trampolines, monoids & other functional

amenities

This is NOT your

father's by Mario Fusco

mario.fusco@gmail.com

twitter: @mariofusco

Laziness,

public static <T> void sort(List<T> list, Comparator<? super T> c)

Essence of Functional Programming

Data and behaviors are the same thing!

Data Behaviors

Collections.sort(persons, (p1, p2) -> p1.getAge() – p2.getAge())

Higher-order functions Are they so mind-blowing?

Higher-order functions Are they so mind-blowing?

… but one of the most influent sw engineering book is almost completely dedicated to them

Command

Template Method

Functions are more general and higher level abstractions

Factory

Strategy

public interface Converter { double convert(double value); } public class AbstractConverter implements Converter { public double convert(double value) { return value * getConversionRate(); } public abstract double getConversionRate(); } public class Mi2KmConverter extends AbstractConverter { public double getConversionRate() { return 1.609; } } public class Ou2GrConverter extends AbstractConverter { public double getConversionRate() { return 28.345; } }

A strategy pattern Converter

public List<Double> convertValues(List<Double> values, Converter converter) { List<Double> convertedValues = new ArrayList<Double>(); for (double value : values) { convertedValues.add(converter.convert(value)); } return convertedValues; } List<Double> values = Arrays.asList(10, 20, 50); List<Double> convertedDistances = convertValues(values, new Mi2KmConverter()); List<Double> convertedWeights = convertValues(values, new Ou2GrConverter());

Using the Converter

A functional Converter

public class Converter implements ExtendedBiFunction<Double, Double, Double> { @Override public Double apply(Double conversionRate, Double value) { return conversionRate * value; } } @FunctionalInterface public interface ExtendedBiFunction<T, U, R> extends BiFunction<T, U, R> { default Function<U, R> curry1(T t) { return u -> apply(t, u); } default Function<T, R> curry2(U u) { return t -> apply(t, u); } }

Currying

Converter converter = new Converter(); double tenMilesInKm = converter.apply(1.609, 10.0);

Function<Double, Double> mi2kmConverter = converter.curry1(1.609); double tenMilesInKm = mi2kmConverter.apply(10.0);

Converter value

rate

result

Mi2km Converter value

rate=1.609

result

List<Double> values = Stream.of(10, 20, 50) .map(new Converter().curry1(1.609)) .collect(toList())

Function Composition

Celsius Fahrenheit : F = C * 9/5 + 32

Converter

value

rate=9/5

Function Composition

Celsius Fahrenheit : F = C * 9/5 + 32

Converter

value

rate=9/5 andThen n -> n+32

result

Function Composition

Celsius Fahrenheit : F = C * 9/5 + 32

Converter

value

rate=9/5 andThen n -> n+32

result

Celsius2FarenheitConverter

Function<Double, Double> c2fConverter = new Converter().curry1(9.0/5) .andThen(n -> n + 32);

More Function Composition

@FunctionalInterface public interface ExtendedBiFunction<T, U, R> extends BiFunction<T, U, R> { default <V> ExtendedBiFunction<V, U, R> compose1(Function<? super V, ? extends T> before) { return (v, u) -> apply(before.apply(v), u); } default <V> ExtendedBiFunction<T, V, R> compose2(Function<? super V, ? extends U> before) { return (t, v) -> apply(t, before.apply(v)); } }

default <V> Function<V, R> compose(Function<? super V, ? extends T> before) { return (V v) -> apply(before.apply(v)); }

More Function Composition

Fahrenheit Celsius : C = (F - 32) * 5/9

Converter rate=5/9

result

More Function Composition

Fahrenheit Celsius : C = (F - 32) * 5/9

Converter rate=5/9

value n -> n-32

result compose2

More Function Composition

Fahrenheit Celsius : C = (F - 32) * 5/9

Converter rate=5/9

value n -> n-32

result

Farenheit2CelsiusConverter

Function<Double, Double> f2cConverter = new Converter().compose2((Double n) -> n - 32) .curry1(5.0/9);

Functions are building blocks to create other functions

compose2

Monoids A monoid is a triple (T, ∗, z) such that ∗ is an associative

binary operation on T, and z ∈ T has the property that for all

x ∈ T it holds that x∗z = z∗x = x.

interface Monoid<T> { T append(T a, T b); T zero(); }

class Appender implements Monoid<String> { public String append(String a, String b) { return a + b; } public String zero() { return ""; } }

class Multiplier implements Monoid<Integer> { public Integer append(Integer a, Integer b) { return a * b; } public Integer zero() { return 1; } }

