Le didacticiel de l'API Java 8 Stream

1. Vue d'ensemble

Dans ce didacticiel détaillé, nous allons passer en revue l'utilisation pratique de Java 8 Streams de la création à l'exécution parallèle.

Pour comprendre ce matériel, les lecteurs doivent avoir une connaissance de base de Java 8 (expressions lambda, facultatif, références de méthodes) et de l'API Stream. Si vous n'êtes pas familier avec ces sujets, veuillez consulter nos articles précédents - Nouvelles fonctionnalités de Java 8 et Introduction aux flux Java 8.

2. Création de flux

Il existe de nombreuses façons de créer une instance de flux de différentes sources. Une fois créée, l'instance ne modifiera pas sa source, permettant ainsi la création de plusieurs instances à partir d'une seule source.

2.1. Flux vide

La méthode empty () doit être utilisée en cas de création d'un flux vide:

Stream streamEmpty = Stream.empty();

C'est souvent le cas où la méthode empty () est utilisée lors de la création pour éviter de renvoyer null pour les flux sans élément:

public Stream streamOf(List list)  return list == null 

2.2. Flux de collection

Stream peut également être créé de tout type de Collection ( Collection, Liste, Ensemble ):

Collection collection = Arrays.asList("a", "b", "c"); Stream streamOfCollection = collection.stream();

2.3. Stream of Array

Array peut également être une source d'un Stream:

Stream streamOfArray = Stream.of("a", "b", "c");

Ils peuvent également être créés à partir d'un tableau existant ou d'une partie d'un tableau:

String[] arr = new String[]{"a", "b", "c"}; Stream streamOfArrayFull = Arrays.stream(arr); Stream streamOfArrayPart = Arrays.stream(arr, 1, 3);

2.4. Stream.builder ()

Lorsque builder est utilisé, le type souhaité doit être spécifié en plus dans la partie droite de l'instruction, sinon la méthode build () créera une instance du Stream:

Stream streamBuilder = Stream.builder().add("a").add("b").add("c").build();

2.5. Stream.generate ()

La méthode generate () accepte un fournisseur pour la génération d'élément. Comme le flux résultant est infini, le développeur doit spécifier la taille souhaitée ou la méthode generate () fonctionnera jusqu'à ce qu'elle atteigne la limite de mémoire:

Stream streamGenerated = Stream.generate(() -> "element").limit(10);

Le code ci-dessus crée une séquence de dix chaînes avec la valeur - «élément» .

2.6. Stream.iterate ()

Une autre façon de créer un flux infini consiste à utiliser la méthode iterate () :

Stream streamIterated = Stream.iterate(40, n -> n + 2).limit(20);

Le premier élément du flux résultant est un premier paramètre de la méthode iterate () . Pour créer chaque élément suivant, la fonction spécifiée est appliquée à l'élément précédent. Dans l'exemple ci-dessus, le deuxième élément sera 42.

2.7. Flux de primitifs

Java 8 offre la possibilité de créer des flux à partir de trois types primitifs: int, long et double. Comme Stream est une interface générique et qu'il n'y a aucun moyen d'utiliser des primitives comme paramètre de type avec des génériques, trois nouvelles interfaces spéciales ont été créées: IntStream, LongStream, DoubleStream.

L'utilisation des nouvelles interfaces réduit l'auto-boxing inutile permet une productivité accrue:

IntStream intStream = IntStream.range(1, 3); LongStream longStream = LongStream.rangeClosed(1, 3);

La méthode range (int startInclusive, int endExclusive) crée un flux ordonné du premier paramètre au deuxième paramètre. Il incrémente la valeur des éléments suivants avec le pas égal à 1. Le résultat n'inclut pas le dernier paramètre, c'est juste une limite supérieure de la séquence.

La méthode rangeClosed (int startInclusive, int endInclusive) fait de même avec une seule différence - le deuxième élément est inclus. Ces deux méthodes peuvent être utilisées pour générer l'un des trois types de flux de primitives.

Depuis Java 8, la classe Random fournit un large éventail de méthodes pour générer des flux de primitives. Par exemple, le code suivant crée un DoubleStream, qui comporte trois éléments:

Random random = new Random(); DoubleStream doubleStream = random.doubles(3);

2.8. Flux de chaîne

La chaîne peut également être utilisée comme source pour créer un flux.

