A Scala Tutorial for Java Programmers

Language

If you are coming to Scala with some Java experience already, this page should give a good overview of the differences, and what to expect when you begin programming with Scala. For best results we suggest to either set up a Scala toolchain on your computer, or try compiling Scala snippets in the browser with Scastie:

At a Glance: Why Scala?

Java without Semicolons: There’s a saying that Scala is Java without semicolons. There is a lot of a truth to this statement: Scala simplifies much of the noise and boilerplate of Java, while building upon the same foundation, sharing the same underlying types and runtime.

Seamless Interop: Scala can use any Java library out of the box; including the Java standard library! And pretty much any Java program will work the same in Scala, just by converting the syntax.

A Scalable Language: the name Scala comes from Scalable Language. Scala scales not only with hardware resources and load requirements, but also with the level of programmer’s skill. If you choose, Scala rewards you with expressive additional features, which when compared to Java, boost developer productivity and readability of code.

It Grows with You: Learning these extras are optional steps to approach at your own pace. The most fun and effective way to learn, in our opinion, is to ensure you are productive first with what knowledge you have from Java. And then, learn one thing at a time following the Scala Book. Pick the learning pace convenient for you and ensure whatever you are learning is fun.

TL;DR: You can start writing Scala as if it were Java with new syntax, then explore from there as you see fit.

Next Steps

Compare Java and Scala

The remainder of this tutorial expands upon some of the key differences between Java and Scala, with further explanations. If you only want a quick reference between the two, read Scala for Java Developers, it comes with many snippets which you can try out in your chosen Scala setup:

Explore Further

When you finish these guides, we recommend to continue your Scala journey by reading the Scala Book or following a number of online MOOCs.

Your First Program

Writing Hello World

As a first example, we will use the standard Hello World program. It is not very fascinating but makes it easy to demonstrate the use of the Scala tools without knowing too much about the language. Here is how it looks:

object HelloWorld {
  def main(args: Array[String]): Unit = {
    println("Hello, World!")
  }
}

The structure of this program should be familiar to Java programmers: it’s entry-point consists of one method called main which takes the command line arguments, an array of strings, as a parameter; the body of this method consists of a single call to the predefined method println with the friendly greeting as argument. The main method does not return a value. Therefore, its return type is declared as Unit (equivalent to void in Java).

What is less familiar to Java programmers is the object declaration containing the main method. Such a declaration introduces what is commonly known as a singleton object, that is a class with a single instance. The declaration above thus declares both a class called HelloWorld and an instance of that class, also called HelloWorld. This instance is created on demand, the first time it is used.

Another difference from Java is that the main method is not declared as static here. This is because static members (methods or fields) do not exist in Scala. Rather than defining static members, the Scala programmer declares these members in singleton objects.

@main def HelloWorld(args: String*): Unit =
  println("Hello, World!")

The structure of this program may not be familiar to Java programmers: there is no method called main, instead the HelloWorld method is marked as an entry-point by adding the @main annotation.

program entry-points optionally take parameters, which are populated by the command line arguments. Here HelloWorld captures all the arguments in a variable-length sequence of strings called args.

The body of the method consists of a single call to the predefined method println with the friendly greeting as argument. The HelloWorld method does not return a value. Therefore, its return type is declared as Unit (equivalent to void in Java).

Even less familiar to Java programmers is that HelloWorld does not need to be wrapped in a class definition. Scala 3 supports top-level method definitions, which are ideal for program entry-points.

The method also does not need to be declared as static. This is because static members (methods or fields) do not exist in Scala. Instead, top-level methods and fields are members of their enclosing package, so can be accessed from anywhere in a program.

Implementation detail: so that the JVM can execute the program, the @main annotation generates a class HelloWorld with a static main method which calls the HelloWorld method with the command line arguments. This class is only visible at runtime.

