Object-Oriented Meets Functional

Have the best of both worlds. Construct elegant class hierarchies for maximum code reuse and extensibility, implement their behavior using higher-order functions. Or anything in-between.

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Scala began life in 2003, created by Martin Odersky and his research group at EPFL, next to Lake Geneva and the Alps, in Lausanne, Switzerland. Scala has since grown into a mature open source programming language, used by hundreds of thousands of developers, and is developed and maintained by scores of people all over the world.
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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.

Traits

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.

Learn More

or visit the Scala Documentation

Author.scala
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: java.io.File): List[Author] = ???
}
App.java
import static scala.collection.JavaConversions.asJavaCollection;

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

    public void sortAuthors(List<Author> authors) {
        Collections.sort(authors);
    }

    public void displaySortedAuthors(File file) {
        List<Author> authors = loadAuthorsFromFile(file);
        sortAuthors(authors);
        for (Author author : authors) {
            System.out.println(
                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 person.name
     | }
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.

Concurrent/Distributed
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.

Traits
abstract class Spacecraft {
  def engage(): Unit
}
trait CommandoBridge extends Spacecraft {
  def engage(): Unit = {
    for (_ <- 1 to 3)
      speedUp()
  }
  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.

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()
}

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.

Go Functional with Higher-Order Functions

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

Scala
val people: Array[Person]

// Partition `people` into two arrays `minors` and `adults`.
// Use the higher-order function `(_.age < 18)` as a predicate for partitioning.
val (minors, adults) = people partition (_.age < 18)
Java
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)
        minors.add(person);
    else
        adults.add(person);
}

In the Scala example on the left, the partition method, available on all collection types (including Array), returns two new collections of the same type. Elements from the original collection are partitioned according to a predicate, which is given as a lambda, i.e., an anonymous function. The _ stands for the parameter to the lambda, i.e., the element that is being tested. This particular lambda can also be written as (x => x.age < 18).

The same program is implemented in Java on the right.

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What's New

blog
SIP Meeting Results: 2 SIPs Numbered and 3 SIPs Updated
date icon Wednesday, July 20, 2016

Last week, the Scala Center ran its first SIP meeting under the newly reformed SIP process, and we’re happy to report that several SIPs saw long-awaited decisions made, or received detailed feedback from the SIP committee!

In the new SIP process, each SIP is assigned a reviewer, who must be someone sitting on the SIP committee. The reviewer’s job is to carefully read and understand the SIP, and to present the SIP before the SIP committee. The reviewer must also present their analysis of the SIP to the committee, and then the floor is opened up for discussion on the SIP. An explicit goal of the new SIP process is for a decision and next steps to be determined for each SIP discussed.

The SIP committee went through five SIPs in the queue. Two new SIPs received a number, meaning they passed the first phase of acceptance – that is, the change to Scala is accepted in theory, so long as the the committee’s design and implementation concerns can be adequately addressed in subsequent discussions on the SIP. The two SIPs which received numbers (first-round accepted) include:

Two other passed their first iteration as proposed by the new process:

The final SIP discussed one was postponed at the request of the authors:

In the case of SIP-22, the authors will reopen it when they feel like taking up the proposal again, and addressing the issues they must look into next; in particular, figuring out how async/await can be used inside of a try/catch block.

Overall, we’re happy to report that progress was made on a number of SIPs in the queue. In particular, we’re excited that 2 SIPs received a first-phase of acceptance in receiving their numbers, and look forward to the addition of these proposals to Scala soon!

Next month, we will propose a reformed SLIP process focused on shaping this new notion of the Scala “platform,” with a completely new committee, consisting of Scala community members. Keep an eye out for developments on the Scala library front this August!

Did you know that each month, we conduct our SIP meetings on-air? You can tune in and ask questions to the SIP committee, and have them answered live! You can even watch past SIP meetings at any time!

Check out the full meeting minutes from the July 13 SIP meeting here, or watch the video of the meeting here.

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A Reformed Scala Improvement Process
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This past May, we held our first advisory board meeting, to discuss some of the areas that the newly-formed Scala Center should focus on. One...
Performance of trait methods
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Scala 2.12.0-M5 is now available!
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We are happy to announce the availability of Scala 2.12.0-M5! Notable changes in M5 (see here for a more extensive list): #5251: concrete trait methods...
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