// 数据准备操作：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
// 建表语句：
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/hadoop/cookie.txt" into table cookie;
select * from cookie;
// SQL语句： 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;
结果：
cookie1 2015-04-16 4 26 26 26 13 13 4
cookie1 2015-04-15 4 22 22 26 16 20 8
cookie1 2015-04-14 2 18 18 26 17 21 10
cookie1 2015-04-13 3 16 16 26 16 18 13
cookie1 2015-04-12 7 13 13 26 13 16 20
cookie1 2015-04-11 5 6 6 26 6 13 25
cookie1 2015-04-10 1 1 1 26 1 6 26
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，如，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
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
–其他AVG，MIN，MAX，和SUM用法一样。
// SQL语句： AVG
SELECT cookieid,createtime,pv,
AVG(pv) OVER(PARTITION BY cookieid ORDER BY createtime) AS pv1, -- 默认为从起点到当前行
AVG(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
AVG(pv) OVER(PARTITION BY cookieid) AS pv3, --分组内所有行
AVG(pv) OVER(PARTITION BY cookieid ORDER BY createtime ROWS BETWEEN 3 PRECEDING AND CURRENT ROW) AS pv4, --当前行+往前3行
AVG(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
AVG(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;
结果：
cookie1 2015-04-16 4 3.7142857142857144 3.7142857142857144 3.7142857142857144 3.25 3.25 4.0
cookie1 2015-04-15 4 3.6666666666666665 3.6666666666666665 3.7142857142857144 4.0 4.0 4.0
cookie1 2015-04-14 2 3.6 3.6 3.7142857142857144 4.25 4.2 3.3333333333333335
cookie1 2015-04-13 3 4.0 4.0 3.7142857142857144 4.0 3.6 3.25
cookie1 2015-04-12 7 4.333333333333333 4.333333333333333 3.7142857142857144 4.333333333333333 4.0 4.0
cookie1 2015-04-11 5 3.0 3.0 3.7142857142857144 3.0 4.333333333333333 4.166666666666667
cookie1 2015-04-10 1 1.0 1.0 3.7142857142857144 1.0 3.0 3.7142857142857144
// SQL语句： MIN
SELECT cookieid, createtime, pv,
MIN(pv) OVER(PARTITION BY cookieid ORDER BY createtime) AS pv1, -- 默认为从起点到当前行
MIN(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
MIN(pv) OVER(PARTITION BY cookieid) AS pv3, --分组内所有行
MIN(pv) OVER(PARTITION BY cookieid ORDER BY createtime ROWS BETWEEN 3 PRECEDING AND CURRENT ROW) AS pv4, --当前行+往前3行
MIN(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
MIN(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;
结果：
cookie1 2015-04-16 4 1 1 1 2 2 4
cookie1 2015-04-15 4 1 1 1 2 2 4
cookie1 2015-04-14 2 1 1 1 2 2 2
cookie1 2015-04-13 3 1 1 1 1 1 2
cookie1 2015-04-12 7 1 1 1 1 1 2
cookie1 2015-04-11 5 1 1 1 1 1 2
cookie1 2015-04-10 1 1 1 1 1 1 1
// SQL语句： MAX
SELECT cookieid,createtime,pv,
MAX(pv) OVER(PARTITION BY cookieid ORDER BY createtime) AS pv1, -- 默认为从起点到当前行
MAX(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
MAX(pv) OVER(PARTITION BY cookieid) AS pv3, --分组内所有行
MAX(pv) OVER(PARTITION BY cookieid ORDER BY createtime ROWS BETWEEN 3 PRECEDING AND CURRENT ROW) AS pv4, --当前行+往前3行
MAX(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
MAX(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;
结果：
cookie1 2015-04-16 4 7 7 7 4 4 4
cookie1 2015-04-15 4 7 7 7 7 7 4
cookie1 2015-04-14 2 7 7 7 7 7 4
cookie1 2015-04-13 3 7 7 7 7 7 4
cookie1 2015-04-12 7 7 7 7 7 7 7
cookie1 2015-04-11 5 5 5 7 5 7 7
cookie1 2015-04-10 1 1 1 7 1 5 7
// 数据准备：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
建表语句：
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/hadoop/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
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
--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
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
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
// 数据准备：cookie3.txt
d1,user1,1000
d1,user2,2000
d1,user3,3000
d2,user4,4000
d2,user5,5000
// 建表语句：
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/hadoop/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
// SQL语句：
SELECT dept, userid, sal,
CUME_DIST() OVER(ORDER BY sal) AS rn1,
CUME_DIST() OVER(PARTITION BY dept ORDER BY sal) AS rn2
FROM cookie3;
// 结果：
d1 user1 1000 0.2 0.3333333333333333
d1 user2 2000 0.4 0.6666666666666666
d1 user3 3000 0.6 1.0
d2 user4 4000 0.8 0.5
d2 user5 5000 1.0 1.0
// SQL语句：
SELECT dept, userid, sal,
CUME_DIST() OVER(ORDER BY sal) AS rn1,
CUME_DIST() OVER(PARTITION BY dept ORDER BY sal desc) AS rn2
FROM cookie3;
结果：
d1 user3 3000 0.6 0.3333333333333333
d1 user2 2000 0.4 0.6666666666666666
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：
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：
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;
// 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
// 建表语句：
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/hadoop/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：
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：
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：
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：
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：
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：
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
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;
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
// 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
// 建表语句：
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/hadoop/cookie5.txt" into table cookie5;
select * from cookie5;
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
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：
SELECT month, day,
COUNT(DISTINCT cookieid) AS uv,
GROUPING__ID
FROM cookie5
GROUP BY month,day
GROUPING SETS (month,day)
ORDER BY GROUPING__ID;
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
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
// 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：
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：
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）
结果：
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。
官网的介绍： https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C+Grouping+and+Rollup