Overcoming immutability in Scala
Joel McCance | March 25, 2016
So you’ve decided to write some Scala. You’ve been sold on the power and flexibility of the language, you’ve gotten over the initial hurdles of the new syntax, and you’re ready to start writing clean, functional code. Besides, you’ve already mastered a handful of other common languages. How hard can it be to pick up one more?
If all your previous languages were mainly imperative, the jump may be harder than you’d expect. Things like strict immutability, functional purity, type classes, and so on are more than just syntactical differences; they require a fundamentally different approach to your code. As a newcomer to functional programming, it’s easy to get frustrated when seemingly simple problems turn into thorny logic puzzles.
This article will tackle one of the most common headaches I’ve seen for new Scala programmers: immutability. I won’t try to convince you that immutability is worth the struggle, though. Other people have already made that argument better than I could. Instead, I’d like to focus on giving you the tools you need to overcome these initial hurdles.
Let’s start with something simple. You want to assign a value to a variable, but the exact value depends on some condition. In Java, you might write something like this:
Even though foo is declared final, Java lets you postpone defining its value until later. Scala, on the other hand, is more strict; you must declare the value of foo right when you define it. So how can we recreate this pattern without introducing a lot of useless, intermediate values?
The trick here is to remember that most things in Scala are expressions, and expressions have values. So there’s no such thing as an “if-else statement”; it’s an if-else expression, whose value is the last expression of whichever branch is followed:
Note that we’re allowing Scala’s type-inference to fill in the type of foo for us. This helps keep the code clean, but be mindful of what type Scala infers for you. Take a look at the following snippets and guess the resulting types.
The answers are: Any, Any, and Foo. (Any is analogous to Java’s Object.) The important thing to remember here is that while Scala will always assign the narrowest type that covers all possible return types, it will happily pick a very wide type in order to make that happen.
In the definition of foo1, our logging statement returns Unit, since that was the last statement in the block. Since Foo and Unit are unrelated, the “narrowest” type that includes both is Any (analogous to Java’s Object).
Something similar happens with foo2. Even though it looks as if there is only one branch, you can think of it as having two: the “if” case (which returns Foo) and the omitted “else” case (which returns Unit).
The final case demonstrates something interesting about how Scala treats exceptions. A block that ends in an exception can’t possibly return anything, so it has the special type Nothing. Nothing is a subclass of everything, in the same way that the empty set is a subset of every set. So since the exception-throwing block is a Nothing and Nothing is a subclass of Foo, the type of foo3 is Foo.
If you’re asking yourself “Why would I ever want Scala to infer Any?”, you’re not alone. This sort of widening is generally a result of programmer error, and it’s simple to manually specify the wide type if you want it. Luckily, there are plugins for SBT like WartRemover that will flag this and other cases of unhelpful implicit type-widening as compile errors.
For short if-then expressions, omitting the expected type is generally fine. It’s reasonable to expect developers to read the block and judge the type for themselves (or use the IDE to find out). That said, feel free to be explicit with your types to help out the reader, or to quickly highlight errors if someone accidentally changes the type while changing the conditional.
- Remember that nearly everything in Scala is an expression and returns a value, including control “statements” like if-then, try-catch, and even simple, brace-enclosed blocks.
- Scala will sometimes infer a different type than you expect. Be explicit with your types (instead of relying on type inference) when you want to be clear about the type you’re expecting. Over time you’ll get a feel for the type inference system and can start relying on them less.
Providing defaults for “null” values
A common subtype of the conditional initialization problem is providing a default value when one isn’t provided. You’ve probably seen this sort of code before in Java:
In Scala code, you will generally not be passing nullable values around; you’d use an Option instead. (You may be familiar with the concept from Java 8’s Optional type.) This helps us avoid common errors where a value is unexpectedly null, resulting in the omnipresent NullPointerException.
