hadoop worldcount小程序

首先在hadoop中建立input文件夹放几个文件，里边写点东西。比如我放了三个，分别写的是
第一个
hello hadoop
bye hadoop
第二个
hello world
bye world
第三个
hello bigdata
然后就有下边这段代码做单词统计：
 1 import java.io.File;
 2 import java.io.IOException;
 3 import java.net.URI;
 4 import java.net.URISyntaxException;
 5 
 6 import org.apache.hadoop.conf.Configuration;
 7 import org.apache.hadoop.fs.FileSystem;
 8 import org.apache.hadoop.fs.Path;
 9 import org.apache.hadoop.io.LongWritable;
10 import org.apache.hadoop.io.Text;
11 import org.apache.hadoop.mapreduce.Job;
12 import org.apache.hadoop.mapreduce.Mapper;
13 import org.apache.hadoop.mapreduce.Reducer;
14 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
15 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
16 
17 public class WorldCount {    
18     
19     static final String INPUT_PATH = "hdfs://masters:9000/user/hadoop/input";
20     static final String OUTPUT_PATH = "hdfs://masters:9000/user/hadoop/output";
21     public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException, URISyntaxException {
22         
23         //添加以下的代码，就可以联通，不知道咋回事
24         String path = new File(".").getCanonicalPath();
25         System.getProperties().put("hadoop.home.dir", path);
26         new File("./bin").mkdirs();
27         new File("./bin/winutils.exe").createNewFile();
28 
29         Configuration conf = new Configuration();
30         Path outpath = new Path(OUTPUT_PATH);
31         
32         Job job = new Job(conf, "WorldCount");
33         
34         FileInputFormat.setInputPaths(job, INPUT_PATH);
35         FileOutputFormat.setOutputPath(job, outpath);
36         
37         //检测输出路径是否存在，如果存在就删除，否则会报错
38         FileSystem fileSystem = FileSystem.get(new URI(OUTPUT_PATH), conf);
39         if(fileSystem.exists(outpath)){
40             fileSystem.delete(outpath, true);
41         }
42         
43         job.setMapperClass(MyMapper.class);
44         job.setReducerClass(MyReducer.class);
45         job.setOutputKeyClass(Text.class);
46         job.setOutputValueClass(LongWritable.class);
47         job.waitForCompletion(true);
48     }
49     
50     //输入，map，即拆分过程
51     static class MyMapper extends Mapper<LongWritable, Text, Text, LongWritable>{
52         
53         /*
54          * 输入为（key,value）输出为（value,count数量）
55          * 所以LongWritable, Text, Text, LongWritable分别代表 key(行号) value value count
56          * 其中LongWritable和Text是hadoop定义的类型，分别代表long和string两种类型
57          * */
58         protected void map(LongWritable k1, Text v1, Context context)throws IOException, InterruptedException{
59             String[] splits = v1.toString().split(" ");//按照空格拆分
60             for(String str: splits){
61                 System.out.println("---" + str);
62                 context.write(new Text(str), new LongWritable(1));//拆分出来的形式为（“单词”，出现次数（这里默认为1））
63             }
64         }
65     }
66     
67     //输出，reduce，汇总过程
68     static class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable>{
69         protected void reduce(
70                 Text k2, //输出的内容，即value
71                 Iterable<LongWritable> v2s, //是一个longwritable类型的数组，所以用了Iterable这个迭代器，且元素为v2s
72                 org.apache.hadoop.mapreduce.Reducer<Text, LongWritable, Text, LongWritable>.Context context)
73                 //这里一定设置好，不然输出会变成单个单词，从而没有统计数量
74                 throws IOException, InterruptedException {
75             //列表求和 初始为0
76             long times = 0L;
77             for(LongWritable count:v2s){
78                 times += count.get();
79             }
80             context.write(k2, new LongWritable(times));
81         }
82     }
83 }
然后就成了，看下结果
第23行到第27行不写就会报错，我也不知道咋回事，如果哪个大牛知道咋回事，非常期待留言解答。