Interface Stream<T>

A sequence of elements supporting sequential and parallel aggregate operations. The following example illustrates an aggregate operation using Stream and IntStream:

int sum = widgets.stream()
                      .filter(w -> w.getColor() == RED)
                      .mapToInt(w -> w.getWeight())
                      .sum();

In addition to Stream, which is a stream of object references, there are primitive specializations for IntStream, LongStream, and DoubleStream, all of which are referred to as "streams" and conform to the characteristics and restrictions described here.

To perform a computation, stream operations are composed into a stream pipeline. A stream pipeline consists of a source (which might be an array, a collection, a generator function, an I/O channel, etc), zero or more intermediate operations (which transform a stream into another stream, such as filter(Predicate)), and a terminal operation (which produces a result or side-effect, such as count() or forEach(Consumer)). Streams are lazy; computation on the source data is only performed when the terminal operation is initiated, and source elements are consumed only as needed.

Collections and streams, while bearing some superficial similarities, have different goals. Collections are primarily concerned with the efficient management of, and access to, their elements. By contrast, streams do not provide a means to directly access or manipulate their elements, and are instead concerned with declaratively describing their source and the computational operations which will be performed in aggregate on that source. However, if the provided stream operations do not offer the desired functionality, the BaseStream.iterator() and BaseStream.spliterator() operations can be used to perform a controlled traversal.

A stream pipeline, like the "widgets" example above, can be viewed as a query on the stream source. Unless the source was explicitly designed for concurrent modification (such as a ConcurrentHashMap), unpredictable or erroneous behavior may result from modifying the stream source while it is being queried.

Most stream operations accept parameters that describe user-specified behavior, such as the lambda expression w -> w.getWeight() passed to mapToInt in the example above. To preserve correct behavior, these behavioral parameters:

• must be non-interfering (they do not modify the stream source); and
• in most cases must be stateless (their result should not depend on any state that might change during execution of the stream pipeline).

Such parameters are always instances of a functional interface such as Function, and are often lambda expressions or method references. Unless otherwise specified these parameters must be non-null.

A stream should be operated on (invoking an intermediate or terminal stream operation) only once. This rules out, for example, "forked" streams, where the same source feeds two or more pipelines, or multiple traversals of the same stream. A stream implementation may throw IllegalStateException if it detects that the stream is being reused. However, since some stream operations may return their receiver rather than a new stream object, it may not be possible to detect reuse in all cases.

Streams have a BaseStream.close() method and implement AutoCloseable, but nearly all stream instances do not actually need to be closed after use. Generally, only streams whose source is an IO channel (such as those returned by Files.lines(Path, Charset)) will require closing. Most streams are backed by collections, arrays, or generating functions, which require no special resource management. (If a stream does require closing, it can be declared as a resource in a try-with-resources statement.)

Stream pipelines may execute either sequentially or in parallel. This execution mode is a property of the stream. Streams are created with an initial choice of sequential or parallel execution. (For example, Collection.stream() creates a sequential stream, and Collection.parallelStream() creates a parallel one.) This choice of execution mode may be modified by the BaseStream.sequential() or BaseStream.parallel() methods, and may be queried with the BaseStream.isParallel() method.

Methods

filter

Stream<T> filter(Predicate<? super T> predicate)

Parameters:

predicate - a non-interfering, stateless predicate to apply to each element to determine if it should be included

Returns:

the new stream

map

<R> Stream<R> map(Function<? super T,? extends R> mapper)

Type Parameters:

R - The element type of the new stream

Parameters:

mapper - a non-interfering, stateless function to apply to each element

Returns:

the new stream

mapToInt

IntStream mapToInt(ToIntFunction<? super T> mapper)

Parameters:

mapper - a non-interfering, stateless function to apply to each element

Returns:

the new stream

mapToLong

LongStream mapToLong(ToLongFunction<? super T> mapper)

Parameters:

mapper - a non-interfering, stateless function to apply to each element

Returns:

the new stream

mapToDouble

DoubleStream mapToDouble(ToDoubleFunction<? super T> mapper)

Parameters:

mapper - a non-interfering, stateless function to apply to each element

Returns:

the new stream

flatMap

<R> Stream<R> flatMap(Function<? super T,? extends Stream<? extends R>> mapper)

API Note:

The flatMap() operation has the effect of applying a one-to-many transformation to the elements of the stream, and then flattening the resulting elements into a new stream.

Examples.

If orders is a stream of purchase orders, and each purchase order contains a collection of line items, then the following produces a stream containing all the line items in all the orders:

orders.flatMap(order -> order.getLineItems().stream())...

