functional groovy

©ASERT2006-2013
Dr Paul King
@paulk_asert
http:/slideshare.net/paulk_asert/functional-groovy
https://github.com/paulk-asert/functional-groovy
Functional Groov

Topics
Intro to Functional Style
• Functional Basics
• Immutability & Persistent Data Structures
• Laziness & Strictness
• Gpars & Concurrency
• Type Safety
• Word Split (bonus material)
• More Info
©ASERT2006-2013

Introduction
• What is functional programming?
– Favour evaluation of composable
expressions over execution of commands
– Encourage particular idioms such as side-
effect free functions & immutability
• And why should I care?
– Declarative understandable code
– Reduction of errors
– Better patterns and approaches to design
– Improved reusability
– Leverage concurrency
©ASERT2006-2013

What makes up functional style?
• Functions, Closures, Lambdas, Blocks as
first-class citizens
• Higher order functions
• Mutable vs Immutable data structures
• Recursion
• Lazy vs Eager evaluation
• Declarative vs Imperative style
• Advanced Techniques
– Memoization, Trampolines, Composition and Curry
• Compile-time safety
• Concurrency

Closures...
©ASERT2006-2013
def twice = { int num -> num + num }
assert twice(5) == 10
assert twice.call(6) == 12
def twice10 = { 2 * 10 }
assert 20 == twice10()
def triple = { arg -> arg * 3 }
assert triple(5) == 15
def alsoTriple = { it * 3 }
assert alsoTriple(6) == 18
def quadruple = { arg = 2 -> twice(arg) * 2 }
assert quadruple(5) == 20
assert quadruple() == 8
// ...

...Closures...
©ASERT2006-2013
// ...
def callWith5(Closure c) {
c(5)
}
assert 15 == callWith5(triple)
def twiceMethod(int num) { num * 2 }
assert twiceMethod(2) == 4
def alsoTwice = this.&twiceMethod
assert alsoTwice(5) == 10
def alsoQuadruple = twice >> twice
assert alsoQuadruple(5) == 20
def forty = quadruple.curry(10)
assert forty() == 40
assert [10, 15, 20] == [twice, triple, quadruple].collect{ it(5) }
assert 45 == [alsoTwice, alsoTriple, alsoQuadruple].sum{ it(5) }

...Closures
• Used for many things in Groovy:
• Iterators
• Callbacks
• Higher-order functions
• Specialized control structures
• Dynamic method definition
• Resource allocation
• Threads
• Continuation-like coding
©ASERT2006-2013
def houston(Closure doit) {
(10..1).each { count ->
doit(count)
}
}
houston { println it }
new File('/x.txt').eachLine {
println it
}
3.times { println 'Hi' }
[0, 1, 2].each { number ->
println number
}
[0, 1, 2].each { println it}
def printit = { println it }
[0, 1, 2].each printit

©ASERT2006-2013
Better Design Patterns: Builder
<html>
<head>
<title>Hello</title>
</head>
<body>
<ul>
<li>world 1</li>
<li>world 2</li>
<li>world 3</li>
<li>world 4</li>
<li>world 5</li>
</ul>
</body>
</html>
import groovy.xml.*
def page = new MarkupBuilder()
page.html {
head { title 'Hello' }
body {
ul {
for (count in 1..5) {
li "world $count"
} } } }
• Markup Builder

...Better File Manipulation
©ASERT2006-2013
def out = new File('result.txt')
out.delete()
new File('..').eachFileRecurse { file ->
if (file.name.endsWith('.groovy')) {
file.eachLine { line, num ->
if (line.toLowerCase().contains('groovy'))
out << "File '$file' on line $numn$linen"
}
}
}

...DSL example...
©ASERT2006-2013
show = { println it }
square_root = { Math.sqrt(it) }
def please(action) {
[the: { what ->
[of: { n -> action(what(n)) }]
}]
}
please show the square_root of 100
// ==> 10.0
Inspiration for this example came from …

...DSL example
©ASERT2006-2013
// Japanese DSL using GEP3 rules
Object.metaClass.を =
Object.metaClass.の =
{ clos -> clos(delegate) }
まず = { it }
表示する = { println it }
平方根 = { Math.sqrt(it) }
まず 100 の平方根を表示する
// First, show the square root of 100
// => 10.0
// source: http://d.hatena.ne.jp/uehaj/20100919/1284906117
// http://groovyconsole.appspot.com/edit/241001

interface Calc {
def execute(n, m)
}
class CalcByMult implements Calc {
def execute(n, m) { n * m }
}
class CalcByManyAdds implements Calc {
def execute(n, m) {
def result = 0
n.times {
result += m
}
return result
}
}
def sampleData = [
[3, 4, 12],
[5, -5, -25]
]
Calc[] multiplicationStrategies = [
new CalcByMult(),
new CalcByManyAdds()
]
sampleData.each {data ->
multiplicationStrategies.each {calc ->
assert data[2] == calc.execute(data[0], data[1])
}
}
def multiplicationStrategies = [
{ n, m -> n * m },
{ n, m ->
def total = 0; n.times{ total += m }; total },
{ n, m -> ([m] * n).sum() }
]
def sampleData = [
[3, 4, 12],
[5, -5, -25]
]
sampleData.each{ data ->
multiplicationStrategies.each{ calc ->
assert data[2] == calc(data[0], data[1])
}
}
Language features instead of Patterns
(c)ASERT2006-2013
Strategy Pattern
with interfaces
with closures

