三节点大数据环境安装教程2

1.主机名和IP配置我们按照【三节点大数据环境安装教程1】已经完成虚拟机的克隆，但是我们克隆出来的三台虚拟机的配置是一样的需要做简单的修改.1.1 启动三台虚拟机1.2 配置三台虚拟机主机名首先使用root用户名和root密码分别登录三台虚拟机分别在三台虚拟机上执行命令:hostnamectl set-hostname nodeXXX(虚拟机名)第一台机器上设置主机名node1第二台机器上设置主机

大锤哥哥Z

2698人浏览 · 2020-10-13 22:47:43

大锤哥哥Z · 2020-10-13 22:47:43 发布

1.主机名和IP配置

我们按照【三节点大数据环境安装教程1】已经完成虚拟机的克隆，但是我们克隆出来的三台虚拟机的配置是一样的需要做简单的修改.

1.1 启动三台虚拟机

1.2 配置三台虚拟机主机名

首先使用root用户名和root密码分别登录三台虚拟机
分别在三台虚拟机上执行命令:hostnamectl set-hostname nodeXXX(虚拟机名)

第一台机器上设置主机名node1

第二台机器上设置主机名node2

第三台机器上设置主机名node3

然后在三台机器上分别执行命令:logout

发现主机名已经修改成node1了，相同的操作大家在其他两台机器上执行下看看效果.

1.3 ip配置

三节点ip规划如下：

节点名称	ip
node1	192.168.200.11
node2	192.168.200.12
node3	192.168.200.13

如下图，将node1上的ip修改为192.168.200.11，修改完后使用命令:systemctl restart network重启网卡

按照上面步骤一次修改node2的ip为:192.168.200.12,修改完后使用systemctl restart nework命令重启网卡，node3的ip修改方法一样，修改为192.168.200.13,修改完后重启网卡.

2.在xshell中创建三台虚拟机连接会话

3.root用户的免密登录配置

3.1 连接三台虚拟机

按住Ctrl依次选择三台虚拟机的会话连接，点击连接，这时会一次性打开三台虚拟机的连接会话

会出现三次安全警告，连续点击三次接受并保存即可.

3.2 生成公钥和私钥

使用此命令：ssh-keygen -t rsa 分别在三台机器中都执行一遍,这里只在node1上做演示，其他两台机器也需要执行此命令。

[root@node1 ~]# ssh-keygen -t rsa  #<--回车
Generating public/private rsa key pair.
Enter file in which to save the key (/root/.ssh/id_rsa): #<--回车
#会在root用户的家目录下生成.ssh目录，此目录中会保存生成的公钥和私钥
Created directory '/root/.ssh'.
Enter passphrase (empty for no passphrase): #<--回车
Enter same passphrase again: 
Your identification has been saved in /root/.ssh/id_rsa.
Your public key has been saved in /root/.ssh/id_rsa.pub.
The key fingerprint is:
SHA256:gpDw08iG9Tq+sGZ48TXirWTY17ajXhIea3drjy+pU3g root@node1
The key's randomart image is:
+---[RSA 2048]----+
|. .              |
| * =             |
|. O o            |
| . + .           |
|  o . + S.       |
| ..+..o*. E      |
|o o+++*.=o..     |
|.=.+oo.=oo+o     |
|+.. .oo.o=o+o    |
+----[SHA256]-----+
You have new mail in /var/spool/mail/root
[root@node1 ~]#

3.3 配置hosts文件

#hosts文件中配置三台机器ip和主机名的映射关系,其他两台机器按照相同的方式操作.
[root@node1 ~]# vi /etc/hosts

127.0.0.1   localhost localhost.localdomain localhost4 localhost4.localdomain4
::1         localhost localhost.localdomain localhost6 localhost6.localdomain6
192.168.200.11 node1
192.168.200.12 node2
192.168.200.13 node3

