Why we cannot ignore Functional Programming

by Massimiliano Dessì
desmax74@yahoo.com
twitter: @desmax74

and Mario Fusco
mario.fusco@gmail.com
twitter: @mariofusco

Moore's
law
Now achieved
by increasing
the number
of cores

The number of transistors on integrated
circuits doubles approximately every two years

Amdahl's
law

The speedup of a program using multiple processors
in parallel computing is limited by the time needed
for the sequential fraction of the program

Concurrency & Parallelism
Concurrent programming
Managing concurrent requests

Parallel programming
Running multiple tasks at
the same time

Both are too hard!

The cause of the problem …

Mutable state +
Parallel processing =
Non-determinism

The cause of the problem …

Mutable state +
Parallel processing =
Non-determinism

Functional
Programming

… and its effects

Race conditions
Starvation

Deadlocks
Livelocks

… and its effects

Race conditions
Starvation

Deadlocks
Livelocks

Too hard to think about them!

The native Java concurrency model
Based on: Threads

Semaphores

Locks Synchronization

They are sometimes plain evil …

… and sometimes a necessary pain …

… but always the wrong default

Different concurrency models

Shared mutable state
(threads + locks)

Isolated mutable state (actors)

Purely immutable (pure functions)

Summing attendants ages (Threads)
class Blackboard {
int sum = 0;
int read() { return sum; }
void write(int value) { sum = value; }
}

class Attendant implements Runnable {
int age;
Blackboard blackboard;

public void run() {
synchronized(blackboard) {
int oldSum = blackboard.read();
int newSum = oldSum + age;
blackboard.write(newSum);
}
}
}

Summing attendants ages (Actors)
class Blackboard extends UntypedActors {
int sum = 0;
public void onReceive(Object message) {
if (message instanceof Integer) {
sum += (Integer)message;
}
}
}
class Attendant {
int age;
Blackboard blackboard;

public void sendAge() {
blackboard.sendOneWay(age);
}
}

Summing attendants ages (Functional)
class Blackboard {
final int sum;
Blackboard(int sum) { this.sum = sum; }
}

class Attendant {
int age;
Attendant next;

public Blackboard addMyAge(Blackboard blackboard) {
final Blackboard b = new Blackboard(blackboard.sum + age);
return next == null ? b : next.myAge(b);
}
}

attendants.foldLeft(new Blackboard(0),
(att, b) -> new Blackboard(b.sum + att.age));

What is a functional program?
A program created using only pure functions
No side effects allowed like:
Reassigning a variable
Modifying a data structure in place
Setting a field on an object
}
Throwing an exception or halting with an error

}
Printing to the console
Reading user input
Reading from or writing to a file
Drawing on the screen
Functional programming is a restriction on how we
write programs, but not on what they can do

The OOP/FP dualism
class Bird OOP vs. …
class Cat {
def capture(b: Bird): Unit = ...
def eat(): Unit = ...
}
val cat = new Cat
val bird = new Bird

cat.capture(bird)
cat.eat()
class Cat
class Bird
trait Catch
… FP The story
trait FullStomach
def capture(prey: Bird, hunter: Cat): Cat with Catch
def eat(consumer: Cat with Catch): Cat with FullStomach

val story = (capture _) andThen (eat _)
story(new Bird, new Cat)

What is a (pure) function?
A function with input type A and output type B
is a computation which relates every value a of
type A to exactly one value b of type B such
that b is determined solely by the value of a

But, if it really is a
function, it will do
nothing else

Referential transparency
An expression e is referentially transparent if for all programs p,
all occurrences of e in p can be replaced by the result of
evaluating e, without affecting the observable behavior of p

A function f is pure if the expression f(x) is referentially
transparent for all referentially transparent x

RT
String x = "purple";
String r1 = x.replace('p', 't');
String r2 = x.replace('p', 't');
vs.
String r1 = "purple".replace('p', 't');
r1: "turtle"
String r2 = "purple".replace('p', 't');
r2: "turtle"
Non-RT
Immutable StringBuilder x = new StringBuilder("Hi");
StringBuilder y = x.append(", mom");
String r1 = y.toString();
Mutable String r2 = y.toString();

String r1 = x.append(", mom").toString();
Expression not r1: "Hi, mom"
replaceable with String r2 = x.append(", mom").toString();
r1: "Hi, mom, mom"
its result

RT wins

Under a developer point of view:
Easier to reason about since effects of evaluation are
purely local
Use of the substitution model: it's possible to replace a
term with an equivalent one

Under a performance point of view:
The JVM is free to optimize the code by safely
reordering the instructions
No need to synchronize access to shared data

Immutability

Immutable objects can be shared among
many threads exactly because none of
them can modify it

In the same way immutable (persistent)
data structures can be shared without any
need to synchronize the different threads
accessing them

Persistent Collections

5

3 8

E 4 7 9

E E E E E E


5

2
3 8

E 4 7 9

E E E E E E

5

3
5

2
3 8

E E
E 4 7 9

E E E E E E

5 New Root

3 Old Root
5

2
3 8
Shared data
E E
E 4 7 9

E E E E E E

Modularity
class Player {
String name; public void declareWinner(Player p) {
int score; System.out.println(p.name + " wins!");
} }
public void winner(Player p1, Player p2) {
if (p1.score > p2.score) declareWinner(p1)
else declareWinner(p2);
}

Modularity
class Player {
} }
}
Separate
computational logic
public Player maxScore(Player p1, Player p2) { from side effects
return p1.score > p2.score ? p1 : p2;
}
declareWinner(maxScore(p1, p2));
}

Modularity
class Player {
} }
}
Separate
computational logic
}
}
declareWinner(players.reduceLeft(maxScore))

reuse maxScore to compute the winner among a list of players

Modularity
class Player {
} }
}
Separate
computational logic
}
}
declareWinner(players.parallel().reduceLeft(maxScore))

It's trivial to parallelize "internal" iteration

A pure functional core

functional
core

Any function with side-effects can be split into a pure function at the
core and a pair of functions with side-effect. This transformation can
be repeated to push side-effects to the outer layers of the program.

Why we cannot ignore Functional Programming

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