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Contents 1 Introduction 1 2 A First Example 3 3 Programming with Actors and Messages 7 4 Expressions and Simple Functions 11 4.1 Expressions And Simple Functions . . . . . . . . . . . . . . . . . . . . . . 11 4.2 Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4.3 Conditional Expressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.4 Example: Square Roots by Newton’s Method . . . . . . . . . . . . . . . . 15 4.5 Nested Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.6 Tail Recursion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 5 First-Class Functions 21 5.1 Anonymous Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 5.2 Currying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 5.3 Example: Finding Fixed Points of Functions . . . . . . . . . . . . . . . . 25 5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 5.5 Language Elements Seen So Far . . . . . . . . . . . . . . . . . . . . . . . 28 6 Classes and Objects 31 7 Case Classes and Pattern Matching 43 7.1 Case Classes and Case Objects . . . . . . . . . . . . . . . . . . . . . . . . 46 7.2 Pattern Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 8 Generic Types and Methods 51 8.1 Type Parameter Bounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 8.2 Variance Annotations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
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iv CONTENTS 8.3 Lower Bounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 8.4 Least Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 8.5 Tuples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 8.6 Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 9 Lists 63 9.1 Using Lists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 9.2 Definition of class List I: First Order Methods . . . . . . . . . . . . . . . 65 9.3 Example: Merge sort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 9.4 Definition of class List II: Higher-Order Methods . . . . . . . . . . . . . 70 9.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 10 For-Comprehensions 79 10.1 The N-Queens Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 10.2 Querying with For-Comprehensions . . . . . . . . . . . . . . . . . . . . . 81 10.3 Translation of For-Comprehensions . . . . . . . . . . . . . . . . . . . . . 82 10.4 For-Loops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 10.5 Generalizing For . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 11 Mutable State 87 11.1 Stateful Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 11.2 Imperative Control Structures . . . . . . . . . . . . . . . . . . . . . . . . . 91 11.3 Extended Example: Discrete Event Simulation . . . . . . . . . . . . . . . 92 11.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 12 Computing with Streams 99 13 Iterators 103 13.1 Iterator Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 13.2 Constructing Iterators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 13.3 Using Iterators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 14 Lazy Values 109 15 Implicit Parameters and Conversions 113
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CONTENTS v 16 Hindley/Milner Type Inference 117 17 Abstractions for Concurrency 125 17.1 Signals and Monitors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 17.2 SyncVars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 17.3 Futures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 17.4 Parallel Computations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 17.5 Semaphores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 17.6 Readers/Writers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 17.7 Asynchronous Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 17.8 Synchronous Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 17.9 Workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 17.10Mailboxes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 17.11Actors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
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Chapter 1 Introduction Scala smoothly integrates object-oriented and functional programming. It is de- signed to express common programming patterns in a concise, elegant, and type- safe way. Scala introduces several innovative language constructs. For instance: • Abstract types and mixin composition unify concepts from object and module systems. • Pattern matching over class hierarchies unifies functional and object- oriented data access. It greatly simplifies the processing of XML trees. • A flexible syntax and type system enables the construction of advanced li- braries and new domain specific languages. At the same time, Scala is compatible with Java. Java libraries and frameworks can be used without glue code or additional declarations. This document introduces Scala in an informal way, through a sequence of exam- ples. Chapters 2 and 3 highlight some of the features that make Scala interesting. The fol- lowing chapters introduce the language constructs of Scala in a more thorough way, starting with simple expressions and functions, and working up through objects and classes, lists and streams, mutable state, pattern matching to more complete exam- ples that show interesting programming techniques. The present informal exposi- tion is meant to be complemented by the Scala Language Reference Manual which specifies Scala in a more detailed and precise way. Acknowledgment. We owe a great debt to Abelson’s and Sussman’s wonderful book “Structure and Interpretation of Computer Programs”[ASS96]. Many of their examples and exercises are also present here. Of course, the working language has in each case been changed from Scheme to Scala. Furthermore, the examples make use of Scala’s object-oriented constructs where appropriate.
