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：

    CREATE EXTERNAL TABLE lxw1234 (
    cookieid string,
    createtime string,   --day 
    pv INT
    ) ROW FORMAT DELIMITED 
    FIELDS TERMINATED BY ',' 
    stored as textfile location '/tmp/lxw11/';
     
    DESC lxw1234;
    cookieid                STRING 
    createtime              STRING 
    pv INT 
     
    hive> select * from lxw1234;
    OK
    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

SUM — 注意,结果和ORDER BY相关,默认为升序

    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 lxw1234;
     
    cookieid createtime     pv      pv1     pv2     pv3     pv4     pv5      pv6 
    -----------------------------------------------------------------------------
    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

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。

    --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 lxw1234; 
    cookieid createtime     pv      pv1     pv2     pv3     pv4     pv5      pv6 
    -----------------------------------------------------------------------------
    cookie1 2015-04-10      1       1.0     1.0     3.7142857142857144      1.0     3.0     3.7142857142857144
    cookie1 2015-04-11      5       3.0     3.0     3.7142857142857144      3.0     4.333333333333333       4.166666666666667
    cookie1 2015-04-12      7       4.333333333333333       4.333333333333333       3.7142857142857144      4.333333333333333       4.0     4.0
    cookie1 2015-04-13      3       4.0     4.0     3.7142857142857144      4.0     3.6     3.25
    cookie1 2015-04-14      2       3.6     3.6     3.7142857142857144      4.25    4.2     3.3333333333333335
    cookie1 2015-04-15      4       3.6666666666666665      3.6666666666666665      3.7142857142857144      4.0     4.0     4.0
    cookie1 2015-04-16      4       3.7142857142857144      3.7142857142857144      3.7142857142857144      3.25    3.25    4.0
    --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 lxw1234;
     
    cookieid createtime     pv      pv1     pv2     pv3     pv4     pv5      pv6 
    -----------------------------------------------------------------------------
    cookie1 2015-04-10      1       1       1       1       1       1       1
    cookie1 2015-04-11      5       1       1       1       1       1       2
    cookie1 2015-04-12      7       1       1       1       1       1       2
    cookie1 2015-04-13      3       1       1       1       1       1       2
    cookie1 2015-04-14      2       1       1       1       2       2       2
    cookie1 2015-04-15      4       1       1       1       2       2       4
    cookie1 2015-04-16      4       1       1       1       2       2       4
    --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 lxw1234;
     
    cookieid createtime     pv      pv1     pv2     pv3     pv4     pv5      pv6 
    -----------------------------------------------------------------------------
    cookie1 2015-04-10      1       1       1       7       1       5       7
    cookie1 2015-04-11      5       5       5       7       5       7       7
    cookie1 2015-04-12      7       7       7       7       7       7       7
    cookie1 2015-04-13      3       7       7       7       7       7       4
    cookie1 2015-04-14      2       7       7       7       7       7       4
    cookie1 2015-04-15      4       7       7       7       7       7       4
    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：

CREATE EXTERNAL TABLE lxw1234 (
cookieid string,
createtime string,   --day 
pv INT
) ROW FORMAT DELIMITED 
FIELDS TERMINATED BY ',' 
stored as textfile location '/tmp/lxw11/';
 
DESC lxw1234;
cookieid                STRING 
createtime              STRING 
pv INT 
 
hive> select * from lxw1234;
OK
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

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 lxw1234 
    ORDER BY cookieid,createtime;
     
    cookieid day           pv       rn1     rn2     rn3
    -------------------------------------------------
    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       3
    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       4
    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 lxw1234;
     
    --rn = 1 的记录，就是我们想要的结果
     
    cookieid day           pv       rn
    ----------------------------------
    cookie1 2015-04-12      7       1
    cookie1 2015-04-11      5       1
    cookie1 2015-04-15      4       1
    cookie1 2015-04-16      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 2015-04-14      3       2
    cookie2 2015-04-11      3       3
    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。

    SELECT 
    cookieid,
    createtime,
    pv,
    ROW_NUMBER() OVER(PARTITION BY cookieid ORDER BY pv desc) AS rn 
    FROM lxw1234;
     
    cookieid day           pv       rn
    ------------------------------------------- 
    cookie1 2015-04-12      7       1
    cookie1 2015-04-11      5       2
    cookie1 2015-04-15      4       3
    cookie1 2015-04-16      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 2015-04-14      3       5
    cookie2 2015-04-11      3       6
    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

    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 lxw1234 
    WHERE cookieid = 'cookie1';
     
    cookieid day           pv       rn1     rn2     rn3 
    -------------------------------------------------- 
    cookie1 2015-04-12      7       1       1       1
    cookie1 2015-04-11      5       2       2       2
    cookie1 2015-04-15      4       3       3       3
    cookie1 2015-04-16      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
     
    rn1: 15号和16号并列第3, 13号排第5
    rn2: 15号和16号并列第3, 13号排第4
    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：

