hadoop wordcount

在hdfs上的文本内容如下

hello world hello javahello chello hadoop map reduce

以下是自己对这个过程的总结

 mapreduce执行的流程 input<k1,v1> -> map -><k2,v2> -> <k2,list<v2>> ->reduce<k3,v3> ->output 具体的步骤:   map输入        <0,hello world hello java>        <22, hello c>        <29, hello hadoop map reduce>  map输出        <hello,1>        <world,1>        <hello,1>        <java, 1>        <hello,1 >        <c, 1>        <hello, 1>        <hadoop , 1>        <map, 1>        <reduce, 1>  进行shuffle        对map的输出进行排序分组        shuffle处理后               <hello, <1,1,1,1>>            <world, <1>>            <java, <1>>            <c, <1>>...    reduce        接受shuffle后的kv，遍历v的列表，进行求和    结果：        <hello, 4>        <world,1>...    output到hdfs

代码

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;public class WordCount { /**  *   * map：  *     将一行句子， 以空格切分 进行输出 <一个单词, 1>  * @author hadoop  *  */  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 {      //String[] strs = value.toString().split("");      StringTokenizer itr = new StringTokenizer(value.toString());      while (itr.hasMoreTokens()) {        word.set(itr.nextToken());        context.write(word, one);      }    }  }  /**   * reduce功能   *    接受map的数据(中间有shuffle过程)   *    计数   * @author hadoop   *   */  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();    Job job = Job.getInstance(conf, "word count");    //设置主要的工作类    job.setJarByClass(WordCount.class);    //设置输入输出路径    FileInputFormat.addInputPath(job, new Path(args[0]));    FileOutputFormat.setOutputPath(job, new Path(args[1]));    //设置map和reduce类    job.setMapperClass(TokenizerMapper.class);      job.setReducerClass(IntSumReducer.class);    //设置输出k， v 格式    job.setOutputKeyClass(Text.class);    job.setOutputValueClass(IntWritable.class);    //job.setCombinerClass(IntSumReducer.class);     //运行任务    System.exit(job.waitForCompletion(true) ? 0 : 1);  }}