Package org.apache.hadoop.mapreduce

Class Mapper<KEYIN,VALUEIN,KEYOUT,VALUEOUT>

java.lang.Object

org.apache.hadoop.mapreduce.Mapper<KEYIN,VALUEIN,KEYOUT,VALUEOUT>

Direct Known Subclasses:

ChainMapper, FieldSelectionMapper, InverseMapper, MultithreadedMapper, RegexMapper, TokenCounterMapper, ValueAggregatorMapper, WrappedMapper

@Public
@Stable
public class Mapper<KEYIN,VALUEIN,KEYOUT,VALUEOUT>
extends Object

Maps input key/value pairs to a set of intermediate key/value pairs.

Maps are the individual tasks which transform input records into a intermediate records. The transformed intermediate records need not be of the same type as the input records. A given input pair may map to zero or many output pairs.

The Hadoop Map-Reduce framework spawns one map task for each InputSplit generated by the InputFormat for the job. Mapper implementations can access the Configuration for the job via the JobContext.getConfiguration().

The framework first calls setup(org.apache.hadoop.mapreduce.Mapper.Context), followed by map(Object, Object, org.apache.hadoop.mapreduce.Mapper.Context) for each key/value pair in the InputSplit. Finally cleanup(org.apache.hadoop.mapreduce.Mapper.Context) is called.

All intermediate values associated with a given output key are subsequently grouped by the framework, and passed to a Reducer to determine the final output. Users can control the sorting and grouping by specifying two key RawComparator classes.

The Mapper outputs are partitioned per Reducer. Users can control which keys (and hence records) go to which Reducer by implementing a custom Partitioner.

Users can optionally specify a combiner, via Job.setCombinerClass(Class), to perform local aggregation of the intermediate outputs, which helps to cut down the amount of data transferred from the Mapper to the Reducer.

Applications can specify if and how the intermediate outputs are to be compressed and which CompressionCodecs are to be used via the Configuration.

If the job has zero reduces then the output of the Mapper is directly written to the OutputFormat without sorting by keys.

Example:

 public class TokenCounterMapper 
     extends Mapper<Object, Text, Text, IntWritable>{
    
   private final static IntWritable one = new IntWritable(1);
   private Text word = new Text();
   
   public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
     StringTokenizer itr = new StringTokenizer(value.toString());
     while (itr.hasMoreTokens()) {
       word.set(itr.nextToken());
       context.write(word, one);
     }
   }
 }
 

Applications may override the run(org.apache.hadoop.mapreduce.Mapper.Context) method to exert greater control on map processing e.g. multi-threaded Mappers etc.

See Also:

• InputFormat
• JobContext
• Partitioner
• Reducer

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
class	org.apache.hadoop.mapreduce.Mapper.Context	The Context passed on to the Mapper implementations.

Constructor Summary

Constructors

Constructor	Description
Mapper()

Method Summary

Modifier and Type	Method	Description
protected void	cleanup(Mapper<KEYIN,VALUEIN,KEYOUT,VALUEOUT>.org.apache.hadoop.mapreduce.Mapper.Context context)	Called once at the end of the task.
protected void	map(KEYIN key, VALUEIN value, Mapper<KEYIN,VALUEIN,KEYOUT,VALUEOUT>.org.apache.hadoop.mapreduce.Mapper.Context context)	Called once for each key/value pair in the input split.
void	run(Mapper<KEYIN,VALUEIN,KEYOUT,VALUEOUT>.org.apache.hadoop.mapreduce.Mapper.Context context)	Expert users can override this method for more complete control over the execution of the Mapper.
protected void	setup(Mapper<KEYIN,VALUEIN,KEYOUT,VALUEOUT>.org.apache.hadoop.mapreduce.Mapper.Context context)	Called once at the beginning of the task.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

Mapper

public Mapper()

Method Details

setup

protected void setup(Mapper<KEYIN,VALUEIN,KEYOUT,VALUEOUT>.org.apache.hadoop.mapreduce.Mapper.Context context)
              throws IOException,
InterruptedException

Called once at the beginning of the task.

Throws:

IOException

InterruptedException

map

protected void map(KEYIN key,
 VALUEIN value,
 Mapper<KEYIN,VALUEIN,KEYOUT,VALUEOUT>.org.apache.hadoop.mapreduce.Mapper.Context context)
            throws IOException,
InterruptedException

Called once for each key/value pair in the input split. Most applications should override this, but the default is the identity function.

Throws:

IOException

InterruptedException

cleanup

protected void cleanup(Mapper<KEYIN,VALUEIN,KEYOUT,VALUEOUT>.org.apache.hadoop.mapreduce.Mapper.Context context)
                throws IOException,
InterruptedException

Called once at the end of the task.

Throws:

IOException

InterruptedException

run

public void run(Mapper<KEYIN,VALUEIN,KEYOUT,VALUEOUT>.org.apache.hadoop.mapreduce.Mapper.Context context)
         throws IOException,
InterruptedException

Expert users can override this method for more complete control over the execution of the Mapper.

Parameters:

context -

Throws:

IOException

InterruptedException