mapreduce中入门中要注意的几点

在 mapreduce中，比如有如下的词：

I love beijing
i love cina
beijing is the captial of china
   统计的时候，如下图：




注意上图中，最左边的偏移量第一个为1，然后I LOVE CHINA中，I是第4个单词了；所以偏移量为4；
然后进行分词，
然后K3就是把每个单词归类的KEY，然后V3（1，1），就是说I这个单词，统计的频率；


相关的mapper的编写：

public class WordCountMapper extends Mapper<LongWritable, Text, Text, LongWritable> {

	@Override
	protected void map(LongWritable key, Text value, Context context)
			throws IOException, InterruptedException {
		/*
		 * key: 输入的key
		 * value: 数据   I love Beijing
		 * context: Map上下文
		 */
		String data= value.toString();
		//分词
		String[] words = data.split(" ");
		
		//输出每个单词
		for(String w:words){
			context.write(new Text(w), new LongWritable(1));
		}
	}

}

reduce:

public class WordCountReducer extends Reducer<Text, LongWritable, Text, LongWritable>{

	@Override
	protected void reduce(Text k3, Iterable<LongWritable> v3,Context context) throws IOException, InterruptedException {
		//v3: 是一个集合，每个元素就是v2
		long total = 0;
		for(LongWritable l:v3){
			total = total + l.get();
		}
		
		//输出
		context.write(k3, new LongWritable(total));
	}

}

主程序：

public class WordCountMain {

	public static void main(String[] args) throws Exception {
		//创建一个job = map + reduce
		Configuration conf = new Configuration();
		
		//创建一个Job
		Job job = Job.getInstance(conf);
		//指定任务的入口
		job.setJarByClass(WordCountMain.class);
		
		//指定job的mapper
		job.setMapperClass(WordCountMapper.class);
		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(LongWritable.class);
		
		//指定job的reducer
		job.setReducerClass(WordCountReducer.class);
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(LongWritable.class);
		
		//指定任务的输入和输出
		FileInputFormat.setInputPaths(job, new Path(args[0]));
		FileOutputFormat.setOutputPath(job, new Path(args[1]));		
		
		//提交任务
		job.waitForCompletion(true);
	}