首先，

最最最重要的事情说三遍！！！

宿主机物理内存至少8G

宿主机物理内存至少8G

宿主机物理内存至少8G

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宿主机就是本地物理主机实际电脑，下方链接了解清楚（本地主机）宿主机与（虚拟机）客户机

物理机（宿主机）与虚拟机（客户机）

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开启本地主机 CPU 虚拟化（主机或笔记本或服务器）【amd 或者 intel 的 CPU 都阔以】
本地安装 vmware 虚拟机，xshell、xftp 工具
安装一台 centos7 (64bit)纯净版【1G 内存 20G 硬盘】，
无界面版安装步骤链接：
centos7 无界面安装（全截图）_闭关苦炼内功的博客-CSDN博客

（也可安装有界面的，这个随意；不过说实话没有界面的开机明显快一些）
并备份一份留作后用（避免出现错误不能恢复）
其次，
从安装好的虚拟机上克隆出完整的三台 centos7 虚拟机
（1）每台虚拟机配置：内存配置 1G（可调大），cpu配置 1 核，具体配置详情见下图

（2）NAT 网络模式，配置虚拟网络 vmnet8，宿主机 ip、子网掩码、默认网关、DNS；
并为三台虚拟机生成不同 mac 物理地址
（3）三台虚拟机开机，查看并配置网络
①配置网关，永久修改机器 hostname，
主机名命名以字母，或者字母+数字命名，可以出现（-）减号，一定不要出现（_）下划线；
配置 ip 地址、DNS 地址；
②设置主机名，配置内网 ip 映射主机名
192.168.40.130 master namenode
192.168.40.131 slave1 datanode
192.168.40.132 slave2 datanode
③重启网络服务
systemctl restart network #重启网络服务
systemctl status network #查看网络状态
④关闭防火墙
systemctl stop firewalld #停止防火墙服务
systemctl disable firewalld #禁止开机启动防火墙
systemctl status firewalld #查看防火墙状态
⑤重启设备
reboot

（二）开始

本次实验三台机器 centos7.2,同时关闭防火墙和 selinux
以 slave2 节点为例


[root@slave2 ~]# systemctl status firewalld
[root@slave2 ~]# getenforce
Enforcing
[root@slave2 ~]# setenforce 0
[root@slave2 ~]#
[root@slave2 ~]# getenforce
Permissive

------------------------------------------------------------

1、在 master 上配置免密登录

配置在 master 上，可以 ssh 无密码登录 master,slave1,slave2


[root@master ~]# ssh-keygen //一路回车
[root@master ~]# ssh-copy-id root@192.168.40.130
[root@master ~]# ssh-copy-id root@192.168.40.131
[root@master ~]# ssh-copy-id root@192.168.40.132
[root@master ~]# ssh master
[root@master ~]# exit
登出
Connection to master closed.
[root@master ~]#
[root@master ~]# ssh slave1
[root@slave1 ~]# exit
登出
Connection to slave1 closed.
[root@master ~]#
[root@master ~]# ssh slave2
[root@slave2 ~]# exit
登出
Connection to slave2 closed.
[root@master ~]#

------------------------------------------------------------

2、三台机器上配置 hosts 文件

首先在 master 节点上配置
然后复制到其他两台机器上
scp /etc/hosts root@192.168.40.131:/etc
scp /etc/hosts root@192.168.40.132:/etc


[root@master ~]# cat /etc/hosts
#127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4
#::1 localhost localhost.localdomain localhost6 localhost6.localdomain6
127.0.0.1 localhost
192.168.40.130 master
192.168.40.131 slave1
192.168.40.132 slave2
[root@master ~]# scp /etc/hosts root@slave1:/etc
[root@master ~]# scp /etc/hosts root@slave2:/etc

------------------------------------------------------------

3、在三台节点上创建运行 Hadoop 用户

useradd -u 8000 hadoop
echo hadoop | passwd --stdin hadoop
三台节点都需要创建 hadoop 用户，保持 UID 一致