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Chapter 2 A First Example As a first example, here is an implementation of Quicksort in Scala. def sort(xs: Array[Int]) { def swap(i: Int, j: Int) { val t = xs(i); xs(i) = xs(j); xs(j) = t } def sort1(l: Int, r: Int) { val pivot = xs((l + r) / 2) var i = l; var j = r while (i <= j) { while (xs(i) < pivot) i += 1 while (xs(j) > pivot) j -= 1 if (i <= j) { swap(i, j) i += 1 j -= 1 } } if (l < j) sort1(l, j) if (j < r) sort1(i, r) } sort1(0, xs.length - 1) } The implementation looks quite similar to what one would write in Java or C. We use the same operators and similar control structures. There are also some minor syntactical differences. In particular: • Definitions start with a reserved word. Function definitions start with def, variable definitions start with var and definitions of values (i.e. read only vari- ables) start with val.
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4 A First Example • The declared type of a symbol is given after the symbol and a colon. The de- clared type can often be omitted, because the compiler can infer it from the context. • Array types are writtenArray[T] rather thanT[], and array selections are writ- ten a(i) rather than a[i]. • Functions can be nested inside other functions. Nested functions can access parameters and local variables of enclosing functions. For instance, the name of the array xs is visible in functions swap and sort1, and therefore need not be passed as a parameter to them. So far, Scala looks like a fairly conventional language with some syntactic peculiar- ities. In fact it is possible to write programs in a conventional imperative or object- oriented style. This is important because it is one of the things that makes it easy to combine Scala components with components written in mainstream languages such as Java, C# or Visual Basic. However, it is also possible to write programs in a style which looks completely dif- ferent. Here is Quicksort again, this time written in functional style. def sort(xs: Array[Int]): Array[Int] = { if (xs.length <= 1) xs else { val pivot = xs(xs.length / 2) Array.concat( sort(xs filter (pivot >)), xs filter (pivot ==), sort(xs filter (pivot <))) } } The functional program captures the essence of the quicksort algorithm in a concise way: • If the array is empty or consists of a single element, it is already sorted, so return it immediately. • If the array is not empty, pick an an element in the middle of it as a pivot. • Partition the array into two sub-arrays containing elements that are less than, respectively greater than the pivot element, and a third array which contains elements equal to pivot. • Sort the first two sub-arrays by a recursive invocation of the sort function.1 • The result is obtained by appending the three sub-arrays together. 1This is not quite what the imperative algorithm does; the latter partitions the array into two sub-arrays containing elements less than or greater or equal to pivot.
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5 Both the imperative and the functional implementation have the same asymptotic complexity – O(N l og(N)) in the average case and O(N2) in the worst case. But where the imperative implementation operates in place by modifying the argument array, the functional implementation returns a new sorted array and leaves the ar- gument array unchanged. The functional implementation thus requires more tran- sient memory than the imperative one. The functional implementation makes it look like Scala is a language that’s special- ized for functional operations on arrays. In fact, it is not; all of the operations used in the example are simple library methods of a sequence class Seq[T] which is part of the standard Scala library, and which itself is implemented in Scala. Because arrays are instances of Seq all sequence methods are available for them. In particular, there is the method filter which takes as argument a predicate func- tion. This predicate function must map array elements to boolean values. The result of filter is an array consisting of all the elements of the original array for which the given predicate function is true. The filter method of an object of type Array[T] thus has the signature def filter(p: T => Boolean): Array[T] Here, T => Boolean is the type of functions that take an element of type t and return a Boolean. Functions like filter that take another function as argument or return one as result are called higher-order functions. Scala does not distinguish between identifiers and operator names. An identifier can be either a sequence of letters and digits which begins with a letter, or it can be a sequence of special characters, such as “+”, “*”, or “:”. Any identifier can be used as an infix operator in Scala. The binary operation E op E′ is always interpreted as the method call E.op(E′). This holds also for binary infix operators which start with a letter. Hence, the expression xs filter (pivot >) is equivalent to the method call xs.filter(pivot >). In the quicksort program, filter is applied three times to an anonymous function argument. The first argument, pivot >, represents a function that takes an argu- ment x and returns the value pivot > x. This is an example of a partially applied function. Another, equivalent way to write this function which makes the missing argument explicit is x => pivot > x. The function is anonymous, i.e. it is not de- fined with a name. The type of the x parameter is omitted because a Scala compiler can infer it automatically from the context where the function is used. To summa- rize, xs.filter(pivot >) returns a list consisting of all elements of the list xs that are smaller than pivot. Looking again in detail at the first, imperative implementation of Quicksort, we find that many of the language constructs used in the second solution are also present, albeit in a disguised form.
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6 A First Example For instance, “standard” binary operators such as +, -, or < are not treated in any special way. Like append, they are methods of their left operand. Consequently, the expression i + 1 is regarded as the invocation i.+(1) of the + method of the integer value x. Of course, a compiler is free (if it is moderately smart, even expected) to recognize the special case of calling the + method over integer arguments and to generate efficient inline code for it. For efficiency and better error diagnostics the while loop is a primitive construct in Scala. But in principle, it could have just as well been a predefined function. Here is a possible implementation of it: def While (p: => Boolean) (s: => Unit) { if (p) { s ; While(p)(s) } } The While function takes as first parameter a test function, which takes no parame- ters and yields a boolean value. As second parameter it takes a command function which also takes no parameters and yields a result of type Unit. While invokes the command function as long as the test function yields true. Scala’s Unit type roughly corresponds to void in Java; it is used whenever a func- tion does not return an interesting result. In fact, because Scala is an expression- oriented language, every function returns some result. If no explicit return expres- sion is given, the value (), which is pronounced “unit”, is assumed. This value is of type Unit. Unit-returning functions are also called procedures. Here’s a more “expression-oriented” formulation of the swap function in the first implementation of quicksort, which makes this explicit: def swap(i: Int, j: Int) { val t = xs(i); xs(i) = xs(j); xs(j) = t () } The result value of this function is simply its last expression – a return keyword is not necessary. Note that functions returning an explicit value always need an “=” before their body or defining expression.
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Chapter 3 Programming with Actors and Mes- sages Here’s an example that shows an application area for which Scala is particularly well suited. Consider the task of implementing an electronic auction service. We use an Erlang-style actor process model to implement the participants of the auction. Actors are objects to which messages are sent. Every actor has a “mailbox” of its in- coming messages which is represented as a queue. It can work sequentially through the messages in its mailbox, or search for messages matching some pattern. For every traded item there is an auctioneer actor that publishes information about the traded item, that accepts offers from clients and that communicates with the seller and winning bidder to close the transaction. We present an overview of a simple implementation here. As a first step, we define the messages that are exchanged during an auction. There are two abstract base classes AuctionMessage for messages from clients to the auc- tion service, and AuctionReply for replies from the service to the clients. For both base classes there exists a number of cases, which are defined in Figure 3.1. For each base class, there are a number of case classes which define the format of particular messages in the class. These messages might well be ultimately mapped to small XML documents. We expect automatic tools to exist that convert between XML documents and internal data structures like the ones defined above. Figure 3.2 presents a Scala implementation of a class Auction for auction actors that coordinate the bidding on one item. Objects of this class are created by indicating • a seller actor which needs to be notified when the auction is over, • a minimal bid, • the date when the auction is to be closed. The behavior of the actor is defined by its act method. That method repeatedly
