Oracle分组函数之Grouping Sets
功能介绍:
自定义分组的字段
创建表:
插入测试数据:
Grouping Sets(null,t.classid,(t.classid,t.studentname)),类似于ROLLUP
Select t.classid,t.studentname,Sum(t.score) From Score t Group By Grouping Sets(null,t.classid,(t.classid,t.studentname));
查询结果:
aaarticlea/png;base64,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" alt="" />
Grouping Sets(null,t.classid,t.studentname,(t.classid,t.studentname)),类似于CUBE
Select t.classid,t.studentname,Sum(t.score) From Score t Group By Grouping Sets(null,t.classid,t.studentname,(t.classid,t.studentname));
查询结果:
aaarticlea/png;base64,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" alt="" />
Oracle分组函数之Grouping Sets的更多相关文章
- 【转】【CUBE】Oracle分组函数之CUBE魅力
http://blog.itpub.net/519536/viewspace-610997/ Oracle的CUBE与ROLLUP功能很相似,也是在数据统计分析领域的一把好手. 关于ROLLUP的查 ...
- Oracle分组函数之CUBE魅力
Oracle的CUBE与ROLLUP功能很相似,也是在数据统计分析领域的一把好手. 关于ROLLUP的查询统计功能请参考文章<Oracle分组函数之ROLLUP魅力>(http://www ...
- TSQL 分组集(Grouping Sets)
分组集(Grouping Sets)是多个分组的并集,用于在一个查询中,按照不同的分组列对集合进行聚合运算,等价于对单个分组使用“union all”,计算多个结果集的并集.使用分组集的聚合查询,返回 ...
- Oracle分组函数之ROLLUP用法
rollup函数 本博客简单介绍一下oracle分组函数之rollup的用法,rollup函数常用于分组统计,也是属于oracle分析函数的一种 环境准备 create table dept as s ...
- Group By 多个分组集小结 --GROUPING SETS,GROUP BY CUBE,GROUP BY ROLLUP,GROUPING(),GROUPING_ID()
T-SQL 多个分组集共有三种 GROUPING SETS, CUBE, 以及ROLLUP, 其中 CUBE和ROLLUP可以当做是GROUPING SETS的简写版 示例数据库下载: http:// ...
- [转]【ROLLUP】Oracle分组函数之ROLLUP魅力
原创:http://blog.itpub.net/519536/viewspace-610995 本文通过演示给出Oracle ROLLUP分组函数的用法,体验一下Oracle在统计查询领域中的函数魅 ...
- Hive函数:GROUPING SETS,GROUPING__ID,CUBE,ROLLUP
参考:lxw大数据田地:http://lxw1234.com/archives/2015/04/193.htm 数据准备: CREATE EXTERNAL TABLE test_data ( mont ...
- oracle 分组函数执行分析
先上例了: select job as "JOB1", avg(sal) as "avg sal" from scott.emp group by " ...
- Oracle分组函数以及数据分组
简单总结一下对于数据的分组和分组函数. 本文所举实例,数据来源oracle用户scott下的emp,dept ,salgrade 3表:数据如下: 一.分组函数 1.sum()求和函数.max()求最 ...
随机推荐
- 【tensorflow使用笔记三】:tensorflow tutorial中的源码阅读
https://blog.csdn.net/victoriaw/article/details/61195620#t0 input_data 没用的另一种解决方法:tensorflow1.8版本及以上 ...
- MDX 入门
之前用到的SQL,解释:结构化查询语言(Structured Query Language)(发音:/ˈes kjuː ˈel/ "S-Q-L"),是一种特殊目的的编程语言,是一种 ...
- 博客图片上传picgo工具安装配置github图传使用
摘要 对于每一个写博客的人来说,图片是至关重要.这一路经历了多次图片的烦恼,之前选择了微博个人文章那里粘贴图片的方式上传,感觉也挺方便的.但是由于新浪的图片显示问题,如果header中不设置 标签就不 ...
- js 创建对象的方法
<script> //1.字面量语法 var rectangle1 = {}; rectangle1.name="mindong"; rectangle1.width ...
- Application.Restore不起作用了
http://www.myexception.cn/delphi/695243.html Application.Restore不起作用了窗体上只有一个Button和一个Timer(1秒计时)代码如下 ...
- Machine Learning 文章导读
Machine Learning Algorithms Linear Regression and Gradient Descent Local Weighted Regression Algorit ...
- 安全运维 - Windows系统应急响应
挖矿病毒应急 传播方式: 通过社工.钓鱼方式下载和运行了挖矿程序(邮件.IM等) 利用计算机系统远程代码执行漏洞下载.上传和执行挖矿程序. 利用i算计Web或第三方软件漏洞获取计算机权限,然后下载和执 ...
- Struts2的核心配置文件
Struts2的详细配置: 配置的是struts2的核心配置文件:,在struts2的核心配置文件中主要有三个标签需要进行配置:package,action,result. 1. 配置package标 ...
- Git-第三篇廖雪峰Git教程学习笔记(2)回退修改,恢复文件
1.工作区 C:\fyliu\lfyTemp\gitLocalRepository\yangjie 2.版本库 我们使用git init命令创建的.git就是我们的版本库.Git的版本库里存了很多东西 ...
- Ad Hoc Distributed Queries / xp_cmdshell 的启用与关闭
启用Ad Hoc Distributed Queries: reconfigure reconfigure 关闭Ad Hoc Distributed Queries: reconfigure reco ...