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]；---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[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
[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
; --切换数据库
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。
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) 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)
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
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;
本地模式
set hive.exec.mode.local.auto=true;
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);
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）
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
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;
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;
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;
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;
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;
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;
desc formatted student;