Hadoop之WordCount

Hadoop的WordCount实例，代码如下：

import java.io.IOException;import java.util.*;import org.apache.hadoop.conf.*;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.*;import org.apache.hadoop.mapreduce.*;import org.apache.hadoop.mapreduce.lib.input.*;import org.apache.hadoop.mapreduce.lib.output.*;import org.apache.hadoop.util.*;public class WordCount extends Configured implements Tool {public static class Map extends Mapper<LongWritable, Text, Text, IntWritable>{ private final static IntWritable one = new IntWritable(1);      private Text word = new Text();      public void map(LongWritable key, Text value, Context context) throws IOException,InterruptedException {        String line = value.toString();StringTokenizer tokenizer = new StringTokenizer(line);        while (tokenizer.hasMoreTokens()) {          word.set(tokenizer.nextToken());          context.write(word, one);        }       }}public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable>{ public void reduce(Text key, Iterator<IntWritable> values, Context context) throws IOException,InterruptedException {        int sum = 0;          while (values.hasNext()) {           sum += values.next().get();        }        context.write(key, new IntWritable(sum));       }}public int run(String[] args) throws Exception{Job job = new Job(getConf());job.setJarByClass(WordCount.class);job.setJobName("wordcount");job.setOutputKeyClass(Text.class);job.setOutputValueClass(IntWritable.class);job.setMapperClass(Map.class);job.setReducerClass(Reduce.class);job.setInputFormatClass(TextInputFormat.class);job.setOutputFormatClass(TextOutputFormat.class);FileInputFormat.setInputPaths(job, new Path(args[0]));FileOutputFormat.setOutputPath(job, new Path(args[1]));boolean success = job.waitForCompletion(true);return success ? 0 : 1;}/** * @param args */public static void main(String[] args) throws Exception{// TODO Auto-generated method stubint ret = ToolRunner.run(new WordCount(), args);System.exit(ret);}}

WordCount工程中Export---java（JAR File）---不要勾选.classpath和.project，生成wordcount_test.jar包，然后放到服务器上去执行。

在hdfs文件系统中创建数据文件，如：

[root@localhost hadoop-0.20.2]# bin/hadoop fs -lsr
drwxr-xr-x   - root supergroup          0 2013-01-09 09:43 /user/root/wordcount
drwxr-xr-x   - root supergroup          0 2013-01-08 08:41 /user/root/wordcount/input
-rw-r--r--   1 root supergroup         43 2013-01-08 08:41 /user/root/wordcount/input/hello1.txt
-rw-r--r--   1 root supergroup         63 2013-01-08 08:41 /user/root/wordcount/input/hello2.txt

运行jar包，对数据进行统计：

[root@localhost hadoop-0.20.2]# bin/hadoop jar wordcount_test.jar WordCount /user/root/wordcount/input/* output
13/01/23 09:19:06 INFO input.FileInputFormat: Total input paths to process : 2
13/01/23 09:19:06 INFO mapred.JobClient: Running job: job_201301220408_0001
13/01/23 09:19:07 INFO mapred.JobClient:  map 0% reduce 0%
13/01/23 09:19:14 INFO mapred.JobClient:  map 100% reduce 0%
13/01/23 09:19:26 INFO mapred.JobClient:  map 100% reduce 100%
13/01/23 09:19:28 INFO mapred.JobClient: Job complete: job_201301220408_0001
13/01/23 09:19:28 INFO mapred.JobClient: Counters: 17
13/01/23 09:19:28 INFO mapred.JobClient:   Job Counters
13/01/23 09:19:28 INFO mapred.JobClient:     Launched reduce tasks=1
13/01/23 09:19:28 INFO mapred.JobClient:     Launched map tasks=2
13/01/23 09:19:28 INFO mapred.JobClient:     Data-local map tasks=2
13/01/23 09:19:28 INFO mapred.JobClient:   FileSystemCounters
13/01/23 09:19:28 INFO mapred.JobClient:     FILE_BYTES_READ=196
13/01/23 09:19:28 INFO mapred.JobClient:     HDFS_BYTES_READ=106
13/01/23 09:19:28 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=462
13/01/23 09:19:28 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=134
13/01/23 09:19:28 INFO mapred.JobClient:   Map-Reduce Framework
13/01/23 09:19:28 INFO mapred.JobClient:     Reduce input groups=5
13/01/23 09:19:28 INFO mapred.JobClient:     Combine output records=0
13/01/23 09:19:28 INFO mapred.JobClient:     Map input records=7
13/01/23 09:19:28 INFO mapred.JobClient:     Reduce shuffle bytes=202
13/01/23 09:19:28 INFO mapred.JobClient:     Reduce output records=14
13/01/23 09:19:28 INFO mapred.JobClient:     Spilled Records=28
13/01/23 09:19:28 INFO mapred.JobClient:     Map output bytes=162
13/01/23 09:19:28 INFO mapred.JobClient:     Combine input records=0
13/01/23 09:19:28 INFO mapred.JobClient:     Map output records=14
13/01/23 09:19:28 INFO mapred.JobClient:     Reduce input records=14

统计结果：

[root@localhost hadoop-0.20.2]# bin/hadoop fs -lsr
drwxr-xr-x   - root supergroup          0 2013-01-23 09:19 /user/root/output
drwxr-xr-x   - root supergroup          0 2013-01-23 09:19 /user/root/output/_logs
drwxr-xr-x   - root supergroup          0 2013-01-23 09:19 /user/root/output/_logs/history
-rw-r--r--   1 root supergroup      17647 2013-01-23 09:19 /user/root/output/_logs/history/localhost_1358845718908_job_201301220408_0001_conf.xml
-rw-r--r--   1 root supergroup       8951 2013-01-23 09:19 /user/root/output/_logs/history/localhost_1358845718908_job_201301220408_0001_root_wordcount
-rw-r--r--   1 root supergroup        134 2013-01-23 09:19 /user/root/output/part-r-00000
drwxr-xr-x   - root supergroup          0 2013-01-09 09:43 /user/root/wordcount
drwxr-xr-x   - root supergroup          0 2013-01-08 08:41 /user/root/wordcount/input
-rw-r--r--   1 root supergroup         43 2013-01-08 08:41 /user/root/wordcount/input/hello1.txt
-rw-r--r--   1 root supergroup         63 2013-01-08 08:41 /user/root/wordcount/input/hello2.txt

最终，得到output中的结果：

bin/hadoop fs -cat /user/root/output/part-r-00000