从零开始hadoop分布式环境搭建

叁歲伎倆 2022-07-13 11:21 302阅读 0赞

## 1. Linux虚机换机环境安装 ##

### 1.1 linux环境安装 ###

1.建议选择虚拟机:VirtualBox  
2.Linux版本：Ubuntu  
3.安装时选择动态扩展磁盘，最大磁盘容量50G(最大磁盘容量太小，hadoop使用过程中容易出现意想不到的的问题）  
4.网络选择桥接网卡（不要选择NAT，不然路由器不会为虚拟机分配独立的IP地址）  
5.安装增强功能开启双向开启共享剪切板  
6.安装用户使用同样的用户名：如hadoop，在建立分布式环境时需要保证通用的用户名  
7.设置hadoop主机名为：hadoop-master hadoop-slave01 hadoop-slave02 (如果忘记设置可以看1.2修改主机名）  
ps:具体安装过程不再赘述  
安装成功后：ifconfig查看机器ip  
机器1的ip  
![hadoop-ip01][]  
机器2的ip  
![hadoop-ip02][]  
机器3的ip  
![hadoop-ip03][]

### 1.2 linux主机名修改 ###

1.  sudo apt-get install vim
2.  sudo vim /etc/hostname
3.  修改为自己想要的域名如：hadoop-master，并保存
4.  reboot（重启）
5.  确认主机名修改成功：hadoop@hadoop-VirtualBox 变为hadoop@hadoop-master

## 2 ssh和polysh工具安装 ##

ssh和polysh的安装的目的是通过自己的机器去访问虚拟机如：本人macbook去访问virtualbox中的hadoop-master hadoop-slave01 hadoop-slave02 3台虚拟机。  
其中ssh需要在四台机器都安装，polysh则只需要在自己本机安装，从而通过一个shell的形式访问3台机器

### 2.1 ssh安装和使用 ###

1.  sudo apt-get install ssh （各个虚拟机都要先安装，下面的步骤都可以同ssh远程登录机器操作）
2.  在本地机器建立虚拟机host和ip映射关系：/etc/hosts 输入如下内容： 192.168.31.88 hadoop-master  
    192.168.31.234 hadoop-slave01  
    192.168.31.186 hadoop-slave02
3.  ssh hadoop@hadoop-master （输入虚拟机密码）
4.  ssh-keygen（生成免密登录的几个文件，全部回车即可）
5.  切换目标cd .ssh 显示文件ls (id\_rsa id\_rsa.pub 文件具体意义自己百度)
6.  如果要免密登录到一台机器，需要把自己机器的id\_rsa.pub文件导入到要登录机器.ssh/authorized\_keys文件中
7.  到虚拟机上执行命令：echo “ssh-rsa xxxxxxxx” >> .ssh/authorized\_keys (”ssh xxx“为本地机器的id\_rsa.pub的内容）
8.  exit关闭虚拟机shell
9.  ssh hadoop@hadoop-master (不再需要密码）
10. 最好是将所有虚拟机的id\_rsa.pub也导入到本地机器的authorized\_keys中使得互相传文件时不用输密码
11. 现在可以无密码的登录三台机器ssh hadoop@hadoop-master ssh hadoop@hadoop-slave01 ssh hadoop@hadoop-slave02

### 2.2 polysh安装 ###

polysh是批量登录多台机器的工具，如上面如果你要传一个hadoop的安装文件包到虚拟机上，你需要建立三个ssh链接，并在每个ssh的shell中上传同样的文件，而polysh的作用就是你可以在polysh的shell中操作三台机器，如上传文件，只需要一次上传，就在三台虚拟机中都上传了文件。

polysh脚本安装

wget http://guichaz.free.fr/polysh/files/polysh-0.4.tar.gz
    tar -zxvf polysh-0.4.tar.gz
    cd polysh-0.4
    sudo python setup.py install
    polysh -help 如果有提示表示安装成功
    # 将polysh命令加入path中
    echo "export PATH=~/polysh/bin:$PATH" >> ~/.bash_profile
    source ~/.bash_profile

polysh使用

echo "alias hadoop-login=\"polysh 'hadoop@hadoop-master' 'hadoop@hadoop-slave<01-02>' \" ">> ~/.bash_profile
    source ~/.bash_profile
    #以后就可通过hadoop-login来登录三台机器
    hadoop-login

