Hadoop SingleNode 单节点部署

Posted 2023-04-11 李昊哲小课

Hadoop SingleNode 单节点部署

升级操作系统和软件

yum -y update

升级后建议重启

安装常用软件

yum -y install gcc gcc-c++ autoconf automake cmake make rsync openssh-server vim man zip unzip net-tools zlib zlib-devel openssl openssl-devel pcre-devel tcpdump lrzsz tar

修改主机名

hostnamectl set-hostname hadoop
或者
vim /etc/hostname 
hadoop

bash

修改IP地址

vim /etc/sysconfig/network-scripts/ifcfg-ens160

网卡 配置文件示例

TYPE=Ethernet
PROXY_METHOD=none
BROWSER_ONLY=no
BOOTPROTO=none
DEFROUTE=yes
IPV4_FAILURE_FATAL=no
IPV6INIT=yes
IPV6_AUTOCONF=yes
IPV6_DEFROUTE=yes
IPV6_FAILURE_FATAL=no
NAME=ens160
UUID=943779e9-249c-44bb-b272-d49ea5831ed4
DEVICE=ens160
ONBOOT=yes
IPADDR=192.168.28.10
PREFIX=24
GATEWAY=192.168.28.2
DNS1=192.168.28.2

保存后
nmcli con up ens160
重启网络服务

关闭防火墙

systemctl stop firewalld
systemctl disable firewalld

vim /etc/selinux/config

# This file controls the state of SELinux on the system.
# SELINUX= can take one of these three values:
#     enforcing - SELinux security policy is enforced.
#     permissive - SELinux prints warnings instead of enforcing.
#     disabled - No SELinux policy is loaded.
# SELINUX=enforcing
# SELINUXTYPE= can take one of three two values:
#     targeted - Targeted processes are protected,
#     minimum - Modification of targeted policy. Only selected processes are protected. 
#     mls - Multi Level Security protection.
# SELINUXTYPE=targeted 

SELINUX=disabled

执行下面命令

setenforce 0

或者

sed -i 's/SELINUX=enforcing/SELINUX=disabled/g' /etc/selinux/config
setenforce 0

创建软件安装目录并上传软件，配置环境变量

mkdir -p /opt/soft
cd /opt/soft
# 上传jdk zookeeper
tar -zxvf jdk-8u361-linux-x64.tar.gz
mv jdk1.8.0_361 jdk8
tar -zxvf hadoop-3.3.5.tar.gz
mv hadoop-3.3.5 hadoop3

vim /etc/profile
	
export JAVA_HOME=/opt/soft/jdk8
export CLASSPATH=.:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar
export PATH=$PATH:$JAVA_HOME/bin

export HADOOP_HOME=/opt/soft/hadoop3

export HADOOP_INSTALL=$HADOOP_HOME
export HADOOP_MAPRED_HOME=$HADOOP_HOME
export HADOOP_COMMON_HOME=$HADOOP_HOME
export HADOOP_HDFS_HOME=$HADOOP_HOME
export YARN_HOME=$HADOOP_HOME
export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin
export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop

export HDFS_NAMENODE_USER=root
export HDFS_DATANODE_USER=root
export HDFS_SECONDARYNAMENODE_USER=root
export YARN_RESOURCEMANAGER_USER=root
export YARN_NODEMANAGER_USER=root

编辑完成后使用source命令使文件~/.bash_profile生效执行以下命令
source /etc/profile
检查环境变量
printenv

修改域名映射

vim /etc/hosts
192.168.28.10 hadoop

修改后建议重启

修改Hadoop配置文件 在hadoop解压后的目录找到 etc/hadoop目录

cd /opt/soft/hadoop3

修改如下配置文件

• hadoop-env.sh
• core-site.xml
• hdfs-site.xml
• workers
• mapred-site.xml
• yarn-site.xml

hadoop-env.sh 文件末尾追加

export JAVA_HOME=/opt/soft/jdk8
export HDFS_NAMENODE_USER=root
export HDFS_DATANODE_USER=root
export HDFS_ZKFC_USER=root
export HDFS_JOURNALNODE_USER=root

export YARN_RESOURCEMANAGER_USER=root
export YARN_NODEMANAGER_USER=root

core-site.xml

<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
    <property>
        <name>fs.defaultFS</name>
        <value>hdfs://hadoop:9000</value>
    </property>
    <property>
    	<name>hadoop.tmp.dir</name>
    	<value>/home/hadoop_data</value>
    </property>
    <property>
    	<name>hadoop.http.staticuser.user</name>
    	<value>root</value>
    </property>
</configuration>

hdfs-site.xml

<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
    <property>
        <name>dfs.replication</name>
        <value>1</value>
    </property>
</configuration>

workers

hadoop

mapred-site.xml

<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
    <property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property>
    <property>
        <name>mapreduce.application.classpath</name>
        <value>$HADOOP_MAPRED_HOME/share/hadoop/mapreduce/*:$HADOOP_MAPRED_HOME/share/hadoop/mapreduce/lib/*</value>
    </property>
</configuration>

yarn-site.xml

<?xml version="1.0"?>
<configuration>
    <property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>
    <property>
        <name>yarn.nodemanager.env-whitelist</name>
        <value>JAVA_HOME,HADOOP_COMMON_HOME,HADOOP_HDFS_HOME,HADOOP_CONF_DIR,CLASSPATH_PREPEND_DISTCACHE,HADOOP_YARN_HOME,HADOOP_HOME,PATH,LANG,TZ,HADOOP_MAPRED_HOME</value>
    </property>
</configuration>

配置ssh免密钥登录

创建本地秘钥并将公共秘钥写入认证文件

ssh-keygen -t rsa -P '' -f ~/.ssh/id_rsa
cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
# 或者
ssh-copy-id hadoop

# 远程登录自己
ssh hadoop
# Are you sure you want to continue connecting (yes/no)? 此处输入yes
# 登录成功后exit或者logout返回
exit

Hadoop初始化

# 创建数据目录
mkdir -p /home/hadoop_data
# 格式化文件系统
hdfs namenode -format
# 启动 NameNode SecondaryNameNode DataNode 
start-dfs.sh
# 查看启动进程
jps
# 看到 DataNode SecondaryNameNode NameNode 三个进程代表启动成功

# 启动 ResourceManager daemon 和 NodeManager
start-yarn.sh
# 看到 DataNode NodeManager SecondaryNameNode NameNode ResourceManager 五个进程代表启动成功

重点提示：

# 关机之前 依关闭服务
stop-yarn.sh
stop-dfs.sh
# 开机后 依次开启服务
start-dfs.sh
start-yarn.sh

或者

# 关机之前关闭服务
stop-all.sh
# 开机后开启服务
start-all.sh

#jps 检查进程正常后开启胡哦关闭在再做其它操作

大数据系列hadoop单节点安装官方文档翻译

Hadoop: Setting up a Single Node Cluster.

HADOOP：建立单节点集群

• Purpose
• Prerequisites
  • Supported Platforms
  • Required Software
  • Installing Software
• Download
• Prepare to Start the Hadoop Cluster
• Standalone Operation
• Pseudo-Distributed Operation
  • Configuration
  • Setup passphraseless ssh
  • Execution
  • YARN on a Single Node
• Fully-Distributed Operation

目的

前置条件

    支持的平台

    需要的软件

   安装软件

下载

准备开始建立hadoop集群

单机操作

伪分布式操作

   配置

  设置ssh免密登陆

  扩展

  单节点中YARN

完全分布式

Purpose

This document describes how to set up and configure a single-node Hadoop installation so that you can quickly perform simple operations using Hadoop MapReduce and the Hadoop Distributed File System (HDFS).

目的

该文档描述了如何安装和配置一个单节点的Hadoop,以便于你可以快速的使用MapReduce和HDFS执行简单的操作。

Prerequisites

前置条件

Supported Platforms

• GNU/Linux is supported as a development and production platform. Hadoop has been demonstrated on GNU/Linux clusters with 2000 nodes.

• Windows is also a supported platform but the followings steps are for Linux only. To set up Hadoop on Windows, see wiki page.

支持的平台

     开发和生产环境支持GUN/linux环境。Hadoop在GUN/LINUX平台下证实可以创建2000个节点。

     windows平台也是支持的，但是如下的操作只是针对linux平台的，在windows上安装hadoop,请参考 wiki page.

Required Software

Required software for Linux include:

1. Java? must be installed. Recommended Java versions are described at HadoopJavaVersions.

2. ssh must be installed and sshd must be running to use the Hadoop scripts that manage remote Hadoop daemons if the optional start and stop scripts are to be used. Additionally, it is recommmended that pdsh also be installed for better ssh resource management.

需要的软件

    Java是必须的，需求的Java版本请查看HadoopJavaVersions.

   ssh是必须的，sshd必须使用hadoop脚本运行，如果使用开启或关闭脚本来管理远程机器上的hadoop进程。此外，为了更好的管理ssh资源pdsh也是需要安装的。

Installing Software

If your cluster doesn’t have the requisite software you will need to install it.

For example on Ubuntu Linux:

  $ sudo apt-get install ssh
  $ sudo apt-get install pdsh

安装软件

如果你的集群没有必要的软件，你需要去安装它。

例如在Ubuntu linux系统上：

   sudo apt-get install ssh

  sudo apt-get install pdsh

Download

To get a Hadoop distribution, download a recent stable release from one of the Apache Download Mirrors.

下载：

为了获取hadoop

Prepare to Start the Hadoop Cluster

Unpack the downloaded Hadoop distribution. In the distribution, edit the file etc/hadoop/hadoop-env.sh to define some parameters as follows:

  # set to the root of your Java installation
  export JAVA_HOME=/usr/java/latest

Try the following command:

  $ bin/hadoop

This will display the usage documentation for the hadoop script.

Now you are ready to start your Hadoop cluster in one of the three supported modes:

• Local (Standalone) Mode
• Pseudo-Distributed Mode
• Fully-Distributed Mode

Standalone Operation

By default, Hadoop is configured to run in a non-distributed mode, as a single Java process. This is useful for debugging.

The following example copies the unpacked conf directory to use as input and then finds and displays every match of the given regular expression. Output is written to the given output directory.

  $ mkdir input
  $ cp etc/hadoop/*.xml input
  $ bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.0.0-alpha4.jar grep input output ‘dfs[a-z.]+‘
  $ cat output/*

Pseudo-Distributed Operation

Hadoop can also be run on a single-node in a pseudo-distributed mode where each Hadoop daemon runs in a separate Java process.

Configuration

Use the following:

etc/hadoop/core-site.xml:

<configuration>
    <property>
        <name>fs.defaultFS</name>
        <value>hdfs://localhost:9000</value>
    </property>
</configuration>

etc/hadoop/hdfs-site.xml:

<configuration>
    <property>
        <name>dfs.replication</name>
        <value>1</value>
    </property>
</configuration>

Setup passphraseless ssh

Now check that you can ssh to the localhost without a passphrase:

  $ ssh localhost

If you cannot ssh to localhost without a passphrase, execute the following commands:

  $ ssh-keygen -t rsa -P ‘‘ -f ~/.ssh/id_rsa
  $ cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
  $ chmod 0600 ~/.ssh/authorized_keys

Execution

The following instructions are to run a MapReduce job locally. If you want to execute a job on YARN, see YARN on Single Node.

1. Format the filesystem:

     $ bin/hdfs namenode -format
   

2. Start NameNode daemon and DataNode daemon:

     $ sbin/start-dfs.sh
   

   The hadoop daemon log output is written to the $HADOOP_LOG_DIR directory (defaults to $HADOOP_HOME/logs).

3. Browse the web interface for the NameNode; by default it is available at:

   • NameNode - http://localhost:9870/

4. Make the HDFS directories required to execute MapReduce jobs:

     $ bin/hdfs dfs -mkdir /user
     $ bin/hdfs dfs -mkdir /user/<username>
   

5. Copy the input files into the distributed filesystem:

     $ bin/hdfs dfs -mkdir input
     $ bin/hdfs dfs -put etc/hadoop/*.xml input
   

6. Run some of the examples provided:

     $ bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.0.0-alpha4.jar grep input output ‘dfs[a-z.]+‘
   

7. Examine the output files: Copy the output files from the distributed filesystem to the local filesystem and examine them:

     $ bin/hdfs dfs -get output output
     $ cat output/*
   

   or

   View the output files on the distributed filesystem:

     $ bin/hdfs dfs -cat output/*
   

8. When you’re done, stop the daemons with:

     $ sbin/stop-dfs.sh
   

YARN on a Single Node

You can run a MapReduce job on YARN in a pseudo-distributed mode by setting a few parameters and running ResourceManager daemon and NodeManager daemon in addition.

The following instructions assume that 1. ~ 4. steps of the above instructions are already executed.

1. Configure parameters as follows:

   etc/hadoop/mapred-site.xml:

   <configuration>
       <property>
           <name>mapreduce.framework.name</name>
           <value>yarn</value>
       </property>
   </configuration>
   

   etc/hadoop/yarn-site.xml:

   <configuration>
       <property>
           <name>yarn.nodemanager.aux-services</name>
           <value>mapreduce_shuffle</value>
       </property>
       <property>
           <name>yarn.nodemanager.env-whitelist</name>
           <value>JAVA_HOME,HADOOP_COMMON_HOME,HADOOP_HDFS_HOME,HADOOP_CONF_DIR,CLASSPATH_PREPEND_DISTCACHE,HADOOP_YARN_HOME,HADOOP_MAPRED_HOME</value>
       </property>
   </configuration>
   

2. Start ResourceManager daemon and NodeManager daemon:

     $ sbin/start-yarn.sh
   

3. Browse the web interface for the ResourceManager; by default it is available at:

   • ResourceManager - http://localhost:8088/

4. Run a MapReduce job.

5. When you’re done, stop the daemons with:

     $ sbin/stop-yarn.sh