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Hadoop集群进行map词频统计

一、首先新建虚拟机

二、配置静态IP

    1、首先查看虚拟网络编辑器 查看起始IP

            ![](https://img-blog.csdnimg.cn/9f9b0fc72fb345e7bb58ae06de2909c9.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_17,color_FFFFFF,t_70,g_se,x_16)

     2.1、修改静态IP

            输入指令：vi /etc/sysconfig/network-scripts/ifcfg-ens33

            修改BOOTPROTO=static

            增加IPADDR、NETWASK、GATEWAY、DNS1

            ![](https://img-blog.csdnimg.cn/de82dc4bfe9b4d98acfd63b1687b5462.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)

            ![](https://img-blog.csdnimg.cn/dedfb9effb6449febeb1be7310e4dd9a.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)

     2.2、输入指令：vi /etc/sysconfig/network增加以下两条

           ![](https://img-blog.csdnimg.cn/53733028997b461da21e3c07bc9752d3.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16) 

             ![](https://img-blog.csdnimg.cn/988e426cef9a4441997763cd903abad4.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_9,color_FFFFFF,t_70,g_se,x_16)

     2.3、输入指令：vi /etc/hosts 添加上IP和主机名

            ![](https://img-blog.csdnimg.cn/42b69a5c48784cb29b4e03f239560e69.png)

     2.4、输入：reboot 重启虚拟机

三、安装JDK

    3.1、在opt目录下创建module、jdk文件夹

            输入命令：cd /opt/

            输入命令：mkdir module

            输入命令：mkdir jdk

            输入命令：mkdir hadoop

    3.2、卸载当前jdk

            输入命令：java -version 查看当前jdk版本

            输入命令：yum remove java* 卸载所有jdk

            ![](https://img-blog.csdnimg.cn/ab75cbda67b24ff0a239fb2dd17602d7.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)

     3.3、使用FileZilla链接虚拟机

             将jdk压缩包上传到hadoop102的opt/jdk目录下、

             hadoop压缩包上传到hadoop102的opt/hadoop目录下。

            ![](https://img-blog.csdnimg.cn/59cce46b63e04b818203b7469f62ddb5.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)

     3.4、解压压缩包到制定目录

            输入指令：cd /opt/jdk

            输入指令：tar -zxvf jdk（jdk压缩包） -C /opt/module/

            ![](https://img-blog.csdnimg.cn/2b19a2f71f3d49f9842ce931280591bb.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)

     3.5、配置profile文件并让其生效

            输入指令：pwd 查看当前目录

            输入指令：vi /etc/profile  在文件末尾添加JAVA_HOME

            ![](https://img-blog.csdnimg.cn/09db62fec3734338a08b2ea1449f028e.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_9,color_FFFFFF,t_70,g_se,x_16)

             输入指令：source /etc/profile

             输入指令：java -version

            ![](https://img-blog.csdnimg.cn/3d0d1f7b1b9e4d79b00ed641182948e4.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)

四、安装hadoop

    4.1、解压hadoop到指定目录

            切换到根目录

            输入指令：mkdir kkb

            输入指令：cd kkb

            输入指令：mkdir install

            输入指令：cd /opt/hadoop

            输入指令：tar -zxvf hadoop（hadoop压缩包） -C /kkb/install

           ![](https://img-blog.csdnimg.cn/e5e4602bec1848e5b25b7207c7fb3531.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)

     4.2、配置profile文件并使其生效

            输入指令：vi /etc/profile 配置HADOOP_HOME环境

            ![](https://img-blog.csdnimg.cn/bcdc5e2ddcb84f52903b8588af60908c.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)

            输入指令：source /etc/profile

            输入指令：hadoop version

            ![](https://img-blog.csdnimg.cn/0eb3abb3e11e4674b51036b445719f15.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)

