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* Copyright (c) 2012, 2013, Oracle and/or its affiliates. All rights reserved.
* ORACLE PROPRIETARY/CONFIDENTIAL. Use is subject to license terms.
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package java.util.stream;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Comparator;
import java.util.Iterator;
import java.util.Objects;
import java.util.Optional;
import java.util.Spliterator;
import java.util.Spliterators;
import java.util.function.BiConsumer;
import java.util.function.BiFunction;
import java.util.function.BinaryOperator;
import java.util.function.Consumer;
import java.util.function.Function;
import java.util.function.IntFunction;
import java.util.function.Predicate;
import java.util.function.Supplier;
import java.util.function.ToDoubleFunction;
import java.util.function.ToIntFunction;
import java.util.function.ToLongFunction;
import java.util.function.UnaryOperator;
/**
* A sequence of elements supporting sequential and parallel aggregate
* operations. The following example illustrates an aggregate operation using
* {@link Stream} and {@link IntStream}:
*
* {@code
* int sum = widgets.stream()
* .filter(w -> w.getColor() == RED)
* .mapToInt(w -> w.getWeight())
* .sum();
* }
*
* In this example, {@code widgets} is a {@code Collection}. We create
* a stream of {@code Widget} objects via {@link Collection#stream Collection.stream()},
* filter it to produce a stream containing only the red widgets, and then
* transform it into a stream of {@code int} values representing the weight of
* each red widget. Then this stream is summed to produce a total weight.
*
* In addition to {@code Stream}, which is a stream of object references,
* there are primitive specializations for {@link IntStream}, {@link LongStream},
* and {@link 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 {@link Stream#filter(Predicate)}), and a
* terminal operation (which produces a result or side-effect, such
* as {@link Stream#count()} or {@link Stream#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 {@link #iterator()} and {@link #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 {@link 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 {@code w -> w.getWeight()} passed to
* {@code 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 {@link java.util.function.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 {@link 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 {@link #close()} method and implement {@link 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 {@link 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 {@code 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,
* {@link Collection#stream() Collection.stream()} creates a sequential stream,
* and {@link Collection#parallelStream() Collection.parallelStream()} creates
* a parallel one.) This choice of execution mode may be modified by the
* {@link #sequential()} or {@link #parallel()} methods, and may be queried with
* the {@link #isParallel()} method.
*
* @param the type of the stream elements
* @since 1.8
* @see IntStream
* @see LongStream
* @see DoubleStream
* @see java.util.stream
*/
public interface Stream extends BaseStream> {
/**
* Returns a stream consisting of the elements of this stream that match
* the given predicate.
*
* This is an intermediate
* operation.
*
* @param predicate a non-interfering,
* stateless
* predicate to apply to each element to determine if it
* should be included
* @return the new stream
*/
Stream filter(Predicate predicate);
/**
* Returns a stream consisting of the results of applying the given
* function to the elements of this stream.
*
* This is an intermediate
* operation.
*
* @param The element type of the new stream
* @param mapper a non-interfering,
* stateless
* function to apply to each element
* @return the new stream
*/
Stream map(Function mapper);
/**
* Returns an {@code IntStream} consisting of the results of applying the
* given function to the elements of this stream.
*
* This is an
* intermediate operation.
*
* @param mapper a non-interfering,
* stateless
* function to apply to each element
* @return the new stream
*/
IntStream mapToInt(ToIntFunction mapper);
/**
* Returns a {@code LongStream} consisting of the results of applying the
* given function to the elements of this stream.
*
*
This is an intermediate
* operation.
*
* @param mapper a non-interfering,
* stateless
* function to apply to each element
* @return the new stream
*/
LongStream mapToLong(ToLongFunction mapper);
/**
* Returns a {@code DoubleStream} consisting of the results of applying the
* given function to the elements of this stream.
*
*
This is an intermediate
* operation.
