The Functional Style - Part 4

Functional programming explained for the pragmatic programmer.

Richard Wild · 19 Sep 2018

First-Class Functions II: Filter, Reduce & more.

In the previous article I introduced the concept of first-class functions and lambdas, demonstrated the technique of 'mapping' a function over an array and advanced it as an alternative to explicit iteration. I went on to assert that the majority of the loops we write are either for that purpose of mapping one type of array into another, or they are for filtering elements from an array, searching for elements in an array, sorting them, or accumulating totals. I promised to show examples of all these. I’d better make good on my promise, so to begin I'm going to plunder classical antiquity for an example.

The Sieve of Eratosthenes.

This is an algorithm for finding prime numbers, discovered by the greek scholar Eratosthenes in the 3rd century BC. It is very simple. To find all the prime numbers up to some number n what you do is:

  1. Iterate all the natural numbers i where 2 ≤ i ≤ (n ∕ 2).
  2. For each i, mark every multiple m of i as non-prime, where 2imn.
  3. When you’re done, all the natural numbers up to n that remain unmarked are prime.

In Java code, that could look like this:

public class Sieve {

    private Set<Integer> sieve = new HashSet<>();
    private int limit;

    public Sieve(int limit) {
        this.limit = limit;
        for (var n = 2; n <= (limit / 2); n++)
            for (var nonPrime = (n * 2); nonPrime <= limit; nonPrime += n)
                sieve.add(nonPrime);
    }

    public List<Integer> primes() {
        var primes = new ArrayList<Integer>();
        for (var candidate = 1; candidate <= limit; candidate++)
            if (!sieve.contains(candidate))
                primes.add(candidate);
        return primes;
    }
}

The constructor creates the sieve and then the primes() method effectively “strains” the numbers through the sieve to extract the primes. We can use it to print out all the prime numbers between 1 and 10,000:

public static void main(String[] args) {
    var sieve = new Sieve(10000);
    var primes = sieve.primes();
    for (var prime : primes)
        System.out.print(prime + " ");
    System.out.println();
}

So far, so imperative. I picked this exercise as an example for filtering because, after all, what is a sieve but a filter? So let’s rewrite the sieve in a functional style:

public List<Integer> primes() {
    return IntStream.range(1, limit)
            .filter(candidate -> !sieve.contains(candidate))
            .boxed()
            .collect(Collectors.toList());
}

Even if the IntStream is new to you hopefully its purpose is clear: it gives us a stream of integers from 1 to limit. The .boxed() call maps the stream of ints to a stream of Integers so that we can collect it to a List in the terminating operation, because you cannot create a List of primitives (if you recall from the previous article, primitives in Java are a pain).

Now, we could collect the primes to a Set instead, and that might in fact be more appropriate because we only want each prime to appear in the output once:

public Set<Integer> primes() {
    return IntStream.range(1, limit)
            .filter(candidate -> !sieve.contains(candidate))
            .boxed()
            .collect(Collectors.toSet());
}

However, that has the undesired effect of producing the result out of order:

1 2 3 4099 5 2053 7 6151 11 13 2063 4111 17 8209 19 6163 2069 23 8219 [...]

We could fix that by streaming and sorting the results too:

public static void main(String[] args) {
    var sieve = new Sieve(10000);
    sieve.primes().stream()
            .sorted()
            .forEach(prime -> System.out.print(prime + " "));
    System.out.println();
}

We can take this further still though, by mapping the Integers to Strings and then using Collectors.joining to append them together and interpose the spaces for us:

public static void main(String[] args) {
    var sieve = new Sieve(10000);
    System.out.println(sieve.primes().stream()
            .sorted()
            .map(Object::toString)
            .collect(Collectors.joining(" ")));
}

Maybe you begin to see the appeal of the functional style. Just declare what you want to happen and have the language arrange it for you. No more worrying about fencepost errors! The previous version of the code appends a space after every number, and if it was important to you that it should not, fixing it would be quite awkward. How many situations have you had to program your way out of before that this could have helped you with? I've been there often.

I really want to stress this point. This is programmer labour saving right here. Not the kind of labour saving in the way some have dreamed of for decades: where people express their wishes in some natural language while the machine writes the program for you. The reason that dream was misguided is that the syntax never really has been the hardest part of programming. Programming has always been about analysing a problem and codifying it so unambiguously that it can be executed by a machine. The functional style does not change that. Rather, the functional style helps you with the problems that are already solved, such as appending these strings together while inserting spaces between them. You shouldn’t have to write code to do this stuff every time.