五、克隆出hadoop103、hadoop104，并向hadoop102一样步骤修改静态IP

六、配置ssh免密登录

     6.1、以102为例配置ssh

            输入指令：cd ~/.ssh

            输入指令：ssh-keygen -t rsa

            连续输入三个回车，生成密匙       

    6.2、分发密匙，优先分发给自己，再分发给103、104

            输入指令：ssh-copy-id 192.168.88.130

            输入指令：ssh-copy-id 192.168.88.131

            输入指令：ssh-copy-id 192.168.88.132

     6.3、在103、104上按照6.1-6.2的步骤配置ssh

            ![](https://img-blog.csdnimg.cn/f5aa11e2a5504575a17c5be21c25b1db.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_9,color_FFFFFF,t_70,g_se,x_16)

             ![](https://img-blog.csdnimg.cn/8470272efc7b472fb2cbc9e1d1db0c3f.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_10,color_FFFFFF,t_70,g_se,x_16)

七、配置集群分发脚本xsync

    7.1、在/usr/local/bin目录下创建xsync文件

            输入指令：vi /usr/local/bin/xsync

    7.2、xsync内容文件如下：

#!/bin/bash
#1 获取输入参数个数，如果没有参数，直接退出
pcount=$#
if((pcount==0)); then
echo no args;
exit;
fi

#2 获取文件名称
p1=$1
fname=`basename $p1`
echo fname=$fname

#3 获取上级目录到绝对路径
pdir=`cd -P $(dirname $p1); pwd`
echo pdir=$pdir

#4 获取当前用户名称
user=`whoami`

#5 循环
for((host=103; host<105; host++)); do
        #echo $pdir/$fname $user@hadoop$host:$pdir
        echo --------------- hadoop$host ----------------
        rsync -rvl $pdir/$fname $user@hadoop$host:$pdir
done

    7.3、修改文件权限

            输入指令：chomd a+x xsync

八、hadoop3的集群配置

    8.1、执行checknative

            输入指令：cd /kkb/install/hadoop-3.1.4/

            输入指令：bin/hadoop checknative

            ![](https://img-blog.csdnimg.cn/12f58f690a964b8aa43a165501d13d2c.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)

    8.2、安装openssl-deve1

            输入指令：yum -y install openssl-deve1

    8.3、修改dfs、yarn配置文件

            输入指令：cd /kkb/install/hadoop-3.1.4/etc/hadoop

            输入指令：vim /hadoop-env.sh   在末尾添加以下内容

export JAVA_HOME=/kkb/install/jdk1.8.0_162

            输入指令：vim core-site.xml  在标签内添加以下内容

<configuration>
    <property>
        <name>fs.defaultFS</name>
        <value>hdfs://hadoop102:8020</value>
    </property>
    <property>
        <name>hadoop.tmp.dir</name>
        <value>/kkb/install/hadoop-3.1.4/hadoopDatas/tempDatas</value>
    </property>
    <!--  缓冲区大小，实际工作中根据服务器性能动态调整；默认值4096 -->
    <property>
        <name>io.file.buffer.size</name>
        <value>4096</value>
    </property>
    <!--  开启hdfs的垃圾桶机制，删除掉的数据可以从垃圾桶中回收，单位分钟；默认值0 -->
    <property>
        <name>fs.trash.interval</name>
        <value>10080</value>
    </property>
</configuration>