列如：hadoop主从机器的host和ip映射

sudo sh -c "echo \"192.168.31.88 hadoop-master\n192.168.31.234 hadoop-slave01\n192.168.31.186 hadoop-slave02\n\" >> /etc/hosts"
    #这里会要求输入密码(输入hadoop)
    cat /etc/hosts
    #可以看到如下内容：（：前面是机器hostname，后面是内容）
    hadoop@hadoop-slave01 : 192.168.31.88 hadoop-master
    hadoop@hadoop-slave01 : 192.168.31.234 hadoop-slave01
    hadoop@hadoop-slave01 : 192.168.31.186 hadoop-slave02
    hadoop@hadoop-master  : 192.168.31.88 hadoop-master
    hadoop@hadoop-master  : 192.168.31.234 hadoop-slave01
    hadoop@hadoop-master  : 192.168.31.186 hadoop-slave02
    hadoop@hadoop-slave02 : 192.168.31.88 hadoop-master
    hadoop@hadoop-slave02 : 192.168.31.234 hadoop-slave01
    hadoop@hadoop-slave02 : 192.168.31.186 hadoop-slave02

## 3 hadoop安装 ##

### 3.1 hadoop安装包下载和上传 ###

*  下载hadoop安装包：hadoop-1.2.1-bin.tar.gz
 *  scp username@192.168.31.19:/Users/username/Downloads/hadoop-1.2.1-bin.tar.gz ./ (通过polysh从本地机器将安装包上传到虚拟机上，如果你没有把虚拟机的id\_rsa.pub设置到本地机器的authorized\_keys文件中，你需要输入本地机器登陆的密码-建议设置）
 *  ls hadoop\*  
    hadoop@hadoop-slave01 : hadoop-1.2.1-bin.tar.gz  
    hadoop@hadoop-slave02 : hadoop-1.2.1-bin.tar.gz  
    hadoop@hadoop-master : hadoop-1.2.1-bin.tar.gz
 *  下载jdk安装包：jdk-8u111-linux-x64.tar.gz
 *  scp username@192.168.31.19:/Users/username/Downloads/jdk-8u111-linux-x64.tar.gz ./

### 3.2 hadoop安装 ###

1.安装jdk和配置环境变量

#解压
    tar -xzvf jdk-8u111-linux-x64.tar.gz
    #设置jdk环境变量path和classpath
    echo "export JAVA_HOME=/home/hadoop/workspace/jdk8" >> .bash_profile
    echo "export PATH=$JAVA_HOME/bin:$PATH" >> .bash_profile
    source .bash_profile
    echo "export CLASSPATH=.:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar" >> .bash_profile
    source .bash_profile

2.安装hadoop和修改配置

#解压
    tar -xzvf hadoop-1.2.1-bin.tar.gz
    #配置环境变量
    echo "export HADOOP_HOME=/home/hadoop/workspace/hadoop" >> .bash_profile
    source .bash_profile
    echo "export PATH=$HADOOP_HOME/bin:$PATH" >> .bash_profile
    source .bash_profile

//修改conf中配置文件

#修改hadoop-env.sh
    #修改JAVA_HOME变量
    
    # The java implementation to use. Required.
    # export JAVA_HOME=/usr/lib/j2sdk1.5-sun
    export JAVA_HOME=/home/hadoop/workspace/jdk8
    # Extra Java CLASSPATH elements. Optional.
    # export HADOOP_CLASSPATH=

#修改core-site.xml 
    #设置namenode的地址和端口
    #设置hadoop的中间结果和中间日志文件等的临时目录
    <configuration>
        <property>
            <name>fs.default.name</name>
            <value>hdfs://hadoop-master:8020</value>
        </property>
        <property>
                    <name>hadoop.tmp.dir</name>
                    <value>/home/hadoop/workspace/hadoop_dir/tmp</value>
            </property>
    </configuration>

#修改hdfs-site.xml
    #配置namenode节点目录
    #配置datanode节点目录
    #配置备份数量
    <configuration>
        <property>
            <name>dfs.name.dir</name>
            <value>/home/hadoop/workspace/hadoop_dir/name</value>
        </property>
        <property>
                    <name>dfs.data.dir</name>
                    <value>/home/hadoop/workspace/hadoop_dir/data</value>
            </property>
        <property>
                    <name>dfs.replication</name>
                    <value>3</value>
            </property>
    </configuration>

#修改mapred-site.xml
    #配置map-reduce中的jobtracker的地址和端口
    <configuration>
        <property>
            <name>mapred.job.tracker</name>
            <value>hadoop-master:8021</value>
        </property>
    </configuration>