Avec l'aide de la méthode chars () de la classe String . Puisqu'il n'y a pas d'interface CharStream dans JDK, l' IntStream est utilisé pour représenter un flux de caractères à la place.

IntStream streamOfChars = "abc".chars();

L'exemple suivant divise une chaîne en sous-chaînes selon RegEx spécifié :

Stream streamOfString = Pattern.compile(", ").splitAsStream("a, b, c");

2.9. Flux de fichier

La classe Java NIO Files permet de générer un Stream d'un fichier texte via la méthode lines () . Chaque ligne du texte devient un élément du flux:

Path path = Paths.get("C:\\file.txt"); Stream streamOfStrings = Files.lines(path); Stream streamWithCharset = Files.lines(path, Charset.forName("UTF-8"));

The Charset can be specified as an argument of the lines() method.

3. Referencing a Stream

It is possible to instantiate a stream and to have an accessible reference to it as long as only intermediate operations were called. Executing a terminal operation makes a stream inaccessible.

To demonstrate this we will forget for a while that the best practice is to chain sequence of operation. Besides its unnecessary verbosity, technically the following code is valid:

Stream stream = Stream.of("a", "b", "c").filter(element -> element.contains("b")); Optional anyElement = stream.findAny();

But an attempt to reuse the same reference after calling the terminal operation will trigger the IllegalStateException:

Optional firstElement = stream.findFirst();

As the IllegalStateException is a RuntimeException, a compiler will not signalize about a problem. So, it is very important to remember that Java 8 streams can't be reused.

This kind of behavior is logical because streams were designed to provide an ability to apply a finite sequence of operations to the source of elements in a functional style, but not to store elements.

So, to make previous code work properly some changes should be done:

List elements = Stream.of("a", "b", "c").filter(element -> element.contains("b")) .collect(Collectors.toList()); Optional anyElement = elements.stream().findAny(); Optional firstElement = elements.stream().findFirst();

4. Stream Pipeline

To perform a sequence of operations over the elements of the data source and aggregate their results, three parts are needed – the source, intermediate operation(s) and a terminal operation.

Intermediate operations return a new modified stream. For example, to create a new stream of the existing one without few elements the skip() method should be used:

Stream onceModifiedStream = Stream.of("abcd", "bbcd", "cbcd").skip(1);

If more than one modification is needed, intermediate operations can be chained. Assume that we also need to substitute every element of current Stream with a sub-string of first few chars. This will be done by chaining the skip() and the map() methods:

Stream twiceModifiedStream = stream.skip(1).map(element -> element.substring(0, 3));

As you can see, the map() method takes a lambda expression as a parameter. If you want to learn more about lambdas take a look at our tutorial Lambda Expressions and Functional Interfaces: Tips and Best Practices.

A stream by itself is worthless, the real thing a user is interested in is a result of the terminal operation, which can be a value of some type or an action applied to every element of the stream. Only one terminal operation can be used per stream.

The right and most convenient way to use streams are by a stream pipeline, which is a chain of stream source, intermediate operations, and a terminal operation. For example:

List list = Arrays.asList("abc1", "abc2", "abc3"); long size = list.stream().skip(1) .map(element -> element.substring(0, 3)).sorted().count();

5. Lazy Invocation

Intermediate operations are lazy. This means that they will be invoked only if it is necessary for the terminal operation execution.

To demonstrate this, imagine that we have method wasCalled(), which increments an inner counter every time it was called:

private long counter; private void wasCalled() { counter++; }

Let's call method wasCalled() from operation filter():

List list = Arrays.asList(“abc1”, “abc2”, “abc3”); counter = 0; Stream stream = list.stream().filter(element -> { wasCalled(); return element.contains("2"); });

As we have a source of three elements we can assume that method filter() will be called three times and the value of the counter variable will be 3. But running this code doesn't change counter at all, it is still zero, so, the filter() method wasn't called even once. The reason why – is missing of the terminal operation.

Let's rewrite this code a little bit by adding a map() operation and a terminal operation – findFirst(). We will also add an ability to track an order of method calls with a help of logging:

Optional stream = list.stream().filter(element -> { log.info("filter() was called"); return element.contains("2"); }).map(element -> { log.info("map() was called"); return element.toUpperCase(); }).findFirst();

Resulting log shows that the filter() method was called twice and the map() method just once. It is so because the pipeline executes vertically. In our example the first element of the stream didn't satisfy filter's predicate, then the filter() method was invoked for the second element, which passed the filter. Without calling the filter() for third element we went down through pipeline to the map() method.