*
* @param mapper a non-interfering,
* stateless
* function to apply to each element
* @return the new stream
*/
DoubleStream mapToDouble(ToDoubleFunction mapper);
/**
* Returns a stream consisting of the results of replacing each element of
* this stream with the contents of a mapped stream produced by applying
* the provided mapping function to each element. Each mapped stream is
* {@link java.util.stream.BaseStream#close() closed} after its contents
* have been placed into this stream. (If a mapped stream is {@code null}
* an empty stream is used, instead.)
*
*
This is an intermediate
* operation.
*
* @apiNote
* The {@code 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 {@code 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:
*
{@code
* orders.flatMap(order -> order.getLineItems().stream())...
* }
*
* If {@code path} is the path to a file, then the following produces a
* stream of the {@code words} contained in that file:
*
{@code
* Stream lines = Files.lines(path, StandardCharsets.UTF_8);
* Stream words = lines.flatMap(line -> Stream.of(line.split(" +")));
* }
* The {@code mapper} function passed to {@code flatMap} splits a line,
* using a simple regular expression, into an array of words, and then
* creates a stream of words from that array.
*
* @param The element type of the new stream
* @param mapper a non-interfering,
* stateless
* function to apply to each element which produces a stream
* of new values
* @return the new stream
*/
Stream flatMap(Function> mapper);
/**
* Returns an {@code IntStream} consisting of the results of replacing each
* element of this stream with the contents of a mapped stream produced by
* applying the provided mapping function to each element. Each mapped
* stream is {@link java.util.stream.BaseStream#close() closed} after its
* contents have been placed into this stream. (If a mapped stream is
* {@code null} an empty stream is used, instead.)
*
* This is an intermediate
* operation.
*
* @param mapper a non-interfering,
* stateless
* function to apply to each element which produces a stream
* of new values
* @return the new stream
* @see #flatMap(Function)
*/
IntStream flatMapToInt(Function mapper);
/**
* Returns an {@code LongStream} consisting of the results of replacing each
* element of this stream with the contents of a mapped stream produced by
* applying the provided mapping function to each element. Each mapped
* stream is {@link java.util.stream.BaseStream#close() closed} after its
* contents have been placed into this stream. (If a mapped stream is
* {@code null} an empty stream is used, instead.)
*
*
This is an intermediate
* operation.
*
* @param mapper a non-interfering,
* stateless
* function to apply to each element which produces a stream
* of new values
* @return the new stream
* @see #flatMap(Function)
*/
LongStream flatMapToLong(Function mapper);
/**
* Returns an {@code DoubleStream} consisting of the results of replacing
* each element of this stream with the contents of a mapped stream produced
* by applying the provided mapping function to each element. Each mapped
* stream is {@link java.util.stream.BaseStream#close() closed} after its
* contents have placed been into this stream. (If a mapped stream is
* {@code null} an empty stream is used, instead.)
*
*
This is an intermediate
* operation.
*
* @param mapper a non-interfering,
* stateless
* function to apply to each element which produces a stream
* of new values
* @return the new stream
* @see #flatMap(Function)
*/
DoubleStream flatMapToDouble(Function mapper);
/**
* Returns a stream consisting of the distinct elements (according to
* {@link Object#equals(Object)}) of this stream.
*
*
For ordered streams, the selection of distinct elements is stable
* (for duplicated elements, the element appearing first in the encounter
* order is preserved.) For unordered streams, no stability guarantees
* are made.
*
*
This is a stateful
* intermediate operation.
*
* @apiNote
* Preserving stability for {@code 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 {@link #generate(Supplier)})
* or removing the ordering constraint with {@link #unordered()} may result
* in significantly more efficient execution for {@code 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 {@code distinct()} in parallel pipelines,
* switching to sequential execution with {@link #sequential()} may improve
* performance.
*
* @return the new stream
*/
Stream distinct();
/**
* Returns a stream consisting of the elements of this stream, sorted
* according to natural order. If the elements of this stream are not
* {@code Comparable}, a {@code java.lang.ClassCastException} may be thrown
* when the terminal operation is executed.
