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Autumn collections : from iterable to lambdas, streams and collectors
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Autumn collections : from iterable to lambdas, streams and collectors

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These are the slides of my talk at Devoxx 2013. ...

These are the slides of my talk at Devoxx 2013.

This presentation introduces the lambda expressions, how we can write them, and what they bring to us developers. The second part presents in details the new patterns introduced by the Stream and Collector APIs. These new APIs will change the way we process large collections content, enabling concurrent and parallel access, with a very simple programming model, and amazing patterns. Complex examples will show how powerful those APIs are, and how they can help us write efficient and clean code.

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    Autumn collections : from iterable to lambdas, streams and collectors Autumn collections : from iterable to lambdas, streams and collectors Presentation Transcript

    • Autumn Collections From Iterable to Lambdas, Streams and Collectors #DV13LBDS @JosePaumard
    • #DV13LBDS @JosePaumard
    • Why were they introduced ? #DV13LBDS @JosePaumard
    • Why were they introduced ? Why are they so important ? #DV13LBDS @JosePaumard
    • Why were they introduced ? Why are they so important ? How will they change the way we build applications ? #DV13LBDS @JosePaumard
    • #DV13LBDS @JosePaumard
    • #DV13LBDS @JosePaumard
    • Questions ? #DV13LBDS #DV13LBDS @JosePaumard
    • Let’s introduce the lambdas #DV13LBDS @JosePaumard
    • Let’s introduce the lambdas on a simple example #DV13LBDS @JosePaumard
    • A very simple example public class Person { A plain old bean… private String name ; private int age ; // constructors // getters / setters } List<Person> list = new ArrayList<>() ; … and a good old list #DV13LBDS @JosePaumard
    • Average of the ages int sum = 0 ; // I need a default value in case // the list is empty int average = 0 ; for (Person person : list) { sum += person.getAge() ; } if (!list.isEmpty()) { average = sum / list.size() ; } #DV13LBDS @JosePaumard
    • Trickier : average of the ages of people older than 20 int sum = 0 ; int n = 0 ; int average = 0 ; for (Person person : list) { if (person.getAge() > 20) { n++ ; sum += person.getAge() ; } } if (n > 0) { average = sum / n ; } #DV13LBDS @JosePaumard
    • Trickier : average of the ages of people older than 20 int sum = 0 ; int n = 0 ; int average = 0 ; for (Person person : list) { if (person.getAge() > 20) { n++ ; sum += person.getAge() ; } } if (n > 0) { average = sum / n ; } « imperative programming » #DV13LBDS @JosePaumard
    • … it does not have to be like that ! select avg(age) from Person where age > 20 This is a description of the result #DV13LBDS @JosePaumard
    • … it does not have to be like that ! select avg(age) from Person where age > 20 © SQL language, 1974 This is a description of the result In that case, the DB server is free to compute the result the way it sees fit #DV13LBDS @JosePaumard
    • map Person age age > 20 sum 1st step : mapping #DV13LBDS @JosePaumard
    • map Person age age > 20 sum 1st step : mapping Mapping : - takes a list of a given type - gives another list of a different type - same number of elements #DV13LBDS @JosePaumard
    • map Person filter age age > 20 sum 2nd step : filtering #DV13LBDS @JosePaumard
    • map Person filter age age > 20 sum 2nd step : filtering Filtering : - takes a list of a given type - gives another list of a the same type - less elements #DV13LBDS @JosePaumard
    • map Person filter age reduce age > 20 sum 3rd step : reduction #DV13LBDS @JosePaumard
    • map Person filter age reduce age > 20 sum 3rd step : reduction Reduction : agregation of all the elements in a single one Ex : average, sum, min, max, etc… #DV13LBDS @JosePaumard
    • How can I model that ? #DV13LBDS @JosePaumard
    • The JDK 7 way Create an interface to model the mapper… public interface Mapper<T, V> { public V map(T t) ; } #DV13LBDS @JosePaumard
    • The JDK 7 way … and create an implementation … public interface Mapper<T, V> { public V map(T t) ; } public class PersonToAgeMapper implements Mapper<Person, Integer> { public Integer map(Person p) { return p.getAge() ; } } #DV13LBDS @JosePaumard
    • The JDK 7 way … that could be anonymous public interface Mapper<T, V> { public V map(T t) ; } Mapper<Person, Integer> mapper = new Mapper<Person, Integer>() { public Integer map(Person p) { return p.getAge() ; } } #DV13LBDS @JosePaumard
    • The JDK 7 way We can do the same for the filtering public interface Predicate<T> { public boolean filter(T t) ; } #DV13LBDS @JosePaumard
    • The JDK 7 way We can do the same for the filtering public interface Predicate<T> { public boolean filter(T t) ; } public class AgePredicate implements Predicate<Integer> { public boolean filter(Integer i) { return i > 20 ; } } #DV13LBDS @JosePaumard
    • The JDK 7 way We can do the same for the filtering public interface Predicate<T> { public boolean filter(T t) ; } AgePredicate predicate = new Predicate<Integer>() { public boolean filter(Integer i) { return i > 20 ; } } #DV13LBDS @JosePaumard
    • The JDK 7 way And for the reduction public interface Reducer<T> { public T reduce(T t1, T t2) ; } #DV13LBDS @JosePaumard
    • The JDK 7 way And for the reduction public interface Reducer<T> { public T reduce(T t1, T t2) ; } public class Sum implements Reducer<Integer> { public Integer reduce(Integer i1, Integer i2) { return i1 + i2 ; } } #DV13LBDS @JosePaumard
    • The JDK 7 way And for the reduction public interface Reducer<T> { public T reduce(T t1, T t2) ; } Reducer<Integer> reduction = new Reducer<Integer>() { public Integer reduce(Integer i1, Integer i2) { return i1 + i2 ; } } #DV13LBDS @JosePaumard
    • The JDK 7 way So the whole map / filter / reduce looks like this 1) Create 3 interfaces public interface Mapper<T, V> { public V map(T t) ; } public interface Predicate<T> { public boolean filter(T t) ; } public interface Reducer<T> { public T reduce(T t1, T t2) ; } #DV13LBDS @JosePaumard
    • The JDK 7 way So the whole map / filter / reduce looks like this 1) Create 3 interfaces 2) Apply the pattern List<Person> persons = ... ; int sum = persons.map( new Mapper<Person, Integer>() { public Integer map(Person p) { return p.getAge() ; } }) .filter( new Filter<Integer>() { public boolean filter(Integer age) { return age > 20 ; } }) .reduce(0, new Reducer<Integer>() { public Integer recude(Integer i1, Integer i2) { return i1 + i2 ; } } }) ; #DV13LBDS @JosePaumard
    • The JDK 7 way So the whole map / filter / reduce looks like this 1) Create 3 interfaces 2) Apply the pattern List<Person> persons = ... ; int sum = persons.map( new Mapper<Person, Integer>() { public Integer map(Person p) { return p.getAge() ; } }) .filter( new Filter<Integer>() { public boolean filter(Integer age) { return age > 20 ; } }) .reduce(0, new Reducer<Integer>() { public Integer recude(Integer i1, Integer i2) { return i1 + i2 ; } } }) ; #DV13LBDS @JosePaumard
    • The JDK 8 way #DV13LBDS @JosePaumard
    • The JDK 8 way Let’s rewrite our mapper mapper = new Mapper<Person, Integer>() { public Integer map(Person person) { return person.getAge() ; } } #DV13LBDS @JosePaumard
    • The JDK 8 way Let’s rewrite our mapper mapper = new Mapper<Person, Integer>() { public Integer map(Person person) { // 1 method return person.getAge() ; } } #DV13LBDS @JosePaumard
    • The JDK 8 way Let’s rewrite our mapper mapper = new Mapper<Person, Integer>() { public Integer map(Person person) { // 1 method return p.getAge() ; we take } a } person p mapper = Person person #DV13LBDS ; @JosePaumard
    • The JDK 8 way Let’s rewrite our mapper mapper = new Mapper<Person, Integer>() { public Integer map(Person person) { // 1 method return person.getAge() ; } } and then … mapper = Person person -> #DV13LBDS ; @JosePaumard
    • The JDK 8 way Let’s rewrite our mapper mapper = new Mapper<Person, Integer>() { public Integer map(Person person) { // 1 method return person.getAge() ; } } … return the age of p mapper = Person person -> person.getAge() ; #DV13LBDS @JosePaumard
    • The JDK 8 way Let’s rewrite our mapper mapper = new Mapper<Person, Integer>() { public Integer map(Person person) { // 1 method return person.getAge() ; } } mapper = Person person -> person.getAge() ; #DV13LBDS @JosePaumard
    • The JDK 8 way Let’s rewrite our mapper mapper = new Mapper<Person, Integer>() { public Integer map(Person person) { // 1 method return person.getAge() ; } } mapper = Person person -> person.getAge() ; The compiler can recognize this as an implementation of Mapper #DV13LBDS @JosePaumard
    • What if … … there is more than one statement ? mapper = Person person -> { System.out.println("Mapping " + person) ; return person.getAge() ; } The return has to be explicit #DV13LBDS @JosePaumard
    • What if … …I have no return value ? consumer = Person person -> p.setAge(p.getAge() + 1) ; #DV13LBDS @JosePaumard
    • What if … …I have more than one argument ? reducer = (int i1, int i2) -> { return i1 + i2 ; } Or : reducer = (int i1, int i2) -> i1 + i2 ; #DV13LBDS @JosePaumard
