Hadoop 之 HDFS 分布式集群配置与使用（三）

本是古典何须时尚 2023-10-13 18:23 1阅读 0赞

### Hadoop 之 分布式集群 ###

*  一.集群实例创建
 *  二.配置
 *   *  1.创建三个虚拟机（Anolis）
     *   *  1.修改 HostName
         *  2.配置免密登录，配置前
     *  2.配置命名节点
     *   *  1.在 nd1 / nd2 部署 hadoop
         *  2.配置
     *  3.查看集群信息
 *  三.测试
 *   *  1.Shell 命令
     *  2.Java & MapReduce

# 一.集群实例创建 #

以三个 Hadoop 实例创建集群，可以用虚拟机，或者Docker容器来实现。本教程直接使用虚拟机演示
    集群架构可参考下图：客户端、命名节点和数据节点

![在这里插入图片描述][f6cfd0b781b94388b9548c7795fc579c.png]

查看 Hadoop 配置文件目录：ll $HADOOP_HOME/etc/hadoop

![在这里插入图片描述][442920ecb0ef4095bc4a7dc906c18274.png]

Hadoop 基本概念与含义

<table> 
 <thead> 
  <tr> 
   <th>名称</th> 
   <th>含义</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>HDFS</td> 
   <td>Hadoop Distributed File System，Hadoop 分布式文件系统的简称</td> 
  </tr> 
  <tr> 
   <td>NameNode</td> 
   <td>指挥其它节点存储的节点，用于映射文件在集群存储的位置</td> 
  </tr> 
  <tr> 
   <td>Secondary NameNode</td> 
   <td>副命名节点，用于备份命名节点数据，并协助命名节点进行管理工作；命名节点崩溃后可以用来恢复其数据，可以有多个</td> 
  </tr> 
  <tr> 
   <td>DataNode</td> 
   <td>用来储存数据块的节点，HDFS基础存储单位，受命名节点协调管理</td> 
  </tr> 
  <tr> 
   <td>core-site.xml</td> 
   <td>Hadoop 核心配置</td> 
  </tr> 
  <tr> 
   <td>hdfs-site.xml</td> 
   <td>HDFS 配置项</td> 
  </tr> 
  <tr> 
   <td>mapred-site.xml</td> 
   <td>MapReduce 配置项，映射和规约，对大型任务分治处理</td> 
  </tr> 
  <tr> 
   <td>yarn-site.xml</td> 
   <td>YARN 配置项</td> 
  </tr> 
  <tr> 
   <td>workers</td> 
   <td>记录所有的数据节点的主机名或 IP 地址</td> 
  </tr> 
 </tbody> 
</table>

# 二.配置 #

## 1.创建三个虚拟机（Anolis） ##

<table> 
 <thead> 
  <tr> 
   <th>虚拟机名称</th> 
   <th>地址</th> 
   <th>Host Name</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>hadoop_1</td> 
   <td>192.168.1.6</td> 
   <td>nn</td> 
  </tr> 
  <tr> 
   <td>hadoop_2</td> 
   <td>192.168.1.7</td> 
   <td>nd1</td> 
  </tr> 
  <tr> 
   <td>hadoop_3</td> 
   <td>192.168.1.8</td> 
   <td>nd2</td> 
  </tr> 
 </tbody> 
</table>

![在这里插入图片描述][9598426a5c224506bbcec34aad485c21.png]

### 1.修改 HostName ###

## 修改 192.168.1.6 服务器
    hostnamectl set-hostname nn
    echo "192.168.1.7 nd1" >> /etc/hosts
    echo "192.168.1.8 nd2" >> /etc/hosts
    ## 修改 192.168.1.7 服务器
    hostnamectl set-hostname nd1
    echo "192.168.1.6 nn" >> /etc/hosts
    echo "192.168.1.8 nd2" >> /etc/hosts
    ## 修改 192.168.1.8 服务器
    hostnamectl set-hostname nd2
    echo "192.168.1.6 nn" >> /etc/hosts
    echo "192.168.1.7 nd1" >> /etc/hosts

以 192.168.1.6 为例

![在这里插入图片描述][a48ec8aa1ab746598015b1ec4aca4871.png]

### 2.配置免密登录，配置前 ###

ssh root@nd1

![在这里插入图片描述][feefbe2c983b436f8cbcab9362d6068a.png]

