2

我正在尝试按照以下链接中给出的步骤运行一个程序来计算单词的数量及其频率：http: //developer.yahoo.com/hadoop/tutorial/module3.html

我已经加载了一个名为input的目录，其中包含三个文本文件。

我能够正确配置所有内容。现在在运行 WordCount.java 时，我在输出目录中的part-00000文件中看不到任何内容。

Mapper的Java代码是：

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reporter;

public class WordCountMapper extends MapReduceBase
implements Mapper<LongWritable, Text, Text, IntWritable> {

private final IntWritable one = new IntWritable(1);
private Text word = new Text();

public void map(WritableComparable key, Writable value,
  OutputCollector output, Reporter reporter) throws IOException {

String line = value.toString();
StringTokenizer itr = new StringTokenizer(line.toLowerCase());
while(itr.hasMoreTokens()) {
  word.set(itr.nextToken());
  output.collect(word, one);
}
}

@Override
public void map(LongWritable arg0, Text arg1,
    OutputCollector<Text, IntWritable> arg2, Reporter arg3)
     throws IOException {
// TODO Auto-generated method stub

 }

}

减少代码是：

public class WordCountReducer extends MapReduceBase
implements Reducer<Text, IntWritable, Text, IntWritable> {

public void reduce(Text key, Iterator values,
  OutputCollector output, Reporter reporter) throws IOException {

int sum = 0;
while (values.hasNext()) {
    //System.out.println(values.next());
  IntWritable value = (IntWritable) values.next();
  sum += value.get(); // process value
}

output.collect(key, new IntWritable(sum));
 }
 }

字计数器的代码是：

public class Counter {

public static void main(String[] args) {
    JobClient client = new JobClient();
    JobConf conf = new JobConf(com.example.Counter.class);

    // TODO: specify output types
    conf.setOutputKeyClass(Text.class);
    conf.setOutputValueClass(IntWritable.class);

    // TODO: specify input and output DIRECTORIES (not files)
    conf.setInputPath(new Path("src"));
    conf.setOutputPath(new Path("out"));

    // TODO: specify a mapper
    conf.setMapperClass(org.apache.hadoop.mapred.lib.IdentityMapper.class);

    // TODO: specify a reducer
    conf
                   .setReducerClass(org.apache.hadoop.mapred.lib.IdentityReducer.class);

    client.setConf(conf);
    try {
        JobClient.runJob(conf);
    } catch (Exception e) {
        e.printStackTrace();
    }
}

}

在控制台中，我得到这些日志：

13/09/10 10:09:20 WARN mapred.JobClient: Use GenericOptionsParser for parsing the       arguments. Applications should implement Tool for the same.
13/09/10 10:09:20 INFO mapred.FileInputFormat: Total input paths to process : 3
13/09/10 10:09:20 INFO mapred.FileInputFormat: Total input paths to process : 3
13/09/10 10:09:20 INFO mapred.JobClient: Running job: job_201309100855_0012
13/09/10 10:09:21 INFO mapred.JobClient:  map 0% reduce 0%
13/09/10 10:09:25 INFO mapred.JobClient:  map 25% reduce 0%
13/09/10 10:09:26 INFO mapred.JobClient:  map 75% reduce 0%
13/09/10 10:09:27 INFO mapred.JobClient:  map 100% reduce 0%
13/09/10 10:09:35 INFO mapred.JobClient: Job complete: job_201309100855_0012
13/09/10 10:09:35 INFO mapred.JobClient: Counters: 15
13/09/10 10:09:35 INFO mapred.JobClient:   File Systems
13/09/10 10:09:35 INFO mapred.JobClient:     HDFS bytes read=54049
13/09/10 10:09:35 INFO mapred.JobClient:     Local bytes read=14
13/09/10 10:09:35 INFO mapred.JobClient:     Local bytes written=214
13/09/10 10:09:35 INFO mapred.JobClient:   Job Counters 
13/09/10 10:09:35 INFO mapred.JobClient:     Launched reduce tasks=1
13/09/10 10:09:35 INFO mapred.JobClient:     Launched map tasks=4
13/09/10 10:09:35 INFO mapred.JobClient:     Data-local map tasks=4
13/09/10 10:09:35 INFO mapred.JobClient:   Map-Reduce Framework
13/09/10 10:09:35 INFO mapred.JobClient:     Reduce input groups=0
13/09/10 10:09:35 INFO mapred.JobClient:     Combine output records=0
13/09/10 10:09:35 INFO mapred.JobClient:     Map input records=326
13/09/10 10:09:35 INFO mapred.JobClient:     Reduce output records=0
13/09/10 10:09:35 INFO mapred.JobClient:     Map output bytes=0
13/09/10 10:09:35 INFO mapred.JobClient:     Map input bytes=50752
13/09/10 10:09:35 INFO mapred.JobClient:     Combine input records=0
13/09/10 10:09:35 INFO mapred.JobClient:     Map output records=0
13/09/10 10:09:35 INFO mapred.JobClient:     Reduce input records=0

我是 Hadoop 的新手。

请以适当的答案回复。

谢谢。

4

2 回答 2

4

您的 Mapper 类中有两种map方法。带有@Override注释的方法是实际被覆盖的方法，并且该方法不做任何事情。所以没有任何东西从你的映射器中出来，也没有任何东西进入reducer，因此没有输出。

删除map用@Override注解标记的方法，并用 标记第一个map方法@Override。然后修复任何方法签名问题，它应该可以工作。

于 2013-09-10T05:37:48.313 回答
0

我遇到了同样的问题。我通过删除覆盖的 map 方法并将第一个参数的 map 方法的签名更改为 of 来解决它LongWritable。更新 map 方法签名如下：

@Override
public void map(LongWritable key, Text value, OutputCollector output, Reporter reporter) 
    throws IOException {
于 2013-12-24T21:21:17.353 回答