[root@master ~]# useradd -u 8000 hadoop
[root@master ~]# echo hadoop | passwd --stdin hadoop
[root@master ~]# ssh slave1
Last login: Wed Mar 25 01:05:48 2020 from master
[root@slave1 ~]# useradd -u 8000 hadoop
[root@slave1 ~]# echo hadoop | passwd --stdin hadoop
[root@slave1 ~]# exit
登出
Connection to slave1 closed.
[root@master ~]# ssh slave2
Last login: Wed Mar 25 01:04:29 2020 from master
[root@slave2 ~]# useradd -u 8000 hadoop
[root@slave2 ~]# echo hadoop | passwd --stdin hadoop
[root@slave2 ~]# exit
登出
Connection to slave2 closed.
[root@master ~]#

4、在三台节点上安装 java 环境 jdk

把 jdk 上传到/home 目录下
1）将 jdk 解压至/usr/local 目录下


[root@master home]# ls
centos hadoop jdk-8u112-linux-x64.tar.gz
[root@master home]# tar -zxvf jdk-8u112-linux-x64.tar.gz -C /usr/local/
[root@master home]# ls /usr/local/
bin etc games include jdk1.8.0_112 lib lib64 libexec sbin share src
[root@master home]#

2）配置 jdk 环境变量
编辑/etc/profile，在原文件最后加


[root@master home]# vim /etc/profile
export JAVA_HOME=/usr/local/jdk1.8.0_112
export JAVA_BIN=/usr/local/jdk1.8.0_112/bin
export PATH=${JAVA_HOME}/bin:$PATH
export CLASSPATH=.:${JAVA_HOME}/lib/dt.jar:${JAVA_HOME}/lib/tools.jar
[root@master home]# cat /etc/profile

3）执行配置文件生效：
source /etc/profile #使配置文件生效
java -version #验证 java 运行环境是否安装成功


[root@master home]# source /etc/profile
[root@master home]# java -version
java version "1.8.0_112"
Java(TM) SE Runtime Environment (build 1.8.0_112-b15)
Java HotSpot(TM) 64-Bit Server VM (build 25.112-b15, mixed mode)
[root@master home]#

4）将 jdk 部署到另外两台机器上


[root@master home]# scp -r /usr/local/jdk1.8.0_112/ slave1:/usr/local/
[root@master home]# scp -r /usr/local/jdk1.8.0_112/ slave2:/usr/local/
scp /etc/profile slave1:/etc/
[root@master home]# scp /etc/profile slave1:/etc/
profile 100% 1945 1.9KB/s 00:00
[root@master home]# scp /etc/profile slave2:/etc/
profile 100% 1945 1.9KB/s 00:00
[root@master home]#
使建立的环境立即生效
[root@slave1 ~]# source /etc/profile
[root@slave1 ~]# java -version
java version "1.8.0_112"
Java(TM) SE Runtime Environment (build 1.8.0_112-b15)
Java HotSpot(TM) 64-Bit Server VM (build 25.112-b15, mixed mode)
[root@slave2 ~]# source /etc/profile
[root@slave2 ~]# java -version
java version "1.8.0_112"
Java(TM) SE Runtime Environment (build 1.8.0_112-b15)
Java HotSpot(TM) 64-Bit Server VM (build 25.112-b15, mixed mode)

5、在 master 安装 hadoop 并创建相应的工作目录


先上传 hadoop 压缩包到/home 目录下
然后解压
[root@master home]# ls
centos hadoop hadoop-2.7.5.tar.gz jdk-8u112-linux-x64.tar.gz
[root@master home]# tar -zxf hadoop-2.7.5.tar.gz -C /home/hadoop/
[root@master home]# cd /home/hadoop/hadoop-2.7.5/
创建 hadoop 相关工作目录
[root@master ~]# mkdir -p /home/hadoop/tmp
[root@master ~]# mkdir -p /home/hadoop/dfs/{name,data}
[root@master ~]# cd /home/hadoop/
[root@master hadoop]# ls
dfs hadoop-2.7.5 tmp

6、在 master 节点上配置 hadoop

配置文件位置：
/home/hadoop/hadoop-2.7.5/etc/hadoop/


[root@master ~]# cd /home/hadoop/hadoop-2.7.5/etc/hadoop/
[root@master hadoop]# ls
capacity-scheduler.xml hadoop-policy.xml kms-log4j.properties ssl-client.xml.example
configuration.xsl hdfs-site.xml kms-site.xml ssl-server.xml.example
container-executor.cfg httpfs-env.sh log4j.properties yarn-env.cmd
core-site.xml httpfs-log4j.properties mapred-env.cmd yarn-env.sh
hadoop-env.cmd httpfs-signature.secret mapred-env.sh yarn-site.xml
hadoop-env.sh httpfs-site.xml mapred-queues.xml.template
hadoop-metrics2.properties kms-acls.xml mapred-site.xml.template
hadoop-metrics.properties kms-env.sh slaves
[root@master hadoop]#

