基于HIVE文件格式的map reduce代码编写

by hugh.wangp

 

我们的数据绝大多数都是在HIVE上，对HIVE的SEQUENCEFILE和RCFILE的存储格式都有利用，为了满足HIVE的数据开放，hive client的方式就比较单一，直接访问HIVE生成的HDFS数据也是一种必要途径，所以本文整理测试了如何编写基于TEXTFILE、SEQUENCEFILE、RCFILE的数据的map reduce的代码。以wordcount的逻辑展示3种MR的代码。


其实只要知道MAP的输入格式是什么，就知道如何在MAP中处理数据；只要知道REDUCE(也可能只有MAP)的输出格式，就知道如何把处理结果转成输出格式。

表1：

 
如下代码片段是运行一个MR的最简单的配置：定义job、配置job、运行job

//map/reduce的job配置类，向hadoop框架描述map-reduce执行的工作 
JobConf conf = new JobConf(WordCountRC.class);
//设置一个用户定义的job名称
conf.setJobName("WordCountRC");

//为job的输出数据设置Key类
conf.setOutputKeyClass(Text.class);
//为job输出设置value类 
conf.setOutputValueClass(IntWritable.class);

//为job设置Mapper类
conf.setMapperClass(MapClass.class);
//为job设置Combiner类
conf.setCombinerClass(Reduce.class);
//为job设置Reduce类
conf.setReducerClass(Reduce.class);

//为map-reduce任务设置InputFormat实现类
conf.setInputFormat(RCFileInputFormat.class);
//为map-reduce任务设置OutputFormat实现类
conf.setOutputFormat(TextOutputFormat.class);

//为map-reduce job设置路径数组作为输入列表
FileInputFormat.setInputPaths(conf, new Path(args[0]));
//为map-reduce job设置路径数组作为输出列表
FileOutputFormat.setOutputPath(conf, new Path(args[1]));

//运行一个job
JobClient.runJob(conf);

而此刻，我们更多的是关注配置InputFormat和OutputFormat的setInputFormat和setOutputFormat。根据我们不同的输入输出做相应的配置，可以选择表1的任何格式。
当我们确定了输入输出格式，接下来就是来在实现map和reduce函数时首选对输入格式做相应的处理，然后处理具体的业务逻辑，最后把处理后的数据转成既定的输出格式。

 

如下是处理textfile、sequencefile、rcfile输入文件的wordcount代码，大家可以比较一下具体区别，应该就能处理更多其它输入文件或者输出文件格式的数据。
代码1：textfile版wordcount

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

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;


public class WordCountTxt{
 
  public static class MapClass extends MapReduceBase
    implements Mapper<LongWritable, Text, Text, IntWritable> {
   
    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();
   
       @Override
       public void map(LongWritable key, Text value,
                     OutputCollector<Text, IntWritable> output,
            Reporter reporter) throws IOException {
              String line = value.toString();
              StringTokenizer itr = new StringTokenizer(line);
              while (itr.hasMoreTokens()) {
                     word.set(itr.nextToken());
                     output.collect(word, one);
              }
  }
  }

  public static class Reduce extends MapReduceBase
    implements Reducer<Text, IntWritable, Text, IntWritable> {
   
       @Override
    public void reduce(Text key, Iterator<IntWritable> values,
                       OutputCollector<Text, IntWritable> output,
                       Reporter reporter) throws IOException {
      int sum = 0;
      while (values.hasNext()) {
        sum += values.next().get();
      }
      output.collect(key, new IntWritable(sum));
    }
  }
 
  public static void main(String[] args) throws Exception {
         JobConf conf = new JobConf(WordCountTxt.class);
         conf.setJobName("wordcounttxt");
        
         conf.setOutputKeyClass(Text.class);
         conf.setOutputValueClass(IntWritable.class);
        
         conf.setMapperClass(MapClass.class);
         conf.setCombinerClass(Reduce.class);
         conf.setReducerClass(Reduce.class);
        
         FileInputFormat.setInputPaths(conf, new Path(args[0]));
         FileOutputFormat.setOutputPath(conf, new Path(args[1]));
              
         JobClient.runJob(conf);   
  }
  
}

代码2：sequencefile版wordcount

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

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.SequenceFileAsTextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;


public class WordCountSeq {

         public static class MapClass extends MapReduceBase
           implements Mapper<Text, Text, Text, IntWritable> {
          
           private final static IntWritable one = new IntWritable(1);
           private Text word = new Text();
          
              @Override
              public void map(Text key, Text value,
                           OutputCollector<Text, IntWritable> output,
                   Reporter reporter) throws IOException {
                     String line = value.toString();
                     StringTokenizer itr = new StringTokenizer(line);
                     while (itr.hasMoreTokens()) {
                           word.set(itr.nextToken());
                           output.collect(word, one);
                     }
         }
         }

         public static class Reduce extends MapReduceBase
           implements Reducer<Text, IntWritable, Text, IntWritable> {
          
              @Override
           public void reduce(Text key, Iterator<IntWritable> values,
                              OutputCollector<Text, IntWritable> output,
                              Reporter reporter) throws IOException {
             int sum = 0;
             while (values.hasNext()) {
               sum += values.next().get();
             }
             output.collect(key, new IntWritable(sum));
           }
         }
         /**
          * @param args
        * @throws IOException
          */
         public static void main(String[] args) throws IOException {
              // TODO Auto-generated method stub
                JobConf conf = new JobConf(WordCountSeq.class);
                conf.setJobName("wordcountseq");
               
                conf.setOutputKeyClass(Text.class);
                conf.setOutputValueClass(IntWritable.class);
               
                conf.setMapperClass(MapClass.class);
                conf.setCombinerClass(Reduce.class);
                conf.setReducerClass(Reduce.class);
               
                conf.setInputFormat(SequenceFileAsTextInputFormat.class);
                conf.setOutputFormat(TextOutputFormat.class);
               
                FileInputFormat.setInputPaths(conf, new Path(args[0]));
                FileOutputFormat.setOutputPath(conf, new Path(args[1]));
                     
                JobClient.runJob(conf);
         }

}

代码3：rcfile版wordcount

import java.io.IOException;
import java.util.Iterator;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hive.ql.io.RCFileInputFormat;
import org.apache.hadoop.hive.serde2.columnar.BytesRefArrayWritable;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.TextOutputFormat;

public class WordCountRC {
    
     public static class MapClass
          extends MapReduceBase implements Mapper<LongWritable, BytesRefArrayWritable, Text, IntWritable> {
         
          private final static IntWritable one = new IntWritable(1);
          private Text word =new Text();
    
          @Override
          public void map(LongWritable key, BytesRefArrayWritable value,
                    OutputCollector<Text, IntWritable> output, Reporter reporter)
                    throws IOException {
               Text txt = new Text();
               txt.set(value.get(0).getData(), value.get(0).getStart(), value.get(0).getLength());
               String[] result = txt.toString().split("\\s");
               for(int i=0; i < result.length; i++){
                    word.set(result[i]);
                    output.collect(word, one);    
               }
          }         
     }

     public static class Reduce
          extends MapReduceBase implements Reducer<Text, IntWritable, Text, IntWritable> {
    
          private IntWritable result = new IntWritable();
         
          @Override
          public void reduce(Text key, Iterator<IntWritable> value,
                    OutputCollector<Text, IntWritable> output, Reporter reporter)
                    throws IOException {
               int sum = 0;
               while (value.hasNext()) {
                    sum += value.next().get();
               }
              
               result.set(sum);
               output.collect(key, result);              
          }
         
     }
     /**
     * @param args
     */
     public static void main(String[] args) throws IOException{
          JobConf conf = new JobConf(WordCountRC.class);
          conf.setJobName("WordCountRC");
         
          conf.setOutputKeyClass(Text.class);
          conf.setOutputValueClass(IntWritable.class);
         
          conf.setMapperClass(MapClass.class);
          conf.setCombinerClass(Reduce.class);
          conf.setReducerClass(Reduce.class);
         
          conf.setInputFormat(RCFileInputFormat.class);
          conf.setOutputFormat(TextOutputFormat.class);
         
          FileInputFormat.setInputPaths(conf, new Path(args[0]));
          FileOutputFormat.setOutputPath(conf, new Path(args[1]));
         
          JobClient.runJob(conf);
     }
}

原始数据：

hadoop fs -text /group/alidw-dev/seq_input/attempt_201201101606_2339628_m_000000_0
12/02/13 17:07:57 INFO util.NativeCodeLoader: Loaded the native-hadoop library
12/02/13 17:07:57 INFO zlib.ZlibFactory: Successfully loaded & initialized native-zlib library
12/02/13 17:07:57 INFO compress.CodecPool: Got brand-new decompressor
12/02/13 17:07:57 INFO compress.CodecPool: Got brand-new decompressor
12/02/13 17:07:57 INFO compress.CodecPool: Got brand-new decompressor
12/02/13 17:07:57 INFO compress.CodecPool: Got brand-new decompressor
        hello, i am ok. are you?
        i am fine too!

编译打包完成后执行：

hadoop jarWordCountSeq.jar WordCountSeq /group/alidw-dev/seq_input/ /group/alidw-dev/rc_output

执行完毕就能看到最终结果：

hadoop fs -cat /group/alidw-dev/seq_output/part-00000
am      2
are     1
fine    1
hello,  1
i       2
ok.     1
too!    1
you?    1