Notable Scala Features

5.1 Case Classes and Sealed Traits81
5.2 Pattern Matching83
5.3 By-Name Parameters89
5.4 Implicit Parameters91
5.5 Typeclass Inference94

@ def getDayMonthYear(s: String) = s match {
    case s"$day-$month-$year" => println(s"found day: $day, month: $month, year: $year")
    case _ => println("not a date")

@ getDayMonthYear("9-8-1965")
found day: 9, month: 8, year: 1965

@ getDayMonthYear("9-8")
not a date
</> 5.1.scala

Snippet 5.1: using Scala's pattern matching feature to parse simple string patterns

This chapter will cover some of the more interesting and unusual features of Scala. For each such feature, we will cover both what the feature does as well as some common use cases to give you an intuition for what it is useful for.

Not every feature in this chapter will be something you use day-to-day. Nevertheless, even these less-commonly-used features are used often enough that it is valuable to have a high-level understanding for when you eventually encounter them in the wild.

5.1 Case Classes and Sealed Traits

5.1.1 Case Classes

case classes are like normal classes, but meant to represent classes which are "just data": where all the data is immutable and public, without any mutable state or encapsulation. Their use case is similar to "structs" in C/C++, "POJOs" in Java or "Data Classes" in Python or Kotlin. Their name comes from the fact that they support pattern matching (5.2) via the case keyword.

Case classes are defined with the case keyword, and can be instantiated without new. All of their constructor parameters are public fields by default.

@ case class Point(x: Int, y: Int)

@ val p = Point(1, 2)
p: Point = Point(1, 2)
</> 5.2.scala
@ p.x
res2: Int = 1

@ p.y
res3: Int = 2
</> 5.3.scala

case classs give you a few things for free:

  • A .toString implemented to show you the constructor parameter values

  • A == implemented to check if the constructor parameter values are equal

  • A .copy method to conveniently create modified copies of the case class instance

@ p.toString
res4: String = "Point(1,2)"

@ val p2 = Point(1, 2)

@ p == p2
res6: Boolean = true
</> 5.4.scala
@ val p = Point(1, 2)

@ val p3 = p.copy(y = 10)
p3: Point = Point(1, 10)

@ val p4 = p3.copy(x = 20)
p4: Point = Point(20, 10)
</> 5.5.scala

Like normal classes, you can define instance methods or properties in the body of the case class:

@ case class Point(x: Int, y: Int) {
    def z = x + y
</> 5.6.scala
@ val p = Point(1, 2)

@ p.z
res12: Int = 3
</> 5.7.scala

case classes are a good replacement for large tuples, since instead of extracting their values via ._1 ._2 ._7 you can extract the values via their names like .x and .y. That is much easier than trying to remember exactly what field ._7 in a large tuple represents!

See example 5.1 - CaseClass

5.1.2 Sealed Traits

traits can also be defined sealed, and only extended by a fixed set of case classes. In the following example, we define a sealed trait Point extended by two case classes: Point2D and Point3D:

@ {
  sealed trait Point
  case class Point2D(x: Double, y: Double) extends Point
  case class Point3D(x: Double, y: Double, z: Double) extends Point

@ def hypotenuse(p: Point) = p match {
    case Point2D(x, y) => math.sqrt(x * x + y * y)
    case Point3D(x, y, z) => math.sqrt(x * x + y * y + z * z)

@ val points: Array[Point] = Array(Point2D(1, 2), Point3D(4, 5, 6))

@ for (p <- points) println(hypotenuse(p))
</> 5.8.scala

The core difference between normal traits and sealed traits can be summarized as follows:

  • Normal traits are open, so any number of classes can inherit from the trait as long as they provide all the required methods, and instances of those classes can be used interchangeably via the trait's required methods.

  • sealed traits are closed: they only allow a fixed set of classes to inherit from them, and all inheriting classes must be defined together with the trait itself in the same file or REPL command (hence the curlies {} surrounding the Point/Point2D/Point3D definitions above).

Because there are only a fixed number of classes inheriting from sealed trait Point, we can use pattern matching in the def hypotenuse function above to define how each kind of Point should be handled.

5.1.3 Use Cases for Normal v.s. Sealed Traits

Both normal traits and sealed traits are common in Scala applications: normal traits for interfaces which may have any number of subclasses, and sealed traits where the number of subclasses is fixed.

Normal traits and sealed traits make different things easy:

  • A normal trait hierarchy makes it easy to add additional sub-classes: just define your class and implement the necessary methods. However, it makes it difficult to add new methods: a new method needs to be added to all existing subclasses, of which there may be many.