    • The JDK 8 way How can the compiler recognize the implementation of map() ? mapper = Person person -> person.getAge() ; #DV13LBDS @JosePaumard
    • The JDK 8 way How can the compiler recognize the implementation of map() ? mapper = Person person -> person.getAge() ; 1) There’s only one method in Mapper #DV13LBDS @JosePaumard
    • The JDK 8 way How can the compiler recognize the implementation of map() ? mapper = Person person -> person.getAge() ; 1) There’s only one method in Mapper 2) Both the parameters and the return types are compatible #DV13LBDS @JosePaumard
    • The JDK 8 way How can the compiler recognize the implementation of map() ? mapper = Person person -> person.getAge() ; 1) There’s only one method in Mapper 2) Both the parameters and the return types are compatible 3) Thrown exceptions (if any) are compatible #DV13LBDS @JosePaumard
    • More lambdas Writing more lambdas becomes natural : mapper = Person person -> person.getAge() ; // mapper filter = int age -> age > 20 ; // filter reducer = (int i1, int i2) -> i1 + i2 ; // reducer #DV13LBDS @JosePaumard
    • More lambdas And most of the time, the compiler understands this : mapper = person -> person.getAge() ; // mapper filter = age -> age > 20 ; // filter reducer = (i1, i2) -> i1 + i2 ; // reducer The « parameter types » can be omitted #DV13LBDS @JosePaumard
    • Just a remark on the reduction How does it really work ? #DV13LBDS @JosePaumard
    • #DV13LBDS @JosePaumard
    • #DV13LBDS @JosePaumard
    • #DV13LBDS @JosePaumard
    • #DV13LBDS @JosePaumard
    • #DV13LBDS @JosePaumard
    • #DV13LBDS @JosePaumard
    • #DV13LBDS @JosePaumard
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    • Reduction 2 examples : Reducer r1 = (i1, i2) -> i1 + i2 ; // Ok Reducer r2 = (i1, i2) -> i1*i1 + i2*i2 ; // Oooops Caveat : the result is always reproductible in serial it’s not in parallel #DV13LBDS @JosePaumard
    • So far A lambda expression is an alternative to write instances of anonymous inner classes #DV13LBDS @JosePaumard
    • Other syntaxes Very often one writes mapper = person -> person.getAge() ; This syntax is also possible : mapper = Person::getAge ; // non static method #DV13LBDS @JosePaumard
    • Other syntaxes Other example : sum = (i1, i2) -> i1 + i2 ; sum = Integer::sum ; // static method, new ! Or : max = (i1, i2) -> i1 > i2 ? i1 : i2 ; max = Integer::max ; // static method, new ! #DV13LBDS @JosePaumard
    • Other syntaxes Other example : toLower = String::toLowerCase ; // !!!! NO NO NO !!!! toLowerFR = String::toLowerCase(Locale.FRANCE) ; #DV13LBDS @JosePaumard
    • More on the lambdas #DV13LBDS @JosePaumard
    • Questions : What model for a lambda ? #DV13LBDS @JosePaumard
    • Questions : What model for a lambda ? Can I put a lambda in a variable ? #DV13LBDS @JosePaumard
    • Questions : What model for a lambda ? Can I put a lambda in a variable ? Is a lambda an object ? #DV13LBDS @JosePaumard
    • Modelization A lambda is an instance of a « functional interface » @FunctionalInterface public interface Consumer<T> { public void accept(T t) ; } #DV13LBDS @JosePaumard
    • Modelization A lambda is an instance of a « functional interface » @FunctionalInterface public interface Consumer<T> { public void accept(T t) ; } - only one abstract method (methods from Object dont count) - can be annotated by @FunctionalInterface (optional) #DV13LBDS @JosePaumard
    • Modelization A lambda is an instance of « functional interface » @FunctionalInterface public interface Consumer<T> { public void accept(T t) ; public default Consumer<T> chain(Consumer<? super T> other) { Objects.requireNonNull(other); return (T t) -> { accept(t); other.accept(t); }; } } #DV13LBDS @JosePaumard
    • Putting a lambda in a variable Example of a consumer Consumer<String> c = new Consumer<String>() { @Override public void accept(String s) { System.out.println(s) ; } } ; So : Consumer<String> c = s -> System.out.println(s) ; #DV13LBDS @JosePaumard
    • Putting a lambda in a variable Example of a consumer Consumer<String> c = new Consumer<String>() { @Override public void accept(String s) { System.out.println(s) ; } } ; Question : what is s ? So : Consumer<String> c = s -> System.out.println(s) ; #DV13LBDS @JosePaumard
    • Putting a lambda in a variable Example of a consumer Consumer<String> c = new Consumer<String>() { @Override public void accept(String s) { System.out.println(s) ; } } ; So : Answer : the compiler infers that it is a String Consumer<String> c = s -> System.out.println(s) ; #DV13LBDS @JosePaumard
    • Putting a lambda in a variable Side question : can one write this ? int i = ... ; Consumer<String> c = new Consumer<String>() { @Override public void accept(String s) { System.out.println("i = " + i) ; } } ; #DV13LBDS @JosePaumard
    • Putting a lambda in a variable Side question : can one write this ? int i = ... ; Consumer<String> c = new Consumer<String>() { @Override public void accept(String s) { System.out.println("i = " + i) ; } } ; JDK 7 : i should be final #DV13LBDS @JosePaumard
    • Putting a lambda in a variable Side question : can one write this ? int i = ... ; Consumer<String> c = new Consumer<String>() { @Override public void accept(String s) { System.out.println("i = " + i) ; } } ; JDK 8 : Yes ! (no final needed) #DV13LBDS @JosePaumard
    • Putting a lambda in a variable But this code … int i = ... ; Consumer<String> c = new Consumer<String>() { @Override public void accept(String s) { System.out.println("i = " + i) ; } } ; i = i + 1 ; does not compile #DV13LBDS @JosePaumard
    • Putting a lambda in a variable Because i is an effectively final variable final int i = ... ; Consumer<String> c = new Consumer<String>() { @Override public void accept(String s) { System.out.println("i = " + i) ; } } ; i = i + 1 ; // Cant modify #DV13LBDS @JosePaumard
    • And what about this ? JDK 7 : this is the anonymous instance Consumer<String> c = new Consumer<String>() { @Override public void accept(String s) { System.out.println("this = " + this) ; } public String toString() { return "I’m in c" ; } } ; Calling accept() will print « I’m in c » #DV13LBDS @JosePaumard
    • And what about this ? JDK 8 : no change, calling accept() still prints « I’m in c » Consumer<String> c = new Consumer<String>() { @Override public void accept(String s) { System.out.println("this = " + this) ; } public String toString() { return "I’m in c" ; } } ; But … #DV13LBDS @JosePaumard
    • And what about this ? Consumer can also be instanced with a lambda Consumer<String> c = s -> System.out.println(this) ; Here this is the enclosing instance #DV13LBDS @JosePaumard
    • Questions : What model for a lambda ? answer : with a functional interface Can I put a lambda in a variable ? answer : yes Is a lambda an object ? #DV13LBDS @JosePaumard
    • Is a lambda an objet ? Let’s play the game of 7 errors (with only 1 error) Consumer<String> c = new Consumer<String>() { @Override public void accept(String s) { System.out.println(s) ; } } ; Consumer<String> c = s -> System.out.println(s) ; #DV13LBDS @JosePaumard
    • Is a lambda an objet ? Let’s play the game of 7 errors (with only 1 error) Consumer<String> c = new Consumer<String>() { @Override public void accept(String s) { System.out.println(s) ; } } ; Consumer<String> c = s -> System.out.println(s) ; #DV13LBDS @JosePaumard
    • Questions : What model for a lambda ? answer : with a functionnal interface Can I put a lambda in a variable ? answer : yes Is a lambda an object ? answer : no #DV13LBDS @JosePaumard
    • Plenty of lambdas : Java.util.functions #DV13LBDS @JosePaumard
    • Java.util.functions This new package holds the functionnal interfaces There are 43 of them #DV13LBDS @JosePaumard
    • Java.util.functions Supplier : alone in its kind Consumer / BiConsumer Function / BiFunction (UnaryOperator / BinaryOperator) Predicate / BiPredicate Plus the primitive types versions #DV13LBDS @JosePaumard
    • Supplier A supplier supplies an object public interface Supplier<T> { T get() ; } #DV13LBDS @JosePaumard
    • Consumer A consumer just … accepts an object public interface Consumer<T> { void accept(T t) ; } Consumer<String> c1 = s -> System.out.println(s) ; Consumer<String> c2 = ... ; Consumer<String> c3 = c1.andThen(c2) ; persons.stream().forEach(c3) ; #DV13LBDS @JosePaumard
    • BiConsumer Takes 2 arguments instead of one Can be chained Primitive types : - ObjIntConsumer - ObjLongConsumer - ObjDoubleConsumer ObjIntConsumer takes an object and an int as arguments #DV13LBDS @JosePaumard
    • Function A function takes an object and returns another one public interface Function<T, R> { R apply(T t) ; } Can be chained and / or composed #DV13LBDS @JosePaumard
    • BiFunction A function that takes 2 arguments Can be chained Primitive type versions : - ToIntBiFunction - ToLongBiFunction - ToDoubleBiFunction #DV13LBDS @JosePaumard
    • UnaryOperator A UnaryOperator is a Function that operates on one type Other UnaryOperators : - IntUnaryOperator - LongUnaryOperator - DoubleUnaryOperator #DV13LBDS @JosePaumard
    • BinaryOperator A BinaryOperator is a BiFunction that operates on one type Other BinaryOperators : - IntBinaryOperator - LongBinaryOperator - DoubleBinaryOperator IntBinaryOperator takes 2 int, and returns an int #DV13LBDS @JosePaumard
    • Predicate A Predicate takes an object and returns a boolean public interface Predicate<T> { boolean test(T t) ; } Can be negated, composed with and / or #DV13LBDS @JosePaumard
