分析窗口函数应用场景：

（1）用于分区排序

（2）动态Group By

（3）Top N

（4）累计计算

（5）层次查询

Hive分析窗口函数(一) SUM,AVG,MIN,MAX

Hive中提供了越来越多的分析函数，用于完成负责的统计分析。抽时间将所有的分析窗口函数理一遍，将陆续发布。

今天先看几个基础的，SUM、AVG、MIN、MAX。

用于实现分组内所有和连续累积的统计。

数据准备：

  1. CREATE EXTERNAL TABLE lxw1234 (
  2. cookieid string,
  3. createtime string, --day
  4. pv INT
  5. ) ROW FORMAT DELIMITED
  6. FIELDS TERMINATED BY ','
  7. stored as textfile location '/tmp/lxw11/';
  8. DESC lxw1234;
  9. cookieid STRING
  10. createtime STRING
  11. pv INT
  12. hive> select * from lxw1234;
  13. OK
  14. cookie1 2015-04-10 1
  15. cookie1 2015-04-11 5
  16. cookie1 2015-04-12 7
  17. cookie1 2015-04-13 3
  18. cookie1 2015-04-14 2
  19. cookie1 2015-04-15 4
  20. cookie1 2015-04-16 4

SUM — 注意,结果和ORDER BY相关,默认为升序

  1. SELECT cookieid,
  2. createtime,
  3. pv,
  4. SUM(pv) OVER(PARTITION BY cookieid ORDER BY createtime) AS pv1, -- 默认为从起点到当前行
  5. 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
  6. SUM(pv) OVER(PARTITION BY cookieid) AS pv3, --分组内所有行
  7. 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
  8. 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
  9. 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
  10. FROM lxw1234;
  11. cookieid createtime pv pv1 pv2 pv3 pv4 pv5 pv6
  12. -----------------------------------------------------------------------------
  13. cookie1 2015-04-10 1 1 1 26 1 6 26
  14. cookie1 2015-04-11 5 6 6 26 6 13 25
  15. cookie1 2015-04-12 7 13 13 26 13 16 20
  16. cookie1 2015-04-13 3 16 16 26 16 18 13
  17. cookie1 2015-04-14 2 18 18 26 17 21 10
  18. cookie1 2015-04-15 4 22 22 26 16 20 8
  19. cookie1 2015-04-16 4 26 26 26 13 13 4

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。

  1. --AVG
  2. SELECT cookieid,
  3. createtime,
  4. pv,
  5. AVG(pv) OVER(PARTITION BY cookieid ORDER BY createtime) AS pv1, -- 默认为从起点到当前行
  6. 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
  7. AVG(pv) OVER(PARTITION BY cookieid) AS pv3, --分组内所有行
  8. 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
  9. 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
  10. 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
  11. FROM lxw1234;
  12. cookieid createtime pv pv1 pv2 pv3 pv4 pv5 pv6
  13. -----------------------------------------------------------------------------
  14. cookie1 2015-04-10 1 1.0 1.0 3.7142857142857144 1.0 3.0 3.7142857142857144
  15. cookie1 2015-04-11 5 3.0 3.0 3.7142857142857144 3.0 4.333333333333333 4.166666666666667
  16. cookie1 2015-04-12 7 4.333333333333333 4.333333333333333 3.7142857142857144 4.333333333333333 4.0 4.0
  17. cookie1 2015-04-13 3 4.0 4.0 3.7142857142857144 4.0 3.6 3.25
  18. cookie1 2015-04-14 2 3.6 3.6 3.7142857142857144 4.25 4.2 3.3333333333333335
  19. cookie1 2015-04-15 4 3.6666666666666665 3.6666666666666665 3.7142857142857144 4.0 4.0 4.0
  20. cookie1 2015-04-16 4 3.7142857142857144 3.7142857142857144 3.7142857142857144 3.25 3.25 4.0
  1. --MIN
  2. SELECT cookieid,
  3. createtime,
  4. pv,
  5. MIN(pv) OVER(PARTITION BY cookieid ORDER BY createtime) AS pv1, -- 默认为从起点到当前行
  6. 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
  7. MIN(pv) OVER(PARTITION BY cookieid) AS pv3, --分组内所有行
  8. 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
  9. 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
  10. 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
  11. FROM lxw1234;
  12. cookieid createtime pv pv1 pv2 pv3 pv4 pv5 pv6
  13. -----------------------------------------------------------------------------
  14. cookie1 2015-04-10 1 1 1 1 1 1 1
  15. cookie1 2015-04-11 5 1 1 1 1 1 2
  16. cookie1 2015-04-12 7 1 1 1 1 1 2
  17. cookie1 2015-04-13 3 1 1 1 1 1 2
  18. cookie1 2015-04-14 2 1 1 1 2 2 2
  19. cookie1 2015-04-15 4 1 1 1 2 2 4
  20. cookie1 2015-04-16 4 1 1 1 2 2 4
  1. --MAX
  2. SELECT cookieid,
  3. createtime,
  4. pv,
  5. MAX(pv) OVER(PARTITION BY cookieid ORDER BY createtime) AS pv1, -- 默认为从起点到当前行
  6. 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
  7. MAX(pv) OVER(PARTITION BY cookieid) AS pv3, --分组内所有行
  8. 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
  9. 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
  10. 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
  11. FROM lxw1234;
  12. cookieid createtime pv pv1 pv2 pv3 pv4 pv5 pv6
  13. -----------------------------------------------------------------------------
  14. cookie1 2015-04-10 1 1 1 7 1 5 7
  15. cookie1 2015-04-11 5 5 5 7 5 7 7
  16. cookie1 2015-04-12 7 7 7 7 7 7 7
  17. cookie1 2015-04-13 3 7 7 7 7 7 4
  18. cookie1 2015-04-14 2 7 7 7 7 7 4
  19. cookie1 2015-04-15 4 7 7 7 7 7 4
  20. cookie1 2015-04-16 4 7 7 7 4 4 4

