MapReduce高级编程——自定义InputFormat——深入理解

0、本文承接上文 MapReduce高级编程——自定义InputFormat

1、环境配置，本文的开发环境请直接参考

基于Eclipse的Hadoop应用开发环境的配置

2、Mapper，Reducer参数解释

 

import java.io.IOException;

import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;

public class Point3DMapper extends Mapper<Text, Point3D, Text, Point3D>{
	protected void map(Text key, Point3D value, Context context) throws IOException, InterruptedException{
		context.write(key, value);
	}
}

 

   进入Mapper源代码可以看到

 

public class Mapper<KEYIN, VALUEIN, KEYOUT, VALUEOUT> {

  public class Context 
    extends MapContext<KEYIN,VALUEIN,KEYOUT,VALUEOUT> {
    public Context(Configuration conf, TaskAttemptID taskid,
                   RecordReader<KEYIN,VALUEIN> reader,
                   RecordWriter<KEYOUT,VALUEOUT> writer,
                   OutputCommitter committer,
                   StatusReporter reporter,
                   InputSplit split) throws IOException, InterruptedException {
      super(conf, taskid, reader, writer, committer, reporter, split);
    }
  }
  
  /**
   * Called once at the beginning of the task.
   */
  protected void setup(Context context
                       ) throws IOException, InterruptedException {
    // NOTHING
  }

  /**
   * Called once for each key/value pair in the input split. Most applications
   * should override this, but the default is the identity function.
   */
  @SuppressWarnings("unchecked")
  protected void map(KEYIN key, VALUEIN value, 
                     Context context) throws IOException, InterruptedException {
    context.write((KEYOUT) key, (VALUEOUT) value);
  }

 

 

即,/**

* Text      -> KEYIN
* Point3D  -> VALUEIN
* Text       -> KEYOUT
* Point3D  -> VALUEOUT
**/
Mapper<Text, Point3D, Text, Point3D>

 

而，// Text -> ball etc. -> KEYOUT

// value -> .5, 12.7, 9.0 etc. -> VALUEOUT

context.write(key, value);

 

同理，我们查看Reducer源代码，可以看到，Mapper的KEYOUT类型和VALUEOUT类型，必须对应Reducer的KEYIN类型和VLUEIN类型。

 

public class Reducer<KEYIN,VALUEIN,KEYOUT,VALUEOUT> {

  public class Context 
    extends ReduceContext<KEYIN,VALUEIN,KEYOUT,VALUEOUT> {
    public Context(Configuration conf, TaskAttemptID taskid,
                   RawKeyValueIterator input, 
                   Counter inputKeyCounter,
                   Counter inputValueCounter,
                   RecordWriter<KEYOUT,VALUEOUT> output,
                   OutputCommitter committer,
                   StatusReporter reporter,
                   RawComparator<KEYIN> comparator,
                   Class<KEYIN> keyClass,
                   Class<VALUEIN> valueClass
                   ) throws IOException, InterruptedException {
      super(conf, taskid, input, inputKeyCounter, inputValueCounter,
            output, committer, reporter, 
            comparator, keyClass, valueClass);
    }
  }

  /**
   * Called once at the start of the task.
   */
  protected void setup(Context context
                       ) throws IOException, InterruptedException {
    // NOTHING
  }

  /**
   * This method is called once for each key. Most applications will define
   * their reduce class by overriding this method. The default implementation
   * is an identity function.
   */
  @SuppressWarnings("unchecked")
  protected void reduce(KEYIN key, Iterable<VALUEIN> values, Context context
                        ) throws IOException, InterruptedException {
    for(VALUEIN value: values) {
      context.write((KEYOUT) key, (VALUEOUT) value);
    }
  }

 

 

本文的目的是因为有些时候文档不是很清楚，而最好的方法是看源代码。“源码之前，了无秘密”。

 

对于MapReduce的细致执行流程，我推荐看例子经典，博客作者一流的Map Reduce – the Free Lunch is not over?。相信看完会有所收获！