Running Hello World

Note: The following assumes you are using Scala on the command line

Compiling From the Command Line

To compile the example, we use scalac, the Scala compiler. scalac works like most compilers: it takes a source file as argument, maybe some options, and produces one or several output files. The outputs it produces are standard Java class files.

If we save the above program in a file called HelloWorld.scala, we can compile it by issuing the following command (the greater-than sign > represents the shell prompt and should not be typed):

> scalac HelloWorld.scala

This will generate a few class files in the current directory. One of them will be called HelloWorld.class, and contains a class which can be directly executed using the scala command, as the following section shows.

Running From the Command Line

Once compiled, a Scala program can be run using the scala command. Its usage is very similar to the java command used to run Java programs, and accepts the same options. The above example can be executed using the following command, which produces the expected output:

> scala -classpath . HelloWorld

Hello, World!

Using Java Libraries

One of Scala’s strengths is that it makes it very easy to interact with Java code. All classes from the java.lang package are imported by default, while others need to be imported explicitly.

Let’s look at an example that demonstrates this. We want to obtain and format the current date according to the conventions used in a specific country, say France. (Other regions such as the French-speaking part of Switzerland use the same conventions.)

Java’s class libraries define powerful utility classes, such as LocalDate and DateTimeFormatter. Since Scala interoperates seamlessly with Java, there is no need to implement equivalent classes in the Scala class library; instead, we can import the classes of the corresponding Java packages:

import java.time.format.{DateTimeFormatter, FormatStyle}
import java.time.LocalDate
import java.util.Locale._

object FrenchDate {
  def main(args: Array[String]): Unit = {
    val now = LocalDate.now
    val df = DateTimeFormatter.ofLocalizedDate(FormatStyle.LONG).withLocale(FRANCE)
    println(df.format(now))
  }
}

Scala’s import statement looks very similar to Java’s equivalent, however, it is more powerful. Multiple classes can be imported from the same package by enclosing them in curly braces as on the first line. Another difference is that when importing all the names of a package or class, in Scala 2 we use the underscore character (_) instead of the asterisk (*).

import java.time.format.{DateTimeFormatter, FormatStyle}
import java.time.LocalDate
import java.util.Locale.*

@main def FrenchDate: Unit =
  val now = LocalDate.now
  val df = DateTimeFormatter.ofLocalizedDate(FormatStyle.LONG).withLocale(FRANCE)
  println(df.format(now))

Scala’s import statement looks very similar to Java’s equivalent, however, it is more powerful. Multiple classes can be imported from the same package by enclosing them in curly braces as on the first line. Like with Java, in Scala 3 we use the asterisk (*) to import all the names of a package or class.

The import statement on the third line therefore imports all members of the Locale enum. This makes the static field FRANCE directly visible.

Inside the entry-point method we first create an instance of Java’s DateTime class, containing today’s date. Next, we define a date format using the DateTimeFormatter.ofLocalizedDate method, passing the LONG format style, then further passing the FRANCE locale that we imported previously. Finally, we print the current date formatted according to the localized DateTimeFormatter instance.

To conclude this section about integration with Java, it should be noted that it is also possible to inherit from Java classes and implement Java interfaces directly in Scala.

Sidepoint: Third-Party Libraries

Usually the standard library is not enough. As a Java programmer, you might already know a lot of Java libraries that you’d like to use in Scala. The good news is that, as with Java, Scala’s library ecosystem is built upon Maven coordinates.

Most Scala projects are built with sbt: Adding third party libraries is usually managed by a build tool. Coming from Java you may be familiar with Maven, Gradle and other such tools. It’s still possible to use these to build Scala projects, however it’s common to use sbt. See setup a Scala Project with sbt for a guide on how to build a project with sbt and add some dependencies.

Everything is an Object

Scala is a pure object-oriented language in the sense that everything is an object, including numbers or functions. It differs from Java in that respect, since Java distinguishes primitive types (such as boolean and int) from reference types.