             输入指令：vim /hdfs-site.xml

<configuration>
<!-- NameNode存储元数据信息的路径，实际工作中，一般先确定磁盘的挂载目录，然后多个目录用，进行分割   -->
    <!--   集群动态上下线
    <property>
        <name>dfs.hosts</name>
        <value>/kkb/install/hadoop-3.1.4/etc/hadoop/accept_host</value>
    </property>
    <property>
        <name>dfs.hosts.exclude</name>
        <value>/kkb/install/hadoop-3.1.4/etc/hadoop/deny_host</value>
    </property>
     -->
     <property>
            <name>dfs.namenode.secondary.http-address</name>
            <value>hadoop102:9868</value>
    </property>
    <property>
        <name>dfs.namenode.http-address</name>
        <value>hadoop102:9870</value>
    </property>
    <!-- namenode保存fsimage的路径 -->
    <property>
        <name>dfs.namenode.name.dir</name>
        <value>file:///kkb/install/hadoop-3.1.4/hadoopDatas/namenodeDatas</value>
    </property>
    <!--  定义dataNode数据存储的节点位置，实际工作中，一般先确定磁盘的挂载目录，然后多个目录用，进行分割  -->
    <property>
        <name>dfs.datanode.data.dir</name>
        <value>file:///kkb/install/hadoop-3.1.4/hadoopDatas/datanodeDatas</value>
    </property>
    <!-- namenode保存editslog的目录 -->
    <property>
        <name>dfs.namenode.edits.dir</name>
        <value>file:///kkb/install/hadoop-3.1.4/hadoopDatas/dfs/nn/edits</value>
    </property>
    <!-- secondarynamenode保存待合并的fsimage -->
    <property>
        <name>dfs.namenode.checkpoint.dir</name>
        <value>file:///kkb/install/hadoop-3.1.4/hadoopDatas/dfs/snn/name</value>
    </property>
    <!-- secondarynamenode保存待合并的editslog -->
    <property>
        <name>dfs.namenode.checkpoint.edits.dir</name>
        <value>file:///kkb/install/hadoop-3.1.4/hadoopDatas/dfs/nn/snn/edits</value>
    </property>
    <property>
        <name>dfs.replication</name>
        <value>3</value>
    </property>
    <property>
        <name>dfs.permissions.enabled</name>
        <value>false</value>
    </property>
        <property>
        <name>dfs.blocksize</name>
        <value>134217728</value>
    </property>
</configuration>

            输入指令：vim mapred-site.xml

<configuration>
<property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property>
    <property>
        <name>mapreduce.job.ubertask.enable</name>
        <value>true</value>
    </property>
    <property>
        <name>mapreduce.jobhistory.address</name>
        <value>hadoop102:10020</value>
    </property>
    <property>
        <name>mapreduce.jobhistory.webapp.address</name>
        <value>hadoop102:19888</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>
</configuration>

            输入指令：vi yarn-site.xml

<configuration>

<!-- Site specific YARN configuration properties -->
<property>
       <name>yarn.resourcemanager.hostname</name>
        <value>hadoop102</value>
    </property>
    <property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>
    <!-- 如果vmem、pmem资源不够，会报错，此处将资源监察置为false -->
    <property>
        <name>yarn.nodemanager.vmem-check-enabled</name>
        <value>false</value>
    </property>
    <property>
        <name>yarn.nodemanager.pmem-check-enabled</name>
        <value>false</value>
    </property>
</configuration>

            输入指令：vi workers

hadoop102
hadoop103
hadoop104

    8.4、创建文件存放目录

            输入指令：mkdir -p /kkb/install/hadoop-3.1.4/hadoopDatas/tempDatas

            输入指令：mkdir -p /kkb/install/hadoop-3.1.4/hadoopDatas/namenodeDatas

            输入指令：mkdir -p /kkb/install/hadoop-3.1.4/hadoopDatas/datanodeDatas

            输入指令：mkdir -p /kkb/install/hadoop-3.1.4/hadoopDatas/dfs/nn/edits

            输入指令：mkdir -p /kkb/install/hadoop-3.1.4/hadoopDatas/dfs/snn/name

            输入指令：mkdir -p /kkb/install/hadoop-3.1.4/hadoopDatas/dfs/nn/snn/edits

    8.5、使用xsync分发配置文件               

            输入指令：xsync hadoop-3.1.4  102在install目录下将hadoop分发给103、104

九、启动hdfs、yarn

    9.1、在102上格式化集群（只能格式一次、不能频繁格式）

            输入指令：hdfs namenode -format

    9.2、在102的hadoop-3.1.4目录下启动dfs、yarn

            输入指令：sbin/start-dfs.sh

            输入指令：sbin/start-yarn.sh

    9.3、jps命令查看启动进程

            ![](https://img-blog.csdnimg.cn/cb57e951621b4506afb0834f85e9206f.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_6,color_FFFFFF,t_70,g_se,x_16)![](https://img-blog.csdnimg.cn/ab14fe00cc614a4ca92ffe2512d036ff.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_7,color_FFFFFF,t_70,g_se,x_16)

