hadoop安装前准备工作

深碍√TFBOYSˉ_ 2024-03-22 21:08 30阅读 0赞

1.  在安装好的Ubuntu系统下添加具有sudo权限的用户。

root@nodeA:~\# sudo adduser zyx

Adding user \`zyx' ...

Adding new group \`zyx' (1001) ...

Adding new user \`zyx' (1001) with group \`zyx' ...

Creating home directory \`/home/zyx' ...

Copying files from \`/etc/skel' ...

Enter new UNIX password:

Retype new UNIX password:

passwd: password updated successfully

Changing the user information for zyx

Enter the new value, or press ENTER for the default

Full Name \[\]: ^Cadduser: \`/usr/bin/chfn zyx' exited from signal 2. Exiting.

root@nodeA:~\#

root@nodeA:~\# sudo usermod -G admin -a zyx

root@nodeA:~\#

1.  建立SSH无密码登陆

（1）namenode上实现无密码登陆本机

zyx@nodeA:~$ ssh-keygen -t dsa -P '' -f ~/.ssh/id\_dsa

Generating public/private dsa key pair.

Created directory '/home/zyx/.ssh'.

Your identification has been saved in /home/zyx/.ssh/id\_dsa.

Your public key has been saved in /home/zyx/.ssh/id\_dsa.pub.

The key fingerprint is:

65:2e:e0:df:2e:61:a5:19:6a:ab:0e:38:45:a9:6a:2b zyx@nodeA

The key's randomart image is:

\+--\[ DSA 1024\]----+

| |

| . |

| o . o |

| o . ..+. |

|. . ..S=. |

|.o o.=o |

|+.. . o... |

|E... . .. |

|.. .o. .. |

\+-----------------+

zyx@nodeA:~$ cat ~/.ssh/id\_dsa.pub >> ~/.ssh/authorized\_keys

zyx@nodeA:~$

（2）实现namenode无密码登陆其他datanode

hadoop@nodeB:~$ scp hadoop@nodea:/home/hadoop/.ssh/id\_dsa.pub /home/hadoop

hadoop@nodea's password:

id\_dsa.pub 100% 602 0.6KB/s 00:00

hadoop@nodeB:~$ cat id\_dsa.pub >> .ssh/authorized\_keys

hadoop@nodeB:~$ sudo ufw disable

1.  复制JDK（jdk-6u20-linux-i586.bin）文件到linux

利用F-Secure SSH File Transfer Trial 工具，直接拖拽

1.  jdk-6u20-linux-i586.bin的安装和配置

（1）安装

zyx@nodeA:~$ ls

Examples jdk

zyx@nodeA:~$ cd jdk

zyx@nodeA:~/jdk$ ls

jdk-6u20-linux-i586.bin

zyx@nodeA:~/jdk$ chmod a+x jdk\*

zyx@nodeA:~/jdk$ ./jdk\*

接下来显示许可协议，然后选择yes， 然后按Enter键，安装结束。

zyx@nodeA:~/jdk$ ls

jdk1.6.0\_20 jdk-6u20-linux-i586.bin

（2）配置

用root@nodeA:/home/zyx\# vi .bashrc 打开bashrc， 然后在最后加入下面几行：

export JAVA\_HOME=/home/zyx/jdk/jdk1.6.0\_20

export JRE\_HOME=/home/zyx/jdk/jdk1.6.0\_20/jre

export CLASS\_PATH=$CLASSPATH:$JAVA\_HOME/lib:$JRE\_HOME/lib

export PATH=$JAVA\_HOME/bin:$JRE\_HOME/bin:$PATH:$HOMR/bin

1.  Hadoop的安装

下载地址：

[http://labs.renren.com/apache-mirror/hadoop/core/hadoop-0.20.2/hadoop-0.20.2.tar.gz][http_labs.renren.com_apache-mirror_hadoop_core_hadoop-0.20.2_hadoop-0.20.2.tar.gz]

把hadoop-0.20.2.tar.gz 放到home/zyx/hadoop下，然后解压该文件

zyx@nodeB:~/hadoop$ tar -zvxf hadoop-0.20.2.tar.gz

设置环境变量， 添加到home/zyx/.bashrc

zyx@nodeA:~$ vi .bashrc

export HADOOP\_HOME=/home/zyx/hadoop/hadoop-0.20.2

export PATH=$HADOOP\_HOME/bin:$PATH

1.  Hadoop的配置

1.  在conf/hadoop-env.sh中配置java环境

export JAVA\_HOME=/home/zyx/jdk/jdk/jdk1.6.0\_20

1.  配置conf/masters, slaves 文件， 只需要在nodename上配置。
2.  配置core-site.xml, hdfs-site.xml, mapred-site.xml

