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CentOS6.4上搭建hadoop-2.4.0集群

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公司Commerce Cloud平台上提供申请主机的服务。昨天试了下，申请了3台机器，搭了个hadoop环境。以下是机器的一些配置：

emi-centos-6.4-x86_64
medium | 6GB 内存| 2 虚拟内核 | 30.0GB 盘

3个机器的主机和ip规划如下：

IP地址           主机名    用途

192.168.0.101  hd1 namenode
192.168.0.102  hd2 datanode
192.168.0.103  hd3 datanode

一、系统设置

（所有步骤都需要在所有节点执行）

1. 修改主机名及ip地址解析

1) 修改主机名 

[root@hd1 toughhou]# hostname hd1

[root@hd1 toughhou]# cat /etc/sysconfig/network

NETWORKING=yes

HOSTNAME=hd1

2) 增加ip和主机映射 

[root@hd1 toughhou]# vi /etc/hosts

127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4

:: localhost localhost.localdomain localhost6 localhost6.localdomain6

192.168.0.101 hd1

192.168.0.102 hd2

192.168.0.103 hd3

3) 验证是否成功 

[toughhou@hd1 ~]$ ping hd2

PING hd2 (192.168.0.102) () bytes of data.

 bytes from hd2 (192.168.0.102): icmp_seq= ttl= time=2.55 ms

[toughhou@hd1 ~]$ ping hd3

PING hd3 (192.168.0.103) () bytes of data.

 bytes from hd3 (192.168.0.103): icmp_seq= ttl= time=2.48 ms

能ping通说明已经OK。

2. 关闭防火墙
[root@hd1 toughhou]# chkconfig iptables off

3. SSH免密码登陆

1) 生成密钥与公钥
登陆到hd1，把生成的id_rsa.pub（公钥）内容cat到authorized_keys文件中。同时登陆到hd2, hd3，生成id_rsa.pub，并把hd2, hd3各自的id_rsa.pub的内容copy到hd1中的authorzied_keys中。最后从hd1中scp到hd2, hd3的.ssh目录中。 

[toughhou@hd1 ~]$ ssh-keygen -t rsa

[toughhou@hd1 ~]$ cat id_rsa.pub >> authorized_keys

[toughhou@hd2 ~]$ ssh-keygen -t rsa

[toughhou@hd2 ~]$ cat id_rsa.pub >> authorized_keys

[toughhou@hd3 ~]$ ssh-keygen -t rsa

[toughhou@hd3 ~]$ cat id_rsa.pub >> authorized_keys

2) scp authorized_keys到hd2, hd3 

[toughhou@hd1 ~]$ scp authorized_keys 192.168.0.102:/home/toughhou/.ssh/

[toughhou@hd1 ~]$ scp authorized_keys 192.168.0.103:/home/toughhou/.ssh/

3) 验证ssh登陆是否是免密码

（第一次需要密码，若配置正确的话之后就不用密码了。） 

[toughhou@hd1 ~]$ ssh 192.168.0.102

[toughhou@hd2 ~]$

[toughhou@hd1 ~]$ ssh 192.168.0.103

[toughhou@hd3 ~]$

关于SSH免密码登陆，也可以参考文章 “SSH时不需输入密码”，它更具体地说了关于SSH设置。

二、安装jdk、hadoop及设置环境变量

1. 下载jdk、hadoop安装包
download.oracle.com/otn-pub/java/jdk/7u65-b17/jdk-7u65-linux-x64.tar.gz
http://mirrors.cnnic.cn/apache/hadoop/common/hadoop-2.4.0/hadoop-2.4.0.tar.gz

2. 解压 

[toughhou@hd1 software]$ tar zxvf jdk-7u65-linux-x64.gz

[toughhou@hd1 software]$ tar zxvf hadoop-2.4..tar.gz

[root@hd1 software]# mv hadoop-2.4. /opt/hadoop-2.4.

[root@hd1 software]# mv jdk1..0_65    /opt/jdk1.7.0

3. 设置Java环境变量

以root用户登陆编辑/etc/profile，加入以下内容： 

[root@hd1 software]# vi /etc/profile

#java

export JAVA_HOME=/opt/jdk1.7.0

export JRE_HOME=$JAVA_HOME/jre

export PATH=$PATH:$JAVA_HOME/bin

export CLASSPATH=./:$JAVA_HOME/lib:$JAVA_HOME/jre/lib

#hadoop

export HADOOP_HOME=/opt/hadoop-2.4.

