Hadoop I/O - Hadoop: The Definitive Guide - page 148

public static class FirstComparator extends WritableComparator {
private static final Text . Comparator TEXT_COMPARATOR = new
Text . Comparator ();
public FirstComparator () {
super ( TextPair . class );
}
@Override
public int compare ( byte [] b1 , int s1 , int l1 ,
byte [] b2 , int s2 , int l2 ) {
try {
int firstL1 = WritableUtils . decodeVIntSize ( b1 [ s1 ]) +
readVInt ( b1 , s1 );
int firstL2 = WritableUtils . decodeVIntSize ( b2 [ s2 ]) +
readVInt ( b2 , s2 );
return TEXT_COMPARATOR . compare ( b1 , s1 , firstL1 , b2 , s2 ,
firstL2 );
} catch ( IOException e ) {
throw new IllegalArgumentException ( e );
}
}
@Override
public int compare ( WritableComparable a , WritableComparable b ) {
if ( a instanceof TextPair && b instanceof TextPair ) {
return (( TextPair ) a ). first . compareTo ((( TextPair ) b ). first );
}
return super . compare ( a , b );
}
}
Serialization Frameworks
Although most MapReduce programs use Writable key and value types, this isn't man-
dated by the MapReduce API. In fact, any type can be used; the only requirement is a
mechanism that translates to and from a binary representation of each type.
To support this, Hadoop has an API for pluggable serialization frameworks. A serializa-
tion framework is represented by an implementation of Serialization (in the
org.apache.hadoop.io.serializer package). WritableSerialization ,
for example, is the implementation of Serialization for Writable types.