Closures.

Enough proselytising. What about if we rewrote the sieve() method like this:

public Set<Integer> primes() {
    return IntStream.range(1, limit)
            .boxed()
            .filter(notInNonPrimesUpTo(limit))
            .collect(Collectors.toSet());
}

What’s going on here? That notInNonPrimesUpTo(int) method returns a Predicate, which is a function that accepts a single argument and returns a boolean value:

private Predicate<Integer> notInNonPrimesUpTo(int limit) {
    var sieve = new HashSet<Integer>();
    for (var n = 2; n <= (limit / 2); n++)
        for (var nonPrime = (n * 2); nonPrime <= limit; nonPrime += n)
            sieve.add(nonPrime);
    return candidate -> !sieve.contains(candidate);
}

So it builds the sieve and returns a lambda that tests whether or not the candidate is in the sieve. Isn’t this hugely inefficient? Won’t it build the sieve every time it tests a candidate? Not so: it builds the sieve only once. When the filter() method is invoked on the stream it calls notInNonPrimesUpTo once, which returns the lambda predicate. It is the lambda that gets executed for every element in the stream. This lambda is also a closure. A pure lambda function depends only on its input arguments, while a closure also has access to variables in the scope it was created from - in this case, the sieve set. Even though the notInNonPrimesUpTo method has exited, the sieve set remains in scope because the lambda has "closed over" it. As long as the lambda itself remains in scope, the resources it closes over will remain available and will not be reclaimed by the garbage collector.

Cool. Let's go too far.

What about the generation of the sieve itself, can that be done with a stream too? Well, yes...

private Predicate<Integer> notInNonPrimesUpTo(int limit) {
    Set<Integer> sieve = IntStream.range(2, (limit / 2))
            .boxed()
            .flatMap(n -> Stream.iterate(n * 2, nonPrime -> nonPrime += n)
                    .takeWhile(nonPrime -> nonPrime <= limit))
            .collect(Collectors.toSet());
    return candidate -> !sieve.contains(candidate);
}

I don’t think I actually would do this for real in Java. This code seems less understandable to me than the imperative version. Just because you can do a thing doesn't mean that you should.

The Stream.iterate part is interesting, but explaining it would be jumping ahead of ourselves, so I will come back to that in a later article. The flatMap is worth explaining though. What we have here is a function being mapped over an array that itself returns an array, so that [2, 3, 4, 5 …] is mapped like this:

  • 2 → [4, 6, 8, 10, 12 …]
  • 3 → [6, 9, 12, 15, 18 …]
  • 4 → [8, 12, 16, 20, 24 …]
  • 5 → [10, 15, 20, 25, 30 …]
  • ...

But we don’t want an array of arrays, we want it flattened down into a one-dimensional array. That is what flatMap does for us, so that instead we get:

[4, 6, 8, 10, 12 … 6, 9, 12, 15, 18 … 8, 12, 16, 20, 24 … 10, 15, 20, 25, 30 …]

and finally Collectors.toSet() weeds out the duplicates for us. That makes our sieve.

Aggregation operations.

If you look through the Java streams API, or LINQ if you are a .NET developer, you will see several other operations that are based on searching: find any, find first, any match, etc. They are all based on predicates and work basically the same way as filtering, so I will not labour the point by detailing them all. Instead I want to turn our attention now to aggregation operations. Recall that earlier in the series, we had a recursive program that calculated factorials which looked like this:

public static int factorial(int number) {
    if (number == 1)
        return number;
    else
        return number * (factorial(number - 1));
}

This algorithm falls into the class of “loops that calculate an accumulated result,” which is another of the cases I promised could be done without looping. We eliminate this loop in Java by using reduce. In order to calculate n factorial, first we need to get a stream of integers from 1 up to n. This will do it for us:

IntStream.iterate(1, n -> n += 1)
        .takeWhile(n -> n <= 10)
        .forEach(System.out::println);

Usually mathematicians define factorial the other way around, i.e. counting downwards:

5! = 5 × 4 × 3 × 2 × 1

We get the same result whether we count downwards or upwards so we may as well count upwards because it is simpler. To calculate a factorial we use reduce as the terminating operation of the stream:

public static int factorial(int number) {
    return IntStream.iterate(1, n -> n += 1)
            .takeWhile(n -> n <= number)
            .reduce(1, (acc, next) -> acc * next);
}

The reduce method takes two arguments, which are:

  • An identity value (1)
  • A lambda that is executed for every element in the stream and itself takes two arguments:
    • One argument receives the value of current stream element.
    • The other receives the identity value on the first iteration, and on every subsequent iteration it receives the result of the previous computation.

