备注:
Hive 版本 2.1.1

文章目录

一.row_number、rank、dense_rank
二.lag、lead
三.first_value、last_value
四.percent_rank、CUME_DIST
五.ntile
参考

测试数据

-- create table
create table dept
(
  deptno int,
  dname  varchar(14),
  loc    varchar(13)
);

insert into dept(deptno, dname, loc)
values ('10', 'accounting', 'new york');

insert into dept(deptno, dname, loc)
values ('20', 'research', 'dallas');

insert into dept(deptno, dname, loc)
values ('30', 'sales', 'chicago');

insert into dept(deptno, dname, loc)
values ('40', 'operations', 'boston');


-- create table
create table emp
(
  empno    int,
  ename    varchar(10),
  job      varchar(9),
  mgr      int,
  hiredate date,
  sal      decimal(7,2),
  comm     decimal(7,2),
  deptno   int
) ;

insert into emp(empno, ename, job, mgr, hiredate, sal, comm, deptno)
values ('7369', 'smith', 'clerk', '7902','1980-12-17', '800', null, '20');

insert into emp(empno, ename, job, mgr, hiredate, sal, comm, deptno)
values ('7499', 'allen', 'salesman', '7698', '1981-02-20', '1600', '300', '30');

insert into emp(empno, ename, job, mgr, hiredate, sal, comm, deptno)
values ('7521', 'ward', 'salesman', '7698', '1981-02-22', '1250', '500', '30');

insert into emp(empno, ename, job, mgr, hiredate, sal, comm, deptno)
values ('7566', 'jones', 'manager', '7839', '1981-04-02', '2975', null, '20');

insert into emp(empno, ename, job, mgr, hiredate, sal, comm, deptno)
values ('7654', 'martin', 'salesman', '7698', '1981-09-28', '1250', '1400', '30');

insert into emp(empno, ename, job, mgr, hiredate, sal, comm, deptno)
values ('7698', 'blake', 'manager', '7839', '1981-05-01', '2850', null, '30');

insert into emp(empno, ename, job, mgr, hiredate, sal, comm, deptno)
values ('7782', 'clark', 'manager', '7839', '1981-06-09', '2450', null, '10');

insert into emp(empno, ename, job, mgr, hiredate, sal, comm, deptno)
values ('7788', 'scott', 'analyst', '7566', '1987-06-13', '3000', null, '20');

insert into emp(empno, ename, job, mgr, hiredate, sal, comm, deptno)
values ('7839', 'king', 'president', null, '1981-11-17', '5000', null, '10');

insert into emp(empno, ename, job, mgr, hiredate, sal, comm, deptno)
values ('7844', 'turner', 'salesman', '7698', '1981-09-08', '1500', '0', '30');

insert into emp(empno, ename, job, mgr, hiredate, sal, comm, deptno)
values ('7876', 'adams', 'clerk', '7788', '1987-06-13', '1100', null, '20');

insert into emp(empno, ename, job, mgr, hiredate, sal, comm, deptno)
values ('7900', 'james', 'clerk', '7698', '1981-12-03', '950', null, '30');

insert into emp(empno, ename, job, mgr, hiredate, sal, comm, deptno)
values ('7902', 'ford', 'analyst', '7566', '1981-12-03', '3000', null, '20');

insert into emp(empno, ename, job, mgr, hiredate, sal, comm, deptno)
values ('7934', 'miller', 'clerk', '7782', '1982-01-23', '1300', null, '10');


create table salgrade
(
  grade int,
  losal int,
  hisal int
) ;


insert into salgrade(grade, losal, hisal)
values ('1', '700', '1200');

insert into salgrade(grade, losal, hisal)
values ('2', '1201', '1400');

insert into salgrade(grade, losal, hisal)
values ('3', '1401', '2000');

insert into salgrade(grade, losal, hisal)
values ('4', '2001', '3000');

insert into salgrade(grade, losal, hisal)
values ('5', '3001', '9999');


create table bonus
(
  ename varchar(10),
  job   varchar(9),
  sal   int,
  comm  int
);

