL语句实例：

SELECT cookieid, createtime, url,
ROW_NUMBER() OVER(PARTITION BY cookieid ORDER BY createtime) AS rn,
LAG(createtime,1,'1970-01-01 00:00:00') OVER(PARTITION BY cookieid ORDER BY
createtime) AS last_1_time,
LAG(createtime,2) OVER(PARTITION BY cookieid ORDER BY createtime) AS last_2_time
FROM cookie4;

结果数据：

cookieid createtime url rn last_1_time
last_2_time
cookie1 2015-04-10 10:00:00 url1 1 1970-01-01 00:00:00 NULL
cookie1 2015-04-10 10:00:02 url2 2 2015-04-10 10:00:00 NULL
cookie1 2015-04-10 10:03:04 1url3 3 2015-04-10 10:00:02 2015-04-10 10:00:00
cookie1 2015-04-10 10:10:00 url4 4 2015-04-10 10:03:04 2015-04-10 10:00:02
cookie1 2015-04-10 10:50:01 url5 5 2015-04-10 10:10:00 2015-04-10 10:03:04
cookie1 2015-04-10 10:50:05 url6 6 2015-04-10 10:50:01 2015-04-10 10:10:00
cookie1 2015-04-10 11:00:00 url7 7 2015-04-10 10:50:05 2015-04-10 10:50:01
cookie2 2015-04-10 10:00:00 url11 1 1970-01-01 00:00:00 NULL
cookie2 2015-04-10 10:00:02 url22 2 2015-04-10 10:00:00 NULL
cookie2 2015-04-10 10:03:04 1url33 3 2015-04-10 10:00:02 2015-04-10 10:00:00
cookie2 2015-04-10 10:10:00 url44 4 2015-04-10 10:03:04 2015-04-10 10:00:02
cookie2 2015-04-10 10:50:01 url55 5 2015-04-10 10:10:00 2015-04-10 10:03:04
cookie2 2015-04-10 10:50:05 url66 6 2015-04-10 10:50:01 2015-04-10 10:10:00
cookie2 2015-04-10 11:00:00 url77 7 2015-04-10 10:50:05 2015-04-10 10:50:01

解释：

last_1_time: 指定了往上第1行的值，default为'1970-01-01 00:00:00'
cookie1第一行，往上1行为NULL,因此取默认值 1970-01-01 00:00:00
cookie1第三行，往上1行值为第二行值，2015-04-10 10:00:02
cookie1第六行，往上1行值为第五行值，2015-04-10 10:50:01
last_2_time: 指定了往上第2行的值，为指定默认值
cookie1第一行，往上2行为NULL
cookie1第二行，往上2行为NULL
cookie1第四行，往上2行为第二行值，2015-04-10 10:00:02
cookie1第七行，往上2行为第五行值，2015-04-10 10:50:01

lead
与LAG相反
LEAD(col,n,DEFAULT) 用于统计窗口内往下第n行值
第一个参数为列名，
第二个参数为往下第n行（可选，默认为1），
第三个参数为默认值（当往下第n行为NULL时候，取默认值，如不指定，则为NULL）
SQL语句实例：

SELECT cookieid, createtime, url,
ROW_NUMBER() OVER(PARTITION BY cookieid ORDER BY createtime) AS rn,
LEAD(createtime,1,'1970-01-01 00:00:00') OVER(PARTITION BY cookieid ORDER BY
createtime) AS next_1_time,
LEAD(createtime,2) OVER(PARTITION BY cookieid ORDER BY createtime) AS
next_2_time
FROM cookie4;

