Purely Functional State

Functional Programming in Java

Created by Mark Perry, @mprry, G+, Blog, LinkedIn, GitHub, maperry78@yahoo.com.au

Introduction

• Learn to manipulate state using RNGs
• Explore issues
• Pattern of any stateful API

Standard RNG

• java.util.Random has imperative API
• Uses side effects

Random rng = new java.util.Random();
rng.nextInt(); // 500348701
rng.nextInt(); // -350347772
rng.nextInt(5); // 4, range = [0, 4]
rng.nextInt(5); // 1
					

• Not referentially transparent
• Harder to test, compose, modularise and parallelise

Testability

int rollDie() {
    Random rng = new java.util.Random();
    return rng.nextInt(6); // [0, 5], but want [1, 6]
}
					

• Off by one error
• Obvious - complicated defects are subtle
• Need to reproduce reliably

Fixing the RNG?

• Pass in the RNG?

int rollDie(Random rng) {
    return rng.nextInt(6); // [0, 5]
}
					

• Generator needs same seed and call sequence to reproduce
• Keep track of call sequence?
• Answer: Reject side-effects!

Functional RNG

• Make the updates explicit!
• Do not mutate RNG
• Return new state as result
• Separate state computation from call sequence

class Rng {
    P2<Rng, Integer> nextInt();
    P2<Rng, Integer> nextInt(int max); // [0, max - 1]
    P2<Rng, Long> nextLong();
    P2<Rng, Double> nextDouble();
}

RNG Implementation

// Linear Congruential Generator
class LcgRng {
    private long seed;
    private LcgRng(long seed) { ... }
    static Rng rng(long seed) { ... }

    P2<Rng, Long> nextLong() {
        long newSeed = (seed * 0x5DEECE66DL + 0xBL) & 0xFFFFFFFFFFFFL;
        long n = (Long) (newSeed >>> 16);
        return P.p(n, rng(newSeed));
    }
}

Rng r = LcgRng.rng(10);
P2<Rng, Integer> p = r.nextLong();
r.nextLong()._2(); // 3847489
r.nextLong()._2(); // 3847489
Rng r2 = r.nextLong()._1();
r2.nextLong()._2(); // 1334288366

RNG Diagram

Pure Stateful APIs

• Make API return next state
• Use same technique

class Foo {
    FooState state;
    Bar bar();
    int baz();
}

class Foo2 {
    P2<FooState, Bar> bar(FooState fs);
    P2<FooState, Int> baz(FooState fs);
}

Using the API

P2<Rng, P2<Long, Long>> randomPair(Rng rng) {
    P2<Rng, Long> t1 = rng.nextLong();
    P2<Rng, Long> t2 = t1._1().nextLong();
    return p(t2._1(), p(t1._2(), t2._2()));
}

• Direct use will be tedious
• Refactor repetition
• Pattern of generating next state

State Abstracton

State Class

class State<S, A> {
    F<S, P2<S, A>> run;
    static <S, A> State<S, A> unit(F<S, P2<S, A>> f) {...}

    P2<S, A> run(S s);
    A eval(S s);
    S exec(S s);
}

Example Usage

    Rng r = LcgRng.rng(10);
    State<Rng, Long> s1 = State.unit(rng -> rng.nextLong());
    P2<Rng, Long> p = s1.run(r);
    Long l = s1.eval(r); // 3847489
    Rng r2 = s1.exec(r);

    // use sequence to get a list of random longs
    // static <S, A, B> State<S, List<A>> sequence(List<State<S, A>> list);
    List<Long> nums = sequence(List.replicate(10, s1)).eval(r);

Exercises

static <S, A> State<S, A> constant(A a);
static <S, A, B> State<S, B> map(State<S, A> s, F<A, B> f);
static <S, A, B, C> State<S, C> map2(State<S, A> s1, State<S, B> s2, F2<A, B, C> f);
static <S, A, B> State<S, B> flatMap(State<S, A> s, F<A, State<S, B>> f);
static <S, A, B> State<S, List<A>> sequence(List<State<S, A>> list);
static <S, A, B> State<S, List<B>> traverse(List<A> list, F<A, State<S, B>> f);
                    

Imperative Programming

• Sequence of statements modifying program state
• Statements are state transitions
• State is every reachable object
• Contrast: Limited immutable state with RT

Exercises

static <S> State<S, Unit> set(S s);
static <S> State<S, S> get();
static <S, A> State<S, Unit> modify(State<S, A> s, F<S, S> f);
static <S> State<S, S> init();
                    

Challenge - Vending Machine

A vending machine has items to dispense and a number of coins inside from previous usage. It is initially locked. When a coin is inserted the machine unlocks and the user opens the door to get their item. After being successfully opened the machine is locked again.

Summary

• FP programs with state
• RNG example to motivate
• Use function that takes state and returns new state and a result
• State functions make this simple

The FP Road

Afterword

• Functional Programming in Scala, Chiusano and Bjarnason
• Chapter 6, Purely Functional State

Created by Mark Perry, @mprry, G+, Blog, LinkedIn, GitHub, maperry78@yahoo.com.au