Numbers are objects

Since numbers are objects, they also have methods. And in fact, an arithmetic expression like the following:

1 + 2 * 3 / x

consists exclusively of method calls, because it is equivalent to the following expression, as we saw in the previous section:

1.+(2.*(3)./(x))

This also means that +, *, etc. are valid identifiers for fields/methods/etc in Scala.

Functions are objects

True to everything being an object, in Scala even functions are objects, going beyond Java’s support for lambda expressions.

Compared to Java, there is very little difference between function objects and methods: you can pass methods as arguments, store them in variables, and return them from other functions, all without special syntax. This ability to manipulate functions as values is one of the cornerstones of a very interesting programming paradigm called functional programming.

To demonstrate, consider a timer function which performs some action every second. The action to be performed is supplied by the caller as a function value.

In the following program, the timer function is called oncePerSecond, and it gets a call-back function as argument. The type of this function is written () => Unit and is the type of all functions which take no arguments and return no useful value (as before, the type Unit is similar to void in Java).

The entry-point of this program calls oncePerSecond by directly passing the timeFlies method.

In the end this program will infitely print the sentence time flies like an arrow every second.

object Timer {
  def oncePerSecond(callback: () => Unit): Unit = {
    while (true) { callback(); Thread.sleep(1000) }
  }
  def timeFlies(): Unit = {
    println("time flies like an arrow...")
  }
  def main(args: Array[String]): Unit = {
    oncePerSecond(timeFlies)
  }
}
def oncePerSecond(callback: () => Unit): Unit =
  while true do { callback(); Thread.sleep(1000) }

def timeFlies(): Unit =
  println("time flies like an arrow...")

@main def Timer: Unit =
  oncePerSecond(timeFlies)

Note that in order to print the string, we used the predefined method println instead of using the one from System.out.

Anonymous functions

In Scala, lambda expressions are known as anonymous functions. They are useful when a function so short it is perhaps unneccesary to give them a name.

Here is a revised version of the timer program, passing an anonymous function to oncePerSecond instead of timeFlies:

object TimerAnonymous {
  def oncePerSecond(callback: () => Unit): Unit = {
    while (true) { callback(); Thread.sleep(1000) }
  }
  def main(args: Array[String]): Unit = {
    oncePerSecond(() =>
      println("time flies like an arrow..."))
  }
}
def oncePerSecond(callback: () => Unit): Unit =
  while true do { callback(); Thread.sleep(1000) }

@main def TimerAnonymous: Unit =
  oncePerSecond(() =>
    println("time flies like an arrow..."))

The presence of an anonymous function in this example is revealed by the right arrow (=>), different from Java’s thin arrow (->), which separates the function’s argument list from its body. In this example, the argument list is empty, so we put empty parentheses on the left of the arrow. The body of the function is the same as the one of timeFlies above.

Classes

As we have seen above, Scala is an object-oriented language, and as such it has a concept of class. (For the sake of completeness, it should be noted that some object-oriented languages do not have the concept of class, but Scala is not one of them.) Classes in Scala are declared using a syntax which is close to Java’s syntax. One important difference is that classes in Scala can have parameters. This is illustrated in the following definition of complex numbers.

class Complex(real: Double, imaginary: Double) {
  def re() = real
  def im() = imaginary
}

This Complex class takes two arguments, which are the real and imaginary part of the complex number. These arguments must be passed when creating an instance of class Complex, as follows:

new Complex(1.5, 2.3)

The class contains two methods, called re and im, which give access to these two parts.

class Complex(real: Double, imaginary: Double):
  def re() = real
  def im() = imaginary

This Complex class takes two arguments, which are the real and imaginary part of the complex number. These arguments must be passed when creating an instance of class Complex, as follows:

new Complex(1.5, 2.3)

where new is optional. The class contains two methods, called re and im, which give access to these two parts.

It should be noted that the return type of these two methods is not given explicitly. It will be inferred automatically by the compiler, which looks at the right-hand side of these methods and deduces that both return a value of type Double.