Endomorphisms & Monoids

interface Endomorphism<A> extends Function<A, A> { }

interface EndoMonoid<A> extends Monoid<Endomorphism<A>> { @Override default Endomorphism<A> append(Endomorphism<A> f1, Endomorphism<A> f2) { return ??? } @Override default Endomorphism<A> zero() { return ??? } }

Endomorphisms & Monoids

interface Endomorphism<A> extends Function<A, A> { }

interface EndoMonoid<A> extends Monoid<Endomorphism<A>> { @Override default Endomorphism<A> append(Endomorphism<A> f1, Endomorphism<A> f2) { return ??? } @Override default Endomorphism<A> zero() { return ??? } }

f1.andThen(f2);

Function.identity();

public class SalaryCalculator { // B = basic + 20% public double plusAllowance(double d) { return d * 1.2; } // C = B + 10% public double plusBonus(double d) { return d * 1.1; } // D = C - 30% public double plusTax(double d) { return d * 0.7; } // E = D - 10% public double plusSurcharge(double d) { return d * 0.9; } public double calculate(double basic, boolean[] flags) { double salary = basic; if (flags[0]) salary = plusAllowance(salary); if (flags[1]) salary = plusBonus(salary); if (flags[2]) salary = plusTax(salary); if (flags[3]) salary = plusSurcharge(salary); return salary; } }

SalaryCalculator

public class FluentEndoMonoid<A> implements EndoMonoid<A> { private final Endomorphism<A> endo; public FluentEndoMonoid(Endomorphism<A> endo) { this.endo = endo; } public FluentEndoMonoid(Endomorphism<A> endo, boolean b) { this.endo = b ? endo : zero(); } public FluentEndoMonoid<A> add(Endomorphism<A> other) { return new FluentEndoMonoid<A>(append(endo, other)); } public FluentEndoMonoid<A> add(Endomorphism<A> other, boolean b) { return add(b ? other : zero()); } public Endomorphism<A> get() { return endo; } public static <A> FluentEndoMonoid<A> endo(Endomorphism<A> f, boolean b) { return new FluentEndoMonoid<A>(f, b); } }

FluentEndoMonoid

public class SalaryCalculator { public double calculate(double basic, boolean [] flags) { return getCalculator(bs).apply(basic); } public Endomorphism<Double> getCalculator(boolean[] flags) { return endo(this::plusAllowance, flags[0]) .add(this::plusBonus, flags[1]) .add(this::plusTax, flags[2]) .add(this::plusSurcharge, flags[3]) .get(); } } Endomorphism<Double> f = salaryCalc.getCalculator(true, false, false, true); double aliceNet = f.apply(alice.getIncome()); double brianNet = f.apply(brian.getIncome());

Functional SalaryCalculator

You can calculate a single salary …

… but also obtain a calculator for a given combination of flags (Factory)

Lazy Evaluation Lazy evaluation (or call-by-name) is an evaluation

strategy which delays the evaluation of an expression until its value is needed

I know what to do. Wake me up when you really need it

Creating a Stream of prime numbers

public static IntStream primes(int n) { return IntStream.iterate(2, i -> i + 1) .filter(n –> isPrime(n)) .limit(n); } public static boolean isPrime(int candidate) { int candidateRoot = (int) Math.sqrt((double) candidate); return IntStream.rangeClosed(2, candidateRoot) .noneMatch(i -> candidate % i == 0); }

Creating a Stream of prime numbers

public static IntStream primes(int n) { return IntStream.iterate(2, i -> i + 1) .filter(n –> isPrime(n)) .limit(n); } public static boolean isPrime(int candidate) { int candidateRoot = (int) Math.sqrt((double) candidate); return IntStream.rangeClosed(2, candidateRoot) .noneMatch(i -> candidate % i == 0); }

It iterates through every number every time to see if it can be exactly divided by a candidate number, but it would be enough to only test numbers that have been already classified as prime

Recursively creating a Stream of primes static Intstream numbers() { return IntStream.iterate(2, n -> n + 1); } static int head(IntStream numbers) { return numbers.findFirst().getAsInt(); } static IntStream tail(IntStream numbers) { return numbers.skip(1); } static IntStream primes(IntStream numbers) { int head = head(numbers); return IntStream.concat( IntStream.of(head), primes(tail(numbers).filter(n -> n % head != 0)) ); }

Recursively creating a Stream of primes static Intstream numbers() { return IntStream.iterate(2, n -> n + 1); } static int head(IntStream numbers) { return numbers.findFirst().getAsInt(); } static IntStream tail(IntStream numbers) { return numbers.skip(1); } static IntStream primes(IntStream numbers) { int head = head(numbers); return IntStream.concat( IntStream.of(head), primes(tail(numbers).filter(n -> n % head != 0)) ); }

Cannot invoke 2 terminal operations on the same Streams

Problems?

