Ubuntu上hadoop集群安装[转]

hadoop-2.6.0集群环境搭建

一、主机规划

            1、准备4台Ubuntu 14.04 64-bit 虚拟机，一台充当resourcemanager和namenode，另外三台充当nodemanager和datanode。由于需要实现主机间ssh无密码访问，主机IP采用静态配置。配置如下：

namenode  　ip:192.168.1.110

datanode1 　ip:192.168.1.111

datanode2 　ip:192.168.1.112

datanode3 　ip:192.168.1.113

分别修改每一台的主机名和hosts文件

$sudo vim /etc/hostname

$sudo vim /etc/hosts

2、新建用户组和用户

$sudo groupadd clsuter

$sudo useradd -m -s /bin/bash -g clsuter -G sudo  hadoop

$sudo passwd hadoop

注销当前用户以hadoop用户登陆，主要是方便之后使用gedit编辑器修改配置文件。

3、安装ssh并配置无密码访问，依次执行下面的命令：

$ sudo apt-get install ssh

$ sudo apt-get install rsync

$ ssh-keygen -t dsa -P '' -f ~/.ssh/id_dsa

$ cat ~/.ssh/id_dsa.pub >> ~/.ssh/authorized_keys


4、配置namenode无密码访问datanode1

在datanode1上切换到/home/hadoop/.ssh执行

$ scp hadoop@namenode:/home/hadoop/.ssh/id_dsa.pub ./namenode_dsa.pub

$ cat namenode_dsa.pub >>authorized_keys

在namenode上执行：

$ ssh hadoop@datanode1(第一次需要输入密码，之后便可无密码访问）

同上分别配置datanode2 和datanode3

二、安装jdk和hadoop-2.6.0

1、安装jdk

到Oracle官网下载jdk-8u25-linux-x64.tar.gz将其拷贝到/usr目录，执行：$ sudo tar -zxf /usr/jdk-8u25-linux-x64.tar.gz

2、安装hadoop-2.6.0

到http://hadoop.apache.org/下载hadoop-2.6.0.tar.gz拷贝到/home/hadoop目录，执行：$ tar -zxf /home/hdoop/hadoop-2.6.0.tar.gz

3、配置环境变量

执行：$ sudo vim /etc/profile 在文件末尾添加如下内容：

export JAVA_HOME=/usr/jdk1.8.0_25
export CLASSPATH=.:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar
export PATH=$PATH:$JAVA_HOME/bin
export HADOOP_PREFIX=/home/hadoop/hadoop-2.6.0
export HADOOP_CONF_DIR=/home/hadoop/hadoop-2.6.0/etc/hadoop
export HADOOP_YARN_HOME=/home/hadoop/hadoop-2.6.0


三、配置hadoop

1、配置/home/hadoop/hadoop-2.6.0/etc/hadoop/core-site.xml

<configuration>
    <property>  
        <name>hadoop.tmp.dir</name>  
        <value>/home/hadoop/hadoop-2.6.0/tmp</value>  
        <description>Abase for other temporary directories.</description>  
    </property>  
    <property>  
        <name>fs.defaultFS</name>  
        <value>hdfs://s3:9000</value>  
    </property>  
    <property>  
        <name>io.file.buffer.size</name>  
        <value>131072</value>  
    </property>  
</configuration>

2、配置/home/hadoop/hadoop-2.6.0/etc/hadoop/hdfs-site.xml

<configuration>
 
    <property>  
        <name>dfs.namenode.name.dir</name>  
        <value>/home/hadoop/hadoop-2.6.0/dfs/name</value>  
    </property>  
    <property>  
        <name>dfs.datanode.data.dir</name>  
        <value>/home/hadoop/hadoop-2.6.0/dfs/data</value>  
    </property>  

    <property>  
        <name>dfs.replication</name>  
        <value>2</value>  
    </property>  
    <property>  
        <name>dfs.blocksize</name>  
        <value>268435456</value>  
    </property>
    <property>  
        <name>dfs.namenode.handler.count</name>  
        <value>100</value>  
    </property>  

</configuration>

3、配置/home/hadoop/hadoop-2.6.0/etc/hadoop/yarn-site.xml

<configuration>

<!-- Site specific YARN configuration properties -->
    <property>  
        <name>yarn.acl.enable</name>  
        <value>true</value>  
    </property> 
    <property>  
        <name>yarn.admin.acl</name>  
        <value>*</value>  
    </property> 
    <property>  
        <name>yarn.log-aggregation-enable</name>  
        <value>false</value>  
    </property> 

    <property>  
        <name>yarn.resourcemanager.address</name>  
        <value>s3:8032</value>  
    </property> 
    <property>  
        <name>yarn.resourcemanager.scheduler.address</name>  
        <value>s3:8030</value>  
    </property>
    <property>  
        <name>yarn.resourcemanager.resource-tracker.address</name>  
        <value>s3:8031</value>  
    </property>
    <property>  
        <name>yarn.resourcemanager.admin.address</name>  
        <value>s3:8033</value>  
    </property> 
    <property>  
        <name>yarn.resourcemanager.webapp.address</name>  
        <value>s3:8088</value>  
    </property>  
     <property>  
        <name>yarn.resourcemanager.hostname</name>  
        <value>s3</value>  
    </property> 
    <property>  
        <name>yarn.resourcemanager.scheduler.class</name>  
        <value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler</value>  
    </property>