函数名	函数用途
row_number	对行进行排序并为每一行增加一个唯一编号。这是一个非确定性函数
rank	将数据行值按照排序后的顺序进行排名，在有并列的情况下排名值将被跳过
dense_rank	将数据行值按照排序后的顺序进行排名，在有并列的情况下也不跳过排名值
lag	访问一个分区或结果集中之前的一行
lead	访问一个分区或结果集中之后的一行
first_value	访问一个分区或结果集中第一行
last_value	访问一个分区或结果集中最后一行
nth_value	访问一个分区或结果集中的任意一行
percent_rank	将计算得到的排名值标准化
CUME_DIST()	将计算得到的排名值标准化
NTILE	将数据进行将数据行分组为单元

分析函数有3个基本组成部分：
1.分区子句
2.排序子句
3.开窗子句

function1 (argument1,argument2,..argumentN)
over  w
window w as ([partition-by-clause] [order-by-clause] [windowing-clause])

窗口说明子句的语法:
默认的窗口子句是rows between unbounded preceding and current row。如果你没有显示声明窗口，就将会使用默认窗口。
并不是所有的分析函数都支持开窗子句

(ROWS | RANGE) BETWEEN (UNBOUNDED | [num]) PRECEDING AND ([num] PRECEDING | CURRENT ROW | (UNBOUNDED | [num]) FOLLOWING)
(ROWS | RANGE) BETWEEN CURRENT ROW AND (CURRENT ROW | (UNBOUNDED | [num]) FOLLOWING)
(ROWS | RANGE) BETWEEN [num] FOLLOWING AND (UNBOUNDED | [num]) FOLLOWING

一.row_number、rank、dense_rank

row_number语法：

row_number() over w
window w as (partition-clause order-by-clause)

row_number不支持开窗子句
rank、dense_rank语法同row_number语法

现在需要对分不同部门来看部门内的工资排名，且从大到小排列：


select a.empno,
       a.ename,
       a.deptno,
       a.sal,
       row_number() over w as num,
       rank() over w as rank,
       dense_rank() over w  as dense_rank
  from emp a
  window w as (partition by a.deptno order by a.sal desc)
;

测试记录:
– 可以看到deptno为30的员工工资有重复的,重复的工资为1250
– row_number() 不关注重复的，直接排名，1-2-3-4-5-6
– rank() 重复排名,会跳过，1-2-3-4-4-6
– dense_rank() 重复排名,不跳过，1-2-3-4-4-5

hive> 
    > select a.empno,
    >        a.ename,
    >        a.deptno,
    >        a.sal,
    >        row_number() over w as num,
    >        rank() over w as rank,
    >        dense_rank() over w  as dense_rank
    >   from emp a
    >   window w as (partition by a.deptno order by a.sal desc)
    > ;
Query ID = root_20201217135518_7a47a785-f5ec-41c6-9de8-34faf8e650b9
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
  set mapreduce.job.reduces=<number>
Starting Job = job_1606698967173_0270, Tracking URL = http://hp1:8088/proxy/application_1606698967173_0270/
Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1606698967173_0270
Hadoop job information for Stage-1: number of mappers: 2; number of reducers: 1
2020-12-17 13:55:29,077 Stage-1 map = 0%,  reduce = 0%
2020-12-17 13:55:35,318 Stage-1 map = 50%,  reduce = 0%, Cumulative CPU 2.05 sec
2020-12-17 13:55:36,351 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 3.97 sec
2020-12-17 13:55:40,485 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 6.16 sec
MapReduce Total cumulative CPU time: 6 seconds 160 msec
Ended Job = job_1606698967173_0270
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 2  Reduce: 1   Cumulative CPU: 6.16 sec   HDFS Read: 18321 HDFS Write: 645 HDFS EC Read: 0 SUCCESS
Total MapReduce CPU Time Spent: 6 seconds 160 msec
OK
a.empno a.ename a.deptno        a.sal   num     rank    dense_rank
7839    king    10      5000.00 1       1       1
7782    clark   10      2450.00 2       2       2
7934    miller  10      1300.00 3       3       3
7788    scott   20      3000.00 1       1       1
7902    ford    20      3000.00 2       1       1
7566    jones   20      2975.00 3       3       2
7876    adams   20      1100.00 4       4       3
7369    smith   20      800.00  5       5       4
7698    blake   30      2850.00 1       1       1
7499    allen   30      1600.00 2       2       2
7844    turner  30      1500.00 3       3       3
7654    martin  30      1250.00 4       4       4
7521    ward    30      1250.00 5       4       4
7900    james   30      950.00  6       6       5
Time taken: 22.644 seconds, Fetched: 14 row(s)
hive>

