mapreduce实现wordcount

WordCount.java

/** * Licensed to the Apache Software Foundation (ASF) under one * or more contributor license agreements.  See the NOTICE file * distributed with this work for additional information * regarding copyright ownership.  The ASF licenses this file * to you under the Apache License, Version 2.0 (the * "License"); you may not use this file except in compliance * with the License.  You may obtain a copy of the License at * *     http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */// Modified by Shimin Chen to demonstrate functionality for Homework 2// April-May 2015import 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.mapred.TextInputFormat;import org.apache.hadoop.mapred.TextOutputFormat;import org.apache.hadoop.util.GenericOptionsParser;public class WordCount {  // This is the Mapper class  // reference: http://hadoop.apache.org/docs/r2.6.0/api/org/apache/hadoop/mapreduce/Mapper.html  //  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);      }    }  }    public static class IntSumCombiner       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);    }  }  // This is the Reducer class  // reference http://hadoop.apache.org/docs/r2.6.0/api/org/apache/hadoop/mapreduce/Reducer.html  //  // We want to control the output format to look at the following:  //  // count of word = count  //  public static class IntSumReducer       extends Reducer<Text,IntWritable,Text,Text> {    private Text result_key= new Text();    private Text result_value= new Text();    private byte[] prefix;    private byte[] suffix;    protected void setup(Context context) {      try {        prefix= Text.encode("count of ").array();        suffix= Text.encode(" =").array();      } catch (Exception e) {        prefix = suffix = new byte[0];      }    }    public void reduce(Text key, Iterable<IntWritable> values,                        Context context                       ) throws IOException, InterruptedException {      int sum = 0;      for (IntWritable val : values) {        sum += val.get();      }      // generate result key      result_key.set(prefix);      result_key.append(key.getBytes(), 0, key.getLength());      result_key.append(suffix, 0, suffix.length);      // generate result value      result_value.set(Integer.toString(sum));      context.write(result_key, result_value);    }  }  public static void main(String[] args) throws Exception {    Configuration conf = new Configuration();    String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();    if (otherArgs.length < 2) {      System.err.println("Usage: wordcount <in> [<in>...] <out>");      System.exit(2);    }    Job job = Job.getInstance(conf, "word count");    job.setJarByClass(WordCount.class);    job.setMapperClass(TokenizerMapper.class);    job.setCombinerClass(IntSumCombiner.class);    job.setReducerClass(IntSumReducer.class);    job.setMapOutputKeyClass(Text.class);    job.setMapOutputValueClass(IntWritable.class);    job.setOutputKeyClass(Text.class);    job.setOutputValueClass(Text.class);    // add the input paths as given by command line    for (int i = 0; i < otherArgs.length - 1; ++i) {      FileInputFormat.addInputPath(job, new Path(otherArgs[i]));    }    // add the output path as given by the command line    FileOutputFormat.setOutputPath(job,      new Path(otherArgs[otherArgs.length - 1]));    System.exit(job.waitForCompletion(true) ? 0 : 1);  }}

WordCount-manifest.txtMain-Class: WordCount

执行命令：

1. start hadoop

   $ start-dfs.sh
   $ start-yarn.sh

2. Example: WordCount.java

   compile and generate jar
   $ javac WordCount.java
   $ jar cfm WordCount.jar WordCount-manifest.txt WordCount*.class

   remove output hdfs directory then run MapReduce job
   $ hdfs dfs -rm -f -r /hw2/output
   $ hadoop jar ./WordCount.jar /hw2/example-input.txt /hw2/output

   display output
   $ hdfs dfs -cat '/hw2/output/part-*'