Scala

History

The design of Scala started in 2001 at the École Polytechnique Fédérale de Lausanne (EPFL) (in Lausanne, Switzerland) by Martin Odersky. It followed on from work on Funnel, a programming language combining ideas from functional programming and Petri nets. Odersky formerly worked on Generic Java, and javac, Sun's Java compiler.

After an internal release in late 2003, Scala was released publicly in early 2004 on the Java platform, A second version (v2.0) followed in March 2006.

On 17 January 2011, the Scala team won a five-year research grant of over €2.3 million from the European Research Council. On 12 May 2011, Odersky and collaborators launched Typesafe Inc. (later renamed Lightbend Inc.), a company to provide commercial support, training, and services for Scala. Typesafe received a $3 million investment in 2011 from Greylock Partners.

Platforms and license

Scala runs on the Java platform (Java virtual machine) and is compatible with existing Java programs. As Android applications are typically written in Java and translated from JVM bytecode into Dalvik bytecode (which may be further translated to native machine code during installation) when packaged, Scala's Java compatibility makes it well-suited to Android development, the more so where functional programming is preferred.

The reference Scala software distribution, including compiler and libraries, is released under the Apache license.

Other compilers and targets

Scala.js is a Scala compiler that compiles to JavaScript, making it possible to write Scala programs that can run in web browsers or Node.js. The compiler, in development since 2013, was announced as no longer experimental in 2015 (v0.6). Version v1.0.0-M1 was released in June 2018 and version 1.1.1 in September 2020.

Scala Native is a Scala compiler that targets the LLVM compiler infrastructure to create executable code that uses a lightweight managed runtime, which uses the Boehm garbage collector. The project is led by Denys Shabalin and had its first release, 0.1, on 14 March 2017. Development of Scala Native began in 2015 with a goal of being faster than just-in-time compilation for the JVM by eliminating the initial runtime compilation of code and also providing the ability to call native routines directly.

A reference Scala compiler targeting the .NET Framework and its Common Language Runtime was released in June 2004, but was officially dropped in 2012.

Examples

"Hello World" example

The Hello World program written in Scala 3 has this form:

Unlike the stand-alone Hello World application for Java, there is no class declaration and nothing is declared to be static.

When the program is stored in file HelloWorld.scala, the user compiles it with the command:

$ scalac HelloWorld.scala

and runs it with

$ scala HelloWorld

This is analogous to the process for compiling and running Java code. Indeed, Scala's compiling and executing model is identical to that of Java, making it compatible with Java build tools such as Apache Ant.

A shorter version of the "Hello World" Scala program is:

Scala includes an interactive shell and scripting support. Saved in a file named HelloWorld2.scala, this can be run as a script using the command:

$ scala HelloWorld2.scala

Commands can also be entered directly into the Scala interpreter, using the option -e:

$ scala -e 'println("Hello, World!")'

Expressions can be entered interactively in the REPL:

Basic example

The following example shows the differences between Java and Scala syntax. The function mathFunction takes an integer, squares it, and then adds the cube root of that number to the natural log of that number, returning the result (i.e.,

n

2

/

3

+

ln

⁡

(

n

2

)

{\displaystyle n^{2/3}+\ln(n^{2})}

):

Some syntactic differences in this code are:

Scala does not require semicolons (;) to end statements.

Value types are capitalized (sentence case): Int, Double, Boolean instead of int, double, boolean.

Parameter and return types follow, as in Pascal, rather than precede as in C.

Methods must be preceded by def.

Local or class variables must be preceded by val (indicates an immutable variable) or var (indicates a mutable variable).

The return operator is unnecessary in a function (although allowed); the value of the last executed statement or expression is normally the function's value.

Instead of the Java cast operator (Type) foo, Scala uses foo.asInstanceOf[Type], or a specialized function such as toDouble or toInt.

Example with classes

The following example contrasts the definition of classes in Java and Scala.