#修改masters
    #配置master机器的hostname列表
    hadoop-master

#修改slaves
    #配置slave机器的hastname列表
    hadoop-slave01
    hadoop-slave02

三台虚拟机按照同样的方式配置成功后，hadoop就安装和配置成功了

## hadoop服务启动 ##

**1.启动hadoop服务**

*  ssh hadoop@hadoop-master
 *  hadoop namenode -format （这个命令只需要执行一次）  
    正常格式化namenode输出结果如下：

************************************************************/
    17/01/07 21:31:32 INFO util.GSet: Computing capacity for map BlocksMap
    17/01/07 21:31:32 INFO util.GSet: VM type       = 64-bit
    17/01/07 21:31:32 INFO util.GSet: 2.0% max memory = 1013645312
    17/01/07 21:31:32 INFO util.GSet: capacity      = 2^21 = 2097152 entries
    17/01/07 21:31:32 INFO util.GSet: recommended=2097152, actual=2097152
    17/01/07 21:31:32 INFO namenode.FSNamesystem: fsOwner=hadoop
    17/01/07 21:31:32 INFO namenode.FSNamesystem: supergroup=supergroup
    17/01/07 21:31:32 INFO namenode.FSNamesystem: isPermissionEnabled=true
    17/01/07 21:31:32 INFO namenode.FSNamesystem: dfs.block.invalidate.limit=100
    17/01/07 21:31:32 INFO namenode.FSNamesystem: isAccessTokenEnabled=false accessKeyUpdateInterval=0 min(s), accessTokenLifetime=0 min(s)
    17/01/07 21:31:32 INFO namenode.FSEditLog: dfs.namenode.edits.toleration.length = 0
    17/01/07 21:31:32 INFO namenode.NameNode: Caching file names occuring more than 10 times
    17/01/07 21:31:33 INFO common.Storage: Image file /home/hadoop/workspace/hadoop_dir/name/current/fsimage of size 112 bytes saved in 0 seconds.
    17/01/07 21:31:33 INFO namenode.FSEditLog: closing edit log: position=4, editlog=/home/hadoop/workspace/hadoop_dir/name/current/edits
    17/01/07 21:31:33 INFO namenode.FSEditLog: close success: truncate to 4, editlog=/home/hadoop/workspace/hadoop_dir/name/current/edits
    17/01/07 21:31:33 INFO common.Storage: Storage directory /home/hadoop/workspace/hadoop_dir/name has been successfully formatted.
    17/01/07 21:31:33 INFO namenode.NameNode: SHUTDOWN_MSG:
    /************************************************************
    SHUTDOWN_MSG: Shutting down NameNode at hadoop-master/192.168.31.88
    ************************************************************/

*  设置hadoop-master到hadoop-slave01 hadoop-slave02的ssh免密登录
 *  cd workspace/hadoop/bin/
 *  sh start-all.sh  
    正常启动输入日志如下：

starting namenode, logging to /home/hadoop/workspace/hadoop/libexec/../logs/hadoop-hadoop-namenode-hadoop-master.out
    hadoop-slave01: starting datanode, logging to /home/hadoop/workspace/hadoop/libexec/../logs/hadoop-hadoop-datanode-hadoop-slave01.out
    hadoop-slave02: starting datanode, logging to /home/hadoop/workspace/hadoop/libexec/../logs/hadoop-hadoop-datanode-hadoop-slave02.out
    hadoop-master: starting secondarynamenode, logging to /home/hadoop/workspace/hadoop/libexec/../logs/hadoop-hadoop-secondarynamenode-hadoop-master.out
    starting jobtracker, logging to /home/hadoop/workspace/hadoop/libexec/../logs/hadoop-hadoop-jobtracker-hadoop-master.out
    hadoop-slave02: starting tasktracker, logging to /home/hadoop/workspace/hadoop/libexec/../logs/hadoop-hadoop-tasktracker-hadoop-slave02.out
    hadoop-slave01: starting tasktracker, logging to /home/hadoop/workspace/hadoop/libexec/../logs/hadoop-hadoop-tasktracker-hadoop-slave01.out

可以看出master会启动：namenode、secondarynamenode、jobtracker三个java进程  
slave会启动：datanode、tasktracker两个java进程  
\- jps 查看是否和日志中启动的进程一致  
 hadoop@hadoop-master : 6549 SecondaryNameNode   
hadoop@hadoop-master : 6629 JobTracker  
hadoop@hadoop-master : 6358 NameNode  
hadoop@hadoop-master : 6874 Jps  
hadoop@hadoop-slave01 : 4096 DataNode  
hadoop@hadoop-slave01 : 4197 TaskTracker  
hadoop@hadoop-slave01 : 4253 Jps   
hadoop@hadoop-slave02 : 5177 TaskTracker  
hadoop@hadoop-slave02 : 5076 DataNode  
hadoop@hadoop-slave02 : 5230 Jps

