functional groovy

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functional groovy

  1. 1. ©ASERT2006-2013 Dr Paul King @paulk_asert http:/slideshare.net/paulk_asert/functional-groovy https://github.com/paulk-asert/functional-groovy Functional Groov
  2. 2. 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
  3. 3. 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
  4. 4. 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
  5. 5. 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 // ...
  6. 6. ...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) }
  7. 7. ...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
  8. 8. ©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 $count" } } } } • Markup Builder
  9. 9. ...Better File Manipulation ©ASERT2006-2013 def out = new File('result.txt') out.delete() new File('..').eachFileRecurse { file -> if (file.name.endsWith('.groovy')) { file.eachLine { line, num -> if (line.toLowerCase().contains('groovy')) out << "File '$file' on line $numn$linen" } } }
  10. 10. ...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 …
  11. 11. ...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
  12. 12. 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
  13. 13. 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
  14. 14. 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
  15. 15. 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
  16. 16. Show me the code PrimesPalindromes.groovy, Composition.groovy, Memoize.groovy, FactorialTrampoline.groovy
  17. 17. 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)
  18. 18. 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
  19. 19. 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
  20. 20. 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
  21. 21. 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’
  22. 22. 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’
  23. 23. 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']
  24. 24. 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 String invention = 'Mouse Trap' List inventions = [invention] //(1) invention = 'Better ' + invention //(2) inventions << invention //(3) assert inventions == ['Mouse Trap', 'Better Mouse Trap'] inventions.removeAll 'Mouse Trap' //(4) assert inventions == ['Better Mouse Trap']
  25. 25. 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’
  26. 26. 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
  27. 27. 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 public class Collections { public static void shuffle(List<?> list) { /* ... */ } /* ... */ }
  28. 28. 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 static List myShuffle(List list) { List result = new ArrayList(list) Collections.shuffle(result) result }
  29. 29. Immutable practices • Avoid using mutator methods – But only marginal gains when using Java’s built-in collections // Avoid 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'] // 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']
  30. 30. 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() animals << 'fish' // => java.lang.UnsupportedOperationException def animals = ['cat', 'dog', 'horse'] ArrayList.metaClass.leftShift = { throw new UnsupportedOperationException() } animals << 'fish' // => java.lang.UnsupportedOperationException
  31. 31. 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’
  32. 32. 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
  33. 33. Immutability – persistent collections • Functional Java @Grab('org.functionaljava:functionaljava:3.1') def pets = fj.data.List.list("cat", "dog", "horse") def newPets = pets.cons("fish") assert [3, 4] == [pets.length(), newPets.length()] // sell the horse def remaining = newPets.removeAll{ it == 'horse' } pets newPets head tail head tailhead tail head tail fish cat dog horse remaining ???
  34. 34. Immutability – persistent collections • Functional Java @Grab('org.functionaljava:functionaljava:3.1') def pets = fj.data.List.list("cat", "dog", "horse") def newPets = pets.cons("fish") assert [3, 4] == [pets.length(), newPets.length()] def remaining = newPets.removeAll{ it == 'horse' } assert [3, 4, 3] == [pets, newPets, remaining]*.length() pets newPets head tail head tailhead tail head tail fish cat dog horse remaining ???
  35. 35. Immutability – persistent collections • Functional Java @Grab('org.functionaljava:functionaljava:3.1') def pets = fj.data.List.list("cat", "dog", "horse") def newPets = pets.cons("fish") assert [3, 4] == [pets.length(), newPets.length()] def remaining = newPets.removeAll{ it == 'horse' } 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
  36. 36. Immutability – persistent collections A B C D E F G H I K ??? J original
  37. 37. Immutability – persistent collections • 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
  38. 38. 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! 
  39. 39. 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)
  40. 40. 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)
  41. 41. 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
  42. 42. @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 + ")"; } }
  43. 43. ...@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
  44. 44. ...@Immutable ©ASERT2006-2013 @Immutable class Person { String first, last }
  45. 45. 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
  46. 46. 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)
  47. 47. Immutability options - collections • This script • Produces this output (order will vary) @GrabResolver('http://repo.bodar.com/') @Grab('com.googlecode.totallylazy:totallylazy:1113') 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
  48. 48. GPars and TotallyLazy library ©ASERT2006-2013 @GrabResolver('http://repo.bodar.com') @Grab('com.googlecode.totallylazy:totallylazy:1113') 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