No lazy evaluation in Java leads to an endless recursion

Lazy evaluation in Scala

def numbers(n: Int): Stream[Int] = n #:: numbers(n+1) def primes(numbers: Stream[Int]): Stream[Int] = numbers.head #:: primes(numbers.tail filter (n -> n % numbers.head != 0))

lazy concatenation

In Scala the #:: method (lazy concatenation) returns immediately and the elements are

evaluated only when needed

interface HeadTailList<T> { T head(); LazyList<T> tail(); default boolean isEmpty() { return true; } }

Implementing a lazy list in Java

class LazyList<T> implements HeadTailList<T> { private final T head; private final Supplier<MyList<T>> tail; public LazyList(T head, Supplier<HeadTailList<T>> tail) { this.head = head; this.tail = tail; } public T head() { return head; } public HeadTailList<T> tail() { return tail.get(); } public boolean isEmpty() { return false; } }

… and its lazy filter

public HeadTailList<T> filter(Predicate<T> p) { return isEmpty() ? this : p.test(head()) ? new LazyList<>(head(), () -> tail().filter(p)) : tail().filter(p); }

2 3 4

5 6 7 8 9

2 3 5

7

Back to generating primes

static HeadTailList<Integer> primes(HeadTailList<Integer> numbers) { return new LazyList<>( numbers.head(), () -> primes(numbers.tail() .filter(n -> n % numbers.head() != 0))); }

static LazyList<Integer> from(int n) { return new LazyList<Integer>(n, () -> from(n+1)); }

LazyList<Integer> numbers = from(2); int two = primes(numbers).head(); int three = primes(numbers).tail().head(); int five = primes(numbers).tail().tail().head();

Printing primes

static <T> void printAll(HeadTailList<T> list) { while (!list.isEmpty()){ System.out.println(list.head()); list = list.tail(); } } printAll(primes(from(2)));

static <T> void printAll(HeadTailList<T> list) { if (list.isEmpty()) return; System.out.println(list.head()); printAll(list.tail()); } printAll(primes(from(2)));

Iteration vs. Recursion

External Iteration public int sumAll(int n) { int result = 0; for (int i = 0; i <= n; i++) { result += i; } return result; }

Recursion public int sumAll(int n) { return n == 0 ? 0 : n + sumAll(n - 1); }

Internal Iteration public static int sumAll(int n) { return IntStream.rangeClosed(0, n).sum(); }

public class PalindromePredicate implements Predicate<String> { @Override public boolean test(String s) { return isPalindrome(s, 0, s.length()-1); } private boolean isPalindrome(String s, int start, int end) { while (start < end && !isLetter(s.charAt(start))) start++; while (start < end && !isLetter(s.charAt(end))) end--; if (start >= end) return true; if (toLowerCase(s.charAt(start)) != toLowerCase(s.charAt(end))) return false; return isPalindrome(s, start+1, end-1); } }

Another Recursive Example

Tail Rescursive Call

What's the problem? List<String> sentences = asList( "Dammit, I’m mad!", "Rise to vote, sir!", "Never odd or even", "Never odd and even", "Was it a car or a cat I saw?", "Was it a car or a dog I saw?", VERY_LONG_PALINDROME ); sentences.stream() .filter(new PalindromePredicate()) .forEach(System.out::println);

Exception in thread "main" java.lang.StackOverflowError at java.lang.Character.getType(Character.java:6924) at java.lang.Character.isLetter(Character.java:5798) at java.lang.Character.isLetter(Character.java:5761) at org.javaz.trampoline.PalindromePredicate.isPalindrome(PalindromePredicate.java:17) at org.javaz.trampoline.PalindromePredicate.isPalindrome(PalindromePredicate.java:21) at org.javaz.trampoline.PalindromePredicate.isPalindrome(PalindromePredicate.java:21) at org.javaz.trampoline.PalindromePredicate.isPalindrome(PalindromePredicate.java:21) ……..