The final result of the reduce method is the result of the lambda function on the last element in the stream. If you’re wondering which way round the lambda arguments go, the documentation states that the function must be associative, so it doesn’t really matter. Whatever the identity value is, it must satisfy the condition that when it is passed to the accumulator function along with any other value, the other value is returned as the result. In mathematical terms, identity is defined such that:

f (x, identity) = x

For multiplication and division the identity value is unity, while for addition and subtraction it is zero.

In C# the operation is called Aggregate rather than reduce but otherwise it is basically the same:

public static int Factorial(int number)
{
    return Enumerable.Range(1, number)
            .Aggregate(1, (acc, next) => acc * next);
}

Robot love.

But don’t think that reduce is limited to calculating arithmetic totals. You can reduce an array of one type of elements down to a result of a completely different type. To illustrate this, let’s use another programming exercise we like to use at Codurance, which we call Mars Rover.

We are simulating a robotic rover vehicle. Its “world” is a grid of integer coordinates, and it can point in any of the four cardinal directions: north, east, south, or west. The rover can turn left, it can turn right, and it can move forwards. It is programmed with a sequence of instructions like LAARA which would mean: left, ahead, ahead, right, ahead.

In Clojure we could define a function to make a robot:

(defn robot [coordinates bearing]
  (hash-map :coordinates coordinates, :bearing bearing))

so that creating a robot gives us a simple data structure that represents the state of a robot, like this:

user=> (robot {:x -2 :y 1} :east)
{:coordinates {:x -2, :y 1}, :bearing :east}

In Clojure a hash-map literal is defined by curly braces enclosing a number of key-value pairs, e.g. {:key1 value1 :key2 value2 ...}. The names prefixed with colons (e.g. :east) are merely symbols; they stand for nothing other than themselves. They do not need to be declared because they have no value except for their names. Symbols can be compared, i.e. (= :foo :foo) is true while (= :foo :bar) is false, which makes them handy for map keys and other uses.

Now, to be able to turn, we need to know the effect of rotating left or right depending on our robot’s bearing. So let's build a data structure to hold the rotations:

(def rotations
  {:north {:left :west, :right :east}
   :east {:left :north, :right :south}
   :south {:left :east, :right :west}
   :west {:left :south, :right :north}})

This tells us what our robot's new bearing will be after turning left or right while pointing in any of the four directions. Using it, we can define a function to turn a robot right:

(defn turn-right [robot]
  (let [bearing (:bearing robot)]
    (assoc robot :bearing (-> rotations bearing :right))))

There is quite a lot going on in those three lines of code. To help you understand it, the first thing to know is that you can get the value from a map for a given key like this:

user=> (def a-map {:key1 "value 1" :key2 "value 2"})
#'user/a-map
user=> (:key1 a-map)
"value 1"
user=> (:key2 a-map)
"value 2"

That is how (:bearing robot) gets the robot's current bearing. The -> symbol is called the “thread-first” macro; it is a shorthand for (:right (bearing rotations)) or, in other words, get the rotations corresponding to the robot’s current bearing and then the new bearing after rotating right. The thread-first and thread-last macros are Clojure's answer to the build-up of close-parens that occurs at the end of Lisp forms, which some people find objectionable about the language. They also allow composed functions to be written in left-to-right order, which some people may find more natural (I do).

The assoc function behaves as if it adds or replaces key-value pairs in a map. In this case, it appears to update the robot's bearing. All data structures in Clojure are immutable, of course, so what it does really is create a new map while leaving the original unchanged.