             ![](https://img-blog.csdnimg.cn/c5001bdb7a434964b2732ffd745599c5.png)

     9.4、验证集群是否启动成功

            在浏览器打开：192.168.88.130:8088

            在浏览器打开：192.168.88.130:9870

             ![](https://img-blog.csdnimg.cn/ebe2bcbdcc3a4c95a49531d32d360afc.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)

             ![](https://img-blog.csdnimg.cn/8b6cd92534b34e21a3cab1cfcee9d0ed.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)

十、在Windows中配置hadoop

    10.1、修改windows的hosts文件

              地址：C:\Windows\System32\drivers\etc\hosts

          ![](https://img-blog.csdnimg.cn/52917d929c504a4f8df2305bf25fcfa1.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)   

  10.2、配置Windows本中配置hadoop环境

             将集群所用的hadoop-3.1.4.tar.gz解压到一个没有中文、空格的目录下

  10.3、配置hadoop的环境变量

            ![](https://img-blog.csdnimg.cn/a18668b854b44e1a80d92bc7eded68b3.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)

             ![](https://img-blog.csdnimg.cn/6d7c6135f15b4c5d88b3549d52448ab1.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_9,color_FFFFFF,t_70,g_se,x_16)

   10.4、将下图的hadoop.dll文件拷贝到C:\\Windows\System32

                ![](https://img-blog.csdnimg.cn/819b5bc96a7b47a1abfb5a28b5be807e.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)         

    10.5、将hadoop集群的一下5个配置文件core-site.xml、hdfs-site.xml、mapred-site.xml、yarn-site.xml、workers，拷贝到windows下hadoop的C:\hadoop-3.1.4\etc\hadoop目录下

    10.6、打开cmd运行hadoop命令

           ![](https://img-blog.csdnimg.cn/cb615145d04349d0aefc810de28ed55c.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_11,color_FFFFFF,t_70,g_se,x_16)

十一、安装maven 

    11.1、下载安装包 apache-maven-3.6.1-bin.zip  并解压到某目录、配置环境变量

            ![](https://img-blog.csdnimg.cn/2273d796aaa749b0805d77439eed8b58.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)

            ![](https://img-blog.csdnimg.cn/a48efdeb546d4aef9849f42ce1e16c69.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_10,color_FFFFFF,t_70,g_se,x_16)

    11.2、cmd中运行mvn -v

           ![](https://img-blog.csdnimg.cn/436eba03149440e08a666d3aea3fa962.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)         

    11.3、找到maven解压的目录，找到settings.xml文件，添加以下内容

            ![](https://img-blog.csdnimg.cn/5bf6ee3a169c4f42b0d4926f90e18e61.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)

             ![](https://img-blog.csdnimg.cn/6c392c7058aa48009ce4546997f06f2f.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)

      11.4、打开IDEA，新建一个maven工程，配置pom文件，内容如下：

<properties>
        <hadoop.version>3.1.4</hadoop.version>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>${hadoop.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>${hadoop.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs</artifactId>
            <version>${hadoop.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-mapreduce-client-core</artifactId>
            <version>${hadoop.version}</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/junit/junit -->
        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>4.11</version>
            <scope>test</scope>
        </dependency>
        <dependency>
            <groupId>org.testng</groupId>
            <artifactId>testng</artifactId>
            <version>RELEASE</version>
        </dependency>
        <dependency>
            <groupId>log4j</groupId>
            <artifactId>log4j</artifactId>
            <version>1.2.17</version>
        </dependency>
    </dependencies>
    <build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.0</version>
                <configuration>
                    <source>1.8</source>
                    <target>1.8</target>
                    <encoding>UTF-8</encoding>
                    <!--   <verbal>true</verbal>-->
                </configuration>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-shade-plugin</artifactId>
                <version>2.4.3</version>
                <executions>
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>shade</goal>
                        </goals>
                        <configuration>
                            <minimizeJar>true</minimizeJar>
                        </configuration>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>

十二、词频统计程序实现

    12.1、编写mapper类

package wordcount;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;
import java.util.ArrayList;
import java.util.List;

public class MyMapper extends Mapper <LongWritable, Text,Text, IntWritable>{
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        //获得当前行的数据
        String line = value.toString();

