hive多表联合查询(GroupLens->Users,Movies,Ratings表)
hive (UserMovieRating)> create table if not exists Users(
> UserID int comment 'user id',
> Gender string comment 'user sex',
> Age int comment '1:Under 18,18:18-24,25:25-34,35:35-44,45:45-49,50:50-55,56:56+',
> Occupation int comment '0-20 represents different jobs',
> ZipCode string comment 'your home zip code')
> row format delimited fields terminated by '\t'
> stored as textfile;
OK
Time taken: 0.249 seconds
hive (UserMovieRating)> load data local inpath '/home/landen/MahoutTest/users.txt' overwrite into table Users;
Copying data from file:/home/landen/MahoutTest/users.txt
Copying file: file:/home/landen/MahoutTest/users.txt
Loading data to table usermovierating.users
Deleted hdfs://Master:9000/home/landen/UntarFile/hive-0.10.0/warehouse/usermovierating.db/users
Table usermovierating.users stats: [num_partitions: 0, num_files: 1, num_rows: 0, total_size: 110208, raw_data_size: 0]
OK
Time taken: 0.745 seconds
hive (UserMovieRating)> select * from Users limit 10;
OK
userid gender age occupation zipcode
1 F 1 10 48067
2 M 56 16 70072
3 M 25 15 55117
4 M 45 7 02460
5 M 25 20 55455
6 F 50 9 55117
7 M 35 1 06810
8 M 25 12 11413
9 M 25 17 61614
10 F 35 1 95370
Time taken: 0.096 seconds
hive (UserMovieRating)> create table if not exists Movies(
> MovieID int comment 'movie id',
> MovieName string comment 'movie name',
> ReleasedDate int comment 'released year',
> MovieType string comment 'movie type')
> row format delimited fields terminated by '\t'
> stored as textfile;
OK
Time taken: 0.183 seconds
hive (UserMovieRating)> show tables;
OK
tab_name
movies
users
Time taken: 0.083 seconds
hive (UserMovieRating)> load data local inpath '/home/landen/MahoutTest/Processedmovies.txt' overwrite into table Movies;
Copying data from file:/home/landen/MahoutTest/Processedmovies.txt
Copying file: file:/home/landen/MahoutTest/Processedmovies.txt
Loading data to table usermovierating.movies
Deleted hdfs://Master:9000/home/landen/UntarFile/hive-0.10.0/warehouse/usermovierating.db/movies
Table usermovierating.movies stats: [num_partitions: 0, num_files: 1, num_rows: 0, total_size: 155900, raw_data_size: 0]
OK
Time taken: 0.695 seconds
hive (UserMovieRating)> select * from Movies limit 10;
OK
movieid moviename releaseddate movietype
1 Toy Story 1995 Animation,Children's,Comedy
2 Jumanji 1995 Adventure,Children's,Fantasy
3 Grumpier Old Men 1995 Comedy,Romance
4 Waiting to Exhale 1995 Comedy,Drama
5 Father of the Bride Part II 1995 Comedy
6 Heat 1995 Action,Crime,Thriller
7 Sabrina 1995 Comedy,Romance
8 Tom and Huck 1995 Adventure,Children's
9 Sudden Death 1995 Action
10 GoldenEye 1995 Action,Adventure,Thriller
Time taken: 0.095 seconds
hive (UserMovieRating)> create table if not exists Rating(
> UserID int comment 'user id',
> MovieID int comment 'movie id(1-3952)',
> Rating int comment 'Ratings are made on a 5-star scale',
> RatingTime string comment 'user rates time')
> row format delimited fields terminated by '\t'
> stored as textfile;
OK
Time taken: 1.3 seconds
hive (UserMovieRating)> load data local inpath '/home/landen/MahoutTest/ProcessedRating.txt' overwrite into table Rating;
Copying data from file:/home/landen/MahoutTest/ProcessedRating.txt
Copying file: file:/home/landen/MahoutTest/ProcessedRating.txt
Loading data to table usermovierating.rating
Deleted hdfs://Master:9000/home/landen/UntarFile/hive-0.10.0/warehouse/usermovierating.db/rating
Table usermovierating.rating stats: [num_partitions: 0, num_files: 1, num_rows: 0, total_size: 21593504, raw_data_size: 0]
OK
Time taken: 3.293 seconds
hive (UserMovieRating)> select * from Rating limit 10;
OK
userid movieid rating ratingtime
1 1193 5 978300760
1 661 3 978302109
1 914 3 978301968
1 3408 4 978300275
1 2355 5 978824291
1 1197 3 978302268
1 1287 5 978302039
1 2804 5 978300719
1 594 4 978302268
1 919 4 978301368
Time taken: 0.658 seconds
hive (UserMovieRating)> describe users;
OK
col_name data_type comment
userid int user id
gender string user sex
age int 1:Under 18,18:18-24,25:25-34,35:35-44,45:45-49,50:50-55,56:56+
occupation int 0-20 represents different jobs
zipcode string your home zip code
Time taken: 0.514 seconds
hive (UserMovieRating)> describe movies;
OK
col_name data_type comment
movieid int movie id
moviename string movie name
releaseddate int released year
movietype string movie type
Time taken: 0.085 seconds
hive (UserMovieRating)> describe ratings;
FAILED: SemanticException [Error 10001]: Table not found ratings
hive (UserMovieRating)> describe rating;
OK
col_name data_type comment
userid int user id
movieid int movie id(1-3952)
rating int Ratings are made on a 5-star scale
ratingtime string user rates time
Time taken: 0.121 seconds
Users,Movies,Rating三表联合查询:
hive (UserMovieRating)> select u.userid,u.occupation,m.moviename,r.rating
> from rating r
> join users u on r.userid = u.userid
> join movies m on r.movieid = m.movieid;
hive多表联合查询(GroupLens->Users,Movies,Ratings表)的更多相关文章
- yii 多表联合查询的几种方法
yii多表联合查询, 第一种,用command,自己拼接sql语句执行查询 第二种,用AR,model需继承下面的ar,执行queryall或queryrow方法 <?php //applica ...