3.4 拷贝公钥文件

将node1的公钥拷贝到node2,node3上
将node2的公钥拷贝到node1,node3上
将node3的公钥拷贝到node1,node2上

以下以node1为例执行秘钥复制命令：ssh-copy-id -i 主机名

#复制到node2上
[root@node1 ~]# ssh-copy-id -i node2
/usr/bin/ssh-copy-id: INFO: Source of key(s) to be installed: "/root/.ssh/id_rsa.pub"
The authenticity of host 'node2 (192.168.200.12)' can't be established.
ECDSA key fingerprint is SHA256:rJzUyoggUP/Zn9v5rvqKpWppnG9xZ4gBZuXqHWxPy5k.
ECDSA key fingerprint is MD5:f3:37:16:c4:bb:00:3e:59:ec:b3:37:23:1b:24:88:e6.
Are you sure you want to continue connecting (yes/no)? yes #询问是否要连接输入yes回车
/usr/bin/ssh-copy-id: INFO: attempting to log in with the new key(s), to filter out any that are already installed
/usr/bin/ssh-copy-id: INFO: 1 key(s) remain to be installed -- if you are prompted now it is to install the new keys
root@node2's password: #输入root用户的密码root后回车

Number of key(s) added: 1

Now try logging into the machine, with:   "ssh 'node2'"
and check to make sure that only the key(s) you wanted were added.

#复制到node3上
[root@node1 ~]# ssh-copy-id -i node3
/usr/bin/ssh-copy-id: INFO: Source of key(s) to be installed: "/root/.ssh/id_rsa.pub"
The authenticity of host 'node3 (192.168.200.13)' can't be established.
ECDSA key fingerprint is SHA256:rJzUyoggUP/Zn9v5rvqKpWppnG9xZ4gBZuXqHWxPy5k.
ECDSA key fingerprint is MD5:f3:37:16:c4:bb:00:3e:59:ec:b3:37:23:1b:24:88:e6.
Are you sure you want to continue connecting (yes/no)? yes #询问是否要连接输入yes回车
/usr/bin/ssh-copy-id: INFO: attempting to log in with the new key(s), to filter out any that are already installed
/usr/bin/ssh-copy-id: INFO: 1 key(s) remain to be installed -- if you are prompted now it is to install the new keys
root@node3's password: #输入root用户的密码root后回车

Number of key(s) added: 1

Now try logging into the machine, with:   "ssh 'node3'"
and check to make sure that only the key(s) you wanted were added.

[root@node1 ~]#

3.4 验证免密登录配置

此操作只在node1上操作，其他机器上大家在验证。

#使用ssh 命令登录node2
[root@node1 ~]# ssh node2
Last login: Sun Jun 30 13:56:53 2019 from node1
#登录成功后这里的命令提示符已经变为[root@node2 ~]#说明登录成功
[root@node2 ~]# logout #退出node2继续 验证登录node3
Connection to node2 closed.
#登录node3
[root@node1 ~]# ssh node3
Last login: Sun Jun 30 13:56:55 2019 from node1
#登录成功
[root@node3 ~]# logout
Connection to node3 closed.
You have new mail in /var/spool/mail/root
[root@node1 ~]#

3.5 添加本地认证公钥到认证文件中

#进入到root用户的家目录下
[root@node1 ~]# cd ~
[root@node1 ~]# cd .ssh/
#讲生成的公钥添加到认证文件中
[root@node1 .ssh]# cat id_rsa.pub >> authorized_keys
[root@node1 .ssh]#

4.安装hadoop

4.1 创建hadoop用户组和hadoop用户

创建hadoop用户组和hadoop用户需要在三台机器上分别操作,这里以node1节点配置过程为例

#1.创建用户组hadoop
[root@node1 ~]# groupadd hadoop
#2.创建用户hadoop并添加到hadoop用户组中
[root@node1 ~]# useradd -g hadoop hadoop
#3.使用id命令查看hadoop用户组和hadoop用户创建是否成功
[root@node1 ~]# id hadoop
#用户uid 			用户组id gid      用户组名
uid=1000(hadoop) gid=1000(hadoop) groups=1000(hadoop)
#设置hadoop用户密码为hadoop
[root@node1 ~]# passwd hadoop
Changing password for user hadoop.
New password: #输入hadoop后回车
BAD PASSWORD: The password is shorter than 8 characters
Retype new password: #再次输入hadoop后回车
passwd: all authentication tokens updated successfully.
[root@node1 ~]# chown -R hadoop:hadoop /home/hadoop/
[root@node1 ~]# chmod -R 755 /home/hadoop/
#把root用户的环境变量文件复制并覆盖hadoop用户下的.bash_profile
[root@node1 ~]# cp .bash_profile /home/hadoop/

重要的话必须说三次，三次，三次，再三次，看看下面的三行红色的字，不做，后面集群启动不了，让你后悔一万年，不懂照着做，啥都不要想,一个字就是干，一路操作猛如虎!