The code above shows some of the conceptual differences between Java and Scala's handling of classes:

Scala has no static variables or methods. Instead, it has singleton objects, which are essentially classes with only one instance. Singleton objects are declared using object instead of class. It is common to place static variables and methods in a singleton object with the same name as the class name, which is then known as a companion object. (The underlying class for the singleton object has a $ appended. Hence, for class Foo with companion object object Foo, under the hood there's a class Foo$ containing the companion object's code, and one object of this class is created, using the singleton pattern.)

In place of constructor parameters, Scala has class parameters, which are placed on the class, similar to parameters to a function. When declared with a val or var modifier, fields are also defined with the same name, and automatically initialized from the class parameters. (Under the hood, external access to public fields always goes through accessor (getter) and mutator (setter) methods, which are automatically created. The accessor function has the same name as the field, which is why it's unnecessary in the above example to explicitly declare accessor methods.) Alternative constructors can also be declared, as in Java. Code that would go into the default constructor (other than initializing the member variables) goes directly at class level.

In Scala it is possible to define operators by using symbols as method names. In place of addPoint, the Scala example defines +=, which is then invoked with infix notation as grid += this.

Default visibility in Scala is public.

Features (with reference to Java)

Scala has the same compiling model as Java and C#, namely separate compiling and dynamic class loading, so that Scala code can call Java libraries.

Scala's operational characteristics are the same as Java's. The Scala compiler generates byte code that is nearly identical to that generated by the Java compiler. In fact, Scala code can be decompiled to readable Java code, with the exception of certain constructor operations. To the Java virtual machine (JVM), Scala code and Java code are indistinguishable. The only difference is one extra runtime library, scala-library.jar.

Scala adds a large number of features compared with Java, and has some fundamental differences in its underlying model of expressions and types, which make the language theoretically cleaner and eliminate several corner cases in Java. From the Scala perspective, this is practically important because several added features in Scala are also available in C#.

Syntactic flexibility

As mentioned above, Scala has a good deal of syntactic flexibility, compared with Java. The following are some examples:

Semicolons are unnecessary; lines are automatically joined if they begin or end with a token that cannot normally come in this position, or if there are unclosed parentheses or brackets.

Any method can be used as an infix operator, e.g. "%d apples".format(num) and "%d apples" format num are equivalent. In fact, arithmetic operators like + and << are treated just like any other methods, since function names are allowed to consist of sequences of arbitrary symbols (with a few exceptions made for things like parens, brackets and braces that must be handled specially); the only special treatment that such symbol-named methods undergo concerns the handling of precedence.

Methods apply and update have syntactic short forms. foo()—where foo is a value (singleton object or class instance)—is short for foo.apply(), and foo() = 42 is short for foo.update(42). Similarly, foo(42) is short for foo.apply(42), and foo(4) = 2 is short for foo.update(4, 2). This is used for collection classes and extends to many other cases, such as STM cells.

Scala distinguishes between no-parens (def foo = 42) and empty-parens (def foo() = 42) methods. When calling an empty-parens method, the parentheses may be omitted, which is useful when calling into Java libraries that do not know this distinction, e.g., using foo.toString instead of foo.toString(). By convention, a method should be defined with empty-parens when it performs side effects.

Method names ending in colon (:) expect the argument on the left-hand-side and the receiver on the right-hand-side. For example, the 4 :: 2 :: Nil is the same as Nil.::(2).::(4), the first form corresponding visually to the result (a list with first element 4 and second element 2).

Class body variables can be transparently implemented as separate getter and setter methods. For trait FooLike { var bar: Int }, an implementation may be object Foo extends FooLike { private var x = 0; def bar = x; def bar_=(value: Int) { x = value }} } }. The call site will still be able to use a concise foo.bar = 42.

Unified type system

Java makes a sharp distinction between primitive data types (e.g., int and boolean) and reference data types (any class). Only reference types are part of the inheritance scheme, deriving from java.lang.Object. In Scala, all types inherit from a top-level class Any, whose immediate children are AnyVal (value types, such as Int and Boolean) and AnyRef (reference types, as in Java). This means that the Java distinction between primitive types and boxed types (e.g. int vs. Integer) is not present in Scala; boxing and unboxing is fully transparent to users. Scala 2.10 allows new value types to be user-defined.