Hive分析窗口函数(二) NTILE,ROW_NUMBER,RANK,DENSE_RANK

本文中介绍前几个序列函数，NTILE,ROW_NUMBER,RANK,DENSE_RANK，下面会一一解释各自的用途。

注意： 序列函数不支持WINDOW子句。（什么是WINDOW子句，点此查看前面的文章）

数据准备：

  1. CREATE EXTERNAL TABLE lxw1234 (
  2. cookieid string,
  3. createtime string, --day
  4. pv INT
  5. ) ROW FORMAT DELIMITED
  6. FIELDS TERMINATED BY ','
  7. stored as textfile location '/tmp/lxw11/';
  8. DESC lxw1234;
  9. cookieid STRING
  10. createtime STRING
  11. pv INT
  12. hive> select * from lxw1234;
  13. OK
  14. cookie1 2015-04-10 1
  15. cookie1 2015-04-11 5
  16. cookie1 2015-04-12 7
  17. cookie1 2015-04-13 3
  18. cookie1 2015-04-14 2
  19. cookie1 2015-04-15 4
  20. cookie1 2015-04-16 4
  21. cookie2 2015-04-10 2
  22. cookie2 2015-04-11 3
  23. cookie2 2015-04-12 5
  24. cookie2 2015-04-13 6
  25. cookie2 2015-04-14 3
  26. cookie2 2015-04-15 9
  27. cookie2 2015-04-16 7

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

  1. SELECT
  2. cookieid,
  3. createtime,
  4. pv,
  5. NTILE(2) OVER(PARTITION BY cookieid ORDER BY createtime) AS rn1, --分组内将数据分成2片
  6. NTILE(3) OVER(PARTITION BY cookieid ORDER BY createtime) AS rn2, --分组内将数据分成3片
  7. NTILE(4) OVER(ORDER BY createtime) AS rn3 --将所有数据分成4片
  8. FROM lxw1234
  9. ORDER BY cookieid,createtime;
  10. cookieid day pv rn1 rn2 rn3
  11. -------------------------------------------------
  12. cookie1 2015-04-10 1 1 1 1
  13. cookie1 2015-04-11 5 1 1 1
  14. cookie1 2015-04-12 7 1 1 2
  15. cookie1 2015-04-13 3 1 2 2
  16. cookie1 2015-04-14 2 2 2 3
  17. cookie1 2015-04-15 4 2 3 3
  18. cookie1 2015-04-16 4 2 3 4
  19. cookie2 2015-04-10 2 1 1 1
  20. cookie2 2015-04-11 3 1 1 1
  21. cookie2 2015-04-12 5 1 1 2
  22. cookie2 2015-04-13 6 1 2 2
  23. cookie2 2015-04-14 3 2 2 3
  24. cookie2 2015-04-15 9 2 3 4
  25. 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

