Wordcount -- MapReduce example -- Mapper

25-02-14 13

本文将为您提供关于Wordcount--MapReduceexample--Mapper的详细介绍，同时，我们还将为您提供关于''com.example.mybatisplus.mapper.Pers

本文将为您提供关于Wordcount -- MapReduce example -- Mapper的详细介绍，同时，我们还将为您提供关于''com.example.mybatisplus.mapper.PersonMapper'' that could not be found.、(一)Hadoop之Mapreduce的基础入门实例WordCount详解、Hadoop 6、第一个 mapreduce 程序 WordCount、Hadoop MapReduce WordCount程序的三种API代码的实用信息。

本文目录一览：

Wordcount -- MapReduce example -- Mapper
''com.example.mybatisplus.mapper.PersonMapper'' that could not be found.
(一)Hadoop之Mapreduce的基础入门实例WordCount详解
Hadoop 6、第一个 mapreduce 程序 WordCount
Hadoop MapReduce WordCount程序的三种API代码

Wordcount -- MapReduce example -- Mapper

Mapper maps input key/value pairs into intermediate key/value pairs. E.g. Input: (docID, doc) Output: (term, 1)

Mapper Class Prototype:

Mapper<Object, Text, Text, IntWritable> 
// Object:: INPUT_KEY
// Text:: INPUT_VALUE
// Text:: OUTPUT_KEY
// IntWritable:: OUTPUT_VALUE

Special Data Type for Mapper

IntWritable

A serializable and comparable object for integer. Example:

private final static IntWritable one = new IntWritable(1);

Text

A serializable, deserializable and comparable object for string at byte level. It stores text in UTF-8 encoding. Example:

private Text word = new Text();

Hadoop defines its own classes for general data types. -- All "values" must have Writable interface; -- All "keys" must have WritableComparable interface;

Map Method for Mapper

Method header

public void map(Object key, Text value, Context context
               ) throws IOException, InterruptedException
// Object key:: Declare data type of input key;
// Text value:: Declare data type of input value;
// Context context:: Declare data type of output. Context is often used for output data collection.

Tokenization

// Use Java built-in StringTokenizer to split input value (document) into words:
StringTokenizer itr = new StringTokenizer(value.toString());

Building (key, value) pairs

// Loop over all words:
while (itr.hasMoreTokens()) {
  // convert built-in String back to Text:
  word.set(itr.nextToken());
  // build (key, value) pairs into Context and emit:
  context.write(word, one);
}

Map Method Summary

Mapper class produces Mapper.Context object, which comprise a series of (key, value) pairs

  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);
    }
  }

Overview of Mapper Class

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);
    }
  }
}

''com.example.mybatisplus.mapper.PersonMapper'' that could not be found.

通常有两种原因，配置原因，或者是 mapper 相关文件，mapper.java 或 mapper.xml 内部错误

如果是配置原因

解决方式 1

统一配置 mapper

//import org.mybatis.spring.annotation.MapperScan;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;

@SpringBootApplication
@MapperScan("com.example.mybatisplus.mapper")  //把这个加上，别写上正确的路径，如果是模块化一般路径为@MapperScan("com.kfit.*.mapper")

public class Application {

    public static void main(String[] args){
        System.out.println("hahahaha");
         SpringApplication.run(Application.class, args);
    }

    
}

解决方式 2

每个 mapper 文件配置 @Mapper

package com.example.mybatisplus.mapper;

//import org.apache.ibatis.annotations.Mapper;

import com.baomidou.mybatisplus.mapper.BaseMapper;
import com.example.mybatisplus.entity.Person;
@Mapper     //每个文件配置这个
public interface PersonMapper extends BaseMapper<Person> { 
  Integer listCount(); 
  Person findPersonById(Integer id); 
}

两者可以结合使用

(一)Hadoop之Mapreduce的基础入门实例WordCount详解

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。

　　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）。而我们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。

Mapreduce的基础实例

　　讲解mapreduce运行原理前，首先我们看看mapreduce里的hello world实例WordCount,这个实例在任何一个版本的hadoop安装程序里都会有。