Topics
• Intro to Functional Style
Functional Basics
• GPars & Concurrency
• Type Safety
• More Info
©ASERT2006-2013

Pure Functions, Closures, Side-effects
©ASERT2006-2013
def x = 4
def increment = { arg -> arg + 1 }
assert 11 == increment (10)
assert x == 4
def incrementWithSideEffect = { arg -> x++; arg + 1 }
assert 11 == incrementWithSideEffect(10)
assert 101 == incrementWithSideEffect(100)
assert x == 6

Referential Transparency
©ASERT2006-2013
def x, y, z, arg
def method = {
// ...
}
y = 3
arg = y
x = y + 1
method(arg)
z = y + 1 // z = x
assert x == z
def pythagorian(x, y) { Math.sqrt(x * x + y * y) }
final int A = 4
final int B = 3
def c = pythagorian(A, B) // c = 5
assert c == 5

Show me the code
PrimesPalindromes.groovy, Composition.groovy, Memoize.groovy, FactorialTrampoline.groovy

Tail Recursion
• https://github.com/jlink/tailrec
©ASERT2006-2013
@TailRecursive
def factorial(n, acc = 1) {
n <= 1 ? acc : factorial(n - 1, n * acc)
}
println factorial(1000G)

Topics
Immutability & Persistent Data Structures
• Type Safety
• More Info
©ASERT2006-2013

Immutability
• An object’s value doesn’t change once created
• Examples
– Groovy has primitives & their wrapper classes,
Strings, null
– “constants” (final reference fields)
• With some caveats about what they point to
– Basic enum values
• With some caveats on complex enums
– Numerous (effectively) immutable classes
• java.awt.Color, java.net.URI, java.util.UUID, java.lang.Class,
java.util.Date, java.math.BigInteger, java.math.BigDecimal
– Your own carefully written classes
– Very careful use of aggregation/collection classes
– Special immutable aggregation/collection classes

Why Immutability?
• Simple
– Exactly one state
– Potentially easier to design, implement, use, reason
about & make secure
• Inherently referentially transparent
– Potential for optimisation
• Can be shared freely
– Including “constant” aggregations of immutables
– Including persistent structures of immutables
– Suitable for caching
– Can even cache “pure” expressions involving
immutables, e.g. 3 + 4, “string”.size(), fib(42)
– Inherently thread safe

Approaches to managing collection storage
• Mutable • Persistent
©ASERT2006-2013
• Immutable
‘c’ ‘a’ ‘c’ ‘a’
Add ‘t’ Add ‘t’ Add ‘t’
‘c’ ‘a’ ‘t’
‘c’ ‘a’
‘c’ ‘a’ ‘t’
X
‘c’
‘a’
‘t’
‘c’
‘a’

Immutable practices
• Using mutating style
String invention = 'Mouse Trap'
List inventions = [invention]
invention = 'Better ' + invention
inventions << invention
assert inventions == ['Mouse Trap', 'Better Mouse Trap']
inventions.removeAll 'Mouse Trap'
assert inventions == ['Better Mouse Trap']

Immutable practices
• Using mutating style
– We could possibly get away with this code here but it
has some debatable code smells
• (1) add a reference to a mutable list
• (2) change string reference losing original
• (3),(4) mutate list
• (4) duplicate first invention because original lost
List inventions = [invention] //(1)
invention = 'Better ' + invention //(2)
inventions << invention //(3)
inventions.removeAll 'Mouse Trap' //(4)

Immutable practices
• Avoid using mutator methods
Avoid Prefer
list.sort() list.sort(false)
list.unique() list.unique(false)
list.reverse(true) list.reverse()
list.addAll list.plus
list.removeAll list.minus
String or List += or << use differently named variables
mutating java.util.Collections
void methods, e.g. shuffle, swap,
fill, copy, rotate
your own non mutating variants

Immutable practices
Avoid Prefer
fill, copy, rotate
public class Collections {
public static void shuffle(List<?> list) { /* ... */ }
/* ... */
}

Immutable practices
Avoid Prefer
fill, copy, rotate
static List myShuffle(List list) {
List result = new ArrayList(list)
Collections.shuffle(result)
result
}

