mapreduce去重

现有一个某电商网站的数据文件，名为buyer_favorite1，记录了用户收藏的商品以及收藏的日期，文件buyer_favorite1中包含（用户id，商品id，收藏日期）三个字段，数据内容以“\t”分割，由于数据很大，所以为了方便统计我们只截取它的一部分数据，内容如下：

买家id   商品id    收藏日期  
10181   1000481   2010-04-04 16:54:31  
20001   1001597   2010-04-07 15:07:52  
20001   1001560   2010-04-07 15:08:27  
20042   1001368   2010-04-08 08:20:30  
20067   1002061   2010-04-08 16:45:33  
20056   1003289   2010-04-12 10:50:55  
20056   1003290   2010-04-12 11:57:35  
20056   1003292   2010-04-12 12:05:29  
20054   1002420   2010-04-14 15:24:12  
20055   1001679   2010-04-14 19:46:04  
20054   1010675   2010-04-14 15:23:53  
20054   1002429   2010-04-14 17:52:45  
20076   1002427   2010-04-14 19:35:39  
20054   1003326   2010-04-20 12:54:44  
20056   1002420   2010-04-15 11:24:49  
20064   1002422   2010-04-15 11:35:54  
20056   1003066   2010-04-15 11:43:01  
20056   1003055   2010-04-15 11:43:06  
20056   1010183   2010-04-15 11:45:24  
20056   1002422   2010-04-15 11:45:49  
20056   1003100   2010-04-15 11:45:54  
20056   1003094   2010-04-15 11:45:57  
20056   1003064   2010-04-15 11:46:04  
20056   1010178   2010-04-15 16:15:20  
20076   1003101   2010-04-15 16:37:27  
20076   1003103   2010-04-15 16:37:05  
20076   1003100   2010-04-15 16:37:18  
20076   1003066   2010-04-15 16:37:31  
20054   1003103   2010-04-15 16:40:14  
20054   1003100   2010-04-15 16:40:16  

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要求用Java编写MapReduce程序，根据商品id进行去重，统计用户收藏商品中都有哪些商品被收藏。

源代码：

package mapreduce;

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

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.NullWritable;
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.Reducer.Context;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

import mapreduce.WordCount.MyMapper;
import mapreduce.WordCount.MyReducer;

public class Filter {
    public static class Map extends Mapper<Object, Text, Text, NullWritable> {
        private static Text newKey = new Text();

        public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            StringTokenizer itr = new StringTokenizer(value.toString());

            while (itr.hasMoreTokens()) {
                String line = itr.nextToken();
                String arr = line.substring(0, line.indexOf("   "));
                newKey.set(arr);
                System.out.println(arr);
                context.write(newKey, NullWritable.get());

            }
        }

    }

    public static class Reduce extends Reducer<Text, NullWritable, Text, NullWritable> {
        public void reduce(Text key, Iterable<NullWritable> values, Context context)
                throws IOException, InterruptedException {

            context.write(key, NullWritable.get());
        }
    }

    public static void main(String[] args) throws Exception {

        Configuration conf = new Configuration();
        System.out.println("start");
        Job job = new Job(conf, "filter");
        job.setJarByClass(Filter.class);
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(NullWritable.class);
        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);
        Path in = new Path("hdfs://localhost:9000/mymapreduce2/in/buyer_favorite1");
        Path out = new Path("hdfs://localhost:9000/mymapreduce2/out");

        FileInputFormat.addInputPath(job, in);
        FileOutputFormat.setOutputPath(job, out);
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

统计数据：

10181
20001
20042
20054
20055
20056
20064
20067
20076
买家id

遇到的问题：

1.这次代码和上次代码很相似，所以这次代码石油上次代码复制粘贴过来改了一下。但是忘了该main函数中"job.setJarByClass(Filter.class);job.setMapperClass(Map.class);job.setReducerClass(Reduce.class);"。所以一直运行的是上次写的代码。

后来改了过来。