前言：

Hadoop3.2 集群是比较新稳定版本的搭建详细讲解过程，从下面第一张官方的图来看：

自己搭建的hadoop集群，顺便记录一下，害怕忘记。

主机

本次操作为root，主要是方便操作，操作系统为：Centos7

1、准备两台主机192.168.0.233、192.168.0.234（资源有限，就用一台主机了）

2、对应的hosts为m1.example.com、m2.example.com

具体命令操作如下

vim /etc/hosts

192.168.0.233 m1.example.com
192.168.0.233 m2.example.com

3、主机免密钥登录

#主机操作
ssh-keygen -t rsa -C m1.example.com

#免登密码操作
cat ~/.ssh/id_rsa.pub >> root@m1.example.com
cat ~/.ssh/id_rsa.pub >> root@m2.example.com

Java

1、下载JDK1.8

#创建目录
mkdir /soft
#上传下载好的安装包
jdk-8u201-linux-x64.tar. gz
#解压
tar -zxvf jdk-8u201-linux-x64.tar.gzmv jdk1.8.0_161 jdk1.8

2、配置Java环境

#配置Java环境
echo -e 'export JAVA_HOME=/soft/jdk1.8 export export CLASSPATH=.:$JAVA_HOME/jre/lib/rt.jar:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar \nexport PATH=$PATH:$JAVA_HONE/bin’ >> /etc/profile

3、环境变量生效 source /etc/profile

Hadoop

1、两台主机创建好hadoop的文件夹（目前我使用的一台主机，创建一份就可以了）

mkdir -p /hadoop/hdfs/data
mkdir -p /hadoop/hdfs/name
mkdir -p /hadoop/hdfs/tmp

2、环境配置 hadoop-3.2.0.tar.gz

#下载
wget http://地扯/hadoop-3.2.0.tar. gz
#解压
tar -zxvf hadoop-3.2.0.tar. gz
#更名
mv hadoop-3.2.0 hadoop
#配置Hadoop环境
echo -e 'export HADOOP_HOME=/soft/hadoop \nexport PATH=$PATH:$HADOOP_HOME/bin : $HADOOP_HOME/sbin' >> /etc/profile

3、参数配置
运行用户操作，添加如下几句到 /soft/hadoop/etc/hadoop/hadoop-env.sh

export JAVA_HOME=/soft/jdk1.8
export YARN__RESOURCEMANAGER_USER=root
export YARN_NODEMANAGER_USER=root
export HDFS_NAMENODE_USER=root
export HDFS_DATANODE_USER=root
export HDFS_SECONDARYNAMENODE_USER=root

4、环境变量生效 source /etc/profile

5、hadoop 配置文件

core-site.xml 配置

<property>
<name>hadoop.tmp.dir</name>
<value>file:/hadoop/hdfs/tmp</value></property>
<property>
<name>io.file.buffer.size</name><value>131072</value>
</property>
<property>
<name>fs.default.name</name>
<value>hdfs://m1.example.com:9000</value></property>
<property>
<name>hadoop.proxyuser.root.hosts</name><value>*</value>
</property>
<property>
<name>hadoop.proxyuser.root.groups</name><value>*</value>
</property>

hdfs-site.xml 配置

<property>
<name>dfs.replication</name><value>2</value>
</property>
<property>
<name>dfs.namenode.name.dir</name><value>file:/hadoop/hdfs/name</value><final>true</final>
</property>
<property>
<name>dfs.datanode.data.dir</name><value>file:/hadoop/hdfs/data</value><final>true</final>
</property>
<property>
<name>dfs.namenode.secondary.http-address</name><value>m2.example.com:9001</value>
</property>
<property>
<name>dfs.webhdfs.enabled</name><value>true</value>
</property>
<property>
<name>dfs.permissions</name><value>false</value>
</property>

yarn-site.xml 配置

<property>
<name>yarn.resourcemanager.hostname</name><value>m1.example.com</value>
</property>
<property>
<name>yarn.resourcemanager.address</name><value>m1.example.com:8050</value>
</property>
<property>
<name>yarn.resourcemanager.scheduler.address</name><value>m1.example.com:8030</value>
</property>
<property>
<name>yarn.resourcemanager.webapp.address</name><value>m1.example.com:8088</value>
</property>
<property>
<name>yarn.resourcemanager.resource-tracker.address</name><value>ml.example.com:8025</value>
</property>
<property>
<name>yarn.resourcemanager.admin.address</name><value>m1.example.com:8041</value>
</property>
<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>

mapred-site.xml 配置

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

6、格式化name文件夹 hadoop namenode -format
7、启动hadoop集群

cd /home/hadoop-3.2.0/

验证是否启动成功

8、验证：访问namenode主节点端口为9870

http://192.168.0.233:9870/dfshealth.html#tab-overview

9、访问yarn管理界面：访问 http://192.168.0.233:8088/cluster

10、写一个简单的python程序测试

# -*- coding: utf-8 -*-
import pandas as pd
import hdfs
_author_ = 'joe'
_date_ = '2021/07/22'


hdfs_user = 'root'
hdfs_addr = 'http://192.168.0.233:9870'
client = hdfs.InsecureClient(hdfs_addr, user=hdfs_user)
df = pd.read_csv("D:\\user_visit_action.csv")
print(df)
hdfs_path = '/test/a.csv'
client.write(hdfs_path, df.to_csv(index=False), overwrite=True, encoding='utf-8')
read = client.read(hdfs_path, encoding='utf-8')
with read as reader:
    for row in reader:
        print(row)

准备工作

运行python程序：

查看写入成功的数据

参考网站：
https://www.jianshu.com/p/3182aaff918d
https://blog.csdn.net/qq_17623363/article/details/102008227
https://blog.csdn.net/weixin_43042683/article/details/105397046
https://blog.csdn.net/daqiang012/article/details/104109578
https://www.jianshu.com/p/181b06293067
https://blog.csdn.net/qq_39314099/article/details/103681298
https://blog.csdn.net/qq_39314099/article/details/103681298?utm_medium=distribute.pc_relevant.none-task-blog-2%7Edefault%7EOPENSEARCH%7Edefault-1.control&depth_1-utm_source=distribute.pc_relevant.none-task-blog-2%7Edefault%7EOPENSEARCH%7Edefault-1.control

原文链接:https://blog.csdn.net/Joe192/article/details/118995804