- MVC5+EF6简单实例---以原有SQLServer数据库两表联合查询为例
有二三年没写代码了,**内的工作就是这样,容易废人!看到园子里这么多大侠朝气蓬勃的,我想也要学点东西并和大家分享,共同进步!快乐每一天,进步每一天!言归正传! 通过最近一段时间对MVC5.EF6的学习 ...
- Dynamic CRM 2013学习笔记(九)CrmFetchKit.js介绍:Fetchxml、多表联合查询, 批量更新
CrmFetchKit.js是一个跨浏览器的一个类库,允许通过JavaScript来执行fetch xml的查询,还可以实现批量更新,分页查询等.目前已支持Chrome 25, Firefox 19 ...
- SharePoint 2013 列表多表联合查询
在SharePoint的企业应用中,遇到复杂的逻辑的时候,我们会需要多表查询:SharePoint和Sql数据表一样,也支持多表联合查询,但是不像Sql语句那样简单,需要使用SPQuery的Joins ...
- MyBatis 多表联合查询及优化 以及自定义返回结果集
下面就来说一下 mybatis 是通过什么来实现多表联合查询的.首先看一下表关系,如图: 这 里,我已经搭好了开发的环境,用到的是 SpringMVC + Spring + MyBatis,当然,为了 ...
- 一步步学Mybatis-实现多表联合查询(4)
上一章节中我们已经完成了对单表的CRUD操作,接下来今天这一讲讲述的是关于Mybatis在多表查询时候的应用,毕竟实际业务中也是多表的联合查询比较多嘛~ 还记得最一开始我们新建过一张Website表吗 ...
- MyBatis之三:多表联合查询
在这篇文章里面主要讲解如何在mybatis里面使用一对一.一对多.多表联合查询(类似视图)操作的例子. 注:阅读本文前请先大概看一下之前两篇文章. 一.表结构 班级表class,学生表student, ...
- MyBatis 多表联合查询,字段重复的解决方法
MyBatis 多表联合查询,两张表中字段重复时,在配置文件中,sql语句联合查询时使用字段别名,resultMap中对应的column属性使用相应的别名: <resultMap type=&q ...
- ormlite 多表联合查询
ormlite 多表联合查询 QueryBuilder shopBrandQueryBuilder = shopBrandDao.queryBuilder(); QueryBuilder shopQu ...
- Mybatis oracle多表联合查询分页数据重复的问题
Mybatis oracle多表联合查询分页数据重复的问题 多表联合查询分页获取数据时出现一个诡异的现象:数据总条数正确,但有些记录多了,有些记录却又少了甚至没了.针对这个问题找了好久,最后发现是由于 ...
随机推荐
- 2018.08.28 洛谷P4556 [Vani有约会]雨天的尾巴(树上差分+线段树合并)
传送门 要求维护每个点上出现次数最多的颜色. 对于每次修改,我们用树上差分的思想,然后线段树合并统计答案就行了. 注意颜色很大需要离散化. 代码: #include<bits/stdc++.h& ...
- csdn的一次回答问题
#coding:utf8 import tushare as ts import pandas as pd import numpy as np import pymysql,datetime imp ...
- CreateDialog()与CreateDialogIndrect()
CreateDialog() 概述 函数功能:CreateDialog宏从一个对话框模板资源创建一个无模式的对话框,CreateDiaog宏使用CreateDialogParam函数. 函数原型:HW ...
- _variant_t与其他数据类型的转换
转自:http://kuaixingdong.blog.hexun.com/29627840_d.html 我们先看看COM所支持的一些类型的基本类: (微软提供,在comdef.h中定义) 在COM ...
- 系统目录APK更新——权限问题
package com.example.wx; import java.io.File;import java.io.FileOutputStream;import java.io.IOExcepti ...
- 洛谷P1600 天天爱跑步(线段树合并)
小c同学认为跑步非常有趣,于是决定制作一款叫做<天天爱跑步>的游戏.<天天爱跑步>是一个养成类游戏,需要玩家每天按时上线,完成打卡任务. 这个游戏的地图可以看作一一棵包含 nn ...
- 老码农冒死揭开行业黑幕:如何编写无法维护的代码[ZZ]
下面是一篇有意思的"代码大全",可谓 逆软件工程. 老码农冒死揭开行业黑幕:如何编写无法维护的代码 原文如下 让自己稳拿铁饭碗 ;-) – Roedy Green(翻译版略有删节) ...
- [C#]C#时间日期操作
一.C# 日期格式 1. DateTime dt = DateTime.Now; 2. dt.ToString();//2005-11-5 13:21:25 3. dt.ToFileTime().To ...
- 什么是fortran语言之fortran语言入门
Fortran源自于“公式翻译”(英语:FormulaTranslation)的缩写,是一种编程语言.它是世界上最早出现的计算机高级程序设计语言,广泛应用于科学和工程计算领域.FORTRAN语言以其特 ...
- mvc 读写txt文档
-----------------写入内容---------------- string userfile = "UserData.txt"; StreamWriter sw = ...