MapReduce实例：编写MapReduce程序，统计每个买家收藏商品数量

现有某电商网站用户对商品的收藏数据，记录了用户收藏的商品id以及收藏日期，名为buyer_favorite1。

buyer_favorite1包含：买家id，商品id，收藏日期这三个字段，数据以“\t”分割，样本数据及格式如下：

1. 买家id   商品id    收藏日期
2. 10181   1000481   2010-04-04 16:54:31
3. 20001   1001597   2010-04-07 15:07:52
4. 20001   1001560   2010-04-07 15:08:27
5. 20042   1001368   2010-04-08 08:20:30
6. 20067   1002061   2010-04-08 16:45:33
7. 20056   1003289   2010-04-12 10:50:55
8. 20056   1003290   2010-04-12 11:57:35
9. 20056   1003292   2010-04-12 12:05:29
10. 20054   1002420   2010-04-14 15:24:12
11. 20055   1001679   2010-04-14 19:46:04
12. 20054   1010675   2010-04-14 15:23:53
13. 20054   1002429   2010-04-14 17:52:45
14. 20076   1002427   2010-04-14 19:35:39
15. 20054   1003326   2010-04-20 12:54:44
16. 20056   1002420   2010-04-15 11:24:49
17. 20064   1002422   2010-04-15 11:35:54
18. 20056   1003066   2010-04-15 11:43:01
19. 20056   1003055   2010-04-15 11:43:06
20. 20056   1010183   2010-04-15 11:45:24
21. 20056   1002422   2010-04-15 11:45:49
22. 20056   1003100   2010-04-15 11:45:54
23. 20056   1003094   2010-04-15 11:45:57
24. 20056   1003064   2010-04-15 11:46:04
25. 20056   1010178   2010-04-15 16:15:20
26. 20076   1003101   2010-04-15 16:37:27
27. 20076   1003103   2010-04-15 16:37:05
28. 20076   1003100   2010-04-15 16:37:18
29. 20076   1003066   2010-04-15 16:37:31
30. 20054   1003103   2010-04-15 16:40:14
31. 20054   1003100   2010-04-15 16:40:16

要求编写MapReduce程序，统计每个买家收藏商品数量。

package mapreduce;
import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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 WordCount {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Job job = Job.getInstance();
        job.setJobName("WordCount");
        job.setJarByClass(WordCount.class);
        job.setMapperClass(doMapper.class);
        job.setReducerClass(doReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        Path in = new Path("hdfs://localhost:9000/mymapreduce1/inyer_favourite9");
        Path out = new Path("hdfs://localhost:9000/mymapreduce1/out");
        FileInputFormat.addInputPath(job, in);
        FileOutputFormat.setOutputPath(job, out);
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
    public static class doMapper extends Mapper<Object, Text, Text, IntWritable>{
        public static final IntWritable one = new IntWritable(1);
        public static Text word = new Text();
        @Override
        protected void map(Object key, Text value, Context context)
                    throws IOException, InterruptedException {
            StringTokenizer tokenizer = new StringTokenizer(value.toString(), "   ");
                word.set(tokenizer.nextToken());
                context.write(word, one);
        }
    }
    public static class doReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
        private IntWritable result = new IntWritable();
        @Override
        protected void reduce(Text key, Iterable<IntWritable> values, Context context)
        throws IOException, InterruptedException {
        int sum = 0;
        for (IntWritable value : values) {
        sum += value.get();
        }
        result.set(sum);
        context.write(key, result);
        }
    }
}

原文地址：https://www.cnblogs.com/jmdd/p/11768679.html