## 修改 192.168.1.6 服务器
    ssh-keygen -t rsa -P "" -f ~/.ssh/id_rsa
    ssh-copy-id -i ~/.ssh/id_rsa root@nd1
    ssh-copy-id -i ~/.ssh/id_rsa root@nd2
    ## 修改 192.168.1.7 服务器
    ssh-keygen -t rsa -P "" -f ~/.ssh/id_rsa
    ssh-copy-id -i ~/.ssh/id_rsa root@nn
    ssh-copy-id -i ~/.ssh/id_rsa root@nd2
    ## 修改 192.168.1.8 服务器
    ssh-keygen -t rsa -P "" -f ~/.ssh/id_rsa
    ssh-copy-id -i ~/.ssh/id_rsa root@nn
    ssh-copy-id -i ~/.ssh/id_rsa root@nd1
    ## 分别配置本地登录
    ssh-copy-id -i ~/.ssh/id_rsa root@localhost

以 192.168.1.6为例，免密访问 nd1

![在这里插入图片描述][a118fa0942a94dc09074920017e6846d.png]

## 2.配置命名节点 ##

### 1.在 nd1 / nd2 部署 hadoop ###

## 解压文件
    mkdir -p /usr/local/java
    mkdir -p /usr/local/hadoop
    tar zxvf jdk-11.0.19_linux-x64_bin.tar.gz -C /usr/local/java/
    tar zxvf hadoop-3.3.6.tar.gz -C /usr/local/hadoop/
    ## 设置环境变量
    echo 'export JAVA_HOME=/usr/local/java/jdk-11.0.19' >> /etc/profile
    echo 'export CLASSPATH=$JAVA_HOME/lib:$CLASSPATH' >> /etc/profile
    echo 'export PATH=$JAVA_HOME/bin:$PATH' >> /etc/profile
    echo 'export HADOOP_HOME=/usr/local/hadoop/hadoop-3.3.6' >> /etc/profile
    echo 'export PATH=${PATH}:${HADOOP_HOME}/bin:${HADOOP_HOME}/sbin' >> /etc/profile
    source /etc/profile

### 2.配置 ###

## 1.进入
    cd $HADOOP_HOME/etc/hadoop
    ## 2.修改配置 core-site.xml
    vim core-site.xml
    ## 增加如下信息
       <property>
           <name>hadoop.http.staticuser.user</name>
           <value>root</value>
       </property>
       <property>
            <name>fs.defaultFS</name>
            <value>hdfs://nn:9000</value>
        </property>
        <property>
            <name>hadoop.tmp.dir</name>
            <value>file:///home/hadoop/tmp</value>
        </property>
    ## 3.修改配置 hdfs-site.xml
    vim hdfs-site.xml
    ## 增加如下信息
        <property>
            <name>dfs.replication</name>
            <value>2</value>
        </property>
        <property>
            <name>dfs.namenode.name.dir</name>
            <value>file:///home/hadoop/hdfs/name</value>
        </property>
        <property>
            <name>dfs.namenode.data.dir</name>
            <value>file:///home/hadoop/hdfs/data</value>
        </property>
    ## 4.修改配置 yarn-site.xml
    vim yarn-site.xml
    ## 增加如下信息
        <property>
            <name>yarn.nodemanager.aux-services</name>
            <value>mapreduce_shuffle</value>
        </property>
        <property>
            <name>yarn.resourcemanager.hostname</name>
            <value>nn</value>
        </property>
    ## 5.修改配置 mapred-site.xml
    vim mapred-site.xml
    ## 增加如下信息
        <property>
            <name>mapreduce.framework.name</name>
            <value>yarn</value>
        </property>
        <property>
    	  <name>yarn.app.mapreduce.am.env</name>
    	  <value>HADOOP_MAPRED_HOME=${
        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>
    ## 6.修改配置 workers
    vim workers
    ## 增加如下信息
    nn
    nd1
    nd2
    ## 7.将以上两个文件从 nn 复制到 nd1/nd2
    scp core-site.xml hdfs-site.xml yarn-site.xml mapred-site.xml hadoop-env.sh workers root@nd1:$HADOOP_HOME/etc/hadoop
    scp core-site.xml hdfs-site.xml yarn-site.xml mapred-site.xml hadoop-env.sh workers root@nd2:$HADOOP_HOME/etc/hadoop

core-site.xml

![在这里插入图片描述][3cfecfd02dc340608096562d971a8380.png]

hdfs-site.xml

![在这里插入图片描述][4b431a0caa164baf81467406b16d9b99.png]

yarn-site.xml

![在这里插入图片描述][edfd35edea0341418c38e52e0dd478e5.png]

mapred-site.xml

![在这里插入图片描述][650311b220f342759a94fd7a6bd02f1a.png]

workers

![在这里插入图片描述][c922118d4c3446a7ac11bb7b8890f0a2.png]

