Introduction to functional programming in Java

Note: This article was originally written for an undergraduate course on the Scheme textbook Structure and Interpretation of Computer Programs (SICP). Students entering this course typically had 1–2 semesters of experience programming in Java and we found that many students could solve the course excercises in Java, but failed to translate the code to Scheme. This article helped bridge the gap between the courses to show students how to write programs that accomplish the same tasks using different techniques and languages, as well as introducing them to some basic functional programming concepts.

This is a new piece of documentation, written just for this course, so let me know if parts of it are unclear to you — or if you like it! Expect to read it fairly slowly (like any technical writing), and consult the Java documentation if there’s methods you don’t recognize — though the most important ones should be linked.

A significant portion of this course is about learning functional programming techniques; functional programming solves problems very differently from object-oriented programming (which is what we use when we’re writing Java), and the difference in tactics that functional programming requires combined with the difference in syntax that Scheme in specific requires can be challenging to Java programmers.

This reading is optional but recommended — if you find yourself thinking that you know how to solve a problem in Java but unsure how to solve it in Scheme, make sure to give it a read!

Object-oriented programming

Quick: off the top of your head, what is object-oriented programming about?

Got an idea yet?

If you thought any of the words “encapsulation”, “inheritance”, “polymorphism”, “information hiding”, “abstraction”, or “vtables”, you are wrong.

If you thought any of the words “class”, “prototype”, or “type”, you are still wrong.

Object-oriented programming is about objects: bundles of state and behavior. The rest is optional fluff. And object-oriented languages are defined only by having built-in support for bundling state and behavior, not by having built-in support for classes. You may notice we don’t call it “class-oriented programming”.

“The controller pattern is awful (and other OO heresy)” by Eevee

Let’s make this clear with some Java code. Here’s a simple Point class, which represents a point in 2-dimensional space with double coordinates.

public class Point {
    public double x;
    public double y;

This class is a bundle of state; the state of an object is what the is; here, the state is its coordinates. We can make Points, modify them, and pass them around, but they don’t do anything.

Point p = new Point();
p.x = 1;
p.y = 3;
System.out.println("p.x = " + p.x);
System.out.println("p.y = " + p.y);

Let’s give our Point some behavior, with a rotate method, which will calculate the new coordinates using the 2-dimensional rotation matrix:

import java.lang.Math;

public class Point {
    public double x;
    public double y;

    Point(double x, double y) {
        this.x = x;
        this.y = y;

    void rotate(double radians) {
        double newX = x * Math.cos(radians) - y * Math.sin(radians);
        double newY = x * Math.sin(radians) + y * Math.cos(radians);
        this.x = newX;
        this.y = newY;

    public String toString() {
        return "Point(" + x + ", " + y +")";

Now we can make and modify Points (just like before), but Points also inherently know how to do something: rotate!

Point p = new Point(1, 3);
System.out.println("p = " + p);
p.rotate(Math.PI / 2); // 90° CCW
System.out.println("p = " + p);

Prints p = Point(1.0, 3.0), and then p = Point(-3.0, 1.0000000000000002) (because doubles round in strange ways). Now we have an object, with state (what it is; the x and y coordinates) and behavior (what it can do; rotate).


The rotate method we made is particularly interesting, because it returns void, storing its result in its own data. We call it mutation when the state of an object is changed.

This can either be problematic (now, if we pass a Point to some method, we don’t have any guarantee that the Point won’t be changed by the method, which bites each new programmer at least once) or advantageous (we can easily give other methods the power to change the objects we care about by mutating them).

One thing, however, is certain: when we add mutability to our objects, our program becomes more complex. What if methods never mutated objects? Functional programming advocates say that objects which can’t be mutated (immutable objects) are simpler to reason about and program with, because we never have to account for data changing without us knowing about it.

Functions which don’t mutate data or cause other side-effects (like modifying files, printing data, or making network requests) are called pure. Let’s rewrite our rotate method to be pure:

Point rotate(double radians) {
    return new Point(
            x * Math.cos(radians) - y * Math.sin(radians),
            x * Math.sin(radians) + y * Math.cos(radians)

The change is pretty minimal. Now, we can use it like this:

Point p = new Point(1, 3);
System.out.println("p = " + p);
System.out.println("rotated = " + p.rotate(Math.PI / 2)); // 90° CCW
System.out.println("p = " + p);

which prints

p = Point(1.0, 3.0)
rotated = Point(-3.0, 1.0000000000000002)
p = Point(1.0, 3.0)

Composing functions together

When a program is made up entirely of pure functions, it can be easier to read, write, and understand the code.

