Question

我正试图在电子书Mahout in Action中运行第6章（列出6.1~6.4）中的推荐示例。有两个映射器/减速器对。这是代码：

Mapper - 1

public class WikipediaToItemPrefsMapper extends 
        Mapper<LongWritable,Text,VarLongWritable,VarLongWritable> {

私有静态最终模式NUMBERS = Pattern.compile（“（\ d +）”）;

@Override
public void map(LongWritable key,
          Text value,
          Context context)
throws IOException, InterruptedException {

    String line = value.toString();
    Matcher m = NUMBERS.matcher(line);
    m.find();
    VarLongWritable userID = new VarLongWritable(Long.parseLong(m.group()));
    VarLongWritable itemID = new VarLongWritable();
    while (m.find()) {
        itemID.set(Long.parseLong(m.group()));
        context.write(userID, itemID);
    }
}

}

Reducer - 1

public class WikipediaToUserVectorReducer extends 
        Reducer<VarLongWritable,VarLongWritable,VarLongWritable,VectorWritable> {
@Override
public void reduce(VarLongWritable userID, 
                    Iterable<VarLongWritable> itemPrefs,
                    Context context)
  throws IOException, InterruptedException {

        Vector userVector = new RandomAccessSparseVector(
        Integer.MAX_VALUE, 100);
        for (VarLongWritable itemPref : itemPrefs) {
            userVector.set((int)itemPref.get(), 1.0f);
        }

        //LongWritable userID_lw = new LongWritable(userID.get());
        context.write(userID, new VectorWritable(userVector));
        //context.write(userID_lw, new VectorWritable(userVector));
}

}

reducer输出userID和userVector，它看起来像这样：98955 {590：1.0 22：1.0 9059：1.0 3：1.0 2：1.0 1：1.0}提供了驱动程序中使用的FileInputformat和TextInputFormat。

我想使用另一对mapper-reducer来进一步处理这些数据：

Mapper - 2

public class UserVectorToCooccurenceMapper extends
Mapper<VarLongWritable,VectorWritable,IntWritable,IntWritable> {

@Override
public void map(VarLongWritable userID,
                VectorWritable userVector,
                Context context)
throws IOException, InterruptedException {

    Iterator<Vector.Element> it = userVector.get().iterateNonZero();
    while (it.hasNext()) {
        int index1 = it.next().index();
        Iterator<Vector.Element> it2 = userVector.get().iterateNonZero();
        while (it2.hasNext()) {
            int index2 = it2.next().index();
                context.write(new IntWritable(index1),
                                new IntWritable(index2));
        }
    }
}

}

Reducer - 2

公共类UserVectorToCooccurenceReducer扩展减速器{

@Override
public void reduce(IntWritable itemIndex1,
          Iterable<IntWritable> itemIndex2s,
          Context context)
throws IOException, InterruptedException {

    Vector cooccurrenceRow = new RandomAccessSparseVector(Integer.MAX_VALUE, 100);
    for (IntWritable intWritable : itemIndex2s) {
        int itemIndex2 = intWritable.get();
        cooccurrenceRow.set(itemIndex2, cooccurrenceRow.get(itemIndex2) + 1.0);
    }
    context.write(itemIndex1, new VectorWritable(cooccurrenceRow));
}

}

这是我正在使用的驱动程序：

public final class RecommenderJob extends Configured implements Tool {

@覆盖 public int run（String [] args）抛出异常{

  Job job_preferenceValues = new Job (getConf());
  job_preferenceValues.setJarByClass(RecommenderJob.class);
  job_preferenceValues.setJobName("job_preferenceValues");

  job_preferenceValues.setInputFormatClass(TextInputFormat.class);
  job_preferenceValues.setOutputFormatClass(SequenceFileOutputFormat.class);

  FileInputFormat.setInputPaths(job_preferenceValues, new Path(args[0]));
  SequenceFileOutputFormat.setOutputPath(job_preferenceValues, new Path(args[1]));

  job_preferenceValues.setMapOutputKeyClass(VarLongWritable.class);
  job_preferenceValues.setMapOutputValueClass(VarLongWritable.class);

  job_preferenceValues.setOutputKeyClass(VarLongWritable.class);
  job_preferenceValues.setOutputValueClass(VectorWritable.class);

  job_preferenceValues.setMapperClass(WikipediaToItemPrefsMapper.class);
  job_preferenceValues.setReducerClass(WikipediaToUserVectorReducer.class);

  job_preferenceValues.waitForCompletion(true);

  Job job_cooccurence = new Job (getConf());
  job_cooccurence.setJarByClass(RecommenderJob.class);
  job_cooccurence.setJobName("job_cooccurence");

  job_cooccurence.setInputFormatClass(SequenceFileInputFormat.class);
  job_cooccurence.setOutputFormatClass(TextOutputFormat.class);

  SequenceFileInputFormat.setInputPaths(job_cooccurence, new Path(args[1]));
  FileOutputFormat.setOutputPath(job_cooccurence, new Path(args[2]));

  job_cooccurence.setMapOutputKeyClass(VarLongWritable.class);
  job_cooccurence.setMapOutputValueClass(VectorWritable.class);

  job_cooccurence.setOutputKeyClass(IntWritable.class);
  job_cooccurence.setOutputValueClass(VectorWritable.class);

  job_cooccurence.setMapperClass(UserVectorToCooccurenceMapper.class);
  job_cooccurence.setReducerClass(UserVectorToCooccurenceReducer.class);

  job_cooccurence.waitForCompletion(true);

  return 0;

}

public static void main(String[] args) throws Exception {
ToolRunner.run(new Configuration(), new RecommenderJob(), args);

} }

我得到的错误是：

java.io.IOException: Type mismatch in key from map: expected org.apache.mahout.math.VarLongWritable, received org.apache.hadoop.io.IntWritable

在Google搜索修补程序的过程中，我发现我的问题类似于this question。但不同的是，我已经在使用SequenceFileInputFormat和SequenceFileOutputFormat，我相信是正确的。我也看到org.apache.mahout.cf.taste.hadoop.item.RecommenderJob或多或少做了类似的事情。在我的理解和Yahoo Tutorial

SequenceFileOutputFormat将任意数据类型快速序列化到文件中;相应的SequenceFileInputFormat会将文件反序列化为相同的类型，并以与上一个Reducer发出的方式相同的方式将数据呈现给下一个Mapper。

我做错了什么？真的很感激某些人的一些指示......我花了一天时间试图解决这个问题并且无处可去:(

Answer 1

您的第二张映射器具有以下签名：

public class UserVectorToCooccurenceMapper extends 
        Mapper<VarLongWritable,VectorWritable,IntWritable,IntWritable>

但是您在驱动程序代码中定义了以下内容：

job_cooccurence.setMapOutputKeyClass(VarLongWritable.class);
job_cooccurence.setMapOutputValueClass(VectorWritable.class);

reducer期望<IntWritable, IntWritable>作为输入，因此您只需将驱动程序代码修改为：

job_cooccurence.setMapOutputKeyClass(IntWritable.class);
job_cooccurence.setMapOutputValueClass(IntWritable.class);

从map中键入不匹配，正确使用SequenceFileInputFormat

1 个答案: