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

Posted jmdd

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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“ ”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);  
        }  
    }  
}

 

技术图片

 

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