三.hadoop mapreduce之WordCount例子

时间：2021-12-01 05:04:05

目录：

目录见文章1

这个案列完成对单词的计数，重写map,与reduce方法，完成对mapreduce的理解。

Mapreduce初析

　　Mapreduce是一个计算框架，既然是做计算的框架，那么表现形式就是有个输入（input），mapreduce操作这个输入（input），通过本身定义好的计算模型，得到一个输出（output），这个输出就是我们所需要的结果。

　　我们要学习的就是这个计算模型的运行规则。在运行一个mapreduce计算任务时候，任务过程被分为两个阶段：map阶段和reduce阶段，每个阶段都是用键值对（key/value）作为输入（input）和输出（output）。而程序员要做的就是定义好这两个阶段的函数：map函数和reduce函数。

 1.准备 w.txt 文件,用于当测试数据

yaojiale hahaha

yaojiale llllll  

2.构建maven项目，将WordCount类打包成mrtest.jar.丢到hadoop所在服务器上

三.hadoop mapreduce之WordCount例子

pom.xml

<!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-common -->

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hadoop-common</artifactId>

            <version>2.7.3</version>

        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-mapreduce-client-core -->

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hadoop-mapreduce-client-core</artifactId>

            <version>2.7.3</version>

        </dependency>

        <!-- 加上这个就不报本地某错了 Cannot initialize Cluster

  https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-mapreduce-client-common -->

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hadoop-mapreduce-client-common</artifactId>

            <version>2.6.4</version>

        </dependency>

WordCount.java 代码：

package org.apache.hadoop.examples;

import java.io.IOException;

import java.util.StringTokenizer;

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.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.hadoop.util.GenericOptionsParser;

public class WordCount {

//WordCOuntMap方法接收LongWritable，Text的参数，返回<Text， IntWriatable>键值对。

  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);

    }

  }

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

    Configuration conf = new Configuration();

    String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();

    if (otherArgs.length != 2) {

      System.err.println("Usage: wordcount <in> <out>");

      System.exit(2);

    }

    Job job = new Job(conf, "word count");

    job.setJarByClass(WordCount.class);

    job.setMapperClass(TokenizerMapper.class);

    job.setCombinerClass(IntSumReducer.class);

    job.setReducerClass(IntSumReducer.class);

    job.setOutputKeyClass(Text.class);

    job.setOutputValueClass(IntWritable.class);

    FileInputFormat.addInputPath(job, new Path(otherArgs[0]));

    FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));

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

  }

}

 2.将w.txt放到hdfs下（folder下有w.txt文件）

bin/hdfs dfs -put /usr/software/folder input

然后查看

root@ubuntu:/usr/software/hadoop# bin/hdfs dfs -ls

Found 1 items

drwxr-xr-x   - root supergroup          0 2018-07-16 21:50 input //内有w.txt文件

3.运行程序统计WordCount

bin/hadoop jar /usr/software/mrtest2.jar input output

然后查看可得



root@ubuntu:/usr/software/hadoop# bin/hdfs dfs -ls
Found 2 items
drwxr-xr-x   - root supergroup          0 2018-07-16 21:50 input
drwxr-xr-x   - root supergroup          0 2018-07-16 22:18 output




root@ubuntu:/usr/software/hadoop# bin/hdfs dfs -cat output/*
hahaha	1
llllll	1
yaojiale	2


 




完毕。


附录：附上一个hadoop自带的例子：


计算圆周率




 bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7..jar pi  1000

result:


　Estimated value of Pi is 3.14000000000000000000




参考文章：


Hadoop之MapReduce的HelloWorld（七）


代码详细解释
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