Important: The inferred result type of a method can change in subtle ways if the implementation changes, which could have a knock-on effect. Hence it is a best practise to put explicit result types for public members of classes.

For local values in methods, it is encouraged to infer result types. Try to experiment by omitting type declarations when they seem to be easy to deduce from the context, and see if the compiler agrees. After some time, the programmer should get a good feeling about when to omit types, and when to specify them explicitly.

Methods without arguments

A small problem of the methods re and im is that, in order to call them, one has to put an empty pair of parenthesis after their name, as the following example shows:

object ComplexNumbers {
  def main(args: Array[String]): Unit = {
    val c = new Complex(1.2, 3.4)
    println("imaginary part: " + c.im())
  }
}
@main def ComplexNumbers: Unit =
  val c = Complex(1.2, 3.4)
  println("imaginary part: " + c.im())

It would be nicer to be able to access the real and imaginary parts like if they were fields, without putting the empty pair of parenthesis. This is perfectly doable in Scala, simply by defining them as methods without arguments. Such methods differ from methods with zero arguments in that they don’t have parenthesis after their name, neither in their definition nor in their use. Our Complex class can be rewritten as follows:

class Complex(real: Double, imaginary: Double) {
  def re = real
  def im = imaginary
}
class Complex(real: Double, imaginary: Double):
  def re = real
  def im = imaginary

Inheritance and overriding

All classes in Scala inherit from a super-class. When no super-class is specified, as in the Complex example of previous section, scala.AnyRef is implicitly used.

It is possible to override methods inherited from a super-class in Scala. It is however mandatory to explicitly specify that a method overrides another one using the override modifier, in order to avoid accidental overriding. As an example, our Complex class can be augmented with a redefinition of the toString method inherited from Object.

class Complex(real: Double, imaginary: Double) {
  def re = real
  def im = imaginary
  override def toString() =
    "" + re + (if (im >= 0) "+" else "") + im + "i"
}
class Complex(real: Double, imaginary: Double):
  def re = real
  def im = imaginary
  override def toString() =
    "" + re + (if im >= 0 then "+" else "") + im + "i"

We can call the overridden toString method as below:

object ComplexNumbers {
  def main(args: Array[String]): Unit = {
    val c = new Complex(1.2, 3.4)
    println("Overridden toString(): " + c.toString)
  }
}
@main def ComplexNumbers: Unit =
  val c = Complex(1.2, 3.4)
  println("Overridden toString(): " + c.toString)

Algebraic Data Types and Pattern Matching

A kind of data structure that often appears in programs is the tree. For example, interpreters and compilers usually represent programs internally as trees; JSON payloads are trees; and several kinds of containers are based on trees, like red-black trees.

We will now examine how such trees are represented and manipulated in Scala through a small calculator program. The aim of this program is to manipulate very simple arithmetic expressions composed of sums, integer constants and variables. Two examples of such expressions are 1+2 and (x+x)+(7+y).

We first have to decide on a representation for such expressions. The most natural one is the tree, where nodes are operations (here, the addition) and leaves are values (here constants or variables).

In Java, before the introduction of records, such a tree would be represented using an abstract super-class for the trees, and one concrete sub-class per node or leaf. In a functional programming language, one would use an algebraic data-type (ADT) for the same purpose.

Scala 2 provides the concept of case classes which is somewhat in between the two. Here is how they can be used to define the type of the trees for our example:

abstract class Tree
object Tree {
  case class Sum(l: Tree, r: Tree) extends Tree
  case class Var(n: String) extends Tree
  case class Const(v: Int) extends Tree
}

The fact that classes Sum, Var and Const are declared as case classes means that they differ from standard classes in several respects:

  • the new keyword is not mandatory to create instances of these classes (i.e., one can write Tree.Const(5) instead of new Tree.Const(5)),
  • getter functions are automatically defined for the constructor parameters (i.e., it is possible to get the value of the v constructor parameter of some instance c of class Tree.Const just by writing c.v),
  • default definitions for methods equals and hashCode are provided, which work on the structure of the instances and not on their identity,
  • a default definition for method toString is provided, and prints the value in a “source form” (e.g., the tree for expression x+1 prints as Sum(Var(x),Const(1))),
  • instances of these classes can be decomposed through pattern matching as we will see below.

