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Old Design Patterns in Scala

Posted 2016-11-29
Benchmarking Scala CollectionsImplicit Design Patterns in Scala

A Design Pattern is something you do over and over when building software, but isn't concrete enough to be made into a helper method, class or other abstraction. The "Gang of Four" Design Patterns book popularized the idea, and discussed in detail many of the design patterns which are common in C++, Java and similar "Object Oriented" languages.

The Scala programming language in 2016 is different from languages common 22 years ago. While some of the traditional design patterns still apply, others have changed significantly, and yet others have been entirely superseded by new language features. This post will explore how some of these old design patterns apply to the Scala programming language.

About the Author: Haoyi is a software engineer, and the author of many open-source Scala tools such as the Ammonite REPL and the Mill Build Tool. If you enjoyed the contents on this blog, you may also enjoy Haoyi's book Hands-on Scala Programming

What is a Design Pattern?

A "Design Pattern" is a common solution to a common problem that isn't concrete enough to be packaged up as a helper method, class or module.

Implementing a design pattern isn't as simple as using a language feature, calling a method or instantiating a class; rather than concrete code, a design pattern is a high-level sketch of a solution, and you still have to write all the code yourself in order to make something useful. If the "commonality" is concrete enough to make into a helper method, then you make it into a helper method and it is no longer called a design pattern! Hence the idea of "design patterns" will always have a bit of vagueness to it.

However, that doesn't mean design patterns are without value. Knowing these patterns helps you quickly identify common techniques in other peoples' code, provides a toolbox of common ways you can approach common problems, and provides a common vocabulary to explain or discuss implementations (real or imagined) with other developers. For example, "This is a builder object" tells you what it's for much faster and more effectively than reading through dozens of method signatures or hundreds of lines of code.

Some say that design patterns are missing language features: this may be true, but that fact is uninteresting. No matter how many features a language has or how flexible they are, they will never be able to satisfy the ever-growing set of things that people will want to do with a programming language! As programming languages gets more flexible and powerful, people will use them for ever more complex things, and there will be always be "missing language features" at the margin.

The Design Patterns book describes a dozen or two different patterns:

There are many other books discussing design patterns, and countless other patterns have been described and analysed. Some of these patterns are broadly applicable to most large codebases, while others are specialized and only relevant within a particular field.

There are too many design patterns to discuss them all in this post. Instead I will bring up just a few to highlight how those design patterns change when applied to Scala, or if they apply at all.


The intent of the Builder design pattern is to separate the construction of a complex object from its representation. - Wikipedia

The Builder pattern takes a class e.g. Car and defines a matching CarBuilder class that you can use to build a Car. For example, in Java syntax:

CarBuilder carBuilder = new CarBuilder()
Car car = carBuilder.build()

This requires a bit of ceremony to define a whole new class with all the set methods, but has distinct advantages over using a constructor:

Car car = new Car(2, true, true, false)

The builder code lets you to associate names with each of the "constructor arguments" you are setting via setXXX methods, and lets you to provide defaults for each argument. In a language like Java, you cannot name the constructor arguments while passing them, making it easy to mix up which true or false is going into which aregument. Furthermore, in Java you also cannot provide arguments with default values. That means that if the Car had a few more arguments that were always false, we'd still need to pass them all in every time, leading to code like:

Car car = new Car(2, true, true, false, false, false, false, false, false)
Car car = new Car(2, true, true, false, false, false, true, false, false)

The builder pattern improves on this by making it clear which arguments you are setting to what values, and letting you ignore the arguments you don't care about. While verbose to use and even more verbose to implement, it's sufficiently more readable than the sea of true, false, false, false, falses that in many cases it's worth it.

In a language like Scala which lets you name arguments while passing them in, the builder pattern is mostly obsolete:

val car = new Car(
  seats = 2, 
  isSportsCar = true, 
  hasTripComputer = true, 
  hasGPS = false
  // no need to pass in arguments with default values

For the vast majority of cases, passing in arguments by name and setting defaults is enough: you can see what each argument is being set to which value, and you don't need to pass in the uninteresting arguments which have a default value. For the majority of cases, having a builder doesn't give you any additional value.