The function for turning a robot left is similar, and we could of course extract the common functionality if we wished to:

(defn turn-left [robot]
  (let [bearing (:bearing robot)]
    (assoc robot :bearing (-> rotations bearing :left))))

We can easily test the turning functions in the REPL:

user=> (turn-left (robot {:x 0 :y 0} :north))
{:coordinates {:x 0, :y 0}, :bearing :west}

user=> (turn-left (robot {:x 0 :y 0} :west))
{:coordinates {:x 0, :y 0}, :bearing :south}

user=> (turn-left (robot {:x 0 :y 0} :south))
{:coordinates {:x 0, :y 0}, :bearing :east}

user=> (turn-left (robot {:x 0 :y 0} :east))
{:coordinates {:x 0, :y 0}, :bearing :north}

user=> (turn-right (robot {:x 0 :y 0} :north))
{:coordinates {:x 0, :y 0}, :bearing :east}

user=> (turn-right (robot {:x 0 :y 0} :east))
{:coordinates {:x 0, :y 0}, :bearing :south}

user=> (turn-right (robot {:x 0 :y 0} :south))
{:coordinates {:x 0, :y 0}, :bearing :west}

user=> (turn-right (robot {:x 0 :y 0} :west))
{:coordinates {:x 0, :y 0}, :bearing :north}

Notice that it never changes the coordinates, only the bearing. This gives us a robot that can turn on the spot. In order to move, we also need to know the effect that moving forwards has on its position, according to its bearing:

(def translations
  {:north {:delta-x 0, :delta-y 1}
   :east {:delta-x 1, :delta-y 0}
   :south {:delta-x 0, :delta-y -1}
   :west {:delta-x -1, :delta-y 0}})

And now we can write a function to move the robot ahead along its current bearing:

(defn move-ahead [robot]
  (let [{ {x :x, y :y} :coordinates, bearing :bearing} robot]
    (let [{delta-x :delta-x, delta-y :delta-y} (translations bearing)]
      (assoc robot :coordinates {:x (+ x delta-x), :y (+ y delta-y)}))))

There is some slightly complex destructuring going on there, but hopefully it is clear enough. We get the coordinates and the bearing from the robot, and then further destructure the coordinates into its x and y components. We then look up the translation to be applied according to the robot’s bearing. Finally, we return a new robot whose bearing is the same as the original robot and whose coordinates are (x + Δx, y + Δy). Once again we can test this function in the REPL:

user=> (move-ahead (robot {:x 0 :y 0} :north))
{:coordinates {:x 0, :y 1}, :bearing :north}

user=> (move-ahead (robot {:x 0 :y 0} :east))
{:coordinates {:x 1, :y 0}, :bearing :east}

user=> (move-ahead (robot {:x 0 :y 0} :south))
{:coordinates {:x 0, :y -1}, :bearing :south}

user=> (move-ahead (robot {:x 0 :y 0} :west))
{:coordinates {:x -1, :y 0}, :bearing :west}

Finally, we need to be able to process a sequence of instructions and determine the final robot state. To do this, we need a function that can decode an instruction and apply the relevant function to the robot:

(defn do-step [robot next-step]
  (case next-step
    \A (move-ahead robot)
    \L (turn-left robot)
    \R (turn-right robot)))

and then it is a simple matter of iterating the instruction sequence, keeping track of the robot state as we go, and calling do-step on each instruction to get the next robot state:

(defn simulate [steps initial-robot]
  (loop [remaining-steps steps, robot initial-robot]
    (if (empty? remaining-steps)
      robot
      (recur (rest remaining-steps) (do-step robot (first remaining-steps))))))

So...?

But wait! This is just another loop that calculates an accumulated result. It is reducible, as if you hadn’t guessed. Of course you did. So this will work too:

(defn simulate [steps initial-robot]
  (reduce do-step initial-robot steps))

Well of course, I hear you saying, but that’s just funky magical Clojure though, right? Not so! You can do exactly the same thing in C#:

private readonly Dictionary<Bearing, Rotation> _rotations = new Dictionary<Bearing,Rotation>
{
    {Bearing.North, new Rotation(Bearing.West, Bearing.East)},
    {Bearing.East, new Rotation(Bearing.North, Bearing.South)},
    {Bearing.South, new Rotation(Bearing.East, Bearing.West)},
    {Bearing.West, new Rotation(Bearing.South, Bearing.North)}
};

private readonly Dictionary<Bearing, Coordinates> _translations = new Dictionary<Bearing, Coordinates>
{
    {Bearing.North, new Coordinates(0, 1)},
    {Bearing.East, new Coordinates(1, 0)},
    {Bearing.South, new Coordinates(0, -1)},
    {Bearing.West, new Coordinates(-1, 0)}
};

private Robot TurnLeft(Robot robot)
{
    var rotation = _rotations[robot.Bearing];
    return new Robot(robot.Coordinates, rotation.Left);
}

