Scala 2.8 Arrays Martin Odersky, EPFL October 1, 2009

The Problem Arrays have turned out to be one of the trickiest concepts to get right in Scala. This has mostly to do with very hard constraints that clash with what’s desirable. On the one hand, we want to use arrays for interoperation with Java, which means that they need to have the same representation as in Java. This low-level representation is also useful to get high performance out of arrays. But on the other hand, arrays in Java are severely limited. First, there’s actually not a single array type representation in Java but nine different ones: One representation for arrays of reference type and another eight for arrays of each of the primitive types byte, char, short, int, long, float, double, and boolean. There is no common type for these different representations which is more specific than just java.lang.Object, even though there are some reflective methods to deal with arrays of arbitrary type in java.lang.reflect.Array. Second, there’s no way to create an array of a generic type; only monomorphic array creations are allowed. Third, the only operations supported by arrays are indexing, updates, and get length. Contrast this with what we would like to have in Scala: Arrays should slot into the collections hierarchy, supporting the hundred or so methods that are defined on sequences. And they should certainly be generic, so that one can create an Array[T] where T is a type variable.

The Past How to combine these desirables with the representation restrictions imposed by Java interoperability and performance? There’s no easy answer, and I believe we got it wrong the first time when we designed Scala. The Scala language up to 2.7.x “magically” wrapped and unwrapped arrays when required in a process called boxing and unboxing, similarly to what is done to treat primitive numeric types as objects. “Magically” means: the compiler generated code to do so based on the static types of expressions. Additional magic made generic array creation work. An expression like new Array[T] where T is a type parameter was converted to new BoxedAnyArray[T]. BoxedAnyArray was a special wrapper class which changed its representation depending on the type of the concrete Java array to which it was cast. This

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scheme worked well enough for most programs but the implementation “leaked” for certain combinations of type tests and type casts, as well as for observing uninitialized arrays. It also could lead to unexpectedly low performance. Some of the problems have been described by David MacIver[Mac08b] and Matt Malone[Mal09] Boxed arrays were also unsound when combined with covariant collections. In summary, the old array implementation technique was problematic because it was a leaky abstraction that was complicated enough so that it would be very tedious to specify where the leaks were to be expected.

Exploring the Solution Space The obvious way to reduce the amount of magic needed for arrays is to have two representations: One which corresponds closely to a Java array and another which forms an integral part of Scala’s collection hierarchy. Implicit conversions can be used to transparently convert between the two representations. This is the gist of the array refactoring proposal of David MacIver (with contributions by Stepan Koltsov) [Mac08a]. The main problem with this proposal, as I see it, is that it would force programmers to choose the kind of array to work with. The choice would not be clear-cut: The Java-like arrays would be fast and interoperable whereas the Scala native arrays would support a much nicer set of operations on them. With a choice like this, one would expect different components and libraries to make different decisions, which would result in incompatibilities and brittle, complex code. MacIver and Koltsov introduce some compiler magic to alleviate this. They propose to automatically split a method taking an array as an argument into two overloaded versions: one taking a Java array and one taking a generic Scala array. I believe this would solve some of the more egregious plumbing issues, but it would simply hide the problem a bit better, not solve it. A similar idea—but with a slightly different slant—is to “dress up” native arrays with an implicit conversion that integrates them into Scala’s collection hierarchy. This is similar to what’s been done with the String to RichString conversion in pre-2.8 Scala. The difference to the MacIver/Koltsov proposal is that one would not normally refer to Scala native arrays in user code, just as one rarely referred to RichString in Scala. One would only rely on the implicit conversion to add the necessary methods and traits to Java arrays. Unfortunately, the String/RichString experience has shown that this is also problematic. In particular, in pre 2.8 versions of Scala, one had the non-intuitive property that "abc".reverse.reverse == "abc"

, yet

"abc" != "abc".reverse.reverse

!

The problem here was that the reverse method was inherited from class Seq where it was defined to return another Seq. Since strings are not sequences, the only feasible type reverse could return when called on a String was RichString. But then the equals method on Strings which is inherited from Java would not recognize that a String could be equal to a RichString.

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2.8 Collections The new scheme of Scala 2.8 solves the problems with both arrays and strings. It makes critical use of the new 2.8 collections framework which accompanies collection traits such as Seq with implementation traits that abstract over the representation of the collection. For instance, in addition to trait Seq there is now a trait trait SeqLike[+Elem, +Repr] { ... }

That trait is parameterized with a representation type Repr. No assumptions need to be made about this representation type; in particular it not required to be a subtype of Seq. Methods such as reverse in trait SeqLike will return values of the representation type Repr rather than Seq. The Seq trait then inherits all its essential operations from SeqLike, instantiating the Repr parameter to Seq. trait Seq[+Elem] extends ... with SeqLike[Elem, Seq[Elem]] { ... }

A similar split into base trait and implementation trait applies to most other kinds of collections, including Traversable, Iterable, and Vector.

Integrating Arrays We can integrate arrays into this collection framework using two implicit conversions. The first conversion will map an Array[T] to an object of type ArrayOps, which is a subtype of type VectorLike[T, Array[T]]. Using this conversion, all sequence operations are available for arrays at the natural types. In particular, methods will yield arrays instead of ArrayOps values as their results. Because the results of these implicit conversions are so short-lived, modern VM’s can eliminate them altogether using escape analysis, so we expect the calling overhead for these added methods to be essentially zero. So far so good. But what if we need to convert an array to a real Seq, not just call a Seq method on it? For this there is another implicit conversion, which takes an array and converts it into a WrappedArray. WrappedArrays are mutable Vectors that implement all vector operations in terms of a given Java array. The difference between a WrappedArray and an ArrayOps object is apparent in the type of methods like reverse: Invoked on a WrappedArray, reverse returns again a WrappedArray, but invoked on an ArrayOps object, it returns an Array. The conversion from Array to WrappedArray is invertible. A dual implicit conversion goes from WrappedArray to Array. WrappedArray and ArrayOps both inherit from an implementation trait ArrayLike. This is to avoid duplication of code between ArrayOps and WrappedArray; all operations are factored out into the common ArrayLike trait.