Immutable practices
– But only marginal gains when using Java’s built-in
collections
// Avoid
List inventions = [invention]
invention = 'Better ' + invention
inventions << invention
inventions.removeAll 'Mouse Trap'
// Prefer
String firstInvention = 'Mouse Trap'
List initialInventions = [firstInvention]
String secondInvention = 'Better ' + firstInvention
List allInventions = initialInventions + secondInvention
assert allInventions == ['Mouse Trap', 'Better Mouse Trap']
List bestInventions = allInventions - firstInvention
assert bestInventions == ['Better Mouse Trap']

Immutability options - collections
• Built-in
• Google Collections
– Numerous improved immutable collection types
• Groovy run-time metaprogramming
import com.google.common.collect.*
List<String> animals = ImmutableList.of("cat", "dog", "horse")
animals << 'fish' // => java.lang.UnsupportedOperationException
def animals = ['cat', 'dog', 'horse'].asImmutable()
def animals = ['cat', 'dog', 'horse']
ArrayList.metaClass.leftShift = {
throw new UnsupportedOperationException() }

Immutability – persistent collections
• Functional Java
– Or Functional Groovy, clj-ds, pcollections, totallylazy
@Grab('org.functionaljava:functionaljava:3.1')
def pets = fj.data.List.list("cat", "dog", "horse")
// buy a fish
def newPets = pets.cons("fish")
assert [3, 4] == [pets.length(), newPets.length()]
pets
newPets
head
tail
head
tail
head
tail
head
tail
fish
cat
dog
horse

• Functional Java
// sell the horse
def remaining = newPets.removeAll{ it == 'horse' }
pets
newPets
head
tail
head
tailhead
tail
head
tail
fish
cat
dog
horse
remaining
???

• Functional Java
assert [3, 4, 3] == [pets, newPets, remaining]*.length()
pets
newPets
head
tail
head
tailhead
tail
head
tail
fish
cat
dog
horse
remaining
???

• Functional Java
assert [3, 4, 3] == [pets, newPets, remaining]*.length()
pets
newPets
head
tail
head
tailhead
tail
head
tail
fish
cat
dog
horse
remaining
head
tail
head
tail
head
tail
fish
cat
dog
copy
copy
copy

A
B C
D E F G
H I K
???
J
original

• You will see the correct results but in general, different
operations may give very differing performance
characteristics from what you expect
– But don’t fret, smart people are working on smart structures to support a variety
of scenarios. You may even have several in your current NoSQL implementation
A
B C
D E F G
H I
A*
C*
G*
KJ
original modified

Reality check
• OK, do I have to write this myself?
– Might pay to try some simple ones. Take a look at Eric
Lippert’s blog on some C# implementations. Here is
the first part (Part 1: Kinds of Immutability):
http://blogs.msdn.com/ericlippert/archive/2007/11/13/
immutability-in-c-part-one-kinds-of-immutability.aspx
– Also consider
• Part 2: Simple Immutable Stack, Part 3: Covariant Immutable
Stack, Part 4: Immutable Queue, Part 6: Simple Binary Tree
– There are probably plenty of implementations you can
already use
– See also: Purely Functional Data Structures by Chris
Okasak, Cambridge University Press (1999)
• It turns out you use trees for nearly everything! 

Reality check
• Functional Java persistent data structures
– Singly-linked list (fj.data.List)
– Lazy singly-linked list (fj.data.Stream)
– Nonempty list (fj.data.NonEmptyList)
– Optional value (a container of length 0 or 1) (fj.data.Option)
– Immutable set using a red/black tree (fj.data.Set)
– Immutable multi-way tree (a.k.a. rose tree) (fj.data.Tree)
– Immutable tree-map using a red/black tree (fj.data.TreeMap)
– Products (tuples) of arity 1-8 (fj.P1..P8)
– Vectors of arity 2-8 (fj.data.vector.V2..V8)
– Pointed lists and trees (fj.data.Zipper and fj.data.TreeZipper)
– Type-safe, generic heterogeneous list (fj.data.hlist.HList)
– Immutable arrays (fj.data.Array)
– Disjoint union datatype (fj.data.Either)
– 2-3 finger trees supporting access to the ends in amortized O(1)
time (fj.data.fingertrees)

Reality check
• OK, have we achieved something simpler?
– It depends. Understanding the insides of persistent
data structures can be very hard
• But as you move towards more complex systems and more
concurrent systems, not having to worry about which
threads are mutating what and when usually outweighs the
complexities of using persistent data structures
• Arguing for impure in Haskell:
http://www.cse.unsw.edu.au/~benl/papers/thesis/lippmeier-
impure-world.pdf
– They still don’t solve all of the problems. For any
significant problem you will have multiple threads
working on the solution. In some sense we have just
moved the problem but at least we have separated
concerns.
• You might combine with message passing (actors) or
dataflow or software transactional memory (STM)

