Hive函数:SUM,AVG,MIN,MAX
转自:http://lxw1234.com/archives/2015/04/176.htm,Hive分析窗口函数(一) SUM,AVG,MIN,MAX
之前看到大数据田地有关于max()over(partition by)的用法,今天恰好工作中用到了它,但是使用中遇到了一个问题:在max(rsrp)over(partition by buildingid,height) as max_rsrp返回的结果不是分组中的最大值。最中找到了问题的原因:max_rsrp数据类型为string而不是double类型,导致的一个bug问题。
再处理的过程中也再次把大数据田地的中关于sum,avg,max,min的函数用法做了demo,因此有了该参考后的文章。
数据准备:
echo ''>data_file.txt
vim data_file.txt
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
cookie2,2015-04-10,6
cookie2,2015-04-11,5
cookie2,2015-04-12,7
cookie2,2015-04-13,4
cookie2,2015-04-14,3
cookie2,2015-04-15,5
cookie2,2015-04-16,5
hadoop fs -rm -r /user/jrf/test_data
hadoop fs -mkdir /user/jrf/test_data
hadoop fs -copyFromLocal data_file.txt /user/jrf/test_data/
drop table if exists test_data;
create EXTERNAL TABLE test_data (
cookieid string,
createtime string, --day
pv INT
) ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
stored as textfile location '/user/jrf/test_data/';
select * from test_data;
+---------------------+-----------------------+---------------+--+
| test_data.cookieid | test_data.createtime | test_data.pv |
+---------------------+-----------------------+---------------+--+
| 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 | 6 |
| cookie2 | 2015-04-11 | 5 |
| cookie2 | 2015-04-12 | 7 |
| cookie2 | 2015-04-13 | 4 |
| cookie2 | 2015-04-14 | 3 |
| cookie2 | 2015-04-15 | 5 |
| cookie2 | 2015-04-16 | 5 |
+---------------------+-----------------------+---------------+--+
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 test_data order by cookieid,createtime;
+-----------+-------------+-----+------+------+------+------+------+------+--+
| 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 |
| cookie2 | 2015-04-10 | 6 | 6 | 6 | 35 | 6 | 11 | 35 |
| cookie2 | 2015-04-11 | 5 | 11 | 11 | 35 | 11 | 18 | 29 |
| cookie2 | 2015-04-12 | 7 | 18 | 18 | 35 | 18 | 22 | 24 |
| cookie2 | 2015-04-13 | 4 | 22 | 22 | 35 | 22 | 25 | 17 |
| cookie2 | 2015-04-14 | 3 | 25 | 25 | 35 | 19 | 24 | 13 |
| cookie2 | 2015-04-15 | 5 | 30 | 30 | 35 | 19 | 24 | 10 |
| cookie2 | 2015-04-16 | 5 | 35 | 35 | 35 | 17 | 17 | 5 |
+-----------+-------------+-----+------+------+------+------+------+------+--+
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 test_data order by cookieid,createtime;
+-----------+-------------+-----+---------------------+---------------------+---------------------+--------------------+--------------------+---------------------+--+
| 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 |
| cookie2 | 2015-04-10 | 6 | 6.0 | 6.0 | 5.0 | 6.0 | 5.5 | 5.0 |
| cookie2 | 2015-04-11 | 5 | 5.5 | 5.5 | 5.0 | 5.5 | 6.0 | 4.833333333333333 |
| cookie2 | 2015-04-12 | 7 | 6.0 | 6.0 | 5.0 | 6.0 | 5.5 | 4.8 |
| cookie2 | 2015-04-13 | 4 | 5.5 | 5.5 | 5.0 | 5.5 | 5.0 | 4.25 |
| cookie2 | 2015-04-14 | 3 | 5.0 | 5.0 | 5.0 | 4.75 | 4.8 | 4.333333333333333 |
| cookie2 | 2015-04-15 | 5 | 5.0 | 5.0 | 5.0 | 4.75 | 4.8 | 5.0 |