一共需要修改 7 个配置文件：

1）hadoop-env.sh，指定 hadoop 的 java 运行环境

/usr/local/jdk1.8.0_112/


[root@master hadoop]# vim hadoop-env.sh
24 # The java implementation to use.
25 export JAVA_HOME=/usr/local/jdk1.8.0_112/

2）yarn-env.sh，指定 yarn 框架的 java 运行环境
/usr/local/jdk1.8.0_112/


[root@master hadoop]# vim yarn-env.sh
25 #echo "run java in $JAVA_HOME"
26 JAVA_HOME=/usr/local/jdk1.8.0_112/

3）slaves，指定 datanode 数据存储服务器
将所有 DataNode 的名字写入此文件中，每个主机名一行，配置如下：


[root@master hadoop]# vim slaves
[root@master hadoop]# cat slaves
#localhost
slave1
slave2

4）core-site.xml，指定访问 hadoop web 界面访问路径
hadoop 的核心配置文件，这里需要配置两个属性，
fs.default.FS 配置了 hadoop 的 HDFS 系统的命名，位置为主机的 9000 端口；
hadoop.tmp.dir 配置了 hadoop 的 tmp 目录的根位置。


[root@master hadoop]# vim core-site.xml
19 <configuration>
20
21 </configuration>
19 <configuration>
20
21 <property>
22 <name>fs.defaultFS</name>
23 <value>hdfs://master:9000</value>
24 </property>
25
26 <property>
27 <name>io.file.buffer.size</name>
28 <value>131072</value>
29 </property>
30
31 <property>
32 <name>hadoop.tmp.dir</name>
33 <value>file:/home/hadoop/tmp</value>
34 <description>Abase for other temporary directories.</description>
35 </property>
36
37 </configuration>


[root@master hadoop]# cat core-site.xml
<configuration>
<property>
<name>fs.defaultFS</name>
<value>hdfs://master:9000</value>
</property>
<property>
<name>io.file.buffer.size</name>
<value>131072</value>
</property>
<property>
<name>hadoop.tmp.dir</name>
<value>file:/home/hadoop/tmp</value>
<description>Abase for other temporary directories.</description>
</property>
</configuration>
[root@master hadoop]#

5）hdfs-site.xml
hdfs 的配置文件，
dfs.http.address 配置了 hdfs 的 http 的访问位置；
dfs.replication 配置了文件块的副本数，一般不大于从机的个数。


[root@master hadoop]# more hdfs-site.xml
<configuration>
</configuration>
[root@master hadoop]# vim hdfs-site.xml
19 <configuration>
20
21 </configuration>
19 <configuration>
20
21 <property>
22 <name>dfs.namenode.secondary.http-address</name>
23 <value>master:9001</value>
24 </property>
25
26 <property>
27 <name>dfs.namenode.name.dir</name>
28 <value>file:/home/hadoop/dfs/name</value>
29 </property>
30 <property>
31 <name>dfs.datanode.data.dir</name>
32 <value>file:/home/hadoop/dfs/data</value>
33 </property>
34
35 <property>
36 <name>dfs.replication</name>
37 <value>2</value>
38 </property>
39
40 <property>
41 <name>dfs.webhdfs.enabled</name>
42 <value>true</value>
43 </property>
44
45 </configuration>


[root@master hadoop]# cat hdfs-site.xml
<property>
<name>dfs.namenode.secondary.http-address</name>
<value>master:9001</value>
</property>
<property>
<name>dfs.namenode.name.dir</name>
<value>file:/home/hadoop/dfs/name</value>
</property>
<property>
<name>dfs.datanode.data.dir</name>
<value>file:/home/hadoop/dfs/data</value>
</property>
<property>
<name>dfs.replication</name>
<value>2</value>
</property>
<property>
<name>dfs.webhdfs.enabled</name>
<value>true</value>
</property>

6）mapred-site.xml
mapreduce 任务的配置，由于 hadoop2.x 使用了 yarn 框架，
所以要实现分布式部署，
必须在 mapreduce.framework.name 属性下配置为 yarn。
mapred.map.tasks 和 mapred.reduce.tasks
分别为 map 和 reduce 的任务数。


