记录在64位CentOS 6.5环境下搭建Hadoop 2.5.2集群的步骤，同时遇到问题的解决办法，这些记录都仅供参考！

1、操作系统环境配置

1.1、操作系统环境

主机名	IP地址	角色	Hadoop用户
hadoop-master	192.168.30.50	Hadoop主节点	hadoop
hadoop-slave01	192.168.30.51	Hadoop从节点	hadoop
hadoop-slave02	192.168.30.52	Hadoop从节点	hadoop

1.2、关闭防火墙和SELinux

1.2.1、关闭防火墙

service iptables stop 
chkconfig iptables off

1.2.2、关闭SELinux

setenforce 0
sed -i 's/enforcing/disabled/' /etc/sysconfig/selinux

1.3、hosts配置

vim /etc/hosts

########## Hadoop host ##########
192.168.30.50   hadoop-master
192.168.30.51   hadoop-slave01
192.168.30.52   hadoop-slave02

注：以上操作需要在root用户，通过ping 主机名可以返回对应的IP即可

1.4、配置无密码访问

在3台主机上使用hadoop用户配置无密码访问，所有主机的操作相同，以hadoop-master为例

生成私钥和公钥
ssh-keygen -t rsa

拷贝公钥到主机（需要输入密码）

ssh-copy-id hadoop@hadoop-master
ssh-copy-id hadoop@hadoop-slave01
ssh-copy-id hadoop@hadoop-slave02

注：以上操作需要在hadoop用户，通过hadoop用户ssh到其他主机不需要密码即可。

2、Java环境配置

2.1、下载JDK

mkdir -p /home/hadoop/app/java
cd /home/hadoop/app/java
wget -c http://download.oracle.com/otn/java/jdk/6u45-b06/jdk-6u45-linux-x64.bin

2.2、安装java

cd /home/hadoop/app/java
chmod +x jdk-6u45-linux-x64.bin
./jdk-6u45-linux-x64.bin

2.3、配置Java环境变量

vim .bash_profile

export JAVA_HOME=/home/hadoop/app/java/jdk1.6.0_45
export JRE_HOME=$JAVA_HOME/jre
export CLASSPATH=.:$CLASSPATH:$JAVA_HOME/lib:$JRE_HOME/lib
export PATH=$PATH:$JAVA_HOME/bin:$JRE_HOME/bin

启用环境变量
source .bash_profile

注：使用hadoop用户在所有机器安装jdk，通过java –version命令返回Java的版本信息即可

3、Hadoop安装配置

hadoop的安装配置使用hadoop用户操作；

3.1、安装Hadoop

• 下载hadoop 1.2.1

  mkdir -p /home/hadoop/app/hadoop
  cd /home/hadoop/app/hadoop
  wget -c https://archive.apache.org/dist/hadoop/common/hadoop-1.2.1/hadoop-1.2.1-bin.tar.gz
  tar -zxf hadoop-1.2.1-bin.tar.gz

• 创建hadoop临时文件目录

  mkdir -p /home/hadoop/app/hadoop/hadoop-1.2.1/tmp

3.2、配置Hadoop

Hadoop配置文件都是XML文件，使用hadoop用户操作即可；

3.2.1、配置core-site.xml

vim /home/hadoop/app/hadoop/hadoop-1.2.1/conf/core-site.xml

<configuration>
    <property>
        <name>hadoop.tmp.dir</name>
        <value>/home/hadoop/app/hadoop/hadoop-1.2.1/tmp</value>
    </property>
    <property>
        <name>fs.default.name</name>
        <value>hdfs://192.168.30.50:9000</value>
    </property>
</configuration>

3.2.2、配置hdfs-site.xml

vim /home/hadoop/app/hadoop/hadoop-1.2.1/conf/hdfs-site.xml

<configuration>
    <property>
        <name>dfs.replication</name>
        <value>2</value>
    </property>
</configuration>