Avoiding Ambiguities So now that we have two implicit conversions from Array to ArrayLike values, how does one choose between them and how does one avoid ambiguities? The trick is to make use of a generalization of overloading and implicit resolution in Scala 2.8. Previously, the most specific overloaded method or implicit 3

conversion would be chosen based solely on the method’s argument types. There was an additional clause which said that the most specific method could not be defined in a proper superclass of any of the other alternatives. This scheme has been replaced in Scala 2.8 by the following, more liberal one: When comparing two different applicable alternatives of an overloaded method or of an implicit, each method gets one point for having more specific arguments, and another point for being defined in a proper subclass. An alternative “wins” over another if it gets a greater number of points in these two comparisons. This means in particular that if alternatives have identical argument types, the one which is defined in a subclass wins. Applied to arrays, this means that we can prioritize the conversion from Array to ArrayOps over the conversion from Array to WrappedArray by placing the former in the standard Predef object and by placing the latter in a class LowPriorityImplicits, which is inherited from Predef. This way, calling a sequence method will always invoke the conversion to ArrayOps. The conversion to WrappedArray will only be invoked when an array needs to be converted to a sequence.

Integrating Strings Essentially the same technique is applied to strings. There are two implicit conversions: The first, which goes from String to StringOps, adds useful methods to class String. The second, which goes from String to WrappedString, converts strings to sequences.

Generic Array Creation and Manifests That’s almost everything. The only remaining question is how to implement generic array creation. Unlike Java, Scala allows an instance creation new Array[T] where T is a type parameter. How can this be implemented, given the fact that there does not exist a uniform array representation in Java? The only way to do this is to require additional runtime information which describes the type T. Scala 2.8 has a new mechanism for this, which is called a Manifest. An object of type Manifest[T] provides complete information about the type T. Manifest values are typically passed in implicit parameters; and the compiler knows how to construct them for statically known types T. There exists also a weaker form named ClassManifest which can be constructed from knowing just the top-level class of a type, without necessarily knowing all its argument types. It is this type of runtime information that’s required for array creation. Here’s an example. Consider the method tabulate which forms an array from the results of applying a given function f on a range of numbers from 0 until a given length. Up to Scala 2.7, tabulate could be written as follows: def tabulate[T](len: Int, f: Int => T) = { val xs = new Array[T](len) for (i T)(implicit m: ClassManifest[T]) = { val xs = new Array[T](len) for (i T) = { val xs = new Array[T](len) for (i T) = tabulate(10, f)

The move away form boxing and to class manifests is bound to break some existing code that generated generic arrays as in the first version of tabulate above. Usually, the necessary changes simply involve adding a context bound to some type parameter.

Class GenericArray For the case where generic array creation is needed but adding manifests is not feasible, Scala 2.8 offers an alternative version of arrays in the GenericArray class. This class is defined in package scala.collection.mutable along the following lines. class GenericArray[T](length: Int) extends Vector[T] { val array: Array[AnyRef] = new Array[AnyRef](length) 1 Generally, a type parameter with a context bound is of the form [T: Bound]; it is expanded to plain type parameter T together

with an implicit parameter of type Bound[T].

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... // all vector operations defined in terms of ‘array’ }

Unlike normal arrays, GenericArrays can be created without a class manifest because they have a uniform representation: all their elements are stored in an Array[AnyRef], which corresponds to an Object[] array in Java. The addition of GenericArray to the Scala collection library does demand a choice from the programmer—should one pick a normal array or a generic array? This choice is easily answered, however: Whenever a class manifest for the element type can easily be produced, it’s better to pick a normal array, because it tends to be faster, is more compact, and has better interoperability with Java. Only when producing a class manifest is infeasible one should revert to a GenericArray. The only place where GenericArray is used in Scala’s current collection framework is in the sortWith method of class Seq. A call xs.sortWith(f) converts its receiver xs first to a GenericArray, passes the resulting array to a Java sorting method defined in java.util.Arrays, and converts the sorted array back to the same type of Seq as xs. Since the conversion to an array is a mere implementation detail of sortWith, we felt that it was unreasonable to demand a class manifest for the element type of the sequence. Hence the choice of a GenericArray.

Conclusion In summary, the new Scala collection framework resolves some long-standing problems with arrays and with strings. It removes a considerable amount of compiler magic and avoids several pitfalls which existed in the previous implementation. It relies on three new features of the Scala language that should be generally useful in the construction of libraries and frameworks: First, the generalization of overloading and implicit resolution allows one to prioritize some implicits over others. Second, manifests provide type information at run-time that was lost through erasure. Third, context bounds are a convenient shorthand for certain forms of implicit arguments. These three language features will be described in more detail in separate notes.

References [Mac08a] David MacIver. Refactoring scala.array. Pre-SIP (Scala Improvement Proposal), October 2008. http://www.drmaciver.com/repos/scala-arrays/sip-arrays.xhtml. [Mac08b] David MacIver. Scala arrays. Blog, June 2008. http://www.drmaciver.com/2008/06/scalaarrays. [Mal09]

Matt Malone. The mystery of the parameterized array. Blog, August 2009. http://oldfashionedsoftware.com/2009/08/05/the-mystery-of-the-parameterized-array.

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