原创 Spark动手实践 1Hadoop2.7.3安装部署实际动手

Posted 2020-09-17 大数据与人工智能探索

目录：

第一部分：操作系统准备工作：

　　1. 安装部署CentOS7.3 1611

　　2. CentOS7软件安装（net-tools, wget, vim等）

　　3. 更新CentOS7的Yum源，更新软件速度更快

　　4. CentOS 用户配置，Sudo授权

第二部分：Java环境准备

　　1. JDK1.8 安装与配置

第三部分：Hadoop配置,启动与验证

　　1. 解压Hadoop2.7.3更新全局变量

　　2. 更新Hadoop配置文件

　　3. 启动Hadoop

　　4. 验证Hadoop

 =============================================================================================

第一部分：操作系统准备工作：

　　1. 安装部署CentOS7.3 1611

　　2. CentOS7软件安装（net-tools, wget, vim等）

　　3. 更新CentOS7的Yum源，更新软件速度更快

　　4. CentOS 用户配置，Sudo授权

1. 安装部署CentOS7.3 1611

2. CentOS7软件安装（net-tools, wget, vim等）

sudo yum install -y net-tools

sudo yum install -y wget

sudo yum install -y vim 

 

3. 更新CentOS7的Yum源(更新为阿里云的CentOS7的源)，更新软件速度更快

 http://mirrors.aliyun.com/help/centos

1、备份

mv /etc/yum.repos.d/CentOS-Base.repo /etc/yum.repos.d/CentOS-Base.repo.backup

2、下载新的CentOS-Base.repo 到/etc/yum.repos.d/

CentOS 5

wget -O /etc/yum.repos.d/CentOS-Base.repo http://mirrors.aliyun.com/repo/Centos-5.repo

CentOS 6

wget -O /etc/yum.repos.d/CentOS-Base.repo http://mirrors.aliyun.com/repo/Centos-6.repo

CentOS 7

wget -O /etc/yum.repos.d/CentOS-Base.repo http://mirrors.aliyun.com/repo/Centos-7.repo

3、之后运行yum makecache生成缓存

 

4、sudo yum -y update 对系统进行升级

 

  sudo vim /etc/hosts # 更新hosts文件，便于用spark02代表本机IP

 

第二部分：Java环境准备

　　1. JDK1.8 安装与配置

通过FileZilla 上传实验所需要用到的文件（JDK，Hadoop，Spark）

 

对JDK和Hadoop进行解压

 tar -zxvf jdk-8u121-linux-x64.tar.gz

 tar -zxvf hadoop-2.7.3.tar.gz

 

在 .bash_profile文件内增加环境便利，便于Java和Hadoop更容易操作

#Add JAVA_HOME and HADOOP_HOME
export JAVA_HOME=/home/spark/jdk1.8.0_121
export PATH=$PATH:$JAVA_HOME/bin
export HADOOP_HOME=/home/spark/hadoop-2.7.3
export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin

source .bash_profile #配置生效

第三部分：Hadoop配置,启动与验证

　　1. 解压Hadoop2.7.3更新全局变量

　　2. 更新Hadoop配置文件

　　3. 启动Hadoop

　　4. 验证Hadoop

 

参考：Hadoop2.7.3 官方文档进行伪分布式的配置

http://hadoop.apache.org/docs/r2.7.3/hadoop-project-dist/hadoop-common/SingleCluster.html  

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:50070/

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 -put etc/hadoop input
   

6. Run some of the examples provided:

     $ bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.3.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>
   </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
   
   

   配置免密码，否则运行的时候会报错。

   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

   Hadoop配置文件具体的配置信息如下:

   1. vim etc/hadoop/hadoop-env.sh

   #export JAVA_HOME=${JAVA_HOME}
   export JAVA_HOME=/home/spark/jdk1.8.0_121

   2. vim etc/hadoop/core-site.xml

   <!-- Put site-specific property overrides in this file. -->

   <configuration>
   <property>
   <name>fs.defaultFS</name>
   <value>hdfs://spark01:9000</value>
   </property>
   <property>
   <name>hadoop.tmp.dir</name>
   <value>/home/spark/hadoopdata</value>
   </property>
   </configuration>

