Functional thinking

Functional design patterns, Part 3

The Interpreter pattern and extending the language


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This content is part of the series:Functional thinking

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This Functional thinking installment continues my investigation of alternate, functional solutions to Gang of Four (GoF) design patterns (see Related topics). In this article, I investigate the least understood but most powerful of those patterns: Interpreter.

The definition of Interpreter is:

Given a language, define a representation for its grammar along with an interpreter that uses the representation to interpret sentences in the language.

In other words, if the language you are using isn't appropriate for the problem, use it to build a language that is. Good examples of this approach appear in web frameworks like Grails and Ruby on Rails (see Related topics), which extend their base languages (Groovy and Ruby, respectively) to make it easier to write web applications.

This pattern is the least understood because it's uncommon to build a new language, so the skills and idioms required are specialized. It is the most powerful of the design patterns because it encourages you to extend your programming language toward the problem you are solving. This is a common ethos in the Lisp (and therefore Clojure) worlds but less common in mainstream languages.

When using languages (such as Java) that disallow extensions to the language itself, developers tend to mold their thoughts into the syntax of the language; it's your only choice. However, when you become accustomed to working in languages that allow painless extension, you start bending the language toward the problem solution, not the other way around.

Java lacks straightforward language-extension mechanisms unless you resort to aspect-oriented programming. However, the next-generation JVM languages (Groovy, Scala, and Clojure) (see Related topics) all allow extensions in a variety of ways. In doing so, they meet the intent of the Interpreter design pattern. First, I show how to implement operator overloading in all three languages, then show how Groovy and Scala let you extend existing classes.

Operator overloading

A common feature of functional languages is operator overloading — the ability to redefine operators (such as +, -, or *) to work with new types and exhibit new behaviors. Omission of operator overloading was a conscious decision during Java's formative period, but virtually every modern language now features it, including the natural successors to Java on the JVM.


Groovy tries to update Java's syntax to the current century while preserving its natural semantics. Thus, Groovy allows operator overloading by automatically mapping operators to method names. For example, if you want to overload Integer's + operator, you override the Integer class's plus() method. The entire list of mappings is available online (see Related topics); Table 1 shows a partial list:

Table 1. Partial list of Groovy operator/method mappings
x +
x * yx.multiply(y)
x / yx.div(y)
x ** yx.power(y)

As an example of operator overloading, I'll create a ComplexNumber class in both Groovy and Scala. Complex numbers are a mathematical concept with both a real and imaginary part, typically written as, for example, 3 + 4i. Complex numbers are common in many scientific fields, including engineering, physics, electromagnetism, and chaos theory. Developers writing applications in those fields greatly benefit from the ability to create operators that mirror their problem domain. (For more information on complex numbers, see Related topics.)

A Groovy ComplexNumber class appears in Listing 1:

Listing 1. ComplexNumber in Groovy
package complexnums

class ComplexNumber {
   def real, imaginary

  public ComplexNumber(real, imaginary) {
    this.real = real
    this.imaginary = imaginary

  def plus(rhs) {
    new ComplexNumber(this.real + rhs.real, this.imaginary + rhs.imaginary)
  def multiply(rhs) {
    new ComplexNumber(
        real * rhs.real - imaginary * rhs.imaginary,
        real * rhs.imaginary + imaginary * rhs.real)

  String toString() {
    real.toString() + ((imaginary < 0 ? "" : "+") + imaginary + "i").toString()

In Listing 1, I create a class that holds both real and imaginary parts, and I create the overloaded plus() and multiply() operators. Adding two complex numbers is straightforward: the plus() operator adds the two numbers' respective real and imaginary parts to each other to produce the result. Multiplying two complex numbers requires this formula:

(x + yi)(u + vi) = (xu - yv) + (xv + yu)i

The multiply() operator in Listing 1 replicates the formula. It multiplies the numbers' real parts, then subtracts the product of the imaginary parts, which is added to the product of the real and imaginary parts both multiplied by each other.