*  停止hadoop服务：sh stop-all.sh

stopping jobtracker
    hadoop-slave02: stopping tasktracker
    hadoop-slave01: stopping tasktracker
    stopping namenode
    hadoop-slave01: stopping datanode
    hadoop-slave02: stopping datanode
    hadoop-master: stopping secondarynamenode

2.状态查看网站  
map-reduce任务情况：[http://hadoop-master:50030/jobtracker.jsp][http_hadoop-master_50030_jobtracker.jsp]  
dfs使用情况：[http://hadoop-master:50070/dfshealth.jsp][http_hadoop-master_50070_dfshealth.jsp]

## 4 执行hadoop任务 ##

百度实现一个wordcount的hadoop jar包任务。百度上很多不在此说明

1.上传本地文件到hdfs文件系统中

*  hadoop fs -mkdir input （在hdfs文件系统上创建input文件夹）
 *  hadoop fs -put input/\* input （将本地文件夹input中的文件上传到hdfs文件夹中）
 *  hadoop fs -ls input

hadoop@hadoop-master:~/workspace$ hadoop fs -ls input
    Found 3 items
    -rw-r--r--   3 hadoop supergroup       2416 2017-01-07 22:58 /user/hadoop/input/wiki01
    -rw-r--r--   3 hadoop supergroup       3475 2017-01-07 22:58 /user/hadoop/input/wiki02
    -rw-r--r--   3 hadoop supergroup       4778 2017-01-07 22:58 /user/hadoop/input/wiki03

*  scp wenyicao@192.168.31.19:/Users/wenyi/workspace/smart-industry-parent/hadoop-wordcount/target/hadoop-wordcount-1.0-SNAPSHOT.jar ./ (将本地的hadoop程序上传到虚拟机）
 *  hadoop jar hadoop-wordcount-1.0-SNAPSHOT.jar com.cweeyii.hadoop/WordCount input output (运行hadoop的wordcount单词计数程序）  
    hadoop调度和运行日志：

