我采用的是网上的电影大数据，共有3个文件，movies.dat、user.dat、ratings.dat。分别有3000/6000和1百万数据，正好做实验。

下面先介绍数据结构：

RATINGS FILE DESCRIPTION
================================================================================
All ratings are contained in the file "ratings.dat" and are in the
following format:

UserID::MovieID::Rating::Timestamp

- UserIDs range between 1 and 6040
- MovieIDs range between 1 and 3952
- Ratings are made on a 5-star scale (whole-star ratings only)
- Timestamp is represented in seconds since the epoch as returned by time(2)
- Each user has at least 20 ratings
USERS FILE DESCRIPTION

================================================================================
User information is in the file "users.dat" and is in the following
format:

UserID::Gender::Age::Occupation::Zip-code

All demographic information is provided voluntarily by the users and is
not checked for accuracy. Only users who have provided some demographic
information are included in this data set.

- Gender is denoted by a "M" for male and "F" for female
- Age is chosen from the following ranges:

* 1: "Under 18"
* 18: "18-24"
* 25: "25-34"
* 35: "35-44"
* 45: "45-49"
* 50: "50-55"
* 56: "56+"

- Occupation is chosen from the following choices:

* 0: "other" or not specified
* 1: "academic/educator"
* 2: "artist"
* 3: "clerical/admin"
* 4: "college/grad student"
* 5: "customer service"
* 6: "doctor/health care"
* 7: "executive/managerial"
* 8: "farmer"
* 9: "homemaker"
* 10: "K-12 student"
* 11: "lawyer"
* 12: "programmer"
* 13: "retired"
* 14: "sales/marketing"
* 15: "scientist"
* 16: "self-employed"
* 17: "technician/engineer"
* 18: "tradesman/craftsman"
* 19: "unemployed"
* 20: "writer"

MOVIES FILE DESCRIPTION
================================================================================

Movie information is in the file "movies.dat" and is in the following
format:

MovieID::Title::Genres

- Titles are identical to titles provided by the IMDB (including
year of release)
- Genres are pipe-separated and are selected from the following genres:

* Action
* Adventure
* Animation
* Children‘s
* Comedy
* Crime
* Documentary
* Drama
* Fantasy
* Film-Noir
* Horror
* Musical
* Mystery
* Romance
* Sci-Fi
* Thriller
* War
* Western

****************************************************************************************************

二、进入重点

开始建库、建表：

create database movies;
use movies;
//试试建表
CREATE TABLE users(userid:Long);
create table users(userid:Bigint);
CREATE TABLE ratings(userid Int,movieid Int,rating Int,timestamp Timestamp)PARTITIONED BY(dt String) ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘::‘;
出错：FAILED: ParseException line 1:55 Failed to recognize predicate ‘timestamp‘. Failed rule: ‘identifier‘ in column specification

timestamp不支持数据结构里的字符串，改之。

CREATE TABLE ratings(userid Int,movieid Int,rating Int,timestamped Timestamp)PARTITIONED BY(dt String) ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘,‘;

LOAD DATA LOCAL INPATH ‘/home/dyq/Documents/movies/ratings-douhao.dat‘ into table ratings PARTITION(dt="20161201");
hive> select * from ratings limit 10;
OK
1 1193 5 NULL 20161201
1 661 3 NULL 20161201
1 914 3 NULL 20161201
1 3408 4 NULL 20161201
1 2355 5 NULL 20161201
1 1197 3 NULL 20161201
1 1287 5 NULL 20161201
1 2804 5 NULL 20161201
1 594 4 NULL 20161201
1 919 4 NULL 20161201

看来用"::"做分隔符有了麻烦，替换成我喜欢的","

drop table ratings;
CREATE TABLE ratings(userid Int,movieid Int,rating Int,timestamped String)PARTITIONED BY(dt String) ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘,‘;

hive> select * from ratings limit 10;
OK
1 1193 5 978300760 20161201
1 661 3 978302109 20161201
1 914 3 978301968 20161201
1 3408 4 978300275 20161201
1 2355 5 978824291 20161201
1 1197 3 978302268 20161201
1 1287 5 978302039 20161201
1 2804 5 978300719 20161201
1 594 4 978302268 20161201
1 919 4 978301368 20161201
Time taken: 0.122 seconds, Fetched: 10 row(s)

一切OK！hive的语义真是不够强大的说。

下面建立Movies和users表。

CREATE TABLE movies(movieid Int,title String,genres String)PARTITIONED BY(dt String) ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘,‘;

LOAD DATA LOCAL INPATH ‘/home/dyq/Documents/movies/movies-douhao.dat‘ into table movies PARTITION(dt="20161201");

CREATE TABLE users(userid Int,gender String,age Int,occupation String,zip-code String)PARTITIONED BY(dt String) ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘,‘;

FAILED: ParseException line 1:73 cannot recognize input near ‘-‘ ‘code‘ ‘String‘ in column type

CREATE TABLE users(userid Int,gender String,age Int,occupation String,zipcode String)PARTITIONED BY(dt String) ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘,‘;

LOAD DATA LOCAL INPATH ‘/home/dyq/Documents/movies/users-douhao.dat‘ into table users PARTITION(dt="20161201");

hive> select * from users limit 10;
OK
1 F 1 10 48067 20161201
2 M 56 16 70072 20161201
3 M 25 15 55117 20161201
4 M 45 7 02460 20161201
5 M 25 20 55455 20161201
6 F 50 9 55117 20161201
7 M 35 1 06810 20161201
8 M 25 12 11413 20161201
9 M 25 17 61614 20161201
10 F 35 1 95370 20161201
Time taken: 0.168 seconds, Fetched: 10 row(s)

*****************************************************************
创建索引：

create index ratings_userid_index on table ratings(userid) as ‘COMPACT‘ with deferred rebuild;
show index on ratings;
drop index ratings_userid_index on ratings;

create index ratings_movieid_index on table ratings(movieid) as ‘COMPACT‘ with deferred rebuild;
show index on ratings;
drop index ratings_movieid_index on ratings;

加索引前的join：
select movies.movieid,movies.title,ratings.rating from movies join ratings on(movies.movieid=ratings.movieid);
Time taken: 40.721 seconds, Fetched: 1000209 row(s)

加索引后的join：
Time taken: 40.816 seconds, Fetched: 1000209 row(s)

查询某一个值：
select movies.movieid,movies.title,ratings.rating from movies join ratings on(movies.movieid=ratings.movieid) where movies.movieid=2716;
Time taken: 33.834 seconds, Fetched: 2181 row(s)

索引后：
drop index ratings_movieid_index on ratings;
drop index ratings_userid_index on ratings;
select movies.movieid,movies.title,ratings.rating from movies join ratings on(movies.movieid=ratings.movieid) where movies.movieid=2716;

Time taken: 29.428 seconds, Fetched: 2181 row(s)

hive1.2.1实战操作电影大数据！