  1. SELECT
  2. cookieid,
  3. createtime,
  4. pv,
  5. NTILE(3) OVER(PARTITION BY cookieid ORDER BY pv DESC) AS rn
  6. FROM lxw1234;
  7. --rn = 1 的记录，就是我们想要的结果
  8. cookieid day pv rn
  9. ----------------------------------
  10. cookie1 2015-04-12 7 1
  11. cookie1 2015-04-11 5 1
  12. cookie1 2015-04-15 4 1
  13. cookie1 2015-04-16 4 2
  14. cookie1 2015-04-13 3 2
  15. cookie1 2015-04-14 2 3
  16. cookie1 2015-04-10 1 3
  17. cookie2 2015-04-15 9 1
  18. cookie2 2015-04-16 7 1
  19. cookie2 2015-04-13 6 1
  20. cookie2 2015-04-12 5 2
  21. cookie2 2015-04-14 3 2
  22. cookie2 2015-04-11 3 3
  23. cookie2 2015-04-10 2 3

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。

  1. SELECT
  2. cookieid,
  3. createtime,
  4. pv,
  5. ROW_NUMBER() OVER(PARTITION BY cookieid ORDER BY pv desc) AS rn
  6. FROM lxw1234;
  7. cookieid day pv rn
  8. -------------------------------------------
  9. cookie1 2015-04-12 7 1
  10. cookie1 2015-04-11 5 2
  11. cookie1 2015-04-15 4 3
  12. cookie1 2015-04-16 4 4
  13. cookie1 2015-04-13 3 5
  14. cookie1 2015-04-14 2 6
  15. cookie1 2015-04-10 1 7
  16. cookie2 2015-04-15 9 1
  17. cookie2 2015-04-16 7 2
  18. cookie2 2015-04-13 6 3
  19. cookie2 2015-04-12 5 4
  20. cookie2 2015-04-14 3 5
  21. cookie2 2015-04-11 3 6
  22. cookie2 2015-04-10 2 7

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

  1. SELECT
  2. cookieid,
  3. createtime,
  4. pv,
  5. RANK() OVER(PARTITION BY cookieid ORDER BY pv desc) AS rn1,
  6. DENSE_RANK() OVER(PARTITION BY cookieid ORDER BY pv desc) AS rn2,
  7. ROW_NUMBER() OVER(PARTITION BY cookieid ORDER BY pv DESC) AS rn3
  8. FROM lxw1234
  9. WHERE cookieid = 'cookie1';
  10. cookieid day pv rn1 rn2 rn3
  11. --------------------------------------------------
  12. cookie1 2015-04-12 7 1 1 1
  13. cookie1 2015-04-11 5 2 2 2
  14. cookie1 2015-04-15 4 3 3 3
  15. cookie1 2015-04-16 4 3 3 4
  16. cookie1 2015-04-13 3 5 4 5
  17. cookie1 2015-04-14 2 6 5 6
  18. cookie1 2015-04-10 1 7 6 7
  19. rn1: 15号和16号并列第3, 13号排第5
  20. rn2: 15号和16号并列第3, 13号排第4
  21. rn3: 如果相等，则按记录值排序，生成唯一的次序，如果所有记录值都相等，或许会随机排吧。

Hive分析窗口函数(三) CUME_DIST,PERCENT_RANK

这两个序列分析函数不是很常用，这里也介绍一下。

注意： 序列函数不支持WINDOW子句。（什么是WINDOW子句，点此查看前面的文章）

数据准备：

  1. CREATE EXTERNAL TABLE lxw1234 (
  2. dept STRING,
  3. userid string,
  4. sal INT
  5. ) ROW FORMAT DELIMITED
  6. FIELDS TERMINATED BY ','
  7. stored as textfile location '/tmp/lxw11/';
  8. hive> select * from lxw1234;
  9. OK
  10. d1 user1 1000
  11. d1 user2 2000
  12. d1 user3 3000
  13. d2 user4 4000
  14. d2 user5 5000

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

  1. SELECT
  2. dept,
  3. userid,
  4. sal,
  5. CUME_DIST() OVER(ORDER BY sal) AS rn1,
  6. CUME_DIST() OVER(PARTITION BY dept ORDER BY sal) AS rn2
  7. FROM lxw1234;
  8. dept userid sal rn1 rn2
  9. -------------------------------------------
  10. d1 user1 1000 0.2 0.3333333333333333
  11. d1 user2 2000 0.4 0.6666666666666666
  12. d1 user3 3000 0.6 1.0
  13. d2 user4 4000 0.8 0.5
  14. d2 user5 5000 1.0 1.0
  15. rn1: 没有partition,所有数据均为1组，总行数为5，
  16. 第一行：小于等于1000的行数为1，因此，1/5=0.2
  17. 第三行：小于等于3000的行数为3，因此，3/5=0.6
  18. rn2: 按照部门分组，dpet=d1的行数为3,
  19. 第二行：小于等于2000的行数为2，因此，2/3=0.6666666666666666