If path is the path to a file, then the following produces a stream of the words contained in that file:

Stream<String> lines = Files.lines(path, StandardCharsets.UTF_8);
     Stream<String> words = lines.flatMap(line -> Stream.of(line.split(" +")));

The mapper function passed to flatMap splits a line, using a simple regular expression, into an array of words, and then creates a stream of words from that array.

Type Parameters:

R - The element type of the new stream

Parameters:

mapper - a non-interfering, stateless function to apply to each element which produces a stream of new values

Returns:

the new stream

flatMapToInt

IntStream flatMapToInt(Function<? super T,? extends IntStream> mapper)

Parameters:

mapper - a non-interfering, stateless function to apply to each element which produces a stream of new values

Returns:

the new stream

See Also:

flatMap(Function)

flatMapToLong

LongStream flatMapToLong(Function<? super T,? extends LongStream> mapper)

Parameters:

mapper - a non-interfering, stateless function to apply to each element which produces a stream of new values

Returns:

the new stream

See Also:

flatMap(Function)

flatMapToDouble

DoubleStream flatMapToDouble(Function<? super T,? extends DoubleStream> mapper)

Parameters:

mapper - a non-interfering, stateless function to apply to each element which produces a stream of new values

Returns:

the new stream

See Also:

flatMap(Function)

distinct

Stream<T> distinct()

API Note:

Preserving stability for distinct() in parallel pipelines is relatively expensive (requires that the operation act as a full barrier, with substantial buffering overhead), and stability is often not needed. Using an unordered stream source (such as generate(Supplier)) or removing the ordering constraint with BaseStream.unordered() may result in significantly more efficient execution for distinct() in parallel pipelines, if the semantics of your situation permit. If consistency with encounter order is required, and you are experiencing poor performance or memory utilization with distinct() in parallel pipelines, switching to sequential execution with BaseStream.sequential() may improve performance.

Returns:

the new stream

sorted

Stream<T> sorted()

Returns:

the new stream

sorted

Stream<T> sorted(Comparator<? super T> comparator)

Parameters:

comparator - a non-interfering, stateless Comparator to be used to compare stream elements

Returns:

the new stream

peek

Stream<T> peek(Consumer<? super T> action)

API Note:

This method exists mainly to support debugging, where you want to see the elements as they flow past a certain point in a pipeline:

Stream.of("one", "two", "three", "four")
         .filter(e -> e.length() > 3)
         .peek(e -> System.out.println("Filtered value: " + e))
         .map(String::toUpperCase)
         .peek(e -> System.out.println("Mapped value: " + e))
         .collect(Collectors.toList());

Parameters:

action - a non-interfering action to perform on the elements as they are consumed from the stream

Returns:

the new stream

limit

Stream<T> limit(long maxSize)

API Note:

While limit() is generally a cheap operation on sequential stream pipelines, it can be quite expensive on ordered parallel pipelines, especially for large values of maxSize, since limit(n) is constrained to return not just any n elements, but the first n elements in the encounter order. Using an unordered stream source (such as generate(Supplier)) or removing the ordering constraint with BaseStream.unordered() may result in significant speedups of limit() in parallel pipelines, if the semantics of your situation permit. If consistency with encounter order is required, and you are experiencing poor performance or memory utilization with limit() in parallel pipelines, switching to sequential execution with BaseStream.sequential() may improve performance.

Parameters:

maxSize - the number of elements the stream should be limited to

Returns:

the new stream

Throws:

IllegalArgumentException - if maxSize is negative

skip

Stream<T> skip(long n)

API Note:

While skip() is generally a cheap operation on sequential stream pipelines, it can be quite expensive on ordered parallel pipelines, especially for large values of n, since skip(n) is constrained to skip not just any n elements, but the first n elements in the encounter order. Using an unordered stream source (such as generate(Supplier)) or removing the ordering constraint with BaseStream.unordered() may result in significant speedups of skip() in parallel pipelines, if the semantics of your situation permit. If consistency with encounter order is required, and you are experiencing poor performance or memory utilization with skip() in parallel pipelines, switching to sequential execution with BaseStream.sequential() may improve performance.