请参考 3.2生成公钥和私钥，3.4 验证免密码登录配置，3.5 添加本地认证公钥到认证文件中,在hadoop用户下，对hadoop用户做免密码登录配置

[hadoop@node1 ~] su - hadoop
[hadoop@node1 ~] source.bash_profile
#使用su - hadoop切换到hadoop用户下执行如下操作
[hadoop@node1 ~]# ssh-keygen -t rsa  #<--回车
Generating public/private rsa key pair.
Enter file in which to save the key (/root/.ssh/id_rsa): #<--回车
#会在root用户的家目录下生成.ssh目录，此目录中会保存生成的公钥和私钥
Created directory '/root/.ssh'.
Enter passphrase (empty for no passphrase): #<--回车
Enter same passphrase again: 
Your identification has been saved in /root/.ssh/id_rsa.
Your public key has been saved in /root/.ssh/id_rsa.pub.
The key fingerprint is:
SHA256:gpDw08iG9Tq+sGZ48TXirWTY17ajXhIea3drjy+pU3g root@node1
The key's randomart image is:
+---[RSA 2048]----+
|. .              |
| * =             |
|. O o            |
| . + .           |
|  o . + S.       |
| ..+..o*. E      |
|o o+++*.=o..     |
|.=.+oo.=oo+o     |
|+.. .oo.o=o+o    |
+----[SHA256]-----+
You have new mail in /var/spool/mail/root
[hadoop@node1 ~]# 

#修改.ssh目录权限
[hadoop@node1 ~]$ chmod -R 755 .ssh/
[hadoop@node1 ~]$ cd .ssh/
[hadoop@node1 .ssh]$ chmod 644 *
[hadoop@node1 .ssh]$ chmod 600 id_rsa
[hadoop@node1 .ssh]$ chmod 600 id_rsa.pub 
[hadoop@node1 .ssh]$

4.2 配置hadoop

在一台机器上配置好后复制到其他机器上即可,这样保证三台机器的hadoop配置是一致的.

1.上传hadoop安装包,进行解压

#1.创建hadoop安装目录
[root@node1 ~]# mkdir -p /opt/bigdata
#2.解压hadoop-3.1.2.tar.gz
[root@node1 ~]# tar -xzvf hadoop-3.1.2.tar.gz -C /opt/bigdata/

2.配置hadoop环境变量

1.配置环境变量

[root@node1 ~]# vi .bash_profile 

# .bash_profile

# Get the aliases and functions
if [ -f ~/.bashrc ]; then
        . ~/.bashrc
fi

# User specific environment and startup programs
JAVA_HOME=/usr/java/jdk1.8.0_211-amd64
HADOOP_HOME=/opt/bigdata/hadoop-3.1.2

PATH=$PATH:$HOME/bin:$JAVA_HOME/bin:$HADOOP_HOME/bin:$HADOOP_HOME/sbin

export JAVA_HOME
export HADOOP_HOME
export PATH
~                                                                                                                                                                                                       
:wq!

2.验证环境变量

#1.使环境变量生效
[root@node1 ~]# source .bash_profile 
#2.显示hadoop的版本信息
[root@node1 ~]# hadoop version
#3.显示出hadoop版本信息表示安装和环境变量成功.
Hadoop 3.1.2
Source code repository https://github.com/apache/hadoop.git -r 1019dde65bcf12e05ef48ac71e84550d589e5d9a
Compiled by sunilg on 2019-01-29T01:39Z
Compiled with protoc 2.5.0
From source with checksum 64b8bdd4ca6e77cce75a93eb09ab2a9
This command was run using /opt/bigdata/hadoop-3.1.2/share/hadoop/common/hadoop-common-3.1.2.jar
[root@node1 ~]#

hadoop用户下也需要按照root用户配置环境变量的方式操作一下

3.配置hadoop-env.sh

这个文件只需要配置JAVA_HOME的值即可,在文件中找到export JAVA_HOME字眼的位置，删除最前面的#

export JAVA_HOME=/usr/java/jdk1.8.0_211-amd64

[root@node1 ~]# cd /opt/bigdata/hadoop-3.1.2/etc/hadoop/
You have new mail in /var/spool/mail/root
[root@node1 hadoop]# pwd
/opt/bigdata/hadoop-3.1.2/etc/hadoop
[root@node1 hadoop]# vi hadoop-env.sh

