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:
Getting Started
Install Scala on your computer and start writing some Scala code!
Scala in the Browser
To start experimenting with Scala right away, use "Scastie" in your browser.
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.
Scala Book
Learn Scala by reading a series of short lessons.
Online Courses
MOOCs to learn Scala, for beginners and experienced programmers.
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 classHelloWorld
with a staticmain
method which calls theHelloWorld
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 functions are 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(left: Tree, right: 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 writeTree.Const(5)
instead ofnew 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 instancec
of classTree.Const
just by writingc.v
), - default definitions for methods
equals
andhashCode
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 expressionx+1
prints asSum(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(left: Tree, right: 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 instancec
of caseTree.Const
just by writingc.v
), - default definitions for methods
equals
andhashCode
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 expressionx+1
prints asSum(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 follows, using a pattern match on a tree value t
:
import Tree._
def eval(t: Tree, ev: Environment): Int = t match {
case Sum(left, right) => eval(left, ev) + eval(right, ev)
case Var(n) => ev(n)
case Const(v) => v
}
import Tree.*
def eval(t: Tree, ev: Environment): Int = t match
case Sum(left, right) => eval(left, ev) + eval(right, ev)
case Var(n) => ev(n)
case Const(v) => v
You can understand the precise meaning of the pattern match as follows:
- it first checks if the tree
t
is aSum
, and if it is, it binds the left sub-tree to a new variable calledleft
and the right sub-tree to a variable calledright
, 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.,left
andright
, - if the first check does not succeed, that is, if the tree is not
a
Sum
, it goes on and checks ift
is aVar
; if it is, it binds the name contained in theVar
node to a variablen
and proceeds with the right-hand expression, - if the second check also fails, that is if
t
is neither aSum
nor aVar
, it checks if it is aConst
, and if it is, it binds the value contained in theConst
node to a variablev
and proceeds with the right-hand side, - 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:
- the derivative of a sum is the sum of the derivatives,
- the derivative of some variable
v
is one ifv
is the variable relative to which the derivation takes place, and zero otherwise, - 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(left, right) => Sum(derive(left, v), derive(right, 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(left, right) => Sum(derive(left, v), derive(right, 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.