3.2.3、配置mapred-site.xml

vim /home/hadoop/app/hadoop/hadoop-1.2.1/conf/mapred-site.xml

<configuration>
    <property>
        <name>mapred.job.tracker</name>
        <value>http://192.168.30.50:9001</value>
    </property>
</configuration>

3.2.4、配置master

vim /home/hadoop/app/hadoop/hadoop-1.2.1/conf/masters

hadoop-master

3.2.5、配置slaves

vim /home/hadoop/app/hadoop/hadoop-1.2.1/conf/slaves

hadoop-slave01
hadoop-slave02

3.2.6、配置hadoop-env.xml

vim /home/hadoop/app/hadoop/hadoop-1.2.1/conf/hadoop-env.sh

将JAVA_HOME修改为如下：

export JAVA_HOME=/home/hadoop/app/java/jdk1.6.0_45

3.3、拷贝Hadoop程序到slave

scp -r app hadoop-slave01:/home/hadoop/
scp -r app hadoop-slave02:/home/hadoop/

3.4、配置Hadoop环境变量

在所有机器hadoop用户家目录下编辑 .bash_profile 文件，在最后追加：
vim /home/hadoop/.bash_profile

### Hadoop PATH
export HADOOP_HOME_WARN_SUPPRESS=1
export HADOOP_HOME=/home/hadoop/app/hadoop/hadoop-1.2.1
export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin

让环境变量生效:
source /home/hadoop/.bash_profile

3.5、启动Hadoop

在hadoop主节点上初始化HDFS文件系统，然后启动hadoop集群

3.5.1、初始化HDFS文件系统

hadoop namenode –format

3.5.2、启动和关闭Hadoop集群

• 启动：
  start-all.sh

• 关闭：
  stop-all.sh

浏览NameNode和JobTracker的网络接口，它们的地址默认为：

• NameNode - http://hadoop-master:50070/
• JobTracker - http://hadoop-master:50030/

3.5.3、hadoop各节点的启动进程

• master

  $ jps
  22262 NameNode
  22422 SecondaryNameNode
  24005 Jps
  22506 JobTracker

• slave

  $ jps
  2700 TaskTracker
  2611 DataNode
  4160 Jps

3.5.4、hadoop启动后验证

• 简单的文件操作

hadoop fs -ls hdfs:/

Found 2 items
drwxr-xr-x   - hadoop supergroup          0 2018-01-09 16:15 /home
drwxr-xr-x   - hadoop supergroup          0 2018-01-10 10:39 /user

• 简单的MapReduce计算

  hadoop jar /home/hadoop/app/hadoop/hadoop-1.2.1/hadoop-examples-1.2.1.jar pi 10 10