| cookie2 | 2015-04-16 | 5 | 5.0 | 5.0 | 5.0 | 4.25 | 4.25 | 5.0 |
+-----------+-------------+-----+---------------------+---------------------+---------------------+--------------------+--------------------+---------------------+--+
--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 test_data order by cookieid,createtime;
+-----------+-------------+-----+------+------+------+------+------+------+--+
| 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 |
| cookie2 | 2015-04-10 | 6 | 6 | 6 | 3 | 6 | 5 | 3 |
| cookie2 | 2015-04-11 | 5 | 5 | 5 | 3 | 5 | 5 | 3 |
| cookie2 | 2015-04-12 | 7 | 5 | 5 | 3 | 5 | 4 | 3 |
| cookie2 | 2015-04-13 | 4 | 4 | 4 | 3 | 4 | 3 | 3 |
| cookie2 | 2015-04-14 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
| cookie2 | 2015-04-15 | 5 | 3 | 3 | 3 | 3 | 3 | 5 |
| cookie2 | 2015-04-16 | 5 | 3 | 3 | 3 | 3 | 3 | 5 |
+-----------+-------------+-----+------+------+------+------+------+------+--+
--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 test_data order by cookieid,createtime;
+-----------+-------------+-----+------+------+------+------+------+------+--+
| 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 |
| cookie2 | 2015-04-10 | 6 | 6 | 6 | 7 | 6 | 6 | 7 |
| cookie2 | 2015-04-11 | 5 | 6 | 6 | 7 | 6 | 7 | 7 |
| cookie2 | 2015-04-12 | 7 | 7 | 7 | 7 | 7 | 7 | 7 |
| cookie2 | 2015-04-13 | 4 | 7 | 7 | 7 | 7 | 7 | 5 |
| cookie2 | 2015-04-14 | 3 | 7 | 7 | 7 | 7 | 7 | 5 |
| cookie2 | 2015-04-15 | 5 | 7 | 7 | 7 | 7 | 7 | 5 |
| cookie2 | 2015-04-16 | 5 | 7 | 7 | 7 | 5 | 5 | 5 |
+-----------+-------------+-----+------+------+------+------+------+------+--+ SELECT cookieid,
createtime,
pv,
min(pv) OVER(PARTITION BY cookieid) AS min_pv,
max(pv) OVER(PARTITION BY cookieid) AS max_pv
FROM test_data;
+-----------+-------------+-----+---------+---------+--+
| cookieid | createtime | pv | min_pv | max_pv |
+-----------+-------------+-----+---------+---------+--+
| cookie1 | 2015-04-10 | 1 | 1 | 7 |
| cookie1 | 2015-04-16 | 4 | 1 | 7 |
| cookie1 | 2015-04-15 | 4 | 1 | 7 |
| cookie1 | 2015-04-14 | 2 | 1 | 7 |
| cookie1 | 2015-04-13 | 3 | 1 | 7 |
| cookie1 | 2015-04-12 | 7 | 1 | 7 |
| cookie1 | 2015-04-11 | 5 | 1 | 7 |
| cookie2 | 2015-04-16 | 5 | 3 | 7 |
| cookie2 | 2015-04-15 | 5 | 3 | 7 |
| cookie2 | 2015-04-14 | 3 | 3 | 7 |
| cookie2 | 2015-04-13 | 4 | 3 | 7 |
| cookie2 | 2015-04-12 | 7 | 3 | 7 |
| cookie2 | 2015-04-11 | 5 | 3 | 7 |
| cookie2 | 2015-04-10 | 6 | 3 | 7 |
+-----------+-------------+-----+---------+---------+--+
Hive函数:SUM,AVG,MIN,MAX的更多相关文章
- Hive分析窗口函数(一) SUM,AVG,MIN,MAX
Hive分析窗口函数(一) SUM,AVG,MIN,MAX 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 ...
- Hive学习之路 (十三)Hive分析窗口函数(一) SUM,AVG,MIN,MAX
数据准备 数据格式 cookie1,, cookie1,, cookie1,, cookie1,, cookie1,, cookie1,, cookie1,, 创建数据库及表 create datab ...
- MybatisPlus Lambda表达式 聚合查询 分组查询 COUNT SUM AVG MIN MAX GroupBy
一.序言 众所周知,MybatisPlus在处理单表DAO操作时非常的方便.在处理多表连接连接查询也有优雅的解决方案.今天分享MybatisPlus基于Lambda表达式优雅实现聚合分组查询. 由于视 ...