There are still cases where you want a builder, but those are relatively uncommon. For example, the Akka-Streams library uses a mutable builder to construct it's data-flow graphs before they are "materialized" and executed:

val g = RunnableGraph.fromGraph(GraphDSL.create() { implicit builder: GraphDSL.Builder[NotUsed] =>
  import GraphDSL.Implicits._
  val in = Source(1 to 10)
  val out = Sink.ignore
  val bcast = builder.add(Broadcast[Int](2))
  val merge = builder.add(Merge[Int](2))
  val f1, f2, f3, f4 = Flow[Int].map(_ + 10)
  in ~> f1 ~> bcast ~> f2 ~> merge ~> f3 ~> out
  bcast ~> f4 ~> merge

In this case the builder is used to construct a relatively complex data structure that can't easily be passed as constructor arguments. These cases still exist, but are relatively few and far between. In the rare case a builder class will save you complexity (e.g. above, separating the messy graph-construction phase from the equally-messy execution phase) you can still use one, but mostly you should not need to.


In software engineering, the singleton pattern is a design pattern that restricts the instantiation of a class to one object. This is useful when exactly one object is needed to coordinate actions across the system. - Wikipedia

The Singleton pattern lets you define a static object with only one instance:

public final class MyFoo {
    private static final MyFoo INSTANCE = new MyFoo();
    private Singleton() {}

    public static MyFoo getInstance() {
        return INSTANCE;
    // ... implementation of Singleton ...

This has the advantage over using static methods directly on MyFoo because the singleton, as a "real" object, can implement interfaces, inherit functionality from other classes, and be passed around as any other object would:

public final class MyFoo extends MySuperclass implements Fooable{
    private static final MyFoo INSTANCE = new MyFoo();
    private Singleton() {}

    public static MyFoo getInstance() {
        return INSTANCE;
    // ... implementation of Singleton class ...
// ... in other code ...

A class with static methods can't do any of that.

class MyFoo{
    // bunch of static methods

FooHandler.processFoo(MyFoo)// doesn't work

The singleton pattern is baked into the Scala language, using the object keyword:

object MyFoo extends MySuperclass with Fooable{
    // ... implementation of Singleton ...

FooHandler.processFoo(MyFoo) // works

Thus in Scala, "Singleton" is obsolete as a design pattern: you no longer need to think about doing the INSTANCE/getInstance/private-constructor dance throughout your codebase. You just use object and get everything that Singleton gives you for free.


In software engineering, the adapter pattern is a software design pattern that allows the interface of an existing class to be used as another interface. It is often used to make existing classes work with others without modifying their source code. - Wikipedia

The Adapter pattern is when you wrap a class that doesn't implement an interface in a wrapper-class which does. For example, if you have a ClassA which you need to make implement the StringProvider interface, but you can't change either of them (maybe ClassA comes from third-party library? And StringProvider from another third-party library?) you can wrap it in an adapter:

public interface StringProvider {
    public String getStringData();

public class ClassAFormatAdapter implements StringProvider {
    private ClassA myA = null;

    public ClassAFormatAdapter1(final ClassA A) {
        myA = A;

    public String getStringData() {
        return Helpers.formatNicely(classA);

// ... in other code ...
ClassA thing = ...
StringHandler.handleStringProvider(new ClassAFormatAdapter(thing))

The adapter pattern is basically identical in Scala, just much less verbose:

class ClassAFormatAdapter(myA: ClassA) extends StringProvider{
  def getStringData() = Helpers.formatNicely(myA)

This is basically identical to the Java example above, just using Scala syntax. Just like in Java, you can new ClassAFormatAdapter(myClassA) to wrap it in something you can pass to some library that only takes StringProviders:

// ... in other code ...
val thing: ClassA = ...
StringHandler.handleStringProvider(new ClassAFormatAdapter(thing))

But in Scala, you can go one step further and make the adapter implicit:

implicit class ClassAFormatAdapter(myA: ClassA) extends StringProvider{
  def getStringData() = Helpers.formatNicely(myA)
// ... in other code ...
val thing: ClassA = ...

With an implicit adapter, the instantiation of the adapter-wrapper happens entirely automatically depending on the expected types. While this may feel magical and confusing if overused, in many cases the new ClassAFormatAdapter(...) calls are just uninteresting boilerplate, and making the instantiation implicit can cut through the boilerplate and let you focus on the underlying logic that actually matters.

In general you use Adapters much more in Scala than you do in Java, largely due to the reduction in verbosity. Many libraries will have adapters to turn built-in types into library-specific types:

While Java requires you to define an adapter in a whole new file, with many lines of boilerplate and more boilerplate at the use-site, Scala lets you define an adapter in just 3 lines of code and the use-site is boilerplate free. Thus if you find yourself needing to define a small adapter class to make a value fit into some parameter, it's easy enough you should just do it without much thought.

