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Hadoop MapReduce 学习笔记(十二) MapReduce实现类似SQL的order by/排序3 改进及改正

 
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      本博客属创文章,转载请注明出处:http://guoyunsky.iteye.com/blog/1235954

 

       本博客已迁移到本人独立博客: http://www.yun5u.com/articles/hadoop-mapreduce-sql-order-by-sort-improve-fix.html

       请先阅读:           

           1.Hadoop MapReduce 学习笔记(一) 序言和准备

           2.Hadoop MapReduce 学习笔记(二) 序言和准备 2

               3.Hadoop MapReduce 学习笔记(八) MapReduce实现类似SQL的order by/排序

                4.Hadoop MapReduce 学习笔记(九) MapReduce实现类似SQL的order by/排序 正确写法

                5.Hadoop MapReduce 学习笔记(十) MapReduce实现类似SQL的order by/排序2 对多个字段排序

                6.Hadoop MapReduce 学习笔记(十一) MapReduce实现类似SQL的order by/排序3 改进

 

          下一篇:

        上一篇博客 Hadoop MapReduce 学习笔记(十一) MapReduce实现类似SQL的order by/排序3 改进 获得的结果并不是正确的结果,折腾了一小时没找到原因.于是参考hadoop/examples下面的SecondarySort.照搬里面的一些做法才纠正.这里先标记一下,待日后了解原理后再找出答案.

 

package com.guoyun.hadoop.mapreduce.study;



import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.RawComparator;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparator;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Partitioner;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

/**
 * 通过MapReduce实现类似SELECT * FROM TABLE ORDER BY COL1 ASC,COL2 DESC功能
 * 也就是对多个字段的排序
 * 相比 @OrderByMultiMapReduceTest,主要引入了Partitioner和GroupingComparator,提升性能
 * 由于生成的数据frameworkName比较固定(具体请查看 @MyMapReduceMultiColumnTest 如何生成的数据)
 * 所以这里获取map输出key的frameworkName属性,交给Partitioner和GroupingComparator来确定相同
 * frameworkName的数据输出到相同的Reduce上,尽可能减少Reduce之前的清洗和排序工作,提升性能.
 * 具体Partitioner和GroupingComparator的用法请查看Hadoop说明.
 * 这里只是我目前对Partitioner和GroupingComparator的理解,刻意安排的输入数据.一切还需要验证中,待有机会
 * 查看map和reduce源码后再来求证.
 * 本类相比 @OrderByMultiMapReduceImproveTest 纠正了结果不正确的错误
 * 
 * 注:
 * 查看结果可以发现,其实这也是一个group by的实现
 */
public class OrderByMultiMapReduceImproveFixTest extends
  OrderByMultiMapReduceTest {
  public static final Logger log=LoggerFactory.getLogger(OrderByMultiMapReduceImproveFixTest.class);
  
  public OrderByMultiMapReduceImproveFixTest(long dataLength, String inputPath,
      String outputPath) throws Exception {
    super(dataLength, inputPath, outputPath);
    // TODO Auto-generated constructor stub
  }

  public OrderByMultiMapReduceImproveFixTest(long dataLength) throws Exception {
    super(dataLength);
    // TODO Auto-generated constructor stub
  }

  public OrderByMultiMapReduceImproveFixTest(String inputPath, String outputPath) {
    super(inputPath, outputPath);
    // TODO Auto-generated constructor stub
  }

  public OrderByMultiMapReduceImproveFixTest(String outputPath)
      throws Exception {
    super(outputPath);
    // TODO Auto-generated constructor stub
  }
  
  /**
   * 继承OrderMultiColumnWritable,新增WritableComparator,并注入到WritableComparator中
   * 增加本类就可以解决OrderByMultiMapReduceImproveTest输出结果不一致的错误,具体原因还待探索
   */
  public static class OrderMultiColumnFixWritable extends OrderMultiColumnWritable{
    /**
     * 增加这个WritableComparator就可以解决OrderByMultiMapReduceImproveTest
     * 原理还不清楚,待探索
     */
    public static class MyComparator extends WritableComparator {
      public MyComparator() {
        super(OrderMultiColumnWritable.class);
      }

      public int compare(byte[] b1, int s1, int l1,
                         byte[] b2, int s2, int l2) {
        return compareBytes(b1, s1, l1, b2, s2, l2);
      }
    }

    static {                                        
      // register this comparator
      WritableComparator.define(OrderMultiColumnWritable.class, new MyComparator());
    }
    
