1、WordCount代码

package com.test;

import java.io.IOException;
import java.net.URI;
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.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 {
	
	public static class WordCountMapper 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()," \"\"\t\n\r\f,.:;![]'");
			
			while (itr.hasMoreTokens()) {
				
				word.set(itr.nextToken());
				context.write(word, one);
			}
		}
	}

	public static class WordCountReducer 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);
		}
	}

	@SuppressWarnings("unused")
	public static void main(String[] args) throws Exception {
		Configuration conf = new Configuration();
		
		Job job = new Job(conf, "WordCount");
		job.setJarByClass(WordCount.class);
		job.setMapperClass(WordCountMapper.class);
		job.setCombinerClass(WordCountReducer.class);
		job.setReducerClass(WordCountReducer.class);
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(IntWritable.class);
		
		FileInputFormat.addInputPath(job, new Path("hdfs://192.168.174.129:9000/wcdata"));
		
		Path owcpath =  new Path("hdfs://192.168.174.129:9000/owcdata");
		
		FileSystem fs = FileSystem.get(URI.create("hdfs://192.168.174.129:9000"),conf);  
		
		if(owcpath == null){
			
			FileOutputFormat.setOutputPath(job, owcpath);
			
		}else{
			
			boolean delResult =fs.delete(owcpath,true); 
			if(delResult==true){
				
				System.out.println("del success");
				FileOutputFormat.setOutputPath(job, owcpath);
				
			}else{
				
				System.out.println("del error");
				FileOutputFormat.setOutputPath(job, owcpath);
			}
			
		}
			
		System.exit(job.waitForCompletion(true)  0 : 1);
	}
}

在/home/hadoop目录下创建WordCount目录mkdir /home/hadoop/WordCount
在WordCount目录下创建WordCount_class目录mkdir /home/hadoop/WordCount/WordCount_class

javac -classpath $HADOOP_HOME/share/hadoop/common/hadoop-common-2.6.0.jar:$HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-client-core-2.6.0.jar -d /home/hadoop/WordCount/WordCount_class /home/hadoop/workspace/HelloWorld/src/com/test/WordCount.java

1）-d 后面的目录是编译后class文件存放的目录

2）/home/hadoop/workspace/HelloWorld/src/com/test/WordCount.java 是WordCount.jar所在工程的目录

4、将上一步中class文件打包成可执行的jar文件，具体命令如下：

jar -cvf ./WordCount.jar -C /home/hadoop/WordCount/WordCount_class .

说明：-C参数是必须要加的，最后面的“.”千万不要丢掉

5、向hadoop集群提交WordCout任务，具体命令如下

bin/hadoop jar WordCount.jar com.test.WordCount

说明： jar 后面的jar文件为编译好的jar文件 jar后面为java源文件的类（形式为包名+类名）

[hadoop@Master WordCount]$ hadoop jar WordCount.jar com.test.WordCount
16/10/06 23:29:18 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
del success
16/10/06 23:29:21 INFO client.RMProxy: Connecting to ResourceManager at Master/192.168.174.129:8032
16/10/06 23:29:23 WARN mapreduce.JobSubmitter: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
16/10/06 23:29:24 INFO input.FileInputFormat: Total input paths to process : 1
16/10/06 23:29:24 INFO mapreduce.JobSubmitter: number of splits:1
16/10/06 23:29:25 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1475805031685_0002
16/10/06 23:29:26 INFO impl.YarnClientImpl: Submitted application application_1475805031685_0002
16/10/06 23:29:26 INFO mapreduce.Job: The url to track the job: http://Master:8088/proxy/application_1475805031685_0002/
16/10/06 23:29:26 INFO mapreduce.Job: Running job: job_1475805031685_0002
16/10/06 23:29:56 INFO mapreduce.Job: Job job_1475805031685_0002 running in uber mode : false
16/10/06 23:29:56 INFO mapreduce.Job:  map 0% reduce 0%
16/10/06 23:30:40 INFO mapreduce.Job:  map 100% reduce 0%
16/10/06 23:31:03 INFO mapreduce.Job:  map 100% reduce 100%
16/10/06 23:31:04 INFO mapreduce.Job: Job job_1475805031685_0002 completed successfully
16/10/06 23:31:04 INFO mapreduce.Job: Counters: 49
	File System Counters
		FILE: Number of bytes read=105
		FILE: Number of bytes written=210923
		FILE: Number of read operations=0
		FILE: Number of large read operations=0
		FILE: Number of write operations=0
		HDFS: Number of bytes read=157
		HDFS: Number of bytes written=63
		HDFS: Number of read operations=6
		HDFS: Number of large read operations=0
		HDFS: Number of write operations=2
	Job Counters 
		Launched map tasks=1
		Launched reduce tasks=1
		Data-local map tasks=1
		Total time spent by all maps in occupied slots (ms)=40978
		Total time spent by all reduces in occupied slots (ms)=19816
		Total time spent by all map tasks (ms)=40978
		Total time spent by all reduce tasks (ms)=19816
		Total vcore-seconds taken by all map tasks=40978
		Total vcore-seconds taken by all reduce tasks=19816
		Total megabyte-seconds taken by all map tasks=41961472
		Total megabyte-seconds taken by all reduce tasks=20291584
	Map-Reduce Framework
		Map input records=2
		Map output records=9
		Map output bytes=81
		Map output materialized bytes=105
		Input split bytes=109
		Combine input records=9
		Combine output records=9
		Reduce input groups=9
		Reduce shuffle bytes=105
		Reduce input records=9
		Reduce output records=9
		Spilled Records=18
		Shuffled Maps =1
		Failed Shuffles=0
		Merged Map outputs=1
		GC time elapsed (ms)=857
		CPU time spent (ms)=6620
		Physical memory (bytes) snapshot=286318592
		Virtual memory (bytes) snapshot=4120993792
		Total committed heap usage (bytes)=137936896
	Shuffle Errors
		BAD_ID=0
		CONNECTION=0
		IO_ERROR=0
		WRONG_LENGTH=0
		WRONG_MAP=0
		WRONG_REDUCE=0
	File Input Format Counters 
		Bytes Read=48
	File Output Format Counters 
		Bytes Written=63
[hadoop@Master WordCount]$