【Hive一】Hive入门

Hive安装与配置

Hive的运行需要依赖于Hadoop，因此需要首先安装Hadoop2.5.2，并且Hive的启动前需要首先启动Hadoop。

Hive安装和配置的步骤

1. 从如下地址下载Hive0.14.0

http://mirror.bit.edu.cn/apache/hive/

 

 2.解压hive，在系统变量里配置如下变量HIVE_HOME和PATH

sudo  vim /etc/profile

export HIVE_HOME=/home/hadoop/apache-hive-0.14.0-bin
export PATH=$HIVE_HOME/bin:$PATH

3. 修改Hive的配置文件

cp hive-env.sh.template hive-env.sh

4. 修改hive-env.sh中修改如下变量

HADOOP_HOME=/home/hadoop/hadoop-2.5.2

export HIVE_CONF_DIR=/home/hadoop/apache-hive-0.14.0-bin/conf

5.

cp hive.default.template hive-site.xml

编辑hive-site.xml，将其中的变量${system:java.io.tmpdir}${system:user.name}改为如下目录，这个目录需要手工创建，需要特别注意的是system:java.io.tmpdir有多处，需要留意将所有都改掉

/home/hadoop/apache-hive-0.14.0-bin/iotmp

如果不进行这一步，在使用hive命令启动Hive时，会包类似如下错误：

Exception in thread "main" java.lang.RuntimeException: java.lang.IllegalArgumentException: java.net.URISyntaxException: Relative path in absolute URI: ${system:java.io.tmpdir%7D/$%7Bsystem:user.name%7D
	at org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:444)
	at org.apache.hadoop.hive.cli.CliDriver.run(CliDriver.java:672)
	at org.apache.hadoop.hive.cli.CliDriver.main(CliDriver.java:616)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:606)
	at org.apache.hadoop.util.RunJar.main(RunJar.java:212)

6. 在Hadoop中执行如下命令用于创建/user/hive/warehouse目录

hdfs dfs -mkdir -p /user/hadoop/warehouse

在HDFS上创建这个目录的原因是在hive-site.xml中，有如下的属性

  <property>
    <name>hive.metastore.warehouse.dir</name>
    <!--This is the dir for hadoop-->
    <value>/user/hive/warehouse</value>
    <description>location of default database for the warehouse</description>
  </property>

运行Hive

1. 使用hive命令启动Hive

>hive

 

2. 执行show tables；用于显示当前的表

hive> show tables ;
OK
Time taken: 0.863 seconds

 3. 创建简单表：

hive> create table abc(a int,b string);
OK
Time taken: 1.144 second

4. 表操作：

    4.1 新建表

hive> create table Word (id int,word string) row format delimited fields terminated by ',' stored as textfile ;             
OK
Time taken: 0.153 seconds

 新建的表Word，有两个字段id和word，分别是整数和字符串类型，行的格式是使用逗号分隔的文本文件

   4.2 在本地系统/user/home/创建一个新文件word.txt, 输入文件

1,MSN
10,QQ
100,Gtalk
1000,Skype

   4.3 将word.txt通过Hive上传到HDFS

hive> load data local inpath '/home/hadoop/word.txt' into table Word;
Loading data to table default.word
Table default.word stats: [numFiles=1, totalSize=20]
OK
Time taken: 2.154 seconds

local指明从本地文件系统(/home/hadoop/word.txt)上传数据到Word表中

  4.4 查询：select * from Word;

hive> select * from Word;
OK
1	MSN
10      QQ
100	Gtalk
1000    Skype
Time taken: 0.671 seconds, Fetched: 3 row(s)

   查询: select * from Word where id = 10;

hive> select * from Word where id = 10; 
OK
1	QQ
Time taken: 0.095 seconds, Fetched: 1 row(s)
hive> select * from Word where idNotExist = 1;
FAILED: SemanticException [Error 10004]: Line 1:25 Invalid table alias or column reference 'id1': (possible column names are: id, word)

查看HDFS

1.

hadoop@tom-Inspiron-3521:~/hadoop-2.5.2/bin$ hdfs dfs  -ls /user/hive/warehouse
Found 2 items
drwxr-xr-x   - hadoop supergroup          0 2014-12-24 23:17 /user/hive/warehouse/abc
drwxr-xr-x   - hadoop supergroup          0 2014-12-24 23:28 /user/hive/warehouse/word

abc和word是Hive创建的两个表的表名

2. 

hadoop@tom-Inspiron-3521:~/hadoop-2.5.2/bin$ hdfs dfs  -ls /user/hive/warehouse/word
Found 1 items
-rw-r--r--   1 hadoop supergroup         20 2014-12-24 23:28 /user/hive/warehouse/word/word.txt

word表下的word.txt是之前的步骤上传的文件，

3.

hadoop@tom-Inspiron-3521:~/hadoop-2.5.2/bin$ hdfs dfs  -cat /user/hive/warehouse/word/word.txt
1	MSN
10      QQ
100	Gtalk
1000    Skype

