hive的基本操作

1.DDL

数据库相关操作

• Hive配置单元包含一个名为 default 默认的数据库.
  create database [if not exists] <database name>；---创建数据库
• 显示库
  show databases; --显示所有数据库

• 删除数据库
  drop database if exists <database name> [restrict|cascade];
  --删除数据库，默认情况下，hive不允许删除含有表的数据库，要先将数据库中的表清空才能drop，否则会报错
  --加入cascade关键字，可以强制删除一个数据库,默认是restrict，表示有限制的

        eg.  hive> drop database if exists users cascade;

• drop database if exists <database name> [restrict|cascade]; --删除数据库，默认情况下，hive不允许删除含有表的数据库，要先将数据库中的表清空才能drop，否则会报错
• use <database name>; --切换数据库

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1.1分区表（PARTITIONED BY）

 分区建表分为2种，一种是单分区，也就是说在表文件夹目录下只有一级文件夹目录。另外一种是多分区，表文件夹下出现多文件夹嵌套模式。

1.1.1单分区建表语句：

  create table day_table (id int, content string) partitioned by (dt string);单分区表，按天分区，在表结构中存在id，content，dt三列

1.1.2双分区建表语句：

  create table day_hour_table (id int, content string) partitioned by (dt string, hour string);双分区表，按天和小时分区，在表结构中新增加了dt和hour两列。

1.1.3导入数据

  单分区导入数据
  LOAD DATA local INPATH '/root/hivedata/dat_table.txt' INTO TABLE day_table partition(dt='2017-07-07');
         
  多分区导入数据      
  LOAD DATA local INPATH '/root/hivedata/dat_table.txt' INTO TABLE day_hour_table PARTITION(dt='2017-07-07', hour='08');

1.1.4基于分区的查询：

SELECT day_table.* FROM day_table WHERE day_table.dt = '2017-07-07';

1.1.5查看分区：

  show partitions day_hour_table;  

总的说来partition就是辅助查询，缩小查询范围，加快数据的检索速度和对数据按照一定的规格和条件进行管理。

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1.2ROW FORMAT DELIMITED（指定分隔符）

create table day_table (id int, content string) partitioned by (dt string) row format delimited fields terminated by ',';   ---指定分隔符创建分区表

复杂类型的数据表指定分隔符

create table complex_array(name string,work_locations array<string>) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' COLLECTION ITEMS TERMINATED BY ',';

数据如下：
 zhangsan    beijing,shanghai,tianjin,hangzhou
 wangwu    shanghai,chengdu,wuhan,haerbin

create table t_map(id int,name string,hobby map<string,string>)
row format delimited 
fields terminated by ','
collection items terminated by '-'
map keys terminated by ':' ;

数据：
1,zhangsan,唱歌:非常喜欢-跳舞:喜欢-游泳:一般般
2,lisi,打游戏:非常喜欢-篮球:不喜欢

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1.3内部表、外部表

• 建内部表

  create table student(Sno int,Sname string,Sex string,Sage int,Sdept string) row format delimited fields terminated by ',';

• 建外部表

  create external table student_ext(Sno int,Sname string,Sex string,Sage int,Sdept string) row format delimited fields terminated by ',' location '/stu';

内、外部表加载数据：
load data local inpath '/root/hivedata/students.txt' overwrite into table student;

load data inpath '/stu' into table student_ext;

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本地模式
set hive.exec.mode.local.auto=true;

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1.5分桶表（cluster by into num buckets）

指定开启分桶

set hive.enforce.bucketing = true;
set mapreduce.job.reduces=4;

TRUNCATE TABLE  stu_buck;
#先删除表
drop table stu_buck;
#在创建
create table stu_buck(Sno int,Sname string,Sex string,Sage int,Sdept string)
clustered by(Sno) 
sorted by(Sno DESC)
into 4 buckets
row format delimited
fields terminated by ',';

分桶表导入数据

insert overwrite table stu_buck
select * from student cluster by(Sno);

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分桶、排序等查询：cluster by 、sort by、distribute by

select * from student cluster by(Sno);

insert overwrite table student_buck
select * from student cluster by(Sno) sort by(Sage);  报错,cluster 和 sort 不能共存

对某列进行分桶的同时，根据另一列进行排序

insert overwrite table stu_buck
select * from student distribute by(Sno) sort by(Sage asc);

总结：
cluster（分且排序，必须一样）==distribute（分） + sort（排序）（可以不一样）

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增加/删除分区

drop table t_partition;
create table t_partition(id int,name string)
partitioned by (dt string)
row format delimited
fields terminated by ',';

增加分区

alter table t_partition add partition (dt='2008-08-08') location 'hdfs://node-21:9000/t_parti/';

执行添加分区时 /t_parti文件夹下的数据不会被移动。并且没有分区目录dt=2008-08-08

删除分区

alter table t_partition drop partition (dt='2008-08-08');

执行删除分区时/t_parti下的数据会被删除并且连同/t_parti文件夹也会被删除

注意区别于load data时候添加分区:会移动数据 会创建分区目录

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Insert查询语句

多重插入：

create table source_table (id int, name string) row format delimited fields terminated by ',';
create table test_insert1 (id int) row format delimited fields terminated by ',';
create table test_insert2 (name string) row format delimited fields terminated by ',';

from source_table                     
insert overwrite table test_insert1 
select id
insert overwrite table test_insert2
select name;

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动态分区插入

set hive.exec.dynamic.partition=true;    #是否开启动态分区功能，默认false关闭。
set hive.exec.dynamic.partition.mode=nonstrict;   #动态分区的模式，默认strict，表示必须指定

