MapReduce经典案例——统计单词数

资源文件file.txt

hello hadoop

hello word

this is my first hadoop program

分析：一个文档中每行的单词通过空格分割后获取，经过map阶段可以将所有的单词整理成如下形式：

key:hello value:1

key:hadoop value:1

key:hello value:1

key:word value:1

key:this value:1

key:is value:1

key:my value:1

key:first value:1

key:hadoop value:1

key:program value:1

经过hadoop整理后以如下形式输入到Reduce中：

key:hello value:{1,1}

key:hadoop value: {1,1}

key:word value:{1}

key:this value: {1}

key:is value:{1}

key:myvalue: {1}

key:first value:{1}

key:program value:{1}

所以Reduce接受的时候是以Iterable<IntWritable> values最为值。在Reduce中我们就可以将value中的值迭代相加就可以得出该单词出现的次数。

实现：

package com.bwzy.hadoop;
import java.io.File;
import java.io.IOException;
import java.util.Iterator;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapreduce.*;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
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 class Mapextendsorg.apache.hadoop.mapreduce.Mapper<LongWritable, Text, Text, IntWritable> {private final static IntWritable one = new IntWritable(1);private Text word = new Text();public void map(LongWritable key,Text value,Context context){String line = value.toString();StringTokenizer tokenizer = new StringTokenizer(line);//读取每一行数据，并将该行数据以空格分割（StringTokenizer默认是以空格分割字符串）while (tokenizer.hasMoreTokens()) {word.set(tokenizer.nextToken());try {context.write(word, one);//输出给Reduce} catch (IOException e) {e.printStackTrace();} catch (InterruptedException e) {e.printStackTrace();}}}}public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {public void reduce(Text key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException{int sum = 0;
//合并每个单词出现的次数for (IntWritable val : values) {sum+=val.get();}context.write(key, new IntWritable(sum));}}public static void main(String[] args) throws Exception {int ret = ToolRunner.run(new WordCount(), args);System.exit(ret);}@Overridepublic int run(String[] arg0) throws Exception {Job job = new Job(getConf());job.setJobName("wordcount");job.setOutputKeyClass(Text.class);//key--设置输出格式job.setOutputValueClass(IntWritable.class);//value--设置输出格式job.setMapperClass(Map.class);job.setReducerClass(Reduce.class);job.setInputFormatClass(TextInputFormat.class);job.setOutputFormatClass(TextOutputFormat.class);FileInputFormat.setInputPaths(job, new Path(arg0[0]));FileOutputFormat.setOutputPath(job, new Path(arg0[1]));boolean success = job.waitForCompletion(true);return success?0:1;}
}

运行：

1：将程序打包

选中打包的类-->右击-->Export-->java-->JAR file--填入保存路径-->完成

2：将jar包拷贝到hadoop的目录下。（因为程序中用到来hadoop的jar包）

3：将资源文件（存储这单词的文件,假设在/home/user/Document/file1.txt）上传到定义的hdfs目录下

创建hdfs目录命令（在hadoop已经成功启动的前提下）：hadoop fs -mkdir /自定义/自定义/input

上传本地资源文件到hdfs上：hadop fs -put -copyFromLocal /home/user/Document/file1.txt /自定义/自定义/input

4：运行MapReduce程序：

hadoop jar /home/user/hadoop-1.0.4/WordCount.jar com.bwzy.hadoop.WordCount /自定义/自定义/input /自定义/自定义/output

说明：hadoop运行后会自动创建/自定义/自定义/output目录，在该目录下会有两个文件，其中一个文件中存放来MapReduce运行的结果。如果重新运行该程序，需要将/自定义/自定义/output目录删除，否则系统认为该结果已经存在了。

5：运行的结果为

hello 2