PERCENT_RANK

–PERCENT_RANK 分组内当前行的RANK值-1/分组内总行数-1
应用场景不了解，可能在一些特殊算法的实现中可以用到吧。

  1. SELECT
  2. dept,
  3. userid,
  4. sal,
  5. PERCENT_RANK() OVER(ORDER BY sal) AS rn1, --分组内
  6. RANK() OVER(ORDER BY sal) AS rn11, --分组内RANK值
  7. SUM(1) OVER(PARTITION BY NULL) AS rn12, --分组内总行数
  8. PERCENT_RANK() OVER(PARTITION BY dept ORDER BY sal) AS rn2
  9. FROM lxw1234;
  10. dept userid sal rn1 rn11 rn12 rn2
  11. ---------------------------------------------------
  12. d1 user1 1000 0.0 1 5 0.0
  13. d1 user2 2000 0.25 2 5 0.5
  14. d1 user3 3000 0.5 3 5 1.0
  15. d2 user4 4000 0.75 4 5 0.0
  16. d2 user5 5000 1.0 5 5 1.0
  17. rn1: rn1 = (rn11-1) / (rn12-1)
  18. 第一行,(1-1)/(5-1)=0/4=0
  19. 第二行,(2-1)/(5-1)=1/4=0.25
  20. 第四行,(4-1)/(5-1)=3/4=0.75
  21. rn2: 按照dept分组，
  22. dept=d1的总行数为3
  23. 第一行，(1-1)/(3-1)=0
  24. 第三行，(3-1)/(3-1)=1

Hive分析窗口函数(四) LAG,LEAD,FIRST_VALUE,LAST_VALUE

继续学习这四个分析函数。

注意： 这几个函数不支持WINDOW子句。（什么是WINDOW子句，点此查看前面的文章）

数据准备：

  1. CREATE EXTERNAL TABLE lxw1234 (
  2. cookieid string,
  3. createtime string, --页面访问时间
  4. url STRING --被访问页面
  5. ) ROW FORMAT DELIMITED
  6. FIELDS TERMINATED BY ','
  7. stored as textfile location '/tmp/lxw11/';
  8. hive> select * from lxw1234;
  9. OK
  10. cookie1 2015-04-10 10:00:02 url2
  11. cookie1 2015-04-10 10:00:00 url1
  12. cookie1 2015-04-10 10:03:04 1url3
  13. cookie1 2015-04-10 10:50:05 url6
  14. cookie1 2015-04-10 11:00:00 url7
  15. cookie1 2015-04-10 10:10:00 url4
  16. cookie1 2015-04-10 10:50:01 url5
  17. cookie2 2015-04-10 10:00:02 url22
  18. cookie2 2015-04-10 10:00:00 url11
  19. cookie2 2015-04-10 10:03:04 1url33
  20. cookie2 2015-04-10 10:50:05 url66
  21. cookie2 2015-04-10 11:00:00 url77
  22. cookie2 2015-04-10 10:10:00 url44
  23. cookie2 2015-04-10 10:50:01 url55

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）

  1. SELECT cookieid,
  2. createtime,
  3. url,
  4. ROW_NUMBER() OVER(PARTITION BY cookieid ORDER BY createtime) AS rn,
  5. LAG(createtime,1,'1970-01-01 00:00:00') OVER(PARTITION BY cookieid ORDER BY createtime) AS last_1_time,
  6. LAG(createtime,2) OVER(PARTITION BY cookieid ORDER BY createtime) AS last_2_time
  7. FROM lxw1234;
  8. cookieid createtime url rn last_1_time last_2_time
  9. -------------------------------------------------------------------------------------------
  10. cookie1 2015-04-10 10:00:00 url1 1 1970-01-01 00:00:00 NULL
  11. cookie1 2015-04-10 10:00:02 url2 2 2015-04-10 10:00:00 NULL
  12. cookie1 2015-04-10 10:03:04 1url3 3 2015-04-10 10:00:02 2015-04-10 10:00:00
  13. cookie1 2015-04-10 10:10:00 url4 4 2015-04-10 10:03:04 2015-04-10 10:00:02
  14. cookie1 2015-04-10 10:50:01 url5 5 2015-04-10 10:10:00 2015-04-10 10:03:04
  15. cookie1 2015-04-10 10:50:05 url6 6 2015-04-10 10:50:01 2015-04-10 10:10:00
  16. cookie1 2015-04-10 11:00:00 url7 7 2015-04-10 10:50:05 2015-04-10 10:50:01
  17. cookie2 2015-04-10 10:00:00 url11 1 1970-01-01 00:00:00 NULL
  18. cookie2 2015-04-10 10:00:02 url22 2 2015-04-10 10:00:00 NULL
  19. cookie2 2015-04-10 10:03:04 1url33 3 2015-04-10 10:00:02 2015-04-10 10:00:00
  20. cookie2 2015-04-10 10:10:00 url44 4 2015-04-10 10:03:04 2015-04-10 10:00:02
  21. cookie2 2015-04-10 10:50:01 url55 5 2015-04-10 10:10:00 2015-04-10 10:03:04
  22. cookie2 2015-04-10 10:50:05 url66 6 2015-04-10 10:50:01 2015-04-10 10:10:00
  23. cookie2 2015-04-10 11:00:00 url77 7 2015-04-10 10:50:05 2015-04-10 10:50:01
  24. last_1_time: 指定了往上第1行的值，default为'1970-01-01 00:00:00'
  25. cookie1第一行，往上1行为NULL,因此取默认值 1970-01-01 00:00:00
  26. cookie1第三行，往上1行值为第二行值，2015-04-10 10:00:02
  27. cookie1第六行，往上1行值为第五行值，2015-04-10 10:50:01
  28. last_2_time: 指定了往上第2行的值，为指定默认值
  29. cookie1第一行，往上2行为NULL
  30. cookie1第二行，往上2行为NULL
  31. cookie1第四行，往上2行为第二行值，2015-04-10 10:00:02
  32. 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）