Parameters:

n - the number of leading elements to skip

Returns:

the new stream

Throws:

IllegalArgumentException - if n is negative

forEach

void forEach(Consumer<? super T> action)

Parameters:

action - a non-interfering action to perform on the elements

forEachOrdered

void forEachOrdered(Consumer<? super T> action)

Parameters:

action - a non-interfering action to perform on the elements

See Also:

forEach(Consumer)

toArray

Object[] toArray()

Returns:

an array containing the elements of this stream

toArray

<A> A[] toArray(IntFunction<A[]> generator)

API Note:

The generator function takes an integer, which is the size of the desired array, and produces an array of the desired size. This can be concisely expressed with an array constructor reference:

Person[] men = people.stream()
                          .filter(p -> p.getGender() == MALE)
                          .toArray(Person[]::new);

Type Parameters:

A - the element type of the resulting array

Parameters:

generator - a function which produces a new array of the desired type and the provided length

Returns:

an array containing the elements in this stream

Throws:

ArrayStoreException - if the runtime type of the array returned from the array generator is not a supertype of the runtime type of every element in this stream

reduce

T reduce(T identity,
         BinaryOperator<T> accumulator)

T result = identity;
     for (T element : this stream)
         result = accumulator.apply(result, element)
     return result;

API Note:

Sum, min, max, average, and string concatenation are all special cases of reduction. Summing a stream of numbers can be expressed as:

Integer sum = integers.reduce(0, (a, b) -> a+b);

or:

Integer sum = integers.reduce(0, Integer::sum);

While this may seem a more roundabout way to perform an aggregation compared to simply mutating a running total in a loop, reduction operations parallelize more gracefully, without needing additional synchronization and with greatly reduced risk of data races.

Parameters:

identity - the identity value for the accumulating function

accumulator - an associative, non-interfering, stateless function for combining two values

Returns:

the result of the reduction

reduce

Optional<T> reduce(BinaryOperator<T> accumulator)

boolean foundAny = false;
     T result = null;
     for (T element : this stream) {
         if (!foundAny) {
             foundAny = true;
             result = element;
         }
         else
             result = accumulator.apply(result, element);
     }
     return foundAny ? Optional.of(result) : Optional.empty();

Parameters:

accumulator - an associative, non-interfering, stateless function for combining two values

Returns:

an Optional describing the result of the reduction

Throws:

NullPointerException - if the result of the reduction is null

See Also:

reduce(Object, BinaryOperator), min(Comparator), max(Comparator)

reduce

<U> U reduce(U identity,
             BiFunction<U,? super T,U> accumulator,
             BinaryOperator<U> combiner)

U result = identity;
     for (T element : this stream)
         result = accumulator.apply(result, element)
     return result;

combiner.apply(u, accumulator.apply(identity, t)) == accumulator.apply(u, t)

API Note:

Many reductions using this form can be represented more simply by an explicit combination of map and reduce operations. The accumulator function acts as a fused mapper and accumulator, which can sometimes be more efficient than separate mapping and reduction, such as when knowing the previously reduced value allows you to avoid some computation.

Type Parameters:

U - The type of the result

Parameters:

identity - the identity value for the combiner function

accumulator - an associative, non-interfering, stateless function for incorporating an additional element into a result

combiner - an associative, non-interfering, stateless function for combining two values, which must be compatible with the accumulator function

Returns:

the result of the reduction

See Also:

reduce(BinaryOperator), reduce(Object, BinaryOperator)

collect

<R> R collect(Supplier<R> supplier,
              BiConsumer<R,? super T> accumulator,
              BiConsumer<R,R> combiner)

R result = supplier.get();
     for (T element : this stream)
         accumulator.accept(result, element);
     return result;

API Note:

There are many existing classes in the JDK whose signatures are well-suited for use with method references as arguments to collect(). For example, the following will accumulate strings into an ArrayList:

List<String> asList = stringStream.collect(ArrayList::new, ArrayList::add,
                                                ArrayList::addAll);

The following will take a stream of strings and concatenates them into a single string:

String concat = stringStream.collect(StringBuilder::new, StringBuilder::append,
                                          StringBuilder::append)
                                 .toString();

Type Parameters:

R - type of the result

Parameters:

supplier - a function that creates a new result container. For a parallel execution, this function may be called multiple times and must return a fresh value each time.

accumulator - an associative, non-interfering, stateless function for incorporating an additional element into a result

combiner - an associative, non-interfering, stateless function for combining two values, which must be compatible with the accumulator function

Returns:

the result of the reduction

collect

<R,A> R collect(Collector<? super T,A,R> collector)

API Note:

The following will accumulate strings into an ArrayList:

List<String> asList = stringStream.collect(Collectors.toList());

The following will classify Person objects by city:

Map<String, List<Person>> peopleByCity
         = personStream.collect(Collectors.groupingBy(Person::getCity));

The following will classify Person objects by state and city, cascading two Collectors together:

Map<String, Map<String, List<Person>>> peopleByStateAndCity
         = personStream.collect(Collectors.groupingBy(Person::getState,
                                                      Collectors.groupingBy(Person::getCity)));