结果：

cookieid createtime url rn next_1_time
next_2_time
cookie1 2015-04-10 10:00:00 url1 1 2015-04-10 10:00:02 2015-04-
10 10:03:04
cookie1 2015-04-10 10:00:02 url2 2 2015-04-10 10:03:04 2015-04-
10 10:10:00
cookie1 2015-04-10 10:03:04 1url3 3 2015-04-10 10:10:00 2015-04-
10 10:50:01
cookie1 2015-04-10 10:10:00 url4 4 2015-04-10 10:50:01 2015-04-
10 10:50:05
cookie1 2015-04-10 10:50:01 url5 5 2015-04-10 10:50:05 2015-04-
10 11:00:00
cookie1 2015-04-10 10:50:05 url6 6 2015-04-10 11:00:00 NULL
cookie1 2015-04-10 11:00:00 url7 7 1970-01-01 00:00:00 NULL
cookie2 2015-04-10 10:00:00 url11 1 2015-04-10 10:00:02 2015-04-
10 10:03:04
cookie2 2015-04-10 10:00:02 url22 2 2015-04-10 10:03:04 2015-04-
10 10:10:00
cookie2 2015-04-10 10:03:04 1url33 3 2015-04-10 10:10:00 2015-04-
10 10:50:01
cookie2 2015-04-10 10:10:00 url44 4 2015-04-10 10:50:01 2015-04-
10 10:50:05
cookie2 2015-04-10 10:50:01 url55 5 2015-04-10 10:50:05 2015-04-
10 11:00:00
cookie2 2015-04-10 10:50:05 url66 6 2015-04-10 11:00:00 NULL
cookie2 2015-04-10 11:00:00 url77 7 1970-01-01 00:00:00 NULL

first_value
取分组内排序后，截止到当前行，第一个值
SQL语句实例：

SELECT cookieid, createtime, url,
ROW_NUMBER() OVER(PARTITION BY cookieid ORDER BY createtime) AS rn,
FIRST_VALUE(url) OVER(PARTITION BY cookieid ORDER BY createtime) AS first1
FROM cookie4;

结果：

cookie1 2015-04-10 10:00:00 url1 1 url1
cookie1 2015-04-10 10:00:02 url2 2 url1
cookie1 2015-04-10 10:03:04 1url3 3 url1
cookie1 2015-04-10 10:10:00 url4 4 url1
cookie1 2015-04-10 10:50:01 url5 5 url1
cookie1 2015-04-10 10:50:05 url6 6 url1
cookie1 2015-04-10 11:00:00 url7 7 url1
cookie2 2015-04-10 10:00:00 url11 1 url11
cookie2 2015-04-10 10:00:02 url22 2 url11
cookie2 2015-04-10 10:03:04 1url33 3 url11
cookie2 2015-04-10 10:10:00 url44 4 url11
cookie2 2015-04-10 10:50:01 url55 5 url11
cookie2 2015-04-10 10:50:05 url66 6 url11
cookie2 2015-04-10 11:00:00 url77 7 url11

last_value
取分组内排序后，截止到当前行，最后一个值
SQL语句实例:

SELECT cookieid, createtime, url,
ROW_NUMBER() OVER(PARTITION BY cookieid ORDER BY createtime) AS rn,
LAST_VALUE(url) OVER(PARTITION BY cookieid ORDER BY createtime) AS last1
FROM cookie4;

结果数据：

cookie1 2015-04-10 10:00:00 url1 1 url1
cookie1 2015-04-10 10:00:02 url2 2 url2
cookie1 2015-04-10 10:03:04 1url3 3 1url3
cookie1 2015-04-10 10:10:00 url4 4 url4
cookie1 2015-04-10 10:50:01 url5 5 url5
cookie1 2015-04-10 10:50:05 url6 6 url6
cookie1 2015-04-10 11:00:00 url7 7 url7
cookie2 2015-04-10 10:00:00 url11 1 url11
cookie2 2015-04-10 10:00:02 url22 2 url22
cookie2 2015-04-10 10:03:04 1url33 3 1url33
cookie2 2015-04-10 10:10:00 url44 4 url44
cookie2 2015-04-10 10:50:01 url55 5 url55
cookie2 2015-04-10 10:50:05 url66 6 url66
cookie2 2015-04-10 11:00:00 url77 7 url77

问题：如果不指定ORDER BY，则默认按照记录在文件中的偏移量进行排序，会出现错误的结果
// 求得每组的最后一个值：排倒序，然后取FIRST_VALUE
SQL语句：

SELECT cookieid, createtime, url,
ROW_NUMBER() OVER(PARTITION BY cookieid ORDER BY createtime) AS rn,
LAST_VALUE(url) OVER(PARTITION BY cookieid ORDER BY createtime) AS last1,
FIRST_VALUE(url) OVER(PARTITION BY cookieid ORDER BY createtime DESC) AS
last2
FROM cookie4
ORDER BY cookieid,createtime;

