Data Types

Data Types

Standard types

We've seen in many of the examples that it's possible for DList objects to be parameterised by normal Scala primitive (value) types. Not surprisingly, Scoobi supports DList objects that are parameterised by any of the Scala primitive types:

val x: DList[Byte] = ...
val x: DList[Char] = ...
val x: DList[Int] = ...
val x: DList[Long] = ...
val x: DList[Double] = ...

And as we've also see, although not a primitive, Scoobi supports DLists of Strings:

val x: DList[String] = ...

Some of the examples also use DList objects that are parameterised by a pair (Scala Tuple2 type). In fact, Scoobi supports DList objects that are parameterised by Scala tuples up to arity 8, and in addition, supports arbitrary nesting:

val x: DList[(Int, String)] = ...
val x: DList[(String, Long)] = ...
val x: DList[(Int, String, Long)] = ...
val x: DList[(Int, (String, String), Int, (Long, Long, Long))] = ...
val x: DList[(Int, (String, (Long, Long)), Char)] = ...

Finally, Scoobi also supports DList objects that are parameterised by the Scala Option
and Either types, which can also be combined with any of the Tuple and primitive types:

val x: Option[Int] = ...
val x: Option[String] = ...
val x: Option[(Long, String)] = ...

val x: Either[Int, String] = ...
val x: Either[String, (Long, Long)] = ...
val x: Either[Long, Either[String, Int]] = ...
val x: Either[Int, Option[Long]] = ...

Notice that in all these cases, the DList object is parameterised by a standard Scala type and not some wrapper type. This is really convenient. It means, for example, that the use of a higher-order function like map can directly call any of the methods associated with those types. In contrast, programming MapReduce jobs directly using Hadoop's API requires that all types implement the Writable interface, resulting in the use of wrapper types such as IntWritable rather than just int. Of course the reason for this is that Writable specifies methods for serialization and deserialization of data within the Hadoop framework. However, given that DList objects eventually result in code that is executed by the Hadoop framework, how is serialization and deserialization specified?

Custom types


Scoobi requires that the type parameterizing a DList object has an implementation of the WireFormat type class (Scala context bound). Thus, the DList class is actually specified as:

class DList[A : WireFormat] { ... }

If the compiler cannot find a WireFormat implementation for the type parameterizing a specific DList object, that code will not compile. Implementations of WireFormat specify serialization and deserialization in their toWire and fromWire methods, which end up finding their way into Writable's write and readFields methods.

To make life easy, the WireFormat object includes WireFormat implementations for the types listed above (that is why they work out of the box). However, the real advantage of using type classes is they allow you to extend the set of types that can be used with DList objects and that set can include types that already exist, maybe even in some other compilation unit. So long as a type has a WireFormat implementation, it can parameterise a DList. This is extremely useful because while, say, you can represent a lot with nested tuples, much can be gained in terms of type safety, readability and maintenance by using custom types. For example, say we were building an application to analyze stock ticker-data. In that situation it would be nice to work with DList[Tick] objects. We can do that if we write a WireFormat implementation for Tick:

case class Tick(val date: Int, val symbol: String, val price: Double)

implicit def TickFmt = new WireFormat[Tick] {
  def toWire(tick: Tick, out: DataOutput) = {
  def fromWire(in: DataInput): Tick = {
    val date = in.readInt
    val symbol = in.readUTF
    val price = in.readDouble
    Tick(date, symbol, price)
  def show(tick: Tick): String = tick.toString

val ticks: DList[Tick] = ...  /* OK */

Then we can actually make use of the Tick type:

/* Function to compute Hi and Low for a stock for a given day */
def hilo(ts: Iterable[Tick]): (Double, Double) = {
  val start = ts.head.price
  ts.tail.foldLeft((start, start)) { case ((high, low), tick) => (max(high, tick.price), min(low, tick.price)) }

/* Group tick data by date and symbol */
val ticks: DList[Tick] = ...
val ticksGrouped = ticks.groupBy(t => (t.symbol,

/* Compute highs and lows for each stock for each day */
val highLow = ticksGrouped map { case ((symbol, date), ticks) => (symbol, date, hilo(ticks)) }

Notice that by using the custom type Tick it's obvious what fields we are using. If instead the type of ticks was DList[(Int, String, Double)], the code would be far less readable, and maintenance would be more difficult if, for example, we added new fields to Tick or modified the order of existing fields.

For case classes

Being able to have DList objects of custom types is a huge productivity boost. However, there is still the boiler-plate, mechanical work associated with the WireFormat implementation. To overcome this, the WireFormat object also provides a utility function called mkCaseWireFormat that automatically constructs a WireFormat for case classes:

case class Tick(date: Int, symbol: String, price: Double)
implicit val tickFmt = mkCaseWireFormat(Tick, Tick.unapply _)

val ticks: DList[Tick] = ...  /* Still OK */

mkCaseWireFormat takes as arguments the case class's automatically generated apply and unapply methods. The only requirement on case classes when using mkCaseWireFormat is that all its fields have WireFormat implementations. If not, your DList objects won't type check. The upside to this is that all of the types above that do have WireFormat implementations can be fields in a case class when used in conjunction with mkCaseWireFormat:

case class Tick(date: Int, symbol: String, price: Double, high_low: (Double, Double))
implicit val tickFmt = mkCaseWireFormat(Tick, Tick.unapply _)

val ticks: DList[Tick] = ...  /* Amazingly, still OK */

Of course, this will also extend to other case classes as long as they have WireFormat implementations. Thus, it's possible to have nested case classes that can parameterise DList objects:

case class PriceAttr(price: Double, high_low: (Double, Double))
implicit val priceAttrFmt = mkCaseWireFormat(PriceAttr, PriceAttr.unapply _)

case class Tick(date: Int, symbol: String, attr: PriceAttr)
implicit val tickFmt = mkCaseWireFormat(Tick, Tick.unapply _)

val ticks: DList[Tick] = ...  /* That's right, amazingly, still OK */

In summary, the way data types work in Scoobi is definitely one of its killer features, basically because they don't get in the way!

Default WireFormat

Temporarily, during your development, you can import a default WireFormat instance which should work with most Java types:

 import com.nicta.scoobi.core.WireFormat.AnythingFmt

 class Timestamp(val date: Date)

 implicit val timestampFormat = AnythingFmt[Timestamp]

 val timestamps: DList[Timestamps] = ...  /* Compiles OK */

The AnythingFmt is a WireFormat using Java serialization to serialise/deserialise the objects. It is however very ineffecient so it is not provided as an implicit conversion, you need to explicitely import it to be able to use it.