zyx@nodeC:~/hadoop-0.20.2/conf$ more core-site.xml

<?xml version="1.0"?>

<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

<name>fs.default.name</name>

\# <value>hdfs://192.168.1.103:54310</value>

</property>

</configuration>

zyx@nodeC:~/hadoop-0.20.2/conf$ more hdfs-site.xml

<?xml version="1.0"?>

<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

<name>dfs.replication</name>

</property>

</configuration>

zyx@nodeC:~/hadoop-0.20.2/conf$ more mapred-site.xml

<?xml version="1.0"?>

<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

<name>mapred.job.tracker</name>

\# <value>hdfs://192.168.1.103:54320</value>

</property>

</configuration>

1.  Hadoop的运行

（0） 格式化：

zyx@nodeC:~/hadoop-0.20.2/bin$ hadoop namenode –format

（1）用jps查看进程：

zyx@nodeC:~/hadoop-0.20.2/bin$ jps

31030 NameNode

31488 TaskTracker

31283 SecondaryNameNode

31372 JobTracker

31145 DataNode

31599 Jps

（2）查看集群状态

zyx@nodeC:~/hadoop-0.20.2/bin$ hadoop dfsadmin -report

Configured Capacity: 304716488704 (283.79 GB)

Present Capacity: 270065557519 (251.52 GB)

DFS Remaining: 270065532928 (251.52 GB)

DFS Used: 24591 (24.01 KB)

DFS Used%: 0%

Under replicated blocks: 0

Blocks with corrupt replicas: 0

Missing blocks: 0

\-------------------------------------------------

Datanodes available: 1 (1 total, 0 dead)

Name: 192.168.1.103:50010

Decommission Status : Normal

Configured Capacity: 304716488704 (283.79 GB)

DFS Used: 24591 (24.01 KB)

Non DFS Used: 34650931185 (32.27 GB)

DFS Remaining: 270065532928(251.52 GB)

DFS Used%: 0%

DFS Remaining%: 88.63%

Last contact: Fri Apr 23 15:39:10 CST 2010

（3）Stop 文件：

zyx@nodeC:~/hadoop-0.20.2/bin$ stop-all.sh

stopping jobtracker

localhost: stopping tasktracker

stopping namenode

localhost: stopping datanode

localhost: stopping secondarynamenode

1.  运行一个简单JAVA 程序

1.  先在本地磁盘建立两个文件file01和file02  
    **\[cuijj@station1 ~\]$ echo "Hello cuijj bye cuijj" > file01**  
    **\[cuijj@station1 ~\]$ echo "Hello Hadoop Goodbye Hadoop" > file02**  
    2）在hdfs中建立一个input目录  
    **\[cuijj@station1 ~\]$ hadoop dfs -mkdir input**

1.  将file01和file02拷贝到hdfs的input目录下

zyx@nodeC:~$ hadoop dfs -copyFromLocal /home/zyx/file0\* input

1.  查看hdfs中有没有input目录

zyx@nodeC:~$ hadoop dfs -ls

Found 1 items

drwxr-xr-x - zyx supergroup 0 2010-04-23 16:40 /user/zyx/input

1.  查看input目录下有没有复制成功file01和file02

zyx@nodeC:~$ hadoop dfs -ls input

Found 2 items

\-rw-r--r-- 1 zyx supergroup 0 2010-04-23 16:40 /user/zyx/input/file01

\-rw-r--r-- 1 zyx supergroup 0 2010-04-23 16:40 /user/zyx/input/file02

1.  执行wordcount（确保hdfs上没有output目录）

zyx@nodeC:~/hadoop-0.20.2$ hadoop jar hadoop-0.20.2-examples.jar wordcount input output

10/04/24 09:25:10 INFO input.FileInputFormat: Total input paths to process : 2

10/04/24 09:25:11 INFO mapred.JobClient: Running job: job\_201004240840\_0001

10/04/24 09:25:12 INFO mapred.JobClient: map 0% reduce 0%

10/04/24 09:25:22 INFO mapred.JobClient: map 100% reduce 0%

10/04/24 09:25:34 INFO mapred.JobClient: map 100% reduce 100%

10/04/24 09:25:36 INFO mapred.JobClient: Job complete: job\_201004240840\_0001

10/04/24 09:25:36 INFO mapred.JobClient: Counters: 17

10/04/24 09:25:36 INFO mapred.JobClient: Job Counters

10/04/24 09:25:36 INFO mapred.JobClient: Launched reduce tasks=1

10/04/24 09:25:36 INFO mapred.JobClient: Launched map tasks=2

10/04/24 09:25:36 INFO mapred.JobClient: Data-local map tasks=2

10/04/24 09:25:36 INFO mapred.JobClient: FileSystemCounters

10/04/24 09:25:36 INFO mapred.JobClient: FILE\_BYTES\_READ=79

10/04/24 09:25:36 INFO mapred.JobClient: HDFS\_BYTES\_READ=50

10/04/24 09:25:36 INFO mapred.JobClient: FILE\_BYTES\_WRITTEN=228

10/04/24 09:25:36 INFO mapred.JobClient: HDFS\_BYTES\_WRITTEN=41

10/04/24 09:25:36 INFO mapred.JobClient: Map-Reduce Framework

10/04/24 09:25:36 INFO mapred.JobClient: Reduce input groups=5

10/04/24 09:25:36 INFO mapred.JobClient: Combine output records=6

10/04/24 09:25:36 INFO mapred.JobClient: Map input records=2

10/04/24 09:25:36 INFO mapred.JobClient: Reduce shuffle bytes=85

10/04/24 09:25:36 INFO mapred.JobClient: Reduce output records=5

10/04/24 09:25:36 INFO mapred.JobClient: Spilled Records=12

10/04/24 09:25:36 INFO mapred.JobClient: Map output bytes=82

10/04/24 09:25:36 INFO mapred.JobClient: Combine input records=8

10/04/24 09:25:36 INFO mapred.JobClient: Map output records=8

10/04/24 09:25:36 INFO mapred.JobClient: Reduce input records=6

1.  查看运行结果

zyx@nodeC:~/hadoop-0.20.2$ hadoop fs -cat output/part-r-00000

Goodbye 1

Hadoop 2

Hello 2

bye 1

cuijj 2

1.  MapReduce的安装
2.  MapReduce程序的运行

11. 对于.java程序的hadoop编译：

root@nodeC:/home/zyx/hadoop-0.20.2\# javac -classpath /home/zyx/hadoop-0.20.2/hadoop-0.20.2-core.jar:/home/zyx/hadoop-0.20.2/lib/commons-cli-1.2.jar -d /home/zyx/wordcount\_class /home/zyx/hadoop-0.20.2/src/examples/org/apache/hadoop/examples/WordCount.java

12. 把 .class 文件生成 .jar 文件

root@nodeC:/home/zyx/wordcount\_class/org/apache/hadoop/examples\# jar -cvf /home/zyx/wordcount.jar /home/zyx/wordcount\_class/ .

added manifest

adding: home/zyx/wordcount\_class/(in = 0) (out= 0)(stored 0%)

adding: home/zyx/wordcount\_class/org/(in = 0) (out= 0)(stored 0%)

adding: home/zyx/wordcount\_class/org/apache/(in = 0) (out= 0)(stored 0%)

adding: home/zyx/wordcount\_class/org/apache/hadoop/(in = 0) (out= 0)(stored 0%)

adding: home/zyx/wordcount\_class/org/apache/hadoop/examples/(in = 0) (out= 0)(stored 0%)

adding: home/zyx/wordcount\_class/org/apache/hadoop/examples/WordCount.class(in = 1911) (out= 996)(deflated 47%)

adding: home/zyx/wordcount\_class/org/apache/hadoop/examples/WordCount$TokenizerMapper.class(in = 1790) (out= 765)(deflated 57%)

adding: home/zyx/wordcount\_class/org/apache/hadoop/examples/WordCount$IntSumReducer.class(in = 1789) (out= 746)(deflated 58%)

adding: WordCount.class(in = 1911) (out= 996)(deflated 47%)

adding: WordCount$TokenizerMapper.class(in = 1790) (out= 765)(deflated 57%)