结果：

cookie1 2015-04-10 10:00:00 url1 1 url1 url7
cookie1 2015-04-10 10:00:02 url2 2 url2 url7
cookie1 2015-04-10 10:03:04 1url3 3 1url3 url7
cookie1 2015-04-10 10:10:00 url4 4 url4 url7
cookie1 2015-04-10 10:50:01 url5 5 url5 url7
cookie1 2015-04-10 10:50:05 url6 6 url6 url7
cookie1 2015-04-10 11:00:00 url7 7 url7 url7
cookie2 2015-04-10 10:00:00 url11 1 url11 url77
cookie2 2015-04-10 10:00:02 url22 2 url22 url77
cookie2 2015-04-10 10:03:04 1url33 3 1url33 url77
cookie2 2015-04-10 10:10:00 url44 4 url44 url77
cookie2 2015-04-10 10:50:01 url55 5 url55 url77
cookie2 2015-04-10 10:50:05 url66 6 url66 url77
cookie2 2015-04-10 11:00:00 url77 7 url77 url77

6、 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
官网介绍：https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2
C+Grouping+and+Rollup
数据准备：cookie5.txt

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

建表导入数据相关：

create database if not exists myhive;
use myhive;
drop table if exists cookie5;
create table cookie5(month string, day string, cookieid string) row format
delimited fields terminated by ',';
load data local inpath "/home/bigdata/cookie5.txt" into table cookie5;
select * from cookie5;

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
SQL语句实例：

SELECT month, day,
COUNT(DISTINCT cookieid) AS uv,
GROUPING__ID
FROM cookie5
GROUP BY month,day
GROUPING SETS (month,day)
ORDER BY GROUPING__ID;

其中的 GROUPING__ID，表示结果属于哪一个分组集合。
结果：

2015-04 NULL 6 1
2015-03 NULL 5 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

其实这个SQL语句等价于下面这个SQL：

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;

执行结果：

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 NULL 5 1
2015-04 NULL 6 1

SQL语句：

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;

其中的 GROUPING__ID，表示结果属于哪一个分组集合。
结果数据：

2015-03 2015-03-10 4 0
2015-04 2015-04-16 2 0
2015-04 2015-04-13 3 0
2015-04 2015-04-12 2 0
2015-04 2015-04-15 2 0
2015-03 2015-03-12 1 0
2015-03 NULL 5 1
2015-04 NULL 6 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

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
UNION ALL
SELECT month,day,COUNT(DISTINCT cookieid) AS uv,3 AS GROUPING__ID FROM cookie5
GROUP BY month,day;

cube
根据GROUP BY的维度的所有组合进行聚合
SQL语句：

SELECT month, day,
COUNT(DISTINCT cookieid) AS uv,
GROUPING__ID
FROM cookie5
GROUP BY month,day
WITH CUBE
ORDER BY GROUPING__ID;

结果：

2015-03 2015-03-10 4 0
2015-04 2015-04-16 2 0
2015-04 2015-04-13 3 0
2015-04 2015-04-12 2 0
2015-04 2015-04-15 2 0
2015-03 2015-03-12 1 0
2015-03 NULL 5 1
2015-04 NULL 6 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
NULL NULL 7 3

等价于：

SELECT NULL,NULL,COUNT(DISTINCT cookieid) AS uv,0 AS GROUPING__ID FROM cookie5
UNION ALL
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;

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，SQL语句：

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
结果：

2015-04 2015-04-16 2 0
2015-04 2015-04-15 2 0
2015-04 2015-04-13 3 0
2015-04 2015-04-12 2 0
2015-03 2015-03-12 1 0
2015-03 2015-03-10 4 0
2015-04 NULL 6 1
2015-03 NULL 5 1
NULL NULL 7 3

把month和day调换顺序，则以day维度进行层级聚合：SQL语句：

SELECT day, month, COUNT(DISTINCT cookieid) AS uv, GROUPING__ID
FROM cookie5
GROUP BY day,month WITH ROLLUP ORDER BY GROUPING__ID;

