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我试图通过制作电影推荐系统来练习Big Data Mapreduce。我的代码：了解Mapreduce代码
*imports
public class MRS {
public static class Map extends Mapper<LongWritable, Text, Text, Text> {
public void map(LongWritable key, Text value, Context con)
throws IOException, InterruptedException {
String line = value.toString();
StringTokenizer token = new StringTokenizer(line);
while(token.hasMoreTokens()){
String userId = token.nextToken();
String movieId = token.nextToken();
String ratings =token.nextToken();
token.nextToken();
con.write(new Text(userId), new Text(movieId + "," + ratings));
}
}
}
public static class Reduce extends
Reducer<Text, IntWritable, Text, Text> {
public void reduce(Text key, Iterable<Text> value,Context con) throws IOException, InterruptedException{
int item_count=0;
int item_sum =0;
String result="[";
for(Text t : value){
String s = t.toString();
StringTokenizer token = new StringTokenizer(s,",");
while(token.hasMoreTokens()){
token.nextToken();
item_sum=item_sum+Integer.parseInt(token.nextToken());
item_count++;
}
result=result+"("+s+"),";
}
result=result.substring(0, result.length()-1);
result=result+"]";
result=String.valueOf(item_count)+","+String.valueOf(item_sum)+","+result;
con.write(key, new Text(result));
}
}
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
Configuration con = new Configuration();
Job job = new Job(con,"Movie Recommendation");
job.setJarByClass(MRS.class);
job.setMapperClass(Map.class);
job.setCombinerClass(Reduce.class);
job.setReducerClass(Reduce.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
我使用从here
的movielens数据集，其中输入文件是u.data
，并运行此代码后，我的输出应该像
用户id ITEM_COUNT ，Item_sum，[带评级的movie_Id列表]
不过，我得到这个
99 173,4
99 288,4
99 66,3
99 203,4
99 105,2
99 12,5
99 1,4
99 741,3
99 895,3
99 619,4
99 742,5
99 294,4
99 196,4
99 328,4
99 120,2
99 246,3
99 232,4
99 181,5
99 201,3
99 978,3
99 123,3
99 433,4
99 345,3
这应该是地图类