大数据与云计算 | Hadoop分布式集群搭建与HDFS、Hadoop、MapReduce常见操作

川长思鸟来 2023-02-23 03:58 18阅读 0赞

# 一、实验目的 #

1.  熟悉Hadoop分布式集群的搭建过程
2.  学习Hadoop分布式集群的使用示例

# 二、实验内容 #

1.  搭建Hadoop分布式集群环境
2.  掌握HDFS常见操作，自行编写一个英文文本文件，上传至HDFS中
3.  使用Hadoop提供的example程序实现统计该文件的wordcount
4.  \*自行编写实现wordcount功能的MapReduce程序

# 三、实验过程 #

## **3.1** **虚拟机环境配置** ##

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70][]

3.1.1 从[https://www.virtualbox.org][https_www.virtualbox.org]下载并安装安装Oracle VM VirtualBox，新建一台Linux虚拟机，分配1024MB内存(master分配2048MB)、50GB虚拟硬盘(使用VDI映像，动态分配空间)

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 1][]

3.1.2 刚新建完成的虚拟机没有操作系统，为了兼顾稳定性和主机负载，我选用了桌面版Ubuntu 16.04([https://ubuntu.com/download/desktop][https_ubuntu.com_download_desktop])，下载镜像后，在存储区挂载Ubuntu安装镜像的虚拟光盘后开机

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 2][]

3.1.3 根据安装程序的提示，在虚拟机中安装Ubuntu，按照默认设置安装即可，静等安装完成，安装完成后卸载ISO虚拟光驱

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 3][]

3.1.4为了便于后续的IP与MAC绑定操作，设置虚拟机的网络连接方式为“桥接网卡”，至此，虚拟机的配置大体完成

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 4][]

## **3.2** **前置配置操作****(****上****)** ##

（为减轻重复配置的工作量，先配置一台虚拟机，配置完成后克隆出三台虚拟机，然后配置虚拟机之间不同部分）

3.2.1 编辑hostname文件，将第一台虚拟机命名为master，保存

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 5][]

3.2.2 编辑hosts文件，修改127.0.1.1的名称为master，保存

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 6][]

3.2.3 安装ssh并配置SSH免密登录

1.  打开终端，运行sudo apt-get install openssh-server，安装ssh
2.  运行sudo ufw disable，关闭防火墙
3.  运行ssh-keygen –t rsa，一直回车，设置密码为空，直至出现RSA密钥
4.  运行cat ~/.ssh/id\_rsa.pub >> ~/.ssh/authorized\_keys，将公钥追加到“authorized\_keys”文件
5.  设定文件权限：chmod 600 .ssh/authorized\_keys
6.  运行ssh localhost，如出现下图界面，说明配置成功

![20200705121001112.png][]

3.2.4 安装JDK和Hadoop

1.  运行sudo apt-get install openjdk-8-jdk，完成JDK的安装
2.  从[https://hadoop.apache.org/][https_hadoop.apache.org]下载Hadoop 2.10.0，下载完成后解压，移动到“/usr/local”目录下，重命名文件夹名为hadoop

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 7][]

jdk安装界面

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 8][]

Hadoop下载页面

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 9][]

Hadoop安装目录

3.2.5 配置JDK与Hadoop的系统环境变量

需要修改配置文件/etc/profile，添加Java和Hadoop的环境变量

1.  export JAVA\_HOME=/usr/lib/jvm/java-1.8.0-openjdk-amd64 
2.  export PATH=$JAVA\_HOME/bin:$PATH 
3.  export CLASSPATH=.:$JAVA\_HOME/lib/dt.jar:$JAVA\_HOME/lib/tools.jar 
4.  
5.  export HADOOP\_HOME=/usr/local/hadoop 
6.  export PATH=$PATH:$HADOOP\_HOME/bin

保存后运行“source /etc/profile”指令使刚刚的配置立即生效

![20200705121001120.png][]

3.2.6 配置Hadoop相关配置文件

3.2.6.1 修改/usr/local/hadoop/etc/hadoop/hadoop-env.sh，配置JDK路径为：

“/usr/lib/jvm/java-1.8.0-openjdk-amd64”

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 10][]