4.配置core-site.xml

切换到cd /opt/bigdata/hadoop-3.1.2/etc/hadoop/目录下

[root@node1 ~]# cd /opt/bigdata/hadoop-3.1.2/etc/hadoop/

<configuration>
 <!-- 指定hdfs的namenode主机的hostname -->
  <property>
      <name>fs.defaultFS</name>
      <value>hdfs://node1:9000</value>
  </property>
<!-- io操作流的配置 -->
  <property>
      <name>io.file.buffer.size</name>
      <value>131072</value>
  </property>	
 <!--hadoop集群临时数据存储目录-->
  <property>
       <name>hadoop.tmp.dir</name>
       <value>/opt/bigdata/hadoop-3.1.2/tmpdata</value>
  </property>
</configuration>

5.配置hdfs-site.xml

配置/opt/bigdata/hadoop-3.1.2/etc/hadoop/目录下的hdfs-site.xml

<configuration>
    <!--namenode元数据存储目录-->
	<property>
	  <name>dfs.namenode.name.dir</name>
	  <value>/opt/bigdata/hadoop-3.1.2/hadoop/hdfs/name/</value>
	</property>
    <!--指定block块的的大小-->
	<property>
	  <name>dfs.blocksize</name>
	  <value>268435456</value>
	</property>
    <!-- -->
	<property>
	  <name>dfs.namenode.handler.count</name>
	  <value>100</value>
	</property>

	<!--工作节点的数据块存储目录 -->
	<property>
	  <name>dfs.datanode.data.dir</name>
	  <value>/opt/bigdata/hadoop-3.1.2/hadoop/hdfs/data/</value>
	</property>
	<!--block的副本数-->
	<property>
		<name>dfs.replication</name>
		<value>3</value>
	</property>
</configuration>

6.配置mapred-site.xml

配置/opt/bigdata/hadoop-3.1.2/etc/hadoop/目录下的mapred-site.xml

<!--指定运行mapreduce的环境是yarn -->
<configuration>
   <property>
       <name>mapreduce.framework.name</name>
       <value>yarn</value>
   </property>
  <property>
    <name>yarn.app.mapreduce.am.env</name>
    <value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value>
  </property>
  <property>
    <name>mapreduce.map.env</name>
    <value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value>
  </property>
  <property>
    <name>mapreduce.reduce.env</name>
    <value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value>
  </property>
    <!--以下配置复制到配置文件中，大家需要修改value中的，（逗号）为英文输入法的逗号-->
    <property>
        <name>mapreduce.application.classpath</name>
  <value>$HADOOP_MAPRED_HOME/share/hadoop/mapreduce/*,				     $HADOOP_MAPRED_HOME/share/hadoop/mapreduce/lib/*</value>
    </property>
</configuration>

7.配置yarn-site.xml

配置/opt/bigdata/hadoop-3.1.2/etc/hadoop/目录下的yarn-site.xml

<configuration>
<!--指定resourcemanager的位置-->
  <property>
    <name>yarn.resourcemanager.hostname</name>
    <value>node1</value>
  </property>
  <property>
    <name>yarn.nodemanager.aux-services</name>
    <value>mapreduce_shuffle</value>
  </property>
  <property>
    <name>yarn.resourcemanager.address</name>
    <value>node1:18040</value>
  </property>
  <property>                
    <name>yarn.resourcemanager.scheduler.address</name>
    <value>node1:18030</value>
  </property>
  <property>
     <name>yarn.resourcemanager.resource-tracker.address</name>
     <value>node1:18025</value>
  </property>
  <property>                
     <name>yarn.resourcemanager.admin.address</name>
     <value>node1:18141</value>
  </property>
  <property>                
      <name>yarn.resourcemanager.webapp.address</name>
      <value>node1:18088</value>
  </property>
</configuration>

8.编辑works

此文件用于配置集群有多少个数据节点,我们把node2，node3作为数据节点,node1作为集群管理节点.