Listing 2 tests the complex-number operators:

Listing 2. Testing complex-number operators
package complexnums

import org.junit.Test
import static org.junit.Assert.assertTrue
import org.junit.Before

class ComplexNumberTest {
  def x, y

  @Before void setup() {
    x = new ComplexNumber(3, 2)
    y = new ComplexNumber(1, 4)

  @Test void plus_test() {
    def z = x + y;
    assertTrue 3 + 1 == z.real
    assertTrue 2 + 4 == z.imaginary
  @Test void multiply_test() {
    def z = x * y
    assertTrue(-5  == z.real)
    assertTrue 14 == z.imaginary

In Listing 2, the plus_test() and multiply_test() methods' use of the overloaded operators — both of which are represented by the same symbols that the domain experts use — is indistinguishable from similar use of built-in types .

Scala (and Clojure)

Scala allows operator overloading by discarding the distinction between operators and methods: operators are merely methods with special names. Thus, to override the multiplication operator in Scala, you override the * method. I create complex numbers in Scala in Listing 3:

Listing 3. Complex numbers in Scala
class ComplexNumber(val real:Int, val imaginary:Int) {
    def +(operand:ComplexNumber):ComplexNumber = {
        new ComplexNumber(real + operand.real, imaginary + operand.imaginary)
    def *(operand:ComplexNumber):ComplexNumber = {
        new ComplexNumber(real * operand.real - imaginary * operand.imaginary,
            real * operand.imaginary + imaginary * operand.real)

    override def toString() = {
        real + (if (imaginary < 0) "" else "+") + imaginary + "i"

The class in Listing 3 includes the familiar real and imaginary members, as well as the + and * operators/methods. As you can see in Listing 4, I can use ComplexNumbers naturally:

Listing 4. Using complex numbers in Scala
val c1 = new ComplexNumber(3, 2)
val c2 = new ComplexNumber(1, 4)
val c3 = c1 + c2
assert(c3.real == 4)
assert(c3.imaginary == 6)

val res = c1 + c2 * c3
printf("(%s) + (%s) * (%s) = %s\n", c1, c2, c3, res)
assert(res.real == -17)
assert(res.imaginary == 24)

By unifying operators and methods, Scala makes operator overloading trivial. Clojure uses the same mechanism to overload operators. For example, this Clojure code defines an overloaded ** operator:

(defn ** [x y] (Math/pow x y))

Extending classes

Similarly to operator overloading, the next-generation JVM languages allow you to extend classes (including core Java classes) in ways that are impossible in the Java language itself. These facilities are often used to build domain-specific languages (DSLs). Although the GoF never considered DSLs — because they weren't common in the popular languages of the time — DSLs exemplify the original purpose of the Interpreter design pattern.

By adding units and other modifiers to core classes such as Integer, you can — as with adding operators — model real-world problems more closely. Both Groovy and Scala allow this, but with different mechanisms.

Groovy's Expando and category classes

Groovy includes two mechanisms for adding methods to existing classes: ExpandoMetaClass and categories. (I covered details of the ExpandoMetaClass in the last installment, in the context of the Adapter pattern.)

Let's say that your company, for bizarre legacy reasons, needs to express speeds in furlongs per fortnight rather than miles per hour (MPH), and developers find themselves performing this conversion often. Using Groovy's ExpandoMetaClass, you can add a FF property to Integer that handles the conversion, as shown in Listing 5:

Listing 5. Using ExpandoMetaClass to add a furlongs/fortnight unit to Integer
static {
  Integer.metaClass.getFF { ->
    delegate * 2688

@Test void test_conversion_with_expando() {
  assertTrue 1.FF == 2688

The alternative to ExpandoMetaClass is to create a category wrapper class, a concept borrowed from Objective-C. In Listing 6, I add a (lowercase) ff property to Integer:

Listing 6. Adding units via a category class
class FFCategory {
  static Integer getFf(Integer self) {
    self * 2688

@Test void test_conversion_with_category() {
  use(FFCategory) {
    assertTrue 1.ff == 2688

A category class is a regular class with a collection of static methods. Each method accepts at least one parameter; the first parameter is the type this method augments. For example, in Listing 6, the FFCategory class has a getFf() method, which accepts an Integer parameter. When this category class is used with the use keyword, all appropriate types within the code block are augmented. In the unit test, I can reference the ff property (remember, Groovy automatically converts get methods to property references) within the code block, as shown at the bottom of Listing 6.

Having two mechanisms to choose from lets you control the scope of augmentations more exactly. For example, if the entire system uses MPH as the default unit of speed but also requires frequent conversion to furlongs per fortnight, a global change using the ExpandoMetaClass would be appropriate.