    • BiPredicate A BiPredicate takes two object and returns a boolean public interface BiPredicate<T, U> { boolean test(T t, U u) ; } #DV13LBDS @JosePaumard
    • Predicate & BiPredicate Primitive types : - IntPredicate - LongPredicate - DoublePredicate Not for BiPredicate #DV13LBDS @JosePaumard
    • Questions ? #DV13LBDS #DV13LBDS @JosePaumard
    • Back to the map / filter / reduce pattern #DV13LBDS @JosePaumard
    • The JDK 7 way How to apply map / filter / reduce to List<Person> ? #DV13LBDS @JosePaumard
    • The JDK 7 way How to apply map / filter / reduce to List<Person> ? The legal way is to iterate over the elements and apply the pattern #DV13LBDS @JosePaumard
    • The JDK 7 way How to apply map / filter / reduce to List<Person> ? The legal way is to iterate over the elements and apply the pattern One can create a helper method #DV13LBDS @JosePaumard
    • Applying map to a List With a helper method, JDK 7 List<Person> persons = new ArrayList<>() ; List<Integer> ages = // ages is a new list Lists.map( persons, new Mapper<Person, Integer>() { public Integer map(Person p) { return p.getAge() ; } } ) ; #DV13LBDS @JosePaumard
    • Applying map to a List With a helper method, JDK 8 & lambdas List<Person> persons = new ArrayList<>() ; List<Integer> ages = Lists.map( persons, person -> person.getAge() ) ; #DV13LBDS @JosePaumard
    • Applying map to a List With a helper method, JDK 8 & lambdas List<Person> persons = new ArrayList<>() ; List<Integer> ages = Lists.map( persons, Person::getAge ) ; #DV13LBDS @JosePaumard
    • Applying map to a List Caveats : beware the generics : what about managers ? List<Manager> managers = new ArrayList<>() ; List<Integer> ages = Lists.map( managers, person -> person.getAge() ) ; We want that to compile ! #DV13LBDS @JosePaumard
    • Applying map to a List Caveats : beware the generics // in Lists public static <T, V> List<V> map( List<? extends T> list, Mapper<T, V> mapper) { List<V> result = new ArrayList<>() ; for (T t : list) { V v = mapper.map(t) ; result.add(v) ; } return result ; } #DV13LBDS @JosePaumard
    • Putting things together The map / filter / reduce pattern would look like this // applying the map / filter / reduce pattern List<Person> persons = ... ; List<Integer> ages = Lists.map( persons, p -> p.getAge()) ; List<Integer> agesGT20 = Lists.filter(ages, a -> a > 20) ; int sum = Lists.reduce(agesGT20, (i1, i2) -> i1 + i2) ; #DV13LBDS @JosePaumard
    • Putting things together The map / filter / reduce pattern would look like this // applying the map / filter / reduce pattern List<Person> persons = ... ; List<Integer> ages = Lists.map( persons, p -> p.getAge()) ; List<Integer> agesGT20 = Lists.filter(ages, a -> a > 20) ; int sum = Lists.reduce(agesGT20, (i1, i2) -> i1 + i2) ; The idea is to push lambdas to the API, and let it apply them on its content #DV13LBDS @JosePaumard
    • Putting things together The map / filter / reduce pattern would look like this // applying the map / filter / reduce pattern List<Person> persons = ... ; List<Integer> ages = Lists.map( persons, p -> p.getAge()) ; List<Integer> agesGT20 = Lists.filter(ages, a -> a > 20) ; int sum = Lists.reduce(agesGT20, (i1, i2) -> i1 + i2) ; Pro : the API can be optimized without touching the code : great ! #DV13LBDS @JosePaumard
    • Putting things together The map / filter / reduce pattern would look like this // applying the map / filter / reduce pattern List<Person> persons = ... ; List<Integer> ages = Lists.map( persons, p -> p.getAge()) ; List<Integer> agesGT20 = Lists.filter(ages, a -> a > 20) ; int sum = Lists.reduce(agesGT20, (i1, i2) -> i1 + i2) ; Pro : the API can be optimized without touching the code Cons … #DV13LBDS @JosePaumard
    • Putting things together 1) Suppose persons is a really BIG list // applying the map / filter / reduce pattern List<Person> persons = ... ; List<Integer> ages = Lists.map( persons, p -> p.getAge()) ; List<Integer> agesGT20 = Lists.filter(ages, a -> a > 20) ; int sum = Lists.reduce(agesGT20, (i1, i2) -> i1 + i2) ; #DV13LBDS @JosePaumard
    • Putting things together 1) Suppose persons is a really BIG list // applying the map / filter / reduce pattern List<Person> persons = ... ; List<Integer> ages = Lists.map( persons, p -> p.getAge()) ; List<Integer> agesGT20 = Lists.filter(ages, a -> a > 20) ; int sum = Lists.reduce(agesGT20, (i1, i2) -> i1 + i2) ; 2 duplications : ages & agesGT20 #DV13LBDS @JosePaumard
    • Putting things together 1) Suppose persons is a really BIG list // applying the map / filter / reduce pattern List<Person> persons = ... ; List<Integer> ages = Lists.map( persons, p -> p.getAge()) ; List<Integer> agesGT20 = Lists.filter(ages, a -> a > 20) ; int sum = Lists.reduce(agesGT20, (i1, i2) -> i1 + i2) ; 2 duplications : ages & agesGT20 What do we do with them ? Send them to the GC #DV13LBDS @JosePaumard
    • Putting things together 2) Suppose the algorithms to compute the map / filter / reduce are optimal // applying the map / filter / reduce pattern List<Person> persons = ... ; List<Integer> ages = Lists.map( persons, p -> p.getAge()) ; List<Integer> agesGT20 = Lists.filter(ages, a -> a > 20) ; int sum = Lists.reduce(agesGT20, (i1, i2) -> i1 + i2) ; #DV13LBDS @JosePaumard
    • Putting things together 2) Suppose the algorithms to compute the map / filter / reduce are optimal // applying the map / filter / reduce pattern List<Person> persons = ... ; List<Integer> ages = Lists.map( persons, p -> p.getAge()) ; List<Integer> agesGT20 = Lists.filter(ages, a -> a > 20) ; int sum = Lists.reduce(agesGT20, (i1, i2) -> i1 + i2) ; … but I need to go even faster ! #DV13LBDS @JosePaumard
    • Putting things together 2) Suppose the algorithms to compute the map / filter / reduce are optimal // applying the map / filter / reduce pattern List<Person> persons = ... ; List<Integer> ages = Lists.map( persons, p -> p.getAge()) ; List<Integer> agesGT20 = Lists.filter(ages, a -> a > 20) ; int sum = Lists.reduce(agesGT20, (i1, i2) -> i1 + i2) ; Only solution : go parallel ! #DV13LBDS @JosePaumard
    • Putting things together 2) Suppose we want to go parallel // applying the map / filter / reduce pattern List<Person> persons = ... ; List<Integer> ages = Lists.map( persons, p -> p.getAge()) ; List<Integer> agesGT20 = Lists.filter(ages, a -> a > 20) ; int sum = Lists.reduce(agesGT20, (i1, i2) -> i1 + i2) ; ages & agesGT20 would better be concurrent aware ! Because they will be accessed concurrently #DV13LBDS @JosePaumard
    • Putting things together 3) Suppose we have a allMatch() reducer // applying the map / filter / reduce pattern List<Person> persons = ... ; List<String> names = Lists.map(persons, p -> p.getName()) ; boolean allMatch = Lists.allMatch(names, n -> n.length() < 20) ; allMatch is true if all the names are shorter than 20 chars let’s see a possible implementation of allMatch() #DV13LBDS @JosePaumard
    • How could one write allMatch ? Here is a basic implementation of allMatch public static <T> boolean allMatch( List<? extends T> list, Filter<T> filter) { for (T t : list) { if (!filter.filter(t)) { return false ; } } return true ; } #DV13LBDS @JosePaumard
    • How could one write allMatch ? Here is a basic implementation of allMatch public static <T> boolean allMatch( List<? extends T> list, Filter<T> filter) { for (T t : list) { if (!filter.filter(t)) { return false ; } } No need to iterate over the whole list return true ; } #DV13LBDS @JosePaumard
    • How could one write allMatch ? Here is a basic implementation of allMatch public static <T> boolean allMatch( List<? extends T> list, Filter<T> filter) { for (T t : list) { if (!filter.filter(t)) { return false ; } } But… return true ; } #DV13LBDS @JosePaumard
    • Putting things together When we apply the allMatch() reducer… // applying the map / filter / reduce pattern List<Person> persons = ... ; List<String> names = Lists.map(persons, p -> p.getName()) ; boolean allMatch = Lists.allMatch(names, n.length() < 20) ; … the list names has already been evaluated ! #DV13LBDS @JosePaumard
    • Putting things together When we apply the allMatch() reducer… // applying the map / filter / reduce pattern List<Person> persons = ... ; List<String> names = Lists.map(persons, p -> p.getName()) ; boolean allMatch = Lists.allMatch(names, n.length() < 20) ; … the list names has already been evaluated ! We lost a big opportunity for optimization ! #DV13LBDS @JosePaumard
    • Putting things together When we apply the allMatch() reducer… // applying the map / filter / reduce pattern List<Person> persons = ... ; List<String> names = Lists.map(persons, p -> p.getName()) ; boolean allMatch = Lists.allMatch(names, n.length() < 20) ; … we should have wait to apply the filter A good way to do that : apply the filter step lazily ! #DV13LBDS @JosePaumard
    • Conclusion Pro : 1 Cons : 3 (at least) // applying the map / filter / reduce pattern List<Person> persons = ... ; List<Integer> ages = Lists.map( persons, p -> p.getAge()) ; List<Integer> agesGT20 = Lists.filter(ages, a -> a > 20) ; int sum = Lists.reduce(agesGT20, (i1, i2) -> i1 + i2) ; #DV13LBDS @JosePaumard
    • Conclusion Pro : 1 Cons : 3 (at least) // applying the map / filter / reduce pattern List<Person> persons = ... ; List<Integer> ages = Lists.map( persons, p -> p.getAge()) ; List<Integer> agesGT20 = Lists.filter(ages, a -> a > 20) ; int sum = Lists.reduce(agesGT20, (i1, i2) -> i1 + i2) ; #DV13LBDS @JosePaumard