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8 Programming with Actors and Messages import scala.actors.Actor abstract class AuctionMessage case class Offer(bid: Int, client: Actor) extends AuctionMessage case class Inquire(client: Actor) extends AuctionMessage abstract class AuctionReply case class Status(asked: Int, expire: Date) extends AuctionReply case object BestOffer extends AuctionReply case class BeatenOffer(maxBid: Int) extends AuctionReply case class AuctionConcluded(seller: Actor, client: Actor) extends AuctionReply case object AuctionFailed extends AuctionReply case object AuctionOver extends AuctionReply Listing 3.1: Message Classes for an Auction Service selects (using receiveWithin) a message and reacts to it, until the auction is closed, which is signaled by a TIMEOUT message. Before finally stopping, it stays active for another period determined by the timeToShutdown constant and replies to further offers that the auction is closed. Here are some further explanations of the constructs used in this program: • The receiveWithin method of class Actor takes as parameters a time span given in milliseconds and a function that processes messages in the mailbox. The function is given by a sequence of cases that each specify a pattern and an action to perform for messages matching the pattern. The receiveWithin method selects the first message in the mailbox which matches one of these patterns and applies the corresponding action to it. • The last case of receiveWithin is guarded by a TIMEOUT pattern. If no other messages are received in the meantime, this pattern is triggered after the time span which is passed as argument to the enclosing receiveWithin method. TIMEOUT is a special message, which is triggered by the Actor implementation itself. • Reply messages are sent using syntax of the form destination ! SomeMessage. ! is used here as a binary operator with an actor and a message as arguments. This is equivalent in Scala to the method call destination.!(SomeMessage), i.e. the invocation of the ! method of the destination actor with the given message as parameter. The preceding discussion gave a flavor of distributed programming in Scala. It might seem that Scala has a rich set of language constructs that support actor pro- cesses, message sending and receiving, programming with timeouts, etc. In fact, the
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9 class Auction(seller: Actor, minBid: Int, closing: Date) extends Actor { val timeToShutdown = 36000000 // msec val bidIncrement = 10 def act() { var maxBid = minBid - bidIncrement var maxBidder: Actor = null var running = true while (running) { receiveWithin ((closing.getTime() - new Date().getTime())) { case Offer(bid, client) => if (bid >= maxBid + bidIncrement) { if (maxBid >= minBid) maxBidder ! BeatenOffer(bid) maxBid = bid; maxBidder = client; client ! BestOffer } else { client ! BeatenOffer(maxBid) } case Inquire(client) => client ! Status(maxBid, closing) case TIMEOUT => if (maxBid >= minBid) { val reply = AuctionConcluded(seller, maxBidder) maxBidder ! reply; seller ! reply } else { seller ! AuctionFailed } receiveWithin(timeToShutdown) { case Offer(_, client) => client ! AuctionOver case TIMEOUT => running = false } } } } } Listing 3.2: Implementation of an Auction Service
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10 Programming with Actors and Messages opposite is true. All the constructs discussed above are offered as methods in the li- brary class Actor. That class is itself implemented in Scala, based on the underlying thread model of the host language (e.g. Java, or .NET). The implementation of all features of class Actor used here is given in Section 17.11. The advantages of the library-based approach are relative simplicity of the core lan- guage and flexibility for library designers. Because the core language need not spec- ify details of high-level process communication, it can be kept simpler and more general. Because the particular model of messages in a mailbox is a library module, it can be freely modified if a different model is needed in some applications. The approach requires however that the core language is expressive enough to provide the necessary language abstractions in a convenient way. Scala has been designed with this in mind; one of its major design goals was that it should be flexible enough to act as a convenient host language for domain specific languages implemented by library modules. For instance, the actor communication constructs presented above can be regarded as one such domain specific language, which conceptually extends the Scala core.