  1. SELECT cookieid,
  2. createtime,
  3. url,
  4. ROW_NUMBER() OVER(PARTITION BY cookieid ORDER BY createtime) AS rn,
  5. LEAD(createtime,1,'1970-01-01 00:00:00') OVER(PARTITION BY cookieid ORDER BY createtime) AS next_1_time,
  6. LEAD(createtime,2) OVER(PARTITION BY cookieid ORDER BY createtime) AS next_2_time
  7. FROM lxw1234;
  8. cookieid createtime url rn next_1_time next_2_time
  9. -------------------------------------------------------------------------------------------
  10. cookie1 2015-04-10 10:00:00 url1 1 2015-04-10 10:00:02 2015-04-10 10:03:04
  11. cookie1 2015-04-10 10:00:02 url2 2 2015-04-10 10:03:04 2015-04-10 10:10:00
  12. cookie1 2015-04-10 10:03:04 1url3 3 2015-04-10 10:10:00 2015-04-10 10:50:01
  13. cookie1 2015-04-10 10:10:00 url4 4 2015-04-10 10:50:01 2015-04-10 10:50:05
  14. cookie1 2015-04-10 10:50:01 url5 5 2015-04-10 10:50:05 2015-04-10 11:00:00
  15. cookie1 2015-04-10 10:50:05 url6 6 2015-04-10 11:00:00 NULL
  16. cookie1 2015-04-10 11:00:00 url7 7 1970-01-01 00:00:00 NULL
  17. cookie2 2015-04-10 10:00:00 url11 1 2015-04-10 10:00:02 2015-04-10 10:03:04
  18. cookie2 2015-04-10 10:00:02 url22 2 2015-04-10 10:03:04 2015-04-10 10:10:00
  19. cookie2 2015-04-10 10:03:04 1url33 3 2015-04-10 10:10:00 2015-04-10 10:50:01
  20. cookie2 2015-04-10 10:10:00 url44 4 2015-04-10 10:50:01 2015-04-10 10:50:05
  21. cookie2 2015-04-10 10:50:01 url55 5 2015-04-10 10:50:05 2015-04-10 11:00:00
  22. cookie2 2015-04-10 10:50:05 url66 6 2015-04-10 11:00:00 NULL
  23. cookie2 2015-04-10 11:00:00 url77 7 1970-01-01 00:00:00 NULL
  24. --逻辑与LAG一样，只不过LAG是往上，LEAD是往下。

FIRST_VALUE

取分组内排序后，截止到当前行，第一个值

  1. SELECT cookieid,
  2. createtime,
  3. url,
  4. ROW_NUMBER() OVER(PARTITION BY cookieid ORDER BY createtime) AS rn,
  5. FIRST_VALUE(url) OVER(PARTITION BY cookieid ORDER BY createtime) AS first1
  6. FROM lxw1234;
  7. cookieid createtime url rn first1
  8. ---------------------------------------------------------
  9. cookie1 2015-04-10 10:00:00 url1 1 url1
  10. cookie1 2015-04-10 10:00:02 url2 2 url1
  11. cookie1 2015-04-10 10:03:04 1url3 3 url1
  12. cookie1 2015-04-10 10:10:00 url4 4 url1
  13. cookie1 2015-04-10 10:50:01 url5 5 url1
  14. cookie1 2015-04-10 10:50:05 url6 6 url1
  15. cookie1 2015-04-10 11:00:00 url7 7 url1
  16. cookie2 2015-04-10 10:00:00 url11 1 url11
  17. cookie2 2015-04-10 10:00:02 url22 2 url11
  18. cookie2 2015-04-10 10:03:04 1url33 3 url11
  19. cookie2 2015-04-10 10:10:00 url44 4 url11
  20. cookie2 2015-04-10 10:50:01 url55 5 url11
  21. cookie2 2015-04-10 10:50:05 url66 6 url11
  22. 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