## 格式化命名节点：$HADOOP_HOME/etc/hadoop
    hdfs namenode -format
    ## 启动服务
    start-dfs.sh

格式化结果

![在这里插入图片描述][a5a8348f109b4adebe3bb502cb719d72.png]

启动结果

![在这里插入图片描述][cc6000a263f34dbe9f00b6243e5799cf.png]

## 启动 Yarn
    start-yarn.sh

![在这里插入图片描述][874509b783364cf8987ac9933dfe4bbe.png]

## 3.查看集群信息 ##

集群信息：http://192.168.1.6:9870/dfshealth.html\#tab-datanode

![在这里插入图片描述][d5e49210cbbe43ac95fe3dec53fcd398.png]

在 Windows 访问 http://nd1:9864 要关虚拟机防火墙，并添加 Host 解析，此处直接通过 IP 地址打开

![在这里插入图片描述][d1a4672106ea48e9b9fa58d344229a36.png]

Yarn 信息：http://192.168.1.6:8088/cluster

![在这里插入图片描述][021225f2fb7e410f9d05f95a9977f673.png]

# 三.测试 #

## 1.Shell 命令 ##

## 上传文件
    hadoop fs -put /home/test.txt /log/
    ## 查看文件
    hadoop fs -cat /log/test.txt

![在这里插入图片描述][ede1805faa03422d9e569b9be5dc8817.png]

## 2.Java & MapReduce ##

[Apache Hadoop 官网][Apache Hadoop]

单词统计代码

package org.example.controller;
    
    import org.apache.hadoop.io.IntWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapreduce.Mapper;
    import org.apache.hadoop.mapreduce.Reducer;
    
    import java.io.IOException;
    import java.util.StringTokenizer;
    
    /**
     * @author Administrator
     * @Description
     * @create 2023-07-17 23:22
     */
    public class WordCount {
        
    
        public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {
        
            private final static IntWritable one = new IntWritable(1);
            private Text word = new Text();
    
            public void map(Object key, Text value, Context context
            ) throws IOException, InterruptedException {
        
                StringTokenizer itr = new StringTokenizer(value.toString());
                while (itr.hasMoreTokens()) {
        
                    word.set(itr.nextToken());
                    context.write(word, one);
                }
            }
        }
    
        public static class IntSumReducer
                extends Reducer<Text,IntWritable,Text,IntWritable> {
        
            private IntWritable result = new IntWritable();
    
            public void reduce(Text key, Iterable<IntWritable> values,
                               Context context
            ) throws IOException, InterruptedException {
        
                int sum = 0;
                for (IntWritable val : values) {
        
                    sum += val.get();
                }
                result.set(sum);
                context.write(key, result);
            }
        }
    
    }

测试类，测试地址：http://127.0.0.1:8080/test/wordCount

package org.example.controller;
    
    import jakarta.annotation.PostConstruct;
    import org.apache.hadoop.conf.Configuration;
    import org.apache.hadoop.fs.FSDataInputStream;
    import org.apache.hadoop.fs.FSDataOutputStream;
    import org.apache.hadoop.fs.FileSystem;
    import org.apache.hadoop.fs.Path;
    import org.apache.hadoop.io.IntWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapreduce.Job;
    import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
    import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
    import org.springframework.web.bind.annotation.GetMapping;
    import org.springframework.web.bind.annotation.RequestMapping;
    import org.springframework.web.bind.annotation.RestController;
    
    import java.io.IOException;
    
    /**
     * @author Administrator
     * @Description
     * @create 2023-07-13 23:19
     */
    @RestController
    @RequestMapping("/test")
    public class TestController {
        
    
        @PostConstruct
        public void init(){
        
            System.setProperty("HADOOP_USER_NAME","root");
        }
    
        @GetMapping("/wordCount")
        public void wordCount() throws IOException, InterruptedException, ClassNotFoundException {
        