   3. vim etc/hadoop/hdfs-site.xml

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

   4. vim etc/hadoop/mapred-site.xml

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

    

   5.vim etc/hadoop/yarn-site.xml

   <configuration>

   <!-- Site specific YARN configuration properties -->
   <property>
   <name>yarn.nodemanager.aux-services</name>
   <value>mapreduce_shuffle</value>
   </property>
   <property>
   <name>yarn.log-aggregation-enable</name>
   <value>true</value>
   </property>
   </configuration>

    

   1. 对HDFS进行格式化

    

   hdfs namenode -format

    

   

    

   2.启动HDFS

   start-dfs.sh

   

    

   3. 启动YARN

   start-yarn.sh

   

    

    

   CentOS 7.2关闭防火墙

   CentOS 7.0默认使用的是firewall作为防火墙，这里改为iptables防火墙步骤。

   firewall-cmd --state #查看默认防火墙状态（关闭后显示notrunning，开启后显示running）

   ?

   1 2 3

   [root@localhost ~]#firewall-cmd --state   not running

    检查防火墙的状态：

   从centos7开始使用systemctl来管理服务和程序，包括了service和chkconfig。

   ?

   1 2 3

   [root@localhost ~]#systemctl list-unit-files|grep firewalld.service      --防火墙处于关闭状态   firewalld.service              disabled

   或者

   ?

   1 2 3 4 5 6 7

   [root@localhost ~]#systemctl status firewalld.service   firewalld.service - firewalld - dynamic firewall daemon     Loaded: loaded (/usr/lib/systemd/system/firewalld.service; disabled; vendor preset: enabled)     Active: inactive (dead)

    关闭防火墙：

   systemctl stop firewalld.service #停止firewall
   systemctl disable firewalld.service #禁止firewall开机启动

   ?

   1 2 3

   [root@localhost ~]#systemctl stop firewalld.service   [root@localhost ~]#systemctl disable firewalld.service

   ?

   1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

   启动一个服务：systemctl start firewalld.service   关闭一个服务：systemctl stop firewalld.service   重启一个服务：systemctl restart firewalld.service   显示一个服务的状态：systemctl status firewalld.service   在开机时启用一个服务：systemctl enable firewalld.service   在开机时禁用一个服务：systemctl disable firewalld.service   查看服务是否开机启动：systemctl is-enabled firewalld.service;echo $?   查看已启动的服务列表：systemctl list-unit-files|grep enabled

   Centos 7 firewall 命令：

   查看已经开放的端口：

   ?

   1	firewall-cmd --list-ports

   开启端口

   ?

   1	firewall-cmd --zone=public --add-port=80/tcp --permanent

   命令含义：

   –zone #作用域

   –add-port=80/tcp #添加端口，格式为：端口/通讯协议

   –permanent #永久生效，没有此参数重启后失效

   重启防火墙

   ?

   1 2 3 4

   firewall-cmd --reload #重启firewall systemctl stop firewalld.service #停止firewall systemctl disable firewalld.service #禁止firewall开机启动 firewall-cmd --state #查看默认防火墙状态（关闭后显示notrunning，开启后显示running）

    

    

   1. 启动防火墙（Firewalld）：

   sudo systemctl start firewalld.service

   2. 查看防火墙（Firewalld）运行状态：

   sudo systemctl status firewalld.service

   3. 配置防火墙（Firewalld）访问规则，打开8088（YARN）和50070（HDFS）两个端口：

   sudo firewall-cmd --zone=public --add-port=8088/tcp --permanent

   sudo firewall-cmd --zone=public --add-port=50070/tcp --permanent

   4. 载入防火墙（Firewalld）规则：

   sudo firewall-cmd --reload

   5. 重新启动防火墙（Firewalld）：

   sudo systemctl restart firewalld.service

   6. 验证防火墙（Firewalld）生效：

   http://spark02:8080

   http://spark02:50070

    

    

    http://spark02:8088

   

   http://spark02:50070

   

    