For-expressions

Instead of the Java "foreach" loops for looping through an iterator, Scala has for-expressions, which are similar to list comprehensions in languages such as Haskell, or a combination of list comprehensions and generator expressions in Python. For-expressions using the yield keyword allow a new collection to be generated by iterating over an existing one, returning a new collection of the same type. They are translated by the compiler into a series of map, flatMap and filter calls. Where yield is not used, the code approximates to an imperative-style loop, by translating to foreach.

A simple example is:

The result of running it is the following vector:

Vector(16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50)

(The expression 1 to 25 is not special syntax. The method to is rather defined in the standard Scala library as an extension method on integers, using a technique known as implicit conversions that allows new methods to be added to existing types.)

A more complex example of iterating over a map is:

Expression (mention, times) <- mentions is an example of pattern matching (see below). Iterating over a map returns a set of key-value tuples, and pattern-matching allows the tuples to easily be destructured into separate variables for the key and value. Similarly, the result of the comprehension also returns key-value tuples, which are automatically built back up into a map because the source object (from the variable mentions) is a map. If mentions instead held a list, set, array or other collection of tuples, exactly the same code above would yield a new collection of the same type.

Functional tendencies

While supporting all of the object-oriented features available in Java (and in fact, augmenting them in various ways), Scala also provides a large number of capabilities that are normally found only in functional programming languages. Together, these features allow Scala programs to be written in an almost completely functional style and also allow functional and object-oriented styles to be mixed.

Examples are:

No distinction between statements and expressions

Type inference

Anonymous functions with capturing semantics (i.e., closures)

Immutable variables and objects

Lazy evaluation

Delimited continuations (since 2.8)

Higher-order functions

Nested functions

Currying

Pattern matching

Algebraic data types (through case classes)

Tuples

Unlike C or Java, but similar to languages such as Lisp, Scala makes no distinction between statements and expressions. All statements are in fact expressions that evaluate to some value. Functions that would be declared as returning void in C or Java, and statements like while that logically do not return a value, are in Scala considered to return the type Unit, which is a singleton type, with only one object of that type. Functions and operators that never return at all (e.g. the throw operator or a function that always exits non-locally using an exception) logically have return type Nothing, a special type containing no objects; that is, a bottom type, i.e. a subclass of every possible type. (This in turn makes type Nothing compatible with every type, allowing type inference to function correctly.)

Object-oriented extensions

Scala is a pure object-oriented language in the sense that every value is an object. Data types and behaviors of objects are described by classes and traits. Class abstractions are extended by subclassing and by a flexible mixin-based composition mechanism to avoid the problems of multiple inheritance.

Traits are Scala's replacement for Java's interfaces. Interfaces in Java versions under 8 are highly restricted, able only to contain abstract function declarations. This has led to criticism that providing convenience methods in interfaces is awkward (the same methods must be reimplemented in every implementation), and extending a published interface in a backwards-compatible way is impossible. Traits are similar to mixin classes in that they have nearly all the power of a regular abstract class, lacking only class parameters (Scala's equivalent to Java's constructor parameters), since traits are always mixed in with a class. The super operator behaves specially in traits, allowing traits to be chained using composition in addition to inheritance. The following example is a simple window system:

A variable may be declared thus:

The result of calling mywin.draw() is:

In other words, the call to draw first executed the code in TitleDecoration (the last trait mixed in), then (through the super() calls) threaded back through the other mixed-in traits and eventually to the code in Window, even though none of the traits inherited from one another. This is similar to the decorator pattern, but is more concise and less error-prone, as it doesn't require explicitly encapsulating the parent window, explicitly forwarding functions whose implementation isn't changed, or relying on run-time initialization of entity relationships. In other languages, a similar effect could be achieved at compile-time with a long linear chain of implementation inheritance, but with the disadvantage compared to Scala that one linear inheritance chain would have to be declared for each possible combination of the mix-ins.

