大型数据库技术实验六 实验6：Mapreduce实例——WordCount

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：

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  

要求编写MapReduce程序，统计每个买家收藏商品数量。

统计结果数据如下：

1. 买家id 商品数量  
2. 10181   1  
3. 20001   2  
4. 20042   1  
5. 20054   6  
6. 20055   1  
7. 20056   12  
8. 20064   1  
9. 20067   1  
10. 20076   5  

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/in/buyer_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);  
        }  
    }  
}  

实验截图：