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Functional Programming

Programming Models

Software consists of two distinct components: what the program knows (data) and what the program knows how to do (operations). Put simply1

A key principle of object-oriented design is to group data with operations. This results in code that is modular and encapsulated. It encourages code reuse through inheritance where child classes can specialize operations for more specific types of objects. With object-oriented code, the functionality and the data are tightly coupled.

Functional programming takes a significantly different approach. Functional programming allows operations to be treated just like data. Here we can define functionality and then pass the functionality to a method where it is applied to a set of data. The functional programming model is well suited for concurrent and event-driven programming tasks. Concurrency — running portions of a task on multiple processors simultaneously — has become increasingly important for performance reasons.

Java is known as an object-oriented programming language because it was originally designed to facilitate writing object-oriented software. Writing Java software using a functional approach has always been possible; however, it was not originally designed to facilitate it. As a result, writing Java using a functional approach was cumbersome in early versions of Java.

More recent versions of Java (particularly Java 8) have brought several functional programming concepts to the Java language. With Java 8 it is now much easier to design and implement software using a functional programming approach. This allows us to use object-oriented techniques when appropriate and functional programming techniques when they make sense.

Functional Programming Characteristics

Conceptually, we can think of functional programming as if each method acted like a mathematical function. For a fixed set of inputs, the function returns the same value each time it is called. This results in the following constraints:

Functional programming requires data to be immutable. If the value must change, a new piece of data is created with the new value.

First-Class Functions

Functional programming involves first-class functions. Put simply, we are able to treat a function like we would data. Specifically:

A lambda expression is Java's implementation mechanism for first-class functions. Consider:

button.setOnAction(event -> {
    // do some work
});

Here the code between the curly braces defines the method void handle(ActionEvent event). Alternatively, we could assign the lambda expression to a variable like this:

EventHandler<ActionEvent> handler = (event -> {
    // do some work
});

button.setOnAction(handler);

Functional Interfaces

A functional interface is an interface that declares a single abstract method. Functional interfaces make use of the @FunctionalInterface annotation. For example, the EventHandler<T> interface is a functional interface. Its declaration looks like this:

@FunctionalInterface
interface EventHandler<T> {
    void handle(T event);
}

Any functional interface may be implemented with a lambda expression. It is best to think of a lambda expression as an anonymous method rather than an object.

Higher-Order Functions

Higher-order functions are functions that accept other functions as argument(s) or return a function as a result. An example of a higher-order function that you've already seen is setOnAction(EventHandler<ActionEvent> event) which must be passed a function (an anonymous method that implements a functional interface).

There are several higher-order functions that are commonly used with streams of data. The Stream interface in Java is often used when applying a functional programming approach to collections of data. The interface includes the following methods:

In addition to these methods, there are several higher-order functions, i.e., methods that accept one or more functions as argument(s).

Predicate Interface

One common type of function that is passed to higher-order functions is a predicate function — a function that evaluates to a boolean value. Java provides a functional interface to help: Predicate<T>.

We can create a predicate function by implementing this interface with a lambda expression. The following defines a predicate function that returns true whenever the string on which the predicate is evaluated is longer than nine characters:

Predicate<String> longerThanNine = (word -> word!=null && word.length()>9);

The Stream interface declares these higher-order functions that require a predicate function:

Function Interface

Another functional interface is the Function<T, R> interface. This interface represents a function that accepts one argument, of type T, and produces a result, of type R. The Stream interface declares these higher-order functions that require a Function:

Consumer Interface

Another functional interface is the Consumer<T> interface which represents an operation that accepts a single input, of type T and returns no result. Unlike most other functional interfaces, this interface is expected to operate via side-effects.

This can be used with the Stream.forEach(Consumer<? super T> action) method. For example, the following code

Consumer<String> prettyDisplay = (word -> System.out.println(word + ": " + word.length()));

List<String> words = Arrays.asList("Gravity", "isn't", "just", "a", "good",
        "idea", "it's", "the", "law");
words.stream().forEach(prettyDisplay);

produces this result:

Gravity: 7
isn't: 5
just: 4
a: 1
good: 4
idea: 4
it's: 4
the: 3
law: 3

Functional Programming and Parallism Note that the order in which forEach() evaluates each element is not guaranteed. In fact, if the last line of the code above is replaced with words.parallelStream().forEach(prettyDisplay);, the result could be:

idea: 4
good: 4
the: 3
law: 3
it's: 4
just: 4
a: 1
isn't: 5
Gravity: 7

By using a parallelStream() the evaluation of the forEach() method may be evaluated in parallel. In this case, the original stream could be partitioned into n streams of k elements each (where n*k is the total number of elements in the stream). Each partitioned stream could be processed on a separate thread. The resulting order in which the elements are processed is non-deterministic.