配置/opt/bigdata/hadoop-3.1.2/etc/hadoop/目录下的works

[root@node1 hadoop]# vi workers 
#将localhost这一行删除掉
node2
node3
~

4.3 远程复制hadoop到集群机器

#1.进入到root用户家目录下
[root@node1 hadoop]# cd ~
#2.使用scp远程拷贝命令将root用户的环境变量配置文件复制到node2
[root@node1 ~]# scp .bash_profile root@node2:~
.bash_profile                                                                   100%  338   566.5KB/s   00:00    
#3.使用scp远程拷贝命令将root用户的环境变量配置文件复制到node3
[root@node1 ~]# scp .bash_profile root@node3:~
.bash_profile                                                                   100%  338   212.6KB/s   00:00    
[root@node1 ~]# 

#4.进入到hadoop的share目录下
[root@node1 ~]# cd /opt/bigdata/hadoop-3.1.2/share/
You have new mail in /var/spool/mail/root
[root@node1 share]# ll
total 0
drwxr-xr-x 3 1001 1002 20 Jan 29 12:05 doc
drwxr-xr-x 8 1001 1002 88 Jan 29 11:36 hadoop
#5.删除doc目录,这个目录存放的是用户手册，比较大，等会儿下面进行远程复制的时候时间比较长，删除后节约复制时间
[root@node1 share]# rm -rf doc/
[root@node1 share]# cd ~
You have new mail in /var/spool/mail/root
[root@node1 ~]# scp -r /opt root@node2:/
[root@node1 ~]# scp -r /opt root@node3:/

4.4 使集群所有机器环境变量生效

在node2，node3的root用户家目录下使环境变量生效

node2节点如下操作:

[root@node2 hadoop-3.1.2]# cd ~
[root@node2 ~]# source .bash_profile 
[root@node2 ~]# hadoop version
Hadoop 3.1.2
Source code repository https://github.com/apache/hadoop.git -r 1019dde65bcf12e05ef48ac71e84550d589e5d9a
Compiled by sunilg on 2019-01-29T01:39Z
Compiled with protoc 2.5.0
From source with checksum 64b8bdd4ca6e77cce75a93eb09ab2a9
This command was run using /opt/bigdata/hadoop-3.1.2/share/hadoop/common/hadoop-common-3.1.2.jar
[root@node2 ~]#

node3节点如下操作:

[root@node3 bin]# cd ~
[root@node3 ~]# source .bash_profile 
[root@node3 ~]# hadoop version
Hadoop 3.1.2
Source code repository https://github.com/apache/hadoop.git -r 1019dde65bcf12e05ef48ac71e84550d589e5d9a
Compiled by sunilg on 2019-01-29T01:39Z
Compiled with protoc 2.5.0
From source with checksum 64b8bdd4ca6e77cce75a93eb09ab2a9
This command was run using /opt/bigdata/hadoop-3.1.2/share/hadoop/common/hadoop-common-3.1.2.jar
[root@node3 ~]#

5.修改hadoop安装目录的权限

node2，node3也需要进行如下操作

#1.修改目录所属用户和组为hadoop:hadoop
[root@node1 ~]# chown -R hadoop:hadoop /opt/
You have new mail in /var/spool/mail/root
You have new mail in /var/spool/mail/root
#2.修改目录所属用户和组的权限值为755
[root@node1 ~]# chmod -R 755  /opt/
[root@node1 ~]# chmod -R g+w /opt/
[root@node1 ~]# chmod -R o+w /opt/
[root@node1 ~]#

6.格式化hadoop

#切换
[root@node1 ~]# su - hadoop
[hadoop@node1 hadoop]$  hdfs namenode -format
2019-06-30 16:11:35,914 INFO namenode.NameNode: STARTUP_MSG: 
/************************************************************
STARTUP_MSG: Starting NameNode
STARTUP_MSG:   host = node1/192.168.200.11
STARTUP_MSG:   args = [-format]
STARTUP_MSG:   version = 3.1.2
#此处省略部分日志
2019-06-30 16:11:36,636 INFO namenode.NameNode: SHUTDOWN_MSG: 
/************************************************************
SHUTDOWN_MSG: Shutting down NameNode at node1/192.168.200.11
************************************************************/
[hadoop@node1 hadoop]$

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-3t23vXeS-1602600359638)(assets/1562380280095.png)]

7.启动集群

[hadoop@node1 ~]$ start-all.sh 
WARNING: Attempting to start all Apache Hadoop daemons as hadoop in 10 seconds.
WARNING: This is not a recommended production deployment configuration.
WARNING: Use CTRL-C to abort.
Starting namenodes on [node1]
Starting datanodes
Starting secondary namenodes [node1]
Starting resourcemanager
Starting nodemanagers
#使用jps显示java进程
[hadoop@node1 ~]$ jps
40852 ResourceManager 
40294 NameNode
40615 SecondaryNameNode
41164 Jps
[hadoop@node1 ~]$

在浏览器地址栏中输入:http://192.168.200.11:9870查看namenode的web界面.