# 生成 mapred-site.xml
[root@master hadoop]# cp mapred-site.xml.template mapred-site.xml
[root@master hadoop]#
编辑：
[root@master hadoop]# vim mapred-site.xml
18
19 <configuration>
20
21 </configuration>
19 <configuration>
20
21 <property>
22 <name>mapreduce.framework.name</name>
23 <value>yarn</value>
24 </property>
25
26 <property>
27 <name>mapreduce.jobhistory.address</name>
28 <value>master:10020</value>
29 </property>
30
31 <property>
32 <name>mapreduce.jobhistory.webapp.address</name>
33 <value>master:19888</value>
34 </property>
35
36 </configuration>


查看：
<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>

7）yarn-site.xml
该文件为 yarn 框架的配置,主要是一些任务的启动位置


[root@master hadoop]# vim yarn-site.xml
15 <configuration>
16
17 <!-- Site specific YARN configuration properties -->
18
19 </configuration>
15 <configuration>
16
17 <!-- Site specific YARN configuration properties -->
18
19 <property>
20 <name>yarn.nodemanager.aux-services</name>
21 <value>mapreduce_shuffle</value>
22 </property>
23
24 <property>
25 <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name>
26 <value>org.apache.hadoop.mapred.ShuffleHandler</value>
27 </property>
28
29 <property>
30 <name>yarn.resourcemanager.address</name>
31 <value>master:8032</value>
32 </property>
33
34 <property>
35 <name>yarn.resourcemanager.scheduler.address</name>
36 <value>master:8030</value>
37 </property>
38
39 <property>
40 <name>yarn.resourcemanager.resource-tracker.address</name>
41 <value>master:8031</value>
42 </property>
43
44 <property>
45 <name>yarn.resourcemanager.admin.address</name>
46 <value>master:8033</value>
47 </property>
48
49 <property>
50 <name>yarn.resourcemanager.webapp.address</name>
51 <value>master:8088</value>
52 </property>
53
54 </configuration>
----------------------------------------------------


查看：
<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.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>

7、修改 hadoop 安装文件的所属者及所属组


chown -R hadoop.hadoop /home/hadoop
[root@master ~]# cd /home/hadoop/
[root@master hadoop]# ll
总用量 4
drwxr-xr-x. 4 root root 28 3 月 25 01:48 dfs
drwxr-xr-x. 9 20415 101 4096 12 月 16 2017 hadoop-2.7.5
drwxr-xr-x. 2 root root 6 3 月 25 01:48 tmp
[root@master hadoop]# chown -R hadoop.hadoop /home/hadoop
[root@master hadoop]# ll
总用量 4
drwxr-xr-x. 4 hadoop hadoop 28 3 月 25 01:48 dfs
drwxr-xr-x. 9 hadoop hadoop 4096 12 月 16 2017 hadoop-2.7.5
drwxr-xr-x. 2 hadoop hadoop 6 3 月 25 01:48 tmp
[root@master hadoop]#

8、设置 master 主机上的 hadoop 普通用户免密登录

生成基于 hadoop 用户的不输入密码登录：
因为后期使用 hadoop 用户启动 datenode 节点需要直接登录到对应的服务器上启动 datenode 相关服务


# step 1：切换 hadoop 用户
[root@master ~]# su - hadoop
上一次登录：三 3 月 25 02:32:57 CST 2020pts/0 上
[hadoop@master ~]$
# step 2：创建密钥文件
[hadoop@master ~]$ ssh-keygen //一路回车
[hadoop@master ~]$
# step 3：将公钥分别 copy 至 master,slave1,slave2
[hadoop@master ~]$ ssh-copy-id hadoop@master
[hadoop@master ~]$ ssh-copy-id hadoop@slave1
[hadoop@master ~]$ ssh-copy-id hadoop@slave2
[hadoop@master ~]$

9、将 hadoop 安装文件复制到其他 DateNode 节点


[hadoop@master ~]$ scp -r /home/hadoop/hadoop-2.7.5/ hadoop@slave1:~/
[hadoop@master ~]$ scp -r /home/hadoop/hadoop-2.7.5/ hadoop@slave2:~/