hadoop@hadoop-master:~/workspace$ hadoop jar hadoop-wordcount-1.0-SNAPSHOT.jar com.cweeyii.hadoop/WordCount input output
    开始执行
    input   output
    17/01/07 23:24:33 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
    17/01/07 23:24:33 INFO util.NativeCodeLoader: Loaded the native-hadoop library
    17/01/07 23:24:33 WARN snappy.LoadSnappy: Snappy native library not loaded
    17/01/07 23:24:33 INFO mapred.FileInputFormat: Total input paths to process : 3
    17/01/07 23:24:34 INFO mapred.JobClient: Running job: job_201701072322_0002
    17/01/07 23:24:35 INFO mapred.JobClient:  map 0% reduce 0%
    17/01/07 23:24:41 INFO mapred.JobClient:  map 33% reduce 0%
    17/01/07 23:24:42 INFO mapred.JobClient:  map 100% reduce 0%
    17/01/07 23:24:49 INFO mapred.JobClient:  map 100% reduce 33%
    17/01/07 23:24:51 INFO mapred.JobClient:  map 100% reduce 100%
    17/01/07 23:24:51 INFO mapred.JobClient: Job complete: job_201701072322_0002
    17/01/07 23:24:51 INFO mapred.JobClient: Counters: 30
    17/01/07 23:24:51 INFO mapred.JobClient:   Map-Reduce Framework
    17/01/07 23:24:51 INFO mapred.JobClient:     Spilled Records=3400
    17/01/07 23:24:51 INFO mapred.JobClient:     Map output materialized bytes=20878
    17/01/07 23:24:51 INFO mapred.JobClient:     Reduce input records=1700
    17/01/07 23:24:51 INFO mapred.JobClient:     Virtual memory (bytes) snapshot=7414968320
    17/01/07 23:24:51 INFO mapred.JobClient:     Map input records=31
    17/01/07 23:24:51 INFO mapred.JobClient:     SPLIT_RAW_BYTES=309
    17/01/07 23:24:51 INFO mapred.JobClient:     Map output bytes=17460
    17/01/07 23:24:51 INFO mapred.JobClient:     Reduce shuffle bytes=20878
    17/01/07 23:24:51 INFO mapred.JobClient:     Physical memory (bytes) snapshot=584658944
    17/01/07 23:24:51 INFO mapred.JobClient:     Map input bytes=10669
    17/01/07 23:24:51 INFO mapred.JobClient:     Reduce input groups=782
    17/01/07 23:24:51 INFO mapred.JobClient:     Combine output records=0
    17/01/07 23:24:51 INFO mapred.JobClient:     Reduce output records=782
    17/01/07 23:24:51 INFO mapred.JobClient:     Map output records=1700
    17/01/07 23:24:51 INFO mapred.JobClient:     Combine input records=0
    17/01/07 23:24:51 INFO mapred.JobClient:     CPU time spent (ms)=1950
    17/01/07 23:24:51 INFO mapred.JobClient:     Total committed heap usage (bytes)=498544640
    17/01/07 23:24:51 INFO mapred.JobClient:   File Input Format Counters
    17/01/07 23:24:51 INFO mapred.JobClient:     Bytes Read=10669
    17/01/07 23:24:51 INFO mapred.JobClient:   FileSystemCounters
    17/01/07 23:24:51 INFO mapred.JobClient:     HDFS_BYTES_READ=10978
    17/01/07 23:24:51 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=285064
    17/01/07 23:24:51 INFO mapred.JobClient:     FILE_BYTES_READ=20866
    17/01/07 23:24:51 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=7918
    17/01/07 23:24:51 INFO mapred.JobClient:   File Output Format Counters
    17/01/07 23:24:51 INFO mapred.JobClient:     Bytes Written=7918
    17/01/07 23:24:51 INFO mapred.JobClient:   Job Counters
    17/01/07 23:24:51 INFO mapred.JobClient:     Launched map tasks=3
    17/01/07 23:24:51 INFO mapred.JobClient:     Launched reduce tasks=1
    17/01/07 23:24:51 INFO mapred.JobClient:     SLOTS_MILLIS_REDUCES=9720
    17/01/07 23:24:51 INFO mapred.JobClient:     Total time spent by all reduces waiting after reserving slots (ms)=0
    17/01/07 23:24:51 INFO mapred.JobClient:     SLOTS_MILLIS_MAPS=13500
    17/01/07 23:24:51 INFO mapred.JobClient:     Total time spent by all maps waiting after reserving slots (ms)=0
    17/01/07 23:24:51 INFO mapred.JobClient:     Data-local map tasks=3

*  hadoop fs -ls output/ （查看执行结果）

# _SUCESS表示执行结果的状态
    # _logs表示执行的日志
    # part-00000表示执行结果
    -rw-r--r--   3 hadoop supergroup          0 2017-01-07 23:24 /user/hadoop/output/_SUCCESS
    drwxr-xr-x   - hadoop supergroup          0 2017-01-07 23:24 /user/hadoop/output/_logs
    -rw-r--r--   3 hadoop supergroup       7918 2017-01-07 23:24 /user/hadoop/output/part-00000

*  hadoop fs -cat output/part-00000 （最终输出结果）

hadoop@hadoop-master:~/workspace$ hadoop fs -cat output/part-00000
    "BeginnerQuestions."    1
    "CamelCase" 1
    "PopularMusic"  1
    "Richard 1
    "RichardWagner" 1
    "TableOfContents"   1
    "WiKi"  1
    "Wiki 1
    "Wiki"  1
    "Wiki").    1
    "WikiNode"  1
    "by 1
    "corrected 1
    "edit"  1
    "editing 1
    "fixed 1
    "free 1
    "history"   1
    "link 1
    "native"    1
    "plain-vanilla" 1
    "popular 1
    "pretty"    1
    "quick".[5][6][7]   1
    "tag"   2
    "the 2
    "wiki 2

*  更加友好的查看结果的方式可以通过：[http://hadoop-master:50070/dfshealth.jsp][http_hadoop-master_50070_dfshealth.jsp] 来查看  
    ![wordcount][]

[hadoop-ip01]: /images/20220713/4e050f546dcd4a7288ba14959b5c3386.png
[hadoop-ip02]: /images/20220713/a6f06e21efa5483e89c04a0ece096ec4.png
[hadoop-ip03]: /images/20220713/cb46442be21f454abbd8aa0f12e36bc2.png
[http_hadoop-master_50030_jobtracker.jsp]: http://hadoop-master:50030/jobtracker.jsp
[http_hadoop-master_50070_dfshealth.jsp]: http://hadoop-master:50070/dfshealth.jsp
[wordcount]: /images/20220713/5ad8046e854341868a1f324f6b0f0702.png