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-u1lf0SDa-1602600359638)(assets/1561886420207.png)]

8.运行mapreduce程序

mapreduce程序(行话程为词频统计程序(中文名),英文名：wordcount)，就是统计一个文件中每一个单词出现的次数，也是我们学习大数据技术最基础，最简单的程序，入门必须要会要懂的第一个程序，其地位和java，php，c#，javascript等编程语言的第一个入门程序HelloWorld(在控制台打印“hello world！”等字样)程序一样，尤为重要,不同的是它们是单机应用程序，我们接下来要运行的程序(wordcount)是一个分布式运行的程序，是在一个大数据集群中运行的程序。**wordcount程序能够正常的运行成功，输入结果，意味着我们的大数据环境正确的安装和配置成功。**好，简单的先介绍到这里，接下来让我们爽一把吧。

#1.使用hdfs dfs -ls /  命令浏览hdfs文件系统，集群刚开始搭建好，由于没有任何目录所以什么都不显示.
[hadoop@node1 ~]$ hdfs dfs -ls /
#2.创建测试目录
[hadoop@node1 ~]$ hdfs dfs -mkdir /test 
#3.在此使用hdfs dfs -ls 发现我们刚才创建的test目录
[hadoop@node1 ~]$ hdfs dfs -ls /
Found 1 items
drwxr-xr-x   - hadoop supergroup          0 2019-06-30 17:23 /test
#4.使用touch命令在linux本地目录创建一个words文件
[hadoop@node1 ~]$ touch words
#5.文件中输入如下内容
[hadoop@node1 ~]$ vi words
i love you
are you ok

#6.将创建的本地words文件上传到hdfs的test目录下
[hadoop@node1 ~]$ hdfs dfs -put words /test
#7.查看上传的文件是否成功
[hadoop@node1 ~]$ hdfs dfs -ls -r /test
Found 1 items
-rw-r--r--   3 hadoop supergroup         23 2019-06-30 17:28 /test/words
#/test/words 是hdfs上的文件存储路径 /test/output是mapreduce程序的输出路径，这个输出路径是不能已经存在的路径，mapreduce程序运行的过程中会自动创建输出路径，数据路径存在的话会报错，这里需要同学注意下.
[hadoop@node1 ~]$ hadoop jar /opt/bigdata/hadoop-3.1.2/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.2.jar  wordcount /test/words /test/output
2019-06-30 17:32:23,685 INFO client.RMProxy: Connecting to ResourceManager at node1/192.168.200.11:18040
2019-06-30 17:32:24,060 INFO mapreduce.JobResourceUploader: Disabling Erasure Coding for path: /tmp/hadoop-yarn/staging/hadoop/.staging/job_1561886252942_0001
2019-06-30 17:32:24,215 INFO input.FileInputFormat: Total input files to process : 1
2019-06-30 17:32:24,291 INFO mapreduce.JobSubmitter: number of splits:1
2019-06-30 17:32:24,394 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1561886252942_0001
2019-06-30 17:32:24,395 INFO mapreduce.JobSubmitter: Executing with tokens: []
2019-06-30 17:32:24,592 INFO conf.Configuration: resource-types.xml not found
2019-06-30 17:32:24,593 INFO resource.ResourceUtils: Unable to find 'resource-types.xml'.
2019-06-30 17:32:24,806 INFO impl.YarnClientImpl: Submitted application application_1561886252942_0001
2019-06-30 17:32:24,861 INFO mapreduce.Job: The url to track the job: http://node1:18088/proxy/application_1561886252942_0001/
2019-06-30 17:32:24,862 INFO mapreduce.Job: Running job: job_1561886252942_0001
2019-06-30 17:32:33,025 INFO mapreduce.Job: Job job_1561886252942_0001 running in uber mode : false
2019-06-30 17:32:33,030 INFO mapreduce.Job:  map 0% reduce 0%
2019-06-30 17:32:39,174 INFO mapreduce.Job:  map 100% reduce 0%
2019-06-30 17:32:43,229 INFO mapreduce.Job:  map 100% reduce 100%
2019-06-30 17:32:43,266 INFO mapreduce.Job: Job job_1561886252942_0001 completed successfully
2019-06-30 17:32:43,369 INFO mapreduce.Job: Counters: 53
	File System Counters
		FILE: Number of bytes read=54
		FILE: Number of bytes written=432335
		FILE: Number of read operations=0
		FILE: Number of large read operations=0
		FILE: Number of write operations=0
		HDFS: Number of bytes read=116
		HDFS: Number of bytes written=28
		HDFS: Number of read operations=8
		HDFS: Number of large read operations=0
		HDFS: Number of write operations=2
	Job Counters 
		Launched map tasks=1
		Launched reduce tasks=1
		Data-local map tasks=1
		Total time spent by all maps in occupied slots (ms)=3346
		Total time spent by all reduces in occupied slots (ms)=2290
		Total time spent by all map tasks (ms)=3346
		Total time spent by all reduce tasks (ms)=2290
		Total vcore-milliseconds taken by all map tasks=3346
		Total vcore-milliseconds taken by all reduce tasks=2290
		Total megabyte-milliseconds taken by all map tasks=3426304
		Total megabyte-milliseconds taken by all reduce tasks=2344960
	Map-Reduce Framework
		Map input records=2
		Map output records=6
		Map output bytes=46
		Map output materialized bytes=54
		Input split bytes=93
		Combine input records=6
		Combine output records=5
		Reduce input groups=5
		Reduce shuffle bytes=54
		Reduce input records=5
		Reduce output records=5
		Spilled Records=10
		Shuffled Maps =1
		Failed Shuffles=0
		Merged Map outputs=1
		GC time elapsed (ms)=472
		CPU time spent (ms)=1380
		Physical memory (bytes) snapshot=492576768
		Virtual memory (bytes) snapshot=5577179136
		Total committed heap usage (bytes)=407371776
		Peak Map Physical memory (bytes)=294256640
		Peak Map Virtual memory (bytes)=2788634624
		Peak Reduce Physical memory (bytes)=198320128
		Peak Reduce Virtual memory (bytes)=2788544512
	Shuffle Errors
		BAD_ID=0
		CONNECTION=0
		IO_ERROR=0
		WRONG_LENGTH=0
		WRONG_MAP=0
		WRONG_REDUCE=0
	File Input Format Counters 
		Bytes Read=23
	File Output Format Counters 
		Bytes Written=28
[hadoop@node1 ~]$ hdfs dfs -ls -r /test/output
Found 2 items
-rw-r--r--   3 hadoop supergroup         28 2019-06-30 17:32 /test/output/part-r-00000
-rw-r--r--   3 hadoop supergroup          0 2019-06-30 17:32 /test/output/_SUCCESS
[hadoop@node1 ~]$ hdfs dfs -text /test/output/part-r-00000
are	1
i	1
love	1
ok	1
you	2
[hadoop@node1 ~]$