  • A sealed trait hierarchy is the opposite: it is easy to add new methods, since a new method can simply pattern match on each sub-class and decide what it wants to do for each. However, adding new sub-classes is difficult, as you need to go to all existing pattern matches and add the case to handle your new sub-class

In general, sealed traits are good for modelling hierarchies where you expect the number of sub-classes to change very little or not-at-all. A good example of something that can be modeled using sealed trait is JSON:

@ {
  sealed trait Json
  case class Null() extends Json
  case class Bool(value: Boolean) extends Json
  case class Str(value: String) extends Json
  case class Num(value: Double) extends Json
  case class Arr(value: Seq[Json]) extends Json
  case class Dict(value: Map[String, Json]) extends Json
</> 5.9.scala
  • A JSON value can only be JSON null, boolean, number, string, array, or dictionary.

  • JSON has not changed in 20 years, so it is unlikely that anyone will need to extend our JSON trait with additional subclasses.

  • While the set of sub-classes is fixed, the range of operations we may want to do on a JSON blob is unbounded: parse it, serialize it, pretty-print it, minify it, sanitize it, etc.

Thus it makes sense to model a JSON data structure as a closed sealed trait hierarchy rather than a normal open trait hierarchy.

See example 5.2 - SealedTrait

5.2 Pattern Matching

5.2.1 Match

Scala allows pattern matching on values using the match keyword. This is similar to the switch statement found in other programming languages, but more flexible: apart from matching on primitive integers and strings, you can also use match to extract values from ("destructure") composite data types like tuples and case classes. Note that in many examples below, there is a case _ => clause which defines the default case if none of the earlier cases matched. Matching on Ints

@ def dayOfWeek(x: Int) = x match {
    case 1 => "Mon"; case 2 => "Tue"
    case 3 => "Wed"; case 4 => "Thu"
    case 5 => "Fri"; case 6 => "Sat"
    case 7 => "Sun"; case _ => "Unknown"

@ dayOfWeek(5)
res19: String = "Fri"

@ dayOfWeek(-1)
res20: String = "Unknown"
</> 5.10.scala Matching on Strings

@ def indexOfDay(d: String) = d match {
    case "Mon" => 1; case "Tue" => 2
    case "Wed" => 3; case "Thu" => 4
    case "Fri" => 5; case "Sat" => 6
    case "Sun" => 7; case _ => -1

@ indexOfDay("Fri")
res22: Int = 5

@ indexOfDay("???")
res23: Int = -1
</> 5.11.scala Matching on tuple (Int, Int)

@ for (i <- Range.inclusive(1, 100)) {
    val s =  (i % 3, i % 5) match {
      case (0, 0) => "FizzBuzz"
      case (0, _) => "Fizz"
      case (_, 0) => "Buzz"
      case _ => i
</> 5.12.scala Matching on tuple (Boolean, Boolean)

@ for (i <- Range.inclusive(1, 100)) {
    val s = (i % 3 == 0, i % 5 == 0) match {
      case (true, true) => "FizzBuzz"
      case (true, false) => "Fizz"
      case (false, true) => "Buzz"
      case (false, false) => i
</> 5.13.scala Matching on Case Classes:

@ case class Point(x: Int, y: Int)

@ def direction(p: Point) = p match {
    case Point(0, 0) => "origin"
    case Point(_, 0) => "horizontal"
    case Point(0, _) => "vertical"
    case _ => "diagonal"

@ direction(Point(0, 0))
res28: String = "origin"

@ direction(Point(1, 1))
res29: String = "diagonal"

@ direction(Point(10, 0))
res30: String = "horizontal"
</> 5.14.scala Matching on String Patterns:

@ def splitDate(s: String) = s match {
    case s"$day-$month-$year" =>
      s"day: $day, mon: $month, yr: $year"
    case _ => "not a date"

@ splitDate("9-8-1965")
res32: String = "day: 9, mon: 8, yr: 1965"

@ splitDate("9-8")
res33: String = "not a date"
</> 5.15.scala

(Note that pattern matching on string patterns only supports simple glob-like patterns, and doesn't support richer patterns like Regular Expressions. For those, you can use the functionality of the scala.util.matching.Regex class)

5.2.2 Nested Matches

Patterns can also be nested, e.g. this example matches a string pattern within a case class pattern:

@ case class Person(name: String, title: String)

@ def greet(p: Person) = p match {
    case Person(s"$firstName $lastName", title) => println(s"Hello $title $lastName")
    case Person(name, title) => println(s"Hello $title $name")

@ greet(Person("Haoyi Li", "Mr"))
Hello Mr Li

@ greet(Person("Who?", "Dr"))
Hello Dr Who?
</> 5.16.scala

Patterns can be nested arbitrarily deeply. The following example matches string patterns, inside a case class, inside a tuple:

@ def greet2(husband: Person, wife: Person) = (husband, wife) match {
    case (Person(s"$first1 $last1", _), Person(s"$first2 $last2", _)) if last1 == last2 =>
      println(s"Hello Mr and Ms $last1")

    case (Person(name1, _), Person(name2, _)) => println(s"Hello $name1 and $name2")

@ greet2(Person("James Bond", "Mr"), Person("Jane Bond", "Ms"))
Hello Mr and Ms Bond

@ greet2(Person("James Bond", "Mr"), Person("Jane", "Ms"))
Hello James Bond and Jane
</> 5.17.scala

5.2.3 Loops and Vals

The last two places you an use pattern matching are inside for-loops and val definitions. Pattern matching in for-loops is useful when you need to iterate over collections of tuples:

@ val a = Array[(Int, String)]((1, "one"), (2, "two"), (3, "three"))

@ for ((i, s) <- a) println(s + i)
</> 5.18.scala

Pattern matching in val statements is useful when you are sure the value will match the given pattern, and all you want to do is extract the parts you want. If the value doesn't match, this fails with an exception:

@ case class Point(x: Int, y: Int)

@ val p = Point(123, 456)

@ val Point(x, y) = p
x: Int = 123
y: Int = 456
</> 5.19.scala
@ val s"$first $second" = "Hello World"
first: String = "Hello"
second: String = "World"

@ val flipped = s"$second $first"
flipped: String = "World Hello"

@ val s"$first $second" = "Hello"
scala.MatchError: Hello
</> 5.20.scala

5.2.4 Pattern Matching on Sealed Traits and Case Classes

Pattern matching lets you elegantly work with structured data comprising case classes and sealed traits. For example, let's consider a simple sealed trait that represents arithmetic expressions:

@ {
  sealed trait Expr
  case class BinOp(left: Expr, op: String, right: Expr) extends Expr
  case class Literal(value: Int) extends Expr
  case class Variable(name: String) extends Expr
</> 5.21.scala

Where BinOp stands for "Binary Operation". This can represent the arithmetic expressions, such as the following

x + 1
BinOp(Variable("x"), "+", Literal(1))
x * (y - 1)
  BinOp(Variable("y"), "-", Literal(1))
</> 5.22.scala
(x + 1) * (y - 1)
  BinOp(Variable("x"), "+", Literal(1)),
  BinOp(Variable("y"), "-", Literal(1))
</> 5.23.scala

For now, we will ignore the parsing process that turns the string on the left into the structured case class structure on the right: we will cover that in Chapter 19: Parsing Structured Text. Let us instead consider two things you may want to do once you have parsed such an arithmetic expression to the case classes we see above: we may want to print it to a human-friendly string, or we may want to evaluate it given some variable values. Stringifying Our Expressions

Converting the expressions to a string can be done using the following approach:

  • If an Expr is a Literal, the string is the value of the literal
  • If an Expr is a Variable, the string is the name of the variable
  • If an Expr is a BinOp, the string is the stringified left expression, followed by the operation, followed by the stringified right expression

Converted to pattern matching code, this can be written as follows:

@ def stringify(expr: Expr): String = expr match {
    case BinOp(left, op, right) => s"(${stringify(left)} $op ${stringify(right)})"
    case Literal(value) => value.toString
    case Variable(name) => name
</> 5.24.scala

We can construct some of Exprs we saw earlier and feed them into our stringify function to see the output:

@ val smallExpr = BinOp(

@ stringify(smallExpr)
res52: String = "(x + 1)"
</> 5.25.scala
@ val largeExpr = BinOp(
    BinOp(Variable("x"), "+", Literal(1)),
    BinOp(Variable("y"), "-", Literal(1))

@ stringify(largeExpr)
res54: String = "((x + 1) * (y - 1))"
</> 5.26.scala Evaluating Our Expressions

Evaluation is a bit more complex than stringifying the expressions, but only slightly. We need to pass in a values map that holds the numeric value of every variable, and we need to treat +, -, and * operations differently:

@ def evaluate(expr: Expr, values: Map[String, Int]): Int = expr match {
    case BinOp(left, "+", right) => evaluate(left, values) + evaluate(right, values)
    case BinOp(left, "-", right) => evaluate(left, values) - evaluate(right, values)
    case BinOp(left, "*", right) => evaluate(left, values) * evaluate(right, values)
    case Literal(value) => value
    case Variable(name) => values(name)

@ evaluate(smallExpr, Map("x" -> 10))
res56: Int = 11

@ evaluate(largeExpr, Map("x" -> 10, "y" -> 20))
res57: Int = 209
</> 5.27.scala

Overall, this looks relatively similar to the stringify function we wrote earlier: a recursive function that pattern matches on the expr: Expr parameter to handle each case class that implements Expr. The cases handling child-free Literal and Variable are trivial, while in the BinOp case we recurse on both left and right children before combining the result. This is a common way of working with recursive data structures in any language, and Scala's sealed traits, case classes and pattern matching make it concise and easy.

This Expr structure and the printer and evaluator we have written are intentionally simplistic, just to give us a chance to see how pattern matching can be used to easily work with structured data modeled as case classes and sealed traits. We will be exploring these techniques much more deeply in Chapter 20: Implementing a Programming Language.