   使用HDFS创建目录，拷贝文件和查看文件

   hdfs dfs -mkdir hdfs://user/jonson/input

   hdfs dfs -cp etc/hadoop hdfs://user/jonson/input

   hdfs dfs -ls hdfs://user/jonson/input

   hdfs dfs -mkdir hdfs://user/jonson/output

   hdfs dfs -rmdir hdfs://user/jonson/output

   hdfs dfs -ls hdfs://user/jonson

    

   尝试使用MapReduce计算框架

    

   [spark@Spark02 hadoop-2.7.3]$ hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.3.jar grep hdfs:///user/jonson/input/hadoop hdfs:///user/jonson/output \'dfs[a-z.]+\'

    

   [spark@Spark02 hadoop-2.7.3]$ hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.3.jar grep hdfs:///user/jonson/input/hadoop hdfs:///user/jonson/output \'dfs[a-z.]+\'
   17/05/07 23:36:17 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
   17/05/07 23:36:18 INFO input.FileInputFormat: Total input paths to process : 30
   17/05/07 23:36:18 INFO mapreduce.JobSubmitter: number of splits:30
   17/05/07 23:36:19 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1494169715431_0003
   17/05/07 23:36:19 INFO impl.YarnClientImpl: Submitted application application_1494169715431_0003
   17/05/07 23:36:19 INFO mapreduce.Job: The url to track the job: http://localhost:8088/proxy/application_1494169715431_0003/
   17/05/07 23:36:19 INFO mapreduce.Job: Running job: job_1494169715431_0003
   17/05/07 23:36:28 INFO mapreduce.Job: Job job_1494169715431_0003 running in uber mode : false
   17/05/07 23:36:28 INFO mapreduce.Job: map 0% reduce 0%
   17/05/07 23:36:58 INFO mapreduce.Job: map 20% reduce 0%
   17/05/07 23:37:25 INFO mapreduce.Job: map 37% reduce 0%
   17/05/07 23:37:26 INFO mapreduce.Job: map 40% reduce 0%
   17/05/07 23:37:50 INFO mapreduce.Job: map 47% reduce 0%
   17/05/07 23:37:51 INFO mapreduce.Job: map 57% reduce 0%
   17/05/07 23:37:54 INFO mapreduce.Job: map 57% reduce 19%
   17/05/07 23:38:04 INFO mapreduce.Job: map 60% reduce 19%
   17/05/07 23:38:06 INFO mapreduce.Job: map 60% reduce 20%
   17/05/07 23:38:12 INFO mapreduce.Job: map 73% reduce 20%
   17/05/07 23:38:15 INFO mapreduce.Job: map 73% reduce 24%
   17/05/07 23:38:18 INFO mapreduce.Job: map 77% reduce 24%
   17/05/07 23:38:21 INFO mapreduce.Job: map 77% reduce 26%
   17/05/07 23:38:33 INFO mapreduce.Job: map 83% reduce 26%
   17/05/07 23:38:34 INFO mapreduce.Job: map 90% reduce 26%
   17/05/07 23:38:35 INFO mapreduce.Job: map 93% reduce 26%
   17/05/07 23:38:36 INFO mapreduce.Job: map 93% reduce 31%
   17/05/07 23:38:43 INFO mapreduce.Job: map 100% reduce 31%
   17/05/07 23:38:44 INFO mapreduce.Job: map 100% reduce 100%
   17/05/07 23:38:45 INFO mapreduce.Job: Job job_1494169715431_0003 completed successfully
   17/05/07 23:38:45 INFO mapreduce.Job: Counters: 49
   File System Counters
   FILE: Number of bytes read=345
   FILE: Number of bytes written=3690573
   FILE: Number of read operations=0
   FILE: Number of large read operations=0
   FILE: Number of write operations=0
   HDFS: Number of bytes read=81841
   HDFS: Number of bytes written=437
   HDFS: Number of read operations=93
   HDFS: Number of large read operations=0
   HDFS: Number of write operations=2
   Job Counters
   Launched map tasks=30
   Launched reduce tasks=1
   Data-local map tasks=30
   Total time spent by all maps in occupied slots (ms)=653035
   Total time spent by all reduces in occupied slots (ms)=77840
   Total time spent by all map tasks (ms)=653035
   Total time spent by all reduce tasks (ms)=77840
   Total vcore-milliseconds taken by all map tasks=653035
