wordcount例子的程序，附带说明

package mapreduce;import java.net.URI;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.FileSystem;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.LongWritable;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;/** * helloyou * hellome * */public class WordCountApp {private static final String INPUT_PATH = "hdfs://chaoren1:9000/files";private static final String OUT_PATH = "hdfs://chaoren1:9000/out";public static void main(String[] args) throws Exception{Configuration conf = new Configuration();final FileSystem filesystem = FileSystem.get(new URI(OUT_PATH), conf);filesystem.delete(new Path(OUT_PATH), true);final Job job = new Job(conf , WordCountApp.class.getSimpleName());job.setJarByClass(WordCountApp.class);FileInputFormat.setInputPaths(job, INPUT_PATH);job.setMapperClass(MyMapper.class);job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(LongWritable.class);job.setReducerClass(MyReducer.class);job.setOutputKeyClass(Text.class);job.setOutputValueClass(LongWritable.class);FileOutputFormat.setOutputPath(job, new Path(OUT_PATH));job.waitForCompletion(true);}public static class MyMapper extends Mapper<LongWritable, Text, Text, LongWritable>{//解析源文件会产生2个键值对，分别是<0,hello you><10,hello me>；所以map函数会被调用2次protected void map(LongWritable key, Text value, org.apache.hadoop.mapreduce.Mapper<LongWritable,Text,Text,LongWritable>.Context context) throws java.io.IOException ,InterruptedException {//为什么要把hadoop类型转换为java类型？final String line = value.toString();final String[] splited = line.split("\t");//产生的<k,v>对少了for (String word : splited) {//在for循环体内，临时变量word的出现次数是常量1context.write(new Text(word), new LongWritable(1));}};}//map函数执行结束后，map输出的<k,v>一共有4个，分别是<hello,1><you,1><hello,1><me,1>//分区，默认只有一个区//排序后的结果：<hello,1><hello,1><me,1><you,1>//分组后的结果：<hello,{1,1}>  <me,{1}>  <you,{1}>//归约(可选)//map产生的<k,v>分发到reduce的过程称作shufflepublic static class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable>{//每一组调用一次reduce函数，一共调用了3次//分组的数量与reduce函数的调用次数有什么关系？//reduce函数的调用次数与输出的<k,v>的数量有什么关系？protected void reduce(Text key, java.lang.Iterable<LongWritable> values, org.apache.hadoop.mapreduce.Reducer<Text,LongWritable,Text,LongWritable>.Context context) throws java.io.IOException ,InterruptedException {//count表示单词key在整个文件中的出现次数long count = 0L;for (LongWritable times : values) {count += times.get();}context.write(key, new LongWritable(count));};}}