5.3 By-Name Parameters

@ def func(arg: => String) = ...

Scala also supports "by-name" method parameters using a : => T syntax, which are evaluated each time they are referenced in the method body. This has three primary use cases:

  1. Avoiding evaluation if the argument does not end up being used
  2. Wrapping evaluation to run setup and teardown code before and after the argument evaluates
  3. Repeating evaluation of the argument more than once

5.3.1 Avoiding Evaluation

The following log method uses a by-name parameter to avoid evaluating the msg: => String unless it is actually going to get printed. This can help avoid spending CPU time constructing log messages (here via "Hello " + 123 + " World") even when logging is disabled:

@ var logLevel = 1

@ def log(level: Int, msg: => String) = {
    if (level > logLevel) println(msg)
</> 5.28.scala
@ log(2, "Hello " + 123 + " World")
Hello 123 World

@ logLevel = 3

@ log(2, "Hello " + 123 + " World")
<no output>
</> 5.29.scala

Often a method does not end up using all of its arguments all the time. In the above example, by not computing log messages when they are not needed, we can save a significant amount of CPU time and object allocations which may make a difference in performance-sensitive applications.

The getOrElse and getOrElseUpdate methods we saw in Chapter 4: Scala Collections are similar: these methods do not use the argument representing the default value if the value we are looking for is already present. By making the default value a by-name parameter, we do not have to evaluate it in the case where it does not get used.

5.3.2 Wrapping Evaluation

Using by-name parameters to "wrap" the evaluation of your method in some setup/teardown code is another common pattern. The following measureTime function defers evaluation of f: => Unit, allowing us to run System.currentTimeMillis() before and after the argument is evaluated and thus print out the time taken:

@ def measureTime(f: => Unit) = {
    val start = System.currentTimeMillis()
    val end = System.currentTimeMillis()
    println("Evaluation took " + (end - start) + " milliseconds")

@ measureTime(new Array[String](10 * 1000 * 1000).hashCode())
Evaluation took 24 milliseconds

@ measureTime { // methods taking a single arg can also be called with curly brackets
    new Array[String](100 * 1000 * 1000).hashCode()
Evaluation took 287 milliseconds
</> 5.30.scala

There are many other use cases for such wrapping:

  • Setting some thread-local context while the argument is being evaluated
  • Evaluating the argument inside a try-catch block so we can handle exceptions
  • Evaluating the argument in a Future so the logic runs asynchronously on another thread

These are all cases where using by-name parameter can help.

5.3.3 Repeating Evaluation

The last use case we will cover for by-name parameters is repeating evaluation of the method argument. The following snippet defines a generic retry method: this method takes in an argument, evaluates it within a try-catch block, and re-executes it on failure with a maximum number of attempts. We test this by using it to wrap a call which may fail, and seeing the retrying messages get printed to the console.

@ def retry[T](max: Int)(f: => T): T = {
    var tries = 0
    var result: Option[T] = None
    while (result == None) {
      try { result = Some(f) }
      catch {case e: Throwable =>
        tries += 1
        if (tries > max) throw e
        else {
          println(s"failed, retry #$tries")
</> 5.31.scala
@ val httpbin = "https://httpbin.org"

@ retry(max = 5) {
    // Only succeeds with a 200 response
    // code 1/3 of the time
call failed, retry #1
call failed, retry #2
res68: requests.Response = Response(
</> 5.32.scala

Above we define retry as a generic function taking a type parameter [T], taking a by-name parameter that computes a value of type T, and returning a T once the code block is successful. We can then use retry to wrap a code block of any type, and it will retry that block and return the first T it successfully computes.

Making retry take a by-name parameter is what allows it to repeat evaluation of the requests.get block where necessary. Other use cases for repetition include running performance benchmarks or performing load tests. In general, by-name parameters aren't something you use very often, but when necessary they let you write code that manipulates the evaluation of a method argument in a variety of useful ways: instrumenting it, retrying it, eliding it, etc.

We will learn more about the requests library that we used in the above snippet in Chapter 12: Working with HTTP APIs.

See example 5.4 - ByName

5.4 Implicit Parameters

An implicit parameter is a parameter that is automatically filled in for you when calling a function. For example, consider the following class Foo and the function bar that takes an implicit foo: Foo parameter:

@ class Foo(val value: Int)

@ def bar(implicit foo: Foo) = foo.value + 10
</> 5.33.scala

If you try to call bar without an implicit Foo in scope, you get a compilation error. To call bar, you need to define an implicit value of the type Foo, such that the call to bar can automatically resolve it from the enclosing scope:

@ bar
cmd4.sc:1: could not find implicit
           value for parameter foo: Foo
val res4 = bar
Compilation Failed
</> 5.34.scala
@ implicit val foo: Foo = new Foo(1)
foo: Foo = ammonite.$sess.cmd1$Foo@451882b2

@ bar // `foo` is resolved implicitly
res72: Int = 11

@ bar(foo) // passing in `foo` explicitly
res73: Int = 11
</> 5.35.scala

Implicit parameters are similar to the default values we saw in Chapter 3: Basic Scala. Both of them allow you to pass in a value explicitly or fall back to some default. The main difference is that while default values are "hard coded" at the definition site of the method, implicit parameters take their default value from whatever implicit is in scope at the call-site.