*
* For ordered streams, the sort is stable. For unordered streams, no
* stability guarantees are made.
*
*
This is a stateful
* intermediate operation.
*
* @return the new stream
*/
Stream sorted();
/**
* Returns a stream consisting of the elements of this stream, sorted
* according to the provided {@code Comparator}.
*
* For ordered streams, the sort is stable. For unordered streams, no
* stability guarantees are made.
*
*
This is a stateful
* intermediate operation.
*
* @param comparator a non-interfering,
* stateless
* {@code Comparator} to be used to compare stream elements
* @return the new stream
*/
Stream sorted(Comparator comparator);
/**
* Returns a stream consisting of the elements of this stream, additionally
* performing the provided action on each element as elements are consumed
* from the resulting stream.
*
* This is an intermediate
* operation.
*
*
For parallel stream pipelines, the action may be called at
* whatever time and in whatever thread the element is made available by the
* upstream operation. If the action modifies shared state,
* it is responsible for providing the required synchronization.
*
* @apiNote This method exists mainly to support debugging, where you want
* to see the elements as they flow past a certain point in a pipeline:
*
{@code
* 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());
* }
*
* @param action a
* non-interfering action to perform on the elements as
* they are consumed from the stream
* @return the new stream
*/
Stream peek(Consumer action);
/**
* Returns a stream consisting of the elements of this stream, truncated
* to be no longer than {@code maxSize} in length.
*
* This is a short-circuiting
* stateful intermediate operation.
*
* @apiNote
* While {@code limit()} is generally a cheap operation on sequential
* stream pipelines, it can be quite expensive on ordered parallel pipelines,
* especially for large values of {@code maxSize}, since {@code 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 {@link #generate(Supplier)}) or removing the
* ordering constraint with {@link #unordered()} may result in significant
* speedups of {@code 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
* {@code limit()} in parallel pipelines, switching to sequential execution
* with {@link #sequential()} may improve performance.
*
* @param maxSize the number of elements the stream should be limited to
* @return the new stream
* @throws IllegalArgumentException if {@code maxSize} is negative
*/
Stream limit(long maxSize);
/**
* Returns a stream consisting of the remaining elements of this stream
* after discarding the first {@code n} elements of the stream.
* If this stream contains fewer than {@code n} elements then an
* empty stream will be returned.
*
* This is a stateful
* intermediate operation.
*
* @apiNote
* While {@code skip()} is generally a cheap operation on sequential
* stream pipelines, it can be quite expensive on ordered parallel pipelines,
* especially for large values of {@code n}, since {@code 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 {@link #generate(Supplier)}) or removing the
* ordering constraint with {@link #unordered()} may result in significant
* speedups of {@code 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
* {@code skip()} in parallel pipelines, switching to sequential execution
* with {@link #sequential()} may improve performance.
*
* @param n the number of leading elements to skip
* @return the new stream
* @throws IllegalArgumentException if {@code n} is negative
*/
Stream skip(long n);
/**
* Performs an action for each element of this stream.
*
* This is a terminal
* operation.
*
*
The behavior of this operation is explicitly nondeterministic.
* For parallel stream pipelines, this operation does not
* guarantee to respect the encounter order of the stream, as doing so
* would sacrifice the benefit of parallelism. For any given element, the
* action may be performed at whatever time and in whatever thread the
* library chooses. If the action accesses shared state, it is
* responsible for providing the required synchronization.
*
* @param action a
* non-interfering action to perform on the elements
*/
void forEach(Consumer action);
/**
* Performs an action for each element of this stream, in the encounter
* order of the stream if the stream has a defined encounter order.
*
*
This is a terminal
* operation.
*
*
This operation processes the elements one at a time, in encounter
* order if one exists. Performing the action for one element
* happens-before
* performing the action for subsequent elements, but for any given element,
* the action may be performed in whatever thread the library chooses.
*
* @param action a
* non-interfering action to perform on the elements
* @see #forEach(Consumer)
*/
void forEachOrdered(Consumer action);
/**
* Returns an array containing the elements of this stream.