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;
/**
 * 
 * 描述：WordCount explains by York
  * @author Hadoop Dev Group
 */
publicclass WordCount {
    /**
     * 建立Mapper类TokenizerMapper继承自泛型类Mapper
     * Mapper类:实现了Map功能基类
     * Mapper接口：
     * WritableComparable接口：实现WritableComparable的类可以相互比较。所有被用作key的类应该实现此接口。
     * Reporter 则可用于报告整个应用的运行进度，本例中未使用。 
     * 
     */
  public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>{
   /**
      * IntWritable, Text 均是 Hadoop 中实现的用于封装 Java 数据类型的类，这些类实现了WritableComparable接口，
      * 都能够被串行化从而便于在分布式环境中进行数据交换，你可以将它们分别视为int,String 的替代品。
      * 声明one常量和word用于存放单词的变量
    */
    privatefinalstatic IntWritable one =new IntWritable(1);
    private Text word =new Text();
    /**
         * Mapper中的map方法：
         * void map(K1 key, V1 value, Context context)
         * 映射一个单个的输入k/v对到一个中间的k/v对
         * 输出对不需要和输入对是相同的类型，输入对可以映射到0个或多个输出对。
         * Context：收集Mapper输出的<k,v>对。
         * Context的write(k, v)方法:增加一个(k,v)对到context
         * 程序员主要编写Map和Reduce函数.这个Map函数使用StringTokenizer函数对字符串进行分隔,通过write方法把单词存入word中
         * write方法存入(单词,1)这样的二元组到context中
     */  
    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();
    /**
      * Reducer类中的reduce方法：
      * void reduce(Text key, Iterable<IntWritable> values, Context context)
      * 中k/v来自于map函数中的context,可能经过了进一步处理(combiner),同样通过context输出           
    */
    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);
    }
  }

  publicstaticvoid main(String[] args) throws Exception {
    /**
      * Configuration：map/reduce的j配置类，向hadoop框架描述map-reduce执行的工作
    */
    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");    //设置一个用户定义的job名称
    job.setJarByClass(WordCount.class);
    job.setMapperClass(TokenizerMapper.class);    //为job设置Mapper类
    job.setCombinerClass(IntSumReducer.class);    //为job设置Combiner类
    job.setReducerClass(IntSumReducer.class);    //为job设置Reducer类
    job.setOutputKeyClass(Text.class);        //为job的输出数据设置Key类
    job.setOutputValueClass(IntWritable.class);    //为job输出设置value类
    FileInputFormat.addInputPath(job, new Path(otherArgs[0]));    //为job设置输入路径
    FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));//为job设置输出路径
    System.exit(job.waitForCompletion(true) ?0 : 1);        //运行job
  }
}

WordCount逐行解析

对于map函数的方法：

public void map(Object key, Text value, Context context) throws IOException, InterruptedException {…}

　　这里有三个参数，前面两个Object key, Text value 就是输入的 key 和 value ，第三个参数Context context这是可以记录输入的key和value，例如：context.write(word, one)。

对于reduce函数的方法：

public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {…}

　　reduce函数的输入也是一个 key/value 的形式，不过它的value是一个迭代器的形式 Iterable<IntWritable> values ，也就是说reduce的输入是一个key对应一组的值的value，reduce也有context和map的context作用一致。至于计算的逻辑则需要程序员编码实现。

对于main函数的调用：

首先是

Configuration conf = new Configuration();

运行MapReduce程序前都要初始化Configuration，该类主要是读取MapReduce系统配置信息，这些信息包括hdfs还有MapReduce，也就是安装hadoop时候的配置文件例如：core-site.xml、hdfs-site.xml和mapred-site.xml等等文件里的信息，有些童鞋不理解为啥要这么做，这个是没有深入思考MapReduce计算框架造成，我们程序员开发MapReduce时候只是在填空，在map函数和reduce函数里编写实际进行的业务逻辑，其它的工作都是交给MapReduce框架自己操作的，但是至少我们要告诉它怎么操作啊，比如hdfs在哪里，MapReduce的jobstracker在哪里，而这些信息就在conf包下的配置文件里。

接下来的代码是：

    String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
    if (otherArgs.length != 2) {
      System.err.println("Usage: wordcount <in> <out>");
      System.exit(2);
    }

　　If的语句好理解，就是运行WordCount程序时候一定是两个参数，如果不是就会报错退出。至于第一句里的GenericOptionsParser类，它是用来解释常用hadoop命令，并根据需要为Configuration对象设置相应的值，其实平时开发里我们不太常用它，而是让类实现Tool接口，然后再main函数里使用ToolRunner运行程序，而ToolRunner内部会调用GenericOptionsParser。

接下来的代码是：

    Job job = new Job(conf, "word count");
    job.setJarByClass(WordCount.class);
    job.setMapperClass(TokenizerMapper.class);
    job.setCombinerClass(IntSumReducer.class);
    job.setReducerClass(IntSumReducer.class);

　　第一行就是在构建一个job，在mapreduce框架里一个mapreduce任务也叫mapreduce作业也叫做一个mapreduce的job，而具体的map和reduce运算就是task了，这里我们构建一个job，构建时候有两个参数，一个是conf这个就不累述了，一个是这个job的名称。

　　第二行就是装载程序员编写好的计算程序，例如我们的程序类名就是WordCount了。这里我要做下纠正，虽然我们编写mapreduce程序只需要实现map函数和reduce函数，但是实际开发我们要实现三个类，第三个类是为了配置mapreduce如何运行map和reduce函数，准确的说就是构建一个mapreduce能执行的job了，例如WordCount类。

　　第三行和第五行就是装载map函数和reduce函数实现类了，这里多了个第四行，这个是装载Combiner类，这个类和mapreduce运行机制有关，其实本例去掉第四行也没有关系，但是使用了第四行理论上运行效率会更好。

接下来的代码：

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

　　这个是定义输出的 key/value 的类型，也就是最终存储在 hdfs 上结果文件的 key/value 的类型。

最后的代码是：

    FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
    FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);

　　第一行就是构建输入的数据文件，第二行是构建输出的数据文件，最后一行如果job运行成功了，我们的程序就会正常退出。

Hadoop 6、第一个 mapreduce 程序 WordCount

1、程序代码

Map:

import java.io.IOException;

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 org.apache.hadoop.util.StringUtils;

public class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
    
    protected void map(LongWritable key, Text value,Context context)
            throws IOException, InterruptedException {
        String[] words = StringUtils.split(value.toString(), '' '');
        for(String word : words){
            context.write(new Text(word), new IntWritable(1));
        }
    }
}

Reduce:

import java.io.IOException;

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

public class wordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
    
    protected void reduce(Text arg0, Iterable<IntWritable> arg1,Context arg2)
            throws IOException, InterruptedException {
        int sum = 0;
        for(IntWritable i : arg1){
            sum += i.get();
        }
        arg2.write(arg0, new IntWritable(sum));
    }
    
}

Main:

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
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.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class RunJob {

    public static void main(String[] args) {
        Configuration config = new Configuration();
        try {
            FileSystem fs = FileSystem.get(config);
            Job job = Job.getInstance(config);
            job.setJobName("wordCount");
            job.setJarByClass(RunJob.class);
            job.setMapperClass(WordCountMapper.class);
            job.setReducerClass(wordCountReducer.class);
            job.setMapOutputKeyClass(Text.class);
            job.setMapOutputValueClass(IntWritable.class);
            
            FileInputFormat.addInputPath(job, new Path("/usr/input/"));
            Path outPath = new Path("/usr/output/wc/");
            if(fs.exists(outPath)){
                fs.delete(outPath, true);
            }
            FileOutputFormat.setOutputPath(job, outPath);
            Boolean result = job.waitForCompletion(true);
            if(result){
                System.out.println("Job is complete!");
            }else{
                System.out.println("Job is fail!");
            }
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

2、打包程序

将 Java 程序打成 Jar 包，并上传到 Hadoop 服务器上（任何一台在启动的 NameNode 节点即可）

3、数据源

数据源是如下：

hadoop java text hdfs
tom jack java text
job hadoop abc lusi
hdfs tom text

将该内容放到 txt 文件中，并放到 HDFS 的 /usr/input (是 HDFS 下不是 Linux 下)，可以使用 Eclipse 插件上传：

4、执行 Jar 包

# hadoop jar jar路径  类的全限定名(Hadoop需要配置环境变量)
$ hadoop jar wc.jar com.raphael.wc.RunJob

执行完成以后会在 HDFS 的 /usr 下新创建一个 output 目录：

查看执行结果：

abc	1
hadoop	2
hdfs	2
jack	1
java	2
job	1
lusi	1
text	3
tom	2

完成了单词个数的统计。

Hadoop MapReduce WordCount程序的三种API代码

旧版API:

package com.chenjun.MRstudy.oldapi;

import java.io.IOException;
import java.util.Iterator;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;

public class Wcmr {
	
	private static final String INPUT_PATH = "/test/in/wc.txt";
	private static final String OUTPUT_PATH = "/test/out/wcresult";

	public static class MyMapper extends MapReduceBase implements Mapper<LongWritable,Text,Text,IntWritable>{
		@Override
		public void map(LongWritable key, Text value, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException {
			String[] splitArray = value.toString().split(" ");
			for(String s : splitArray){
				output.collect(new Text(s), new IntWritable(1));
			}
		}
	}
	
	public static class MyReducer extends MapReduceBase implements Reducer<Text, IntWritable, Text, IntWritable>{
		@Override
		public void reduce(Text key, Iterator<IntWritable> values, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException {
			int sum = 0;
			while(values.hasNext()){
				sum += values.next().get();
			}
			output.collect(key, new IntWritable(sum));
		}
	}

