Hadoop MapReduce 入门 单词统计

覆盖 mapper类的map方法和reducer类的reduce方法

感兴趣的可以一起交流，只是单纯的分享代码，如果需要详细指导可以私聊

package mapreduce;
import java.io.IOException;
import java.util.StringTokenizer;
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;

// word counter main class
public class WordCount {
	
	public static void main(String[] args) throws IOException,ClassNotFoundException,InterruptedException{
		Job job = Job.getInstance();
		job.setJobName("WordCount");
		job.setJarByClass(WordCount.class);
		job.setMapperClass(doMapper.class);
		job.setReducerClass(doReducer.class);
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(IntWritable.class);
		Path in = new Path("hdfs://localhost:9000/mymapreduce1/in/buyer_favorite1");
		Path out = new Path("hdfs://localhost:9000/mymapreduce1/out");
		FileInputFormat.addInputPath(job, in);
		FileOutputFormat.setOutputPath(job, out);
		System.exit(job.waitForCompletion(true)? 0: 1);
	}
	// map
	public static class doMapper extends Mapper<Object, Text, Text, IntWritable>{
		public static final IntWritable count_one = new IntWritable(1);
		public static Text word = new Text();
		@Override
		protected void map(Object key, Text value, Context context)
					throws IOException, InterruptedException{
			StringTokenizer st = new StringTokenizer(value.toString(), "\t");
			
			while (st.hasMoreTokens()){
				word.set(st.nextToken());
				context.write(word, count_one);
			}
			
		}
	}
	// reduce
	public static class doReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
		private IntWritable result = new IntWritable();
		@Override
		protected void reduce(Text key, Iterable<IntWritable> values, Context context)
			throws IOException, InterruptedException{
			int sum = 0;
			for(IntWritable value:values){
				sum += value.get();
			}
			result.set(sum);
			context.write(key,result);
		}
	}
}