*
*
This is a terminal
* operation.
*
* @return an array containing the elements of this stream
*/
Object[] toArray();
/**
* Returns an array containing the elements of this stream, using the
* provided {@code generator} function to allocate the returned array, as
* well as any additional arrays that might be required for a partitioned
* execution or for resizing.
*
*
This is a terminal
* operation.
*
* @apiNote
* 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:
*
{@code
* Person[] men = people.stream()
* .filter(p -> p.getGender() == MALE)
* .toArray(Person[]::new);
* }
*
* @param the element type of the resulting array
* @param generator a function which produces a new array of the desired
* type and the provided length
* @return 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
*/
A[] toArray(IntFunction generator);
/**
* Performs a reduction on the
* elements of this stream, using the provided identity value and an
* associative
* accumulation function, and returns the reduced value. This is equivalent
* to:
* {@code
* T result = identity;
* for (T element : this stream)
* result = accumulator.apply(result, element)
* return result;
* }
*
* but is not constrained to execute sequentially.
*
* The {@code identity} value must be an identity for the accumulator
* function. This means that for all {@code t},
* {@code accumulator.apply(identity, t)} is equal to {@code t}.
* The {@code accumulator} function must be an
* associative function.
*
*
This is a terminal
* operation.
*
* @apiNote Sum, min, max, average, and string concatenation are all special
* cases of reduction. Summing a stream of numbers can be expressed as:
*
*
{@code
* Integer sum = integers.reduce(0, (a, b) -> a+b);
* }
*
* or:
*
* {@code
* 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.
*
* @param identity the identity value for the accumulating function
* @param accumulator an associative,
* non-interfering,
* stateless
* function for combining two values
* @return the result of the reduction
*/
T reduce(T identity, BinaryOperator accumulator);
/**
* Performs a reduction on the
* elements of this stream, using an
* associative accumulation
* function, and returns an {@code Optional} describing the reduced value,
* if any. This is equivalent to:
* {@code
* 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();
* }
*
* but is not constrained to execute sequentially.
*
* The {@code accumulator} function must be an
* associative function.
*
*
This is a terminal
* operation.
*
* @param accumulator an associative,
* non-interfering,
* stateless
* function for combining two values
* @return an {@link Optional} describing the result of the reduction
* @throws NullPointerException if the result of the reduction is null
* @see #reduce(Object, BinaryOperator)
* @see #min(Comparator)
* @see #max(Comparator)
*/
Optional reduce(BinaryOperator accumulator);
/**
* Performs a reduction on the
* elements of this stream, using the provided identity, accumulation and
* combining functions. This is equivalent to:
* {@code
* U result = identity;
* for (T element : this stream)
* result = accumulator.apply(result, element)
* return result;
* }
*
* but is not constrained to execute sequentially.
*
* The {@code identity} value must be an identity for the combiner
* function. This means that for all {@code u}, {@code combiner(identity, u)}
* is equal to {@code u}. Additionally, the {@code combiner} function
* must be compatible with the {@code accumulator} function; for all
* {@code u} and {@code t}, the following must hold:
*
{@code
* combiner.apply(u, accumulator.apply(identity, t)) == accumulator.apply(u, t)
* }
*
* This is a terminal
* operation.
*
* @apiNote Many reductions using this form can be represented more simply
* by an explicit combination of {@code map} and {@code reduce} operations.
* The {@code 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.
*
* @param The type of the result
* @param identity the identity value for the combiner function
* @param accumulator an associative,
* non-interfering,
* stateless
* function for incorporating an additional element into a result
* @param combiner an associative,
* non-interfering,
* stateless
* function for combining two values, which must be
* compatible with the accumulator function
* @return the result of the reduction
* @see #reduce(BinaryOperator)
* @see #reduce(Object, BinaryOperator)
*/
U reduce(U identity,
BiFunction accumulator,
BinaryOperator combiner);
/**
* Performs a mutable
* reduction operation on the elements of this stream. A mutable
* reduction is one in which the reduced value is a mutable result container,
* such as an {@code ArrayList}, and elements are incorporated by updating
* the state of the result rather than by replacing the result. This
* produces a result equivalent to:
* {@code
* R result = supplier.get();
* for (T element : this stream)
* accumulator.accept(result, element);
* return result;
* }
*
* Like {@link #reduce(Object, BinaryOperator)}, {@code collect} operations
* can be parallelized without requiring additional synchronization.
