实验内容
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:
买家id 商品数量
10181 1
20001 2
20042 1
20054 6
20055 1
20056 12
20064 1
20067 1
20076 5
源码:
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 class doMapper extends Mapper<Object, Text, Text, IntWritable>{ //第一个Object表示输入key的类型;第二个Text表示输入value的类型;第三个Text表示表示输出键的类型;第四个IntWritable表示输出值的类型 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 //抛出异常 { StringTokenizer tokenizer = new StringTokenizer(value.toString(),"\t"); //StringTokenizer是Java工具包中的一个类,用于将字符串进行拆分 word.set(tokenizer.nextToken()); //返回当前位置到下一个分隔符之间的字符串 context.write(word, one); //将word存到容器中,记一个数 } } public static class doReducer extends Reducer<Text, IntWritable, Text, IntWritable>{ //参数同Map一样,依次表示是输入键类型,输入值类型,输出键类型,输出值类型 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(); } //for循环遍历,将得到的values值累加 result.set(sum); context.write(key, result); } } 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"); Path out = new Path("hdfs://localhost:9000/mymapreduce1/out"); FileInputFormat.addInputPath(job, in); FileOutputFormat.setOutputPath(job, out); System.exit(job.waitForCompletion(true) ? 0 : 1); } }
结果截图: