# hive数据导入
可以通过多种方式将数据导入hive表
## 1.通过外部表导入
用户在hive上建external表，建表的同时指定hdfs路径，在数据拷贝到指定hdfs路径的同时，也同时完成数据插入external表。
例如：
编辑文件test.txt
~~~
$ cat test.txt
1 hello
2 world
3 test
4 case
~~~
字段之间以'\t'分割
启动hive：
`$ hive`
建external表：
~~~
hive> CREATE EXTERNAL TABLE MYTEST(num INT, name STRING)
> COMMENT 'this is a test'
> ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
> STORED AS TEXTFILE
> LOCATION '/data/test';
OK
Time taken: 0.714 seconds
hive> show tables;
OK
mytest
partition_test
partition_test_input
test
Time taken: 0.07 seconds
hive> desc mytest ;
OK
num int
name string
Time taken: 0.121 seconds|
~~~
数据拷贝到hdfs：
$ hadoop fs -put test.txt /data/test
查看hive表数据：
~~~
hive> select * from mytest;
OK
1 hello
2 world
3 test
4 case
Time taken: 0.375 seconds
hive> select num from mytest;
Total MapReduce jobs = 1
Launching Job 1 out of 1
......
Total MapReduce CPU Time Spent: 510 msec
OK
1
2
3
4
Time taken: 27.157 seconds
~~~
这种方式常常用于当hdfs上有一些历史数据，而我们需要在这些数据上做一些hive的操作时使用。这种方式避免了数据拷贝开销
## 2.从本地导入
数据不在hdfs上，直接从本地导入hive表
文件/home/work/test.txt内容同上
建表：
~~~
hive> CREATE TABLE MYTEST2(num INT, name STRING)
> COMMENT 'this is a test2'
> ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
> STORED AS TEXTFILE;
OK
Time taken: 0.077 seconds
~~~
导数据入表：
~~~
hive> LOAD DATA LOCAL INPATH '/home/work/test.txt' INTO TABLE MYTEST2;
Copying data from file:/home/work/test.txt
Copying file: file:/home/work/test.txt
Loading data to table default.mytest2
OK
Time taken: 0.24 seconds
~~~
查看数据：
~~~
hive> select * from MYTEST2;
OK
1 hello
2 world
3 test
4 case
Time taken: 0.11 seconds
~~~
这种方式导入的本地数据可以是一个文件，一个文件夹或者通配符，需要注意的是，如果是文件夹，文件夹内不能包含子目录，同样，通配符只能通配文件。
## 3.从hdfs导入
上述test.txt文件已经导入/data/test
则可以使用下述命令直接将数据导入hive表：
~~~
hive> CREATE TABLE MYTEST3(num INT, name STRING)
> COMMENT "this is a test3"
> ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
> STORED AS TEXTFILE;
OK
Time taken: 4.735 seconds
hive> LOAD DATA INPATH '/data/test/test.txt' INTO TABLE MYTEST3;
Loading data to table default.mytest3
OK
Time taken: 0.337 seconds
hive> select * from MYTEST3 ;
OK
1 hello
2 world
3 test
4 case
Time taken: 0.227 seconds
~~~
## 4\. 从其它表导入数据：
~~~
hive> CREATE EXTERNAL TABLE MYTEST4(num INT) ;
OK
Time taken: 0.091 seconds
hive> FROM MYTEST3 test3
> INSERT OVERWRITE TABLE MYTEST4
> select test3.num where name='world';
Total MapReduce jobs = 2
Launching Job 1 out of 2
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_201207230024_0002, Tracking URL = http://localhost:50030/jobdetails.jsp?jobid=job_201207230024_0002
Kill Command = /home/work/hadoop/hadoop-1.0.3/libexec/../bin/hadoop job -Dmapred.job.tracker=localhost:9001 -kill job_201207230024_0002
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
2012-07-23 18:59:02,365 Stage-1 map = 0%, reduce = 0%
2012-07-23 18:59:08,417 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.62 sec
2012-07-23 18:59:09,435 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.62 sec
2012-07-23 18:59:10,445 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.62 sec
2012-07-23 18:59:11,455 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.62 sec
2012-07-23 18:59:12,470 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.62 sec
2012-07-23 18:59:13,489 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.62 sec
2012-07-23 18:59:14,508 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 0.62 sec
MapReduce Total cumulative CPU time: 620 msec
Ended Job = job_201207230024_0002
Ended Job = -174856900, job is filtered out (removed at runtime).
Moving data to: hdfs://localhost:9000/tmp/hive-work/hive_2012-07-23_18-58-44_166_189728317691010041/-ext-10000
Loading data to table default.mytest4
Deleted hdfs://localhost:9000/user/hive/warehouse/mytest4
Table default.mytest4 stats: [num_partitions: 0, num_files: 1, num_rows: 0, total_size: 2, raw_data_size: 0]
1 Rows loaded to mytest4
MapReduce Jobs Launched:
Job 0: Map: 1 Accumulative CPU: 0.62 sec HDFS Read: 242 HDFS Write: 2 SUCESS
Total MapReduce CPU Time Spent: 620 msec
OK
Time taken: 30.663 seconds
hive> select * from mytest4;
OK
2
Time taken: 0.103 seconds
~~~