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Chapter 4 Expressions and Simple Functions The previous examples gave an impression of what can be done with Scala. We now introduce its constructs one by one in a more systematic fashion. We start with the smallest level, expressions and functions. 4.1 Expressions And Simple Functions A Scala system comes with an interpreter which can be seen as a fancy calculator. A user interacts with the calculator by typing in expressions. The calculator returns the evaluation results and their types. For example: scala> 87 + 145 unnamed0: Int = 232 scala> 5 + 2 * 3 unnamed1: Int = 11 scala> "hello" + " world!" unnamed2: java.lang.String = hello world! It is also possible to name a sub-expression and use the name instead of the expres- sion afterwards: scala> def scale = 5 scale: Int scala> 7 * scale unnamed3: Int = 35 scala> def pi = 3.141592653589793 pi: Double
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12 Expressions and Simple Functions scala> def radius = 10 radius: Int scala> 2 * pi * radius unnamed4: Double = 62.83185307179586 Definitions start with the reserved word def; they introduce a name which stands for the expression following the = sign. The interpreter will answer with the intro- duced name and its type. Executing a definition such as def x = e will not evaluate the expression e. In- stead e is evaluated whenever x is used. Alternatively, Scala offers a value defini- tion val x = e, which does evaluate the right-hand-side e as part of the evaluation of the definition. If x is then used subsequently, it is immediately replaced by the pre-computed value of e, so that the expression need not be evaluated again. How are expressions evaluated? An expression consisting of operators and operands is evaluated by repeatedly applying the following simplification steps. • pick the left-most operation • evaluate its operands • apply the operator to the operand values. A name defined by def is evaluated by replacing the name by the (unevaluated) definition’s right hand side. A name defined by val is evaluated by replacing the name by the value of the definitions’s right-hand side. The evaluation process stops once we have reached a value. A value is some data item such as a string, a number, an array, or a list. Example 4.1.1 Here is an evaluation of an arithmetic expression. (2 * pi) * radius → (2 * 3.141592653589793) * radius → 6.283185307179586 * radius → 6.283185307179586 * 10 → 62.83185307179586 The process of stepwise simplification of expressions to values is called reduction. 4.2 Parameters Using def, one can also define functions with parameters. For example:
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4.2 Parameters 13 scala> def square(x: Double) = x * x square: (Double)Double scala> square(2) unnamed0: Double = 4.0 scala> square(5 + 3) unnamed1: Double = 64.0 scala> square(square(4)) unnamed2: Double = 256.0 scala> def sumOfSquares(x: Double, y: Double) = square(x) + square(y) sumOfSquares: (Double,Double)Double scala> sumOfSquares(3, 2 + 2) unnamed3: Double = 25.0 Function parameters follow the function name and are always enclosed in paren- theses. Every parameter comes with a type, which is indicated following the param- eter name and a colon. At the present time, we only need basic numeric types such as the type scala.Double of double precision numbers. Scala defines type aliases for some standard types, so we can write numeric types as in Java. For instance double is a type alias of scala.Double and int is a type alias for scala.Int. Functions with parameters are evaluated analogously to operators in expressions. First, the arguments of the function are evaluated (in left-to-right order). Then, the function application is replaced by the function’s right hand side, and at the same time all formal parameters of the function are replaced by their corresponding ac- tual arguments. Example 4.2.1 sumOfSquares(3, 2+2) → sumOfSquares(3, 4) → square(3) + square(4) → 3 * 3 + square(4) → 9 + square(4) → 9 + 4 * 4 → 9 + 16 → 25 The example shows that the interpreter reduces function arguments to values be- fore rewriting the function application. One could instead have chosen to apply the function to unreduced arguments. This would have yielded the following reduction sequence:
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14 Expressions and Simple Functions sumOfSquares(3, 2+2) → square(3) + square(2+2) → 3 * 3 + square(2+2) → 9 + square(2+2) → 9 + (2+2) * (2+2) → 9 + 4 * (2+2) → 9 + 4 * 4 → 9 + 16 → 25 The second evaluation order is known as call-by-name, whereas the first one is known as call-by-value. For expressions that use only pure functions and that there- fore can be reduced with the substitution model, both schemes yield the same final values. Call-by-value has the advantage that it avoids repeated evaluation of arguments. Call-by-name has the advantage that it avoids evaluation of arguments when the parameter is not used at all by the function. Call-by-value is usually more efficient than call-by-name, but a call-by-value evaluation might loop where a call-by-name evaluation would terminate. Consider: scala> def loop: Int = loop loop: Int scala> def first(x: Int, y: Int) = x first: (Int,Int)Int Then first(1, loop) reduces with call-by-name to 1, whereas the same term re- duces with call-by-value repeatedly to itself, hence evaluation does not terminate. first(1, loop) → first(1, loop) → first(1, loop) → ... Scala uses call-by-value by default, but it switches to call-by-name evaluation if the parameter type is preceded by =>. Example 4.2.2 scala> def constOne(x: Int, y: => Int) = 1 constOne: (Int,=> Int)Int scala> constOne(1, loop) unnamed0: Int = 1 scala> constOne(loop, 2) // gives an infinite loop.
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