Question

我遇到了一个非常奇怪的问题。 Reducer确实有效但如果我检查输出文件，我只找到了映射器的输出。当我尝试调试时，在将映射器的输出值类型从Longwritable更改为Text

之后，我发现了单词count sample的相同问题

    package org.myorg;

import java.io.IOException;
import java.util.*;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapreduce.*;
import org.apache.hadoop.mapreduce.lib.input.*;
import org.apache.hadoop.mapreduce.lib.output.*;
import org.apache.hadoop.util.*;

public class WordCount extends Configured implements Tool {

   public static class Map
       extends Mapper<LongWritable, Text, Text, Text> {
     private final static IntWritable one = new IntWritable(1);
     private Text word = new Text();

     public void map(LongWritable key, Text wtf, Context context)
         throws IOException, InterruptedException {
       String line = wtf.toString();
       StringTokenizer tokenizer = new StringTokenizer(line);
       while (tokenizer.hasMoreTokens()) {
         word.set(tokenizer.nextToken());
         context.write(word, new Text("frommapper"));
       }
     }
   }

   public static class Reduce
       extends Reducer<Text, Text, Text, Text> {
     public void reduce(Text key, Text wtfs,
         Context context) throws IOException, InterruptedException {
/*
       int sum = 0;
       for (IntWritable val : wtfs) {
         sum += val.get();
       }
       context.write(key, new IntWritable(sum));*/
    context.write(key,new Text("can't output"));
     }
   }

   public int run(String [] args) throws Exception {
     Job job = new Job(getConf());
     job.setJarByClass(WordCount.class);
     job.setJobName("wordcount");


     job.setOutputKeyClass(Text.class);
     job.setMapOutputValueClass(Text.class);
       job.setOutputValueClass(Text.class);
     job.setMapperClass(Map.class);
     //job.setCombinerClass(Reduce.class);
     job.setReducerClass(Reduce.class);

     job.setInputFormatClass(TextInputFormat.class);
     job.setOutputFormatClass(TextOutputFormat.class);

     FileInputFormat.setInputPaths(job, new Path(args[0]));
     FileOutputFormat.setOutputPath(job, new Path(args[1]));

     boolean success = job.waitForCompletion(true);
     return success ? 0 : 1;
         }

   public static void main(String[] args) throws Exception {
     int ret = ToolRunner.run(new WordCount(), args);
     System.exit(ret);
   }
}

这是结果

JobClient:     Combine output records=0
12/06/13 17:37:46 INFO mapred.JobClient:     Map input records=7
12/06/13 17:37:46 INFO mapred.JobClient:     Reduce shuffle bytes=116
12/06/13 17:37:46 INFO mapred.JobClient:     Reduce output records=7
12/06/13 17:37:46 INFO mapred.JobClient:     Spilled Records=14
12/06/13 17:37:46 INFO mapred.JobClient:     Map output bytes=96
12/06/13 17:37:46 INFO mapred.JobClient:     Combine input records=0
12/06/13 17:37:46 INFO mapred.JobClient:     Map output records=7
12/06/13 17:37:46 INFO mapred.JobClient:     Reduce input records=7

然后我在outfile中发现了奇怪的结果。这个问题发生在我将地图的输出值类型和输入键的reducer类型更改为Text之后，无论我是否更改了reduce输出值的类型。我也被迫改变job.setOutputValue（Text.class）

a   frommapper
a   frommapper
a   frommapper
gg  frommapper
h   frommapper
sss frommapper
sss frommapper

帮助！

Answer 1

您的reduce函数参数应如下所示：

public void reduce(Text key, Iterable <Text> wtfs,
     Context context) throws IOException, InterruptedException {

通过定义参数的方式，reduce操作不会获取值列表，因此它只输出从map函数获取的任何输入，因为

sum+ = val.get()

每次都从0变为1，因为<key, value>形式的每个<word, one>对分别与减速器分开。

此外，映射器函数通常不会写入输出文件（我从未听说过它，但我不知道是否可能）。在通常情况下，始终是reducer写入输出文件。 Mapper输出是由Hadoop透明处理的中间数据。因此，如果您在输出文件中看到某些内容，那么必须是reducer输出，而不是mapper输出。如果要验证这一点，可以转到所运行作业的日志，并分别查看每个映射器和减速器中发生的情况。

希望这能为你解决一些问题。

Hadoop：Reducer将Mapper输出写入输出文件

1 个答案: