What are the connections and differences between Hadoop Writable and java.io.serialization?
Underlying storage differences:
Java Serializable
Serializable does not assume the class of stored values is known and tags instances with its class ie. it writes the metadata about the object, which includes the class name, field names and types, and its superclass. ObjectOutputStream and ObjectInputStream optimize this somewhat, so that 5-byte handles are written for instances of a class after the first. But object sequences with handles cannot be then accessed randomly, since they rely on stream state. This complicates things like sorting.
Hadoop Writable
While defining a "Writable", you know the expected class. So Writables don't store their type in the serialized representation as while deserializing, you know what is expected. eg. if the input key is a LongWritable, so an empty LongWritable instance is asked to populate itself from the input data stream.As no meta info needs to be stored (classname, fields, their types, super classes) is done, this results in considerably more compact binary files, straightforward random access and higher performance.
Some good reads:
For Java Serializable:
- http://docs.oracle.com/javase/7/docs/platform/serialization/spec/serial-arch.html
- http://www.javaworld.com/javaworld/jw-02-2006/jw-0227-control.html
Hadoop Writable
In words of Doug Cutting :
The Writable interface is subtly different than Serializable. Serializable does not assume the class of stored values is known. So each instance is tagged with its class. ObjectOutputStream and ObjectInputStream optimize this somewhat, so that 5-byte handles are written for instances of a class after the first. But object sequences with handles cannot be then accessed randomly, since they rely on stream state. This complicates things like sorting.
Writable, on the other hand, assumes that the application knows the expected class. The application must be able to create an instance in order to call readFields(). So the class need not be stored with each instance. This results in considerably more compact binary files, straightforward random access and generally higher performance.
Arguably Hadoop could use Serializable. One could override writeObject or writeExternal for each class whose serialization was performance critical. (MapReduce is very i/o intensive, so nearly every class's serialization is performance critical.) One could implement ObjectOutputStream.writeObjectOverride() and ObjectInputStream.readObjectOverride() to use a more compact representation, that, e.g., did not need to tag each top-level instance in a file with its class. This would probably require as least as much code as Haddop has in Writable, ObjectWritable, etc., and the code would be a bit more complicated, since it would be trying to work around a different typing model. But it might have the advantage of better built-in version control. Or would it?
Serializable's version mechanism is to have classes define a static named serialVersionUID. This permits one to protect against incompatible changes, but does not easily permit one to implement back-compatibility. For that, the application must explicitly deal with versions. It must reason, in a class-specific manner, about the version that was written while reading, to decide what to do. But Serializeable's version mechanism does not support this any more or less than Writable.
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