But even though Option helps you distinguish nullable types from non-nullable types, you still need to somehow unpack it. We can solve this in problem using an if-then expression as in the previous section:
There’s some issues with this approach though. For one thing, it’s generally accepted that calling Option#get should be avoided. But even aside from that, doesn’t it seem like an awful lot of typing for something that’s easily abstracted?
This is an important intuition to develop about Scala: if something seems like it should be easier, it almost always is. Let’s take a look at the Scaladoc for Option to see if there’s anything there to help us.
Bingo! This does exactly what we want in one simple, clear line.
- The Scala standard library is a rich resource. It’s almost always worth checking to see if there’s a baked-in function that does what you need.
- Scaladoc has a number of helpful features to make finding these helpful functions easier. Almost all of the major libraries make their Scaladoc available online.
In Java, you typically start from an empty collection and build it up by adding things to it:
Once again, this relies on mutability: the state of fullNames isn’t fixed, so you can freely add new items to it. Scala collections are immutable by default, so this “build it up” style isn’t available. Instead, you should think of the operation in terms of transforming one collection into another:
The filter operation will also come in handy, if for example you wanted only users whose last names start with “M”:
If you’ve programmed with Java 8 lambdas, this may be recognizable as a more streamlined version of what you would do with streams:
- In imperative languages, we get used to thinking in terms of loops and steps. In functional languages, the correct solutions are best thought of in terms of transformations, filters, and reductions.
Building a map
Building a map has the same problem as building a collection, but the solution isn’t quite as obvious. Say you wanted to build a map to look up users by ID
From the last section, you should now be thinking about how to describe this change as a transformation of some kind. But in previous examples, we were keeping the general structure of “list” and just changing the contents. Now we need to actually transform the list into a different structure.
One way to figure this out is to look at the Scaladoc for Map and see how you define a new Map in the first place:
Note the type of elems is (A, B)*. T* is Scala’s syntax for varargs argument of type T. So effectively, a Map is built from a list of tuples (A, B), where the first element is the key and the second is the value. This is actually what’s going on when you initialize a Map by hand. The -> operator is just a convenience for creating pairs.
Now that we know that a Map can be build from a list of pairs, we can leverage that to initialize our Map:
This works, but it seems kind of wordy. Since we now know that there’s a natural transformation from Seq[(A, B)] to Map[A, B], let’s take a look at the Scaladoc for Seq to see if there’s anything to help us out.
With this, our map can be created with an elegant one-liner:
You might wonder what would happen if you called toMap on a Seq that had something in it other than Tuple2s (the type of what we’ve been calling “pairs”). If you’re from a Java background, you might expect an exception to be thrown at runtime when the JVM is unable to cast the elements to the type it needs.
If you try it out (which you definitely should!), you’ll see that you actually get an error at compile time, not runtime. I won’t go into the details here, but if you look at the full signature for toMap (either by looking at the source or expanding the “full signature” section of the Scaladoc) you’ll notice an peculiar implicit parameter being passed. This parameter tells the compiler to try to prove that your sequence contains Tuple2s at compile time. If it can’t, it won’t let you use the function. This is a great example of how Scala is able to support certain special-case utility functions without sacrificing type safety.
- When converting between different types, look at how your target type is created. This should give you an idea of what your intermediary type will be.
- Once you know what your intermediary type is, check to see if it provides a function to convert directly to your target type.
It’s easy for immutability to discourage new Scala developers. When you dive into a new language, it’s usually because you want to get something done, not fiddle around with basic things like initialization and collection twiddling. But immutability is fundamental to functional programming; without it, writing pure functions would be meaningless. Hopefully these examples will help you to overcome this initial hurdle and enjoy the benefits of working with values that will never change.
Joel McCance is a solution architect with Slalom’s Cross-Market Delivery Center in Chicago, where he strives to demonstrate that functional programming can be accessible, practical, and powerful. Ask him about it on Twitter: @jmccance.