    • Conclusion And the same goes for this one // applying the map / filter / reduce pattern List<Person> persons = ... ; List<Integer> ages = persons.map(p -> p.getAge()) ; List<Integer> agesGT20 = ages.filter(a -> a > 20) ; int sum = agesGT20.reduce(ages, (i1, i2) -> i1 + i2) ; #DV13LBDS @JosePaumard
    • What about « in place » operations ? Not possible for the map step, because the type of the list changes Could be evaluated for the filter, with concurrency issues Doesnt make sense for the reduction #DV13LBDS @JosePaumard
    • Conclusion (again) We need a new concept to handle BIG lists efficiently #DV13LBDS @JosePaumard
    • Conclusion (again) We need a new concept to handle BIG lists efficiently The Collection framework is not the right one… #DV13LBDS @JosePaumard
    • Conclusion (again) We need a new concept to handle BIG lists efficiently The Collection framework is not the right one We need something else ! #DV13LBDS @JosePaumard
    • So what pattern to choose ? #DV13LBDS @JosePaumard
    • Introduction Putting map / filter / reduce methods on Collection would have led to : // map / filter / reduce pattern on collections int sum = persons .map(p -> p.getAge()) .filter(a -> a > 20) .reduce(0, (a1, a2) -> a1 + a2) ; And even if it doesnt lead to viable patterns, it is still a pleasant way of writing things #DV13LBDS @JosePaumard
    • Introduction Let’s keep the same kind of pattern and add a stream() // map / filter / reduce pattern on collections int sum = persons.stream() .map(p -> p.getAge()) .filter(a -> a > 20) .reduce(0, (a1, a2) -> a1 + a2) ; #DV13LBDS @JosePaumard
    • Introduction Collection.stream() returns a Stream : new interface // map / filter / reduce pattern on collections int sum = persons.stream() .map(p -> p.getAge()) .filter(a -> a > 20) .reduce(0, (a1, a2) -> a1 + a2) ; New interface = our hands are free ! #DV13LBDS @JosePaumard
    • New Collection interface So we need a new method on Collection public interface Collection<E> { // our good old methods Stream<E> stream() ; } #DV13LBDS @JosePaumard
    • New Collection interface Problem : ArrayList doesnt compile anymore… Something has to be done ! public interface Collection<E> { // our good old methods Stream<E> stream() ; } #DV13LBDS @JosePaumard
    • Interfaces Problem : ArrayList doesnt compile anymore… Something has to be done ! Something that does not need to change or recompile all the existing implementations of Collection #DV13LBDS @JosePaumard
    • Questions ? #DV13LBDS #DV13LBDS @JosePaumard
    • New interfaces #DV13LBDS @JosePaumard
    • Java 8 interfaces Problem : ArrayList doesnt compile anymore… Something has to be done ! Something that does not need to change or recompile all the existing implementations of Collection Problem : how to add methods to an interface, without changing any of the implementations ? #DV13LBDS @JosePaumard
    • Java 8 interfaces Problem : how to add methods to an interface, without changing any of the implementations ? Solution : change the way interfaces work in Java #DV13LBDS @JosePaumard
    • Java 8 interfaces ArrayList needs to know the implementation of stream()… public interface Collection<E> { // our good old methods Stream<E> stream() ; } #DV13LBDS @JosePaumard
    • Java 8 interfaces Let’s put it in the interface ! public interface Collection<E> { // our good old methods default Stream<E> stream() { return ... ; } } #DV13LBDS @JosePaumard
    • Java 8 interfaces Let’s introduce default methods in Java public interface Collection<E> { // our good old methods default Stream<E> stream() { return ... ; } } Default methods are about interface evolution Allows the extension of old interfaces #DV13LBDS @JosePaumard
    • Default methods Does it bring multiple inheritance to Java ? #DV13LBDS @JosePaumard
    • Default methods Does it bring multiple inheritance to Java ? Yes ! But we already have it #DV13LBDS @JosePaumard
    • Default methods Does it bring multiple inheritance to Java ? Yes ! But we already have it public class String implements Serializable, Comparable<String>, CharSequence { // ... } #DV13LBDS @JosePaumard
    • Default methods What we have so far is multiple inheritance of type #DV13LBDS @JosePaumard
    • Default methods What we have so far is multiple inheritance of type What we get in Java 8 is multiple inheritance of behavior #DV13LBDS @JosePaumard
    • Default methods What we have so far is multiple inheritance of type What we get in Java 8 is multiple inheritance of behavior What we dont have is multiple inheritance of state #DV13LBDS @JosePaumard
    • Default methods What we have so far is multiple inheritance of type What we get in Java 8 is multiple inheritance of behavior What we dont have is multiple inheritance of state … and this is where the trouble is ! #DV13LBDS @JosePaumard
    • Default methods Will we have conflicts ? #DV13LBDS @JosePaumard
    • Default methods Will we have conflicts ? Oooh yes… #DV13LBDS @JosePaumard
    • Default methods Will we have conflicts ? Oooh yes… So we need rules to handle them #DV13LBDS @JosePaumard
    • Default methods public class C implements A, B { // ... } #DV13LBDS @JosePaumard
    • Default methods public class C implements A, B { // ... } public interface A { default String a() { ... } } #DV13LBDS public interface B { default String a() { ... } } @JosePaumard
    • Default methods public class C implements A, B { public String a() { ... } } Example #0 public interface A { default String a() { ... } } public interface B { default String a() { ... } } Class wins ! No default if an implementation is provided #DV13LBDS @JosePaumard
    • Default methods public class C implements A, B { // ... } Example #1 public interface A { default String a() { ... } } #DV13LBDS public interface B { default String a() { ... } } @JosePaumard
    • Default methods public class C implements A, B { // ... } Example #1 public interface A { default String a() { ... } } public interface B { default String a() { ... } } Compile time error : class C inherits unrelated defaults for a() from types A and B #DV13LBDS @JosePaumard
    • Default methods public class C implements A, B { // ... } Example #2 public interface A extends B { default String a() { ... } } public interface B { default String a() { ... } } A is more specific than B : A.a() is chosen #DV13LBDS @JosePaumard
    • Default methods public class D extends C implements B { // ... } public class C implements A { // ... } Example #2 public interface A extends B { default String a() { ... } } #DV13LBDS public interface B { default String a() { ... } } @JosePaumard
    • Default methods public class D extends C implements B { // ... } public class C implements A { // ... } Example #2 public interface A extends B { default String a() { ... } } public interface B { default String a() { ... } } A is still more specific than B : A.a() is chosen #DV13LBDS @JosePaumard
    • Default methods public class C implements A, B { public String a() { return B.super.a() ; } } Back to example #1 public interface A { default String a() { ... } } public interface B { default String a() { ... } } We can also make an explicit call #DV13LBDS @JosePaumard
    • 3 simple rules To handle the conflict of multiple inheritance of behavior #DV13LBDS @JosePaumard
    • 3 simple rules To handle the conflict of multiple inheritance of behavior 1) Class wins ! #DV13LBDS @JosePaumard
    • 3 simple rules To handle the conflict of multiple inheritance of behavior 1) Class wins ! 2) More specific interfaces wins over less specific #DV13LBDS @JosePaumard
    • 3 simple rules To handle the conflict of multiple inheritance of behavior 1) Class wins ! 2) More specific interfaces wins over less specific 3) In fact there’s no rule #3 #DV13LBDS @JosePaumard
    • An example Everybody who writes an implementation of Iterator writes this : public class MyIterator implements Iterator<E> { // some critical business code public void remove() { throw new UnsupportedOperationException("You shouldnt do that") ; } ; } #DV13LBDS @JosePaumard
    • An example Thanks to Java 8 : public interface Iterator<E> { default void remove() { throw new UnsupportedOperationException("remove") ; } ; } No silly implementation of remove() anymore ! #DV13LBDS @JosePaumard
    • Java 8 interfaces So the concept of « interface » changed in Java 8 public class HelloWorld { // souvenir souvenir public static void main(String[] args) { System.out.println("Hello world!") ; } ; } #DV13LBDS @JosePaumard
    • Java 8 interfaces So the concept of « interface » changed in Java 8 I mean, really public interface HelloWorld { // souvenir souvenir public static void main(String[] args) { System.out.println("Hello world!") ; } ; } #DV13LBDS @JosePaumard
    • Java 8 interfaces 1) Default methods, multiple behavior inheritance Rules to handle conflicts, solved at compile time 2) Static methods in interfaces Will bring new patterns and new ways to write APIs #DV13LBDS @JosePaumard
    • Where are we ? 1) We have a new syntax 2) We have a new pattern to implement 3) We have new interfaces to keep the backward compatibily #DV13LBDS @JosePaumard
    • Where are we going to ? 1) 2) 3) 4) The Stream API Reductions Performances Collectors But for now… #DV13LBDS @JosePaumard