Immutable Classes
• Some Rules
– Don’t provide mutators
– Ensure that no methods can
be overridden
• Easiest to make the class final
• Or use static factories & non-public
constructors
– Make all fields final
– Make all fields private
• Avoid even public immutable constants
– Ensure exclusive access to any mutable components
• Don’t leak internal references
• Defensive copying in and out
– Optionally provide equals and hashCode methods
– Optionally provide toString method

@Immutable...
• Java Immutable Class
– As per Joshua Bloch
Effective Java
©ASERT2006-2013
public final class Person {
private final String first;
private final String last;
public String getFirst() {
return first;
}
public String getLast() {
return last;
}
@Override
public int hashCode() {
final int prime = 31;
int result = 1;
result = prime * result + ((first == null)
? 0 : first.hashCode());
result = prime * result + ((last == null)
? 0 : last.hashCode());
return result;
}
public Person(String first, String last) {
this.first = first;
this.last = last;
}
// ...
// ...
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (getClass() != obj.getClass())
return false;
Person other = (Person) obj;
if (first == null) {
if (other.first != null)
return false;
} else if (!first.equals(other.first))
return false;
if (last == null) {
if (other.last != null)
return false;
} else if (!last.equals(other.last))
return false;
return true;
}
@Override
public String toString() {
return "Person(first:" + first
+ ", last:" + last + ")";
}
}

...@Immutable...
• Java Immutable Class
– As per Joshua Bloch
Effective Java
©ASERT2006-2013
public final class Person {
private final String first;
private final String last;
public String getFirst() {
return first;
}
public String getLast() {
return last;
}
@Override
public int hashCode() {
final int prime = 31;
int result = 1;
result = prime * result + ((first == null)
? 0 : first.hashCode());
result = prime * result + ((last == null)
? 0 : last.hashCode());
return result;
}
public Person(String first, String last) {
this.first = first;
this.last = last;
}
// ...
// ...
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (getClass() != obj.getClass())
return false;
Person other = (Person) obj;
if (first == null) {
if (other.first != null)
return false;
} else if (!first.equals(other.first))
return false;
if (last == null) {
if (other.last != null)
return false;
} else if (!last.equals(other.last))
return false;
return true;
}
@Override
public String toString() {
return "Person(first:" + first
+ ", last:" + last + ")";
}
}
boilerplate

...@Immutable
©ASERT2006-2013
@Immutable class Person {
String first, last
}

Topics
Laziness & Strictness
• Type Safety
• More Info
©ASERT2006-2013

totallylazy library
• Similar to Groovy’s collection
GDK methods …
• Except … lazy …
@GrabResolver('http://repo.bodar.com/')
@Grab('com.googlecode.totallylazy:totallylazy:1113')
import static com.googlecode.totallylazy.Sequences.map
import static com.googlecode.totallylazy.numbers.Numbers.*
assert range(6, 10) == [6,7,8,9,10]
assert range(6, 10, 2).forAll(even)
assert range(6, 10).reduce{ a, b -> a + b } == 40
assert range(6, 10).foldLeft(0, add) == 40
assert map(range(6, 10), { it + 100 }) == [106,107,108,109,110]
assert primes().take(10) == [2,3,5,7,11,13,17,19,23,29]
assert range(1, 4).cycle().drop(2).take(8) == [3,4,1,2,3,4,1,2]
println range(6, 1_000_000_000_000).filter(even).drop(1).take(5)
// => 8,10,12,14,16 (a handful of millis later)

Immutability options - collections
• This script
• Produces this output (order will vary)
@GrabResolver('http://repo.bodar.com/')
import static com.googlecode.totallylazy.Sequences.flatMapConcurrently
import static com.googlecode.totallylazy.numbers.Numbers.*
println flatMapConcurrently(range(6, 10)) {
println it // just for logging
even(it) ? [it, it+100] : []
}
//9
//7
//8
//6
//10
//6,106,8,108,10,110

GPars and TotallyLazy library
©ASERT2006-2013
@GrabResolver('http://repo.bodar.com')
import static groovyx.gpars.GParsExecutorsPool.withPool
import static com.googlecode.totallylazy.Callables.asString
import static com.googlecode.totallylazy.Sequences.sequence
withPool { pool ->
assert ['5', '6'] == sequence(4, 5, 6)
.drop(1)
.mapConcurrently(asString(), pool)
.toList()
}
withPool {
assert ['5', '6'] == [4, 5, 6]
.drop(1)
.collectParallel{ it.toString() }
}
<= Plain GPars equivalent