CREATE EXTERNAL TABLE lxw1234 (
dept STRING,
userid string,
sal INT
) ROW FORMAT DELIMITED 
FIELDS TERMINATED BY ',' 
stored as textfile location '/tmp/lxw11/';
 
 
hive> select * from lxw1234;
OK
d1      user1   1000
d1      user2   2000
d1      user3   3000
d2      user4   4000
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

    SELECT 
    dept,
    userid,
    sal,
    CUME_DIST() OVER(ORDER BY sal) AS rn1,
    CUME_DIST() OVER(PARTITION BY dept ORDER BY sal) AS rn2 
    FROM lxw1234;
     
    dept    userid   sal   rn1       rn2 
    -------------------------------------------
    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
     
    rn1: 没有partition,所有数据均为1组，总行数为5，
         第一行：小于等于1000的行数为1，因此，1/5=0.2
         第三行：小于等于3000的行数为3，因此，3/5=0.6
    rn2: 按照部门分组，dpet=d1的行数为3,
         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。

    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 lxw1234;
     
    dept    userid  sal     rn1    rn11     rn12    rn2
    ---------------------------------------------------
    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
     
    rn1: rn1 = (rn11-1) / (rn12-1) 
    	 第一行,(1-1)/(5-1)=0/4=0
    	 第二行,(2-1)/(5-1)=1/4=0.25
    	 第四行,(4-1)/(5-1)=3/4=0.75
    rn2: 按照dept分组，
         dept=d1的总行数为3
         第一行，(1-1)/(3-1)=0
         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：

CREATE EXTERNAL TABLE lxw1234 (
cookieid string,
createtime string,  --页面访问时间
url STRING       --被访问页面
) ROW FORMAT DELIMITED 
FIELDS TERMINATED BY ',' 
stored as textfile location '/tmp/lxw11/';
 
 
hive> select * from lxw1234;
OK
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

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）

    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 lxw1234;
     
     
    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
     
     
    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）

    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 lxw1234;
     
     
    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
     
    --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

    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 lxw1234;
     
    cookieid  createtime            url     rn      first1
    ---------------------------------------------------------
    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

    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 lxw1234;
     
     
    cookieid  createtime            url    rn       last1  
    -----------------------------------------------------------------
    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

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

    SELECT cookieid,
    createtime,
    url,
    FIRST_VALUE(url) OVER(PARTITION BY cookieid) AS first2  
    FROM lxw1234;
     
    cookieid  createtime            url     first2
    ----------------------------------------------
    cookie1 2015-04-10 10:00:02     url2    url2
    cookie1 2015-04-10 10:00:00     url1    url2
    cookie1 2015-04-10 10:03:04     1url3   url2
    cookie1 2015-04-10 10:50:05     url6    url2
    cookie1 2015-04-10 11:00:00     url7    url2
    cookie1 2015-04-10 10:10:00     url4    url2
    cookie1 2015-04-10 10:50:01     url5    url2
    cookie2 2015-04-10 10:00:02     url22   url22
    cookie2 2015-04-10 10:00:00     url11   url22
    cookie2 2015-04-10 10:03:04     1url33  url22
    cookie2 2015-04-10 10:50:05     url66   url22
    cookie2 2015-04-10 11:00:00     url77   url22
    cookie2 2015-04-10 10:10:00     url44   url22
    cookie2 2015-04-10 10:50:01     url55   url22
     
    SELECT cookieid,
    createtime,
    url,
    LAST_VALUE(url) OVER(PARTITION BY cookieid) AS last2  
    FROM lxw1234;
     
    cookieid  createtime            url     last2
    ----------------------------------------------
    cookie1 2015-04-10 10:00:02     url2    url5
    cookie1 2015-04-10 10:00:00     url1    url5
    cookie1 2015-04-10 10:03:04     1url3   url5
    cookie1 2015-04-10 10:50:05     url6    url5
    cookie1 2015-04-10 11:00:00     url7    url5
    cookie1 2015-04-10 10:10:00     url4    url5
    cookie1 2015-04-10 10:50:01     url5    url5
    cookie2 2015-04-10 10:00:02     url22   url55
    cookie2 2015-04-10 10:00:00     url11   url55
    cookie2 2015-04-10 10:03:04     1url33  url55
    cookie2 2015-04-10 10:50:05     url66   url55
    cookie2 2015-04-10 11:00:00     url77   url55
    cookie2 2015-04-10 10:10:00     url44   url55
    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：

    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 lxw1234 
    ORDER BY cookieid,createtime;
     
    cookieid  createtime            url     rn     last1    last2
    -------------------------------------------------------------
    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
<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：

CREATE EXTERNAL TABLE lxw1234 (
month STRING,
day STRING, 
cookieid STRING 
) ROW FORMAT DELIMITED 
FIELDS TERMINATED BY ',' 
stored as textfile location '/tmp/lxw11/';
 
 
hive> select * from lxw1234;
OK
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