    • #DV13LBDS @JosePaumard
    • The Stream API #DV13LBDS @JosePaumard
    • What is a Stream From a technical point of view : a typed interface « a stream of String » #DV13LBDS @JosePaumard
    • What is a Stream From a technical point of view : a typed interface « a stream of String » Other classes for primitive types : IntStream, LongStream, DoubleStream #DV13LBDS @JosePaumard
    • It’s a new notion It‘ looks like a collection, but… - It is built on a « source », that can be a collection - No data needs to be provided at build time - No limit on what the source can produce #DV13LBDS @JosePaumard
    • It’s a new notion A stream can be built on : - A collection or an array - An iterator - An I/O source Some of those can be « infinite » #DV13LBDS @JosePaumard
    • It’s a new notion 1) A stream doesnt hold any data It’s just an object on which one can declare operations Streams are about « specifying computations » #DV13LBDS @JosePaumard
    • It’s a new notion 1) A stream doesnt hold any data 2) A stream cant modify its source Consequence : it can use parallel processing #DV13LBDS @JosePaumard
    • It’s a new notion 1) A stream doesnt hold any data 2) A stream cant modify its source 3) A source can be infinite So we need a way to guarantee that computations will be conducted in finite time #DV13LBDS @JosePaumard
    • It’s a new notion 1) 2) 3) 4) A stream doesnt hold any data A stream cant modify its source A source can be infinite A stream works lazily Then optimizations can be made among the different operations We need a way / convention to trigger the computations #DV13LBDS @JosePaumard
    • Is a Stream the same as a collection ? Answer is no #DV13LBDS @JosePaumard
    • Is a Stream the same as a collection ? Answer is no A Collection provides means to access the elements one by one #DV13LBDS @JosePaumard
    • Is a Stream the same as a collection ? Answer is no A Collection provides means to access the elements one by one A Stream does not #DV13LBDS @JosePaumard
    • Is a Stream the same as a collection ? Answer is no A Collection does not provide any mean to process its elements with lambdas #DV13LBDS @JosePaumard
    • Is a Stream the same as a collection ? Answer is no A Collection does not provide any mean to process its elements with lambdas A Stream does #DV13LBDS @JosePaumard
    • How can we build a stream ? Many ways… 1) From a collection : method Collection.stream Collection<String> collection = ... ; Stream<String> stream = collection.stream() ; #DV13LBDS @JosePaumard
    • How can we build a stream ? Many ways… 1) From a collection : method Collection.stream 2) From an array : Arrays.stream(Object []) Stream<String> stream2 = Arrays.stream(new String [] {"one", "two", "three"}) ; #DV13LBDS @JosePaumard
    • How can we build a stream ? Many ways… 1) From a collection : method Collection.stream 2) From an array : Arrays.stream(Object []) 3) From factory methods in Stream, IntStream, … Stream<String> stream1 = Stream.of("one", "two", "three") ; #DV13LBDS @JosePaumard
    • How can we build a stream ? Some last patterns Stream.empty() ; // returns an empty Stream Stream.of(T t) ; // a stream with a single element Stream.generate(Supplier<T> s) ; Stream.iterate(T seed, UnaryOperator<T> f) ; #DV13LBDS @JosePaumard
    • How can we build a stream ? Other ways scattered in the JDK string.chars() ; // returns a IntStream lineNumberReader.lines() ; // returns a Stream<String> random.ints() ; // return a IntStream #DV13LBDS @JosePaumard
    • How can we build a stream ? Factory methods : a closer look Stream.build() ; // returns a Stream.Builder A stream builder is a consumer - one sends it elements, that are accepted - it builds back a stream on the build() call with the accepted elemts - it has a life cycle #DV13LBDS @JosePaumard
    • How can we build a stream ? And as a last resort : StreamSupport + Spliterator « This class is mostly for library writers presenting stream views of data structures; most static stream methods intended for end users are in the various Stream classes » #DV13LBDS @JosePaumard
    • A 1st example Back to our map / filter / reduce example Build a stream collections on a List // map / filter / reduce pattern on int sum = persons.stream() .map(p -> p.getAge()) .filter(a -> a > 20) .reduce(0, (a1, a2) -> a1 + a2) ; #DV13LBDS @JosePaumard
    • A 1st example Back to our map / filter / reduce example Declare collections operations // map / filter / reduce pattern on int sum = persons.stream() .map(p -> p.getAge()) .filter(a -> a > 20) .reduce(0, (a1, a2) -> a1 + a2) ; #DV13LBDS @JosePaumard
    • A 1st example Back to our map / filter / reduce example Triggers the collections computation // map / filter / reduce pattern on int sum = persons.stream() .map(p -> p.getAge()) .filter(a -> a > 20) .reduce(0, (a1, a2) -> a1 + a2) ; #DV13LBDS @JosePaumard
    • A 1st example Back to our map / filter / reduce example // map / filter / reduce pattern on collections int sum = persons.stream() .map(p -> p.getAge()) .filter(a -> a > 20) .reduce(0, (a1, a2) -> a1 + a2) ; Default value, in case the stream is empty #DV13LBDS @JosePaumard
    • Two types of operations One can declare operations on a Stream Two types of operations : 1) Intermediate operations : declarations, processed lazily ex : map, filter #DV13LBDS @JosePaumard
    • Two types of operations One can declare operations on a Stream Two types of operations : 1) Intermediate operations : declarations, processed lazily ex : map, filter 2) Terminal operations : trigger the computations ex : reduce #DV13LBDS @JosePaumard
    • State of a stream The implementation of Stream has a state : - SIZED : the number of elements is known - ORDERED : the order matters (List) - DISTINCT : all elements are unique (Set) - SORTED : all elements have been sorted (SortedSet) #DV13LBDS @JosePaumard
    • State of a stream Some operations change that : - filtering removes the SIZED - mapping removes DISTINCT and SORTED #DV13LBDS @JosePaumard
    • State of a stream And it allows nice optimizations ! - a HashSet cant have doubles so… #DV13LBDS @JosePaumard
    • State of a stream And it allows nice optimizations ! - a HashSet cant have doubles so… - distinct() is NOPed on a stream built on a HashSet #DV13LBDS @JosePaumard
    • State of a stream And it allows nice optimizations ! - a HashSet cant have doubles so… - distinct() is NOPed on a stream built on a HashSet - A TreeSet is sorted so… #DV13LBDS @JosePaumard
    • State of a stream And it allows nice optimizations ! - a HashSet cant have doubles so… - distinct() is NOPed on a stream built on a HashSet - A TreeSet is sorted so… - sorted() is NOPed on a stream built on a TreeSet #DV13LBDS @JosePaumard
    • A 2nd example Sorting a list of String List<String> strings = new ArrayList<>() ; // populate the list with strings // then somewhere else in the code : List<String> result = strings.stream() .sorted() .collect(Collector.toList()) ; #DV13LBDS @JosePaumard
    • A 2nd example Sorting a list of String // then some smart guy changes the implementation SortedSet<String> strings = new TreeSet<>() ; // populate the list with strings // then somewhere else in the code : List<String> result = strings.stream() .sorted() .collect(Collector.toList()) ; #DV13LBDS @JosePaumard
    • A 2nd example Sorting a list of String // then some smart guy changes the implementation SortedSet<String> strings = new TreeSet<>() ; // populate the list with strings // then somewhere else in the code : List<String> result = strings.stream() .sorted() .collect(Collector.toList()) ; In this case, no sorting is required, and executed ! #DV13LBDS @JosePaumard
    • Stateless / statefull operations Stateless / statefull operations Some operations are stateless : persons.stream().map(p -> p.getAge()) ; It means that they dont need more information than the one held by their parameters #DV13LBDS @JosePaumard
    • Stateless / statefull operations Stateless / statefull operations Some operations are stateless : persons.stream().map(p -> p.getAge()) ; Some need to retain information : stream.limit(10_000_000) ; // select the 10M first elements #DV13LBDS @JosePaumard
    • Example Let’s sort an array of String Random rand = new Random() ; String [] strings = new String[10_000_000] ; for (int i = 0 ; i < strings.length ; i++) { strings[i] = Long.toHexString(rand.nextLong()) ; } #DV13LBDS @JosePaumard
    • Example Let’s sort an array of String Random rand = new Random() ; String [] strings = new String[10_000_000] ; for (int i = 0 ; i < strings.length ; i++) { strings[i] = Long.toHexString(rand.nextLong()) ; } Soooo Java 7… #DV13LBDS @JosePaumard
    • Example Let’s sort an array of String Random rand = new Random() ; Stream<String> stream = Stream.generate( () -> Long.toHexString(rand.nextLong()) ) ; Better ? #DV13LBDS @JosePaumard
    • Example Let’s sort an array of String // Random rand = new Random() ; Stream<String> stream = Stream.generate( () -> Long.toHexString(ThreadLocalRandom.current().nextLong()) ) ; Better ! #DV13LBDS @JosePaumard
    • Example Let’s sort an array of String // other way Stream<String> stream = ThreadLocalRandom .current() .longs() // returns a LongStream .mapToObj(l -> Long.toHexString(l)) ; #DV13LBDS @JosePaumard