5.插入数据：

hive> insert into table my_word values(10, "WeChat");
Query ID = hadoop_20150308231111_f2c753b4-e528-4081-887e-cf310dc76695
Total jobs = 3
Launching Job 1 out of 3
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_1425868733189_0001, Tracking URL = http://hadoop.master:8088/proxy/application_1425868733189_0001/
Kill Command = /home/hadoop/software/hadoop-2.5.2/bin/hadoop job  -kill job_1425868733189_0001
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
2015-03-08 23:11:32,345 Stage-1 map = 0%,  reduce = 0%
2015-03-08 23:11:43,706 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 2.23 sec
MapReduce Total cumulative CPU time: 2 seconds 230 msec
Ended Job = job_1425868733189_0001
Stage-4 is selected by condition resolver.
Stage-3 is filtered out by condition resolver.
Stage-5 is filtered out by condition resolver.
Moving data to: hdfs://hadoop.master:9000/tmp/hive/hadoop/70d6d067-6898-4d12-9190-1431ddb4ff9a/hive_2015-03-08_23-11-15_056_4782682719483814130-1/-ext-10000
Loading data to table default.my_word
Table default.my_word stats: [numFiles=2, numRows=1, totalSize=51, rawDataSize=9]
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 1   Cumulative CPU: 2.23 sec   HDFS Read: 288 HDFS Write: 81 SUCCESS
Total MapReduce CPU Time Spent: 2 seconds 230 msec
OK
Time taken: 30.149 seconds
hive> select * from my_word;
OK
10	WeChat
1	MSN  
10	QQ  
100	Gtalk  
1000	Skype 
NULL	NULL
Time taken: 0.05 seconds, Fetched: 6 row(s)

 插入数据时，HDFS上的变化如下：

[hadoop@hadoop bin]$ ./hdfs dfs -ls /user/hive/warehouse/my_word
Found 2 items
-rw-r--r--   2 hadoop supergroup         10 2015-03-08 23:11 /user/hive/warehouse/my_word/000000_0
-rw-r--r--   2 hadoop supergroup         41 2015-03-08 23:09 /user/hive/warehouse/my_word/word.txt
[hadoop@hadoop bin]$ ./hdfs dfs -ls cat /user/hive/warehouse/my_word.txt
ls: `cat': No such file or directory
ls: `/user/hive/warehouse/my_word.txt': No such file or directory
[hadoop@hadoop bin]$ ./hdfs dfs -cat /user/hive/warehouse/my_word.txt
cat: `/user/hive/warehouse/my_word.txt': No such file or directory
[hadoop@hadoop bin]$ ./hdfs dfs -cat /user/hive/warehouse/my_word/word.txt
1,MSN  
10,QQ  
100,Gtalk  
1000,Skype 

[hadoop@hadoop bin]$ ./hdfs dfs -cat /user/hive/warehouse/my_word/000000_0
10,WeChat
[hadoop@hadoop bin]$

初始的word.txt没有变化，新增了一个000000_0文件，其中是新增的数据

 

 6. 删除数据

 Hive不支持更新操作，delete和update，只能通过其它方式，迂回的完成

hive> delete from my_word where id = 100;
FAILED: SemanticException [Error 10294]: Attempt to do update or delete using transaction manager that does not support these operations.
hive> insert overwrite table my_word  select * from my_word  where id != 100;
Query ID = hadoop_20150308232020_d8e7491c-006e-4377-8962-8a01dc651a82
Total jobs = 3
Launching Job 1 out of 3
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_1425868733189_0002, Tracking URL = http://hadoop.master:8088/proxy/application_1425868733189_0002/
Kill Command = /home/hadoop/software/hadoop-2.5.2/bin/hadoop job  -kill job_1425868733189_0002
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
2015-03-08 23:20:34,938 Stage-1 map = 0%,  reduce = 0%
2015-03-08 23:20:53,232 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 2.55 sec
MapReduce Total cumulative CPU time: 2 seconds 550 msec
Ended Job = job_1425868733189_0002
Stage-4 is selected by condition resolver.
Stage-3 is filtered out by condition resolver.
Stage-5 is filtered out by condition resolver.
Moving data to: hdfs://hadoop.master:9000/tmp/hive/hadoop/70d6d067-6898-4d12-9190-1431ddb4ff9a/hive_2015-03-08_23-20-20_635_4212544169044890358-1/-ext-10000
Loading data to table default.my_word
Table default.my_word stats: [numFiles=1, numRows=4, totalSize=38, rawDataSize=34]
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 1   Cumulative CPU: 2.55 sec   HDFS Read: 346 HDFS Write: 109 SUCCESS
Total MapReduce CPU Time Spent: 2 seconds 550 msec
OK
Time taken: 36.279 seconds
hive> select * from my_word;
OK
10	WeChat
1	MSN  
10	QQ  
1000	Skype 
Time taken: 0.086 seconds, Fetched: 4 row(s)
hive> 

此时查看HDFS的状态

[hadoop@hadoop bin]$ ./hdfs dfs -ls /user/hive/warehouse/my_word/
Found 1 items
-rw-r--r--   2 hadoop supergroup         38 2015-03-08 23:20 /user/hive/warehouse/my_word/000000_0
[hadoop@hadoop bin]$ ./hdfs dfs -cat /user/hive/warehouse/my_word/000000_0
10,WeChat
1,MSN  
10,QQ  
1000,Skype 
[hadoop@hadoop bin]$ 

 可见，最初的word.txt已经被覆盖掉，只有000000.0文件，其中不包含id为100的那个记录

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