More than 50,000 people signed up for Martin Odersky's course "Functional Programming Principles in Scala" at Coursera. That's a huge number of developers for whom this might have been the first contact with Scala, functional programming, or both.
If you are reading this, maybe you are one of them, or maybe you have started to learn Scala by some other means. In any case, if you have started to learn Scala, you are excited to delve deeper into this beautiful language, but it all still feels a little exotic or foggy to you, then the series of articles that is beginning with this one is for you.
Even though the Coursera course covered quite a lot of what you need to know about Scala, the given time constraints made it impossible to explain everything in detail. As a result, some Scala features might seem like magic to you if you are new to the language. You are able to use them somehow, but you haven't fully grasped how they work and, more importantly, why they work as they do.
In this article and the ones following in the coming weeks, I would like to clear things up and remove those question marks. I will also explain some of the features of the Scala language and library that I had trouble with when I started learning the language, partially because I didn't find any good explanations for them, but instead just stumbled upon them in the wild. Where appropriate, I will also try to give guidance on how to use these features in an idiomatic™ way.
Enough of the introductions. Before I begin, keep in mind that, while having attended the Coursera course is not a prerequisite for following this series, having roughly the knowledge of Scala as can be acquired in that course is definitely helpful, and I will sometimes refer to the course.
So how does this pattern matching thingie actually work?
In the Coursera course, you came across one very powerful language feature of Scala: Pattern matching. It allows you to decompose a given data structure, binding the values it was constructed from to variables. It's not an idea that is unique to Scala, though. Other prominent languages in which pattern matching plays an important role are Haskell and Erlang, for instance.
If you followed the video lectures, you saw that you can decompose various kinds of data structures using pattern matching, among them lists, streams, and any instances of case classes. So is this list of data structures that can be destructured fixed, or can you extend it somehow? And first of all, how does this actually work? Is there some kind of magic involved that allows you to write things like the following?
case class User(firstName: String, lastName: String, score: Int)
def advance(xs: List[User]) = xs match {
case User(_, _, score1) :: User(_, _, score2) :: _ => score1 - score2
case _ => 0
}
As it turns out, there isn't. At least not much. The reason why you are able to write the above code (no matter how little sense this particular example makes) is the existence of so-called extractors.
In its most widely applied form, an extractor has the opposite role of a constructor: While the latter creates an object from a given list of parameters, an extractor extracts the parameters from which an object passed to it was created.
The Scala library contains some predefined extractors, and we will have a look at one of them shortly. Case classes are special because Scala automatically creates a companion object for them: a singleton object that contains not only an apply method for creating new instances of the case class, but also an unapply method – the method that needs to be implemented by an object in order for it to be an extractor.
Our first extractor, yay!
There is more than one possible signature for a valid unapply method, but we will start with the ones that are most widely used. Let's pretend that our User class is not a case class after all, but instead a trait, with two classes extending it, and for the moment, it only contains a single field:
trait User {
def name: String
}
class FreeUser(val name: String) extends User
class PremiumUser(val name: String) extends User
We want to implement extractors for the FreeUser and PremiumUser classes in respective companion objects, just as Scala would have done were these case classes. If your extractor is supposed to only extract a single parameter from a given object, the signature of an unapply method looks like this:
def unapply(object: S): Option[T]
The method expects some object of type S and returns an Option of type T, which is the type of the parameter it extracts. Remember that Option is Scala's safe alternative to the existence of null values. There will be a separate article about it, but for now, it's enough to know that the unapply method returns either Some[T] (if it could successfully extract the parameter from the given object) or None, which means that the parameters could not be extracted, as per the rules determined by the extractor implementation.
Here are our extractors:
trait User {
def name: String
}
class FreeUser(val name: String) extends User
class PremiumUser(val name: String) extends User
object FreeUser {
def unapply(user: FreeUser): Option[String] = Some(user.name)
}
object PremiumUser {
def unapply(user: PremiumUser): Option[String] = Some(user.name)
}
We can now use this in the REPL:
scala> FreeUser.unapply(new FreeUser("Daniel"))
res0: Option[String] = Some(Daniel)
But you wouldn't usually call this method directly. Scala calls an extractor's unapply method if the extractor is used as an extractor pattern.
If the result of calling unapply is Some[T], this means that the pattern matches, and the extracted value is bound to the variable declared in the pattern. If it is None, this means that the pattern doesn't match and the next case statement is tested.
Let's use our extractors for pattern matching:
val user: User = new PremiumUser("Daniel")
user match {
case FreeUser(name) => "Hello " + name
case PremiumUser(name) => "Welcome back, dear " + name
}
As you will already have noticed, our two extractors never return None. The example shows that this makes more sense than it might seem at first. If you have an object that could be of some type or another, you can check its type and destructure it at the same time.
In the example, the FreeUser pattern will not match because it expects an object of a different type than we pass it. Since it wants an object of type FreeUser, not one of type PremiumUser, this extractor is never even called. Hence, the user value is now passed to the unapply method of the PremiumUser companion object, as that extractor is used in the second pattern. This pattern will match, and the returned value is bound to the name parameter.
Later in this article, we will see an example of an extractor that does not always return Some[T].
Extracting several values
Now, let's assume that our classes against which we want to match have some more fields:
trait User {
def name: String
def score: Int
}
class FreeUser(val name: String, val score: Int, val upgradeProbability: Double)
extends User
class PremiumUser(val name: String, val score: Int) extends User
If an extractor pattern is supposed to decompose a given data structure into more than one parameter, the signature of the extractor's unapply method looks like this:
def unapply(object: S): Option[(T1, ..., Tn)]
The method expects some object of type S and returns an Option of type TupleN, where N is the number of parameters to extract.
Let's adapt our extractors to the modified classes:
trait User {
def name: String
def score: Int
}
class FreeUser(val name: String, val score: Int, val upgradeProbability: Double)
extends User
class PremiumUser(val name: String, val score: Int) extends User
object FreeUser {
def unapply(user: FreeUser): Option[(String, Int, Double)] =
Some((user.name, user.score, user.upgradeProbability))
}
object PremiumUser {
def unapply(user: PremiumUser): Option[(String, Int)] = Some((user.name, user.score))
}
We can now use this extractor for pattern matching, just like we did with the previous version:
val user: User = new FreeUser("Daniel", 3000, 0.7d)
user match {
case FreeUser(name, _, p) =>
if (p > 0.75) name + ", what can we do for you today?" else "Hello " + name
case PremiumUser(name, _) => "Welcome back, dear " + name
}
A Boolean extractor
Sometimes, you don't really have the need to extract parameters from a data structure against which you want to match – instead, you just want to do a simple boolean check. In this case, the third and last of the available unapply method signatures comes in handy, which expects a value of type S and returns a Boolean:
def unapply(object: S): Boolean
Used in a pattern, the pattern will match if the extractor returns true. Otherwise the next case, if available, is tried.
In the previous example, we had some logic that checks whether a free user is likely to be susceptible to being persuaded to upgrade their account. Let's place this logic in its own boolean extractor:
object premiumCandidate {
def unapply(user: FreeUser): Boolean = user.upgradeProbability > 0.75
}
As you can see here, it is not necessary for an extractor to reside in the companion object of the class for which it is applicable. Using such a boolean extractor is as simple as this:
val user: User = new FreeUser("Daniel", 2500, 0.8d)
user match {
case freeUser @ premiumCandidate() => initiateSpamProgram(freeUser)
case _ => sendRegularNewsletter(user)
}
This example shows that a boolean extractor is used by just passing it an empty parameter list, which makes sense because it doesn't really extract any parameters to be bound to variables.
There is one other peculiarity in this example: I am pretending that our fictional initiateSpamProgram function expects an instance of FreeUser because premium users are never to be spammed. Our pattern matching is against any type of User, though, so I cannot pass user to the initiateSpamProgram function – not without ugly type casting anyway.
Luckily, Scala's pattern matching allows to bind the value that is matched to a variable, too, using the type that the used extractor expects. This is done using the @ operator. Since our premiumCandidate extractor expects an instance of FreeUser, we have therefore bound the matched value to a variable freeUser of type FreeUser.
Personally, I haven't used boolean extractors that much, but it's good to know they exist, as sooner or later you will probably find yourself in a situation where they come in handy.
Infix operation patterns
If you followed the Scala course at Coursera, you learned that you can destructure lists and streams in a way that is akin to one of the ways you can create them, using the cons operator, :: or #::, respectively:
val xs = 58 #:: 43 #:: 93 #:: Stream.empty
xs match {
case first #:: second #:: _ => first - second
case _ => -1
}
Maybe you have wondered why that is possible. The answer is that as an alternative to the extractor pattern notation we have seen so far, Scala also allows extractors to be used in an infix notation. So, instead of writing e(p1, p2), where e is the extractor and p1 and p2 are the parameters to be extracted from a given data structure, it's always possible to write p1 e p2.
Hence, the infix operation pattern head #:: tail could also be written as #::(head, tail), and our PremiumUser extractor could also be used in a pattern that reads name PremiumUser score. However, this is not something you would do in practice. Usage of infix operation patterns is only recommended for extractors that indeed are supposed to read like operators, which is true for the cons operators of List and Stream, but certainly not for our PremiumUser extractor.
A closer look at the Stream extractor
Even though there is nothing special about how the #:: extractor can be used in pattern matching, let's take a look at it, to better understand what is going on in our pattern matching code above. Also, this is a good example of an extractor that, depending on the state of the passed in data structure, may return None and thus not match.
Here is the complete extractor, taken from the sources of Scala 2.9.2:
object #:: {
def unapply[A](xs: Stream[A]): Option[(A, Stream[A])] =
if (xs.isEmpty) None
else Some((xs.head, xs.tail))
}
If the given Stream instance is empty, it just returns None. Thus, case head #:: tail will not match for an empty stream. Otherwise, a Tuple2 is returned, the first element of which is the head of the stream, while the second element of the tuple is the tail, which is itself a Stream again. Hence, case head #:: tail will match for a stream of one or more elements. If it has only one element, tail will be bound to the empty stream.
To understand how this extractor works for our pattern matching example, let's rewrite that example, going from infix operation patterns to the usual extractor pattern notation:
val xs = 58 #:: 43 #:: 93 #:: Stream.empty
xs match {
case #::(first, #::(second, _)) => first - second
case _ => -1
}
First, the extractor is called for the intitial stream xs that is passed to the pattern matching block. The extractor returns Some((xs.head, xs.tail)), so first is bound to 58, while the tail of xs is passed to the extractor again, which is used again inside of the first one. Again, it returns the head and and tail as a Tuple2 wrapped in a Some, so that second is bound to the value 43, while the tail is bound to the wildcard _ and thus thrown away.
Using extractors
So when and how should you actually make use of custom extractors, especially considering that you can get some useful extractors for free if you make use of case classes?
While some people point out that using case classes and pattern matching against them breaks encapsulation, coupling the way you match against data with its concrete representation, this criticism usually stems from an object-oriented point of view. It's a good idea, if you want to do functional programming in Scala, to use case classes as algebraic data types (ADTs) that contain pure data and no behaviour whatsoever.
Usually, implementing your own extractors is only necessary if you want to extract something from a type you have no control over, or if you need additional ways of pattern matching against certain data. For example, a common usage of extractors is to extract meaningful values from some string. As an exercise, think about how you would implement and use a URLExtractor that takes String representations of URLs.
Conclusion
In this first part of the series, we have examined extractors, the workhorse behind pattern matching in Scala. You have learned how to implement your own extractors and how the implementation of an extractor relates to its usage in a pattern.
We haven't covered all there is to say about extractors, because this article is already long enough as it is. In the next part of this series, I am going to revisit extractors, covering how to implement them if you you want to bind a variable number of extracted parameters in a pattern.
Please do let me know if this article was helpful to you or if something is not clear to you.