            Configuration conf = new Configuration();
            conf.set("fs.defaultFS", "hdfs://192.168.1.6:9000");
            // 打开文件并读取输出
            Job job = Job.getInstance(conf, "word count");
            job.setJarByClass(WordCount.class);
            job.setMapperClass(WordCount.TokenizerMapper.class);
            job.setCombinerClass(WordCount.IntSumReducer.class);
            job.setReducerClass(WordCount.IntSumReducer.class);
            job.setOutputKeyClass(Text.class);
            job.setOutputValueClass(IntWritable.class);
            FileInputFormat.addInputPath(job, new Path("/testData"));
            FileOutputFormat.setOutputPath(job, new Path("/output"));
            boolean result = job.waitForCompletion(true);
            // System.exit(result ? 0 : 1);
            // 打开文件并读取输出
            FileSystem fs = FileSystem.get(conf);
            Path path = new Path("/output/part-r-00000");
            FSDataInputStream ins = fs.open(path);
            StringBuilder builder = new StringBuilder();
            int ch = ins.read();
            while (ch != -1) {
        
                builder.append((char)ch);
                ch = ins.read();
            }
            System.out.println(builder.toString());
        }
    }

测试结果

![在这里插入图片描述][b6d4d48943ab45c09ecbf349dad0b998.png]

![在这里插入图片描述][1a31a0d374f34ab69b4c8038f98ab348.png]

[f6cfd0b781b94388b9548c7795fc579c.png]: https://img-blog.csdnimg.cn/f6cfd0b781b94388b9548c7795fc579c.png
[442920ecb0ef4095bc4a7dc906c18274.png]: https://img-blog.csdnimg.cn/442920ecb0ef4095bc4a7dc906c18274.png
[9598426a5c224506bbcec34aad485c21.png]: https://img-blog.csdnimg.cn/9598426a5c224506bbcec34aad485c21.png
[a48ec8aa1ab746598015b1ec4aca4871.png]: https://img-blog.csdnimg.cn/a48ec8aa1ab746598015b1ec4aca4871.png
[feefbe2c983b436f8cbcab9362d6068a.png]: https://img-blog.csdnimg.cn/feefbe2c983b436f8cbcab9362d6068a.png
[a118fa0942a94dc09074920017e6846d.png]: https://img-blog.csdnimg.cn/a118fa0942a94dc09074920017e6846d.png
[3cfecfd02dc340608096562d971a8380.png]: https://img-blog.csdnimg.cn/3cfecfd02dc340608096562d971a8380.png
[4b431a0caa164baf81467406b16d9b99.png]: https://img-blog.csdnimg.cn/4b431a0caa164baf81467406b16d9b99.png
[edfd35edea0341418c38e52e0dd478e5.png]: https://img-blog.csdnimg.cn/edfd35edea0341418c38e52e0dd478e5.png
[650311b220f342759a94fd7a6bd02f1a.png]: https://img-blog.csdnimg.cn/650311b220f342759a94fd7a6bd02f1a.png
[c922118d4c3446a7ac11bb7b8890f0a2.png]: https://img-blog.csdnimg.cn/c922118d4c3446a7ac11bb7b8890f0a2.png
[a5a8348f109b4adebe3bb502cb719d72.png]: https://img-blog.csdnimg.cn/a5a8348f109b4adebe3bb502cb719d72.png
[cc6000a263f34dbe9f00b6243e5799cf.png]: https://img-blog.csdnimg.cn/cc6000a263f34dbe9f00b6243e5799cf.png
[874509b783364cf8987ac9933dfe4bbe.png]: https://img-blog.csdnimg.cn/874509b783364cf8987ac9933dfe4bbe.png
[d5e49210cbbe43ac95fe3dec53fcd398.png]: https://img-blog.csdnimg.cn/d5e49210cbbe43ac95fe3dec53fcd398.png
[d1a4672106ea48e9b9fa58d344229a36.png]: https://img-blog.csdnimg.cn/d1a4672106ea48e9b9fa58d344229a36.png
[021225f2fb7e410f9d05f95a9977f673.png]: https://img-blog.csdnimg.cn/021225f2fb7e410f9d05f95a9977f673.png
[ede1805faa03422d9e569b9be5dc8817.png]: https://img-blog.csdnimg.cn/ede1805faa03422d9e569b9be5dc8817.png
[Apache Hadoop]: https://hadoop.apache.org/docs/stable/
[b6d4d48943ab45c09ecbf349dad0b998.png]: https://img-blog.csdnimg.cn/b6d4d48943ab45c09ecbf349dad0b998.png
[1a31a0d374f34ab69b4c8038f98ab348.png]: https://img-blog.csdnimg.cn/1a31a0d374f34ab69b4c8038f98ab348.png