Chain of Responsibility

In object-oriented design, the chain-of-responsibility pattern is a design pattern consisting of a source of command objects and a series of processing objects. Each processing object contains logic that defines the types of command objects that it can handle; the rest are passed to the next processing object in the chain. A mechanism also exists for adding new processing objects to the end of this chain. - Wikipedia

Using an example from Wikipedia, which models a chain of individuals in an organization who can approve a purchase (depending on price), you first define a bunch of PurchasePower classes:

class ManagerPPower extends PurchasePower {
    protected double getAllowable(){
        return BASE*10;

    protected String getRole(){
        return "Manager";

Each one has a getAllowable() method or similar, which defines what kind of input it is allowed to process. The PurchasePower class then has logic to take an input, process it if it's allowed to, and otherwise delegate the processing to the next processor in the chain:

public void processRequest(PurchaseRequest request){
    if (request.getAmount() < this.getAllowable()) {
        System.out.println(this.getRole() + " will approve $" + request.getAmount());
    } else if (successor != null) {

To use this, you instantiate a bunch of PurchasePower processors, link them into a chain, and then ask the chain to process some input:

ManagerPPower manager = new ManagerPPower();
DirectorPPower director = new DirectorPPower();
VicePresidentPPower vp = new VicePresidentPPower();
PresidentPPower president = new PresidentPPower();

System.out.println("Enter the amount to check who should approve your expenditure.");
double d = Double.parseDouble(new BufferedReader(new InputStreamReader(System.in)).readLine());
manager.processRequest(new PurchaseRequest(d, "General"));

The Chain of Responsibility pattern is still present and still common in Scala, but with two tweaks:

Pattern Matching Partial Functions

Scala already has a basic implementation of the Chain of Command pattern built into the language: PartialFunction[T, V]. For example, here is some code from the Ammonite project which defines an error-handling partial function, checking the input Throwable against a list of possible conditions and handling each one separately:

val userCodeExceptionHandler: PartialFunction[Throwable, Res.Failing] = {
  // Exit
  case Ex(_: InvEx, _: InitEx, ReplExit(value))  => Res.Exit(value)

  // Interrupted during pretty-printing
  case Ex(e: ThreadDeath)                 =>  interrupted(e)

  // Interrupted during evaluation
  case Ex(_: InvEx, e: ThreadDeath)       =>  interrupted(e)

  case Ex(_: InvEx, _: InitEx, userEx@_*) => Res.Exception(userEx(0), "")
  case Ex(_: InvEx, userEx@_*)            => Res.Exception(userEx(0), "")
  case Ex(userEx@_*)                      => Res.Exception(userEx(0), "")

While a pattern-match is similar to a switch statement or chain of if-elses, by assigning it to a partial function you can then manipulate it in some limited ways. For example, you can combine PartialFunctions using orElse, meaning the above could be written as:

val exitHandler: PartialFunction[Throwable, Res.Failing] = {
  case Ex(_: InvEx, _: InitEx, ReplExit(value))  => Res.Exit(value)
val prettyPrintFailedHandler: PartialFunction[Throwable, Res.Failing] = {
  case Ex(e: ThreadDeath)                 =>  interrupted(e)

val simpleFailureHandler: PartialFunction[Throwable, Res.Failing] = {
  case Ex(_: InvEx, e: ThreadDeath)       =>  interrupted(e)

  case Ex(_: InvEx, _: InitEx, userEx@_*) => Res.Exception(userEx(0), "")
  case Ex(_: InvEx, userEx@_*)            => Res.Exception(userEx(0), "")
  case Ex(userEx@_*)                      => Res.Exception(userEx(0), "")
val userCodeExceptionHandler = {

What you can't do with PartialFunctions is inspect them in any way, as they are entirely opaque:

If you do not need to do these things, pattern-matching PartialFunctions works great. For example, the Play Framework's "SIRD" Router uses partial functions to define routes:

val router = Router.from {
  case GET(p"/hello/$to") => Action {
    Results.Ok(s"Hello $to")
val server = NettyServer.fromRouter() {
 case GET(p"/posts/") => Action {
    Results.Ok(”All posts")
  case GET(p"/posts/$id") => Action {
    Results.Ok(“Post:" + id ) 

For simple use cases, it works great. But if you do want to do any of the things that partial functions don't support, you will have to use a normal Chain of Command with structured objects as handlers.