  }
    
  /**
   * map,get the source datas,and generate a (key,value) pair as (MultiWritable,NullWritable) 
   */
  public static class MyMapper extends Mapper<LongWritable,Text,OrderMultiColumnWritable,LongWritable>{
    private OrderMultiColumnWritable writeKey=new OrderMultiColumnWritable();
    private LongWritable writeValue=new LongWritable(0);
    
    @Override
    protected void map(LongWritable key, Text value, Context context)
        throws IOException, InterruptedException {
      log.debug("begin to map");
      String[] splits=null;
      
      try {
        splits=value.toString().split("\\t");
        if(splits!=null&&splits.length==2){
          writeKey.set(splits[0],Long.parseLong(splits[1].trim()));
          writeValue.set(writeKey.getNumber());
        }
      } catch (NumberFormatException e) {
        log.error("map error:"+e.getMessage());
      }
      
      context.write(writeKey, writeValue);
    }
    
  }
  
  /**
   * reduce,only use to output the result
   */
  public static class MyReducer 
    extends Reducer<OrderMultiColumnWritable,LongWritable,Text,LongWritable>{
    private Text writeKey=new Text();
    @Override
    protected void reduce(OrderMultiColumnWritable key,
        Iterable<LongWritable> values,Context context) throws IOException,
        InterruptedException {
      
      writeKey.set(key.getFrameworkName());
      for(LongWritable value:values){ 
        context.write(writeKey, value);
      }
      
    }
    
  }
  
  /**
   * partitioner
   */
  public static class MyPartitioner extends Partitioner<OrderMultiColumnWritable,LongWritable>{

    @Override
    public int getPartition(OrderMultiColumnWritable key, LongWritable value,
        int numbers) {
      return (int)Math.abs(key.getFrameworkName().hashCode()%numbers);
    }
    
  }

  
  
  /**
   * GroupingComparator
   */
  public static class MyGroupingComparator implements RawComparator<OrderMultiColumnWritable>{

    @Override
    public int compare(OrderMultiColumnWritable o1,
        OrderMultiColumnWritable o2) {
      return o1.getFrameworkName().compareTo(o2.getFrameworkName());
    }

    @Override
    public int compare(byte[] b1, int s1, int l1, byte[] b2, int s2,
        int l2) {
      return WritableComparator.compareBytes(b1,s1,l1,b2,s2,l2);
    }
    
  }
  
  public static void main(String[] args){
    MyMapReduceTest mapReduceTest=null;
    Configuration conf=null;
    Job job=null;
    FileSystem fs=null;
    Path inputPath=null;
    Path outputPath=null;
    long begin=0;
    String input="testDatas/mapreduce/MRInput_Multi_OrderBy";
    String output="testDatas/mapreduce/MROutput_Multi_OrderBy_Improve_Fix";
    
    
    try {
      // 直接使用MRInput_Single_OrderBy的输入数据,不重新生成数据,以便比对结果是否正确
      // 和MROutput_Multi_OrderBy输出结果进行比对
      mapReduceTest=new OrderByMultiMapReduceImproveFixTest(input,output);
      
      inputPath=new Path(mapReduceTest.getInputPath());
      outputPath=new Path(mapReduceTest.getOutputPath());
      
      conf=new Configuration();
      job=new Job(conf,"OrderBy");
      
      fs=FileSystem.getLocal(conf);
      if(fs.exists(outputPath)){
        if(!fs.delete(outputPath,true)){
          System.err.println("Delete output file:"+mapReduceTest.getOutputPath()+" failed!");
          return;
        }
      }
      
      
      job.setJarByClass(OrderByMultiMapReduceImproveFixTest.class);
      job.setMapOutputKeyClass(OrderMultiColumnWritable.class);
      job.setMapOutputValueClass(LongWritable.class);
      job.setOutputKeyClass(Text.class);
      job.setOutputValueClass(LongWritable.class);
      job.setMapperClass(MyMapper.class);
      job.setReducerClass(MyReducer.class);
      
      job.setPartitionerClass(MyPartitioner.class);
      job.setGroupingComparatorClass(MyGroupingComparator.class);
      
      job.setNumReduceTasks(2);
      
      FileInputFormat.addInputPath(job, inputPath);
      FileOutputFormat.setOutputPath(job, outputPath);
      
      begin=System.currentTimeMillis();
      job.waitForCompletion(true);
      
      System.out.println("===================================================");
      if(mapReduceTest.isGenerateDatas()){
        System.out.println("The maxValue is:"+mapReduceTest.getMaxValue());
        System.out.println("The minValue is:"+mapReduceTest.getMinValue());
      }
      System.out.println("Spend time:"+(System.currentTimeMillis()-begin));
      // Spend time:1270
      
    } catch (Exception e) {
      // TODO Auto-generated catch block
      e.printStackTrace();
    }
    
  }
}

 

 

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