hadoop编程实践

项目文件：Github


 Mapreduce流程：





package test.wordcount;

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


/*
 * @author:Kouch
 * 
 * "词频统计"思路：
 *         1 input：从输入文件读取数据；
 *      2 split :一行 为一个<key,value>对 - value：行内容;
 *         3 map ：将一行切分成一个一个单词，每个单词输出为一个 <word,1> 的兼键值对；
 *         4 shuffle：将相同的key（一行的内容）累计 - <key,value-list> ：<  word , n > ;
 *                    hadoop会自动统计每一次map后的单词频率;
 *         5 reduce：汇总时统计整体的同一个单词的频率；
 *         6 output：输出；
 * 
 */

public class WordCount {

    //map
    public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {
        
        //hadoop的数据类型：有一个单词，就记录一个 ‘1’；
        private static final IntWritable one = new IntWritable(1);
        
        //（在循环map时）保存每一个单词；
        private Text word = new Text();
        
        public void map(Object key, Text value, Mapper<Object, Text, Text, IntWritable>.Context context) throws IOException, InterruptedException {
            
            //强调：具体的切分依据数据的保存格式；
            //文档数据格式：单词以空格分割；
            //一行内容：Text value 转成 StringTokenizer；
            StringTokenizer itr = new StringTokenizer(value.toString());
            
            //将每个单词输出到context
            while(itr.hasMoreTokens()) {
                this.word.set(itr.nextToken());
                context.write(this.word, one);
            }

        }
    }


    //reduce
    public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
        
        //
        private IntWritable result = new IntWritable();
        
        public void reduce(Text key, Iterable<IntWritable> values, Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {
            int sum = 0;
            
            //迭代累计频率；
            IntWritable val;
            for(Iterator i$ = values.iterator(); i$.hasNext(); sum += val.get()) {
                val = (IntWritable)i$.next();
            }

            this.result.set(sum);
            context.write(key, this.result);
        }
    }


    //main
    public static void main(String[] args) throws Exception {
        
        //配置类
        Configuration conf = new Configuration();
        
        //传参设置
        //String[] ioArgs = (new GenericOptionsParser(conf, args)).getRemainingArgs(); //可用于打包jar
        String[] ioArgs =new String[] {"input","output"};
        if(ioArgs.length < 2) {
            System.err.println("Usage: wordcount <in> [<in>...] <out>");
            System.exit(2);
        }

        //job相关配置
        Job job = Job.getInstance(conf, "word count");
        job.setJarByClass(WordCount.class);
        job.setMapperClass(WordCount.TokenizerMapper.class);
        job.setCombinerClass(WordCount.IntSumReducer.class);
        job.setReducerClass(WordCount.IntSumReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        for(int i = 0; i < ioArgs.length - 1; ++i) {
            FileInputFormat.addInputPath(job, new Path(ioArgs[i]));
        }

        FileOutputFormat.setOutputPath(job, new Path(ioArgs[ioArgs.length - 1]));
        
        //等待job完成之后再返回结果并退出程序
        System.exit(job.waitForCompletion(true)?0:1);
        
    }

}