3.2.6.2 修改/usr/local/hadoop/etc/hadoop/core-site.xml —— HDFS默认地址和Web UI配置

1.  **<configuration>**
2.  **<property>**
3.  **<name>**hadoop.tmp.dir**</name>**
4.  **<value>**file:/usr/local/hadoop/tmp**</value>**
5.  **<description>**Abase for other temporary directories.**</description>**
6.  **</property>**
7.  **<property>**
8.  **<name>**fs.defaultFS**</name>**
9.  **<value>**hdfs://master:9000**</value>**
10. **</property>**
11. **</configuration>**

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 11][]

3.2.6.3 修改/usr/local/hadoop/etc/hadoop/hdfs-site.xml —— HDFS副本

1.  **<configuration>**
2.  **<property>**
3.  **<name>**dfs.replication**</name>**
4.  **<value>**3**</value>**
5.  **</property>**
6.  **<property>**
7.  **<name>**dfs.name.dir**</name>**
8.  **<value>**/usr/local/hadoop/hdfs/name**</value>**
9.  **</property>**
10. **<property>**
11. **<name>**dfs.data.dir**</name>**
12. **<value>**/usr/local/hadoop/hdfs/data**</value>**
13. **</property>**
14.  <!-- 
15. **<property>**
16. **<name>**dfs.namenode.datanode.registration.ip-hostname-check**</name>**
17. **<value>**false**</value>**
18. **</property>**
19.  \--**>**
20. **</configuration>**

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 12][]

3.2.6.4 修改/usr/local/hadoop/etc/hadoop/mapred-site.xml——设置MapReduce使用的框架：拷贝mapred-site.xml.template，命名为“mapred-site.xml”，对其做如下配置：

1.  **<configuration>**
2.  **<property>**
3.  **<name>**mapreduce.framework.name**</name>**
4.  **<value>**yarn**</value>**
5.  **</property>**
6.  **<property>**
7.  **<name>**mapred.job.tracker**</name>**
8.  **<value>**http://master:9001**</value>**
9.  **</property>**
10. **<property>**
11. **<name>**mapreduce.reduce.maxattempts**</name>**
12. **<value>**100**</value>**
13. **</property>**
14. **<property>**
15. **<name>**mapreduce.map.maxattempts**</name>**
16. **<value>**100**</value>**
17. **</property>**
18. **</configuration>**

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 13][]

3.2.6.5 修改/usr/local/hadoop/etc/hadoop/yarn-site.xml

1.  **<configuration>**
2.  
3.  **<property>**
4.  **<name>**yarn.nodemanager.aux-services**</name>**
5.  **<value>**mapreduce\_shuffle**</value>**
6.  **</property>**
7.  **<property>**
8.  **<name>**yarn.resourcemanager.hostname**</name>**
9.  **<value>**master**</value>**
10. **</property>**
11. **</configuration>**

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 14][]

3.2.6.6 修改/usr/local/hadoop/etc/hadoop/slaves，添加以下内容(即hostname文件中要设置的主机名)：

1.  data1 
2.  data2 
3.  data3

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 15][]

## **3.3** **克隆虚拟机** ##

将虚拟机复制3份，重新生成MAC地址，使用“完全复制”，作为从机

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 16][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 3][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 17][]

VM VirtualBox中的四台虚拟机，全部启动

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 18][]

虚拟机全部启动后，系统资源使用情况

## **3.4** **前置配置操作****(****下****)** ##

3.4.1 使用ifconfig命令查询每一台虚拟机的IP地址

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 19][]

经查询，master的IP地址为10.0.0.103，三台从机的IP地址为10.0.0.104、10.0.105、10.0.107

3.4.2 编辑hostname文件，将三台从机分别命名为data1、data2、data3，保存

![20200705121001257.png][]

3.4.3 编辑hosts文件，修改127.0.1.1的名称为本机的hostname，加入四个IP地址对应虚拟机的host，保存

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 20][]

3.4.4 为了避免各虚拟机的内网IP地址发生变化，在路由器设置界面将IP与MAC地址绑定，或将DHCP地址租期设置为最大值，以免反复调整hosts文件

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 21][]

![20200705121001286.png][]

