Hadoop集群搭建

说明：

集群25号搭好，今日凌晨整理完本文方才发布。

本文目录

（一）准备

（二）开始

1、在 master 上配置免密登录

2、三台机器上配置 hosts 文件

3、在三台节点上创建运行 Hadoop 用户

4、在三台节点上安装 java 环境 jdk

5、在 master 安装 hadoop 并创建相应的工作目录

6、在 master 节点上配置 hadoop

7、修改 hadoop 安装文件的所属者及所属组

8、设置 master 主机上的 hadoop 普通用户免密登录

9、将 hadoop 安装文件复制到其他 DateNode 节点

10、master 上启动 Hadoop

---

 

（一）准备

首先，
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】，
*面版安装步骤链接：
https://blog.csdn.net/frdevolcqzyxynjds/article/details/104644252
（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）内存配置 1G（可调大）
（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

[[e-mail] ~]# systemctl status firewalld
[[e-mail] ~]# getenforce
Enforcing
[[e-mail] ~]# setenforce 0
[[e-mail] ~]#
[[e-mail] ~]# 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

[[e-mail] ~]# ssh-****** //一路回车
[[e-mail] ~]# ssh-copy-id [e-mail]
[[e-mail] ~]# ssh-copy-id [e-mail]
[[e-mail] ~]# ssh-copy-id [e-mail]
[[e-mail] ~]# ssh master
[[e-mail] ~]# exit
登出
Connection to master closed.
[[e-mail] ~]#
[[e-mail] ~]# ssh slave1
[[e-mail] ~]# exit
登出
Connection to slave1 closed.
[[e-mail] ~]#
[[e-mail] ~]# ssh slave2
[[e-mail] ~]# exit
登出
Connection to slave2 closed.
[[e-mail] ~]#

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

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 [e-mail]:/etc
scp /etc/hosts [e-mail]:/etc

[[e-mail] ~]# 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
[[e-mail] ~]# scp /etc/hosts [e-mail]:/etc
[[e-mail] ~]# scp /etc/hosts [e-mail]:/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

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

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

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

2）配置 jdk 环境变量
编辑/etc/profile， 在原文件最后加

[[e-mail] 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
[[e-mail] 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

[[e-mail] home]# source /etc/profile
[[e-mail] 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)
[[e-mail] home]#

4）将 jdk 部署到另外两台机器上

[[e-mail] home]# scp -r /usr/local/jdk1.8.0_112/ slave1:/usr/local/
[[e-mail] home]# scp -r /usr/local/jdk1.8.0_112/ slave2:/usr/local/
scp /etc/profile slave1:/etc/
[[e-mail] home]# scp /etc/profile slave1:/etc/
profile 100% 1945 1.9KB/s 00:00
[[e-mail] home]# scp /etc/profile slave2:/etc/
profile 100% 1945 1.9KB/s 00:00
[[e-mail] home]#
使建立的环境立即生效
[[e-mail] ~]# source /etc/profile
[[e-mail] ~]# 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)
[[e-mail] ~]# source /etc/profile
[[e-mail] ~]# 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
[[e-mail] home]# ls
centos hadoop hadoop-2.7.5.tar.gz jdk-8u112-linux-x64.tar.gz
[[e-mail] home]# tar -zxf hadoop-2.7.5.tar.gz -C /home/hadoop/
[[e-mail] home]# cd /home/hadoop/hadoop-2.7.5/
创建 hadoop 相关工作目录
[[e-mail] ~]# mkdir -p /home/hadoop/tmp
[[e-mail] ~]# mkdir -p /home/hadoop/dfs/{name,data}
[[e-mail] ~]# cd /home/hadoop/
[[e-mail] 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/

[[e-mail] ~]# cd /home/hadoop/hadoop-2.7.5/etc/hadoop/
[[e-mail] 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
[[e-mail] 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/

[[e-mail] 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/

[[e-mail] 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：
 

[[e-mail] hadoop]# vim slaves
[[e-mail] 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。

[[e-mail] 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>

 

[[e-mail] 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>
[[e-mail] 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。

[[e-mail] hadoop]# more hdfs-site.xml
<configuration>
</configuration>
[[e-mail] 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>

 

[[e-mail] 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
[[e-mail] hadoop]# cp mapred-site.xml.template mapred-site.xml
[[e-mail] hadoop]#
编辑：
[[e-mail] 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

[[e-mail] 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
[[e-mail] ~]# cd /home/hadoop/
[[e-mail] 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
[[e-mail] hadoop]# chown -R hadoop.hadoop /home/hadoop
[[e-mail] 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
[[e-mail] 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
[[e-mail] ~]# su - hadoop
上一次登录：三 3 月 25 02:32:57 CST 2020pts/0 上
[[e-mail] ~]$
# step 2：创建**文件
[[e-mail] ~]$ ssh-****** //一路回车
[[e-mail] ~]$
# step 3：将公钥分别 copy 至 master,slave1,slave2
[[e-mail] ~]$ ssh-copy-id [e-mail]
[[e-mail] ~]$ ssh-copy-id [e-mail]
[[e-mail] ~]$ ssh-copy-id [e-mail]
[[e-mail] ~]$

 

9、将 hadoop 安装文件复制到其他 DateNode 节点

[[e-mail] ~]$ scp -r /home/hadoop/hadoop-2.7.5/ [e-mail]:~/
[[e-mail] ~]$ scp -r /home/hadoop/hadoop-2.7.5/ [e-mail]:~/

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。

[[e-mail] ~]$ cd /home/hadoop/hadoop-2.7.5/bin/
[[e-mail] bin]$
[[e-mail] 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
************************************************************/
[[e-mail] bin]$
---------------------------------------------------------

2）查看下格式化后生成的文件；

[[e-mail] ~]$ tree /home/hadoop/dfs/
bash: tree: 未找到命令...
[[e-mail] ~]$

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

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

[[e-mail] ~]# su - hadoop
上一次登录：三 3 月 25 02:33:32 CST 2020pts/0 上
[[e-mail] ~]$ cd /home/hadoop/hadoop-2.7.5/sbin/
[[e-mail] sbin]$
[[e-mail] 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
[[e-mail] sbin]$

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

[[e-mail] 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
[[e-mail] 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。

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

6）查看hdfs

[[e-mail] 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
[[e-mail] bin]$

7）查看进程

[[e-mail] ~]$ jps
57920 NameNode
58821 Jps
58087 SecondaryNameNode
58602 JobHistoryServer
58287 ResourceManager
[[e-mail] ~]$ jps
54626 NodeManager
54486 DataNode
54888 Jps
[[e-mail] ~]$ jps
54340 DataNode
54473 NodeManager
54735 Jps

 

 

 

 

 

--

待续……