  1. SELECT cookieid,
  2. createtime,
  3. url,
  4. ROW_NUMBER() OVER(PARTITION BY cookieid ORDER BY createtime) AS rn,
  5. LAST_VALUE(url) OVER(PARTITION BY cookieid ORDER BY createtime) AS last1
  6. FROM lxw1234;
  7. cookieid createtime url rn last1
  8. -----------------------------------------------------------------
  9. cookie1 2015-04-10 10:00:00 url1 1 url1
  10. cookie1 2015-04-10 10:00:02 url2 2 url2
  11. cookie1 2015-04-10 10:03:04 1url3 3 1url3
  12. cookie1 2015-04-10 10:10:00 url4 4 url4
  13. cookie1 2015-04-10 10:50:01 url5 5 url5
  14. cookie1 2015-04-10 10:50:05 url6 6 url6
  15. cookie1 2015-04-10 11:00:00 url7 7 url7
  16. cookie2 2015-04-10 10:00:00 url11 1 url11
  17. cookie2 2015-04-10 10:00:02 url22 2 url22
  18. cookie2 2015-04-10 10:03:04 1url33 3 1url33
  19. cookie2 2015-04-10 10:10:00 url44 4 url44
  20. cookie2 2015-04-10 10:50:01 url55 5 url55
  21. cookie2 2015-04-10 10:50:05 url66 6 url66
  22. cookie2 2015-04-10 11:00:00 url77 7 url77

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

  1. SELECT cookieid,
  2. createtime,
  3. url,
  4. FIRST_VALUE(url) OVER(PARTITION BY cookieid) AS first2
  5. FROM lxw1234;
  6. cookieid createtime url first2
  7. ----------------------------------------------
  8. cookie1 2015-04-10 10:00:02 url2 url2
  9. cookie1 2015-04-10 10:00:00 url1 url2
  10. cookie1 2015-04-10 10:03:04 1url3 url2
  11. cookie1 2015-04-10 10:50:05 url6 url2
  12. cookie1 2015-04-10 11:00:00 url7 url2
  13. cookie1 2015-04-10 10:10:00 url4 url2
  14. cookie1 2015-04-10 10:50:01 url5 url2
  15. cookie2 2015-04-10 10:00:02 url22 url22
  16. cookie2 2015-04-10 10:00:00 url11 url22
  17. cookie2 2015-04-10 10:03:04 1url33 url22
  18. cookie2 2015-04-10 10:50:05 url66 url22
  19. cookie2 2015-04-10 11:00:00 url77 url22
  20. cookie2 2015-04-10 10:10:00 url44 url22
  21. cookie2 2015-04-10 10:50:01 url55 url22
  22. SELECT cookieid,
  23. createtime,
  24. url,
  25. LAST_VALUE(url) OVER(PARTITION BY cookieid) AS last2
  26. FROM lxw1234;
  27. cookieid createtime url last2
  28. ----------------------------------------------
  29. cookie1 2015-04-10 10:00:02 url2 url5
  30. cookie1 2015-04-10 10:00:00 url1 url5
  31. cookie1 2015-04-10 10:03:04 1url3 url5
  32. cookie1 2015-04-10 10:50:05 url6 url5
  33. cookie1 2015-04-10 11:00:00 url7 url5
  34. cookie1 2015-04-10 10:10:00 url4 url5
  35. cookie1 2015-04-10 10:50:01 url5 url5
  36. cookie2 2015-04-10 10:00:02 url22 url55
  37. cookie2 2015-04-10 10:00:00 url11 url55
  38. cookie2 2015-04-10 10:03:04 1url33 url55
  39. cookie2 2015-04-10 10:50:05 url66 url55
  40. cookie2 2015-04-10 11:00:00 url77 url55
  41. cookie2 2015-04-10 10:10:00 url44 url55
  42. cookie2 2015-04-10 10:50:01 url55 url55