    • Example Let’s sort an array of String // other way Stream<String> stream = ThreadLocalRandom .current() .longs() .mapToObj(Long::toHexString) ; #DV13LBDS @JosePaumard
    • Example Let’s sort an array of String // other way Stream<String> stream = ThreadLocalRandom .current() .longs() .mapToObj(Long::toHexString) .limit(10_000_000) .sorted() ; T = 4 ms #DV13LBDS @JosePaumard
    • Example Let’s sort an array of String // other way Stream<String> stream = ThreadLocalRandom .current() .longs() .mapToObj(Long::toHexString) .limit(10_000_000) .sorted() ; Object [] sorted = stream.toArray() ; #DV13LBDS @JosePaumard
    • Example Let’s sort an array of String // other way Stream<String> stream = ThreadLocalRandom .current() .longs() .mapToObj(Long::toHexString) .limit(10_000_000) .sorted() ; Object [] sorted = stream.toArray() ; T = 14 s #DV13LBDS @JosePaumard
    • Example Let’s sort an array of String // other way Stream<String> stream = ThreadLocalRandom .current() .longs() .mapToObj(Long::toHexString) .limit(10_000_000) .sorted() ; Intermediate calls ! Object [] sorted = stream.toArray() ; T = 14 s #DV13LBDS @JosePaumard
    • More on stream operations 1) There are intermediate and terminal operations 2) A stream is processed on a terminal operation call #DV13LBDS @JosePaumard
    • More on stream operations 1) 2) 3) 4) There are intermediate and terminal operations A stream is processed on a terminal operation call Only one terminal operation is allowed It cannot be processed again If needed another stream should be built on the source #DV13LBDS @JosePaumard
    • Parallel Streams #DV13LBDS @JosePaumard
    • Optimization The first optimization (well, laziness was first !) we need is parallelism The fork / join enable parallel programming since JDK 7 But writing tasks is hard and does not always lead to better performances #DV13LBDS @JosePaumard
    • Optimization The first optimization (well, laziness was first !) we need is parallelism The fork / join enable parallel programming since JDK 7 But writing tasks is hard and does not always lead to better performances Using the parallel stream API is much more secure #DV13LBDS @JosePaumard
    • How to build a parallel stream ? Two patterns 1) Call parallelStream() instead of stream() Stream<String> s = strings.parallelStream() ; 2) Call parallel() on an existing stream Stream<String> s = strings.stream().parallel() ; #DV13LBDS @JosePaumard
    • Can we decide the # of cores ? Answer is no #DV13LBDS @JosePaumard
    • Can we decide the # of cores ? Answer is no But… do we really need it ? #DV13LBDS @JosePaumard
    • Is parallelism really that simple ? Well… yes ! #DV13LBDS @JosePaumard
    • Is parallelism really that simple ? Well… yes ! … and no #DV13LBDS @JosePaumard
    • Is parallelism really that simple ? Example 1 : persons.stream().limit(10_000_000) ; « returns the 10M first elements of the source » #DV13LBDS @JosePaumard
    • Is parallelism really that simple ? Example 1 : persons.parallelStream().limit(10_000_000) ; « returns the 10M first elements of the source » #DV13LBDS @JosePaumard
    • Is parallelism really that simple ? Example 1 : persons.parallelStream().limit(10_000_000) ; « returns the 10M first elements of the source » But how can I track the first 10M of the source on several cores of my CPU ? #DV13LBDS @JosePaumard
    • Is parallelism really that simple ? Example 1 : performances Code 1 List<Long> list = new ArrayList<>(10_000_100) ; for (int i = 0 ; i < 10_000_000 ; i++) { list1.add(ThreadLocalRandom.current().nextLong()) ; } #DV13LBDS @JosePaumard
    • Is parallelism really that simple ? Example 1 : performances Code 2 Stream<Long> stream = Stream.generate(() -> ThreadLocalRandom.current().nextLong()) ; List<Long> list1 = stream.limit(10_000_000).collect(Collectors.toList()) ; #DV13LBDS @JosePaumard
    • Is parallelism really that simple ? Example 1 : performances Code 3 Stream<Long> stream = ThreadLocalRandom.current().longs(10_000_000).mapToObj(Long::new) ; List<Long> list = stream.collect(Collectors.toList()) ; #DV13LBDS @JosePaumard
    • Is parallelism really that simple ? Example 1 : performances Code 1 (for) Code 2 (limit) Code 3 (longs) #DV13LBDS Serial 270 ms 310 ms 250 ms Parallel @JosePaumard
    • Is parallelism really that simple ? Example 1 : performances Code 1 (for) Code 2 (limit) Code 3 (longs) #DV13LBDS Serial 270 ms 310 ms 250 ms Parallel 500 ms 320 ms @JosePaumard
    • Is parallelism really that simple ? Example 2 : Stream s3 = Stream.concat(stream1, stream2) ; « returns a stream with all the elements of the first stream followed by all the elements of the second stream » #DV13LBDS @JosePaumard
    • Parallelism Parallelism implies overhead most of the time Badly configured parallel operations can lead to unneeded computations that will slow down the overall process Unnecessary ordering of a stream will lead to performance hits #DV13LBDS @JosePaumard
    • Back to our sorting example Parallel version Stream<String> stream = ... ; // created before the test Object [] sorted = stream.parallel() .sorted() // or not .toArray() ; #DV13LBDS @JosePaumard
    • Sorting performances In fact it’s not that simple # 10 100 1K 10K 100K 1M 10M 20M 30M #DV13LBDS Serial 33 ms 1 ms 2 ms 5 ms 65 ms 540 ms 9,7 s 19,1 s 31,0 s Parallel 32 ms 1 ms 1 ms 7 ms 49 ms 153 ms 2,2 s 4,78 s 8,13 s Ratio Ratio n.log(n) @JosePaumard
    • Sorting performances In fact it’s not that simple # 10 100 1K 10K 100K 1M 10M 20M 30M #DV13LBDS Serial 33 ms 1 ms 2 ms 5 ms 65 ms 540 ms 9,7 s 19,1 s 31,0 s Parallel 32 ms 1 ms 1 ms 7 ms 49 ms 153 ms 2,2 s 4,78 s 8,13 s Ratio 1 1 2 0,7 1,3 3,5 4,4 4,0 3,8 Ratio n.log(n) @JosePaumard
    • Sorting performances In fact it’s not that simple # 10 100 1K 10K 100K 1M 10M 20M 30M #DV13LBDS Serial 33 ms 1 ms 2 ms 5 ms 65 ms 540 ms 9,7 s 19,1 s 31,0 s Parallel 32 ms 1 ms 1 ms 7 ms 49 ms 153 ms 2,2 s 4,78 s 8,13 s Ratio 1 1 2 0,7 1,3 3,5 4,4 4,0 3,8 Ratio n.log(n) 1 664 4982 26,6K 25,6K 36,9K 23,9K 25,3K 24,1K @JosePaumard
    • Questions ? #DV13LBDS #DV13LBDS @JosePaumard
    • Reductions #DV13LBDS @JosePaumard
    • What is a reduction 2 kinds of reduction : 1) The reduction « A reduction operation (also called a fold) takes a sequence of input elements and combines them into a single summary result by repeated application of a combining operation » #DV13LBDS @JosePaumard
    • What is a reduction 2 kinds of reduction : 2) The mutable reduction « A mutable reduction operation accumulates input elements into a mutable result container, such as a Collection or StringBuilder, as it processes the elements in the stream » #DV13LBDS @JosePaumard
    • A simple reduction Sum of ages // map / filter / reduce pattern on collections int sum = persons.stream() .map(p -> p.getAge()) .filter(a -> a > 20) .reduce(0, (a1, a2) -> a1 + a2) ; #DV13LBDS @JosePaumard
    • A simple reduction It would be nice to be able to write : // map / filter / reduce pattern on collections int sum = persons.stream() .map(p -> p.getAge()) .filter(a -> a > 20) .sum() ; But there’s no sum() on Stream<T> What would be the sense of « adding persons » ? #DV13LBDS @JosePaumard
    • A simple reduction It would be nice to be able to write : // map / filter / reduce pattern on collections int sum = persons.stream() .map(p -> p.getAge()) .filter(a -> a > 20) .sum() ; But there’s no sum() on Stream<T> There’s one on IntStream ! #DV13LBDS @JosePaumard
    • A simple reduction 2nd version : // map / filter / reduce pattern on collections int sum = persons.stream() .map(Person::getAge) .filter(a -> a > 20) .mapToInt(Integer::intValue) .sum() ; Value if persons is an empty list : 0 #DV13LBDS @JosePaumard
    • A simple reduction 2nd version (even better) : // map / filter / reduce pattern on collections int sum = persons.stream() .mapToInt(Person::getAge) .filter(a -> a > 20) // .mapToInt(Integer::intValue) .sum() ; Value if persons is an empty list : 0 #DV13LBDS @JosePaumard
    • A simple reduction What about max() and min() ? // map / filter / reduce pattern on collections ....... = persons.stream() .mapToInt(Person::getAge) .filter(a -> a > 20) .max() ; How can one chose a default value ? #DV13LBDS @JosePaumard
    • Problem with the default value The « default value » is a tricky notion #DV13LBDS @JosePaumard
    • Problem with the default value The « default value » is a tricky notion 1) The « default value » is the reduction of the empty set #DV13LBDS @JosePaumard
    • Problem with the default value The « default value » is a tricky notion 1) The « default value » is the reduction of the empty set 2) But it’s also the identity element for the reduction #DV13LBDS @JosePaumard
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    • Problem with the default value The « default value » is a tricky notion 1) The « default value » is the reduction of the empty set 2) But it’s also the identity element for the reduction #DV13LBDS @JosePaumard
    • Problem with the default value Problem : max() and min() have no identity element eg : an element e for which max(e, a) = a #DV13LBDS @JosePaumard
    • Problem with the default value Problem : max() and min() have no identity element eg : an element e for which max(e, a) = a 0 cant be, max(0, -1) is not -1… −∞ is not an integer #DV13LBDS @JosePaumard
    • Problem with the default value So what is the default value of max() and min() ? #DV13LBDS @JosePaumard