Expressive type system

Scala is equipped with an expressive static type system that mostly enforces the safe and coherent use of abstractions. The type system is, however, not sound. In particular, the type system supports:

Classes and abstract types as object members

Structural types

Path-dependent types

Compound types

Explicitly typed self references

Generic classes

Polymorphic methods

Upper and lower type bounds

Type variance

Annotation

Views

Scala is able to infer types by use. This makes most static type declarations optional. Static types need not be explicitly declared unless a compiler error indicates the need. In practice, some static type declarations are included for the sake of code clarity.

Type enrichment

A common technique in Scala, known as "enrich my library" (originally termed "pimp my library" by Martin Odersky in 2006; concerns were raised about this phrasing due to its negative connotations and immaturity), allows new methods to be used as if they were added to existing types. This is similar to the C# concept of extension methods but more powerful, because the technique is not limited to adding methods and can, for instance, be used to implement new interfaces. In Scala, this technique involves declaring an implicit conversion from the type "receiving" the method to a new type (typically, a class) that wraps the original type and provides the additional method. If a method cannot be found for a given type, the compiler automatically searches for any applicable implicit conversions to types that provide the method in question.

This technique allows new methods to be added to an existing class using an add-on library such that only code that imports the add-on library gets the new functionality, and all other code is unaffected.

The following example shows the enrichment of type Int with methods isEven and isOdd:

Importing the members of MyExtensions brings the implicit conversion to extension class IntPredicates into scope.

Concurrency

Scala's standard library includes support for futures and promises, in addition to the standard Java concurrency APIs. Originally, it also included support for the actor model, which is now available as a separate source-available platform Akka licensed by Lightbend Inc. Akka actors may be distributed or combined with software transactional memory (transactors). Alternative communicating sequential processes (CSP) implementations for channel-based message passing are Communicating Scala Objects, or simply via JCSP.

An Actor is like a thread instance with a mailbox. It can be created by system.actorOf, overriding the receive method to receive messages and using the ! (exclamation point) method to send a message.

The following example shows an EchoServer that can receive messages and then print them.

Scala also comes with built-in support for data-parallel programming in the form of Parallel Collections integrated into its Standard Library since version 2.9.0.

The following example shows how to use Parallel Collections to improve performance.

Besides futures and promises, actor support, and data parallelism, Scala also supports asynchronous programming with software transactional memory, and event streams.

Cluster computing

The most well-known open-source cluster-computing solution written in Scala is Apache Spark. Additionally, Apache Kafka, the publish–subscribe message queue popular with Spark and other stream processing technologies, is written in Scala.

Testing

There are several ways to test code in Scala. ScalaTest supports multiple testing styles and can integrate with Java-based testing frameworks. ScalaCheck is a library similar to Haskell's QuickCheck. specs2 is a library for writing executable software specifications. ScalaMock provides support for testing high-order and curried functions. JUnit and TestNG are popular testing frameworks written in Java.

Comparison with other JVM languages

Scala is often compared with Groovy and Clojure, two other programming languages also using the JVM. Substantial differences between these languages exist in the type system, in the extent to which each language supports object-oriented and functional programming, and in the similarity of their syntax to that of Java.

Scala is statically typed, while both Groovy and Clojure are dynamically typed. This makes the type system more complex and difficult to understand but allows almost all type errors to be caught at compile-time and can result in significantly faster execution. By contrast, dynamic typing requires more testing to ensure program correctness, and thus is generally slower, to allow greater programming flexibility and simplicity. Regarding speed differences, current versions of Groovy and Clojure allow optional type annotations to help programs avoid the overhead of dynamic typing in cases where types are practically static. This overhead is further reduced when using recent versions of the JVM, which has been enhanced with an invoke dynamic instruction for methods that are defined with dynamically typed arguments. These advances reduce the speed gap between static and dynamic typing, although a statically typed language, like Scala, is still the preferred choice when execution efficiency is very important.

Regarding programming paradigms, Scala inherits the object-oriented model of Java and extends it in various ways. Groovy, while also strongly object-oriented, is more focused in reducing verbosity. In Clojure, object-oriented programming is deemphasised with functional programming being the main strength of the language. Scala also has many functional programming facilities, including features found in advanced functional languages like Haskell, and tries to be agnostic between the two paradigms, letting the developer choose between the two paradigms or, more frequently, some combination thereof.

Regarding syntax similarity with Java, Scala inherits much of Java's syntax, as is the case with Groovy. Clojure on the other hand follows the Lisp syntax, which is different in both appearance and philosophy.

Adoption

Language rankings

Back in 2013, when Scala was in version 2.10, the ThoughtWorks Technology Radar, which is an opinion based biannual report of a group of senior technologists, recommended Scala adoption in its languages and frameworks category.

In July 2014, this assessment was made more specific and now refers to a "Scala, the good parts", which is described as "To successfully use Scala, you need to research the language and have a very strong opinion on which parts are right for you, creating your own definition of Scala, the good parts.".

In the 2018 edition of the State of Java survey, which collected data from 5160 developers on various Java-related topics, Scala places third in terms of use of alternative languages on the JVM. Relative to the prior year's edition of the survey, Scala's use among alternative JVM languages fell from 28.4% to 21.5%, overtaken by Kotlin, which rose from 11.4% in 2017 to 28.8% in 2018.

The Popularity of Programming Language Index, which tracks searches for language tutorials, ranked Scala 15th in April 2018 with a small downward trend, and 17th in Jan 2021. This makes Scala the 3rd most popular JVM-based language after Java and Kotlin, ranked 12th.

The RedMonk Programming Language Rankings, which establishes rankings based on the number of GitHub projects and questions asked on Stack Overflow, in January 2021 ranked Scala 14th. Here, Scala was placed inside a second-tier group of languages–ahead of Go, PowerShell, and Haskell, and behind Swift, Objective-C, TypeScript, and R.

The TIOBE index of programming language popularity employs internet search engine rankings and similar publication counting to determine language popularity. In September 2021, it showed Scala in 31st place. In this ranking, Scala was ahead of Haskell (38th) and Erlang, but below Go (14th), Swift (15th), and Perl (19th).

As of 2022, JVM-based languages such as Clojure, Groovy, and Scala are highly ranked, but still significantly less popular than the original Java language, which is usually ranked in the top three places.

Companies

In April 2009, Twitter announced that it had switched large portions of its backend from Ruby to Scala and intended to convert the rest.

Tesla, Inc. uses Akka with Scala in the backend of the Tesla Virtual Power Plant. Thereby, the Actor model is used for representing and operating devices that together with other components make up an instance of the virtual power plant, and Reactive Streams are used for data collection and data processing.

Apache Kafka is implemented in Scala with regards to most of its core and other critical parts. It is maintained and extended through the open source project and by the company Confluent.

Gilt uses Scala and Play Framework.

Foursquare uses Scala and Lift.

Coursera uses Scala and Play Framework.

Apple Inc. uses Scala in certain teams, along with Java and the Play framework.

The Guardian newspaper's high-traffic website guardian.co.uk announced in April 2011 that it was switching from Java to Scala.

The New York Times revealed in 2014 that its internal content management system Blackbeard is built using Scala, Akka, and Play.

The Huffington Post newspaper started to employ Scala as part of its content delivery system Athena in 2013.

Swiss bank UBS approved Scala for general production use.

LinkedIn uses the Scalatra microframework to power its Signal API.

Meetup uses Unfiltered toolkit for real-time APIs.

Criticism

In November 2011, Yammer moved away from Scala for reasons that included the learning curve for new team members and incompatibility from one version of the Scala compiler to the next. In March 2015, former VP of the Platform Engineering group at Twitter Raffi Krikorian, stated that he would not have chosen Scala in 2011 due to its learning curve. The same month, LinkedIn SVP Kevin Scott stated their decision to "minimize [their] dependence on Scala".