基于HIVE文件格式的map reduce代码编写

更清晰的code格式版本可以移步：http://hugh-wangp.iteye.com/blog/1405804

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，hiveclient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mapreduce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。

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, newPath(args[0]));

//为map-reduce job设置路径数组作为输出列表

FileOutputFormat.setOutputPath(conf, newPath(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。

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(LongWritablekey, Text value,

                     OutputCollector<Text,IntWritable> output,

           Reporter reporter) throws IOException{

              Stringline = value.toString();

              StringTokenizeritr = 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(Textkey, 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("wordcount");

        

         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(Textkey, Text value,

                           OutputCollector<Text,IntWritable> output,

                  Reporter reporter) throws IOException{

                     Stringline = value.toString();

                     StringTokenizeritr = 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(Textkey, 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 IOException{

              // TODO Auto-generated methodstub

               JobConf conf = new JobConf(WordCountSeq.class);

               conf.setJobName("wordcount");

               

               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;
importorg.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 implementsMapper<LongWritable, BytesRefArrayWritable, Text,IntWritable> {
         
         private final static IntWritable one = new IntWritable(1);
         private Text word =new Text();
    
         @Override
         public void map(LongWritable key, BytesRefArrayWritablevalue,
                   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);              
         }
         
    }
    
    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 thenative-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-newdecompressor

12/02/13 17:07:57 INFO compress.CodecPool: Got brand-newdecompressor

12/02/13 17:07:57 INFO compress.CodecPool: Got brand-newdecompressor

12/02/13 17:07:57 INFO compress.CodecPool: Got brand-newdecompressor

        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