Scala 3 provides the concept of enums which can be used like Java’s enum, but also to implement ADTs. Here is how they can be used to define the type of the trees for our example:

enum Tree:
  case Sum(l: Tree, r: Tree)
  case Var(n: String)
  case Const(v: Int)

The cases of the enum Sum, Var and Const are similar to standard classes, but differ in several respects:

  • getter functions are automatically defined for the constructor parameters (i.e., it is possible to get the value of the v constructor parameter of some instance c of case Tree.Const just by writing c.v),
  • default definitions for methods equals and hashCode are provided, which work on the structure of the instances and not on their identity,
  • a default definition for method toString is provided, and prints the value in a “source form” (e.g., the tree for expression x+1 prints as Sum(Var(x),Const(1))),
  • instances of these enum cases can be decomposed through pattern matching as we will see below.

Now that we have defined the data-type to represent our arithmetic expressions, we can start defining operations to manipulate them. We will start with a function to evaluate an expression in some environment. The aim of the environment is to give values to variables. For example, the expression x+1 evaluated in an environment which associates the value 5 to variable x, written { x -> 5 }, gives 6 as result.

We therefore have to find a way to represent environments. We could of course use some associative data-structure like a hash table, but we can also directly use functions! An environment is really nothing more than a function which associates a value to a (variable) name. The environment { x -> 5 } given above can be written as follows in Scala:

type Environment = String => Int
val ev: Environment = { case "x" => 5 }

This notation defines a function which, when given the string "x" as argument, returns the integer 5, and fails with an exception otherwise.

Above we defined a type alias called Environment which is more readable than the plain function type String => Int, and makes future changes easier.

We can now give the definition of the evaluation function. Here is a brief specification: the value of a Sum is the addition of the evaluations of its two inner expressions; the value of a Var is obtained by lookup of its inner name in the environment; and the value of a Const is its inner value itself. This specification translates exactly into Scala as followa, using a pattern match on a tree value t:

import Tree._

def eval(t: Tree, ev: Environment): Int = t match {
  case Sum(l, r) => eval(l, ev) + eval(r, ev)
  case Var(n)    => ev(n)
  case Const(v)  => v
}
import Tree.*

def eval(t: Tree, ev: Environment): Int = t match
  case Sum(l, r) => eval(l, ev) + eval(r, ev)
  case Var(n)    => ev(n)
  case Const(v)  => v

You can understand the precise meaning of the pattern match as follows:

  1. it first checks if the tree t is a Sum, and if it is, it binds the left sub-tree to a new variable called l and the right sub-tree to a variable called r, and then proceeds with the evaluation of the expression following the arrow; this expression can (and does) make use of the variables bound by the pattern appearing on the left of the arrow, i.e., l and r,
  2. if the first check does not succeed, that is, if the tree is not a Sum, it goes on and checks if t is a Var; if it is, it binds the name contained in the Var node to a variable n and proceeds with the right-hand expression,
  3. if the second check also fails, that is if t is neither a Sum nor a Var, it checks if it is a Const, and if it is, it binds the value contained in the Const node to a variable v and proceeds with the right-hand side,
  4. finally, if all checks fail, an exception is raised to signal the failure of the pattern matching expression; this could happen here only if more sub-classes of Tree were declared.

We see that the basic idea of pattern matching is to attempt to match a value to a series of patterns, and as soon as a pattern matches, extract and name various parts of the value, to finally evaluate some code which typically makes use of these named parts.