Functional programming has become more popular in recent years. One of the big reasons for this is the ease with which code can be modified to run in a distributed fashion. When working with really large datasets, being able to distribute the work associated with a task across multiple processes, computers, or even data centers means that we can buy/rent more hardware to make our code run faster instead of having to buy/rent faster hardware.

This is particularly important if we have a workload that may vary significantly with time of day, for example.

Acting on Streams Using a Functional Approach

There are several other methods in the Stream interface that you can investigate in more detail. The following are frequently used:

Note: the descriptions above are over-simplifications. Each of the first three operations create a new stream rather than changing the contents of the current stream.

At this point, it may be good to see a few examples.

Filtering

The following takes a stream of integers and produces a new list containing only the even integers:

Stream<Integer> numbers = Stream.of(3, 8, 7, 2, 1, 9, 8);
List<Integer> evens = numbers.filter(num -> num%2==0)
        .collect(Collectors.toList());

The following counts how many strings end in tion:

long count = Stream.of("happy", "discussion" /* ... */, "locomotion")
        .filter(word -> word.endsWith("tion"))
        .count();

Filtering and Mapping

The following takes a stream of Shapes and produces a new list of circles:

List<Circle> circles = Stream.of(new Circle(), new Rectangle(), new Circle(), new Triangle())
        .filter(shape -> shape instanceof Circle)
        .map(shape -> (Circle)shape)
        .collect(Collectors.toList());

Mapping and Reducing

The following calculates the total area of all the shapes:

double totalArea = Stream.of(new Circle(), new Rectangle(), new Circle(), new Triangle())
        .mapToDouble(shape -> shape.getArea())
        .sum();

Note: we can specify the mapping function as a method reference instead of a lambda expression. (See the argument passed to mapToDouble() in the next example). Like a lambda expression, a method reference is turned into an instance of a functional interface. The following calculates the average area of the shapes:

OptionalDouble averageArea = Stream.of(new Circle(), new Rectangle(), new Circle(), new Triangle())
        .mapToDouble(Shape::getArea)
        .average();

Note: .average() returns an OptionalDouble since it is possible that this calculation doesn't make sense (if the list is empty). We can check if the average was calculated by calling averageArea.isPresent(). If it is, then we can get the value by calling averageArea.getAsDouble().

Since calculating statistics is pretty common, there is a DoubleSummaryStatistics class that can be used with summaryStatistics() to calculate several statistics:

List<Shape> shapes = Arrays.asList(new Circle(), new Rectangle(), new Circle(), new Triangle());
DoubleSummaryStatistics areaStatistics = shapes.stream()
        .mapToDouble(Shape::getArea)
        .summaryStatistics();
double totalArea = areaStatistics.getSum();       
double averageArea = areaStatistics.getAverage();       
double maximumArea = areaStatistics.getMax();       
double minimumArea = areaStatistics.getMin();       
long numberOfShapes = areaStatistics.getCount();

Lazy Evaluation

One subtle but powerful concept related to streams is that streams are lazy. They are lazy in the following sense:

Computations on the source data are only performed when a terminal stream operation (e.g., .collect(), .reduce(), .count(), etc...) is performed and the source elements are only evaluated as needed.

Consider the following LifeGrid.countAliveCells() method:

public int countAliveCells(){
    int numAlive = 0;
    for(List<Cell> row : cells) {
        for(Cell cell : row) {
            if(cell.isAlive()) {
                numAlive++;
            }
        }
    }
    return numAlive;
}

Implementing this method using a functional programming approach could look like:

public int countAliveCells(){
    return (int)cells.parallelStream()
        .flatMap(List::stream)
        .filter(Cell::isAlive)
        .count();
}

The cells instance variable is a list of lists. It looks like we:

Because the stream is evaluated in lazily, the compiler can determine how best to optimize the order of operations. It could make one long stream or chose to have each stream representing a row of cells be sent to a different processor where subtotal is tallied and then combined to produce the total count.

Generally speaking, functional programming describes what we want done, but not how it should be done. In the countAliveCells() example, the first approach specified exactly how to count the cells whereas the functional approach allows the compiler and/or runtime environment to optimize how the work gets done.

1

This is an oversimplification that will be expanded upon below.