    • Problem with the default value So what is the default value of max() and min() ? Answer : there is no default value for max() and min() #DV13LBDS @JosePaumard
    • Problem with the default value So what is the returned value of the max() reduction ? // map / filter / reduce pattern on collections ....... = persons.stream() .mapToInt(Person::getAge) .filter(a -> a > 20) .max() ; #DV13LBDS @JosePaumard
    • Problem with the default value So what is the returned value of the max() reduction ? // map / filter / reduce pattern on collections ....... = persons.stream() .mapToInt(Person::getAge) .filter(a -> a > 20) .max() ; If it’s int, then the default value will be 0… #DV13LBDS @JosePaumard
    • Optionals Since there’s none, we need another notion // map / filter / reduce pattern on collections OptionalInt optionalMax = persons.stream() .mapToInt(Person::getAge) .filter(a -> a > 20) .max() ; « there might be no result » #DV13LBDS @JosePaumard
    • Optionals What can I do with OptionalInt ? 1st pattern : test if it holds a value OptionalInt optionalMax = ... ; int max ; if (optionalMax.isPresent()) { max = optionalMax.get() ; } else { max = ... ; // decide a « default value » } #DV13LBDS @JosePaumard
    • Optionals What can I do with OptionalInt ? 2nd pattern : read the held value, can get an exception OptionalInt optionalMax = ... ; // throws NoSuchElementException if no held value int max = optionalMax.getAsInt() ; #DV13LBDS @JosePaumard
    • Optionals What can I do with OptionalInt ? 3rd pattern : read the held value, giving a default value OptionalInt optionalMax = ... ; // get 0 if no held value int max = optionalMax.orElse(0) ; #DV13LBDS @JosePaumard
    • Optionals What can I do with OptionalInt ? 4th pattern : read the held value, or throw an exception OptionalInt optionalMax = ... ; // exceptionSupplier will supply an exception, if no held value int max = optionalMax.orElseThrow(exceptionSupplier) ; #DV13LBDS @JosePaumard
    • Available optionals Optional<T> OptionalInt, OptionalLong, OptionalDouble #DV13LBDS @JosePaumard
    • Available reductions On Stream<T> : - reduce(), with different parameters - count(), min(), max() - anyMatch(), allMatch(), noneMatch() - findFirst(), findAny() - toArray() - forEach(), forEachOrdered() #DV13LBDS @JosePaumard
    • Available reductions On IntStream, LongStream, DoubleStream : - average() - summaryStatistics() #DV13LBDS @JosePaumard
    • Available reductions SummaryStatistics is an object that holds : - Count, Min, Max, Sum, Average It’s a consumer, so it can be easily extended to compute other statistics #DV13LBDS @JosePaumard
    • Mutable reductions : example 1 Use of the helper class : Collectors ArrayList<String> strings = stream .map(Object::toString) .collect(Collectors.toList()) ; #DV13LBDS @JosePaumard
    • Mutable reductions : example 2 Concatenating strings with a helper String names = persons .stream() .map(Person::getName) .collect(Collectors.joining()) ; #DV13LBDS @JosePaumard
    • Mutable reductions Common things : - have a container : Collection or StringBuilder - have a way to add an element to the container - have a way to merge two containers (for parallelization) #DV13LBDS @JosePaumard
    • Mutable reductions The general form works with 3 objects : - a supplier : supplies a new instance of the mutable result container Supplier<ArrayList<String>> supplier = () -> new ArrayList<String>() ; #DV13LBDS @JosePaumard
    • Mutable reductions The general form works with 3 objects : - a supplier : supplies a new instance of the mutable result container - an accumulator : takes an partly filled container and adds an element to it BiConsumer<ArrayList<String>, ? super String> accumulator = (suppliedList, s) -> suppliedList.add(s) ; #DV13LBDS @JosePaumard
    • Mutable reductions The general form works with 3 objects : - a supplier : supplies a new instance of the mutable result container - an accumulator : takes an partly filled container and adds an element to it - a combiner : takes two partly filled containers and combines them BiConsumer<ArrayList<String>, ArrayList<String>> combiner = (supplied1, supplied2) -> supplied1.addAll(supplied2) ; #DV13LBDS @JosePaumard
    • Mutable reductions : example 1 Putting things together : ArrayList<String> strings = stream .map(Object::toString) .collect( () -> new ArrayList<String>(), // the supplier (suppliedList, s) -> suppliedList.add(s), // the accumulator (supplied1, supplied2) -> supplied1.addAll(supplied2) // the combiner ) ; #DV13LBDS @JosePaumard
    • Mutable reductions : example 1 Putting things together : ArrayList<String> strings = stream .map(Object::toString) .collect( ArrayList::new, // the supplier ArrayList::add, // the accumulator ArrayList::addAll // the combiner ) ; #DV13LBDS @JosePaumard
    • Collectors #DV13LBDS @JosePaumard
    • The Collectors class A rich toolbox (37 methods) for various types of reductions - counting, minBy, maxBy - summing, averaging, summarizing - joining - toList, toSet And - mapping, groupingBy, partionningBy #DV13LBDS @JosePaumard
    • The Collectors class Average, Sum, Count persons .stream() .collect(Collectors.averagingDouble(Person::getAge)) ; persons .stream() .collect(Collectors.counting()) ; #DV13LBDS @JosePaumard
    • The Collectors class Concatenating the names in a String String names = persons .stream() .map(Person::getName) .collect(Collectors.joining(", ")) ; #DV13LBDS @JosePaumard
    • The Collectors class Accumulating in a List, Set Set<Person> setOfPersons = persons .stream() .collect( Collectors.toSet()) ; #DV13LBDS @JosePaumard
    • The Collectors class Accumulating in a custom collection TreeSet<Person> treeSetOfPersons = persons .stream() .collect( Collectors.toCollection(TreeSet::new)) ; #DV13LBDS @JosePaumard
    • The Collectors class Getting the max according to a comparator Optional<Person> optionalPerson = persons .stream() .collect( Collectors.maxBy( Comparator.comparing(Person::getAge)) ; Bonus : new API to build comparators #DV13LBDS @JosePaumard
    • Building comparators New API to build comparators in a declarative way Comparator<Person> comp = Comparator.comparing(Person::getLastName) .thenComparing(Person::getFirstName) .thenComparing(Person:getAge) ; #DV13LBDS @JosePaumard
    • The Collectors class : mapping The mapping helper method takes 2 parameter : - a function, that maps the elements of the stream - a collector, called a « downstream », that is applied to the mapped values #DV13LBDS @JosePaumard
    • The Collectors class : mapping Accumulating names in a set Set<String> set = persons .stream() .collect( Collectors.mapping( Person::getLastName, Collectors.toSet())) ; #DV13LBDS @JosePaumard
    • The Collectors class : mapping Mapping the stream, then accumulating in a collection TreeSet<String> set = persons .stream() .collect( Collectors.mapping( Person::getLastName, Collectors.toCollection(TreeSet::new)) ) ; #DV13LBDS @JosePaumard
    • The Collector API : groupingBy « Grouping by » builds hash maps - must explain how the keys are built - by default the values are put in a list - may specify a downstream (ie a collector) #DV13LBDS @JosePaumard
    • The Collector API : groupingBy Grouping a list of persons by their age Map<Integer, List<Person>> map = persons .stream() .collect( Collectors.groupingBy(Person::getAge)) ; #DV13LBDS @JosePaumard
    • The Collector API : groupingBy Grouping a list of persons by their age Map<Integer, Set<Person>> map = persons .stream() .collect( Collectors.groupingBy( Person::getAge, Collectors.toSet() // the downstream ) ; #DV13LBDS @JosePaumard
    • The Collector API : groupingBy Grouping a list of persons names by their age Map<Integer, Set<String>> map = persons .stream() .collect( Collectors.groupingBy( Person::getAge, Collectors.mapping( // Person::getLastName, // the downstream Collectors.toSet() // ) ) ; #DV13LBDS @JosePaumard
    • The Collector API : groupingBy Grouping a list of persons names by their age Map<Integer, TreeSet<String>> map = persons .stream() .collect( Collectors.groupingBy( Person::getAge, Collectors.mapping( Person::getLastName, Collectors.toCollection(TreeSet::new) ) ) ; #DV13LBDS @JosePaumard
    • The Collector API : groupingBy Grouping a list of blah blah blah TreeMap<Integer, TreeSet<String>> map = persons .stream() .collect( Collectors.groupingBy( Person::getAge, TreeMap::new, Collectors.mapping( Person::getLastName, Collectors.toCollection(TreeSet::new) ) ) ; #DV13LBDS @JosePaumard
    • The Collector API : groupingBy Example : creating an age histogram Map<Integer, Long> map = persons .stream() .collect( Collectors.groupingBy(Person::getAge, Collectors.counting()) ) ; Gives the # of persons by age #DV13LBDS @JosePaumard
    • The Collector API : partionningBy Creates a Map<Boolean, …> on a predicate - the map has 2 keys : TRUE and FALSE - may specify a downstream #DV13LBDS @JosePaumard
    • The Collector API : partionningBy Creates a Map<Boolean, …> on a predicate Map<Boolean, List<Person>> map = persons .stream() .collect( Collectors.partitioningBy(p -> p.getAge() > 20) ) ; map.get(TRUE) returns the list people older than 20 #DV13LBDS @JosePaumard