export HADOOP_COMMON_HOME=$HADOOP_HOME

export HADOOP_HDFS_HOME=$HADOOP_HOME

export HADOOP_MAPRED_HOME=$HADOOP_HOME

export HADOOP_YARN_HOME=$HADOOP_HOME

export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop

export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$HADOOP_HOME/lib

export HADOOP_COMMON_LIB_NATIVE_DIR=$HADOOP_HOME/lib/native

export HADOOP_OPTS="-Djava.library.path=$HADOOP_HOME/lib"

export JAVA_LIBRARY_PATH=$HADOOP_HOME/lib/native

4. 验证环境变量 

[toughhou@hd1 hadoop]$ java -version

[toughhou@hd1 hadoop]$ hadoop

Usage: hadoop [--config confdir] COMMAND

三、hadoop集群设置

1. 修改hadoop配置文件

[toughhou@hd1 hadoop]$ cd /opt/hadoop-2.4.0/etc/hadoop

1) hadoop-env.sh、yarn-env.sh 设置JAVA_HOME环境变量

最开始以为已经在/etc/profile设置了JAVA_HOME，所以在hadoop-env.sh和yarn-env.sh中已经能成功获取到JAVA_HOME，所以就不用再设置了。最终发现这在hadoop-2.4.0中行不通，start-all.sh的时候出错了（hd1: Error: JAVA_HOME is not set and could not be found.）。

