Hadoop WordCount改进实现正确识别单词以及词频降序排序

参考资料：

http://radarradar.javaeye.com/blog/289257

http://blog.chinaunix.net/u3/99156/showart_2157576.html

package org.apache.hadoop.examples;import java.io.IOException;import java.util.Random;import java.util.StringTokenizer;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.io.WritableComparable;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.SequenceFileInputFormat;import org.apache.hadoop.mapreduce.lib.map.InverseMapper;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;import org.apache.hadoop.util.GenericOptionsParser;public class WordCount2 {public static class TokenizerMapper extendsMapper<Object, Text, Text, IntWritable> {private final static IntWritable one = new IntWritable(1);private Text word = new Text();private String pattern = "[^//w]"; // 正则表达式，代表不是0-9, a-z, A-Z的所有其它字符,其中还有下划线public void map(Object key, Text value, Context context)throws IOException, InterruptedException {String line = value.toString().toLowerCase(); // 全部转为小写字母line = line.replaceAll(pattern, " "); // 将非0-9, a-z, A-Z的字符替换为空格StringTokenizer itr = new StringTokenizer(line);while (itr.hasMoreTokens()) {word.set(itr.nextToken());context.write(word, one);}}}public static class IntSumReducer extendsReducer<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);}} private static class IntWritableDecreasingComparator extends IntWritable.Comparator { public int compare(WritableComparable a, WritableComparable b) { return -super.compare(a, b); } public int compare(byte[] b1, int s1, int l1, byte[] b2, int s2, int l2) { return -super.compare(b1, s1, l1, b2, s2, l2); } }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> <out>");System.exit(2);} Path tempDir = new Path("wordcount-temp-" + Integer.toString( new Random().nextInt(Integer.MAX_VALUE))); //定义一个临时目录Job job = new Job(conf, "word count");job.setJarByClass(WordCount2.class);try{job.setMapperClass(TokenizerMapper.class);job.setCombinerClass(IntSumReducer.class);job.setReducerClass(IntSumReducer.class);job.setOutputKeyClass(Text.class);job.setOutputValueClass(IntWritable.class);FileInputFormat.addInputPath(job, new Path(otherArgs[0]));FileOutputFormat.setOutputPath(job, tempDir);//先将词频统计任务的输出结果写到临时目 //录中, 下一个排序任务以临时目录为输入目录。job.setOutputFormatClass(SequenceFileOutputFormat.class);if(job.waitForCompletion(true)){Job sortJob = new Job(conf, "sort");sortJob.setJarByClass(WordCount2.class);FileInputFormat.addInputPath(sortJob, tempDir);sortJob.setInputFormatClass(SequenceFileInputFormat.class);/*InverseMapper由hadoop库提供，作用是实现map()之后的数据对的key和value交换*/ sortJob.setMapperClass(InverseMapper.class); /*将 Reducer 的个数限定为1, 最终输出的结果文件就是一个。*/ sortJob.setNumReduceTasks(1); FileOutputFormat.setOutputPath(sortJob, new Path(otherArgs[1])); sortJob.setOutputKeyClass(IntWritable.class);sortJob.setOutputValueClass(Text.class);/*Hadoop 默认对 IntWritable 按升序排序，而我们需要的是按降序排列。 * 因此我们实现了一个 IntWritableDecreasingComparator 类,　 * 并指定使用这个自定义的 Comparator 类对输出结果中的 key (词频)进行排序*/ sortJob.setSortComparatorClass(IntWritableDecreasingComparator.class); System.exit(sortJob.waitForCompletion(true) ? 0 : 1);}}finally{FileSystem.get(conf).deleteOnExit(tempDir);}}}