### **例子：WordCount v1.0** ###

在深入细节之前，让我们先看一个Map/Reduce的应用示例，以便对它们的工作方式有一个初步的认识。

WordCount是一个简单的应用，它可以计算出指定数据集中每一个单词出现的次数。

这个应用适用于 [单 机模式][Link 1]， [伪 分布式模式][Link 2] 或 [完 全分布式模式][Link 3] 三种Hadoop安装方式。

#### **源代码** ####

<table> 
 <tbody> 
 <tr> 
 <td> </td> 
 <td> WordCount.java </td> 
 </tr> 
 <tr> 
 <td> 1. </td> 
 <td> package org.myorg; </td> 
 </tr> 
 <tr> 
 <td> 2. </td> 
 <td> </td> 
 </tr> 
 <tr> 
 <td> 3. </td> 
 <td> import java.io.IOException; </td> 
 </tr> 
 <tr> 
 <td> 4. </td> 
 <td> import java.util.*; </td> 
 </tr> 
 <tr> 
 <td> 5. </td> 
 <td> </td> 
 </tr> 
 <tr> 
 <td> 6. </td> 
 <td> import org.apache.hadoop.fs.Path; </td> 
 </tr> 
 <tr> 
 <td> 7. </td> 
 <td> import org.apache.hadoop.conf.*; </td> 
 </tr> 
 <tr> 
 <td> 8. </td> 
 <td> import org.apache.hadoop.io.*; </td> 
 </tr> 
 <tr> 
 <td> 9. </td> 
 <td> import org.apache.hadoop.mapred.*; </td> 
 </tr> 
 <tr> 
 <td> 10. </td> 
 <td> import org.apache.hadoop.util.*; </td> 
 </tr> 
 <tr> 
 <td> 11. </td> 
 <td> </td> 
 </tr> 
 <tr> 
 <td> 12. </td> 
 <td> public class WordCount { </td> 
 </tr> 
 <tr> 
 <td> 13. </td> 
 <td> </td> 
 </tr> 
 <tr> 
 <td> 14. </td> 
 <td> &nbsp;&nbsp; public static class Map extends MapReduceBase implements Mapper&lt;LongWritable, Text, Text, IntWritable&gt; { </td> 
 </tr> 
 <tr> 
 <td> 15. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp; private final static IntWritable one = new IntWritable(1); </td> 
 </tr> 
 <tr> 
 <td> 16. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp; private Text word = new Text(); </td> 
 </tr> 
 <tr> 
 <td> 17. </td> 
 <td> </td> 
 </tr> 
 <tr> 
 <td> 18. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp; public void map(LongWritable key, Text value, OutputCollector&lt;Text, IntWritable&gt; output, Reporter reporter) throws IOException { </td> 
 </tr> 
 <tr> 
 <td> 19. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; String line = value.toString(); </td> 
 </tr> 
 <tr> 
 <td> 20. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; StringTokenizer tokenizer = new StringTokenizer(line); </td> 
 </tr> 
 <tr> 
 <td> 21. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; while (tokenizer.hasMoreTokens()) { </td> 
 </tr> 
 <tr> 
 <td> 22. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; word.set(tokenizer.nextToken()); </td> 
 </tr> 
 <tr> 
 <td> 23. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; output.collect(word, one); </td> 
 </tr> 
 <tr> 
 <td> 24. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; } </td> 
 </tr> 
 <tr> 
 <td> 25. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp; } </td> 
 </tr> 
 <tr> 
 <td> 26. </td> 
 <td> &nbsp;&nbsp; } </td> 
 </tr> 
 <tr> 
 <td> 27. </td> 
 <td> </td> 
 </tr> 
 <tr> 
 <td> 28. </td> 
 <td> &nbsp;&nbsp; public static class Reduce extends MapReduceBase implements Reducer&lt;Text, IntWritable, Text, IntWritable&gt; { </td> 
 </tr> 
 <tr> 
 <td> 29. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp; public void reduce(Text key, Iterator&lt;IntWritable&gt; values, OutputCollector&lt;Text, IntWritable&gt; output, Reporter reporter) throws IOException { </td> 
 </tr> 
 <tr> 
 <td> 30. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; int sum = 0; </td> 
 </tr> 
 <tr> 
 <td> 31. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; while (values.hasNext()) { </td> 
 </tr> 
 <tr> 
 <td> 32. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; sum += values.next().get(); </td> 
 </tr> 
 <tr> 
 <td> 33. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; } </td> 
 </tr> 
 <tr> 
 <td> 34. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; output.collect(key, new IntWritable(sum)); </td> 
 </tr> 
 <tr> 
 <td> 35. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp; } </td> 
 </tr> 
 <tr> 
 <td> 36. </td> 
 <td> &nbsp;&nbsp; } </td> 
 </tr> 
 <tr> 
 <td> 37. </td> 
 <td> </td> 
 </tr> 
 <tr> 
 <td> 38. </td> 
 <td> &nbsp;&nbsp; public static void main(String[] args) throws Exception { </td> 
 </tr> 
 <tr> 
 <td> 39. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp; JobConf conf = new JobConf(WordCount.class); </td> 
 </tr> 
 <tr> 
 <td> 40. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp; conf.setJobName("wordcount"); </td> 
 </tr> 
 <tr> 
 <td> 41. </td> 
 <td> </td> 
 </tr> 
 <tr> 
 <td> 42. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp; conf.setOutputKeyClass(Text.class); </td> 
 </tr> 
 <tr> 
 <td> 43. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp; conf.setOutputValueClass(IntWritable.class); </td> 
 </tr> 
 <tr> 
 <td> 44. </td> 
 <td> </td> 
 </tr> 
 <tr> 
 <td> 45. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp; conf.setMapperClass(Map.class); </td> 
 </tr> 
 <tr> 
 <td> 46. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp; conf.setCombinerClass(Reduce.class); </td> 
 </tr> 
 <tr> 
 <td> 47. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp; conf.setReducerClass(Reduce.class); </td> 
 </tr> 
 <tr> 
 <td> 48. </td> 
 <td> </td> 
 </tr> 
 <tr> 
 <td> 49. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp; conf.setInputFormat(TextInputFormat.class); </td> 
 </tr> 
 <tr> 
 <td> 50. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp; conf.setOutputFormat(TextOutputFormat.class); </td> 
 </tr> 
 <tr> 
 <td> 51. </td> 
 <td> </td> 
 </tr> 
 <tr> 
 <td> 52. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp; FileInputFormat.setInputPaths(conf, new Path(args[0])); </td> 
 </tr> 
 <tr> 
 <td> 53. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp; FileOutputFormat.setOutputPath(conf, new Path(args[1])); </td> 
 </tr> 
 <tr> 
 <td> 54. </td> 
 <td> </td> 
 </tr> 
 <tr> 
 <td> 55. </td> 
 <td> &nbsp;&nbsp;&nbsp;&nbsp; JobClient.runJob(conf); </td> 
 </tr> 
 <tr> 
 <td> 57. </td> 
 <td> &nbsp;&nbsp; } </td> 
 </tr> 
 <tr> 
 <td> 58. </td> 
 <td> } </td> 
 </tr> 
 <tr> 
 <td> 59. </td> 
 <td> </td> 
 </tr> 
 </tbody> 
</table>