9.停止集群

[hadoop@node1 ~]$ stop-all.sh

至此三节点的hadoop集群环境搭建完成，谢谢大家能够有耐心的学习完!

mory (bytes)=2788634624
Peak Reduce Physical memory (bytes)=198320128
Peak Reduce Virtual memory (bytes)=2788544512
Shuffle Errors
BAD_ID=0
CONNECTION=0
IO_ERROR=0
WRONG_LENGTH=0
WRONG_MAP=0
WRONG_REDUCE=0
File Input Format Counters
Bytes Read=23
File Output Format Counters
Bytes Written=28
[hadoop@node1 ~]$ hdfs dfs -ls -r /test/output
Found 2 items
-rw-r–r-- 3 hadoop supergroup 28 2019-06-30 17:32 /test/output/part-r-00000
-rw-r–r-- 3 hadoop supergroup 0 2019-06-30 17:32 /test/output/_SUCCESS
[hadoop@node1 ~]$ hdfs dfs -text /test/output/part-r-00000
are 1
i 1
love 1
ok 1
you 2
[hadoop@node1 ~]$


# 9.停止集群

[hadoop@node1 ~]$ stop-all.sh




至此三节点的hadoop集群环境搭建完成，谢谢大家能够有耐心的学习完!

华为开发者空间

华为开发者空间，是为全球开发者打造的专属开发空间，汇聚了华为优质开发资源及工具，致力于让每一位开发者拥有一台云主机，基于华为根生态开发、创新。

更多推荐

解析openGauss账本数据库

华为开发者空间

GaussDB的向量化处理技术

华为开发者空间

丽水学院·华为云HCSD校园沙龙圆满落幕，共绘数字教育新篇章！

华为开发者空间

所有评论(0)

查看更多评论

大锤哥哥Z

@weixin_42646459

已为社区贡献4条内容