The Scala language encourages immutability, and thus instead of using .setSuccessor methods, Chain of Command chains tend to be constructed in-place as one large expression. Examples in the wild include Akka-HTTP's routes chains together the handlers using the ~ operator:

val route = get {
  pathSingleSlash {
    complete(HttpEntity(ContentTypes.`text/html(UTF-8)`,"<html><body>Hello world!</body></html>"))
  } ~
  path("ping") {
  } ~
  path("crash") {

Each path(string){body} call defines the URL that that handler can accept, as well as the logic that handler will actually perform when it accepts something. The above example defines three route-handlers that will be tried one after the other when a HTTP request comes in. Unlike the partial functions shown earlier, these Route data structures aren't totally opaque, and in theory you could take advantage of that to optimize or transform them after construction.


The basic idea is to have a class for each symbol (terminal or nonterminal) in a specialized computer language. The syntax tree of a sentence in the language is an instance of the composite pattern and is used to evaluate (interpret) the sentence for a client. - Wikipedia

The Interpreter pattern centers around having a base interface with a single abstract method. To use an example from springframework.guru, here is an interface representing an arithmetic expression, with an interpret method:

public interface Expression {
    int interpret();

From there you define multiple subclasses of Expression: one or more terminal expression types, e.g.

public class NumberExpression implements Expression{
    private int number;
    public NumberExpression(int number){
    public int interpret(){
        return this.number;

As well as one or more "compound" expression types, such as:

public class AdditionExpression implements Expression {
    private Expression firstExpression, secondExpression;
    public AdditionExpression(Expression firstExpression, Expression secondExpression){
        this.firstExpression = firstExpression;
        this.secondExpression = secondExpression;
    public int interpret(){
        return this.firstExpression.interpret()+this.secondExpression.interpret();

Using this, you construct a tree of expressions, call interpret, which recursively calls the equivalent interpret method on all its children and grandchildren in the tree, eventually returning the final result. Here the result is an int that represents the sum of all the expression objects, representing an arithmetic expression, but there can be endless variations:

Often, you end up writing a parser to construct your expression objects from a String. While not strictly part of the Interpreter pattern, almost all examples of it include a parser because constructing everything as nested new invocations gets verbose:

new AdditionExpression(
    new NumberExpression(1),
    new AdditionExpression(
        new NumberExpression(2),
        new NumberExpression(3)

Instead, you take a string as input:


And parse it into the Expression objects before calling interpret. The parser logic isn't shown here due to verbosity but the Wikipedia example shows one possible implementation.

In Scala, the Interpreter pattern still exists mostly unchanged, and is used in libraries like:

However, there are some tweaks that are distinct to using Interpreter in Scala:

Defining the Expression classes is much simpler:

class NumberExpression(number: Int) extends Expression{
  def interpret = number
class AdditionExpression(firstExpression: Expression, 
                         secondExpression: Expression) extends Expression {
  def interpret() = firstExpression.interpret() + secondExpression.interpret()

No need to deal with private variables and constructors initializing those variables and all that. This is purely a syntactic change (the underlying model is exactly the same) but makes it much quicker to get started defining your Expressions so you can start using them

You often do not use a parser

While initializing nested Expressions is verbose and annoying in Java or C++, in Scala it is possible to make the syntax much more concise. For example, Scalatags lets you define your nested Frag objects as such:

val frag = html(
    h1(id := "my-title")(
    p(backgroundColor := "red")(


Which in Java would be something like

Frag frag = new HtmlFrag(new Attr[]{},
  new HtmlFrag(
    new Header1Frag(new Attr[]{new Attr("id", "my-title")},
      new StringFrag("Hello")
    new ParagraphFrag(new Attr[]{new Attr("background-color", "red")},
      new StringFrag("Hello")


Similarly, FastParse lets you define a parser as:

val myParser = P( "a".rep ~ ("b" | "c" | "d") ~ End)


Using custom operators like ~ and | to create the Sequence and Either parsers for you, extension methods to add the .rep method on the string literal "a", and macros to easily capture the name "myParser" to use in the debugging output.

The naive equivalent in Java would be

Parser myParser = new NamedParser("parser",
    new SequenceParser(
        new RepeatParser(
            new StringParser("a")
        new EitherParser(
            new StringParser("a"),
            new StringParser("b"),
            new StringParser("c")


This is a relatively "naive" approach to instantiating the Frag and Parser objects in code, and it could be cleaned up with some cleverness. Nonetheless, the "in-code" instantiations of complex Expression objects is generally always many times more verbose in Java or C++ than in Scala, and hence while the Interpreter pattern traditionally is almost always paired with a parser to build the Expression from a string, in Scala people prefer to create their Expressions in code using Scala's lightweight syntax

Apart from the reduction in verbosity, and the fact that people tend to prefer creating their expression-trees in-code rather than parsing it from a string, the interpreter pattern is still heavily used in Scala libraries and projects.


The observer pattern is a software design pattern in which an object, called the subject, maintains a list of its dependents, called observers, and notifies them automatically of any state changes, usually by calling one of their methods. - Wikipedia

The Observer pattern is another pattern that is still prevalant in Scala. It's common in UI programming, such as in Swing or using the HTML DOM (using Scala.js) where you have inputs such as text-boxes or select-dropdowns whose values can change, and you want to perform some action when they change.

Typical usage code in Java would look like:

EventSource eventSource = ...

// New java 8 style with lambda expressions
eventSource.addObserver( (Observable obj, Object arg) -> { 
    System.out.println("Received response: " + arg);

Where eventSource usually comes from elsewhere, and you want to do something whenever it updates. In Scala (Scala.js), the equivalent code may look like

val element: html.Element =   ...
element.addEventListener("mouseenter", (e: dom.Event) => {
  currentlyShown = true

Which is more or less identical.

While the "raw" Observer pattern is still common in Scala, there are a number of wrappers around the Observer pattern that are commonly used in particular use cases


In the case where an event can only trigger once, it is usually best to encapsulate the observer pattern using a scala.concurrent.Future:

val myPromise = Promise[Int]()
val myFuture: Future[Int] = p.future

  case x => println(x)

myPromise.success(123) // prints 123

A Promise[T] is essentially a generic one-shot event source, and it's .future: Future[T] is interface that external observers can register themselves. The .success(...) method fires the event, causing all observers to be notified.

Futures tend to be much easier to work with than raw callback/observer-style interfaces: you can easily .map them to transform the output, .zip them if you want the results when two Futures are both complete, Future.sequence if you want to wait on a whole list of Futures, or .flatMap if you want to kick off a new Future depending on the result of the previous one.

Under the hood, Future[T] and Promise[T] is all still implemented using Observers and callbacks, but most of the gnarly and error-prone parts are nicely encapsulate in the Future so you don't trip up over them. Things like:

Not every callback/observer-style API can be replaced by Futures: apart from only allowing a single event to trigger, Futures also don't provide any flexibility over managing/de-registering observers after they've subscribed: once you've called .foreach on a callback, there's no turning back. Nevertheless, in Scala you tend to use scala.concurrent.Future over raw Observers whenever possible.


Futures only allow for a single event; that is how they are defined. However, there are other related projects that provide a similar style API that works for long-lasting, multi-event streams. Some of those are:

Each of these has a different design, style and tradeoffs, and none of them are as "standard" as scala.concurrent.Future that is in the standard library. Nonetheless, they are all easily available on Maven Central, and can be used in your own projects just by adding the dependency in your build file. All of them provide a nice layer over raw Observers, and are worth looking at if you find your observer/callback-style code is getting out of hand.


While you shouldn't obsess over design patterns, neither should you ignore them complete and re-invent the world from first principles. Design patterns are a common vocabulary of common solutions to common problems, and being able to identify them helps you analyze programs and discuss them in a higher-level way without getting lost in the weeds. "you need to register an Observer" or "X is a builder for Y" is enough for someone to understand the high-level shape of a piece of code, even if he hasn't dug through all the method names, signatures, and thousands of lines of code.

While many of the design patterns as originally described in 1994 do not directly apply to the Scala language, many of them still apply with some tweaks or modifications. Some, like Singleton, have been folded into the language so well you no longer think of them as a "pattern". Others, like Builder or Chain of Responsibility, have had much of their use-cases replaced by core language features, but are still used as design-patterns in more complex cases. Then there are those like Adapter or Interpreter which apart from a significant reduction in verbosity, work about the same as they always have.

This post only looks at how historical "Design Patterns" apply to the Scala programming language, and does not look at what new design patterns have emerged that are unique to the Scala language. That would be a topic for a future post!

About the Author: Haoyi is a software engineer, and the author of many open-source Scala tools such as the Ammonite REPL and the Mill Build Tool. If you enjoyed the contents on this blog, you may also enjoy Haoyi's book Hands-on Scala Programming

Benchmarking Scala CollectionsImplicit Design Patterns in Scala

Updated 2016-11-30 2016-11-29