## hive窗口函数/分析函数详细剖析

My-sunmy 2021-01-21 23:21:03

# hive窗口函数/分析函数

## sum,avg,min,max 函数

``````建表语句:
create table bigdata_t1(
createtime string, --day
pv int
) row format delimited
fields terminated by ',';

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

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
sum(pv) over(partition by cookieid order by createtime) as pv1
from bigdata_t1;
#pv2
sum(pv) over(partition by cookieid order by createtime rows between unbounded preceding and current row) as pv2
from bigdata_t1;
#pv3
sum(pv) over(partition by cookieid) as pv3
from bigdata_t1;
#pv4
sum(pv) over(partition by cookieid order by createtime rows between 3 preceding and current row) as pv4
from bigdata_t1;
#pv5
sum(pv) over(partition by cookieid order by createtime rows between 3 preceding and 1 following) as pv5
from bigdata_t1;
#pv6
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
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``````

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

``````cookie1,2018-04-10,1
CREATE TABLE bigdata_t2 (
createtime string, --day
pv INT
) ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
stored as textfile;

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
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
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
• 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
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

# 其他一些窗口函数

• 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'
last_2_time: 指定了往上第2行的值，为指定默认值

与LAG相反
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,
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;``````

`````` 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

`````` SELECT cookieid,
createtime,
url,
FIRST_VALUE(url) OVER(PARTITION BY cookieid) AS first2
FROM bigdata_t4;``````

## cume_dist,percent_rank 函数

• 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;

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，

rn2: 按照部门分组，dpet=d1的行数为3,

• 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)

rn2: 按照dept分组，
dept=d1的总行数为3

## grouping sets,grouping__id,cube,rollup 函数

• 数据准备
`````` 2018-03,2018-03-10,cookie1
CREATE TABLE bigdata_t5 (
month STRING,
day STRING,
) ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
stored as textfile;

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,
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，

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;``````

`````` SELECT
month,
day,
GROUPING__ID
FROM bigdata_t5
GROUP BY month,day
GROUPING SETS (month,day,(month,day))
ORDER BY GROUPING__ID;

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,
GROUPING__ID
FROM bigdata_t5
GROUP BY month,day
WITH CUBE
ORDER BY GROUPING__ID;

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,
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,