        //获得一个个的单词
            String lineBuffer = line;

            String[] keys = new String[]{" ", "\t", "    ", ".", "（", "）", "(", ")"};

            for (String k : keys){
            lineBuffer = lineBuffer.replace(k, ",");
        }

        String[] wordsBuffer = lineBuffer.split(",");

        List<String> words = new ArrayList<>();

        for (String w : wordsBuffer){
            if (!w.equals("")){
                words.add(w);
            }
        }

        //每个单词编程kv对
        for (String word : words) {
            //将kv对输出出去
            context.write(new Text(word),new IntWritable(1));
        }

    }
}

    12.2、编写Reducer类

package wordcount;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;

public class MyReducer extends Reducer<Text, IntWritable,Text,IntWritable> {
    //bear,List(2,3,3)
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        int sum=0;
        for (IntWritable value : values) {
            int count = value.get();
            sum +=count;
        }

        context.write(key,new IntWritable(sum));
    }
}

    12.3、组装main程序

package wordcount;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
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.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class WordCount extends Configured implements Tool {
public static void main(String[] args) throws Exception {
int run = ToolRunner.run(new Configuration(), new WordCount(), args); // 集群代码
System.exit(run);

}
@Override
public int run(String[] args) throws Exception {
Job job = Job.getInstance(super.getConf(), "wordcount");
        job.setJarByClass(WordCount.class);

        job.setInputFormatClass(TextInputFormat.class);

        TextInputFormat.addInputPath(job, new Path(args[0]));

        job.setMapperClass(MyMapper.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);

        job.setReducerClass(MyReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        job.setOutputFormatClass(TextOutputFormat.class);
        TextOutputFormat.setOutputPath(job, new Path(args[1]));

        job.setNumReduceTasks(Integer.parseInt(args[2]));

        boolean b = job.waitForCompletion(true);
        return b ? 0 : 1;

    }
}

    12.4、将程序打包、点击maven的package

           ![](https://img-blog.csdnimg.cn/2dcbc6cb2d3d4798bc9232932430b158.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)

十三、在集群中实现

    13.1、使用FileZilla链接hadoop102

               找到打包文件，和测试文件，一起上传到hadoop102

             ![](https://img-blog.csdnimg.cn/2ca791df8d40411fb619e28eabc95528.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)       

    13.2、将测试文件上传到hdfs

               在hadoop-3.1.4目录下

               输入指令：bin/hdfs dfs -mkdir -p /test-wrh  在hdfs上创建test-wrh文件夹

               输入指令：bin/hdfs dfs -put 测试文件地址  /test-wrh/  将测试文件上传到test-wrh

             ![](https://img-blog.csdnimg.cn/94ba732d10704ecdb085471fbd19a8bd.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)  

             ![](https://img-blog.csdnimg.cn/48277b092321420ba133fc8136fa387d.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)

     13.3、在IDEA中拷贝地址

                 ![](https://img-blog.csdnimg.cn/7a0c004ee11e4a858255f0224554afd2.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)   

     13.4、运行程序

               在包含程序的目录中：

               输入指令：hadoop jar jar包名  Reference /输入路径  /输出路径  3个节点

13.5、将结果从hdfs上下载

            ![](https://img-blog.csdnimg.cn/4e716e2a62d341eea3859f10c4a1c676.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_12,color_FFFFFF,t_70,g_se,x_16)

            输入指令：hadoop fs -get /输出路径/part-r-00000 /下载路径


      13.6、查看结果

                 输入指令：vim part-r-00000

            ![](https://img-blog.csdnimg.cn/6bc02f11f7664a858a9ad913210bc552.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBAd2VpeGluXzQ3MjMxNzEz,size_10,color_FFFFFF,t_70,g_se,x_16)
标签： hadoop 大数据 big data
本文转载自: https://blog.csdn.net/weixin_47231713/article/details/122156621
版权归原作者 陈信宇是大聪明 所有， 如有侵权，请联系我们删除。
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