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Scala in a Nutshell

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Seamless Java Interop

Scala runs on the JVM, so Java and Scala stacks can be freely mixed for totally seamless integration.

Type Inference

So the type system doesn’t feel so static. Don’t work for the type system. Let the type system work for you!

Concurrency & Distribution

Use data-parallel operations on collections, use actors for concurrency and distribution, or futures for asynchronous programming.

class Author(val firstName: String,
    val lastName: String) extends Comparable[Author] {

  override def compareTo(that: Author) = {
    val lastNameComp = this.lastName compareTo that.lastName
    if (lastNameComp != 0) lastNameComp
    else this.firstName compareTo that.firstName

object Author {
  def loadAuthorsFromFile(file: List[Author] = ???
import static scala.collection.JavaConversions.asJavaCollection;

public class App {
    public List<Author> loadAuthorsFromFile(File file) {
        return new ArrayList<Author>(asJavaCollection(

    public void sortAuthors(List<Author> authors) {

    public void displaySortedAuthors(File file) {
        List<Author> authors = loadAuthorsFromFile(file);
        for (Author author : authors) {
                author.lastName() + ", " + author.firstName());

Combine Scala and Java seamlessly

Scala classes are ultimately JVM classes. You can create Java objects, call their methods and inherit from Java classes transparently from Scala. Similarly, Java code can reference Scala classes and objects.

In this example, the Scala class Author implements the Java interface Comparable<T> and works with Java Files. The Java code uses a method from the companion object Author, and accesses fields of the Author class. It also uses JavaConversions to convert between Scala collections and Java collections.

Type inference
scala> class Person(val name: String, val age: Int) {
     |   override def toString = s"$name ($age)"
     | }
defined class Person

scala> def underagePeopleNames(persons: List[Person]) = {
     |   for (person <- persons; if person.age < 18)
     |     yield
     | }
underagePeopleNames: (persons: List[Person])List[String]

scala> def createRandomPeople() = {
     |   val names = List("Alice", "Bob", "Carol",
     |       "Dave", "Eve", "Frank")
     |   for (name <- names) yield {
     |     val age = (Random.nextGaussian()*8 + 20).toInt
     |     new Person(name, age)
     |   }
     | }
createRandomPeople: ()List[Person]

scala> val people = createRandomPeople()
people: List[Person] = List(Alice (16), Bob (16), Carol (19), Dave (18), Eve (26), Frank (11))

scala> underagePeopleNames(people)
res1: List[String] = List(Alice, Bob, Frank)

Let the compiler figure out the types for you

The Scala compiler is smart about static types. Most of the time, you need not tell it the types of your variables. Instead, its powerful type inference will figure them out for you.

In this interactive REPL session (Read-Eval-Print-Loop), we define a class and two functions. You can observe that the compiler infers the result types of the functions automatically, as well as all the intermediate values.

val x = Future { someExpensiveComputation() }
val y = Future { someOtherExpensiveComputation() }
val z = for (a <- x; b <- y) yield a*b
for (c <- z) println("Result: " + c)
println("Meanwhile, the main thread goes on!")

Go Concurrent or Distributed with Futures & Promises

In Scala, futures and promises can be used to process data asynchronously, making it easier to parallelize or even distribute your application.

In this example, the Future{} construct evaluates its argument asynchronously, and returns a handle to the asynchronous result as a Future[Int]. For-comprehensions can be used to register new callbacks (to post new things to do) when the future is completed, i.e., when the computation is finished. And since all this is executed asynchronously, without blocking, the main program thread can continue doing other work in the meantime.


Combine the flexibility of Java-style interfaces with the power of classes. Think principled multiple-inheritance.

Pattern Matching

Think “switch” on steroids. Match against class hierarchies, sequences, and more.

Higher-order functions

Functions are first-class objects. Compose them with guaranteed type safety. Use them anywhere, pass them to anything.

abstract class Spacecraft {
  def engage(): Unit
trait CommandoBridge extends Spacecraft {
  def engage(): Unit = {
    for (_ <- 1 to 3)
  def speedUp(): Unit
trait PulseEngine extends Spacecraft {
  val maxPulse: Int
  var currentPulse: Int = 0
  def speedUp(): Unit = {
    if (currentPulse < maxPulse)
      currentPulse += 1
class StarCruiser extends Spacecraft
                     with CommandoBridge
                     with PulseEngine {
  val maxPulse = 200

Flexibly Combine Interface & Behavior

In Scala, multiple traits can be mixed into a class to combine their interface and their behavior.

Here, a StarCruiser is a Spacecraft with a CommandoBridge that knows how to engage the ship (provided a means to speed up) and a PulseEngine that specifies how to speed up.

Switch on the structure of your data

In Scala, case classes are used to represent structural data types. They implicitly equip the class with meaningful toString, equals and hashCode methods, as well as the ability to be deconstructed with pattern matching.

In this example, we define a small set of case classes that represent binary trees of integers (the generic version is omitted for simplicity here). In inOrder, the match construct chooses the right branch, depending on the type of t, and at the same time deconstructs the arguments of a Node.

Pattern matching
// Define a set of case classes for representing binary trees.
sealed abstract class Tree
case class Node(elem: Int, left: Tree, right: Tree) extends Tree
case object Leaf extends Tree

// Return the in-order traversal sequence of a given tree.
def inOrder(t: Tree): List[Int] = t match {
  case Node(e, l, r) => inOrder(l) ::: List(e) ::: inOrder(r)
  case Leaf          => List()

Go Functional with Higher-Order Functions

In Scala, functions are values, and can be defined as anonymous functions with a concise syntax.

val people: Array[Person]

// Partition `people` into two arrays `minors` and `adults`.
// Use the anonymous function `(_.age < 18)` as a predicate for partitioning.
val (minors, adults) = people partition (_.age < 18)
List<Person> people;

List<Person> minors = new ArrayList<Person>(people.size());
List<Person> adults = new ArrayList<Person>(people.size());
for (Person person : people) {
    if (person.getAge() < 18)

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Scala ecosystem

The Scala Library Index (or Scaladex) is a representation of a map of all published Scala libraries. With Scaladex, a developer can now query more than 175,000 releases of Scala libraries. Scaladex is officially supported by Scala Center.

The Scala Library Index

What’s New


Tuples bring generic programming to Scala 3

Friday, February 26, 2021

Tuples allow developers to create new types by associating existing types. In doing so, they are very similar to case classes but unlike them they retain only the structure of the types (e.g., which type is in which order) rather than giving each element a name. A tuple can also be seen as a sequence and therefore a collection of objects, however, whereas homogeneous collections such as List[A] or Set[A] accumulate elements retaining only one type (A), tuples are capable of storing data of different types while preserving the type of each entry.

In Scala 3, tuples gain power thanks to new operations, additional type safety and fewer restrictions, pointing in the direction of a construct called Heterogeneous Lists (HLists), one of the core data structures in generic programming.

In this post I will take you on a tour of the new Tuple API before looking at how a new language feature, dependent match types, allows to implement such API. I hope that through the two proposed examples, you will develop an intuition about the usage and power of a few new exciting features of Scala 3.

Why generic programming?

HLists and case classes can both be used to define products of types. However HLists do not require the developer to declare class or field names. This makes them more convenient in some scenarios, for example in return types. If we consider List, you can see that def splitAt(n: Int) produces a (List[A], List[A]) and not a case class SplitResult(left: List[A], right: List[A]) because of the cognitive cost of introducing new names (SplitResult, left and right).

Moreover, there are infinitely many case classes which share a common structure, which means that they have the same number and type of fields. We might want to apply the same transformations to them, so that such transformations can be defined only once. The Type Astronaut’s Guide to Shapeless proposes the following simple example:

case class Employee(name: String, number: Int, manager: Boolean)
case class IceCream(name: String, numCherries: Int, inCone: Boolean)

If you are implementing an operation such as serializing instances of these types to CSV or JSON, you will realize that the logic is exactly the same and you will want to implement it only once. This is equivalent to defining the serialization algorithm for the (String, Int, Boolean) HList, assuming that you can map both case classes to it.

A simple CSV encoder

Let’s consider a simple CSV encoder for our Employee and IceCream case classes. Each record, or line, of a CSV file is a sequence of values separated by a delimiter, usually a comma or a semicolon. In Scala we can represent each value as text, using the String type, and thus each record can be a list of values, with type List[String]. Therefore, in order to encode case classes to CSV, we need to extract each field of the case class and to turn it into a String, and then collect all the fields in a list. In this setting, Employee and IceCream could be treated in the same way, because they can be simply be seen as a (String, Int, Boolean) which needs to be transformed into a List[String]. We will first see how to handle this simple scenario before briefly looking at how to obtain a tuple from a case class.

Assuming that we know how to transform each element of a tuple into a List[String], can we transform any tuple into a List[String]?

The answer is yes, and this is possible because Scala 3 introduces types *:, EmptyTuple and NonEmptyTuple but also methods head and tail which allow us to define recursive operations on tuples.

Set up

Let’s define the RowEncoder[A] type-class, which describes the capability of values of type A to be converted into Row. To encode a type to Row, we first need to convert each field of the type into a String: this capability is defined by the FieldEncoder type-class.

trait FieldEncoder[A]:
  def encodeField(a: A): String

type Row = List[String]

trait RowEncoder[A]:
  def encodeRow(a: A): Row

We can then add some instances for our base types:

object BaseEncoders:
  given FieldEncoder[Int] with
    def encodeField(x: Int) = x.toString

  given FieldEncoder[Boolean] with
    def encodeField(x: Boolean) = if x then "true" else "false"

  given FieldEncoder[String] with
    def encodeField(x: String) = x // Ideally, we should also escape commas and double quotes
end BaseEncoders


Now that all these tools are in place, let’s focus on the hard part: implementing the transformation of a tuple with an arbitrary number of elements into a Row. Similarly to how you may be used to recurse on lists, on tuples we need to manage two scenarios: the base case (EmptyTuple) and the inductive case (NonEmptyTuple).

In the following snippet, I prefer to use the context bound syntax even if I need a handle for the instances because it concentrates all the constraints in the type parameter list (and I do not need to come up with any name). After this personal preference disclaimer, let’s see the two cases:

object TupleEncoders:
  // Base case
  given RowEncoder[EmptyTuple] with
    def encodeRow(empty: EmptyTuple) =

  // Inductive case
  given [H: FieldEncoder, T <: Tuple: RowEncoder]: RowEncoder[H *: T] with
    def encodeRow(tuple: H *: T) =
      summon[FieldEncoder[H]].encodeField(tuple.head) :: summon[RowEncoder[T]].encodeRow(tuple.tail)
end TupleEncoders

If the tuple is empty, we produce an empty list. To encode a non-empty tuple we invoke the encoder for the first element and we prepend the result to the Row created by the encoder of the tail of the tuple.

We can create an entrypoint function and test this implementation:

def tupleToCsv[X <: Tuple : RowEncoder](tuple: X): List[String] =

tupleToCsv(("Bob", 42, false)) // List("Bob", 42, false)

How to obtain a tuple from a case class?

Scala 3 introduces the Mirror type-class which provides type-level information about the components and labels of types. A paragraph from that documentation is particularly interesting for our use case:

The compiler automatically generates instances of Mirror for enums and their cases, case classes and case objects, sealed classes or traits having only case classes and case objects as children.

That’s why we can obtain a tuple from a case class using:

val bob: Employee = Employee("Bob", 42, false)
val bobTuple: (String, Int, Boolean) = Tuple.fromProductTyped(bob)

But that is also why we can revert the operation:

val bobAgain: Employee = summon[Mirror.Of[Employee]].fromProduct(bobTuple)

New tuples operations

In the previous example, we saw that we can use .head and .tail on tuples, but Scala 3 introduces many other operations, here is a quick overview:

Operation Example Result
size (1, 2, 3).size 3
head (3 *: 4 *: 5 *: EmptyTuple).head 3
tail (3 *: 4 *: 5 *: EmptyTuple).tail (4, 5)
*: 3 *: 4 *: 5 *: 6 *: EmptyTuple (3, 4, 5, 6)
++ (1, 2, 3) ++ (4, 5, 6) (1, 2, 3, 4, 5, 6)
drop (1, 2, 3).drop(2) (3)
take (1, 2, 3).take(2) (1, 2)
apply (1, 2, 3)(2) 3
splitAt (1, 2, 3, 4, 5).splitAt(2) ((1, 2), (3, 4, 5))
zip (1, 2, 3).zip(('a', 'b')) ((1 'a'), (2, 'b'))
toList (1, 'a', 2).toList List(1, 'a', 2) : List[Int | Char]
toArray (1, 'a', 2).toArray Array(1, '1', 2) : Array[AnyRef]
toIArray (1, 'a', 2).toIArray IArray(1, '1', 2) : IArray[AnyRef]
map (1, 'a').map[[X] =>> Option[X]]([T] => (t: T) => Some(t)) (Some(1), Some('a')) : (Option[Int], Option[Char])

Under the hood: Scala 3 introduces match types

All the operations in the above table use very precise types. For example, the compiler ensures that 3 *: (4, 5, 6) is a (Int, Int, Int, Int) or that the index provided to apply is strictly inferior to the size of the tuple.