Comparison to OOP

A programmer familiar with the object-oriented paradigm might wonder why define a single function for eval outside the scope of Tree, and not make eval and abstract method in Tree, providing overrides in each subclass of Tree.

We could have done it actually, it is a choice to make, which has important implications on extensibility:

  • when using method overriding, adding a new operation to manipulate the tree implies far-reaching changes to the code, as it requires to add the method in all sub-classes of Tree, however, adding a new subclass only requires implementing the operations in one place. This design favours a few core operations and many growing subclasses,
  • when using pattern matching, the situation is reversed: adding a new kind of node requires the modification of all functions which do pattern matching on the tree, to take the new node into account; on the other hand, adding a new operation only requires defining the function in one place. If your data structure has a stable set of nodes, it favours the ADT and pattern matching design.

Adding a New Operation

To explore pattern matching further, let us define another operation on arithmetic expressions: symbolic derivation. The reader might remember the following rules regarding this operation:

  1. the derivative of a sum is the sum of the derivatives,
  2. the derivative of some variable v is one if v is the variable relative to which the derivation takes place, and zero otherwise,
  3. the derivative of a constant is zero.

These rules can be translated almost literally into Scala code, to obtain the following definition:

import Tree._

def derive(t: Tree, v: String): Tree = t match {
  case Sum(l, r)        => Sum(derive(l, v), derive(r, v))
  case Var(n) if v == n => Const(1)
  case _                => Const(0)
}
import Tree.*

def derive(t: Tree, v: String): Tree = t match
  case Sum(l, r)        => Sum(derive(l, v), derive(r, v))
  case Var(n) if v == n => Const(1)
  case _                => Const(0)

This function introduces two new concepts related to pattern matching. First of all, the case expression for variables has a guard, an expression following the if keyword. This guard prevents pattern matching from succeeding unless its expression is true. Here it is used to make sure that we return the constant 1 only if the name of the variable being derived is the same as the derivation variable v. The second new feature of pattern matching used here is the wildcard, written _, which is a pattern matching any value, without giving it a name.

We did not explore the whole power of pattern matching yet, but we will stop here in order to keep this document short. We still want to see how the two functions above perform on a real example. For that purpose, let’s write a simple main function which performs several operations on the expression (x+x)+(7+y): it first computes its value in the environment { x -> 5, y -> 7 }, then computes its derivative relative to x and then y.

import Tree._

object Calc {
  type Environment = String => Int
  def eval(t: Tree, ev: Environment): Int = ...
  def derive(t: Tree, v: String): Tree = ...

  def main(args: Array[String]): Unit = {
    val exp: Tree = Sum(Sum(Var("x"),Var("x")),Sum(Const(7),Var("y")))
    val env: Environment = { case "x" => 5 case "y" => 7 }
    println("Expression: " + exp)
    println("Evaluation with x=5, y=7: " + eval(exp, env))
    println("Derivative relative to x:\n " + derive(exp, "x"))
    println("Derivative relative to y:\n " + derive(exp, "y"))
  }
}
import Tree.*

@main def Calc: Unit =
  val exp: Tree = Sum(Sum(Var("x"),Var("x")),Sum(Const(7),Var("y")))
  val env: Environment = { case "x" => 5 case "y" => 7 }
  println("Expression: " + exp)
  println("Evaluation with x=5, y=7: " + eval(exp, env))
  println("Derivative relative to x:\n " + derive(exp, "x"))
  println("Derivative relative to y:\n " + derive(exp, "y"))

Executing this program, we should get the following output:

Expression: Sum(Sum(Var(x),Var(x)),Sum(Const(7),Var(y)))
Evaluation with x=5, y=7: 24
Derivative relative to x:
  Sum(Sum(Const(1),Const(1)),Sum(Const(0),Const(0)))
Derivative relative to y:
  Sum(Sum(Const(0),Const(0)),Sum(Const(0),Const(1)))

By examining the output, we see that the result of the derivative should be simplified before being presented to the user. Defining a basic simplification function using pattern matching is an interesting (but surprisingly tricky) problem, left as an exercise for the reader.