The findFirst() operation satisfies by just one element. So, in this particular example the lazy invocation allowed to avoid two method calls – one for the filter() and one for the map().

6. Order of Execution

From the performance point of view, the right order is one of the most important aspects of chaining operations in the stream pipeline:

long size = list.stream().map(element -> { wasCalled(); return element.substring(0, 3); }).skip(2).count();

Execution of this code will increase the value of the counter by three. This means that the map() method of the stream was called three times. But the value of the size is one. So, resulting stream has just one element and we executed the expensive map() operations for no reason twice out of three times.

If we change the order of the skip() and the map() methods, the counter will increase only by one. So, the method map() will be called just once:

long size = list.stream().skip(2).map(element -> { wasCalled(); return element.substring(0, 3); }).count();

This brings us up to the rule: intermediate operations which reduce the size of the stream should be placed before operations which are applying to each element. So, keep such methods as skip(), filter(), distinct() at the top of your stream pipeline.

7. Stream Reduction

The API has many terminal operations which aggregate a stream to a type or to a primitive, for example, count(), max(), min(), sum(), but these operations work according to the predefined implementation. And what if a developer needs to customize a Stream's reduction mechanism? There are two methods which allow to do this – the reduce()and the collect() methods.

7.1. The reduce() Method

There are three variations of this method, which differ by their signatures and returning types. They can have the following parameters:

identity – the initial value for an accumulator or a default value if a stream is empty and there is nothing to accumulate;

accumulator – a function which specifies a logic of aggregation of elements. As accumulator creates a new value for every step of reducing, the quantity of new values equals to the stream's size and only the last value is useful. This is not very good for the performance.

combiner – a function which aggregates results of the accumulator. Combiner is called only in a parallel mode to reduce results of accumulators from different threads.

So, let's look at these three methods in action:

OptionalInt reduced = IntStream.range(1, 4).reduce((a, b) -> a + b);

reduced = 6 (1 + 2 + 3)

int reducedTwoParams = IntStream.range(1, 4).reduce(10, (a, b) -> a + b);

reducedTwoParams = 16 (10 + 1 + 2 + 3)

int reducedParams = Stream.of(1, 2, 3) .reduce(10, (a, b) -> a + b, (a, b) -> { log.info("combiner was called"); return a + b; });

The result will be the same as in the previous example (16) and there will be no login which means, that combiner wasn't called. To make a combiner work, a stream should be parallel:

int reducedParallel = Arrays.asList(1, 2, 3).parallelStream() .reduce(10, (a, b) -> a + b, (a, b) -> { log.info("combiner was called"); return a + b; });

The result here is different (36) and the combiner was called twice. Here the reduction works by the following algorithm: accumulator ran three times by adding every element of the stream to identity to every element of the stream. These actions are being done in parallel. As a result, they have (10 + 1 = 11; 10 + 2 = 12; 10 + 3 = 13;). Now combiner can merge these three results. It needs two iterations for that (12 + 13 = 25; 25 + 11 = 36).

7.2. The collect() Method

Reduction of a stream can also be executed by another terminal operation – the collect() method. It accepts an argument of the type Collector, which specifies the mechanism of reduction. There are already created predefined collectors for most common operations. They can be accessed with the help of the Collectors type.

In this section we will use the following List as a source for all streams:

List productList = Arrays.asList(new Product(23, "potatoes"), new Product(14, "orange"), new Product(13, "lemon"), new Product(23, "bread"), new Product(13, "sugar"));

Converting a stream to the Collection (Collection, List or Set):

List collectorCollection = productList.stream().map(Product::getName).collect(Collectors.toList());

Reducing to String:

String listToString = productList.stream().map(Product::getName) .collect(Collectors.joining(", ", "[", "]"));

The joiner() method can have from one to three parameters (delimiter, prefix, suffix). The handiest thing about using joiner() – developer doesn't need to check if the stream reaches its end to apply the suffix and not to apply a delimiter. Collector will take care of that.

