hive窗口函数/分析函数

在sql中有一类函数叫做聚合函数,例如sum()、avg()、max()等等,这类函数可以将多行数据按照规则聚集为一行,一般来讲聚集后的行数是要少于聚集前的行数的。但是有时我们想要既显示聚集前的数据,又要显示聚集后的数据,这时我们便引入了窗口函数。窗口函数又叫OLAP函数/分析函数，窗口函数兼具分组和排序功能。

窗口函数最重要的关键字是 partition by 和 order by。

具体语法如下：over (partition by xxx order by xxx)

sum,avg,min,max 函数

准备数据

建表语句:

create table bigdata_t1(

cookieid string,

createtime string,   --day

pv int

) row format delimited

fields terminated by ',';

加载数据：

load data local inpath '/root/hivedata/bigdata_t1.dat' into table bigdata_t1;

cookie1,2018-04-10,1

cookie1,2018-04-11,5

cookie1,2018-04-12,7

cookie1,2018-04-13,3

cookie1,2018-04-14,2

cookie1,2018-04-15,4

cookie1,2018-04-16,4

开启智能本地模式

SET hive.exec.mode.local.auto=true;

SUM函数和窗口函数的配合使用：结果和ORDER BY相关,默认为升序。

#pv1

select cookieid,createtime,pv,

sum(pv) over(partition by cookieid order by createtime) as pv1

from bigdata_t1;

#pv2

select cookieid,createtime,pv,

sum(pv) over(partition by cookieid order by createtime rows between unbounded preceding and current row) as pv2

from bigdata_t1;

#pv3

select cookieid,createtime,pv,

sum(pv) over(partition by cookieid) as pv3

from bigdata_t1;

#pv4

select cookieid,createtime,pv,

sum(pv) over(partition by cookieid order by createtime rows between 3 preceding and current row) as pv4

from bigdata_t1;

#pv5

select cookieid,createtime,pv,

sum(pv) over(partition by cookieid order by createtime rows between 3 preceding and 1 following) as pv5

from bigdata_t1;

#pv6

select cookieid,createtime,pv,

sum(pv) over(partition by cookieid order by createtime rows between current row and unbounded following) as pv6

from bigdata_t1;

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

AVG，MIN，MAX，和SUM用法一样。

row_number,rank,dense_rank,ntile 函数

准备数据

cookie1,2018-04-10,1

cookie1,2018-04-11,5

cookie1,2018-04-12,7

cookie1,2018-04-13,3

cookie1,2018-04-14,2

cookie1,2018-04-15,4

cookie1,2018-04-16,4

cookie2,2018-04-10,2

cookie2,2018-04-11,3

cookie2,2018-04-12,5

cookie2,2018-04-13,6

cookie2,2018-04-14,3

cookie2,2018-04-15,9

cookie2,2018-04-16,7

CREATE TABLE bigdata_t2 (

cookieid string,

createtime string,   --day

pv INT

) ROW FORMAT DELIMITED

FIELDS TERMINATED BY ','

stored as textfile;

加载数据：

load data local inpath '/root/hivedata/bigdata_t2.dat' into table bigdata_t2;

ROW_NUMBER()使用

ROW_NUMBER()从1开始，按照顺序，生成分组内记录的序列。

SELECT

cookieid,

createtime,

pv,

ROW_NUMBER() OVER(PARTITION BY cookieid ORDER BY pv desc) AS rn

FROM bigdata_t2;

RANK 和 DENSE_RANK使用

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 bigdata_t2

WHERE cookieid = 'cookie1';

NTILE

有时会有这样的需求:如果数据排序后分为三部分，业务人员只关心其中的一部分，如何将这中间的三分之一数据拿出来呢?NTILE函数即可以满足。

ntile可以看成是：把有序的数据集合平均分配到指定的数量（num）个桶中, 将桶号分配给每一行。如果不能平均分配，则优先分配较小编号的桶，并且各个桶中能放的行数最多相差1。

然后可以根据桶号，选取前或后 n分之几的数据。数据会完整展示出来，只是给相应的数据打标签；具体要取几分之几的数据，需要再嵌套一层根据标签取出。

SELECT

cookieid,

createtime,

pv,

NTILE(2) OVER(PARTITION BY cookieid ORDER BY createtime) AS rn1,

NTILE(3) OVER(PARTITION BY cookieid ORDER BY createtime) AS rn2,

NTILE(4) OVER(ORDER BY createtime) AS rn3

FROM bigdata_t2

ORDER BY cookieid,createtime;

其他一些窗口函数

lag,lead,first_value,last_value 函数

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 bigdata_t4;

  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 bigdata_t4;

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 bigdata_t4;

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 bigdata_t4;

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 bigdata_t4

  ORDER BY cookieid,createtime;