至少一个分区为静态分区，nonstrict模式表示允许所有的分区字段都可以使用动态分区。

需求：
将dynamic_partition_table中的数据按照时间(day)，插入到目标表d_p_t的相应分区中。

原始表：

create table dynamic_partition_table(day string,ip string)row format delimited fields terminated by ","; 

load data local inpath '/root/hivedata/dynamic_partition_table.txt' into table dynamic_partition_table;

2015-05-10,ip1
2015-05-10,ip2
2015-06-14,ip3
2015-06-14,ip4
2015-06-15,ip1
2015-06-15,ip2

目标表：

create table d_p_t(ip string) partitioned by (month string,day string);

动态插入：

insert overwrite table d_p_t partition (month,day) 
select ip,substr(day,1,7) as month,day from dynamic_partition_table;

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查询结果导出到文件系统

3、将查询结果保存到指定的文件目录（可以是本地，也可以是hdfs）

insert overwrite local directory '/root/123456'
select * from t_p;

insert overwrite directory '/aaa/test'
select * from t_p;

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关于hive中的各种join

准备数据

1,a
2,b
3,c
4,d
7,y
8,u

2,bb
3,cc
7,yy
9,pp

建表：

create table a(id int,name string)
row format delimited fields terminated by ',';

create table b(id int,name string)
row format delimited fields terminated by ',';

导入数据：

load data local inpath '/root/hivedata/a.txt' into table a;
load data local inpath '/root/hivedata/b.txt' into table b;

实验：
** inner join

select * from a inner join b on a.id=b.id;

select a.id,a.name from a join b on a.id = b.id;

select a.* from a join b on a.id = b.id;
+-------+---------+-------+---------+--+
| a.id  | a.name  | b.id  | b.name  |
+-------+---------+-------+---------+--+
| 2     | b       | 2     | bb      |
| 3     | c       | 3     | cc      |
| 7     | y       | 7     | yy      |
+-------+---------+-------+---------+--+

**left join

select * from a left join b on a.id=b.id;
+-------+---------+-------+---------+--+
| a.id  | a.name  | b.id  | b.name  |
+-------+---------+-------+---------+--+
| 1     | a       | NULL  | NULL    |
| 2     | b       | 2     | bb      |
| 3     | c       | 3     | cc      |
| 4     | d       | NULL  | NULL    |
| 7     | y       | 7     | yy      |
| 8     | u       | NULL  | NULL    |
+-------+---------+-------+---------+--+

**right join

select * from a right join b on a.id=b.id;

select * from b right join a on b.id=a.id;
+-------+---------+-------+---------+--+
| a.id  | a.name  | b.id  | b.name  |
+-------+---------+-------+---------+--+
| 2     | b       | 2     | bb      |
| 3     | c       | 3     | cc      |
| 7     | y       | 7     | yy      |
| NULL  | NULL    | 9     | pp      |
+-------+---------+-------+---------+--+

**

select * from a full outer join b on a.id=b.id;
+-------+---------+-------+---------+--+
| a.id  | a.name  | b.id  | b.name  |
+-------+---------+-------+---------+--+
| 1     | a       | NULL  | NULL    |
| 2     | b       | 2     | bb      |
| 3     | c       | 3     | cc      |
| 4     | d       | NULL  | NULL    |
| 7     | y       | 7     | yy      |
| 8     | u       | NULL  | NULL    |
| NULL  | NULL    | 9     | pp      |
+-------+---------+-------+---------+--+

**hive中的特别join

select * from a left semi join b on a.id = b.id;

select a.* from a inner join b on a.id=b.id;

+-------+---------+--+
| a.id  | a.name  |
+-------+---------+--+
| 2     | b       |
| 3     | c       |
| 7     | y       |
+-------+---------+--+

相当于

select a.id,a.name from a where a.id in (select b.id from b); 在hive中效率极低

select a.id,a.name from a join b on (a.id = b.id);

select * from a inner join b on a.id=b.id;

cross join（##慎用）
返回两个表的笛卡尔积结果，不需要指定关联键。
select a.,b. from a cross join b;

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内置jason函数

select get_json_object(line,'$.movie') as moive,get_json_object(line,'$.rate') as rate  from rat_json limit 10;

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transform案例:

1、先加载rating.json文件到hive的一个原始表 rat_json

create table rat_json(line string) row format delimited;
load data local inpath '/root/hivedata/rating.json' into table rat_json;

2、需要解析json数据成四个字段，插入一张新的表 t_rating

drop table if exists t_rating;
create table t_rating(movieid string,rate int,timestring string,uid string)
row format delimited fields terminated by '\t';

insert overwrite table t_rating
select get_json_object(line,'$.movie') as moive,get_json_object(line,'$.rate') as rate,get_json_object(line,'$.timeStamp') as timestring, get_json_object(line,'$.uid') as uid from rat_json limit 10;

3、使用transform+python的方式去转换unixtime为weekday
先编辑一个python脚本文件

python代码

vi weekday_mapper.py

#!/bin/python
import sys
import datetime

for line in sys.stdin:
  line = line.strip()
  movieid, rating, unixtime,userid = line.split('\t')
  weekday = datetime.datetime.fromtimestamp(float(unixtime)).isoweekday()
  print '\t'.join([movieid, rating, str(weekday),userid])

保存文件
然后，将文件加入hive的classpath：
hive>add FILE /root/hivedata/weekday_mapper.py;

create table u_data_new as select
  transform (movieid, rate, timestring,uid)
  using 'python weekday_mapper.py'
  as (movieid, rate, weekday,uid)
from t_rating;

select distinct(weekday) from u_data_new limit 10;

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desc formatted student;