关于hive:hive窗口函数分析函数详细剖析

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hive 窗口函数 / 剖析函数

在 sql 中有一类函数叫做聚合函数, 例如 sum()、avg()、max()等等, 这类函数能够将多行数据依照规定汇集为一行, 一般来讲汇集后的行数是要少于汇集前的行数的。然而有时咱们想要既显示汇集前的数据, 又要显示汇集后的数据, 这时咱们便引入了窗口函数。窗口函数又叫 OLAP 函数 / 剖析函数,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 byorder by。

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

sum,avg,min,max 函数

筹备数据

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应用:后果和 ORDER BY 相干, 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: 分组内从终点到以后行的 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: 分组内以后行 + 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: 分组内以后行 + 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: 分组内以后行 + 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, 默认为从终点到以后行;

如果不指定 order by,则将分组内所有值累加;

要害是了解 rows between 含意, 也叫做window 子句

preceding:往前

following:往后

current row:以后行

unbounded:终点

unbounded preceding 示意从后面的终点

unbounded following:示意到前面的起点

AVG,MIN,MAX,和 SUM 用法一样。

row_number,rank,dense_rank,ntile 函数

筹备数据

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()应用

    ROW_NUMBER()从 1 开始,依照程序,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 应用

    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

    有时会有这样的需要: 如果数据排序后分为三局部,业务人员只关怀其中的一部分,如何将这两头的三分之一数据拿进去呢?NTILE 函数即能够满足。

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

    而后能够依据桶号,选取前或后 n 分之几的数据。数据会残缺展现进去,只是给相应的数据打标签;具体要取几分之几的数据,须要再嵌套一层依据标签取出。

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;

其余一些窗口函数

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, 因而取默认值 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 相同
    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,截止到以后行,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,截止到以后行,最初一个值

  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初一个值,则须要变通一下:

  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;

特地留神 order by

如果不指定 ORDER BY,则进行排序凌乱,会呈现谬误的后果

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

cume_dist,percent_rank 函数

这两个序列剖析函数不是很罕用,留神:序列函数不反对 WINDOW 子句

  • 数据筹备
  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 的排序程序有关系

    CUME_DIST 小于等于以后值的行数 / 分组内总行数 order 默认程序 正序 升序
    比方,统计小于等于以后薪水的人数,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,因而,1/5=0.2
       第三行:小于等于 3000 的行数为 3,因而,3/5=0.6
  rn2: 依照部门分组,dpet=d1 的行数为 3,
       第二行:小于等于 2000 的行数为 2,因而,2/3=0.6666666666666666
  • PERCENT_RANK

    PERCENT_RANK 分组内以后行的 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: 依照 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

这几个剖析函数通常用于 OLAP 中,不能累加,而且须要依据不同维度上钻和下钻的指标统计,比方,分小时、天、月的 UV 数。

  • 数据筹备
  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 语句中的便当写法。

    等价于将不同维度的 GROUP BY 后果集进行 UNION ALL。

    GROUPING__ID,示意后果属于哪一个分组汇合。

  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 示意这一组后果属于哪个分组汇合,依据 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

    依据 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。

  比方,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 调换程序,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,依据天和月进行聚合,和依据天聚合后果一样,因为有父子关系,如果是其余维度组合的话,就会不一样)

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