搭建Hadoop伪分布式集群

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版本与环境

• 虚拟机：VMware Workstation Pro 15
• Linux镜像：Ubuntu-18.04.2-live-server-amd64.iso
• Java版本：jdk-8u231-linux-x64.tar.gz
• Hadoop版本：version-3.1.3

准备

• （PS:以下配置需在克隆slave之前完成）
• 安装Ubuntu（PS:记得安装OpenSSH）
• 解压hadoop和jdk：tar -zxvf xxx.tar.gz
• 移动hadoop根目录：mv hadoop-3.1.3 /usr/local/hadoop3
• 移动jdk根目录：mv jdk-1.8.0_231 /usr/local/jdk1.8

添加环境变量

• 执行以下命令将环境变量写入.bashrc

# cd ~
# vim .bashrc

• java variables

export JAVA_HOME=/usr/local/jdk1.8/ 
export JRE_HOME=$JAVA_HOME/jre
export CLASSPATH=.:$JAVA_HOME/lib:$JRE_HOME/lib
export PATH=$JAVA_HOME/bin:$PATH

• hadoop variables

export HADOOP_HOME=/usr/local/hadoop3
export PATH=$PATH:$HADOOP_HOME/bin
export PATH=$PATH:$HADOOP_HOME/sbin
export HADOOP_MAPRED_HOME=$HADOOP_HOME
export HADOOP_COMMON_HOME=$HADOOP_HOME
export HADOOP_HDFS_HOME=$HADOOP_HOME
export YARN_HOME=$HADOOP_HOME
export HADOOP_COMMON_LIB_NATIVE_DIR=$HADOOP_HOME/lib/native
export HADOOP_OPTS="-Djava.library.path=$HADOOP_HOME/lib"

• 保存环境变量

# source .bashrc

配置Hadoop

• 进入目录：cd /usr/local/hadoop3/etc/hadoop
• 配置文件hadoop-env.sh

    <property>
        <name>hadoop.tmp.dir</name>
        <value>/usr/local/hadoop3/tmp</value>
        <description>文件临时存储目录</description>
    </property>
    <property>
        <name>fs.defaultFS</name>
        <!-- 1.x name>fs.default.name</name -->
        <value>hdfs://master:9000</value>
        <description>hdfs namenode访问地址</description>
    </property>
    <property>
         <name>io.file.buffer.size</name>
         <value>102400</value>
         <description>文件块大小</description>
    </property>

• 配置文件hdfs-site.xml

    <property>
        <name>dfs.namenode.secondary.http-address</name>
        <value>slave1:50080</value>
    </property>
    <property>
        <name>dfs.replication</name>
        <value>1</value>
        <description>文件块的副本数</description>
    </property> </property>
    <property>
        <name>dfs.name.dir</name>
        <value>/usr/local/hadoop3/hdfs/name</value>
        <description>namenode目录</description>
    </property>
    <property>
        <name>dfs.data.dir</name>
        <value>/usr/local/hadoop3/hdfs/data</value>
        <description>datanode目录</description>
    </property>

• 配置文件mapred-site.xml

    <property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property>
    <property>
        <name>mapreduce.jobhistory.address</name>
        <value>master:10020</value>
    </property>
    <property>
        <name>mapreduce.jobhistory.webapp.address</name>
        <value>master:19888</value>
    </property>
    <property>
        <name>yarn.app.mapreduce.am.env</name>
        <value>HADOOP_MAPRED_HOME=/usr/local/hadoop3</value>
    </property>
    <property>
        <name>mapreduce.map.env</name>
        <value>HADOOP_MAPRED_HOME=/usr/local/hadoop3</value>
    </property>
    <property>
        <name>mapreduce.reduce.env</name>
        <value>HADOOP_MAPRED_HOME=/usr/local/hadoop3</value>
    </property>

• 配置文件yarn-site.xml

    <property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>
    <property>
        <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name>
        <value>org.apache.hadoop.mapred.ShuffleHandler</value>
    </property>
    <property>
        <name>yarn.resourcemanager.hostname</name>
        <value>master</value>
    </property>
    <property>
        <name>yarn.resourcemanager.address</name>
        <value>master:8032</value>
    </property>
    <property>
        <name>yarn.resourcemanager.scheduler.address</name>
        <value>master:8030</value>
    </property>
    <property>
        <name>yarn.resourcemanager.resource-tracker.address</name>
        <value>master:8031</value>
    </property>
    <property>
        <name>yarn.resourcemanager.admin.address</name>
        <value>master:8033</value>
    </property>
    <property>
        <name>yarn.resourcemanager.webapp.address</name>
        <value>master:8088</value>
    </property>
    <property>  
        <name>yarn.nodemanager.vmem-check-enabled</name>  
        <value>false</value>  
    </property>

克隆节点

• 完成以上配置后，便可以此为模板克隆多个节点
• 博主以两个slave节点为例

配置主机名与IP

• 分别修改主机名为：master、slave1、slave2

# hostnamectl set-hostname xxx

• 若有/etc/cloud/cloud.cfg文件，则修改preserve_hostname为true
• 分别修改静态IP：192.168.127.134、192.168.127.135、192.168.127.136

# vim /etc/netplan/50-cloud-init.yaml

• 使IP配置生效：# netplan apply
• 修改每个节点的静态DNS解析，例如：

# vim /etc/hosts

192.168.127.134 master
192.168.127.135 slave1
192.168.127.136 slave2

设置节点间免密登录

• master、slave1、slave2中输入：ssh-keygen -t rsa -P ""
• 在master中将slave1、slave2的配置合成keys

# cd ~/.ssh
# scp -P 22 slave1:~/.ssh/id_rsa.pub id_rsa.pub1
# scp -P 22 slave2:~/.ssh/id_rsa.pub id_rsa.pub2
# cat id_rsa.pub >> authorized_keys
# cat id_rsa.pub1 >> authorized_keys
# cat id_rsa.pub2 >> authorized_keys

• 将配置传给slave1、slave2

# scp -P 22 authorized_keys slave1:~/.ssh/
# scp -P 22 authorized_keys slave2:~/.ssh/

配置脚本文件

• 配置master节点即可
• 进入存放指令的目录：cd /usr/local/hadoop3/sbin
• 修改start-dfs.sh、stop-dfs.sh：

HDFS_DATANODE_USER=root
HADOOP_SECURE_DN_USER=hdfs
HDFS_NAMENODE_USER=root
HDFS_SECONDARYNAMENODE_USER=root

• 修改start-yarn.sh、stop-yarn.sh：

YARN_RESOURCEMANAGER_USER=root
HADOOP_SECURE_DN_USER=yarn
YARN_NODEMANAGER_USER=root

启动并验证

• 启动集群：# /usr/local/hadoop3/sbin/start-all.sh
• 显示当前所有java进程：jps
• 登录master:8088、master:9870查看hadoop自带的web服务

运行测试用例

• 进入目录：/usr/local/hadoop3
• 在HDFS中创建文件夹：# hdfs dfs -mkdir -p /data/input
• 将任一txt文件放入：# hdfs dfs -put README.txt /data/input
• 执行mapreduce测试用例：# hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar wordcount /data/input /data/output/result
• 查看结果：# hdfs dfs -cat /data/output/result/part-r-00000

搭建Hadoop伪分布式集群

标签：buffer   rop   net   src   serve   ima   运行   ica   安装   

原文地址：https://www.cnblogs.com/chien-wong/p/11746282.html