*
*
This is a terminal
* operation.
*
* @apiNote There are many existing classes in the JDK whose signatures are
* well-suited for use with method references as arguments to {@code collect()}.
* For example, the following will accumulate strings into an {@code ArrayList}:
*
{@code
* List asList = stringStream.collect(ArrayList::new, ArrayList::add,
* ArrayList::addAll);
* }
*
* The following will take a stream of strings and concatenates them into a
* single string:
*
{@code
* String concat = stringStream.collect(StringBuilder::new, StringBuilder::append,
* StringBuilder::append)
* .toString();
* }
*
* @param type of the result
* @param 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.
* @param accumulator an associative,
* non-interfering,
* stateless
* function for incorporating an additional element into a result
* @param combiner an associative,
* non-interfering,
* stateless
* function for combining two values, which must be
* compatible with the accumulator function
* @return the result of the reduction
*/
R collect(Supplier supplier,
BiConsumer accumulator,
BiConsumer combiner);
/**
* Performs a mutable
* reduction operation on the elements of this stream using a
* {@code Collector}. A {@code Collector}
* encapsulates the functions used as arguments to
* {@link #collect(Supplier, BiConsumer, BiConsumer)}, allowing for reuse of
* collection strategies and composition of collect operations such as
* multiple-level grouping or partitioning.
*
* If the stream is parallel, and the {@code Collector}
* is {@link Collector.Characteristics#CONCURRENT concurrent}, and
* either the stream is unordered or the collector is
* {@link Collector.Characteristics#UNORDERED unordered},
* then a concurrent reduction will be performed (see {@link Collector} for
* details on concurrent reduction.)
*
*
This is a terminal
* operation.
*
*
When executed in parallel, multiple intermediate results may be
* instantiated, populated, and merged so as to maintain isolation of
* mutable data structures. Therefore, even when executed in parallel
* with non-thread-safe data structures (such as {@code ArrayList}), no
* additional synchronization is needed for a parallel reduction.
*
* @apiNote
* The following will accumulate strings into an ArrayList:
*
{@code
* List asList = stringStream.collect(Collectors.toList());
* }
*
* The following will classify {@code Person} objects by city:
*
{@code
* Map> peopleByCity
* = personStream.collect(Collectors.groupingBy(Person::getCity));
* }
*
* The following will classify {@code Person} objects by state and city,
* cascading two {@code Collector}s together:
*
{@code
* Map>> peopleByStateAndCity
* = personStream.collect(Collectors.groupingBy(Person::getState,
* Collectors.groupingBy(Person::getCity)));
* }
*
* @param the type of the result
* @param the intermediate accumulation type of the {@code Collector}
* @param collector the {@code Collector} describing the reduction
* @return the result of the reduction
* @see #collect(Supplier, BiConsumer, BiConsumer)
* @see Collectors
*/
R collect(Collector collector);
/**
* Returns the minimum element of this stream according to the provided
* {@code Comparator}. This is a special case of a
* reduction.
*
* This is a terminal operation.
*
* @param comparator a non-interfering,
* stateless
* {@code Comparator} to compare elements of this stream
* @return an {@code Optional} describing the minimum element of this stream,
* or an empty {@code Optional} if the stream is empty
* @throws NullPointerException if the minimum element is null
*/
Optional min(Comparator comparator);
/**
* Returns the maximum element of this stream according to the provided
* {@code Comparator}. This is a special case of a
* reduction.
*
* This is a terminal
* operation.