  49. 49. 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] ]
  50. 50. 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]
  51. 51. Functional Groovy • https://github.com/mperry/functionalgroovy @GrabResolver('https://oss.sonatype.org/content/groups/public') @Grab('com.github.mperry:functionalgroovy-core:0.2-SNAPSHOT') @Grab('org.functionaljava:functionaljava:3.1') 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]
  52. 52. Show me the code LazyMain.groovy
  53. 53. 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
  54. 54. 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
  55. 55. 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
  56. 56. 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
  57. 57. 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
  58. 58. 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
  59. 59. GPars Parallel Collections… ©ASERT2006-2013 import static groovyx.gpars.GParsPool.withPool withPool { def oneStarters = (1..30) .collectParallel { it ** 2 } .findAllParallel { it ==~ '1.*' } assert oneStarters == [1, 16, 100, 121, 144, 169, 196] assert oneStarters.maxParallel() == 196 assert oneStarters.sumParallel() == 747 }
  60. 60. …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 import static groovyx.gpars.GParsPool.withPool withPool { def oneStarters = (1..30) .collectParallel { it ** 2 } .findAllParallel { it ==~ '1.*' } assert oneStarters == [1, 16, 100, 121, 144, 169, 196] assert oneStarters.maxParallel() == 196 assert oneStarters.sumParallel() == 747 }
  61. 61. Parallel Collection Variations • Apply some Groovy metaprogramming ©ASERT2006-2013 import static groovyx.gpars.GParsPool.withPool 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 }
  62. 62. 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
  63. 63. GPars: Map-Reduce ©ASERT2006-2013 import static groovyx.gpars.GParsPool.withPool 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 }
  64. 64. 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
  65. 65. Parallel Collections vs Map-Reduce Fork Fork JoinJoin Map Map Reduce Map Map Reduce Reduce Map Filter FilterMap
  66. 66. 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
  67. 67. …Concurrency challenge… • We can analyse the example’s task graph: ©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 f2 f3 f1 f4 aa b c d f
  68. 68. …Concurrency challenge… • Manually using asynchronous functions: ©ASERT2006-2013 // example adapted from Parallel Programming with .Net def (f1, f2, f3, f4) = [{ sleep 1000; it }] * 3 + [{ x, y -> x + y }] import static groovyx.gpars.GParsPool.withPool 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
  69. 69. …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
  70. 70. …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 { 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
  71. 71. 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 *
  72. 72. ...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?
  73. 73. ...GPars: Dataflows... • Naive attempt for loops ©ASERT2006-2013 import groovyx.gpars.dataflow.Dataflows import static groovyx.gpars.dataflow.Dataflow.task 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. ... task { flow.a = 10 } ... task { flow.a = 20 } Don’t do this! X
  74. 74. ...GPars: Dataflows... ©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
  75. 75. ...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 import groovyx.gpars.dataflow.Dataflows import static groovyx.gpars.dataflow.Dataflow.task final flow = new Dataflows() task { flow.x = flow.y } task { flow.y = flow.x }
  76. 76. …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 Sorry, please try again Sorry, please try again
  77. 77. 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() } } // ...
  78. 78. …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()
  79. 79. 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
  80. 80. Show me the code JScience, SPrintfChecker, GenericStackTest
  81. 81. 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
  82. 82. 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
  83. 83. Word Split… ©ASERT2006-2013 def swords = { s -> def result = [] def word = '' s.each{ ch -> if (ch == ' ') { if (word) result += word word = '' } else word += ch } if (word) result += word 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']
  84. 84. Word Split… ©ASERT2006-2013 def swords = { s -> def result = [] def word = '' s.each{ ch -> if (ch == ' ') { if (word) result += word word = '' } else word += ch } if (word) result += word result }
  85. 85. Word Split… ©ASERT2006-2013 def swords = { s -> def result = [] def word = '' s.each{ ch -> if (ch == ' ') { if (word) result += word word = '' } else word += ch } if (word) result += word result }
  86. 86. …Word Split… ©ASERT2006-2013
  87. 87. …Word Split… ©ASERT2006-2013
  88. 88. Segment(left1, m1, right1) Segment(left2, m2, right2) Segment(left1, m1 + [ ? ] + m2, right2) …Word Split… ©ASERT2006-2013
  89. 89. …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) } }
  90. 90. …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']
  91. 91. …Word Split… ©ASERT2006-2013
  92. 92. …Word Split… ©ASERT2006-2013
  93. 93. …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() }
  94. 94. …Word Split… ©ASERT2006-2013 import static groovyx.gpars.GParsPool.withPool 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() } }
  95. 95. …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
  96. 96. …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
  97. 97. …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
  98. 98. 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
  99. 99. More Information: Groovy in Action

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