  得到的计算结果是：

  Number of Maps  = 10
  Samples per Map = 10
  Wrote input for Map #0
  Wrote input for Map #1
  Wrote input for Map #2
  Wrote input for Map #3
  Wrote input for Map #4
  Wrote input for Map #5
  Wrote input for Map #6
  Wrote input for Map #7
  Wrote input for Map #8
  Wrote input for Map #9
  Starting Job
  18/01/10 13:49:35 INFO mapred.FileInputFormat: Total input paths to process : 10
  18/01/10 13:49:36 INFO mapred.JobClient: Running job: job_201801101031_0002
  18/01/10 13:49:37 INFO mapred.JobClient:  map 0% reduce 0%
  18/01/10 13:49:49 INFO mapred.JobClient:  map 10% reduce 0%
  18/01/10 13:49:50 INFO mapred.JobClient:  map 30% reduce 0%
  18/01/10 13:49:51 INFO mapred.JobClient:  map 40% reduce 0%
  18/01/10 13:49:59 INFO mapred.JobClient:  map 50% reduce 0%
  18/01/10 13:50:00 INFO mapred.JobClient:  map 60% reduce 0%
  18/01/10 13:50:02 INFO mapred.JobClient:  map 80% reduce 0%
  18/01/10 13:50:07 INFO mapred.JobClient:  map 100% reduce 0%
  18/01/10 13:50:12 INFO mapred.JobClient:  map 100% reduce 33%
  18/01/10 13:50:14 INFO mapred.JobClient:  map 100% reduce 100%
  18/01/10 13:50:16 INFO mapred.JobClient: Job complete: job_201801101031_0002
  18/01/10 13:50:16 INFO mapred.JobClient: Counters: 30
  18/01/10 13:50:16 INFO mapred.JobClient:   Job Counters 
  18/01/10 13:50:16 INFO mapred.JobClient:     Launched reduce tasks=1
  18/01/10 13:50:16 INFO mapred.JobClient:     SLOTS_MILLIS_MAPS=95070
  18/01/10 13:50:16 INFO mapred.JobClient:     Total time spent by all reduces waiting after reserving slots (ms)=0
  18/01/10 13:50:16 INFO mapred.JobClient:     Total time spent by all maps waiting after reserving slots (ms)=0
  18/01/10 13:50:16 INFO mapred.JobClient:     Launched map tasks=10
  18/01/10 13:50:16 INFO mapred.JobClient:     Data-local map tasks=10
  18/01/10 13:50:16 INFO mapred.JobClient:     SLOTS_MILLIS_REDUCES=25054
  18/01/10 13:50:16 INFO mapred.JobClient:   File Input Format Counters 
  18/01/10 13:50:16 INFO mapred.JobClient:     Bytes Read=1180
  18/01/10 13:50:16 INFO mapred.JobClient:   File Output Format Counters 
  18/01/10 13:50:16 INFO mapred.JobClient:     Bytes Written=97
  18/01/10 13:50:16 INFO mapred.JobClient:   FileSystemCounters
  18/01/10 13:50:16 INFO mapred.JobClient:     FILE_BYTES_READ=226
  18/01/10 13:50:16 INFO mapred.JobClient:     HDFS_BYTES_READ=2450
  18/01/10 13:50:16 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=682653
  18/01/10 13:50:16 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=215
  18/01/10 13:50:16 INFO mapred.JobClient:   Map-Reduce Framework
  18/01/10 13:50:16 INFO mapred.JobClient:     Map output materialized bytes=280
  18/01/10 13:50:16 INFO mapred.JobClient:     Map input records=10
  18/01/10 13:50:16 INFO mapred.JobClient:     Reduce shuffle bytes=280
  18/01/10 13:50:16 INFO mapred.JobClient:     Spilled Records=40
  18/01/10 13:50:16 INFO mapred.JobClient:     Map output bytes=180
  18/01/10 13:50:16 INFO mapred.JobClient:     Total committed heap usage (bytes)=1146068992
  18/01/10 13:50:16 INFO mapred.JobClient:     CPU time spent (ms)=7050
  18/01/10 13:50:16 INFO mapred.JobClient:     Map input bytes=240
  18/01/10 13:50:16 INFO mapred.JobClient:     SPLIT_RAW_BYTES=1270
  18/01/10 13:50:16 INFO mapred.JobClient:     Combine input records=0
  18/01/10 13:50:16 INFO mapred.JobClient:     Reduce input records=20
  18/01/10 13:50:16 INFO mapred.JobClient:     Reduce input groups=20
  18/01/10 13:50:16 INFO mapred.JobClient:     Combine output records=0
  18/01/10 13:50:16 INFO mapred.JobClient:     Physical memory (bytes) snapshot=1843138560
  18/01/10 13:50:16 INFO mapred.JobClient:     Reduce output records=0
  18/01/10 13:50:16 INFO mapred.JobClient:     Virtual memory (bytes) snapshot=7827865600
  18/01/10 13:50:16 INFO mapred.JobClient:     Map output records=20
  Job Finished in 41.091 seconds
  Estimated value of Pi is 3.20000000000000000000

hadoop搭建完成，如果有错误，可以查看日志信息。

4、参考资料

• [hadoop文档]<http://hadoop.apache.org/docs/r1.0.4/cn/cluster_setup.html>;