二.lag、lead

lag语法:

lag (expression, offset, default) over w
window w as  (partition-clause order-by-clause)

lag不支持开窗子句
lead同lag语法

代码:

-- 根据分组，取值上n条和下n条   如果是第一条或最后一条，就给个默认值
SELECT a.empno,
       a.deptno,
       a.hiredate,
       a.sal,
       lag(sal, 1, 0) over w as pre_sal,
       lead(sal, 1, 0) over w as next_sal,
       lag(sal, 2, 0) over w  as pre2_sal,
       lead(sal, 2, 0) over w as next_2sal
  FROM emp a
  window w  as (PARTITION BY a.deptno ORDER BY hiredate ASC)
;

-- 没有比自己小我的我们设为AAA,没有比自己大的，我们设置为ZZZ
select deptno,
       ename,
       lag(ename, 1, 'AAA') over w as lower_name,
       lead(ename, 1, 'ZZZ') over w as higher_name
  from emp
window w as(PARTITION BY deptno ORDER BY ename)
;

-- 部门重复的话值输出第一行的部门编号  
select (case when deptno= lag(deptno,1)over w then null else deptno end) as deptno,
        ename,
       lag(ename, 1, 'AAA') over w  as lower_name,
       lead(ename, 1, 'ZZZ') over w as higher_name
  from emp
window w  as (PARTITION BY deptno ORDER BY ename)
;

测试记录:

hive> -- 根据分组，取值上n条和下n条   如果是第一条或最后一条，就给个默认值
hive> SELECT a.empno,
    >        a.deptno,
    >        a.hiredate,
    >        a.sal,
    >        lag(sal, 1, 0) over w as pre_sal,
    >        lead(sal, 1, 0) over w as next_sal,
    >        lag(sal, 2, 0) over w  as pre2_sal,
    >        lead(sal, 2, 0) over w as next_2sal
    >   FROM emp a
    >   window w  as (PARTITION BY a.deptno ORDER BY hiredate ASC)
    > ;
Query ID = root_20201217140032_63a2024b-7a8f-452a-8cf7-c27653105565
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
  set mapreduce.job.reduces=<number>
Starting Job = job_1606698967173_0271, Tracking URL = http://hp1:8088/proxy/application_1606698967173_0271/
Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1606698967173_0271
Hadoop job information for Stage-1: number of mappers: 2; number of reducers: 1
2020-12-17 14:00:39,812 Stage-1 map = 0%,  reduce = 0%
2020-12-17 14:00:46,008 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 3.99 sec
2020-12-17 14:00:52,200 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 6.39 sec
MapReduce Total cumulative CPU time: 6 seconds 390 msec
Ended Job = job_1606698967173_0271
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 2  Reduce: 1   Cumulative CPU: 6.39 sec   HDFS Read: 19017 HDFS Write: 1021 HDFS EC Read: 0 SUCCESS
Total MapReduce CPU Time Spent: 6 seconds 390 msec
OK
a.empno a.deptno        a.hiredate      a.sal   pre_sal next_sal        pre2_sal        next_2sal
7782    10      1981-06-09      2450.00 0.00    5000.00 0.00    1300.00
7839    10      1981-11-17      5000.00 2450.00 1300.00 0.00    0.00
7934    10      1982-01-23      1300.00 5000.00 0.00    2450.00 0.00
7369    20      1980-12-17      800.00  0.00    2975.00 0.00    3000.00
7566    20      1981-04-02      2975.00 800.00  3000.00 0.00    3000.00
7902    20      1981-12-03      3000.00 2975.00 3000.00 800.00  1100.00
7788    20      1987-06-13      3000.00 3000.00 1100.00 2975.00 0.00
7876    20      1987-06-13      1100.00 3000.00 0.00    3000.00 0.00
7499    30      1981-02-20      1600.00 0.00    1250.00 0.00    2850.00
7521    30      1981-02-22      1250.00 1600.00 2850.00 0.00    1500.00
7698    30      1981-05-01      2850.00 1250.00 1500.00 1600.00 1250.00
7844    30      1981-09-08      1500.00 2850.00 1250.00 1250.00 950.00
7654    30      1981-09-28      1250.00 1500.00 950.00  2850.00 0.00
7900    30      1981-12-03      950.00  1250.00 0.00    1500.00 0.00
Time taken: 20.831 seconds, Fetched: 14 row(s)
hive> 
    > -- 没有比自己小我的我们设为AAA,没有比自己大的，我们设置为ZZZ
    > select deptno,
    >        ename,
    >        lag(ename, 1, 'AAA') over w as lower_name,
    >        lead(ename, 1, 'ZZZ') over w as higher_name
    >   from emp
    > window w as(PARTITION BY deptno ORDER BY ename)
    > ;
Query ID = root_20201217140106_f847f43a-b525-40dc-a276-9f79d2c538f4
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
  set mapreduce.job.reduces=<number>
Starting Job = job_1606698967173_0272, Tracking URL = http://hp1:8088/proxy/application_1606698967173_0272/
Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1606698967173_0272
Hadoop job information for Stage-1: number of mappers: 2; number of reducers: 1
2020-12-17 14:01:13,271 Stage-1 map = 0%,  reduce = 0%
2020-12-17 14:01:19,458 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 3.96 sec
2020-12-17 14:01:24,610 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 6.37 sec
MapReduce Total cumulative CPU time: 6 seconds 370 msec
Ended Job = job_1606698967173_0272
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 2  Reduce: 1   Cumulative CPU: 6.37 sec   HDFS Read: 17623 HDFS Write: 537 HDFS EC Read: 0 SUCCESS
Total MapReduce CPU Time Spent: 6 seconds 370 msec
OK
deptno  ename   lower_name      higher_name
10      clark   AAA     king
10      king    clark   miller
10      miller  king    ZZZ
20      adams   AAA     ford
20      ford    adams   jones
20      jones   ford    scott
20      scott   jones   smith
20      smith   scott   ZZZ
30      allen   AAA     blake
30      blake   allen   james
30      james   blake   martin
30      martin  james   turner
30      turner  martin  ward
30      ward    turner  ZZZ
Time taken: 20.52 seconds, Fetched: 14 row(s)
hive> -- 部门重复的话值输出第一行的部门编号  
hive> select (case when deptno= lag(deptno,1)over w then null else deptno end) as deptno,
    >         ename,
    >        lag(ename, 1, 'AAA') over w  as lower_name,
    >        lead(ename, 1, 'ZZZ') over w as higher_name
    >   from emp
    > window w  as (PARTITION BY deptno ORDER BY ename)
    > ;
Query ID = root_20201217140128_f6137d9e-0feb-434e-9a28-766833bec6d2
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
  set mapreduce.job.reduces=<number>
Starting Job = job_1606698967173_0273, Tracking URL = http://hp1:8088/proxy/application_1606698967173_0273/
Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1606698967173_0273
Hadoop job information for Stage-1: number of mappers: 2; number of reducers: 1
2020-12-17 14:01:37,413 Stage-1 map = 0%,  reduce = 0%
2020-12-17 14:01:43,610 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 4.06 sec
2020-12-17 14:01:49,800 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 7.55 sec
MapReduce Total cumulative CPU time: 7 seconds 550 msec
Ended Job = job_1606698967173_0273
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 2  Reduce: 1   Cumulative CPU: 7.55 sec   HDFS Read: 18183 HDFS Write: 537 HDFS EC Read: 0 SUCCESS
Total MapReduce CPU Time Spent: 7 seconds 550 msec
OK
deptno  ename   lower_name      higher_name
10      clark   AAA     king
NULL    king    clark   miller
NULL    miller  king    ZZZ
20      adams   AAA     ford
NULL    ford    adams   jones
NULL    jones   ford    scott
NULL    scott   jones   smith
NULL    smith   scott   ZZZ
30      allen   AAA     blake
NULL    blake   allen   james
NULL    james   blake   martin
NULL    martin  james   turner
NULL    turner  martin  ward
NULL    ward    turner  ZZZ
Time taken: 21.956 seconds, Fetched: 14 row(s)
hive>

三.first_value、last_value

first_value、last_value语法:

first_value(expression) over w 
window w as  (partition-clause order-by-clause windowing-clause)
last_value(expression) over w
window w as  (partition-clause order-by-clause windowing-clause)

代码:

/*
需求:求每个部门工资最高的和工资最低的
*/

-- 默认不带开窗子句,从第一行到当前行
select a.empno,a.deptno,a.sal,
       first_value(a.sal)  over w as first,
       last_value(a.sal) over w as last
  from emp a
  window w  as (partition by a.deptno order by sal)
;

-- rows between unbounded preceding and current row  从第一行到当前行
select a.empno,a.deptno,a.sal,
       first_value(a.sal)  over w as first,
       last_value(a.sal) over w as last
  from emp a
   window w  as (partition by a.deptno order by sal rows between unbounded preceding and current row)
;
  

-- rows between unbounded preceding and unbounded following 从第一行到最后一行
 select a.empno,a.deptno,a.sal,
       first_value(a.sal)  over w as first,
       last_value(a.sal) over w as last
  from emp a
 window w  as (partition by a.deptno order by sal rows between unbounded preceding and unbounded following)
; 
 
 -- 1 preceding and 1 following   当前行的前一行到当前行的后一行 
  select a.empno,a.deptno,a.sal,
       first_value(a.sal)  over w as first,
       last_value(a.sal) over w as last
  from emp a
 window w  as (partition by a.deptno order by sal rows between 1 preceding and 1 following)
;

测试记录:

hive> 
    > -- 默认不带开窗子句,从第一行到当前行
    > select a.empno,a.deptno,a.sal,
    >        first_value(a.sal)  over w as first,
    >        last_value(a.sal) over w as last
    >   from emp a
    >   window w  as (partition by a.deptno order by sal)
    > ;
Query ID = root_20201217140513_e49fe743-41eb-4291-a7a3-afef68e208f3
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
  set mapreduce.job.reduces=<number>
Starting Job = job_1606698967173_0274, Tracking URL = http://hp1:8088/proxy/application_1606698967173_0274/
Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1606698967173_0274
Hadoop job information for Stage-1: number of mappers: 2; number of reducers: 1
2020-12-17 14:05:20,777 Stage-1 map = 0%,  reduce = 0%
2020-12-17 14:05:26,956 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 4.07 sec
2020-12-17 14:05:33,130 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 6.59 sec
MapReduce Total cumulative CPU time: 6 seconds 590 msec
Ended Job = job_1606698967173_0274
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 2  Reduce: 1   Cumulative CPU: 6.59 sec   HDFS Read: 18024 HDFS Write: 688 HDFS EC Read: 0 SUCCESS
Total MapReduce CPU Time Spent: 6 seconds 590 msec
OK
a.empno a.deptno        a.sal   first   last
7934    10      1300.00 1300.00 1300.00
7782    10      2450.00 1300.00 2450.00
7839    10      5000.00 1300.00 5000.00
7369    20      800.00  800.00  800.00
7876    20      1100.00 800.00  1100.00
7566    20      2975.00 800.00  2975.00
7788    20      3000.00 800.00  3000.00
7902    20      3000.00 800.00  3000.00
7900    30      950.00  950.00  950.00
7654    30      1250.00 950.00  1250.00
7521    30      1250.00 950.00  1250.00
7844    30      1500.00 950.00  1500.00
7499    30      1600.00 950.00  1600.00
7698    30      2850.00 950.00  2850.00
Time taken: 20.46 seconds, Fetched: 14 row(s)
hive> -- rows between unbounded preceding and current row  从第一行到当前行
hive> select a.empno,a.deptno,a.sal,
    >        first_value(a.sal)  over w as first,
    >        last_value(a.sal) over w as last
    >   from emp a
    >    window w  as (partition by a.deptno order by sal rows between unbounded preceding and current row)
    > ;
Query ID = root_20201217140542_13d4a59c-1986-4c22-a894-83b31f8b49ab
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
  set mapreduce.job.reduces=<number>
Starting Job = job_1606698967173_0275, Tracking URL = http://hp1:8088/proxy/application_1606698967173_0275/
Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1606698967173_0275
Hadoop job information for Stage-1: number of mappers: 2; number of reducers: 1
2020-12-17 14:05:49,767 Stage-1 map = 0%,  reduce = 0%
2020-12-17 14:05:55,936 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 4.01 sec
2020-12-17 14:06:01,081 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 6.19 sec
MapReduce Total cumulative CPU time: 6 seconds 190 msec
Ended Job = job_1606698967173_0275
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 2  Reduce: 1   Cumulative CPU: 6.19 sec   HDFS Read: 17930 HDFS Write: 688 HDFS EC Read: 0 SUCCESS
Total MapReduce CPU Time Spent: 6 seconds 190 msec
OK
a.empno a.deptno        a.sal   first   last
7934    10      1300.00 1300.00 1300.00
7782    10      2450.00 1300.00 2450.00
7839    10      5000.00 1300.00 5000.00