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

    SELECT 
    month,
    day,
    COUNT(DISTINCT cookieid) AS uv,
    GROUPING__ID 
    FROM lxw1234 
    GROUP BY month,day 
    GROUPING SETS (month,day) 
    ORDER BY GROUPING__ID;
     
    month      day            uv      GROUPING__ID
    ------------------------------------------------
    2015-03    NULL            5       1
    2015-04    NULL            6       1
    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
     
     
    等价于 
    SELECT month,NULL,COUNT(DISTINCT cookieid) AS uv,1 AS GROUPING__ID FROM lxw1234 GROUP BY month 
    UNION ALL 
    SELECT NULL,day,COUNT(DISTINCT cookieid) AS uv,2 AS GROUPING__ID FROM lxw1234 GROUP BY day

再如：

    SELECT 
    month,
    day,
    COUNT(DISTINCT cookieid) AS uv,
    GROUPING__ID 
    FROM lxw1234 
    GROUP BY month,day 
    GROUPING SETS (month,day,(month,day)) 
    ORDER BY GROUPING__ID;
     
    month         day             uv      GROUPING__ID
    ------------------------------------------------
    2015-03       NULL            5       1
    2015-04       NULL            6       1
    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       2015-03-10      4       3
    2015-03       2015-03-12      1       3
    2015-04       2015-04-12      2       3
    2015-04       2015-04-13      3       3
    2015-04       2015-04-15      2       3
    2015-04       2015-04-16      2       3
     
     
    等价于
    SELECT month,NULL,COUNT(DISTINCT cookieid) AS uv,1 AS GROUPING__ID FROM lxw1234 GROUP BY month 
    UNION ALL 
    SELECT NULL,day,COUNT(DISTINCT cookieid) AS uv,2 AS GROUPING__ID FROM lxw1234 GROUP BY day
    UNION ALL 
    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。

    SELECT 
    month,
    day,
    COUNT(DISTINCT cookieid) AS uv,
    GROUPING__ID 
    FROM lxw1234 
    GROUP BY month,day 
    WITH CUBE 
    ORDER BY GROUPING__ID;
     
     
    month           day             uv     GROUPING__ID
    --------------------------------------------
    NULL            NULL            7       0
    2015-03         NULL            5       1
    2015-04         NULL            6       1
    NULL            2015-04-12      2       2
    NULL            2015-04-13      3       2
    NULL            2015-04-15      2       2
    NULL            2015-04-16      2       2
    NULL            2015-03-10      4       2
    NULL            2015-03-12      1       2
    2015-03         2015-03-10      4       3
    2015-03         2015-03-12      1       3
    2015-04         2015-04-16      2       3
    2015-04         2015-04-12      2       3
    2015-04         2015-04-13      3       3
    2015-04         2015-04-15      2       3
     
     
     
    等价于
    SELECT NULL,NULL,COUNT(DISTINCT cookieid) AS uv,0 AS GROUPING__ID FROM lxw1234
    UNION ALL 
    SELECT month,NULL,COUNT(DISTINCT cookieid) AS uv,1 AS GROUPING__ID FROM lxw1234 GROUP BY month 
    UNION ALL 
    SELECT NULL,day,COUNT(DISTINCT cookieid) AS uv,2 AS GROUPING__ID FROM lxw1234 GROUP BY day
    UNION ALL 
    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。

    比如，以month维度进行层级聚合：
    SELECT 
    month,
    day,
    COUNT(DISTINCT cookieid) AS uv,
    GROUPING__ID  
    FROM lxw1234 
    GROUP BY month,day
    WITH ROLLUP 
    ORDER BY GROUPING__ID;
     
    month  	     day             uv     GROUPING__ID
    ---------------------------------------------------
    NULL             NULL            7       0
    2015-03          NULL            5       1
    2015-04          NULL            6       1
    2015-03          2015-03-10      4       3
    2015-03          2015-03-12      1       3
    2015-04          2015-04-12      2       3
    2015-04          2015-04-13      3       3
    2015-04          2015-04-15      2       3
    2015-04          2015-04-16      2       3
     
    可以实现这样的上钻过程：
    月天的UV->月的UV->总UV
    --把month和day调换顺序，则以day维度进行层级聚合：
     
    SELECT 
    day,
    month,
    COUNT(DISTINCT cookieid) AS uv,
    GROUPING__ID  
    FROM lxw1234 
    GROUP BY day,month 
    WITH ROLLUP 
    ORDER BY GROUPING__ID;
     
     
    day  	    month              uv     GROUPING__ID
    -------------------------------------------------------
    NULL            NULL               7       0
    2015-04-13      NULL               3       1
    2015-03-12      NULL               1       1
    2015-04-15      NULL               2       1
    2015-03-10      NULL               4       1
    2015-04-16      NULL               2       1
    2015-04-12      NULL               2       1
    2015-04-12      2015-04            2       3
    2015-03-10      2015-03            4       3
    2015-03-12      2015-03            1       3
    2015-04-13      2015-04            3       3
    2015-04-15      2015-04            2       3
    2015-04-16      2015-04            2       3
     
    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）

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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