private Robot TurnRight(Robot robot)
{
    var rotation = _rotations[robot.Bearing];
    return new Robot(robot.Coordinates, rotation.Right);
}

private Robot MoveAhead(Robot robot)
{
    var delta = _translations[robot.Bearing];
    return new Robot(
        new Coordinates(
        robot.Coordinates.X + delta.X,
        robot.Coordinates.Y + delta.Y), 
        robot.Bearing);
}

private Robot DoStep(Robot robot, char nextStep)
{
    switch (nextStep)
    {
        case 'L': return TurnLeft(robot);
        case 'R': return TurnRight(robot);
        default: return MoveAhead(robot);
    }
}

The imperative code to drive a robot could be:

public Robot Simulate(string instructions, Robot initialRobot)
{
    var robot = initialRobot;
    foreach (var step in instructions)
        robot = DoStep(robot, step);
    return robot;
}

but we can just as readily do:

public Robot Simulate(string instructions, Robot initialRobot)
{
    return instructions.Aggregate(
        initialRobot, 
        (robot, step) => DoStep(robot, step));
}

MapReduce.

I should not close without mentioning this, because you've almost certainly heard this term. It's a “Big Data” technique and that, along with functional programming, is one of today's most fashionable topics. In the last two episodes we've examined techniques called “map” and “reduce” so you could quite reasonably figure that you know now how MapReduce works. However, it's slightly more complicated. MapReduce crunches data in three or four general processing steps, though they will all look familiar to you. We call it MapReduce because FilterMapShuffleReduce doesn't have the same ring to it, I guess:

  1. Filter the dataset (if necessary) to get only the records you are interested in.
  2. Transform the dataset by applying some function to each record (the 'map' step).
  3. Group together records in the transformed dataset. This is sometimes called the 'shuffle' step, but other explanations (like the MongoDB documentation) lump this operation in with the map step. The grouping works by computing some identity value for each record and using these as keys in a hashmap - every key is associated with an array of records that all share the same key.
  4. Aggregate down the transformed dataset by applying a reducing function in the same way we described above to each array (the 'reduce' step). The final result of the MapReduce operation is a set of key-value pairs.

If you've been following the last two episodes of this series closely then you know now how to do all of that. So be sure to polish up your resume and go get yourself a high-paying gig as a data scientist!

Next time.

Hopefully by now I’ve begun to convince you that looping is a construct you should only reach for when you really need it, and in most cases you don’t. LINQ, Streams and similar features in other modern languages can achieve a lot of these use cases for you with far less typing and administrative work. Just as we saw with the example of sorting in the beginning of the previous article, the languages can take care of the parts of the problem that are already solved: how to sort an array efficiently, to search for an item, to remove duplicates, to group by certain keys, etc. You are free to concentrate on the aspects that are particular to your problem domain. The signal-to-noise ratio in your code is thus improved, making it cleaner, more understandable, and less likely to harbour bugs.

In last two articles we have taken a pretty thorough look at first-class functions. Next time, we will introduce a closely related concept, higher-order functions. We will also take a look at function composition, and I will attempt to explain the dreaded Monad pattern. In fact I hope that I will be able not only to help you understand it, but also convince you of its utility.


The whole series:

  1. Introduction
  2. First Steps
  3. First-Class Functions I: Lambda Functions & Map
  4. First-Class Functions II: Filter, Reduce & More
  5. Higher-Order Functions I: Function Composition and Monads
  6. Higher-Order Functions II: Currying
  7. Lazy Evaluation
  8. Persistent Data Structures
  9. Pragmatism
Richard Wild Image

Richard Wild

Richard has been programming for his living since 1998, but he has loved the craft ever since teaching himself assembly language as a teenager on the family computer.

He apprenticed for many years grinding data on Oracle databases, picking up C, Java, C# and other languages along the way. At present he primarily programs in Java, but he is very interested in functional programming with Clojure.

Richard was switched on to Software Craftsmanship by some excellent agile coaches who taught XP, and through the association of some inspiring Software Craftspeople he has been fortunate to work with. In addition to honing his craft, he loves mentoring and teaching others how to practice TDD and how to write clean pragmatic code.

Aside from programming, Richard is a keen club runner with the Bedford Harriers, doing all distances from 5K to marathons. He also enjoys cycling, blogging and making music. Richard lives in Northampton with his partner Sarah.

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