public class WordCount extends Configured implements Tool {
public static class MapClass extends MapReduceBase implements 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, OutputCollector<Text, IntWritable> output, Reporter reporter） throws IOException { String line = value.toString（）; StringTokenizer itr = new StringTokenizer（line）; while （itr.hasMoreTokens（）） { word.set（itr.nextToken（））; output.collect（word, one）; } } }
/** * A reducer class that just emits the sum of the input values. */ public static class Reduce extends MapReduceBase implements Reducer<Text, IntWritable, Text, IntWritable> {
public void reduce（Text key, Iterator<IntWritable> values, OutputCollector<Text, IntWritable> output, Reporter reporter） throws IOException { int sum = 0; while （values.hasNext（）） { sum += values.next（）.get（）; } output.collect（key, new IntWritable（sum））; } }
/** * The main driver for word count map/reduce program. Invoke this method to * submit the map/reduce job. * * @throws IOException * When there is communication problems with the job tracker. */ public int run（String[] args） throws Exception { JobConf conf = new JobConf（getConf（）, WordCount.class）; conf.setJobName（"wordcount"）;
// the keys are words （strings） conf.setOutputKeyClass（Text.class）; // the values are counts （ints） conf.setOutputValueClass（IntWritable.class）;
List<String> other_args = new ArrayList<String>（）; for （int i = 0; i < args.length; ++i） { try { if （"-m".equals（args[i]）） { conf.setNumMapTasks（Integer.parseInt（args[++i]））; } else if （"-r".equals（args[i]）） { conf.setNumReduceTasks（Integer.parseInt（args[++i]））; } else { other_args.add（args[i]）; } } catch （NumberFormatException except） { System.out.println（"ERROR: Integer expected instead of " + args[i]）; return printUsage（）; } catch （ArrayIndexOutOfBoundsException except） { System.out.println（"ERROR: Required parameter missing from " + args[i - 1]）; return printUsage（）; } }
// Make sure there are exactly 2 parameters left. if （other_args.size（） ！= 2） { System.out.println（"ERROR: Wrong number of parameters: " + other_args.size（） + " instead of 2."）; return printUsage（）; } FileInputFormat.setInputPaths（conf, other_args.get（0））; FileOutputFormat.setOutputPath（conf, new Path（other_args.get（1）））;
JobClient.runJob（conf）; return 0; }
public static void main（String[] args） throws Exception { int res = ToolRunner.run（new Configuration（）, new WordCount（）, args）; System.exit（res）; }
} 2. 保证hadoop集群是配置好了的，单机的也好。新建一个目录，比如 /home/admin/WordCount 编译WordCount.java程序。javac -classpath /home/admin/hadoop/hadoop-0.19.1-core.jar WordCount.java -d /home/admin/WordCount 3. 编译完后在/home/admin/WordCount目录会发现三个class文件 WordCount.class，WordCount$Map.class，WordCount$Reduce.class。 cd 进入 /home/admin/WordCount目录,然后执行：jar cvf WordCount.jar *.class 就会生成 WordCount.jar 文件。 4. 构造一些输入数据 input1.txt和input2.txt的文件里面是一些单词。如下：[admin@host WordCount]$ cat input1.txt Hello, i love china are you ok？ [admin@host WordCount]$ cat input2.txt hello, i love word You are ok 在hadoop上新建目录，和put程序运行所需要的输入文件:hadoop fs -mkdir /tmp/input hadoop fs -mkdir /tmp/output hadoop fs -put input1.txt /tmp/input/ hadoop fs -put input2.txt /tmp/input/ 5. 运行程序,会显示job运行时的一些信息。[admin@host WordCount]$ hadoop jar WordCount.jar WordCount /tmp/input /tmp/output 10/09/16 22:49:43 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same. 10/09/16 22:49:43 INFO mapred.FileInputFormat: Total input paths to process :2 10/09/16 22:49:43 INFO mapred.JobClient: Running job: job_201008171228_76165 10/09/16 22:49:44 INFO mapred.JobClient: map 0% reduce 0% 10/09/16 22:49:47 INFO mapred.JobClient: map 100% reduce 0% 10/09/16 22:49:54 INFO mapred.JobClient: map 100% reduce 100% 10/09/16 22:49:55 INFO mapred.JobClient: Job complete: job_201008171228_76165 10/09/16 22:49:55 INFO mapred.JobClient: Counters: 16 10/09/16 22:49:55 INFO mapred.JobClient: File Systems 10/09/16 22:49:55 INFO mapred.JobClient: HDFS bytes read=62 10/09/16 22:49:55 INFO mapred.JobClient: HDFS bytes written=73 10/09/16 22:49:55 INFO mapred.JobClient: Local bytes read=152 10/09/16 22:49:55 INFO mapred.JobClient: Local bytes written=366 10/09/16 22:49:55 INFO mapred.JobClient: Job Counters 10/09/16 22:49:55 INFO mapred.JobClient: Launched reduce tasks=1 10/09/16 22:49:55 INFO mapred.JobClient: Rack-local map tasks=2 10/09/16 22:49:55 INFO mapred.JobClient: Launched map tasks=2 10/09/16 22:49:55 INFO mapred.JobClient: Map-Reduce Framework 10/09/16 22:49:55 INFO mapred.JobClient: Reduce input groups=11 10/09/16 22:49:55 INFO mapred.JobClient: Combine output records=14 10/09/16 22:49:55 INFO mapred.JobClient: Map input records=4 10/09/16 22:49:55 INFO mapred.JobClient: Reduce output records=11 10/09/16 22:49:55 INFO mapred.JobClient: Map output bytes=118 10/09/16 22:49:55 INFO mapred.JobClient: Map input bytes=62 10/09/16 22:49:55 INFO mapred.JobClient: Combine input records=14 10/09/16 22:49:55 INFO mapred.JobClient: Map output records=14 10/09/16 22:49:55 INFO mapred.JobClient: Reduce input records=14 6. 查看运行结果[admin@host WordCount]$ hadoop fs -ls /tmp/output/ Found 2 items drwxr-x--- - admin admin 0 2010-09-16 22:43 /tmp/output/_logs -rw-r----- 1 admin admin 102 2010-09-16 22:44 /tmp/output/part-00000 [admin@host WordCount]$ hadoop fs -cat /tmp/output/part-00000 Hello, 1 You 1 are 2 china 1 hello, 1 i 2 love 2 ok 1 ok？ 1 word 1 you 1
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