hadoop入门（WordCount实例详解）

package wordcount;
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 {//继承泛型类Mapper
        public static class TokenizerMapper
    extends Mapper<Object, Text, Text, IntWritable>{

//定义hadoop数据类型IntWritable实例one，并且赋值为1
private final static IntWritable one = new IntWritable(1);
//定义hadoop数据类型Text实例word
private Text word = new Text();


//实现map函数   
public void map(Object key, Text value, Context context
                 ) throws IOException, InterruptedException {

//Java的字符串分解类，默认分隔符“空格”、“制表符(‘\t’)”、“换行符(‘\n’)”、“回车符(‘\r’)”
   StringTokenizer itr = new StringTokenizer(value.toString());

//循环条件表示返回是否还有分隔符。
   while (itr.hasMoreTokens()) {

/*****
nextToken()：返回从当前位置到下一个分隔符的字符串
word.set()Java数据类型与hadoop数据类型转换
****/
     word.set(itr.nextToken());
//hadoop全局类context输出函数write;
     context.write(word, one);
   }
}
}


//继承泛型类Reducer
public static class IntSumReducer
    extends Reducer<Text,IntWritable,Text,IntWritable> {

//实例化IntWritable
private IntWritable result = new IntWritable();

//实现reduce
public void reduce(Text key, Iterable<IntWritable> values,
                    Context context
                    ) throws IOException, InterruptedException {
   int sum = 0;

//循环values，并记录单词个数
   for (IntWritable val : values) {
     sum += val.get();
   }
//Java数据类型sum，转换为hadoop数据类型result
   result.set(sum);
//输出结果到hdfs
   context.write(key, result);
}
}

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

//实例化Configuration
Configuration conf = new Configuration();
/***********
GenericOptionsParser是hadoop框架中解析命令行参数的基本类。
getRemainingArgs();返回数组【一组路径】
***********/
/**********
函数实现
public String[] getRemainingArgs() {
    return (commandLine == null) ? new String[]{} : commandLine.getArgs();
  }


/********
//总结上面：返回数组【一组路径】
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();

//如果只有一个路径，则输出需要有输入路径和输出路径
if (otherArgs.length < 2) {
   System.err.println("Usage: wordcount <in> [<in>...] <out>");
   System.exit(2);
}

//实例化job
Job job = Job.getInstance(conf, "word count");

//为了能够找到wordcount这个类
job.setJarByClass(wordcount.class);
//指定map类型
job.setMapperClass(TokenizerMapper.class);
/********
指定CombinerClass类
这里很多人对CombinerClass不理解
************/
job.setCombinerClass(IntSumReducer.class);
//指定reduce类
job.setReducerClass(IntSumReducer.class);

//rduce输出Key的类型，是Text
job.setOutputKeyClass(Text.class);
// rduce输出Value的类型
job.setOutputValueClass(IntWritable.class);
//添加输入路径
for (int i = 0; i < otherArgs.length - 1; ++i) {
   FileInputFormat.addInputPath(job, new Path(otherArgs));
}
//添加输出路径
FileOutputFormat.setOutputPath(job,
   new Path(otherArgs[otherArgs.length - 1]));
//提交job
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}