Groovy Streams
• https://github.com/timyates/groovy-stream
@Grab('com.bloidonia:groovy-stream:0.5.2')
import groovy.stream.Stream
// Repeat an object indefinitely
Stream s = Stream.from { 1 }
assert s.take( 5 ).collect() == [ 1, 1, 1, 1, 1 ]
// Use an Iterable
s = Stream.from 1..3
assert s.collect() == [ 1, 2, 3 ]
// Use an iterator
def iter = [ 1, 2, 3 ].iterator()
s = Stream.from iter
assert s.collect() == [ 1, 2, 3 ]
// Use a map of iterables
s = Stream.from x:1..2, y:3..4
assert s.collect() == [ [x:1,y:3],[x:1,y:4],[x:2,y:3],[x:2,y:4] ]

Groovy Streams
• https://github.com/dsrkoc/monadologie
import static hr.helix.monadologie.MonadComprehension.foreach
def res = foreach {
a = takeFrom { [1, 2, 3] }
b = takeFrom { [4, 5] }
yield { a + b }
}
assert res == [5, 6, 6, 7, 7, 8]

Functional Groovy
• https://github.com/mperry/functionalgroovy
@GrabResolver('https://oss.sonatype.org/content/groups/public')
@Grab('com.github.mperry:functionalgroovy-core:0.2-SNAPSHOT')
import static com.github.mperry.fg.Comprehension.foreach
1.to(5).each { println it }
def result = foreach {
num { 1.to(2) }
yield { num + 1 }
}
assert result.toJList() == [2, 3]

Show me the code
LazyMain.groovy

Topics
GPars & Concurrency
• Type Safety
• More Info
©ASERT2006-2013

Ralph Johnson: Parallel Programming
• Styles of parallel programming
– Threads and locks
• Nondeterministic, low-level, rumored humans can do this
– Asynchronous messages e.g. Actors –
no or limited shared memory
• Nondeterministic, ok for I/O but be careful with side-effects
– Sharing with deterministic restrictions
e.g. Fork-join
• Hopefully deterministic semantics, not designed for I/O
– Data parallelism
• Deterministic semantics, easy, efficient, not designed for I/O
©ASERT2006-2013
http://strangeloop2010.com/talk/presentation_file/14485/Johnson-DataParallelism.pdf
Each approach has some caveats

GPars
• http://gpars.codehaus.org/
• Library classes and DSL sugar providing
intuitive ways for Groovy developers to
handle tasks concurrently. Logical parts:
– Data Parallelism features use JSR-166y Parallel Arrays
to enable multi-threaded collection processing
– Asynchronous functions extend the Java 1.5 built-in
support for executor services to enable multi-threaded
closure processing
– Dataflow Concurrency supports natural shared-memory
concurrency model, using single-assignment variables
– Actors provide an implementation of Erlang/Scala-like
actors including "remote" actors on other machines
– Safe Agents provide a non-blocking mt-safe reference to
mutable state; inspired by "agents" in Clojure
©ASERT2006-2013

Coordination approachesSource:ReGinA–GroovyinAction,2ndedition
Data Parallelism:
Fork/Join
Map/Reduce
Fixed coordination
(for collections)
Actors Explicit coordination
Safe Agents Delegated coordination
Dataflow Implicit coordination

GPars: Choosing approachesFormoredetailssee:http://gpars.codehaus.org/Concepts+compared
Parallel
Collections
Data Parallelism
Task
Parallelism
Streamed Data
Parallelism
Fork/
Join
Dataflow
operators
CSP
Actors
Dataflow tasks
Actors
Asynch fun’s
CSP
Fork/
Join
Immutable
Stm, Agents
Special collections
Synchronization
Linear Recursive
Linear
Recursive
Shared
Data
Irregular
Regular

Groovy Sequential Collection
©ASERT2006-2013
def oneStarters = (1..30)
.collect { it ** 2 }
.findAll { it ==~ '1.*' }
assert oneStarters == [1, 16, 100, 121, 144, 169, 196]
assert oneStarters.max() == 196
assert oneStarters.sum() == 747

GPars Parallel Collections…
©ASERT2006-2013
import static groovyx.gpars.GParsPool.withPool
withPool {
.collectParallel { it ** 2 }
.findAllParallel { it ==~ '1.*' }
assert oneStarters.maxParallel() == 196
assert oneStarters.sumParallel() == 747
}

…GPars Parallel Collections
• Suitable when
– Each iteration is independent, i.e. not:
fact[index] = index * fact[index - 1]
– Iteration logic doesn’t use non-thread safe code
– Size and indexing of iteration are important
©ASERT2006-2013
withPool {
.collectParallel { it ** 2 }
.findAllParallel { it ==~ '1.*' }
assert oneStarters.maxParallel() == 196
assert oneStarters.sumParallel() == 747
}