找到里面的JAVA_HOME,修改为实际路径

2) slaves
这个文件配置所有datanode节点，以便namenode搜索 

[toughhou@hd1 hadoop]$ vi slaves

hd2

hd3

3) core-site.xml 

<configuration>

    <property>

        <name>fs.defaultFS</name>

        <value>hdfs://hd1:9000</value>

    </property>

    <property>

        <name>io.file.buffer.size</name>

        <value>131072</value>

    </property>

    <property>

        <name>hadoop.tmp.dir</name>

        <value>/hadoop/temp</value>

        <description>A base for other temporary directories.</description>

    </property>

    <property>

        <name>hadoop.proxyuser.root.hosts</name>

        <value>hd1</value>

    </property>

    <property>

        <name>hadoop.proxyuser.root.groups</name>

        <value>*</value>

    </property>

</configuration>

4) hdfs-site.xml 

<configuration>

    <property>

        <name>dfs.namenode.name.dir</name>

        <value>/hadoop/name</value>

    <final>true</final>

    </property>

    <property>

        <name>dfs.datanode.data.dir</name>

        <value>/hadoop/data</value>

    <final>true</final>

    </property>

    <property>

        <name>dfs.replication</name>

    <value>2</value>

    </property>

    <property>

        <name>dfs.permissions</name>

        <value>false</value>

    </property>

</configuration>

5) mapred-site.xml 

<configuration>

    <property>

        <name>fs.defaultFS</name>

        <value>hdfs://hd1:9000</value>

    </property>

    <property>

        <name>io.file.buffer.size</name>

        <value>131072</value>

    </property>

    <property>

        <name>hadoop.tmp.dir</name>

        <value>/hadoop/temp</value>

        <description>A base for other temporary directories.</description>

    </property>

    <property>

        <name>hadoop.proxyuser.root.hosts</name>

        <value>hd1</value>

    </property>

    <property>

        <name>hadoop.proxyuser.root.groups</name>

        <value>*</value>

    </property>

</configuration>

6) yarn-site.xml 

<configuration>

<property>

<name>yarn.resourcemanager.address</name>

<value>hd1:18040</value>

</property>

<property>

<name>yarn.resourcemanager.scheduler.address</name>

<value>hd1:18030</value>

</property>

<property>

<name>yarn.resourcemanager.resource-tracker.address</name>

<value>hd1:18025</value>

</property>

<property>

<name>yarn.resourcemanager.admin.address</name>

<value>hd1:18041</value>

</property>

<property>

<name>yarn.resourcemanager.webapp.address</name>

<value>hd1:8088</value>

</property>

<property>

<name>yarn.nodemanager.local-dirs</name>

<value>/hadoop/mynode/my</value>

</property>

<property>

<name>yarn.nodemanager.log-dirs</name>

<value>/hadoop/mynode/logs</value>

</property>

<property>

<name>yarn.nodemanager.log.retain-seconds</name>

<value>10800</value>

</property>

<property>

<name>yarn.nodemanager.remote-app-log-dir</name>

<value>/logs</value>

</property>

<property>

<name>yarn.nodemanager.remote-app-log-dir-suffix</name>

<value>logs</value>

</property>

<property>

<name>yarn.log-aggregation.retain-seconds</name>

<value>-1</value>

</property>

<property>

<name>yarn.log-aggregation.retain-check-interval-seconds</name>

<value>-1</value>

</property>

<property>

<name>yarn.nodemanager.aux-services</name>

<value>mapreduce_shuffle</value>

</property>

</configuration>

2. 把以下文件复制到其它节点 

[root@hd1 toughhou]# scp -R /opt/hadoop-2.4./ hd2:/opt/

[root@hd1 toughhou]# scp -R /opt/hadoop-2.4./ hd3:/opt/

[root@hd1 toughhou]# scp -R /opt/jdk1.7.0/ hd2:/opt/

[root@hd1 toughhou]# scp -R /opt/jdk1.7.0/ hd3:/opt/

[root@hd1 toughhou]# scp /etc/profile hd2:/etc/profile

[root@hd1 toughhou]# scp /etc/profile hd3:/etc/profile

[root@hd1 toughhou]# scp /etc/hosts hd2:/etc/hosts

[root@hd1 toughhou]# scp /etc/hosts hd3:/etc/hosts

配置完成之后需要重启电脑

3. namenode初始化

只需要第一次的时候初始化，之后就不需要了 

[toughhou@hd1 bin]$ hdfs namenode -format

如果“Exiting with status 0”，就说明OK。
14/07/23 03:26:33 INFO util.ExitUtil: Exiting with status 0

4. 启动集群 

[toughhou@hd1 sbin]$ cd /opt/hadoop-2.4./sbin

[toughhou@hd1 sbin]$ ./start-all.sh 

This script is Deprecated. Instead use start-dfs.sh and start-yarn.sh

Starting namenodes on [hd1]

hd1: namenode running as process . Stop it first.

hd2: starting datanode, logging to /opt/hadoop-2.4./logs/hadoop-toughhou-datanode-hd2.out

hd3: starting datanode, logging to /opt/hadoop-2.4./logs/hadoop-toughhou-datanode-hd3.out

Starting secondary namenodes [0.0.0.0]

0.0.0.0: secondarynamenode running as process . Stop it first.

starting yarn daemons

resourcemanager running as process . Stop it first.

hd3: starting nodemanager, logging to /opt/hadoop-2.4./logs/yarn-toughhou-nodemanager-hd3.out

hd2: starting nodemanager, logging to /opt/hadoop-2.4./logs/yarn-toughhou-nodemanager-hd2.out

5. 查看各节点的状态 

[toughhou@hd1 sbin]$ jps

 NameNode

 SecondaryNameNode

 Jps

 ResourceManage

[toughhou@hd2 ~]$ jps

 NodeManager

 Jps

 DataNode

[toughhou@hd3 ~]$ jps

 NodeManager

 Jps

 DataNode

以上说明都OK。

6. windows添加快捷访问

为了方便访问，我们也可以编辑 %systemroot%\system32\drivers\etc\hosts 文件，加入以下的 ip和主机映射 

192.168.0.101 hd1

192.168.0.102 hd2

192.168.0.103 hd3

这样，我们在自己机器上也可以通过 http://hd2:8042/node 方式访问节点，而没必要用 http://192.168.0.102:8042/node。

7. wordcount 测试

为了更进一步验证hadoop环境，我们可以运行hadoop自带的例子。

wordcount是hadoop最经典的mapreduce例子。我们进入到相应目录运行自带的jar包，来测试hadoop环境是否OK。

具体步骤：

1) hdfs上创建目录 

[toughhou@hd1 ~]$ hadoop fs -mkdir /in/wordcount

[toughhou@hd1 ~]$ hadoop fs -mkdir /out/

2) 上传文件到hdfs 

[toughhou@hd1 ~]$ cat in1.txt

Hello World , Hello China, Hello Shanghai

I love China

How are you

[toughhou@hd1 ~]$ hadoop fs -put in1.txt /in/wordcount

3) 运行wordcount 

[toughhou@hd1 ~]$ cd /opt/hadoop-2.4./share/hadoop/mapreduce/

[toughhou@hd2 mapreduce]$ hadoop jar hadoop-mapreduce-examples-2.4..jar wordcount /in/wordcount /out/out1

// :: INFO client.RMProxy: Connecting to ResourceManager at hd1/192.168.0.101:

// :: INFO input.FileInputFormat: Total input paths to process :

// :: INFO mapreduce.JobSubmitter: number of splits:

// :: INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1406105556378_0003

// :: INFO impl.YarnClientImpl: Submitted application application_1406105556378_0003

// :: INFO mapreduce.Job: The url to track the job: http://hd1:8088/proxy/application_1406105556378_0003/

// :: INFO mapreduce.Job: Running job: job_1406105556378_0003

// :: INFO mapreduce.Job: Job job_1406105556378_0003 running in uber mode : false

// :: INFO mapreduce.Job: map % reduce %

// :: INFO mapreduce.Job: map % reduce %

// :: INFO mapreduce.Job: map % reduce %

4) 查看运行结果 

[toughhou@hd2 mapreduce]$ hadoop fs -cat /out/out4/part-r-

,

China

China,

Hello

How

I

Shanghai

World

are

love

you

到此，全部结束。整个hadoop-2.4.0集群搭建过程全部结束。

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