- C# 中奇妙的函数–6. 五个序列聚合运算(Sum, Average, Min, Max,Aggregate)
今天,我们将着眼于五个用于序列的聚合运算.很多时候当我们在对序列进行操作时,我们想要做基于这些序列执行某种汇总然后,计算结果. Enumerable 静态类的LINQ扩展方法可以做到这一点 .就像之前 ...
- SQL模糊查询,sum,AVG,MAX,min函数
cmd mysql -hlocalhost -uroot -p select * from emp where ename like '___' -- 三个横线, - 代表字符,可以查询 三个enam ...
- 三、函数 (SUM、MIN、MAX、COUNT、AVG)
第八章 使用数据处理函数 8.1 函数 SQL支持利用函数来处理数据.函数一般是在数据上执行的,给数据的转换和处理提供了方便. 每一个DBMS都有特定的函数.只有少数几个函数被所有主要的DBMS等同的 ...
- LINQ to SQL Count/Sum/Min/Max/Avg Join
public class Linq { MXSICEDataContext Db = new MXSICEDataContext(); // LINQ to SQL // Count/Sum/Min/ ...
- LINQ to SQL 语句(3) 之 Count/Sum/Min/Max/Avg
LINQ to SQL 语句(3) 之 Count/Sum/Min/Max/Avg [1] Count/Sum 讲解 [2] Min 讲解 [3] Max 讲解 [4] Average 和 Agg ...
- [转]LINQ语句之Select/Distinct和Count/Sum/Min/Max/Avg
在讲述了LINQ,顺便说了一下Where操作,这篇开始我们继续说LINQ语句,目的让大家从语句的角度了解LINQ,LINQ包括LINQ to Objects.LINQ to DataSets.LINQ ...
随机推荐
- 托管ASP.NET Core应用程序到Windows服务中
由于公司程序前置Nginx反向代理,所以在Windows中部署过程中没有采用IIS托管.Net Core应用,一直采用控制台dotnet命令直接运行.但是测试过程中,发现程序内Session一直无法覆 ...
- Angular组件——父子组件通讯
Angular组件间通讯 组件树,1号是根组件AppComponent. 组件之间松耦合,组件之间知道的越少越好. 组件4里面点击按钮,触发组件5的初始化逻辑. 传统做法:在按钮4的点击事件里调用组件 ...
- nodejs简单数据迁移demo
近期做数据迁移,采用nodejs框架,数据库为mysql.作为一枚菜鸟,在编码过程中,遇到众多奇葩问题,感谢民少给予的支持. 由于旧数据库中的数据,在之前设计中存在众多不合理的情况,因此在数据迁移中, ...
- Node.JS开发环境准备
1.安装Nodejs的Windows包. 官网:http://nodejs.org/ 2.可以使用cmd运行nodejs项目,命令格式: node 文件名.js node 文件名 3.对于不熟悉的 ...
- npm包使用语义化版本号
npm 采用语义版本管理软件包.所谓语义版本,就是指版本号为a.b.c的形式,其中a是大版本号,b是小版本号,c是补丁号. 一个软件发布的时候,默认就是1.0.0版.如果以后发布补丁,就增加最后一位数 ...
- openjudge(四)
关于switch的应用: #include <iostream>#include<iomanip>using namespace std;int main(){int a,b; ...
- Vue基础
1.可以使用 methods 来替代 computed,效果上两个都是一样的. 但是 computed 是基于它的依赖缓存,只有相关依赖发生改变时才会重新取值. {{ reversedMessage ...
- 如何修改HTML5 input placeholder 颜色
有三种实现方式:伪元素(pseudo-elements).伪类( pseudo-classes)和Notihing. WebKit和Blink(Safari,Google Chrome, Opera1 ...
- 【java并发系列】Fork/Join任务(转)
原文链接 当我们需要执行大量的小任务时,有经验的Java开发人员都会采用线程池来高效执行这些小任务.然而,有一种任务,例如,对超过1000万个元素的数组进行排序,这种任务本身可以并发执行,但如何拆解成 ...
- Beta冲刺 第一天
Beta冲刺 第一天 1. 昨天的困难 由于今天还是第一天,所以暂时没有昨天的困难. 2. 今天解决的进度 潘伟靖: 对代码进行了review 1.将某些硬编码改为软编码 2.合并了一些方法,简化代码 ...