7369    20      800.00  800.00  800.00
7876    20      1100.00 800.00  1100.00
7566    20      2975.00 800.00  2975.00
7788    20      3000.00 800.00  3000.00
7902    20      3000.00 800.00  3000.00
7900    30      950.00  950.00  950.00
7654    30      1250.00 950.00  1250.00
7521    30      1250.00 950.00  1250.00
7844    30      1500.00 950.00  1500.00
7499    30      1600.00 950.00  1600.00
7698    30      2850.00 950.00  2850.00
Time taken: 20.402 seconds, Fetched: 14 row(s)
hive> -- rows between unbounded preceding and unbounded following 从第一行到最后一行
hive>  select a.empno,a.deptno,a.sal,
    >        first_value(a.sal)  over w as first,
    >        last_value(a.sal) over w as last
    >   from emp a
    >  window w  as (partition by a.deptno order by sal rows between unbounded preceding and unbounded following)
    > ; 
Query ID = root_20201217140615_8d331653-75fd-429f-8989-55fcd0e86cfb
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
  set mapreduce.job.reduces=<number>
Starting Job = job_1606698967173_0276, Tracking URL = http://hp1:8088/proxy/application_1606698967173_0276/
Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1606698967173_0276
Hadoop job information for Stage-1: number of mappers: 2; number of reducers: 1
2020-12-17 14:06:22,884 Stage-1 map = 0%,  reduce = 0%
2020-12-17 14:06:30,087 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 3.99 sec
2020-12-17 14:06:35,236 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 6.39 sec
MapReduce Total cumulative CPU time: 6 seconds 390 msec
Ended Job = job_1606698967173_0276
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 2  Reduce: 1   Cumulative CPU: 6.39 sec   HDFS Read: 17896 HDFS Write: 690 HDFS EC Read: 0 SUCCESS
Total MapReduce CPU Time Spent: 6 seconds 390 msec
OK
a.empno a.deptno        a.sal   first   last
7934    10      1300.00 1300.00 5000.00
7782    10      2450.00 1300.00 5000.00
7839    10      5000.00 1300.00 5000.00
7369    20      800.00  800.00  3000.00
7876    20      1100.00 800.00  3000.00
7566    20      2975.00 800.00  3000.00
7788    20      3000.00 800.00  3000.00
7902    20      3000.00 800.00  3000.00
7900    30      950.00  950.00  2850.00
7654    30      1250.00 950.00  2850.00
7521    30      1250.00 950.00  2850.00
7844    30      1500.00 950.00  2850.00
7499    30      1600.00 950.00  2850.00
7698    30      2850.00 950.00  2850.00
Time taken: 20.875 seconds, Fetched: 14 row(s)
hive>  -- 1 preceding and 1 following   当前行的前一行到当前行的后一行 
hive>   select a.empno,a.deptno,a.sal,
    >        first_value(a.sal)  over w as first,
    >        last_value(a.sal) over w as last
    >   from emp a
    >  window w  as (partition by a.deptno order by sal rows between 1 preceding and 1 following)
    > ; 
Query ID = root_20201217140640_8e3c39f1-104b-42ad-a8fc-8d0294cb895c
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
  set mapreduce.job.reduces=<number>
Starting Job = job_1606698967173_0277, Tracking URL = http://hp1:8088/proxy/application_1606698967173_0277/
Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1606698967173_0277
Hadoop job information for Stage-1: number of mappers: 2; number of reducers: 1
2020-12-17 14:06:47,701 Stage-1 map = 0%,  reduce = 0%
2020-12-17 14:06:52,960 Stage-1 map = 50%,  reduce = 0%, Cumulative CPU 1.99 sec
2020-12-17 14:06:53,990 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 4.12 sec
2020-12-17 14:06:59,134 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 6.23 sec
MapReduce Total cumulative CPU time: 6 seconds 230 msec
Ended Job = job_1606698967173_0277
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 2  Reduce: 1   Cumulative CPU: 6.23 sec   HDFS Read: 17864 HDFS Write: 697 HDFS EC Read: 0 SUCCESS
Total MapReduce CPU Time Spent: 6 seconds 230 msec
OK
a.empno a.deptno        a.sal   first   last
7934    10      1300.00 1300.00 2450.00
7782    10      2450.00 1300.00 5000.00
7839    10      5000.00 2450.00 5000.00
7369    20      800.00  800.00  1100.00
7876    20      1100.00 800.00  2975.00
7566    20      2975.00 1100.00 3000.00
7788    20      3000.00 2975.00 3000.00
7902    20      3000.00 3000.00 3000.00
7900    30      950.00  950.00  1250.00
7654    30      1250.00 950.00  1250.00
7521    30      1250.00 1250.00 1500.00
7844    30      1500.00 1250.00 1600.00
7499    30      1600.00 1500.00 2850.00
7698    30      2850.00 1600.00 2850.00
Time taken: 19.959 seconds, Fetched: 14 row(s)
hive>