We'll now look into a more concrete example where using implicit parameters can help keep your code clean and readable, before going into a more advanced use case of the feature for Typeclass Inference (5.5).

5.4.1 Passing ExecutionContext to Futures

As an example, code using Future needs an ExecutionContext value in order to work. As a result, we end up passing this ExecutionContext everywhere, which is tedious and verbose:

def getEmployee(ec: ExecutionContext, id: Int): Future[Employee] = ...
def getRole(ec: ExecutionContext, employee: Employee): Future[Role] = ...

val executionContext: ExecutionContext = ...

val bigEmployee: Future[EmployeeWithRole] = {
  getEmployee(executionContext, 100).flatMap(
    e =>
      getRole(executionContext, e)
        .map(executionContext, r => EmployeeWithRole(e, r))
</> 5.36.scala

getEmployee and getRole perform asynchronous actions, which we then map and flatMap to do further work. Exactly how the Futures work is beyond the scope of this section: for now, what is notable is how every operation needs to be passed the executionContext to do their work. We will will revisit these APIs in Chapter 13: Fork-Join Parallelism with Futures.

Without implicit parameters, we have the following options:

  • Passing executionContext explicitly is verbose and can make your code harder to read: the logic we care about is drowned in a sea of boilerplate executionContext passing

  • Making executionContext global would be concise, but would lose the flexibility of passing different values in different parts of your program

  • Putting executionContext into a thread-local variable would maintain flexibility and conciseness, but it is error-prone and easy to forget to set the thread-local before running code that needs it

All of these options have tradeoffs, forcing us to either sacrifice conciseness, flexibility, or safety. Scala's implicit parameters provide a fourth option: passing executionContext implicitly, which gives us the conciseness, flexibility, and safety that the above options are unable to give us.

5.4.2 Dependency Injection via Implicits

To resolve these issues, we can make all these functions take the executionContext as an implicit parameter. This is already the case for standard library operations like flatMap and map on Futures, and we can modify our getEmployee and getRole functions to follow suit. By defining executionContext as an implicit, it will automatically get picked up by all the method calls below.

def getEmployee(id: Int)(implicit ec: ExecutionContext): Future[Employee] = ...
def getRole(employee: Employee)(implicit ec: ExecutionContext): Future[Role] = ...

implicit val executionContext: ExecutionContext = ...

val bigEmployee: Future[EmployeeWithRole] = {
  getEmployee(100).flatMap(e =>
    getRole(e).map(r =>
      EmployeeWithRole(e, r)
</> 5.37.scala

Using implicit parameters can help clean up code where we pass the same shared context or configuration object throughout your entire application:

  • By making the "uninteresting" parameter passing implicit, it can focus the reader's attention on the core logic of your application.

  • Since implicit parameters can be passed explicitly, they preserve the flexibility for the developer in case they want to manually specify or override the implicit parameter being passed.

  • The fact that missing implicits are a compile time error makes their usage much less error-prone than thread-locals. A missing implicit will be caught early on before code is compiled and deployed to production.

5.5 Typeclass Inference

A second way that implicit parameters are useful is by using them to associate values to types. This is often called a typeclass, the term originating from the Haskell programming language, although it has nothing to do with types and classes in Scala. While typeclasses are a technique built on the same implicit language feature described earlier, they are an interesting and important enough technique to deserve their own section in this chapter.

5.5.1 Problem Statement: Parsing Command Line Arguments

Let us consider the task of parsing command-line arguments, given as Strings, into Scala values of various types: Ints, Booleans, Doubles, etc. This is a common task that almost every program has to deal with, either directly or by using a library.

A first sketch may be writing a generic method to parse the values. The signature might look something like this:

def parseFromString[T](s: String): T = ...

val args = Seq("123", "true", "7.5")
val myInt = parseFromString[Int](args(0))
val myBoolean = parseFromString[Boolean](args(1))
val myDouble = parseFromString[Double](args(2))
</> 5.38.scala

On the surface this seems impossible to implement:

  • How does the parseCliArgument know how to convert the given String into an arbitrary T?

  • How does it know what types T a command-line argument can be parsed into, and which it cannot? For example, we should not be able to parse a java.net.DatagramSocket from an input string.