如果想要取分组内排序后最后一个值，则需要变通一下：

  1. SELECT cookieid,
  2. createtime,
  3. url,
  4. ROW_NUMBER() OVER(PARTITION BY cookieid ORDER BY createtime) AS rn,
  5. LAST_VALUE(url) OVER(PARTITION BY cookieid ORDER BY createtime) AS last1,
  6. FIRST_VALUE(url) OVER(PARTITION BY cookieid ORDER BY createtime DESC) AS last2
  7. FROM lxw1234
  8. ORDER BY cookieid,createtime;
  9. cookieid createtime url rn last1 last2
  10. -------------------------------------------------------------
  11. cookie1 2015-04-10 10:00:00 url1 1 url1 url7
  12. cookie1 2015-04-10 10:00:02 url2 2 url2 url7
  13. cookie1 2015-04-10 10:03:04 1url3 3 1url3 url7
  14. cookie1 2015-04-10 10:10:00 url4 4 url4 url7
  15. cookie1 2015-04-10 10:50:01 url5 5 url5 url7
  16. cookie1 2015-04-10 10:50:05 url6 6 url6 url7
  17. cookie1 2015-04-10 11:00:00 url7 7 url7 url7
  18. cookie2 2015-04-10 10:00:00 url11 1 url11 url77
  19. cookie2 2015-04-10 10:00:02 url22 2 url22 url77
  20. cookie2 2015-04-10 10:03:04 1url33 3 1url33 url77
  21. cookie2 2015-04-10 10:10:00 url44 4 url44 url77
  22. cookie2 2015-04-10 10:50:01 url55 5 url55 url77
  23. cookie2 2015-04-10 10:50:05 url66 6 url66 url77
  24. cookie2 2015-04-10 11:00:00 url77 7 url77 url77 
<span style="font-weight: bold; color: rgb(255, 0, 0); font-family: Arial, Helvetica, sans-serif; background-color: rgb(255, 255, 255);">提示：在使用分析函数的过程中，要特别注意ORDER BY子句，用的不恰当，统计出的结果就不是你所期望的。</span>

Hive分析窗口函数(五) GROUPING SETS,GROUPING__ID,CUBE,ROLLUP

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：

  1. CREATE EXTERNAL TABLE lxw1234 (
  2. month STRING,
  3. day STRING,
  4. cookieid STRING
  5. ) ROW FORMAT DELIMITED
  6. FIELDS TERMINATED BY ','
  7. stored as textfile location '/tmp/lxw11/';
  8. hive> select * from lxw1234;
  9. OK
  10. 2015-03 2015-03-10 cookie1
  11. 2015-03 2015-03-10 cookie5
  12. 2015-03 2015-03-12 cookie7
  13. 2015-04 2015-04-12 cookie3
  14. 2015-04 2015-04-13 cookie2
  15. 2015-04 2015-04-13 cookie4
  16. 2015-04 2015-04-16 cookie4
  17. 2015-03 2015-03-10 cookie2
  18. 2015-03 2015-03-10 cookie3
  19. 2015-04 2015-04-12 cookie5
  20. 2015-04 2015-04-13 cookie6
  21. 2015-04 2015-04-15 cookie3
  22. 2015-04 2015-04-15 cookie2
  23. 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

  1. SELECT
  2. month,
  3. day,
  4. COUNT(DISTINCT cookieid) AS uv,
  5. GROUPING__ID
  6. FROM lxw1234
  7. GROUP BY month,day
  8. GROUPING SETS (month,day)
  9. ORDER BY GROUPING__ID;
  10. month day uv GROUPING__ID
  11. ------------------------------------------------
  12. 2015-03 NULL 5 1
  13. 2015-04 NULL 6 1
  14. NULL 2015-03-10 4 2
  15. NULL 2015-03-12 1 2
  16. NULL 2015-04-12 2 2
  17. NULL 2015-04-13 3 2
  18. NULL 2015-04-15 2 2
  19. NULL 2015-04-16 2 2
  20. 等价于
  21. SELECT month,NULL,COUNT(DISTINCT cookieid) AS uv,1 AS GROUPING__ID FROM lxw1234 GROUP BY month
  22. UNION ALL
  23. SELECT NULL,day,COUNT(DISTINCT cookieid) AS uv,2 AS GROUPING__ID FROM lxw1234 GROUP BY day