四.percent_rank、CUME_DIST

percent_rank语法:

percent_rank() over w
window w as  ([partition-by-clause] [order-by-clause] )

CUME_DIST语法

cume_dist() over w
window w as  ([partition-by-clause] [order-by-clause] )

percent_rank：
– percent_rank函数以0到1之间的分数形式返回某个值在数据分区中的排名
– percent_rank的计算公式为(rank-1)/(n-1)

CUME_DIST:
–一个5行的组中，返回的累计分布值为0.2,0.4,0.6,0.8,1.0;
–注意对于重复行，计算时取重复行中的最后一行的位置。

代码:

SELECT a.empno,
       a.ename,
       a.deptno,
       a.sal,
       percent_rank() over w as num,
       cume_dist() over w as cume
  FROM emp a
  window w  as (PARTITION BY a.deptno ORDER BY a.sal DESC)
;

测试记录:

hive> 
    > 
    > SELECT a.empno,
    >        a.ename,
    >        a.deptno,
    >        a.sal,
    >        percent_rank() over w as num,
    >        cume_dist() over w as cume
    >   FROM emp a
    >   window w  as (PARTITION BY a.deptno ORDER BY a.sal DESC)
    > ;
Query ID = root_20201217140833_0c946fa7-a362-4141-a863-3f8a4674afba
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
  set mapreduce.job.reduces=<number>
Starting Job = job_1606698967173_0278, Tracking URL = http://hp1:8088/proxy/application_1606698967173_0278/
Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1606698967173_0278
Hadoop job information for Stage-1: number of mappers: 2; number of reducers: 1
2020-12-17 14:08:40,917 Stage-1 map = 0%,  reduce = 0%
2020-12-17 14:08:48,122 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 3.91 sec
2020-12-17 14:08:54,301 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 6.37 sec
MapReduce Total cumulative CPU time: 6 seconds 370 msec
Ended Job = job_1606698967173_0278
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 2  Reduce: 1   Cumulative CPU: 6.37 sec   HDFS Read: 18317 HDFS Write: 765 HDFS EC Read: 0 SUCCESS
Total MapReduce CPU Time Spent: 6 seconds 370 msec
OK
a.empno a.ename a.deptno        a.sal   num     cume
7839    king    10      5000.00 0.0     0.3333333333333333
7782    clark   10      2450.00 0.5     0.6666666666666666
7934    miller  10      1300.00 1.0     1.0
7788    scott   20      3000.00 0.0     0.4
7902    ford    20      3000.00 0.0     0.4
7566    jones   20      2975.00 0.5     0.6
7876    adams   20      1100.00 0.75    0.8
7369    smith   20      800.00  1.0     1.0
7698    blake   30      2850.00 0.0     0.16666666666666666
7499    allen   30      1600.00 0.2     0.3333333333333333
7844    turner  30      1500.00 0.4     0.5
7654    martin  30      1250.00 0.6     0.8333333333333334
7521    ward    30      1250.00 0.6     0.8333333333333334
7900    james   30      950.00  1.0     1.0
Time taken: 21.471 seconds, Fetched: 14 row(s)
hive>

五.ntile

Ntile语法:

Ntile(expr) OVER w
window w as   ([ query_partition_clause ] order_by_clause)

Ntile 把数据行分成N个桶。每个桶会有相同的行数，正负误差为1

将员工表emp按照工资分为2、3个桶

代码:

-- 分成2个桶
SELECT ENAME, SAL, NTILE(2) OVER w as n FROM EMP
window w  as (ORDER BY SAL ASC)
;

-- 分成3个桶
SELECT ENAME, SAL, NTILE(3) OVER w as n FROM EMP
window w  as (ORDER BY SAL ASC)
;

测试记录:

hive> 
    > -- 分成2个桶
    > SELECT ENAME, SAL, NTILE(2) OVER w as n FROM EMP
    > window w  as (ORDER BY SAL ASC)
    > ;
Query ID = root_20201217141022_8d69be19-e30a-4d30-9e20-63e13d915521
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
  set mapreduce.job.reduces=<number>
Starting Job = job_1606698967173_0279, Tracking URL = http://hp1:8088/proxy/application_1606698967173_0279/
Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1606698967173_0279
Hadoop job information for Stage-1: number of mappers: 2; number of reducers: 1
2020-12-17 14:10:28,769 Stage-1 map = 0%,  reduce = 0%
2020-12-17 14:10:34,943 Stage-1 map = 50%,  reduce = 0%, Cumulative CPU 2.04 sec
2020-12-17 14:10:35,973 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 3.97 sec
2020-12-17 14:10:41,117 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 6.4 sec
MapReduce Total cumulative CPU time: 6 seconds 400 msec
Ended Job = job_1606698967173_0279
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 2  Reduce: 1   Cumulative CPU: 6.4 sec   HDFS Read: 17143 HDFS Write: 477 HDFS EC Read: 0 SUCCESS
Total MapReduce CPU Time Spent: 6 seconds 400 msec
OK
ename   sal     n
smith   800.00  1
james   950.00  1
adams   1100.00 1
ward    1250.00 1
martin  1250.00 1
miller  1300.00 1
turner  1500.00 1
allen   1600.00 2
clark   2450.00 2
blake   2850.00 2
jones   2975.00 2
scott   3000.00 2
ford    3000.00 2
king    5000.00 2
Time taken: 19.869 seconds, Fetched: 14 row(s)
hive> -- 分成3个桶
hive> SELECT ENAME, SAL, NTILE(3) OVER w as n FROM EMP
    > window w  as (ORDER BY SAL ASC)
    > ;
Query ID = root_20201217141102_5483203c-32bc-45cc-b7ab-65eeaa061696
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
  set mapreduce.job.reduces=<number>
Starting Job = job_1606698967173_0280, Tracking URL = http://hp1:8088/proxy/application_1606698967173_0280/
Kill Command = /opt/cloudera/parcels/CDH-6.3.1-1.cdh6.3.1.p0.1470567/lib/hadoop/bin/hadoop job  -kill job_1606698967173_0280
Hadoop job information for Stage-1: number of mappers: 2; number of reducers: 1
2020-12-17 14:11:08,889 Stage-1 map = 0%,  reduce = 0%
2020-12-17 14:11:15,086 Stage-1 map = 50%,  reduce = 0%, Cumulative CPU 2.04 sec
2020-12-17 14:11:16,115 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 4.01 sec
2020-12-17 14:11:21,255 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 6.22 sec
MapReduce Total cumulative CPU time: 6 seconds 220 msec
Ended Job = job_1606698967173_0280
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 2  Reduce: 1   Cumulative CPU: 6.22 sec   HDFS Read: 17143 HDFS Write: 477 HDFS EC Read: 0 SUCCESS
Total MapReduce CPU Time Spent: 6 seconds 220 msec
OK
ename   sal     n
smith   800.00  1
james   950.00  1
adams   1100.00 1
ward    1250.00 1
martin  1250.00 1
miller  1300.00 2
turner  1500.00 2
allen   1600.00 2
clark   2450.00 2
blake   2850.00 2
jones   2975.00 3
scott   3000.00 3
ford    3000.00 3
king    5000.00 3
Time taken: 19.511 seconds, Fetched: 14 row(s)
hive>

参考

1.https://cwiki.apache.org/confluence/display/Hive/LanguageManual+WindowingAndAnalytics

大数据开发之Hive篇11-Hive高级分析函数及窗口语句

文章目录

一.row_number、rank、dense_rank

二.lag、lead

三.first_value、last_value

四.percent_rank、CUME_DIST

五.ntile

参考

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