Parallel Collection Variations
• Apply some Groovy metaprogramming
©ASERT2006-2013
withPool {
def oneStarters = (1..30).makeConcurrent()
.collect { it ** 2 }
.findAll { it ==~ '1.*' }
.findAll { it ==~ '...' }
assert oneStarters == [100, 121, 144, 169, 196]
}
import groovyx.gpars.ParallelEnhancer
def nums = 1..5
ParallelEnhancer.enhanceInstance(nums)
assert [1, 4, 9, 16, 25] == nums.collectParallel{ it * it }

GPars parallel methods for collections
Transparent Transitive? Parallel Lazy?
any { ... } anyParallel { ... } yes
collect { ... } yes collectParallel { ... }
count(filter) countParallel(filter)
each { ... } eachParallel { ... }
eachWithIndex { ... } eachWithIndexParallel { ... }
every { ... } everyParallel { ... } yes
find { ... } findParallel { ... }
findAll { ... } yes findAllParallel { ... }
findAny { ... } findAnyParallel { ... }
fold { ... } foldParallel { ... }
fold(seed) { ... } foldParallel(seed) { ... }
grep(filter) yes grepParallel(filter)
groupBy { ... } groupByParallel { ... }
max { ... } maxParallel { ... }
max() maxParallel()
min { ... } minParallel { ... }
min() minParallel()
split { ... } yes splitParallel { ... }
sum sumParallel // foldParallel +
Transitive means result is automatically transparent; Lazy means fails fast
FormoredetailsseeReGinAortheGParsdocumentation

GPars: Map-Reduce
©ASERT2006-2013
withPool {
def oneStarters = (1..30).parallel
.map { it ** 2 }
.filter { it ==~ '1.*' }
assert oneStarters.collection ==
[1, 16, 100, 121, 144, 169, 196]
// aggregations/reductions
assert oneStarters.max() == 196
assert oneStarters.reduce { a, b -> a + b } == 747
assert oneStarters.sum() == 747
}

GPars parallel array methods
Method Return Type
combine(initValue) { ... } Map
filter { ... } Parallel array
collection Collection
groupBy { ... } Map
map { ... } Parallel array
max() T
max { ... } T
min() T
min { ... } T
reduce { ... } T
reduce(seed) { ... } T
size() int
sort { ... } Parallel array
sum() T
parallel // on a Collection Parallel array
FormoredetailsseeReGinAortheGParsdocumentation

Parallel Collections vs Map-Reduce
Fork Fork
JoinJoin
Map
Map
Reduce
Map
Map
Reduce
Reduce
Map
Filter
FilterMap

Concurrency challenge…
• Suppose we have the following
calculation involving several functions:
• And we want to use our available cores …
©ASERT2006-2013
// example adapted from Parallel Programming with .Net
def (f1, f2, f3, f4) = [{ sleep 1000; it }] * 3 +
[{ x, y -> x + y }]
def a = 5
def b = f1(a)
def c = f2(a)
def d = f3(c)
def f = f4(b, d)
assert f == 10

…Concurrency challenge…
• We can analyse the example’s task graph:
©ASERT2006-2013
def (f1, f2, f3, f4) = [{ sleep 1000; it }] * 3 +
[{ x, y -> x + y }]
def a = 5
def b = f1(a)
def c = f2(a)
def d = f3(c)
def f = f4(b, d)
assert f == 10
f2
f3
f1
f4
aa
b
c
d
f

…Concurrency challenge…
• Manually using asynchronous functions:
©ASERT2006-2013
def (f1, f2, f3, f4) = [{ sleep 1000; it }] * 3 +
[{ x, y -> x + y }]
withPool(2) {
def a = 5
def futureB = f1.callAsync(a)
def c = f2(a)
def d = f3(c)
def f = f4(futureB.get(), d)
assert f == 10
}
f2
f3
f1
f4
aa
b
c
d
f

…Concurrency challenge
• And with GPars Dataflows:
©ASERT2006-2013
def (f1, f2, f3, f4) = [{ sleep 1000; it }] * 3 +
[{ x, y -> x + y }]
import groovyx.gpars.dataflow.Dataflows
import static groovyx.gpars.dataflow.Dataflow.task
new Dataflows().with {
task { a = 5 }
task { b = f1(a) }
task { c = f2(a) }
task { d = f3(c) }
task { f = f4(b, d) }
assert f == 10
}
f2
f3
f1
f4
aa
b
c
d
f

…Concurrency challenge
• And with GPars Dataflows:
©ASERT2006-2013
def (f1, f2, f3, f4) = [{ sleep 1000; it }] * 3 +
[{ x, y -> x + y }]
new Dataflows().with {
task { f = f4(b, d) }
task { d = f3(c) }
task { c = f2(a) }
task { b = f1(a) }
task { a = 5 }
assert f == 10
}
f2
f3
f1
f4
aa
b
c
d
f