5.5.2 Separate Parser Objects

A second sketch at a solution may be to define separate parser objects, one for each type we need to be able to parse. For example:

trait StrParser[T]{ def parse(s: String): T }
object ParseInt extends StrParser[Int]{ def parse(s: String) = s.toInt }
object ParseBoolean extends StrParser[Boolean]{ def parse(s: String) = s.toBoolean }
object ParseDouble extends StrParser[Double]{ def parse(s: String) = s.toDouble }
</> 5.39.scala

We can then call these as follows:

val args = Seq("123", "true", "7.5")
val myInt = ParseInt.parse(args(0))
val myBoolean = ParseBoolean.parse(args(1))
val myDouble = ParseDouble.parse(args(2))
</> 5.40.scala

This works. However, it then leads to another problem: if we wanted to write a method that didn't parse a String directly, but parsed a value from the console, how would we do that? We have two options. Re-Using Our StrParsers

The first option is writing a whole new set of objects dedicated to parsing from the console:

trait ConsoleParser[T]{ def parse(): T }
object ConsoleParseInt extends ConsoleParser[Int]{
  def parse() = scala.Console.in.readLine().toInt
object ConsoleParseBoolean extends ConsoleParser[Boolean]{
  def parse() = scala.Console.in.readLine().toBoolean
object ConsoleParseDouble extends ConsoleParser[Double]{
  def parse() = scala.Console.in.readLine().toDouble

val myInt = ConsoleParseInt.parse()
val myBoolean = ConsoleParseBoolean.parse()
val myDouble = ConsoleParseDouble.parse()
</> 5.41.scala

The second option is defining a helper method that receives a StrParser[T] as an argument, which we would need to pass in to tell it how to parse the type T

def parseFromConsole[T](parser: StrParser[T]) = parser.parse(scala.Console.in.readLine())

val myInt = parseFromConsole[Int](ParseInt)
val myBoolean = parseFromConsole[Boolean](ParseBoolean)
val myDouble = parseFromConsole[Double](ParseDouble)
</> 5.42.scala

Both of these solutions are clunky:

  1. The first because we need to duplicate all the Int/Boolean/Double/etc. parsers. What if we need to parse input from the network? From files? We would need to duplicate every parser for each case.

  2. The second because we need to pass these ParseFoo objects everywhere. Often there is only a single StrParser[Int] we can pass to parseFromConsole[Int]. Why can't the compiler infer it for us?

5.5.3 Solution: Implicit StrParser

The solution to the problems above is to make the instances of StrParser implicit:

trait StrParser[T]{ def parse(s: String): T }
object StrParser{
  implicit object ParseInt extends StrParser[Int]{
    def parse(s: String) = s.toInt
  implicit object ParseBoolean extends StrParser[Boolean]{
    def parse(s: String) = s.toBoolean
  implicit object ParseDouble extends StrParser[Double]{
    def parse(s: String) = s.toDouble
</> 5.43.scala

We put the implicit object ParseInt, ParseBoolean, etc. in an object StrParser with the same name as the trait StrParser next to it. An object with the same name as a class that it is defined next to is called a companion object. Companion objects are often used to group together implicits, static methods, factory methods, and other functionality that is related to a trait or class but does not belong to any specific instance. Implicits in the companion object are also treated specially, and do not need to be imported into scope in order to be used as an implicit parameter.

Note that if you are entering this into the Ammonite Scala REPL, you need to surround both declarations with an extra pair of curly brackets {...} so that both the trait and object are defined in the same REPL command.

Now, while we can still explicitly call ParseInt.parse(args(0)) to parse literal strings as before, we can now write a generic function that automatically uses the correct instance of StrParser depending on what type we asked it to parse:

def parseFromString[T](s: String)(implicit parser: StrParser[T]) = {

val args = Seq("123", "true", "7.5")
val myInt = parseFromString[Int](args(0))
val myBoolean = parseFromString[Boolean](args(1))
val myDouble = parseFromString[Double](args(2))
</> 5.44.scala

This looks similar to our initial sketch, except by taking an (implicit parser: StrParser[T]) parameter the function can now automatically infer the correct StrParser for each type it is trying to parse. Re-Using Our Implicit StrParsers

Making our StrParser[T]s implicit means we can re-use them without duplicating our parsers or passing them around manually. For example, we can write a function that parses strings from the console:

def parseFromConsole[T](implicit parser: StrParser[T]) = {

val myInt = parseFromConsole[Int]
</> 5.45.scala

The call to parseFromConsole[Int] automatically infers the StrParser.ParseInt implicit in the StrParser companion object, without needing to duplicate it or tediously pass it around. That makes it very easy to write code that works with a generic type T as long as T has a suitable StrParser. Context-Bound Syntax

This technique of taking an implicit parameter with a generic type is common enough that the Scala language provides dedicated syntax for it. The following method signature:

def parseFromString[T](s: String)(implicit parser: StrParser[T]) = ...

Can be written more concisely as:

def parseFromString[T: StrParser](s: String) = ...