	public static void main(String[] args) throws IOException {
		JobConf conf = new JobConf(Wcmr.class);
		conf.setJobName("wc");
		
		conf.setOutputKeyClass(Text.class);
		conf.setOutputValueClass(IntWritable.class);
		
		conf.setMapperClass(MyMapper.class);
		conf.setCombinerClass(MyReducer.class);
		conf.setReducerClass(MyReducer.class);
		
		conf.setNumReduceTasks(1);
		conf.setInputFormat(TextInputFormat.class);
		conf.setOutputFormat(TextOutputFormat.class);
		
		FileInputFormat.setInputPaths(conf, new Path(INPUT_PATH));
		FileOutputFormat.setOutputPath(conf, new Path(OUTPUT_PATH));
		
		JobClient.runJob(conf);
	}
}

新版API

package com.chenjun.MRstudy.newapi;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

public class WordCountNewApi {
	
	private static final String INPUT_PATH = "/test/in/wc.txt";
	private static final String OUTPUT_PATH = "/test/out/wcresult";
	
	
	public static class MyMapper extends Mapper<LongWritable,Text,Text,IntWritable>{
		public void map(LongWritable key,Text value, Context context) 
				throws IOException,InterruptedException{
			String line = value.toString();
			String[] strArray = line.split(" ");
			for(int i=0; i< strArray.length; i++){
				context.write(new Text(strArray[i]), new IntWritable(1));
			}
		}
	}
	
	public static class MyReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
		public void reduce(Text key, Iterable<IntWritable> values, Context context)
				throws IOException,InterruptedException{
			int sum = 0;
			for(IntWritable intWritable : values){
				sum ++;
			}
			context.write(key, new IntWritable(sum));
		}
	}
	
	public static void main(String[] args) {
		try {
			Job job = Job.getInstance(new Configuration());
			job.setJarByClass(WordCountNewApi.class);
			job.setOutputKeyClass(Text.class);
			job.setOutputValueClass(IntWritable.class);
			job.setMapperClass(MyMapper.class);
			job.setReducerClass(MyReducer.class);
			
			job.setInputFormatClass(TextInputFormat.class);
			job.setOutputFormatClass(TextOutputFormat.class);
			
			FileInputFormat.setInputPaths(job, new Path(INPUT_PATH));
			FileOutputFormat.setOutputPath(job, new Path(OUTPUT_PATH));
			
			boolean status = job.waitForCompletion(true);
			if(status){
				System.exit(0);
			}else{
				System.exit(1);
			}
		} catch (IOException e) {
			e.printStackTrace();
		} catch (ClassNotFoundException e) {
			e.printStackTrace();
		} catch (InterruptedException e) {
			e.printStackTrace();
		}
	}
}

类 extends Configured implements Tool 形式的API

package com.chenjun.MRstudy.toolrun;

import java.io.IOException;

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.LongWritable;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class WorldCountUsingToolRunner extends Configured implements Tool {
	
	private final static IntWritable one = new IntWritable(1);

	public static class MyMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
		@Override
		public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
			String line = value.toString();
			String[] strArray = line.split(" ");
			for(int i=0; i< strArray.length; i++){
				context.write(new Text(strArray[i]), new IntWritable(1));
			}
		}
	}
	
	public static class MyReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
		@Override
		public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
			int cnt = 0;
			for(IntWritable iw : values) {
				cnt += iw.get();
			}
			context.write(key, new IntWritable(cnt));
		}
	}

	public int run(String[] allArgs) throws Exception {
		Job job = Job.getInstance(getConf());
		job.setJarByClass(WorldCountUsingToolRunner.class);
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(IntWritable.class);
		
		job.setMapperClass(MyMapper.class);
		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(IntWritable.class);
		
		job.setReducerClass(MyReducer.class);
		job.setNumReduceTasks(1);
		
		
		job.setInputFormatClass(TextInputFormat.class);
		job.setOutputFormatClass(TextOutputFormat.class);
		
		String[] args = new GenericOptionsParser(getConf(), allArgs).getRemainingArgs();
		
		FileInputFormat.setInputPaths(job, new Path(args[0]));
		FileOutputFormat.setOutputPath(job, new Path(args[1]));
		
		return job.waitForCompletion(true) ? 0 : 1;
	}
	
	public static void main(String[] args) throws Exception {
		Configuration configuration = new Configuration();
		ToolRunner.run(configuration,new WorldCountUsingToolRunner(), args);
	}
}

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