1. Información general
En este tutorial detallado, veremos el uso práctico de Java 8 Streams desde la creación hasta la ejecución paralela.
Para comprender este material, los lectores deben tener un conocimiento básico de Java 8 (expresiones lambda, opcionales, referencias de métodos) y de la API Stream. Si no está familiarizado con estos temas, consulte nuestros artículos anteriores: Nuevas funciones en Java 8 e Introducción a las secuencias de Java 8.
2. Creación de transmisiones
Hay muchas formas de crear una instancia de flujo de diferentes fuentes. Una vez creada, la instancia no modificará su fuente, por lo que permitirá la creación de múltiples instancias desde una sola fuente.
2.1. Secuencia vacía
El método empty () debe usarse en caso de que se cree una secuencia vacía:
Stream streamEmpty = Stream.empty();A menudo es el caso que el método vacío () se usa en la creación para evitar devolver un valor nulo para transmisiones sin elemento:
public Stream streamOf(List list) return list == null 2.2. Corriente de colección
La secuencia también se puede crear de cualquier tipo de colección ( colección, lista, conjunto ):
Collection collection = Arrays.asList("a", "b", "c"); Stream streamOfCollection = collection.stream();2.3. Flujo de matriz
Array también puede ser una fuente de Stream:
Stream streamOfArray = Stream.of("a", "b", "c");También se pueden crear a partir de una matriz existente o de una parte de una matriz:
String[] arr = new String[]{"a", "b", "c"}; Stream streamOfArrayFull = Arrays.stream(arr); Stream streamOfArrayPart = Arrays.stream(arr, 1, 3);2.4. Stream.builder ()
Cuando se usa el constructor, el tipo deseado debe especificarse adicionalmente en la parte derecha de la declaración; de lo contrario , el método build () creará una instancia de Stream:
Stream streamBuilder = Stream.builder().add("a").add("b").add("c").build();2.5. Stream.generate ()
El método generate () acepta un Proveedor para la generación de elementos. Como el flujo resultante es infinito, el desarrollador debe especificar el tamaño deseado o el método generate () funcionará hasta que alcance el límite de memoria:
Stream streamGenerated = Stream.generate(() -> "element").limit(10);El código anterior crea una secuencia de diez cadenas con el valor - "elemento" .
2.6. Stream.iterate ()
Otra forma de crear una secuencia infinita es mediante el método iterate () :
Stream streamIterated = Stream.iterate(40, n -> n + 2).limit(20);El primer elemento de la secuencia resultante es un primer parámetro del método iterate () . Para crear cada elemento siguiente, la función especificada se aplica al elemento anterior. En el ejemplo anterior, el segundo elemento será 42.
2.7. Corriente de primitivos
Java 8 ofrece la posibilidad de crear flujos de tres tipos primitivos: int, long y double. Como Stream es una interfaz genérica y no hay forma de usar primitivas como parámetro de tipo con genéricos, se crearon tres nuevas interfaces especiales: IntStream, LongStream, DoubleStream.
El uso de las nuevas interfaces alivia el autoempaquetado innecesario permite una mayor productividad:
IntStream intStream = IntStream.range(1, 3); LongStream longStream = LongStream.rangeClosed(1, 3);El método range (int startInclusive, int endExclusive) crea una secuencia ordenada desde el primer parámetro al segundo parámetro. Incrementa el valor de los elementos subsiguientes con el paso igual a 1. El resultado no incluye el último parámetro, es solo un límite superior de la secuencia.
El método rangeClosed (int startInclusive, int endInclusive) hace lo mismo con una sola diferencia: el segundo elemento está incluido. Estos dos métodos se pueden utilizar para generar cualquiera de los tres tipos de flujos de primitivas.
Desde Java 8, la clase Random proporciona una amplia gama de métodos para generar flujos de primitivas. Por ejemplo, el siguiente código crea un DoubleStream, que tiene tres elementos:
Random random = new Random(); DoubleStream doubleStream = random.doubles(3);2.8. Corriente de cuerda
La cadena también se puede utilizar como fuente para crear una transmisión.
Con la ayuda del método chars () de la clase String . Dado que no hay una interfaz CharStream en JDK, IntStream se utiliza para representar un flujo de caracteres.
IntStream streamOfChars = "abc".chars();El siguiente ejemplo divide una cadena en subcadenas de acuerdo con la expresión regular especificada :
Stream streamOfString = Pattern.compile(", ").splitAsStream("a, b, c");2.9. Flujo de archivo
Los archivos de clase Java NIO permiten generar una secuencia de un archivo de texto a través del método lines () . Cada línea del texto se convierte en un elemento de la secuencia:
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
Stream API es un conjunto de herramientas potente pero fácil de entender para procesar la secuencia de elementos. Nos permite reducir una gran cantidad de código estándar, crear programas más legibles y mejorar la productividad de la aplicación cuando se usa correctamente.
En la mayoría de las muestras de código que se muestran en este artículo, los flujos se dejaron sin consumir (no aplicamos el método close () o una operación de terminal). En una aplicación real, no dejes secuencias instanciadas sin consumir, ya que eso provocará pérdidas de memoria.
Los ejemplos de código completos que acompañan al artículo están disponibles en GitHub.