    <property>  
        <name>yarn.scheduler.minimum-allocation-mb</name>  
        <value>1024</value>  
    </property>  

    <property>  
        <name>yarn.scheduler.maximum-allocation-mb</name>  
        <value>8192</value>  
    </property>  


    <property>  
        <name>yarn.nodemanager.resource.memory-mb</name>  
        <value>8192</value>  
    </property>  

    <property>  
        <name>yarn.nodemanager.log.retain-seconds</name>  
        <value>10800</value>  
    </property>  
    <property>  
        <name>yarn.nodemanager.aux-services</name>  
        <value>mapreduce_shuffle</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.health-checker.script.path</name>  
        <value>-1</value>  
    </property>
</configuration>

4、配置/home/hadoop/hadoop-2.6.0/etc/hadoop/mapred-site.xml

    <configuration>  
        <property>  
            <name>mapreduce.framework.name</name>  
            <value>yarn</value>  
        </property>  
        <property>  
            <name>mapreduce.map.memory.mb</name>  
            <value>1536</value>  
        </property>  
        <property>  
            <name>mapreduce.map.java.opts</name>  
            <value>-Xmx1024M</value>  
        </property>  
        <property>  
            <name>mapreduce.reduce.memory.mb</name>  
            <value>3072</value>  
        </property>  
        <property>  
            <name>mapreduce.reduce.java.opts</name>  
            <value>-Xmx2560M</value>  
        </property> 

        <property>  
            <name>mapreduce.task.io.sort.mb</name>  
            <value>512</value>  
        </property> 
        <property>  
            <name>mapreduce.task.io.sort.factor</name>  
            <value>100</value>  
        </property> 
        <property>  
            <name>mapreduce.reduce.shuffle.parallelcopies</name>  
            <value>50</value>  
        </property>  


        <property>  
            <name>mapreduce.jobhistory.address</name>  
            <value>s3:10020</value>  
        </property>  
        <property>  
            <name>mapreduce.jobhistory.webapp.address</name>  
            <value>s3:19888</value>  
        </property>  
        <property>  
            <name>mapreduce.jobhistory.intermediate-done-dir</name>  
            <value>/mr-history/tmp</value>  
        </property>  
        <property>  
            <name>mapreduce.jobhistory.done-dir</name>  
            <value>/mr-history/done</value>  
        </property>  
    </configuration>

5、配置/home/hadoop/hadoop-2.6.0/etc/hadoop/slaves 把datanode的ip或主机名一行一个写入文件中

datanode1 或者192.168.1.111

datanode2 或者192.168.1.112

datanode3 或者192.168.1.113


6、启动hadoop

在namenode主机上启动如下进程：

格式化hdfs文件系统

$ $HADOOP_PREFIX/bin/hdfs namenode -format

启动namenode

$ $HADOOP_PREFIX/sbin/hadoop-daemon.sh --config $HADOOP_CONF_DIR --script hdfs start namenode

启动resourcemanager

$ $HADOOP_YARN_HOME/sbin/yarn-daemon.sh --config $HADOOP_CONF_DIR start resourcemanager

启动MapReduce JobHistory Server

$ $HADOOP_PREFIX/sbin/mr-jobhistory-daemon.sh start historyserver --config $HADOOP_CONF_DIR

使用jps命令查看运行情况：


分别在每台datanode上启动：

启动datanode

$ $HADOOP_PREFIX/sbin/hadoop-daemon.sh --config $HADOOP_CONF_DIR --script hdfs start datanode

启动nodemanager

$ $HADOOP_YARN_HOME/sbin/yarn-daemon.sh --config $HADOOP_CONF_DIR start nodemanager


7、停止服务

要停止相应的服务只需把上面的命令中的start改为stop即可。

可通过浏览器访问如下地址查看hadoop的运行状态：

NameNode http://namenode:50070

ResourceManager http://namenode:8088

MapReduce JobHistory Server http://namenode:19888

四、运行测试

1、在hdfs文件系统上新建输入、输出目录

$ $HADOOP_PREFIX/bin/hdfs -mkdir /input

$ $HADOOP_PREFIX/bin/hdfs -mkdir /output

此次用hadoop自带的wordcount来做测试，在当前目录下新建测试文件test.txt

$ touch test.txt 并向文件中写入一定量的单词文本

$ $HADOOP_PREFIX/bin/hdfs -copyFromLocal test.txt /input

运行程序

$ $HADOOP_PREFIX/bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.0.jar wordcount /input/ /output/result

查看结果

$ $HADOOP_PREFIX/bin/hdfs -cat /output/result/*

欢迎指正！

文章来源：http://blog.csdn.net/fteworld/article/details/41944597

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原文地址：https://www.cnblogs.com/ddblog/p/3736916.html