Processing the average value of all numeric elements of the stream:

double averagePrice = productList.stream() .collect(Collectors.averagingInt(Product::getPrice));

Processing the sum of all numeric elements of the stream:

int summingPrice = productList.stream() .collect(Collectors.summingInt(Product::getPrice));

Methods averagingXX(), summingXX() and summarizingXX() can work as with primitives (int, long, double) as with their wrapper classes (Integer, Long, Double). One more powerful feature of these methods is providing the mapping. So, developer doesn't need to use an additional map() operation before the collect() method.

Collecting statistical information about stream’s elements:

IntSummaryStatistics statistics = productList.stream() .collect(Collectors.summarizingInt(Product::getPrice));

By using the resulting instance of type IntSummaryStatistics developer can create a statistical report by applying toString() method. The result will be a String common to this one “IntSummaryStatistics{count=5, sum=86, min=13, average=17,200000, max=23}”.

It is also easy to extract from this object separate values for count, sum, min, average by applying methods getCount(), getSum(), getMin(), getAverage(), getMax(). All these values can be extracted from a single pipeline.

Grouping of stream’s elements according to the specified function:

Map
    
      collectorMapOfLists = productList.stream() .collect(Collectors.groupingBy(Product::getPrice));
    

In the example above the stream was reduced to the Map which groups all products by their price.

Dividing stream’s elements into groups according to some predicate:

Map
    
      mapPartioned = productList.stream() .collect(Collectors.partitioningBy(element -> element.getPrice() > 15));
    

Pushing the collector to perform additional transformation:

Set unmodifiableSet = productList.stream() .collect(Collectors.collectingAndThen(Collectors.toSet(), Collections::unmodifiableSet));

In this particular case, the collector has converted a stream to a Set and then created the unmodifiable Set out of it.

Custom collector:

If for some reason, a custom collector should be created, the most easier and the less verbose way of doing so – is to use the method of() of the type Collector.

Collector
    
      toLinkedList = Collector.of(LinkedList::new, LinkedList::add, (first, second) -> { first.addAll(second); return first; }); LinkedList linkedListOfPersons = productList.stream().collect(toLinkedList);
    

In this example, an instance of the Collector got reduced to the LinkedList.

Parallel Streams

Before Java 8, parallelization was complex. Emerging of the ExecutorService and the ForkJoin simplified developer’s life a little bit, but they still should keep in mind how to create a specific executor, how to run it and so on. Java 8 introduced a way of accomplishing parallelism in a functional style.

The API allows creating parallel streams, which perform operations in a parallel mode. When the source of a stream is a Collection or an array it can be achieved with the help of the parallelStream() method:

Stream streamOfCollection = productList.parallelStream(); boolean isParallel = streamOfCollection.isParallel(); boolean bigPrice = streamOfCollection .map(product -> product.getPrice() * 12) .anyMatch(price -> price > 200);

If the source of stream is something different than a Collection or an array, the parallel() method should be used:

IntStream intStreamParallel = IntStream.range(1, 150).parallel(); boolean isParallel = intStreamParallel.isParallel();

Under the hood, Stream API automatically uses the ForkJoin framework to execute operations in parallel. By default, the common thread pool will be used and there is no way (at least for now) to assign some custom thread pool to it. This can be overcome by using a custom set of parallel collectors.

When using streams in parallel mode, avoid blocking operations and use parallel mode when tasks need the similar amount of time to execute (if one task lasts much longer than the other, it can slow down the complete app’s workflow).

The stream in parallel mode can be converted back to the sequential mode by using the sequential() method:

IntStream intStreamSequential = intStreamParallel.sequential(); boolean isParallel = intStreamSequential.isParallel();

Conclusions

L'API Stream est un ensemble d'outils puissant mais simple à comprendre pour traiter une séquence d'éléments. Cela nous permet de réduire une énorme quantité de code standard, de créer des programmes plus lisibles et d'améliorer la productivité de l'application lorsqu'elle est utilisée correctement.

Dans la plupart des exemples de code présentés dans cet article, les flux n'ont pas été utilisés (nous n'avons pas appliqué la méthode close () ou une opération de terminal). Dans une vraie application, ne laissez pas un flux instancié non consommé car cela entraînerait des fuites de mémoire.

Les exemples de code complets qui accompagnent l'article sont disponibles à l'adresse over sur GitHub.