Type Parameters:

R - the type of the result

A - the intermediate accumulation type of the Collector

Parameters:

collector - the Collector describing the reduction

Returns:

the result of the reduction

See Also:

collect(Supplier, BiConsumer, BiConsumer), Collectors

min

Optional<T> min(Comparator<? super T> comparator)

Parameters:

comparator - a non-interfering, stateless Comparator to compare elements of this stream

Returns:

an Optional describing the minimum element of this stream, or an empty Optional if the stream is empty

Throws:

NullPointerException - if the minimum element is null

max

Optional<T> max(Comparator<? super T> comparator)

Parameters:

comparator - a non-interfering, stateless Comparator to compare elements of this stream

Returns:

an Optional describing the maximum element of this stream, or an empty Optional if the stream is empty

Throws:

NullPointerException - if the maximum element is null

count

long count()

return mapToLong(e -> 1L).sum();

Returns:

the count of elements in this stream

anyMatch

boolean anyMatch(Predicate<? super T> predicate)

API Note:

This method evaluates the existential quantification of the predicate over the elements of the stream (for some x P(x)).

Parameters:

predicate - a non-interfering, stateless predicate to apply to elements of this stream

Returns:

true if any elements of the stream match the provided predicate, otherwise false

allMatch

boolean allMatch(Predicate<? super T> predicate)

API Note:

This method evaluates the universal quantification of the predicate over the elements of the stream (for all x P(x)). If the stream is empty, the quantification is said to be vacuously satisfied and is always true (regardless of P(x)).

Parameters:

predicate - a non-interfering, stateless predicate to apply to elements of this stream

Returns:

true if either all elements of the stream match the provided predicate or the stream is empty, otherwise false

noneMatch

boolean noneMatch(Predicate<? super T> predicate)

API Note:

This method evaluates the universal quantification of the negated predicate over the elements of the stream (for all x ~P(x)). If the stream is empty, the quantification is said to be vacuously satisfied and is always true, regardless of P(x).

Parameters:

predicate - a non-interfering, stateless predicate to apply to elements of this stream

Returns:

true if either no elements of the stream match the provided predicate or the stream is empty, otherwise false

findFirst

Optional<T> findFirst()

Returns:

an Optional describing the first element of this stream, or an empty Optional if the stream is empty

Throws:

NullPointerException - if the element selected is null

findAny

Optional<T> findAny()

Returns:

an Optional describing some element of this stream, or an empty Optional if the stream is empty

Throws:

NullPointerException - if the element selected is null

See Also:

findFirst()

builder

static <T> Stream.Builder<T> builder()

Returns a builder for a Stream.

Type Parameters:

T - type of elements

Returns:

a stream builder

empty

static <T> Stream<T> empty()

Returns an empty sequential Stream.

Type Parameters:

T - the type of stream elements

Returns:

an empty sequential stream

of

static <T> Stream<T> of(T t)

Returns a sequential Stream containing a single element.

Type Parameters:

T - the type of stream elements

Parameters:

t - the single element

Returns:

a singleton sequential stream

of

@SafeVarargs
static <T> Stream<T> of(T... values)

Returns a sequential ordered stream whose elements are the specified values.

Type Parameters:

T - the type of stream elements

Parameters:

values - the elements of the new stream

Returns:

the new stream

iterate

static <T> Stream<T> iterate(T seed,
                             UnaryOperator<T> f)

Type Parameters:

T - the type of stream elements

Parameters:

seed - the initial element

f - a function to be applied to the previous element to produce a new element

Returns:

a new sequential Stream

generate

static <T> Stream<T> generate(Supplier<T> s)

Returns an infinite sequential unordered stream where each element is generated by the provided Supplier. This is suitable for generating constant streams, streams of random elements, etc.

Type Parameters:

T - the type of stream elements

Parameters:

s - the Supplier of generated elements

Returns:

a new infinite sequential unordered Stream

concat

static <T> Stream<T> concat(Stream<? extends T> a,
                            Stream<? extends T> b)

Creates a lazily concatenated stream whose elements are all the elements of the first stream followed by all the elements of the second stream. The resulting stream is ordered if both of the input streams are ordered, and parallel if either of the input streams is parallel. When the resulting stream is closed, the close handlers for both input streams are invoked.

Implementation Note:

Use caution when constructing streams from repeated concatenation. Accessing an element of a deeply concatenated stream can result in deep call chains, or even StackOverflowException.

Type Parameters:

T - The type of stream elements

Parameters:

a - the first stream

b - the second stream

Returns:

the concatenation of the two input streams