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

人面猴
序言：七十年代末，一起剥皮案震惊了整个滨河市，随后出现的几起案子，更是在滨河造成了极大的恐慌，老刑警刘岩，带你破解...
沈念sama阅读 197,737评论 5赞 462
死咒
序言：滨河连续发生了三起死亡事件，死亡现场离奇诡异，居然都是意外死亡，警方通过查阅死者的电脑和手机，发现死者居然都...
沈念sama阅读 83,103评论 2赞 375
救了他两次的神仙让他今天三更去死
文/潘晓璐我一进店门，熙熙楼的掌柜王于贵愁眉苦脸地迎上来，“玉大人，你说我怎么就摊上这事。” “怎么了？”我有些...
开封第一讲书人阅读 144,710评论 0赞 326
道士缉凶录：失踪的卖姜人
文/不坏的土叔我叫张陵，是天一观的道长。经常有香客问我，道长，这世上最难降的妖魔是什么？我笑而不...
开封第一讲书人阅读 52,909评论 1赞 267
港岛之恋（遗憾婚礼）
正文为了忘掉前任，我火速办了婚礼，结果婚礼上，老公的妹妹穿的比我还像新娘。我一直安慰自己，他们只是感情好，可当我...
茶点故事阅读 61,794评论 5赞 358
恶毒庶女顶嫁案：这布局不是一般人想出来的
文/花漫我一把揭开白布。她就那样静静地躺着，像睡着了一般。火红的嫁衣衬着肌肤如雪。梳的纹丝不乱的头发上，一...
开封第一讲书人阅读 46,557评论 1赞 275
城市分裂传说
那天，我揣着相机与录音，去河边找鬼。笑死，一个胖子当着我的面吹牛，可吹牛的内容都是我干的。我是一名探鬼主播，决...
沈念sama阅读 36,939评论 3赞 388
双鸳鸯连环套：你想象不到人心有多黑
文/苍兰香墨我猛地睁开眼，长吁一口气：“原来是场噩梦啊……” “哼！你这毒妇竟也来了？” 一声冷哼从身侧响起，我...
开封第一讲书人阅读 35,572评论 0赞 254
万荣杀人案实录
序言：老挝万荣一对情侣失踪，失踪者是张志新（化名）和其女友刘颖，没想到半个月后，有当地人在树林里发现了一具尸体，经...
沈念sama阅读 39,852评论 1赞 293
护林员之死
正文独居荒郊野岭守林人离奇死亡，尸身上长有42处带血的脓包…… 初始之章·张勋以下内容为张勋视角年9月15日...
茶点故事阅读 34,871评论 2赞 314
白月光启示录
正文我和宋清朗相恋三年，在试婚纱的时候发现自己被绿了。大学时的朋友给我发了我未婚夫和他白月光在一起吃饭的照片。...
茶点故事阅读 36,692评论 1赞 328
活死人
序言：一个原本活蹦乱跳的男人离奇死亡，死状恐怖，灵堂内的尸体忽然破棺而出，到底是诈尸还是另有隐情，我是刑警宁泽，带...
沈念sama阅读 32,490评论 3赞 316
日本核电站爆炸内幕
正文年R本政府宣布，位于F岛的核电站，受9级特大地震影响，放射性物质发生泄漏。R本人自食恶果不足惜，却给世界环境...
茶点故事阅读 37,939评论 3赞 300
男人毒药：我在死后第九天来索命
文/蒙蒙一、第九天我趴在偏房一处隐蔽的房顶上张望。院中可真热闹，春花似锦、人声如沸。这庄子的主人今日做“春日...
开封第一讲书人阅读 29,114评论 0赞 19
一桩弑父案，背后竟有这般阴谋
文/苍兰香墨我抬头看了看天上的太阳。三九已至，却和暖如春，着一层夹袄步出监牢的瞬间，已是汗流浃背。一阵脚步声响...
开封第一讲书人阅读 30,409评论 1赞 255
情欲美人皮
我被黑心中介骗来泰国打工，没想到刚下飞机就差点儿被人妖公主榨干…… 1. 我叫王不留，地道东北人。一个月前我还...
沈念sama阅读 41,971评论 2赞 343
代替公主和亲
正文我出身青楼，却偏偏与公主长得像，于是被迫代替她去往敌国和亲。传闻我的和亲对象是个残疾皇子，可洞房花烛夜当晚...
茶点故事阅读 41,176评论 2赞 339

Hive窗口分析函数

1、概念

2、sum, avg, max, min

3、ntile, row_number, rank, dense_rank

4、cume_dist, percent_rank

5、lag, lead, frist_value, last_value

6、 grouping sets, grouping__id, cube,rollup

推荐阅读更多精彩内容