java—如何在mapreduce hadoop框架中对值(及其对应的键)进行排序?

8wigbo56  于 2021-05-27  发布在  Hadoop
关注(0)|答案(1)|浏览(448)

我正在尝试使用hadoopmapreduce对输入数据进行排序。问题是,我只能按键对键值对进行排序,而我正在尝试按值对它们进行排序。每个值的键都是用一个计数器创建的,所以第一个值(234)有键1,第二个值(944)有键2,依此类推。你知道我该怎么做并按值排序输入吗?

import java.io.IOException;
import java.util.StringTokenizer;
import java.util.ArrayList;
import java.util.List;
import java.util.Collections;
import org.apache.hadoop.conf.Configuration;
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 Sortt {

  public static class TokenizerMapper
       extends Mapper<Object, Text, Text ,IntWritable >{
    int k=0;
    int v=0;
    int va=0;
    public Text ke = new Text();
   private final static IntWritable val = new IntWritable();

    public void map(Object key, Text value, Context context) throws 
    IOException, InterruptedException 
{
      StringTokenizer itr = new StringTokenizer(value.toString());

        while (itr.hasMoreTokens()) 
{
        val.set(Integer.parseInt(itr.nextToken()));
        v=val.get();
        k=k+1;
        ke.set(Integer.toString(k));

        context.write(ke, new IntWritable(v));}
}

    }

  public static class SortReducer
       extends Reducer<Text,IntWritable,Text,IntWritable> {
        int a=0;
        int v=0;
       private IntWritable va = new IntWritable();
    public void reduce(Text key, Iterable<IntWritable> values,
                       Context context
                       ) throws IOException, InterruptedException {
    List<Integer> sorted = new ArrayList<Integer>();

    for (IntWritable val : values) {
           a= val.get();
          sorted.add(a);

}
    Collections.sort(sorted);
    for(int i=0;i<sorted.size();i++) {
    v=sorted.get(i);
    va.set(v);

     context.write(key, va);
}
    }
  }

  public static void main(String[] args) throws Exception {
   long startTime=0;
   long Time=0;
   long duration=0;
Configuration conf = new Configuration();
    Job job = Job.getInstance(conf, "sort");
    job.setJarByClass(Sortt.class);
    job.setMapperClass(TokenizerMapper.class);
    job.setCombinerClass(SortReducer.class);
    job.setReducerClass(SortReducer.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);
    FileInputFormat.addInputPath(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
    Time = System.currentTimeMillis();
  //duration = (endTime-startTime)/1000000;
    System.out.println("time="+Time+"MS");
  }
}

输入:
234
944
241
130
369
470
250
100
250
735
856
659
425
756
123
756
459
754
654
951
753
254
698
741
预期产量:
8 100
15 123
4 130
1 234
3 241
24 241
7 250
9 250
22 254
5 369
13 425
17 459
6 470
19 654
12 659
23 698
10 735
21 753
18 754
14 756
16 756
11 856
2 944
20 951
电流输出:
1 234
10 735
11 856
12 659
13 425
14 757
15 123
16 756
17 459
18 754
19 654
2 944
20 951
21 753
22 254
23 698
24 741
3 241
4 130
5 369
6 470
7 250
8 100
9 250

oogrdqng

oogrdqng1#

mapreduce输出默认按键排序,要按值排序,可以使用辅助排序。二次排序是根据值对减速机输出进行排序的最佳技术之一,下面是一个完整的示例。

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