You may be skeptical of the usefulness of reopening core JVM classes, worrying about the broad-reaching implications. Category classes let you limit the scope of potentially dangerous enhancements. Here is an example from a real-world open source project that makes excellent use of this mechanism.

The easyb project (see Related topics) lets you write tests that verify aspects of classes under test. Consider the snippet from an easyb test shown in Listing 7:

Listing 7. easyb testing a queue class
it "should dequeue items in same order enqueued", {
    [1..5].each {val ->
    [1..5].each {val ->

The queue class doesn't include a shouldBe() method, which I call during the verification phase of the test. The easyb framework has added the method for me; the it() method definition in easyb's source, shown in Listing 8, shows how:

Listing 8. easyb's it() method definition
def it(spec, closure) {
  stepStack.startStep(BehaviorStepType.IT, spec)
  closure.delegate = new EnsuringDelegate()
  try {
    if (beforeIt != null) {
    listener.gotResult(new Result(Result.SUCCEEDED))
    use(categories) {
    if (afterIt != null) {
  } catch (Throwable ex) {
    listener.gotResult(new Result(ex))
  } finally {

class BehaviorCategory {
  // ...

  static void shouldBe(Object self, value) {
    shouldBe(self, value, null)


In Listing 8, the it() method accepts a spec (a string describing the test) and a closure block representing the body of the test. At the midway point of the method, the closure executes within the BehaviorCategory block, which appears at the bottom of the listing. The BehaviorCategory augments Object, allowing any instance in the Java universe to verify its value.

By allowing selective augmentation of Object, which resides at the top of the hierarchy, Groovy's open-class mechanism makes it possible to verify results easily for any instance but limits that change to the body of the use block.

Scala's implicit casts

Scala uses implicit casts to simulate the augmentation of existing classes. Implicit casts don't add methods to classes but allow the language to automatically convert an object to an appropriate type that does have the desired method. For example, I can't add an isBlank() method to the String class, but I can create an implicit conversion that automatically converts Strings to a class that does have that method.

As an example, I want to add an append() method to Array, which lets me add Person instances easily to an appropriately typed array, as shown in Listing 9:

Listing 9. Adding a method to Array to add people
case class Person (firstName: String, lastName: String) {}

class PersonWrapper(a: Array[Person]) {
  def append(other: Person) = {
    a ++ Array(other)
  def +(other: Person) = {
    a ++ Array(other)
implicit def listWrapper(a: Array[Person]) = new PersonWrapper(a)

In Listing 9, I create a simple Person class with a couple of properties. To make Array[Person] (in Scala, generics use [ ] rather than < > as delimiters) Person aware, I create a PersonWrapper class, which includes the desired append() method. At the bottom of the listing, I create the implicit conversion that will automatically convert an Array[Person] to a PersonWrapper when I call the append() method on the array. Listing 10 tests the conversion:

Listing 10. Testing natural extensions to existing classes
val p1 = new Person("John", "Doe")
var people = Array[Person]()
people = people.append(p1)

In Listing 9, I also add a + method to the PersonWrapper class. Listing 11 shows how I use this nicely intuitive version of the operator:

Listing 11. Modifying the language to enhance readability
people = people + new Person("Fred", "Smith")
for (p <- people)
  printf("%s, %s\n", p.lastName, p.firstName)

Scala isn't actually adding a method to the original class, but it provides the appearance of doing so by automatically converting to a suitable type. The same diligence required for metaprogramming in languages like Groovy is required in Scala to avoid creating convoluted webs of interconnected classes using too many implicit casts. But when used correctly, implicit casts help you write very expressive code.


The original Interpreter design pattern from the GoF suggested creating a new language, but their base languages didn't support the graceful extension mechanisms we have at our disposal today. All the next-generation Java languages support extensibility at the language level, using a variety of techniques. In this installment, I demonstrated how operator overloading works in Groovy, Scala, and Clojure, and investigated class extension in Groovy and Scala.

In a future installment, I'll show how a combination of Scala-style pattern matching and generics enable you to replace a couple of traditional design patterns. Essential to that discussion is a concept that also plays a role in functional-style error handling, which is the next installment's topic.

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Zone=Java development
ArticleTitle=Functional thinking: Functional design patterns, Part 3