Hadoop I/O - Hadoop: The Definitive Guide - page 143

icWritable to wrap the elements in an ArrayWritable . Alternatively, you could
write a general ListWritable using the ideas from MapWritable .
Implementing a Custom Writable
Hadoop comes with a useful set of Writable implementations that serve most purposes;
however, on occasion, you may need to write your own custom implementation. With a
custom Writable , you have full control over the binary representation and the sort or-
der. Because Writable s are at the heart of the MapReduce data path, tuning the binary
representation can have a significant effect on performance. The stock Writable imple-
mentations that come with Hadoop are well tuned, but for more elaborate structures, it is
often better to create a new Writable type rather than composing the stock types.
TIP
If you are considering writing a custom Writable , it may be worth trying another serialization frame-
work, like Avro, that allows you to define custom types declaratively. See Serialization Frameworks and
Chapter 12 .
To demonstrate how to create a custom Writable , we shall write an implementation
that represents a pair of strings, called TextPair . The basic implementation is shown in
Example 5-7 .
Example 5-7. A Writable implementation that stores a pair of Text objects
import java.io.* ;
import org.apache.hadoop.io.* ;
public class TextPair implements WritableComparable < TextPair > {
private Text first ;
private Text second ;
public TextPair () {
set ( new Text (), new Text ());
}
public TextPair ( String first , String second ) {
set ( new Text ( first ), new Text ( second ));
}
public TextPair ( Text first , Text second ) {