10、master 上启动 Hadoop

1）格式化 namenode
首先切换到 hadoop 用户，执行 hadoop namenode 的初始化，只需要第一次的时候初始化，之后就不需要了。


[hadoop@master ~]$ cd /home/hadoop/hadoop-2.7.5/bin/
[hadoop@master bin]$
[hadoop@master bin]$ ./hdfs namenode -format
format 成功
---------------------------------------------------------
20/03/25 02:48:24 INFO namenode.FSImage: Allocated new BlockPoolId: BP-1015689718-192.168.40.130-
1585075704098
20/03/25 02:48:24 INFO common.Storage: Storage directory /home/hadoop/dfs/name has been successfully
formatted.
20/03/25  02:48:24  INFO  namenode.FSImageFormatProtobuf:  Saving  image  file
/home/hadoop/dfs/name/current/fsimage.ckpt_0000000000000000000 using no compression
20/03/25  02:48:24  INFO  namenode.FSImageFormatProtobuf:  Image  file
/home/hadoop/dfs/name/current/fsimage.ckpt_0000000000000000000 of size 323 bytes saved in 0 seconds.
20/03/25 02:48:24 INFO namenode.NNStorageRetentionManager: Going to retain 1 images with txid >= 0
20/03/25 02:48:24 INFO util.ExitUtil: Exiting with status 0
20/03/25 02:48:24 INFO namenode.NameNode: SHUTDOWN_MSG:
/************************************************************
SHUTDOWN_MSG: Shutting down NameNode at master/192.168.40.130
************************************************************/
[hadoop@master bin]$
---------------------------------------------------------

2）查看下格式化后生成的文件；


[hadoop@master ~]$ tree /home/hadoop/dfs/
bash: tree: 未找到命令...
[hadoop@master ~]$

-----------------------------------------------------------
没有 tree 命令，等一下再专门搞 tree 命令


yum provides tree
yum -y install tree

然后


[hadoop@master ~]$ tree /home/hadoop/dfs/
/home/hadoop/dfs/
├── data
└── name
    └── current
        ├── edits_0000000000000000001-0000000000000000002
        ├── edits_0000000000000000003-0000000000000000011
        ├── edits_0000000000000000012-0000000000000000013
        ├── edits_0000000000000000014-0000000000000000015
        ├── edits_0000000000000000016-0000000000000000017
        ├── edits_0000000000000000018-0000000000000000019
        ├── edits_0000000000000000020-0000000000000000021
        ├── edits_0000000000000000022-0000000000000000023
        ├── edits_0000000000000000024-0000000000000000025
        ├── edits_0000000000000000026-0000000000000000027
        ├── edits_0000000000000000028-0000000000000000029
        ├── edits_0000000000000000030-0000000000000000031
        ├── edits_0000000000000000032-0000000000000000032
        ├── edits_0000000000000000033-0000000000000000034
        ├── edits_0000000000000000035-0000000000000000036
        ├── edits_0000000000000000037-0000000000000000037
        ├── edits_0000000000000000038-0000000000000000039
        ├── edits_inprogress_0000000000000000040
        ├── fsimage_0000000000000000037
        ├── fsimage_0000000000000000037.md5
        ├── fsimage_0000000000000000039
        ├── fsimage_0000000000000000039.md5
        ├── seen_txid
        └── VERSION
 
3 directories, 24 files
[hadoop@master ~]$

-----------------------------------------------------------

3）启动 hdfs：./sbin/start-dfs.sh，即启动 HDFS 分布式存储


[root@master ~]# su - hadoop
上一次登录：三 3 月 25 02:33:32 CST 2020pts/0 上
[hadoop@master ~]$ cd /home/hadoop/hadoop-2.7.5/sbin/
[hadoop@master sbin]$
[hadoop@master sbin]$ ./start-dfs.sh
Starting namenodes on [master]
master: starting namenode, logging to /home/hadoop/hadoop-2.7.5/logs/hadoop-hadoop-namenode-
master.out
slave1: starting datanode, logging to /home/hadoop/hadoop-2.7.5/logs/hadoop-hadoop-datanode-
slave1.out
slave2: starting datanode, logging to /home/hadoop/hadoop-2.7.5/logs/hadoop-hadoop-datanode-
slave2.out
Starting secondary namenodes [master]
master: starting secondarynamenode, logging to /home/hadoop/hadoop-2.7.5/logs/hadoop-hadoop-
secondarynamenode-master.out
[hadoop@master sbin]$