This syntax is referred to as a context bound, and it is semantically equivalent to the (implicit parser: StrParser[T]) syntax above. When using the context bound syntax, the implicit parameter isn't given a name, and so we cannot call parser.parse like we did earlier. Instead, we can resolve the implicit values via the implicitly function, e.g. implicitly[StrParser[T]].parse. Compile-Time Implicit Safety

As Typeclass Inference uses the the same implicit language feature we saw earlier, mistakes such as attempting to call parseFromConsole with an invalid type produce a compile error:

@ val myDatagramSocket = parseFromConsole[java.net.DatagramSocket]
cmd19.sc:1: could not find implicit value for parameter parser:
val myDatagramSocket = parseFromConsole[java.net.DatagramSocket]
Compilation Failed
</> 5.46.scala

Similarly, if you try to call a method taking an (implicit parser: StrParser[T]) from another method that does not have such an implicit available, the compiler will also raise an error:

@ def genericMethodWithoutImplicit[T](s: String) = parseFromString[T](s)
cmd2.sc:1: could not find implicit value for parameter parser:
def genericMethodWithoutImplicit[T](s: String) = parseFromString[T](s)
Compilation Failed
</> 5.47.scala

Most of the things we have done with Typeclass Inference could also be achieved using runtime reflection. However, relying on runtime reflection is fragile, and it is very easy for mistakes, bugs, or mis-configurations to make it to production before failing catastrophically. In contrast, Scala's implicit feature lets you achieve the same outcome but in a safe fashion: mistakes are caught early at compile-time, and you can fix them at your leisure rather than under the pressure of a ongoing production outage.

5.5.4 Recursive Typeclass Inference

We have already seen how we can use the typeclass technique to automatically pick which StrParser to use based on the type we want to parse to. This can also work for more complex types, where we tell the compiler we want a Seq[Int], (Int, Boolean), or even nested types like Seq[(Int, Boolean)], and the compiler will automatically assemble the logic necessary to parse the type we want. Parsing Sequences

For example, the following ParseSeq function provides a StrParser[Seq[T]] for any T which itself has an implicit StrParser[T] in scope:

implicit def ParseSeq[T](implicit p: StrParser[T]) = new StrParser[Seq[T]]{
  def parse(s: String) = s.split(',').toSeq.map(p.parse)
</> 5.48.scala

Note that unlike the implicit objects we defined earlier which are singletons, here we have an implicit def. Depending on the type T, we would need a different StrParser[T], and thus need a different StrParser[Seq[T]]. implicit def ParseSeq would thus return a different StrParser each time it is called with a different type T.

From this one defintion, we can now parse Seq[Boolean]s, Seq[Int]s, etc.

@ parseFromString[Seq[Boolean]]("true,false,true")
res99: Seq[Boolean] = ArraySeq(true, false, true)

@ parseFromString[Seq[Int]]("1,2,3,4")
res100: Seq[Int] = ArraySeq(1, 2, 3, 4)
</> 5.49.scala

What we are effectively doing is teaching the compiler how to produce a StrParser[Seq[T]] for any type T as long as it has an implicit StrParser[T] available. Since we already have StrParser[Int], StrParser[Boolean], and StrParser[Double] available, the ParseSeq method gives StrParser[Seq[Int]], StrParser[Seq[Boolean]], and StrParser[Seq[Double]] for free.

The StrParser[Seq[T]] we are instantiating has a parse method that receives a parameter s: String and returns a Seq[T]. We just needed to implement the logic necessary to do that transformation, which we have done in the code snippet above. Parsing Tuples

Similar to how we defined an implicit def to parse Seq[T]s, we could do the same to parse tuples. We do so below by assuming that tuples are represented by key=value pairs in the input string:

implicit def ParseTuple[T, V](implicit p1: StrParser[T], p2: StrParser[V]) =
  new StrParser[(T, V)]{
    def parse(s: String) = {
      val Array(left, right) = s.split('=')
      (p1.parse(left), p2.parse(right))
</> 5.50.scala

This definition produces a StrParser[(T, V)], but only for a type T and type V for which there are StrParsers available. Now we can parse tuples, as =-separated pairs:

@ parseFromString[(Int, Boolean)]("123=true")
res102: (Int, Boolean) = (123, true)

@ parseFromString[(Boolean, Double)]("true=1.5")
res103: (Boolean, Double) = (true, 1.5)
</> 5.51.scala Parsing Nested Structures

The two definitions above, implicit def ParseSeq and implicit def ParseTuple, are enough to let us also parse sequences of tuples, or tuples of sequences:

@ parseFromString[Seq[(Int, Boolean)]]("1=true,2=false,3=true,4=false")
res104: Seq[(Int, Boolean)] = ArraySeq((1, true), (2, false), (3, true), (4, false))

@ parseFromString[(Seq[Int], Seq[Boolean])]("1,2,3,4,5=true,false,true")
res105: (Seq[Int], Seq[Boolean]) = (ArraySeq(1, 2, 3, 4, 5), ArraySeq(true, false, true))
</> 5.52.scala

Note that in this case we cannot handle nested Seq[Seq[T]]s or nested tuples due to how we're naively splitting the input string. A more structured parser handles such cases without issues, allowing us to specify an arbitrarily complex output type and automatically inferring the necessary parser. We will use a serialization library that uses this technique in Chapter 8: JSON and Binary Data Serialization.