    • The Collector API : partionningBy Can further process the list of persons Map<Boolean, TreeSet<String>> map = persons .stream() .collect( Collectors.partitioningBy( p -> p.getAge() > 20, Collectors.mapping( Person::getLastName, Collectors.toCollection(TreeSet::new)) ) ) ) ; #DV13LBDS @JosePaumard
    • The Collector API : partionningBy Can further process the list of persons Map<Boolean, TreeSet<String>> map = Partition the persons persons .stream() on age > 20 .collect( Collectors.partitioningBy( p -> p.getAge() > 20, Collectors.mapping( Person::getLastName, Collectors.toCollection(TreeSet::new)) ) ) ) ; #DV13LBDS @JosePaumard
    • The Collector API : partionningBy Can further process the list of persons Map<Boolean, TreeSet<String>> map = Gather their persons .stream() .collect( Collectors.partitioningBy( p -> p.getAge() > 20, Collectors.mapping( Person::getLastName, Collectors.toCollection(TreeSet::new)) ) ) ) ; #DV13LBDS names… @JosePaumard
    • The Collector API : partionningBy Can further process the list of persons Map<Boolean, TreeSet<String>> map = … in TreeSets persons .stream() .collect( Collectors.partitioningBy( p -> p.getAge() > 20, Collectors.mapping( Person::getLastName, Collectors.toCollection(TreeSet::new)) ) ) ) ; #DV13LBDS @JosePaumard
    • The Collector API : collectingAndThen Collect data with a downstream Then apply a function called a « finisher » Useful for putting the result in a immutable collection #DV13LBDS @JosePaumard
    • The Collector API : collectingAndThen Set<Map.Entry<Integer, List<Person>>> set = persons .stream() .collect( Collectors.collectingAndThen( Collectors.groupingBy( Person::getAge), // downstream, builds a map Map::entrySet // finisher, applied on the map ) ; In this case « Map::entrySet » is a finisher #DV13LBDS @JosePaumard
    • Questions ? #DV13LBDS #DV13LBDS @JosePaumard
    • Some real examples #DV13LBDS @JosePaumard
    • 1st example Optional<Entry<Integer, Long>> opt = movies.stream().parallel() .collect( Collectors.collectingAndThen( Collectors.groupingBy( movie -> movie.releaseYear(), Collectors.counting() ), Map::entrySet ) ) .stream() .max(Map.Entry.comparingByValue()) ; #DV13LBDS @JosePaumard
    • 1st example A Stream of movies Optional<Entry<Integer, Long>> opt = movies.stream().parallel() .collect( Collectors.collectingAndThen( Collectors.groupingBy( movie -> movie.releaseYear(), Collectors.counting() ), Map::entrySet ) ) .stream() .max(Map.Entry.comparingByValue()) ; #DV13LBDS @JosePaumard
    • 1st example Building a map of year / # of movies Optional<Entry<Integer, Long>> opt = movies.stream().parallel() .collect( Collectors.collectingAndThen( Collectors.groupingBy( movie -> movie.releaseYear(), Collectors.counting() ), Map::entrySet ) ) .stream() .max(Map.Entry.comparingByValue()) ; #DV13LBDS @JosePaumard
    • 1st example Optional<Entry<Integer, Long>> opt = movies.stream().parallel() .collect( Collectors.collectingAndThen( Collectors.groupingBy( movie -> movie.releaseYear(), Collectors.counting() ), Map::entrySet ) ) .stream() .max(Map.Entry.comparingByValue()) ; #DV13LBDS Then getting the entry set @JosePaumard
    • 1st example Optional<Entry<Integer, Long>> opt = movies.stream().parallel() .collect( Collectors.collectingAndThen( Collectors.groupingBy( movie -> movie.releaseYear(), Collectors.counting() ), Map::entrySet ) ) .stream() .max(Map.Entry.comparingByValue()) ; #DV13LBDS Then getting the max by value @JosePaumard
    • 1st example Optional<Entry<Integer, Long>> opt = movies.stream().parallel() .collect( Collectors.collectingAndThen( Collectors.groupingBy( movie -> movie.releaseYear(), Collectors.counting() ), Map::entrySet ) ) .stream() .max(Map.Entry.comparingByValue()) ; #DV13LBDS Returns the year with the most movies released @JosePaumard
    • Some thoughts on parallel #DV13LBDS @JosePaumard
    • Going parallel To ways to do it : - create a parallel stream up front - call the parallel() method on a given stream, or sequential() to go sequential When a terminal operation is initiated, the operations are executed in parallel or not, depending on the mode of the stream #DV13LBDS @JosePaumard
    • Things that will go wrong Some operations, like findAny() dont give reproductible results in parallel Changing the source (which is forbidden) can lead to unpredictable results even if the source is concurrent aware #DV13LBDS @JosePaumard
    • Things that will not go wrong, but… The parallel processing of a stream has to be freed from constraints - ordering is costly, relaxing ordering may lead to better performances - Collecting must be concurrent aware #DV13LBDS @JosePaumard
    • Conclusion #DV13LBDS @JosePaumard
    • Conclusion Why are lambdas introduced in Java 8 ? #DV13LBDS @JosePaumard
    • Conclusion Why are lambdas introduced in Java 8 ? Because it’s in the mood ! #DV13LBDS @JosePaumard
    • #DV13LBDS @JosePaumard
    • Conclusion Why are lambdas introduced in Java 8 ? Because it’s in the mood ! #DV13LBDS @JosePaumard
    • Conclusion Why are lambdas introduced in Java 8 ? Because it’s in the mood ! Because it allows to write more compact code ! #DV13LBDS @JosePaumard
    • Compact code is better ! An example of compact code (in C) #include "stdio.h" main() { int b=0,c=0,q=60,_=q;for(float i=-20,o,O=0,l=0,j,p;j=O*O,p=l*l, (!_--|(j+p>4)?fputc(b?q+(_/3):10,(i+=!b,p=j=O=l=0,c++,stdout)), _=q:l=2*O*l+i/20,O=j-p+o),b=c%q,c<2400;o=-2+b*.05) ; } http://www.codeproject.com/Articles/2228/Obfuscating-your-Mandelbrot-code #DV13LBDS @JosePaumard
    • Compact code is better ! An example of compact code (in C) ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ ++++++++++++++++++++++++++++++++++++++++++++ +++++++++++++++ ++++++++++++++++++++++++++++++++++++++++++ ++++++++++++++ ++++++++++++++++++++++++++++++++++++++++++ ++++++++++++++ ++++++++++++++++++++++++++++++++++++++++++ ++++++++++++++ ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ ++++++++++++++++++++++++++++++++++++ ++ +++++++++++ +++++++++++++++++++++++++++++++++++ +++++++ ++++++++++++++++++++++++++++++++++++ +++++++ +++++++++++++++++++++++++++++++++++ ++++++++ ++++++++++++++++++++++++++++++++++ +++++++ +++++++++++++++++++++++++++++++++ +++++ +++++++++++++++++++++++++++++++++ ++++++ +++++++++++++++++++++++ + +++++ ++++++ +++++++++++++++++++++++ ++ ++++++ ++++++++++++++++++++++ + ++++++ ++++++++++++++++++++++ + ++++++ ++++++++++++++++++++ + + +++++++ ++++++ ++++++++ ++++++++++++++++++++ + + +++++++ ++++++++++++++++++++++ + ++++++ ++++++++++++++++++++++ + ++++++ +++++++++++++++++++++++ ++ ++++++ +++++++++++++++++++++++ + +++++ ++++++ +++++++++++++++++++++++++++++++++ ++++++ +++++++++++++++++++++++++++++++++ +++++ ++++++++++++++++++++++++++++++++++ +++++++ +++++++++++++++++++++++++++++++++++ ++++++++ ++++++++++++++++++++++++++++++++++++ +++++++ +++++++++++++++++++++++++++++++++++ +++++++ ++++++++++++++++++++++++++++++++++++ ++ +++++++++++ ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ ++++++++++++++++++++++++++++++++++++++++++ ++++++++++++++ ++++++++++++++++++++++++++++++++++++++++++ ++++++++++++++ ++++++++++++++++++++++++++++++++++++++++++ ++++++++++++++ ++++++++++++++++++++++++++++++++++++++++++++ +++++++++++++++ ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #include "stdio.h" main() { int b=0,c=0,q=60,_=q;for(float i=-20,o,O=0,l=0,j,p;j=O*O,p=l*l, (!_--|(j+p>4)?fputc(b?q+(_/3):10,(i+=!b,p=j=O=l=0,c++,stdout)), _=q:l=2*O*l+i/20,O=j-p+o),b=c%q,c<2400;o=-2+b*.05) ; } http://www.codeproject.com/Articles/2228/Obfuscating-your-Mandelbrot-code #DV13LBDS @JosePaumard
    • Conclusion Why are lambdas introduced in Java 8 ? Because it’s in the mood ! Because it allows to write more compact code ! #DV13LBDS @JosePaumard
    • Conclusion Why are lambdas introduced in Java 8 ? Because it brings new and efficient patterns, that enable easy and safe parallelization, - needed by modern applications - needed by API writers #DV13LBDS @JosePaumard
    • Conclusion Java 8 is coming, it’s the biggest update in 15 years #DV13LBDS @JosePaumard
    • Conclusion Java 8 is coming, it’s the biggest update in 15 years Moving to Java 8 means a lot of work for us developers ! - Self training - Changing our habits #DV13LBDS @JosePaumard
    • Conclusion Java 8 is coming, it’s the biggest update in 15 years Moving to Java 8 means a lot of work for us developers ! - Self training - Changing our habits - Convincing our bosses (sigh…) #DV13LBDS @JosePaumard
    • Conclusion Java 8 is coming, it’s the biggest update in 15 years Download the develop preview version on Open JDK site Release date : 18th march 2014 ! #DV13LBDS @JosePaumard
    • Conclusion Java 8 is coming, it’s the biggest update in 15 years Devoxx openning keynote Brian Goetz : « Lamda a peek under the hood », Wed 12:00 Paul Sandoz : « In full flow : Java 8 lambdas in the stream », Wed 14:00 #DV13LBDS @JosePaumard
    • Conclusion Java 8 is coming, it’s the biggest update in 15 years « Java is a blue collar language. It’s not PhD thesis material but a language for a job » – James Gosling, 1997 #DV13LBDS @JosePaumard
    • Conclusion Java 8 is coming, it’s the biggest update in 15 years « Language features are not a goal unto themselves; language features are enablers, encouraging or discouraging certain styles and idioms » – Brian Goetz, 2013 #DV13LBDS @JosePaumard
    • Conclusion Java 8 is coming, it’s the biggest update in 15 years The good news is : Java is still Java ! #DV13LBDS @JosePaumard
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