3.4.5 ssh免密登录配置

把自己的密钥拷贝到需要免密的机器上，对每台虚拟机执行以下命令：

ssh-copy-id -i ~/.ssh/id\_rsa.pub 用户名@IP地址【注意是用户名，不是主机名！】

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 22][]

## **3.5** **启动****Hadoop** ##

3.5.1 启动HDFS

3.5.1.1 格式化文件系统：在master的/usr/local/hadoop目录运行以下命令：

1.  bin/hdfs namenode –format

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 23][]

3.5.1.2 开启HDFS

在master的/usr/local/hadoop目录运行以下命令：

sbin/start-all.sh

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 24][]

出现上图所示界面，Hadoop启动成功

## **3.6** **上传一个文件至****HDFS** ##

1.  在hadoop目录下新建一个文本文件，填充英文文本，命名为U201717126.txt，执行以下命令：hadoop fs -copyFromLocal U201717126.txt /dest
2.  执行命令hadoop fs -ls /dest，显示相应的文件，表明上传成功

![20200705121001768.png][]

## **3.7** **使用****example****程序统计文件****wordcount** ##

1.  输入命令： hadoop jar usr/local/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.10.0.jar wordcount /dest /out
2.  在虚拟机中打开[http://master:8088][http_master_8088]，可以看到wordcount正在运行

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 25][]

静等其运行完成：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 26][]

从localhost:50070/explorer.html打开hadoop的文件浏览器，在out文件夹下有\_SUCCESS和part-r-00000两个文件

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 27][]

下载part-r-00000文件并打开，词频统计与文档U201717126.txt内容一致

![20200705121001733.png][]

## **3.8** **编写实现****wordcount****功能的****MapReduce****程序****(****选做****)** ##

3.8.1 编写WordMain.java:

1.  **package** wordcount; 
2.  
3.  **import** java.io.IOException; 
4.  
5.  **import** org.apache.hadoop.conf.Configuration; 
6.  **import** org.apache.hadoop.fs.Path; 
7.  **import** org.apache.hadoop.io.IntWritable; 
8.  **import** org.apache.hadoop.io.Text; 
9.  **import** org.apache.hadoop.mapreduce.Job; 
10. **import** org.apache.hadoop.mapreduce.lib.input.FileInputFormat; 
11. **import** org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; 
12. **import** org.apache.hadoop.util.GenericOptionsParser; 
13. 
14. **public** **class** WordMain \{ 
15. **public** **static** **void** main(String\[\] args) **throws** IOException, ClassNotFoundException, InterruptedException \{ 
16. // Configuration类：读取配置文件内容\-core-site.xml
17.  Configuration conf = **new** Configuration(); 
18. 
19. // 读取命令行参数，并设置到conf
20.  String\[\] otherArgs = **new** GenericOptionsParser(conf, args).getRemainingArgs(); 
21. 
22. // 添加内容
23.  conf.set("fs.defaultFS", "hdfs://10.13.7.72:9000"); 
24.  conf.set("hadoop.job.user", "hadoop"); 
25.  conf.set("mapreduce.framework.name", "yarn"); 
26.  conf.set("mapreduce.jobtracker.address", "10.13.7.72:9001"); 
27.  conf.set("yarn.resourcemanager.hostname", "10.13.7.72"); 
28.  conf.set("yarn.resourcemanager.admin.adress", "10.13.7.72:8033"); 
29.  conf.set("yarn.resourcemanager.adress", "10.13.7.72:8032"); 
30.  conf.set("yarn.resourcemanager.resource-tracker.address", "10.13.7.72:8036"); 
31.  conf.set("yarn.resourcemanager.scheduler.address", "10.13.7.72:8030"); 
32. 
33. 
34. **if** (otherArgs.length != 2) \{ // 输入目录 输出目录
35.  System.err.println("Usage: wordcount <in><out>"); 
36.  System.exit(2); 
37.  \} 
38. 
39.  Job job = **new** Job(conf, "word count"); // 新建一个job
40.  job.setJar("wordcount.jar"); 
41. 
42.  job.setJarByClass(WordMain.**class**); // 设置主类
43.  job.setMapperClass(WordMapper.**class**); // 设置Mapper类
44.  job.setCombinerClass(WordReducer.**class**); // 设置作业合成类
45.  job.setReducerClass(WordReducer.**class**); // 设置Reducer类
46.  job.setOutputKeyClass(Text.**class**); // 设置输出数据的关键类
47.  job.setOutputValueClass(IntWritable.**class**); // 设置输出值类
48.  FileInputFormat.addInputPath(job, **new** Path(otherArgs\[0\])); 
49.  FileOutputFormat.setOutputPath(job, **new** Path(otherArgs\[1\])); 
50.  System.exit(job.waitForCompletion(**true**) ? 0 : 1); 
51.  \} 
52. \}