GPars: Dataflows...
©ASERT2006-2013
import groovyx.gpars.dataflow.DataFlows
import static groovyx.gpars.dataflow.DataFlow.task
final flow = new DataFlows()
task { flow.result = flow.x + flow.y }
task { flow.x = 10 }
task { flow.y = 5 }
assert 15 == flow.result
new DataFlows().with {
task { result = x * y }
task { x = 10 }
task { y = 5 }
assert 50 == result
}
510
yx
*

...GPars: Dataflows...
• Evaluating:
©ASERT2006-2013
import groovyx.gpars.dataflow.DataFlows
import static groovyx.gpars.dataflow.DataFlow.task
final flow = new DataFlows()
task { flow.a = 10 }
task { flow.b = 5 }
task { flow.x = flow.a - flow.b }
task { flow.y = flow.a + flow.b }
task { flow.result = flow.x * flow.y }
assert flow.result == 75
b
10 5
a
+-
*
result = (a – b) * (a + b)
x y
Question: what happens if I change the order of the task statements here?

• Naive attempt for loops
©ASERT2006-2013
final flow = new Dataflows()
[10, 20].each { thisA ->
[4, 5].each { thisB ->
task { flow.a = thisA }
task { flow.b = thisB }
task { flow.x = flow.a - flow.b }
task { flow.y = flow.a + flow.b }
task { flow.result = flow.x * flow.y }
println flow.result
}
}
// => java.lang.IllegalStateException:
A DataflowVariable can only be assigned once.
...
...
Don’t do this!
X

©ASERT2006-2013
import groovyx.gpars.dataflow.DataflowStream
import static groovyx.gpars.dataflow.Dataflow.*
final streamA = new DataflowStream()
final streamB = new DataflowStream()
final streamX = new DataflowStream()
final streamY = new DataflowStream()
final results = new DataflowStream()
operator(inputs: [streamA, streamB],
outputs: [streamX, streamY]) {
a, b -> streamX << a - b; streamY << a + b
}
operator(inputs: [streamX, streamY],
outputs: [results]) { x, y -> results << x * y }
[[10, 20], [4, 5]].combinations().each{ thisA, thisB ->
task { streamA << thisA }
task { streamB << thisB }
}
4.times { println results.val }
b
10
10
20
20
4
5
4
5
a
+-
*
84
75
384
375

...GPars: Dataflows
• Suitable when:
– Your algorithms can be expressed as mutually-
independent logical tasks
• Properties:
– Inherently safe and robust (no race conditions or
livelocks)
– Amenable to static analysis
– Deadlocks “typically” become repeatable
– “Beautiful” (declarative) code
©ASERT2006-2013
final flow = new Dataflows()
task { flow.x = flow.y }
task { flow.y = flow.x }

…GPars: Actors...
©ASERT2006-2013
import static groovyx.gpars.actor.Actors.*
def votes = reactor {
it.endsWith('y') ? "You voted for $it" : "Sorry, please try again"
}
println votes.sendAndWait('Groovy')
println votes.sendAndWait('JRuby')
println votes.sendAndWait('Go')
def languages = ['Groovy', 'Dart', 'C++']
def booth = actor {
languages.each{ votes << it }
loop {
languages.size().times {
react { println it }
}
stop()
}
}
booth.join(); votes.stop(); votes.join()
You voted for Groovy
You voted for JRuby
Sorry, please try again
You voted for Groovy

Software Transactional Memory…
©ASERT2006-2013
@Grab('org.multiverse:multiverse-beta:0.7-RC-1')
import org.multiverse.api.references.LongRef
import static groovyx.gpars.stm.GParsStm.atomic
import static org.multiverse.api.StmUtils.newLongRef
class Account {
private final LongRef balance
Account(long initial) {
balance = newLongRef(initial)
}
void setBalance(long newBalance) {
if (newBalance < 0) throw new RuntimeException("not enough money")
balance.set newBalance
}
long getBalance() {
balance.get()
}
}
// ...

…Software Transactional Memory
©ASERT2006-2013
// ...
def from = new Account(20)
def to = new Account(20)
def amount = 10
def watcher = Thread.start {
15.times {
atomic { println "from: ${from.balance}, to: ${to.balance}" }
sleep 100
}
}
sleep 150
try {
atomic {
from.balance -= amount
to.balance += amount
sleep 500
}
println 'transfer success'
} catch(all) {
println all.message
}
atomic { println "from: $from.balance, to: $to.balance" }
watcher.join()

Show me the code
JScience, SPrintfChecker, GenericStackTest

Word Split with Fortress
©ASERT2006-2013
Guy Steele’s StrangeLoop keynote (from slide 52 onwards for several slides):
http://strangeloop2010.com/talk/presentation_file/14299/GuySteele-parallel.pdf