Most statically typed programming languages can infer types to some degree: even if not every expression is annotated with an explicit type, the compiler can still figure out the types based on the program structure. Typeclass derivation is effectively the reverse: by providing an explicit type, the compiler can infer the program structure necessary to provide a value of the type we are looking for.

In the example above, we just need to define how to handle the basic types - how to produce a StrParser[Boolean], StrParser[Int], StrParser[Seq[T]], StrParser[(T, V)] - and the compiler is able to figure out how to produce a StrParser[Seq[(Int, Boolean)]] when we need it.

5.6 Conclusion

In this chapter, we have explored some of the more unique features of Scala. Case Classes or Pattern Matching you will use on a daily basis, while By-Name Parameters, Implicit Parameters, or Typeclass Inference are more advanced tools that you might only use when dictated by a framework or library. Nevertheless, these are the features that make the Scala language what it is, providing a way to tackle difficult problems more elegantly than most mainstream languages allow.

We have walked through the basic motivation and use cases for these features in this chapter. You will get familiar with more use cases as we see the features in action throughout the rest of this book.

This chapter will be the last in which we discuss the Scala programming language in isolation: subsequent chapters will introduce you to much more complex topics like working with your operating system, remote services, and third-party libraries. The Scala language fundamentals you have learned so far will serve you well as you broaden your horizons, from learning about the Scala language itself to using the Scala language to solve real-world problems.

Exercise: Define a function that uses pattern matching on the Exprs we saw earlier to perform simple algebraic simplifications:

(1 + 1)
((1 + 1) * x)
(2 * x)
((2 - 1) * x)
(((1 + 1) * y) + ((1 - 1) * x))
(2 * y)
See example 5.7 - Simplify

Exercise: Modify the def retry function earlier that takes a by-name parameter and make it perform an exponential backoff, sleeping between retries, with a configurable initial delay in milliseconds:

retry(max = 50, delay = 100 /*milliseconds*/) {
</> 5.53.scala
See example 5.8 - Backoff

Exercise: Modify the typeclass-based parseFromString method we saw earlier to take a JSON-like format, where lists are demarcated by square brackets with comma-separated elements. This should allow it to parse and construct arbitrarily deep nested data structures automatically via typeclass inference:

@ parseFromString[Seq[Boolean]]("[true,false,true]") // 1 layer of nesting
res1: Seq[Boolean] = List(true, false, true)

@ parseFromString[Seq[(Seq[Int], Seq[Boolean])]]( // 3 layers of nesting
res2: Seq[(Seq[Int], Seq[Boolean])] = List(
  (List(1), List(true)),
  (List(2, 3), List(false, true)),
  (List(4, 5, 6), List(false, true, false))

@ parseFromString[Seq[(Seq[Int], Seq[(Boolean, Double)])]]( // 4 layers of nesting
res3: Seq[(Seq[Int], Seq[(Boolean, Double)])] = List(
  (List(1), List((true, 0.5))),
  (List(2, 3), List((false, 1.5), (true, 2.5)))
</> 5.54.scala

A production-ready version of this parseFromString method exists in upickle.default.read, which we will see in Chapter 8: JSON and Binary Data Serialization.

See example 5.9 - Deserialize

Exercise: How about using typeclasses to generate JSON, rather than parse it? Write a writeToString method that uses a StrWriter typeclass to take nested values parsed by parseFromString, and serialize them to the same strings they were parsed from.

@ writeToString[Seq[Boolean]](Seq(true, false, true))
res1: String = "[true,false,true]"

@ writeToString(Seq(true, false, true)) // type can be inferred
res2: String = "[true,false,true]"

@ writeToString[Seq[(Seq[Int], Seq[Boolean])]](
      (Seq(1), Seq(true)),
      (Seq(2, 3), Seq(false, true)),
      (Seq(4, 5, 6), Seq(false, true, false))
res3: String = "[[[1],[true]],[[2,3],[false,true]],[[4,5,6],[false,true,false]]]"

@ writeToString(
      (Seq(1), Seq((true, 0.5))),
      (Seq(2, 3), Seq((false, 1.5), (true, 2.5)))
res4: String = "[[[1],[[true,0.5]]],[[2,3],[[false,1.5],[true,2.5]]]]"
</> 5.55.scala
See example 5.10 - Serialize
Discuss Chapter 5 online at https://www.handsonscala.com/discuss/5