小试牛刀 - WordCount_wordcount cluster init-优快云博客

本文链接：https://blog.youkuaiyun.com/program_red/article/details/54093238

博由

    上一篇简单介绍了通过Docker简单搭建Hadoop集群环境，终于可以简单试试开发环境的操作，因此先感受一下Hadoop的开发。通过简单的单词统计来展示，还未正式学习Hadoop代码库，先直观感受一下。

环境

    [1] 系统：Mac Osx 
    [2] hadoop环境：本地单机环境 version:2.7.1
    [3] 语言: java
    [4] 开发工具：idea + maven

WordCount操作步骤

搭建本地Hadoop环境

配置core-site.xml

<configuration>
     <property>
         <name>fs.default.name</name>
         <value>hdfs://localhost:9000</value>
     </property>
     <property>
          <name>hadoop.tmp.dir</name>
          <value>file:/Users/wangzhiping/hadoop-2.7.1/tmp</value>
      </property>
  </configuration>

配置hdfs-site.xml

<configuration>
     <!-- 这里配置1就行，因为只是单机环境 -->
     <property>
         <name>dfs.replication</name>
         <value>1</value>
     </property>
     <property>
         <name>dfs.namenode.name.dir</name>
         <value>file:/Users/wangzhiping/hadoop-2.7.1/namenodedir</value>
     </property>
     <property>
         <name>dfs.datanode.name.dir</name>
         <value>file:/Users/wangzhiping/hadoop-2.7.1/datanodedir</value>
     </property>
</configuration>

配置mapred-site.xml

<configuration>
     <property>
          <name>mapred.job.tracker</name>
          <value>localhost:9001</value>
     </property>
</configuration>

启动本地环境

运行：start-all.sh 通过jps命令查看运行情况是否启动成功
% jps                                                                                                                                                                                                                                                                                                                !10061
96434 Launcher
95812 NameNode
96004 SecondaryNameNode
96551 Jps
96118 ResourceManager
95898 DataNode
93917 RemoteMavenServer
96206 NodeManager
这样就标识已经成功启动本地环境。

搭建idea+maven环境

    使用idea创建maven项目即可,命名为： hadoop-hello（按照新建的步骤来进行即可）

配置pom.xml

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>hadoop-learn</groupId>
    <artifactId>hello</artifactId>
    <version>1.0-SNAPSHOT</version>

    <dependencies>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-mapreduce-client-common</artifactId>
            <version>2.7.1</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>2.7.1</version>
        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-core -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-core</artifactId>
            <version>1.2.1</version>
        </dependency>

    </dependencies>
</project>

在这个过程中，遇到一个小插曲,见：

Exception in thread "main" java.io.IOException: Cannot initialize Cluster. Please check your configuration for mapreduce.framework.name and the correspond server addresses.  
    at org.apache.hadoop.mapreduce.Cluster.initialize(Cluster.java:120)  
    at org.apache.hadoop.mapreduce.Cluster.<init>(Cluster.java:82)  
    at org.apache.hadoop.mapreduce.Cluster.<init>(Cluster.java:75)  
    at org.apache.hadoop.mapreduce.Job$9.run(Job.java:1238)  
    at org.apache.hadoop.mapreduce.Job$9.run(Job.java:1234) 
解决方式是：hadoop-mapreduce-client-common 这个包没有导入导致，只需要导入包即可(在maven添加下载完成即可)

具体代码实现

package com.hadoop.hello.wordcount;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.tools.ant.filters.TokenFilter;


import java.io.IOException;
import java.util.StringTokenizer;

/**
 * 思路：
 * 第一步：将每一行的文本进行分词，并且形成(word, 1)的元素形式； Mapper过程
 * 第二步：combineKey 将相同的word做聚合操作，value相加即可；  Reducer过程
 * Created by wangzhiping on 17/1/5.
 */

/**
 * 分词Mapper
 * 读取每一行的数据 输入 Text, Text 输出 Text IntWritable （单词,次数）的形式
 */
class WordTokenizerMapper extends Mapper<Object, Text, Text, IntWritable>{
    // notice: Mapper的四个参数，前两个是输入参数（key,value）,后两个参数是输出(key,value)
    // 在这个例子中，第一参数是每行的字符位置索引，value是每一行文本
    @Override
    protected void map(Object key, Text value, Context context) throws IOException, InterruptedException {

        System.out.println("key: " + key + ",value: " + value);

        // 逗号分词
        StringTokenizer st = new StringTokenizer(value.toString(), " ");

        // 循环取词
        while(st.hasMoreTokens()){
            // 设置每个单词(word, 1)的形式
            context.write(new Text(st.nextToken()), new IntWritable(1));
        }
    }
}

/**
 * reduce分词结果
 * 输入：List(<Text, IntWritable>)
 * 输出：Set(<Text, IntWritable>)
 */
class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {

    /**
     * 计算单词个数
     * @param key mapper过程中的输出key
     * @param values combine相同key的value
     * @param context 
     * @throws IOException
     * @throws InterruptedException
     */
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {

        int count = 0;

        for (IntWritable value : values){
            count += value.get();
        }

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

public class WordCount {

    public static void main(String[] args) throws Exception{

        Configuration conf = new Configuration();
        // 看到这一串都懵了，目前刚开始接触，反正从这个实现，个人觉得太烂了，
        // 之后看看是不是有好的实现方式

        Job job = Job.getInstance(conf, "word count");
        job.setJarByClass(WordCount.class);
        job.setMapperClass(WordTokenizerMapper.class);
        job.setReducerClass(WordCountReducer.class);
        job.setCombinerClass(WordCountReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        // 设置输入文件地址
        FileInputFormat.addInputPath(job, new Path("/Users/wangzhiping/workspace/hadoop-hello/src/main/resources/word.txt"));

        // 设置输出文件地址
        FileOutputFormat.setOutputPath(job, new Path("/Users/wangzhiping/workspace/hadoop-hello/src/main/resources/output"));

        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }

}

运行

输入文件 word.txt

hello world hello world hello world
hello world hello world
hello world
hello world hello world hello world hello world

输出文件output

这里写图片描述

输出结果：part-r-00000 文件内容
hello   10
world   10

参考

[1] http://blog.youkuaiyun.com/panguoyuan/article/details/38727273