#### **用法** ####

假设环境变量HADOOP\_HOME对应安装时的根目录，HADOOP\_VERSION对应Hadoop的当前安装版本，编译WordCount.java来创建jar包，可如下操作：

$ mkdir wordcount\_classes  
$ javac -classpath $\{HADOOP\_HOME\}/hadoop-$\{HADOOP\_VERSION\}-core.jar -d wordcount\_classes WordCount.java  
$ jar -cvf /usr/joe/wordcount.jar -C wordcount\_classes/ .

假设：

*  /usr/joe/wordcount/input - 是HDFS中的输入路径
 *  /usr/joe/wordcount/output - 是HDFS中的输出路径

用示例文本文件做为输入：

$ bin/hadoop dfs -ls /usr/joe/wordcount/input/  
/usr/joe/wordcount/input/file01  
/usr/joe/wordcount/input/file02  
  
$ bin/hadoop dfs -cat /usr/joe/wordcount/input/file01  
Hello World Bye World  
  
$ bin/hadoop dfs -cat /usr/joe/wordcount/input/file02  
Hello Hadoop Goodbye Hadoop

运行应用程序：

$ bin/hadoop jar /usr/joe/wordcount.jar org.myorg.WordCount /usr/joe/wordcount/input /usr/joe/wordcount/output

输出是：

$ bin/hadoop dfs -cat /usr/joe/wordcount/output/part-00000  
Bye 1  
Goodbye 1  
Hadoop 2  
Hello 2  
World 2

应用程序能够使用-files选项来指定一个由逗号分隔的路径列表，这些路径是 task的当前工作目录。使用选项-libjars可以向map和reduce的 classpath中添加jar包。使用-archives选项程序可以传递档案文件做为 参数，这些档案文件会被解压并且在task的当前工作目录下会创建一个指向解压生成的目录的符号链接（以压缩包的名字命名）。 有关命令行选项的更多细节请参考 [Commands manual][]。

使用-libjars和-files运 行wordcount例子：  
hadoop jar hadoop-examples.jar wordcount -files cachefile.txt -libjars mylib.jar input output

[http_labs.renren.com_apache-mirror_hadoop_core_hadoop-0.20.2_hadoop-0.20.2.tar.gz]: http://labs.renren.com/apache-mirror/hadoop/core/hadoop-0.20.2/hadoop-0.20.2.tar.gz
[Link 1]: http://hadoop.apache.org/common/docs/r0.18.2/cn/quickstart.html#Standalone+Operation
[Link 2]: http://hadoop.apache.org/common/docs/r0.18.2/cn/quickstart.html#SingleNodeSetup
[Link 3]: http://hadoop.apache.org/common/docs/r0.18.2/cn/quickstart.html#Fully-Distributed+Operation
[Commands manual]: http://hadoop.apache.org/common/docs/r0.18.2/cn/commands_manual.html