Suppose we have a list of points represented as a string, like this:

3.36588,2.16121 3.93673,0.708629 3.90825,-0.851834 3.28476,-2.28261
2.16121,-3.36588 0.708629,-3.93673 -0.851834,-3.90825 -2.28261,-3.28476
-3.36588,-2.16121 -3.93673,-0.708629 -3.90825,0.851834 -3.28476,2.28261
-2.16121,3.36588 -0.708629,3.93673 0.851834,3.90825 2.28261,3.28476
3.36588,2.16121 3.93673,0.708629

That is, points are separated by space characters, and coordinates within a point are separated by a comma.

Here’s how an introductory Java programmer might try to parse the string into a list of Point objects:

public static List<Point> parsePoints(String str) {
    try (Scanner scanner = new Scanner(str)) {
        ArrayList<String> pointStrings = new ArrayList<>();
        while (scanner.hasNext()) {

        ArrayList<Point> points = new ArrayList<>();
        for (int i = 0; i < pointStrings.size(); i++) {
            String[] numbers = pointStrings.get(i).split(",");
            double x = Double.parseDouble(numbers[0]);
            double y = Double.parseDouble(numbers[1]);
            points.add(new Point(x, y));

        return points;

This implementation of parsePoints works fine, but it’s got some problems.

  1. We use too much data; first, we split str by whitespace with the Scanner and store it in pointStrings, which uses almost as much data as storing str originally did. Then, the very next thing we do is throw it all out after we make points.
  2. The code isn’t very clear; we build up pointStrings from the Scanner, we go through pointStrings again, then there’s… indexes into the numbers array? It’s weird, and if someone passed bad data to this method, they’d probably be confused to get an ArrayIndexOutOfBoundsException.

But, under the hood, we’re only really doing a few things:

  1. Splitting str by whitespace.
  2. Splitting the resulting strings by commas.
  3. Parsing all the elements as doubles.
  4. Turning the parsed doubles into Points.

Note that each operation here goes directly to the next operation. With that in mind, let’s split up our parsePoints method a bit:

static String[] splitWhitespace(String str) {
    return str.split("\\s+");

static String[] splitCommas(String str) {
    return str.split(",");

static List<Double> parseDoubles(String[] strs) {
    ArrayList<Double> doubles = new ArrayList<>();
    for (String str : strs) {
    return doubles;

static Point toPoint(List<Double> doubles) {
    return new Point(doubles.get(0), doubles.get(1));

public static List<Point> parsePoints(String str) {
    String[] coords = splitWhitespace(str);

    ArrayList<String[]> splitByCommas = new ArrayList<>();
    for (String coord : coords) {

    ArrayList<List<Double>> doubles = new ArrayList<>();
    for (String[] doubleStrings : splitByCommas) {

    ArrayList<Point> points = new ArrayList<>();
    for (List<Double> rawPoint : doubles) {

    return points;

This code is a bit clearer, but it’s much longer. Further, astute readers might have noticed that this pattern is repeated several times:

ArrayList<NewType> newList = new ArrayList<>();
for (OldType element : oldList) {

That is, building up a list by calling a method on every element of another list. Functional programmers have a name for this operation, and it’s called mapping a list, because it shows you a map from a list of one type to a list of another type.

What do we know about the method oldTypeToNewType? It must look something like this, because it has one parameter of type OldType and returns a value of type NewType:

NewType oldTypeToNewType(OldType o) {
    // ...

Java actually already includes this as an interface called Funtion<T, R>, where T is OldType (the function’s input) and R is NewType (the function’s return-type). With that in mind, let’s write a mapList function that turns a list of T into a list of R:

static <T, R> List<R> mapList(List<T> input, Function<T, R> mappingFunction) {
    ArrayList<R> ret = new ArrayList<>();
    for (T t : input) {
    return ret;

Then, we can create classes to call mapList with. For example, here’s parseDoubles using mapList:

static List<Double> parseDoubles(String[] strs) {
    return mapList(Arrays.asList(strs), new Function<String, Double>() {
        public Double apply(String s) {
            return Double.parseDouble(s);