How is this possible?

The core new feature that allows such a flexible implementation of tuples are match types. I invite you to read more about them here.

Let’s see how we can implement the ++ operator using this powerful construct. We will call our naive version concat.

Defining tuples

First let’s define our own tuple:

enum Tup:
  case EmpT
  case TCons[H, T <: Tup](head: H, tail: T)

That is a tuple is either empty, or an element head which precedes another tuple. Using this recursive definition we can create a tuple in the following way:

import Tup.*

val myTup = TCons(1, TCons(2,  EmpT))

It is not very pretty, but it can be easily adapted to provide the same ease of use as the previous examples. To do so we can use another Scala 3 feature: extension methods

import Tup.*

extension [A, T <: Tup] (a: A) def *: (t: T): TCons[A, T] =
  TCons(a, t)

So that we can write:

1 *: "2" *: EmpT

Concatenating tuples

Now let’s focus on concat, which could look like this:

import Tup.*

def concat[L <: Tup, R <: Tup](left: L, right: R): Tup =
  left match
    case EmpT => right
    case TCons(head, tail) => TCons(head, concat(tail, right))

Let’s analyze the algorithm line by line: L and R are the type of the left and right tuple. We require them to be a subtype of Tup because we want to concatenate tuples. Then we proceed recursively by case: if the left tuple is empty, the result of the concatenation is just the right tuple. Otherwise the result is the current head followed by the result of concatenating the tail with the other tuple.

If we test the function, it seems to work:

val left = 1 *: 2 *: EmpT
val right = 3 *: 4 *: EmpT

concat(left, right) // TCons(1,TCons(2,TCons(3, TCons(4,EmpT))))

So everything seems good. However we can ask the compiler to verify that the function behaves as expected. For instance the following code type-checks:

def concat[L <: Tup, R <: Tup](left: L, right: R): Tup = left

More problematic is the fact that this signature prevents us from using a more specific type for our variables or methods:

// This does not compile
val res: TCons[Int, TCons[Int, TCons[Int, TCons[Int, EmpT.type]]]] = concat(left, right)

Because the returned type is just a tuple, we do not check anything else. This means that the function can return an arbitrary tuple, the compiler cannot check that returned value consists of the concatenation of the two tuples. In other words, we need a type to indicate that the return of this function is all the types of left followed by all the types of the elements of right.

Can we make it so that the compiler verifies that we are indeed returning a tuple consisting of the correct elements?

In Scala 3 it is now possible, without requiring external libraries!

A new type for the result of concat

We know that we need to focus on the return type. We can define it exactly as we have just described it. Let’s call this type Concat to mirror the name of the function.

type Concat[L <: Tup, R <: Tup] <: Tup = L match
  case EmpT.type => R
  case TCons[headType, tailType] => TCons[headType, Concat[tailType, R]]

You can see that the implementation closely follows the one above for the method. The syntax can be read in the following way: the Concat type is a subtype of Tup and is obtained by combining types L and R which are both subtypes of Tup. To use it we need to massage a bit the method implementation and to change its return type:

def concat[L <: Tup, R <: Tup](left: L, right: R): Concat[L, R] =
  left match
    case _: EmpT.type => right
    case cons: TCons[_, _] => TCons(cons.head, concat(cons.tail, right))

We use here a combination of match types and a form of dependent types called dependent match types (docs here and here). There are some quirks to it as you might have noticed: using lower case types means using type variables and we cannot use pattern matching on the object. I think however that this implementation is extremely concise and readable.

Now the compiler will prevent us from making the above mistake:

def wrong[L <: Tup, R <: Tup](left: L, right: R): Concat[L, R] = left
// This does not compile!

We can use an extension method to allow users to write (1, 2) ++ (3, 4) instead of concat((1, 2), (3, 4)), similarly to how we implemented *:.

We can use the same approach for other functions on tuples, I invite you to have a look at the source code of the standard library to see how the other operators are implemented.

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