Traits

Apart from inheriting code from a super-class, a Scala class can also import code from one or several traits.

Maybe the easiest way for a Java programmer to understand what traits are is to view them as interfaces which can also contain code. In Scala, when a class inherits from a trait, it implements that trait’s interface, and inherits all the code contained in the trait.

(Note that since Java 8, Java interfaces can also contain code, either using the default keyword, or as static methods.)

To see the usefulness of traits, let’s look at a classical example: ordered objects. It is often useful to be able to compare objects of a given class among themselves, for example to sort them. In Java, objects which are comparable implement the Comparable interface. In Scala, we can do a bit better than in Java by defining our equivalent of Comparable as a trait, which we will call Ord.

When comparing objects, six different predicates can be useful: smaller, smaller or equal, equal, not equal, greater or equal, and greater. However, defining all of them is fastidious, especially since four out of these six can be expressed using the remaining two. That is, given the equal and smaller predicates (for example), one can express the other ones. In Scala, all these observations can be nicely captured by the following trait declaration:

trait Ord {
  def < (that: Any): Boolean
  def <=(that: Any): Boolean =  (this < that) || (this == that)
  def > (that: Any): Boolean = !(this <= that)
  def >=(that: Any): Boolean = !(this < that)
}
trait Ord:
  def < (that: Any): Boolean
  def <=(that: Any): Boolean =  (this < that) || (this == that)
  def > (that: Any): Boolean = !(this <= that)
  def >=(that: Any): Boolean = !(this < that)

This definition both creates a new type called Ord, which plays the same role as Java’s Comparable interface, and default implementations of three predicates in terms of a fourth, abstract one. The predicates for equality and inequality do not appear here since they are by default present in all objects.

The type Any which is used above is the type which is a super-type of all other types in Scala. It can be seen as a more general version of Java’s Object type, since it is also a super-type of basic types like Int, Float, etc.

To make objects of a class comparable, it is therefore sufficient to define the predicates which test equality and inferiority, and mix in the Ord class above. As an example, let’s define a Date class representing dates in the Gregorian calendar. Such dates are composed of a day, a month and a year, which we will all represent as integers. We therefore start the definition of the Date class as follows:

class Date(y: Int, m: Int, d: Int) extends Ord {
  def year = y
  def month = m
  def day = d
  override def toString(): String = s"$year-$month-$day"

  // rest of implementation will go here
}
class Date(y: Int, m: Int, d: Int) extends Ord:
  def year = y
  def month = m
  def day = d
  override def toString(): String = s"$year-$month-$day"

  // rest of implementation will go here
end Date

The important part here is the extends Ord declaration which follows the class name and parameters. It declares that the Date class inherits from the Ord trait.

Then, we redefine the equals method, inherited from Object, so that it correctly compares dates by comparing their individual fields. The default implementation of equals is not usable, because as in Java it compares objects by their identity. We arrive at the following definition:

class Date(y: Int, m: Int, d: Int) extends Ord {
  // previous decls here

  override def equals(that: Any): Boolean = that match {
    case d: Date => d.day == day && d.month == month && d.year == year
    case _ => false
  }

  // rest of implementation will go here
}
class Date(y: Int, m: Int, d: Int) extends Ord:
  // previous decls here

  override def equals(that: Any): Boolean = that match
    case d: Date => d.day == day && d.month == month && d.year == year
    case _ => false

  // rest of implementation will go here
end Date

While in Java (pre 16) you might use the instanceof operator followed by a cast (equivalent to calling that.isInstanceOf[Date] and that.asInstanceOf[Date] in Scala); in Scala it is more idiomatic to use a type pattern, shown in the example above which checks if that is an instance of Date, and binds it to a new variable d, which is then used in the right hand side of the case.