4）启动 yarn：./sbin/start-yarn.sh 即，启动分布式计算


[hadoop@master sbin]$ ./start-yarn.sh
starting yarn daemons
starting resourcemanager, logging to /home/hadoop/hadoop-2.7.5/logs/yarn-hadoop-resourcemanager-
master.out
slave2: starting nodemanager, logging to /home/hadoop/hadoop-2.7.5/logs/yarn-hadoop-nodemanager-
slave2.out
slave1: starting nodemanager, logging to /home/hadoop/hadoop-2.7.5/logs/yarn-hadoop-nodemanager-
slave1.out
[hadoop@master sbin]$

注意：
其实也可以使用 start-all.sh 脚本依次启动 HDFS 分布式存储及分布式计算。
/home/hadoop/hadoop-2.7.5/sbin/start-all.sh #启动脚本
/home/hadoop/hadoop-2.7.5/sbin/stop-all.sh # 关闭脚本

5）启动历史服务

Hadoop 自带历史服务器，可通过历史服务器查看已经运行完的 Mapreduce 作业记录，
比如用了多少个 Map、用了多少个 Reduce、作业提交时间、作业启动时间、作业完成时间等信息。
默认情况下，Hadoop 历史服务器是没有启动的，我们可以通过下面的命令来启动 Hadoop 历史服务器。


[hadoop@master sbin]$ ./mr-jobhistory-daemon.sh start historyserver
starting  historyserver,  logging  to  /home/hadoop/hadoop-2.7.5/logs/mapred-hadoop-historyserver-
master.out
[hadoop@master sbin]$ cd /home/hadoop/hadoop-2.7.5/bin/

6）查看hdfs


[hadoop@master bin]$ ./hdfs dfsadmin -report
Configured Capacity: 37492883456 (34.92 GB)
Present Capacity: 20249128960 (18.86 GB)
DFS Remaining: 20249120768 (18.86 GB)
DFS Used: 8192 (8 KB)
DFS Used%: 0.00%
Under replicated blocks: 0
Blocks with corrupt replicas: 0
Missing blocks: 0
Missing blocks (with replication factor 1): 0
-------------------------------------------------
Live datanodes (2):
Name: 192.168.40.132:50010 (slave2)
Hostname: slave2
Decommission Status : Normal
Configured Capacity: 18746441728 (17.46 GB)
DFS Used: 4096 (4 KB)
Non DFS Used: 7873466368 (7.33 GB)
DFS Remaining: 10872971264 (10.13 GB)
DFS Used%: 0.00%
DFS Remaining%: 58.00%
Configured Cache Capacity: 0 (0 B)
Cache Used: 0 (0 B)
Cache Remaining: 0 (0 B)
Cache Used%: 100.00%
Cache Remaining%: 0.00%
Xceivers: 1
Last contact: Wed Mar 25 05:45:40 CST 2020
Name: 192.168.40.131:50010 (slave1)
Hostname: slave1
Decommission Status : Normal
Configured Capacity: 18746441728 (17.46 GB)
DFS Used: 4096 (4 KB)
Non DFS Used: 9370288128 (8.73 GB)
DFS Remaining: 9376149504 (8.73 GB)
DFS Used%: 0.00%
DFS Remaining%: 50.02%
Configured Cache Capacity: 0 (0 B)
Cache Used: 0 (0 B)
Cache Remaining: 0 (0 B)
Cache Used%: 100.00%
Cache Remaining%: 0.00%
Xceivers: 1
Last contact: Wed Mar 25 05:45:42 CST 2020
[hadoop@master bin]$

7）查看进程


[hadoop@master ~]$ jps
57920 NameNode
58821 Jps
58087 SecondaryNameNode
58602 JobHistoryServer
58287 ResourceManager
[hadoop@slave1 ~]$ jps
54626 NodeManager
54486 DataNode
54888 Jps
[hadoop@slave2 ~]$ jps
54340 DataNode
54473 NodeManager
54735 Jps

查看Hadoop面板，浏览器访问

http://192.168.40.130:50070/

查看集群节点，浏览器访问

http://192.168.40.130:8088/

查看历史服务器，浏览器访问

http://192.168.40.130:19888/

若想访问域名，则本地win7/10配置 hosts，

先修改hosts权限


192.168.40.130 master
192.168.40.131 slave1
192.168.40.132 slave2

然后再访问：

http://master:50070/

http://master:8088/

http://master:19888/

待续……

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