特别注意order by

如果不指定ORDER BY，则进行排序混乱，会出现错误的结果

  SELECT cookieid,

  createtime,

  url,

  FIRST_VALUE(url) OVER(PARTITION BY cookieid) AS first2

  FROM bigdata_t4;

cume_dist,percent_rank 函数

这两个序列分析函数不是很常用，注意：序列函数不支持WINDOW子句

数据准备

  d1,user1,1000

  d1,user2,2000

  d1,user3,3000

  d2,user4,4000

  d2,user5,5000

  CREATE EXTERNAL TABLE bigdata_t3 (

  dept STRING,

  userid string,

  sal INT

  ) ROW FORMAT DELIMITED

  FIELDS TERMINATED BY ','

  stored as textfile;

  加载数据：

  load data local inpath '/root/hivedata/bigdata_t3.dat' into table bigdata_t3;

CUME_DIST 和order by的排序顺序有关系

CUME_DIST 小于等于当前值的行数/分组内总行数 order 默认顺序正序升序

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 bigdata_t3;

  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

  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 bigdata_t3;

  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

grouping sets,grouping__id,cube,rollup 函数

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

  2018-03,2018-03-10,cookie1

  2018-03,2018-03-10,cookie5

  2018-03,2018-03-12,cookie7

  2018-04,2018-04-12,cookie3

  2018-04,2018-04-13,cookie2

  2018-04,2018-04-13,cookie4

  2018-04,2018-04-16,cookie4

  2018-03,2018-03-10,cookie2

  2018-03,2018-03-10,cookie3

  2018-04,2018-04-12,cookie5

  2018-04,2018-04-13,cookie6

  2018-04,2018-04-15,cookie3

  2018-04,2018-04-15,cookie2

  2018-04,2018-04-16,cookie1

  CREATE TABLE bigdata_t5 (

  month STRING,

  day STRING,

  cookieid STRING

  ) ROW FORMAT DELIMITED

  FIELDS TERMINATED BY ','

  stored as textfile;

  加载数据：

  load data local inpath '/root/hivedata/bigdata_t5.dat' into table bigdata_t5;

GROUPING SETS

grouping sets是一种将多个group by 逻辑写在一个sql语句中的便利写法。

等价于将不同维度的GROUP BY结果集进行UNION ALL。

GROUPING__ID，表示结果属于哪一个分组集合。

  SELECT

  month,

  day,

  COUNT(DISTINCT cookieid) AS uv,

  GROUPING__ID

  FROM bigdata_t5

  GROUP BY month,day

  GROUPING SETS (month,day)

  ORDER BY GROUPING__ID;

  grouping_id表示这一组结果属于哪个分组集合，

  根据grouping sets中的分组条件month，day，1是代表month，2是代表day

  等价于

  SELECT month,NULL,COUNT(DISTINCT cookieid) AS uv,1 AS GROUPING__ID FROM bigdata_t5 GROUP BY month UNION ALL

  SELECT NULL as month,day,COUNT(DISTINCT cookieid) AS uv,2 AS GROUPING__ID FROM bigdata_t5 GROUP BY day;

再如：

  SELECT

  month,

  day,

  COUNT(DISTINCT cookieid) AS uv,

  GROUPING__ID

  FROM bigdata_t5

  GROUP BY month,day

  GROUPING SETS (month,day,(month,day))

  ORDER BY GROUPING__ID;

  等价于

  SELECT month,NULL,COUNT(DISTINCT cookieid) AS uv,1 AS GROUPING__ID FROM bigdata_t5 GROUP BY month

  UNION ALL

  SELECT NULL,day,COUNT(DISTINCT cookieid) AS uv,2 AS GROUPING__ID FROM bigdata_t5 GROUP BY day

  UNION ALL

  SELECT month,day,COUNT(DISTINCT cookieid) AS uv,3 AS GROUPING__ID FROM bigdata_t5 GROUP BY month,day;

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 bigdata_t5

  GROUP BY month,day

  WITH CUBE

  ORDER BY GROUPING__ID;

  等价于

  SELECT NULL,NULL,COUNT(DISTINCT cookieid) AS uv,0 AS GROUPING__ID FROM bigdata_t5

  UNION ALL

  SELECT month,NULL,COUNT(DISTINCT cookieid) AS uv,1 AS GROUPING__ID FROM bigdata_t5 GROUP BY month

  UNION ALL

  SELECT NULL,day,COUNT(DISTINCT cookieid) AS uv,2 AS GROUPING__ID FROM bigdata_t5 GROUP BY day

  UNION ALL

  SELECT month,day,COUNT(DISTINCT cookieid) AS uv,3 AS GROUPING__ID FROM bigdata_t5 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 bigdata_t5

  GROUP BY month,day

  WITH ROLLUP

  ORDER BY GROUPING__ID;

  --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 bigdata_t5

  GROUP BY day,month

  WITH ROLLUP

  ORDER BY GROUPING__ID;

  （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）

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