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：

 import java.io.File;
 import java.io.IOException;
 import java.net.URI;
 import java.net.URISyntaxException;

 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;

 public class WorldCount {    

     static final String INPUT_PATH = "hdfs://masters:9000/user/hadoop/input";
     static final String OUTPUT_PATH = "hdfs://masters:9000/user/hadoop/output";
     public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException, URISyntaxException {

         //添加以下的代码，就可以联通，不知道咋回事
         String path = new File(".").getCanonicalPath();
         System.getProperties().put("hadoop.home.dir", path);
         new File("./bin").mkdirs();
         new File("./bin/winutils.exe").createNewFile();

         Configuration conf = new Configuration();
         Path outpath = new Path(OUTPUT_PATH);

         Job job = new Job(conf, "WorldCount");

         FileInputFormat.setInputPaths(job, INPUT_PATH);
         FileOutputFormat.setOutputPath(job, outpath);

         //检测输出路径是否存在，如果存在就删除，否则会报错
         FileSystem fileSystem = FileSystem.get(new URI(OUTPUT_PATH), conf);
         if(fileSystem.exists(outpath)){
             fileSystem.delete(outpath, true);
         }

         job.setMapperClass(MyMapper.class);
         job.setReducerClass(MyReducer.class);
         job.setOutputKeyClass(Text.class);
         job.setOutputValueClass(LongWritable.class);
         job.waitForCompletion(true);
     }

     //输入，map，即拆分过程
     static class MyMapper extends Mapper<LongWritable, Text, Text, LongWritable>{

         /*
          * 输入为（key,value）输出为（value,count数量）
          * 所以LongWritable, Text, Text, LongWritable分别代表 key(行号) value value count
          * 其中LongWritable和Text是hadoop定义的类型，分别代表long和string两种类型
          * */
         protected void map(LongWritable k1, Text v1, Context context)throws IOException, InterruptedException{
             String[] splits = v1.toString().split(" ");//按照空格拆分
             for(String str: splits){
                 System.out.println("---" + str);
                 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））
             }
         }
     }

     //输出，reduce，汇总过程
     static class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable>{
         protected void reduce(
                 Text k2, //输出的内容，即value
                 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
                 org.apache.hadoop.mapreduce.Reducer<Text, LongWritable, Text, LongWritable>.Context context)
                 //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
                 throws IOException, InterruptedException {
             //列表求和 初始为0
             long times = 0L;
             for(LongWritable count:v2s){
                 times += count.get();
             }
             context.write(k2, new LongWritable(times));
         }
     }
 }

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。