Word Split…
©ASERT2006-2013
def swords = { s ->
def result = []
def word = ''
s.each{ ch ->
if (ch == ' ') {
if (word) result += word
word = ''
} else word += ch
}
result
}
assert swords("This is a sample") == ['This', 'is', 'a', 'sample']
assert swords("Here is a sesquipedalian string of words") ==
['Here', 'is', 'a', 'sesquipedalian', 'string', 'of', 'words']

Word Split…
©ASERT2006-2013
def swords = { s ->
def result = []
def word = ''
s.each{ ch ->
if (ch == ' ') {
word = ''
} else word += ch
}
result
}

Segment(left1, m1, right1) Segment(left2, m2, right2)
Segment(left1, m1 + [ ? ] + m2, right2)
…Word Split…
©ASERT2006-2013

…Word Split…
©ASERT2006-2013
class Util { static maybeWord(s) { s ? [s] : [] } }
import static Util.*
@Immutable class Chunk {
String s
public static final ZERO = new Chunk('')
def plus(Chunk other) { new Chunk(s + other.s) }
def plus(Segment other) {
new Segment(s + other.l, other.m, other.r) }
def flatten() { maybeWord(s) }
}
@Immutable class Segment {
String l; List m; String r
public static final ZERO = new Segment('', [], '')
def plus(Chunk other) { new Segment(l, m, r + other.s) }
def plus(Segment other) {
new Segment(l, m + maybeWord(r + other.l) + other.m, other.r) }
def flatten() { maybeWord(l) + m + maybeWord(r) }
}

…Word Split…
©ASERT2006-2013
def processChar(ch) { ch == ' ' ?
new Segment('', [], '') :
new Chunk(ch) }
def swords(s) { s.inject(Chunk.ZERO) { result, ch ->
result + processChar(ch) } }
assert swords("Here is a sesquipedalian string of words").flatten()
== ['Here', 'is', 'a', 'sesquipedalian', 'string', 'of', 'words']

…Word Split…
©ASERT2006-2013
THREADS = 4
def pwords(s) {
int n = (s.size() + THREADS - 1) / THREADS
def map = new ConcurrentHashMap()
(0..<THREADS).collect { i ->
Thread.start {
def (min, max) = [
[s.size(), i * n].min(), [s.size(), (i + 1) * n].min()
]
map[i] = swords(s[min..<max])
}
}*.join()
(0..<THREADS).collect { i -> map[i] }.sum().flatten()
}

…Word Split…
©ASERT2006-2013
THRESHHOLD = 10
def partition(piece) {
piece.size() <= THRESHHOLD ? piece :
[piece[0..<THRESHHOLD]] + partition(piece.substring(THRESHHOLD))
}
def pwords = { input ->
withPool(THREADS) {
partition(input).parallel.map(swords).reduce{ a, b -> a + b }.flatten()
}
}

…Guy Steele example in Groovy…
©ASERT2006-2013
def words = { s ->
int n = (s.size() + THREADS - 1) / THREADS
def min = (0..<THREADS).collectEntries{ [it, [s.size(),it*n].min()] }
def max = (0..<THREADS).collectEntries{ [it, [s.size(),(it+1)*n].min()] }
def result = new DataFlows().with {
task { a = swords(s[min[0]..<max[0]]) }
task { b = swords(s[min[1]..<max[1]]) }
task { c = swords(s[min[2]..<max[2]]) }
task { d = swords(s[min[3]..<max[3]]) }
task { sum1 = a + b }
task { sum2 = c + d }
task { sum = sum1 + sum2 }
println 'Tasks ahoy!'
sum
}
switch(result) {
case Chunk: return maybeWord(result.s)
case Segment: return result.with{ maybeWord(l) + m + maybeWord(r) }
}
}
DataFlow version: partially hard-coded to 4 partitions for easier reading

…Guy Steele example in Groovy…
©ASERT2006-2013
GRANULARITY_THRESHHOLD = 10
THREADS = 4
println GParsPool.withPool(THREADS) {
def result = runForkJoin(0, input.size(), input){ first, last, s ->
def size = last - first
if (size <= GRANULARITY_THRESHHOLD) {
swords(s[first..<last])
} else { // divide and conquer
def mid = first + ((last - first) >> 1)
forkOffChild(first, mid, s)
forkOffChild(mid, last, s)
childrenResults.sum()
}
}
switch(result) {
case Chunk: return maybeWord(result.s)
case Segment: return result.with{ maybeWord(l) + m + maybeWord(r) }
}
}
Fork/Join version

…Guy Steele example in Groovy
©ASERT2006-2013
println GParsPool.withPool(THREADS) {
def ans = input.collectParallel{ processChar(it) }.sum()
switch(ans) {
case Chunk: return maybeWord(ans.s)
case Segment: return ans.with{ maybeWord(l) + m + maybeWord(r) }
}
}
Just leveraging the algorithm’s parallel nature

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