*
* @param comparator a non-interfering,
* stateless
* {@code Comparator} to compare elements of this stream
* @return an {@code Optional} describing the maximum element of this stream,
* or an empty {@code Optional} if the stream is empty
* @throws NullPointerException if the maximum element is null
*/
Optional max(Comparator comparator);
/**
* Returns the count of elements in this stream. This is a special case of
* a reduction and is
* equivalent to:
* {@code
* return mapToLong(e -> 1L).sum();
* }
*
* This is a terminal operation.
*
* @return the count of elements in this stream
*/
long count();
/**
* Returns whether any elements of this stream match the provided
* predicate. May not evaluate the predicate on all elements if not
* necessary for determining the result. If the stream is empty then
* {@code false} is returned and the predicate is not evaluated.
*
*
This is a short-circuiting
* terminal operation.
*
* @apiNote
* This method evaluates the existential quantification of the
* predicate over the elements of the stream (for some x P(x)).
*
* @param predicate a non-interfering,
* stateless
* predicate to apply to elements of this stream
* @return {@code true} if any elements of the stream match the provided
* predicate, otherwise {@code false}
*/
boolean anyMatch(Predicate predicate);
/**
* Returns whether all elements of this stream match the provided predicate.
* May not evaluate the predicate on all elements if not necessary for
* determining the result. If the stream is empty then {@code true} is
* returned and the predicate is not evaluated.
*
*
This is a short-circuiting
* terminal operation.
*
* @apiNote
* 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 {@code true} (regardless of P(x)).
*
* @param predicate a non-interfering,
* stateless
* predicate to apply to elements of this stream
* @return {@code true} if either all elements of the stream match the
* provided predicate or the stream is empty, otherwise {@code false}
*/
boolean allMatch(Predicate predicate);
/**
* Returns whether no elements of this stream match the provided predicate.
* May not evaluate the predicate on all elements if not necessary for
* determining the result. If the stream is empty then {@code true} is
* returned and the predicate is not evaluated.
*
*
This is a short-circuiting
* terminal operation.
*
* @apiNote
* 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 {@code true}, regardless of P(x).
*
* @param predicate a non-interfering,
* stateless
* predicate to apply to elements of this stream
* @return {@code true} if either no elements of the stream match the
* provided predicate or the stream is empty, otherwise {@code false}
*/
boolean noneMatch(Predicate predicate);
/**
* Returns an {@link Optional} describing the first element of this stream,
* or an empty {@code Optional} if the stream is empty. If the stream has
* no encounter order, then any element may be returned.
*
*
This is a short-circuiting
* terminal operation.
*
* @return an {@code Optional} describing the first element of this stream,
* or an empty {@code Optional} if the stream is empty
* @throws NullPointerException if the element selected is null
*/
Optional findFirst();
/**
* Returns an {@link Optional} describing some element of the stream, or an
* empty {@code Optional} if the stream is empty.
*
* This is a short-circuiting
* terminal operation.
*
*
The behavior of this operation is explicitly nondeterministic; it is
* free to select any element in the stream. This is to allow for maximal
* performance in parallel operations; the cost is that multiple invocations
* on the same source may not return the same result. (If a stable result
* is desired, use {@link #findFirst()} instead.)
*
* @return an {@code Optional} describing some element of this stream, or an
* empty {@code Optional} if the stream is empty
* @throws NullPointerException if the element selected is null
* @see #findFirst()
*/
Optional findAny();
// Static factories
/**
* Returns a builder for a {@code Stream}.
*
* @param type of elements
* @return a stream builder
*/
// public static Builder builder() {
// return new Streams.StreamBuilderImpl<>();
// }
/**
* Returns an empty sequential {@code Stream}.
*
* @param the type of stream elements
* @return an empty sequential stream
*/
// public static Stream empty() {
// return StreamSupport.stream(Spliterators.emptySpliterator(), false);
// }
/**
* Returns a sequential {@code Stream} containing a single element.