Tail Call Optimization int func_a(int data) { data = do_this(data); return do_that(data); } ... | executing inside func_a()

push EIP | push current instruction pointer on stack push data | push variable 'data' on the stack jmp do_this | call do_this() by jumping to its address ... | executing inside do_this() push EIP | push current instruction pointer on stack push data | push variable 'data' on the stack jmp do_that | call do_that() by jumping to its address ... | executing inside do_that() pop data | prepare to return value of 'data' pop EIP | return to do_this() pop data | prepare to return value of 'data' pop EIP | return to func_a() pop data | prepare to return value of 'data' pop EIP | return to func_a() caller ...

Tail Call Optimization int func_a(int data) { data = do_this(data); return do_that(data); } ... | executing inside func_a()

push EIP | push current instruction pointer on stack push data | push variable 'data' on the stack jmp do_this | call do_this() by jumping to its address ... | executing inside do_this() push EIP | push current instruction pointer on stack push data | push variable 'data' on the stack jmp do_that | call do_that() by jumping to its address ... | executing inside do_that() pop data | prepare to return value of 'data' pop EIP | return to do_this() pop data | prepare to return value of 'data' pop EIP | return to func_a() pop data | prepare to return value of 'data' pop EIP | return to func_a() caller ...

caller

avoid putting instruction on stack

from Recursion to Tail Recursion

Recursion public int sumAll(int n) { return n == 0 ? 0 : n + sumAll(n - 1); }

Tail Recursion public int sumAll(int n) { return sumAll(n, 0); } private int sumAll(int n, int acc) { return n == 0 ? acc : sumAll(n – 1, acc + n); }

Tail Recursion in Scala def isPalindrome(s: String): Boolean = isPalindrome(s, 0, s.length-1) @tailrec def isPalindrome(s: String, start: Int, end: Int): Boolean = { val pos1 = nextLetter(s, start, end) val pos2 = prevLetter(s, start, end) if (pos1 >= pos2) return true if (toLowerCase(s.charAt(pos1)) != toLowerCase(s.charAt(pos2))) return false isPalindrome(s, pos1+1, pos2-1) } def nextLetter(s: String, start: Int, end: Int): Int = if (start > end || isLetter(s.charAt(start))) start else nextLetter(s, start+1, end) def prevLetter(s: String, start: Int, end: Int): Int = if (start > end || isLetter(s.charAt(end))) end else prevLetter(s, start, end-1)

Tail Recursion in Java?

Scala (and many other functional languages) automatically perform tail call optimization at compile time

@tailrec annotation ensures the compiler will optimize a tail recursive function (i.e. you will get a compilation failure if you use it on a function that is not really tail recursive)

Java compiler doesn't perform any tail call optimization (and very likely won't do it in a near future)

How can we overcome this limitation and have StackOverflowError-free functions also

in Java tail recursive methods?

Trampolines to the rescue

A trampoline is an iteration applying a list of functions. Each function returns the next function for the loop to run.

Func1 return

apply

Func2 return

apply

Func3 return

apply

FuncN

apply

…

result

return

Implementing the TailCall … @FunctionalInterface public interface TailCall<T> { TailCall<T> apply(); default boolean isComplete() { return false; } default T result() { throw new UnsupportedOperationException(); } default T invoke() { return Stream.iterate(this, TailCall::apply) .filter(TailCall::isComplete) .findFirst() .get() .result(); } // ... missing terminal TailCall }

… and the terminal TailCall

public static <T> TailCall<T> done(final T value) { return new TailCall<T>() { @Override public boolean isComplete() { return true; } @Override public T result() { return value; } @Override public TailCall<T> apply() { throw new UnsupportedOperationException(); } }; }

Using the Trampoline public class PalindromePredicate implements Predicate<String> { @Override public boolean test(String s) { return isPalindrome(s, 0, s.length()-1).invoke(); } private TailCall<Boolean> isPalindrome(String s, int start, int end) { while (start < end && !isLetter(s.charAt(start))) start++; while (end > start && !isLetter(s.charAt(end))) end--; if (start >= end) return done(true); if (toLowerCase(s.charAt(start)) != toLowerCase(s.charAt(end))) return done(false); int newStart = start + 1; int newEnd = end - 1; return () -> isPalindrome(s, newStart, newEnd); } }

Mario Fusco Red Hat – Senior Software Engineer

mario.fusco@gmail.com twitter: @mariofusco

Q A

Thanks … Questions?