3.8.2 编写WordMapper.java:

1.  **package** wordcount; 
2.  
3.  
4.  **import** java.io.IOException; 
5.  **import** java.util.StringTokenizer; 
6.  
7.  **import** org.apache.hadoop.io.IntWritable; 
8.  **import** org.apache.hadoop.io.Text; 
9.  **import** org.apache.hadoop.mapreduce.Mapper; 
10. 
11. **public** **class** WordMapper **extends** Mapper<Object, Text, Text, IntWritable> \{ 
12. **private** **final** **static** IntWritable one = **new** IntWritable(1); 
13. **private** Text word = **new** Text(); 
14. 
15. // Mapper类的核心方法
16. /\*\*
17.  \* key 首字符偏移量
18.  \* value 文件的一行内容
19.  \* context Mapper端的上下文
20.  \* @throws InterruptedException 
21.  \* @throws IOException 
22.  \*/
23. **public** **void** map(Object key, Text value, Context context) **throws** IOException, InterruptedException \{ 
24.  StringTokenizer itr = **new** StringTokenizer(value.toString()); 
25. **while** (itr.hasMoreTokens()) \{ 
26.  word.set(itr.nextToken()); 
27.  context.write(word, one); 
28.  \} 
29.  \} 
30. \}

3.8.3 编写WordReducer.java：

1.  **package** wordcount; 
2.  
3.  **import** java.io.IOException; 
4.  
5.  **import** org.apache.hadoop.io.IntWritable; 
6.  **import** org.apache.hadoop.io.Text; 
7.  **import** org.apache.hadoop.mapreduce.Reducer; 
8.  
9.  **public** **class** WordReducer **extends** Reducer<Text, IntWritable, Text, IntWritable> \{ 
10. **private** IntWritable result = **new** IntWritable(); // 记录词的频数
11. 
12. // Reducer抽象类的核心方法
13. **public** **void** reduce (Text key, Iterable<IntWritable> values, Context context) **throws** IOException, InterruptedException \{ 
14. **int** sum = 0; 
15. // 遍历values 将 list<value> 叠加
16. **for** (IntWritable value : values) \{ 
17.  sum += value.get(); 
18.  \} 
19.  result.set(sum); 
20.  context.write(key, result); 
21.  \} 
22. \}

3.8.4 将Java程序打包为my\_wordcount.jar，置于hadoop目录，运行：

hadoop my\_wordcount.jar wordcount.WordMain /U201717126.txt /out2

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 28][]

出现上图界面，顺利运行完成

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 29][]

在hadoop中的out2文件夹生成了两个文件

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZTFob25n_size_16_color_FFFFFF_t_70 30][]

下载part-r-00000文件并打开，词频统计与文档U201717126.txt内容一致

![20200705121001751.png][]

(U201717126.txt的文件内容)

#  #

# 四、实验结果 #

1.  完成Hadoop分布式集群环境搭建
2.  完成编写一个英文文本文件，上传至HDFS中
3.  完成用Hadoop提供的example程序实现统计该文件的wordcount
4.  自行编写实现wordcount功能的MapReduce程序并运行成功

# 五、体会 #

通过本次实验，我对Hadoop分布式集群、HDFS分布式存储、MapReduce分布式计算、Yarn资源调度有了更深刻的理解，掌握了Hadoop分布式集群环境搭建、HDFS文件的操作与MapReduce分布式计算程序的编译运行，对大数据与云计算的功能与原理有了进一步的了解。

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[https_ubuntu.com_download_desktop]: https://ubuntu.com/download/desktop
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