   Total vcore-milliseconds taken by all reduce tasks=77840
   Total megabyte-milliseconds taken by all map tasks=668707840
   Total megabyte-milliseconds taken by all reduce tasks=79708160
   Map-Reduce Framework
   Map input records=2103
   Map output records=24
   Map output bytes=590
   Map output materialized bytes=519
   Input split bytes=3804
   Combine input records=24
   Combine output records=13
   Reduce input groups=11
   Reduce shuffle bytes=519
   Reduce input records=13
   Reduce output records=11
   Spilled Records=26
   Shuffled Maps =30
   Failed Shuffles=0
   Merged Map outputs=30
   GC time elapsed (ms)=8250
   CPU time spent (ms)=13990
   Physical memory (bytes) snapshot=6025490432
   Virtual memory (bytes) snapshot=64352063488
   Total committed heap usage (bytes)=4090552320
   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=78037
   File Output Format Counters
   Bytes Written=437
   17/05/07 23:38:45 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
   17/05/07 23:38:46 INFO input.FileInputFormat: Total input paths to process : 1
   17/05/07 23:38:46 INFO mapreduce.JobSubmitter: number of splits:1
   17/05/07 23:38:46 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1494169715431_0004
   17/05/07 23:38:46 INFO impl.YarnClientImpl: Submitted application application_1494169715431_0004
   17/05/07 23:38:46 INFO mapreduce.Job: The url to track the job: http://localhost:8088/proxy/application_1494169715431_0004/
   17/05/07 23:38:46 INFO mapreduce.Job: Running job: job_1494169715431_0004
   17/05/07 23:39:00 INFO mapreduce.Job: Job job_1494169715431_0004 running in uber mode : false
   17/05/07 23:39:00 INFO mapreduce.Job: map 0% reduce 0%
   17/05/07 23:39:06 INFO mapreduce.Job: map 100% reduce 0%
   17/05/07 23:39:13 INFO mapreduce.Job: map 100% reduce 100%
   17/05/07 23:39:14 INFO mapreduce.Job: Job job_1494169715431_0004 completed successfully
   17/05/07 23:39:14 INFO mapreduce.Job: Counters: 49
   File System Counters
   FILE: Number of bytes read=291
   FILE: Number of bytes written=237535
   FILE: Number of read operations=0
   FILE: Number of large read operations=0
   FILE: Number of write operations=0
   HDFS: Number of bytes read=566
   HDFS: Number of bytes written=197
   HDFS: Number of read operations=7
   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)=3838
   Total time spent by all reduces in occupied slots (ms)=3849
   Total time spent by all map tasks (ms)=3838
   Total time spent by all reduce tasks (ms)=3849
   Total vcore-milliseconds taken by all map tasks=3838
   Total vcore-milliseconds taken by all reduce tasks=3849
   Total megabyte-milliseconds taken by all map tasks=3930112
   Total megabyte-milliseconds taken by all reduce tasks=3941376
   Map-Reduce Framework
   Map input records=11
   Map output records=11
   Map output bytes=263
   Map output materialized bytes=291
   Input split bytes=129
   Combine input records=0
   Combine output records=0
   Reduce input groups=5
   Reduce shuffle bytes=291
   Reduce input records=11
   Reduce output records=11
   Spilled Records=22
   Shuffled Maps =1
   Failed Shuffles=0
   Merged Map outputs=1
   GC time elapsed (ms)=143
   CPU time spent (ms)=980
   Physical memory (bytes) snapshot=306675712
   Virtual memory (bytes) snapshot=4157272064
   Total committed heap usage (bytes)=165810176
   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=437
   File Output Format Counters
   Bytes Written=197