Finally, the last method to define is the < test, as follows. It makes use of another method, error from the package object scala.sys, which throws an exception with the given error message.

class Date(y: Int, m: Int, d: Int) extends Ord {
  // previous decls here

  def <(that: Any): Boolean = that match {
    case d: Date =>
      (year < d.year) ||
      (year == d.year && (month < d.month ||
                         (month == d.month && day < d.day)))

    case _ => sys.error("cannot compare " + that + " and a Date")
  }
}
class Date(y: Int, m: Int, d: Int) extends Ord:
  // previous decls here

  def <(that: Any): Boolean = that match
    case d: Date =>
      (year < d.year) ||
      (year == d.year && (month < d.month ||
                         (month == d.month && day < d.day)))

    case _ => sys.error("cannot compare " + that + " and a Date")
  end <
end Date

This completes the definition of the Date class. Instances of this class can be seen either as dates or as comparable objects. Moreover, they all define the six comparison predicates mentioned above: equals and < because they appear directly in the definition of the Date class, and the others because they are inherited from the Ord trait.

Traits are useful in other situations than the one shown here, of course, but discussing their applications in length is outside the scope of this document.

Genericity

The last characteristic of Scala we will explore in this tutorial is genericity. Java programmers should be well aware of the problems posed by the lack of genericity in their language, a shortcoming which is addressed in Java 1.5.

Genericity is the ability to write code parametrized by types. For example, a programmer writing a library for linked lists faces the problem of deciding which type to give to the elements of the list. Since this list is meant to be used in many different contexts, it is not possible to decide that the type of the elements has to be, say, Int. This would be completely arbitrary and overly restrictive.

Java programmers resort to using Object, which is the super-type of all objects. This solution is however far from being ideal, since it doesn’t work for basic types (int, long, float, etc.) and it implies that a lot of dynamic type casts have to be inserted by the programmer.

Scala makes it possible to define generic classes (and methods) to solve this problem. Let us examine this with an example of the simplest container class possible: a reference, which can either be empty or point to an object of some type.

class Reference[T] {
  private var contents: T = _
  def set(value: T): Unit = { contents = value }
  def get: T = contents
}

The class Reference is parametrized by a type, called T, which is the type of its element. This type is used in the body of the class as the type of the contents variable, the argument of the set method, and the return type of the get method.

The above code sample introduces variables in Scala, which should not require further explanations. It is however interesting to see that the initial value given to that variable is _, which represents a default value. This default value is 0 for numeric types, false for the Boolean type, () for the Unit type and null for all object types.

import compiletime.uninitialized

class Reference[T]:
  private var contents: T = uninitialized
  def set(value: T): Unit = contents = value
  def get: T = contents

The class Reference is parametrized by a type, called T, which is the type of its element. This type is used in the body of the class as the type of the contents variable, the argument of the set method, and the return type of the get method.

The above code sample introduces variables in Scala, which should not require further explanations. It is however interesting to see that the initial value given to that variable is uninitialized, which represents a default value. This default value is 0 for numeric types, false for the Boolean type, () for the Unit type and null for all object types.

To use this Reference class, one needs to specify which type to use for the type parameter T, that is the type of the element contained by the cell. For example, to create and use a cell holding an integer, one could write the following:

object IntegerReference {
  def main(args: Array[String]): Unit = {
    val cell = new Reference[Int]
    cell.set(13)
    println("Reference contains the half of " + (cell.get * 2))
  }
}
@main def IntegerReference: Unit =
  val cell = new Reference[Int]
  cell.set(13)
  println("Reference contains the half of " + (cell.get * 2))

As can be seen in that example, it is not necessary to cast the value returned by the get method before using it as an integer. It is also not possible to store anything but an integer in that particular cell, since it was declared as holding an integer.

Conclusion

This document gave a quick overview of the Scala language and presented some basic examples. The interested reader can go on, for example, by reading the Tour of Scala, which contains more explanations and examples, and consult the Scala Language Specification when needed.

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