再如：

  1. SELECT
  2. month,
  3. day,
  4. COUNT(DISTINCT cookieid) AS uv,
  5. GROUPING__ID
  6. FROM lxw1234
  7. GROUP BY month,day
  8. GROUPING SETS (month,day,(month,day))
  9. ORDER BY GROUPING__ID;
  10. month day uv GROUPING__ID
  11. ------------------------------------------------
  12. 2015-03 NULL 5 1
  13. 2015-04 NULL 6 1
  14. NULL 2015-03-10 4 2
  15. NULL 2015-03-12 1 2
  16. NULL 2015-04-12 2 2
  17. NULL 2015-04-13 3 2
  18. NULL 2015-04-15 2 2
  19. NULL 2015-04-16 2 2
  20. 2015-03 2015-03-10 4 3
  21. 2015-03 2015-03-12 1 3
  22. 2015-04 2015-04-12 2 3
  23. 2015-04 2015-04-13 3 3
  24. 2015-04 2015-04-15 2 3
  25. 2015-04 2015-04-16 2 3
  26. 等价于
  27. SELECT month,NULL,COUNT(DISTINCT cookieid) AS uv,1 AS GROUPING__ID FROM lxw1234 GROUP BY month
  28. UNION ALL
  29. SELECT NULL,day,COUNT(DISTINCT cookieid) AS uv,2 AS GROUPING__ID FROM lxw1234 GROUP BY day
  30. UNION ALL
  31. 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。

  1. SELECT
  2. month,
  3. day,
  4. COUNT(DISTINCT cookieid) AS uv,
  5. GROUPING__ID
  6. FROM lxw1234
  7. GROUP BY month,day
  8. WITH CUBE
  9. ORDER BY GROUPING__ID;
  10. month day uv GROUPING__ID
  11. --------------------------------------------
  12. NULL NULL 7 0
  13. 2015-03 NULL 5 1
  14. 2015-04 NULL 6 1
  15. NULL 2015-04-12 2 2
  16. NULL 2015-04-13 3 2
  17. NULL 2015-04-15 2 2
  18. NULL 2015-04-16 2 2
  19. NULL 2015-03-10 4 2
  20. NULL 2015-03-12 1 2
  21. 2015-03 2015-03-10 4 3
  22. 2015-03 2015-03-12 1 3
  23. 2015-04 2015-04-16 2 3
  24. 2015-04 2015-04-12 2 3
  25. 2015-04 2015-04-13 3 3
  26. 2015-04 2015-04-15 2 3
  27. 等价于
  28. SELECT NULL,NULL,COUNT(DISTINCT cookieid) AS uv,0 AS GROUPING__ID FROM lxw1234
  29. UNION ALL
  30. SELECT month,NULL,COUNT(DISTINCT cookieid) AS uv,1 AS GROUPING__ID FROM lxw1234 GROUP BY month
  31. UNION ALL
  32. SELECT NULL,day,COUNT(DISTINCT cookieid) AS uv,2 AS GROUPING__ID FROM lxw1234 GROUP BY day
  33. UNION ALL
  34. 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。

  1. 比如，以month维度进行层级聚合：
  2. SELECT
  3. month,
  4. day,
  5. COUNT(DISTINCT cookieid) AS uv,
  6. GROUPING__ID
  7. FROM lxw1234
  8. GROUP BY month,day
  9. WITH ROLLUP
  10. ORDER BY GROUPING__ID;
  11. month day uv GROUPING__ID
  12. ---------------------------------------------------
  13. NULL NULL 7 0
  14. 2015-03 NULL 5 1
  15. 2015-04 NULL 6 1
  16. 2015-03 2015-03-10 4 3
  17. 2015-03 2015-03-12 1 3
  18. 2015-04 2015-04-12 2 3
  19. 2015-04 2015-04-13 3 3
  20. 2015-04 2015-04-15 2 3
  21. 2015-04 2015-04-16 2 3
  22. 可以实现这样的上钻过程：
  23. 月天的UV->月的UV->总UV
  1. --把month和day调换顺序，则以day维度进行层级聚合：
  2. SELECT
  3. day,
  4. month,
  5. COUNT(DISTINCT cookieid) AS uv,
  6. GROUPING__ID
  7. FROM lxw1234
  8. GROUP BY day,month
  9. WITH ROLLUP
  10. ORDER BY GROUPING__ID;
  11. day month uv GROUPING__ID
  12. -------------------------------------------------------
  13. NULL NULL 7 0
  14. 2015-04-13 NULL 3 1
  15. 2015-03-12 NULL 1 1
  16. 2015-04-15 NULL 2 1
  17. 2015-03-10 NULL 4 1
  18. 2015-04-16 NULL 2 1
  19. 2015-04-12 NULL 2 1
  20. 2015-04-12 2015-04 2 3
  21. 2015-03-10 2015-03 4 3
  22. 2015-03-12 2015-03 1 3
  23. 2015-04-13 2015-04 3 3
  24. 2015-04-15 2015-04 2 3
  25. 2015-04-16 2015-04 2 3
  26. 可以实现这样的上钻过程：
  27. 天月的UV->天的UV->总UV
  28. （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。

官网的介绍： https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C+Grouping+and+Rollup

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