   

    

   

    

    

    

   

    

    

    

   

    

   利用命令行来查看运行结果：

   [spark@Spark02 hadoop-2.7.3]$ hadoop fs -cat hdfs:///user/jonson/output/*
   6 dfs.audit.logger
   4 dfs.class
   3 dfs.server.namenode.
   2 dfs.period
   2 dfs.audit.log.maxfilesize
   2 dfs.audit.log.maxbackupindex
   1 dfsmetrics.log
   1 dfsadmin
   1 dfs.servers
   1 dfs.replication
   1 dfs.file
   [spark@Spark02 hadoop-2.7.3]$ hadoop fs -cat hdfs:///user/jonson/output/part-r-00000
   6 dfs.audit.logger
   4 dfs.class
   3 dfs.server.namenode.
   2 dfs.period
   2 dfs.audit.log.maxfilesize
   2 dfs.audit.log.maxbackupindex
   1 dfsmetrics.log
   1 dfsadmin
   1 dfs.servers
   1 dfs.replication
   1 dfs.file

   

    

   

    

   

   

    

    

   ====================================

   免密码登录原理和方法

    

   背景：搭建Hadoop环境需要设置无密码登陆，所谓无密码登陆其实是指通过证书认证的方式登陆，使用一种被称为"公私钥"认证的方式来进行ssh登录。

    　　在linux系统中,ssh是远程登录的默认工具,因为该工具的协议使用了RSA/DSA的加密算法.该工具做linux系统的远程管理是非常安全的。telnet,因为其不安全性,在linux系统中被搁置使用了。

   　　" 公私钥"认证方式简单的解释:首先在客户端上创建一对公私钥 （公钥文件：~/.ssh/id_rsa.pub； 私钥文件：~/.ssh/id_rsa）。然后把公钥放到服务器上（~/.ssh/authorized_keys）, 自己保留好私钥.在使用ssh登录时,ssh程序会发送私钥去和服务器上的公钥做匹配.如果匹配成功就可以登录了。

   工具/原料

    

   • linux系统

   方法/步骤

    

   1.  

      确认系统已经安装了SSH。

      rpm –qa | grep openssh

      rpm –qa | grep rsync

           -->出现如下图的信息表示已安装

       

      假设没有安装ssh和rsync，可以通过下面命令进行安装。

      yum install ssh -->安装SSH协议

      yum install rsync -->rsync是一个远程数据同步工具，可通过LAN/WAN快速同步多台主机间的文件

      service sshd restart -->启动服务

      
   2.  

      生成秘钥对

      ssh-keygen –t rsa –P \'\'  -->直接回车生成的密钥对：id_rsa和id_rsa.pub，默认存储在"/home/hadoop/.ssh"目录下。

      

      
   3.  

      把id_rsa.pub追加到授权的key里面去。

      cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys

      
   4.  

      修改授权key的权限

      chmod 600 ~/.ssh/authorized_keys

      
   5.  

      修改SSH配置文件

      su -  -->登陆root用户修改配置文件

      vim /etc/ssh/sshd_config -->去掉下图中三行的注释

      
   6.  

      测试连接

      service sshd restart -->重启ssh服务,

      exit   -->退出root用户，回到普通用户

      ssh localhost -->连接普通用户测试

       

      这只是配置好了单机环境上的SSH服务，要远程连接其它的服务器，接着看下面。

      
   7.  

      现在秘钥对已经生成好了，客户端SSH服务也已经配置好了，现在就把我们的钥匙（公钥）送给服务器。

      scp ~/.ssh/id_rsa.pub 远程用户名@远程服务器IP:~/  -->将公钥复制到远程服务器的~/目录下

      如: scp ~/.ssh/id_rsa.pub hadoop@192.168.1.134:~/

       

      可以看到我们复制的时候需要我们输入服务器的密码，等我们把SSH配置好之后这些步骤就可以不用输入密码了。

      
   8. 8

      上一步把公钥发送到192.168.1.134服务器上去了，我们去134机器上把公钥追加到授权key中去。（注意：如果是第一次运行SSH，那么.ssh目录需要手动创建，或者使用命令ssh-keygen -t rsa生成秘钥，它会自动在用户目录下生成.ssh目录。特别注意的是.ssh目录的权限问题，记得运行下chmod 700 .ssh命令）

      在134机器上使用命令：

      cat ~/id_rsa.pub >> ~/.ssh/authorized_keys  -->追加公钥到授权key中

      rm ~/id_rsa.pub  -->保险起见，删除公钥

      同样在134机器上重复第四步和第五步，

      service sshd restart  -->重启ssh服务

   9. 9

      回到客户机来，输入:

      ssh 192.168.1.134   -->应该就能直接连接服务器咯。