The syntax new Function<...>() { ... } is just like declaring class MyClass<...> implements Function<...> { ... } and then constructing it exactly once. Fortunately, Java gives us shorter syntax for any functional interface, which represents the exact same thing:

static List<Double> parseDoubles(String[] strs) {
    return mapList(Arrays.asList(strs), s -> Double.parseDouble(s));

This shortcut is called a lambda expression. Further, it turns out that the pattern of writing lambdas of the form var -> MyClass.method(var) is so common there’s another special syntax for it, called method references, which look like MyClass::method (in this case, Double::parseDouble):

static List<Double> parseDoubles(String[] strs) {
    return mapList(Arrays.asList(strs), Double::parseDouble);

Now, we can rewrite our Point parser using mapList:

public static List<Point> parsePoints(String str) {
    String[] coords = splitWhitespace(str);
    List<String[]> splitByCommas = mapList(Arrays.asList(coords), PointParser::splitCommas);
    List<List<Double>> doubles = mapList(splitByCommas, PointParser::parseDoubles);
    return mapList(doubles, PointParser::toPoint);

Much better! This code is very declarative: we write what we mean. First, we split by whitespace. Then, we split by commas. Then, we parse as doubles. Then, we map to points.

This is still fairly wasteful in terms of memory, though — we create not just one but three intermediate lists, each of which gets discarded after being used only once.

We can solve this problem with a stream (sometimes called a lazy iterator). Streams are specifically designed to be composable with functions, so that we can generate a stream from some source (like reading from a file, finding matches in a string, or more traditionally from some data structure like an array or tree) and then specify that it be mapped over in various ways.

Unlike our mapList function, mapping over a stream doesn’t read any data from the underlying source; it just writes down a note saying the stream is also being mapped over by a particular value. Therefore, we don’t need to generate any intermediate lists — we can just add our mapping functions one by one, and the stream remembers how to transform its data before using it when we start reading values from the stream.

And how do we read values from a stream? Unlike arrays or other simple data structures, streams are designed to let us ask more questions than just “what element is at index i in this structure?” Streams have methods that let us ask:

We can generate streams a few different ways in Java, but for the most part the method, the method, and the Collectors methods are suitable for converting most data to and from streams.

Rewriting the point parser with Streams, we get:

public class PointParser {
    static final Pattern COMMA = Pattern.compile(",");
    static final Pattern WHITESPACE = Pattern.compile("\\s+");

    static Point toPoint(Iterator<Double> doubles) {
        return new Point(,;

    public static List<Point> parsePoints(String str) {
        return WHITESPACE.splitAsStream(str)  // (1)
            .map(s -> COMMA.splitAsStream(s)  // (2)
                    .iterator())              // (3)
            .collect(Collectors.toList());    // (4)


  1. The Pattern.splitAsStream method is exactly like String.split except it returns a Stream instead of an array.

  2. Inside one of our calls to map, we create another Stream; streams work best together!

    These calls to map don’t actually get items out of the stream; they’re lazy, and all they do is make the stream remember that it’s mapping by that function.

  3. The call to iterator turns a Stream into an Iterator; it’s a finalizing operation, which means that it consumes the elements of the stream and returns a result. Once we have an Iterator, we can’t map on it any more.

  4. Finally, we collect the elements of the stream into a List with the collect method and the Collectors.toList() utility method.

This code is (I think) much clearer than our first attempt, and a bit shorter as an extra bonus. I’ll admit that code writtten with streams can look a bit alien at first, and it takes some practice to get used to!

In this code, we don’t use a single index or for loop — and why should we? After all, none of the operations need to know anything about the elements before or after them; the indexes were only to overcome limitations in the ways we can refer to data structures.

Additionally, no intermediate lists are created; when the stream is being collected, each element from the pattern-splitter is applied to all our mapping functions in a row before being returned. The processing steps aren’t separated from each other, which saves memory, even though we were able to declare the processing steps separately. In APIs focused on streams, it’s often easier to return (and take as parameters) streams themselves, rather than frequently collecting data to structures like lists at method boundaries.

There’s another advantage of this style of programming, in addition to saving memory; because we never reassign values, the functions are immutable and pure. In Java, we’re not inherently motivated to write pure functions. But in other languages (notably pure functional programming languages like Haskell, or other functional programming languages like Scheme), we’re forced to write pure functions; learning to construct pure functions and immutable data structures will help you reason about and solve problems in a functional programming style.