*
* @param t the single element
* @param the type of stream elements
* @return a singleton sequential stream
*/
// public static Stream of(T t) {
// return StreamSupport.stream(new Streams.StreamBuilderImpl<>(t), false);
// }
/**
* Returns a sequential ordered stream whose elements are the specified values.
*
* @param the type of stream elements
* @param values the elements of the new stream
* @return the new stream
*/
// @SafeVarargs
// @SuppressWarnings("varargs") // Creating a stream from an array is safe
// public static Stream of(T... values) {
// return Arrays.stream(values);
// }
/**
* Returns an infinite sequential ordered {@code Stream} produced by iterative
* application of a function {@code f} to an initial element {@code seed},
* producing a {@code Stream} consisting of {@code seed}, {@code f(seed)},
* {@code f(f(seed))}, etc.
*
* The first element (position {@code 0}) in the {@code Stream} will be
* the provided {@code seed}. For {@code n > 0}, the element at position
* {@code n}, will be the result of applying the function {@code f} to the
* element at position {@code n - 1}.
*
* @param the type of stream elements
* @param seed the initial element
* @param f a function to be applied to to the previous element to produce
* a new element
* @return a new sequential {@code Stream}
*/
// public static Stream iterate(final T seed, final UnaryOperator f) {
// Objects.requireNonNull(f);
// final Iterator iterator = new Iterator() {
// @SuppressWarnings("unchecked")
// T t = (T) Streams.NONE;
//
// @Override
// public boolean hasNext() {
// return true;
// }
//
// @Override
// public T next() {
// return t = (t == Streams.NONE) ? seed : f.apply(t);
// }
// };
// return StreamSupport.stream(Spliterators.spliteratorUnknownSize(
// iterator,
// Spliterator.ORDERED | Spliterator.IMMUTABLE), false);
// }
/**
* Returns an infinite sequential unordered stream where each element is
* generated by the provided {@code Supplier}. This is suitable for
* generating constant streams, streams of random elements, etc.
*
* @param the type of stream elements
* @param s the {@code Supplier} of generated elements
* @return a new infinite sequential unordered {@code Stream}
*/
// public static Stream generate(Supplier s) {
// Objects.requireNonNull(s);
// return StreamSupport.stream(
// new StreamSpliterators.InfiniteSupplyingSpliterator.OfRef<>(Long.MAX_VALUE, s), false);
// }
/**
* 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.
*
* @implNote
* Use caution when constructing streams from repeated concatenation.
* Accessing an element of a deeply concatenated stream can result in deep
* call chains, or even {@code StackOverflowException}.
*
* @param The type of stream elements
* @param a the first stream
* @param b the second stream
* @return the concatenation of the two input streams
*/
// public static Stream concat(Stream a, Stream b) {
// Objects.requireNonNull(a);
// Objects.requireNonNull(b);
//
// @SuppressWarnings("unchecked")
// Spliterator split = new Streams.ConcatSpliterator.OfRef<>(
// (Spliterator) a.spliterator(), (Spliterator) b.spliterator());
// Stream stream = StreamSupport.stream(split, a.isParallel() || b.isParallel());
// return stream.onClose(Streams.composedClose(a, b));
// }
/**
* A mutable builder for a {@code Stream}. This allows the creation of a
* {@code Stream} by generating elements individually and adding them to the
* {@code Builder} (without the copying overhead that comes from using
* an {@code ArrayList} as a temporary buffer.)
*
* A stream builder has a lifecycle, which starts in a building
* phase, during which elements can be added, and then transitions to a built
* phase, after which elements may not be added. The built phase begins
* when the {@link #build()} method is called, which creates an ordered
* {@code Stream} whose elements are the elements that were added to the stream
* builder, in the order they were added.
*
* @param the type of stream elements
* @see Stream#builder()
* @since 1.8
*/
public interface Builder extends Consumer {
/**
* Adds an element to the stream being built.
*
* @throws IllegalStateException if the builder has already transitioned to
* the built state
*/
@Override
void accept(T t);
/**
* Adds an element to the stream being built.
*
* @implSpec
* The default implementation behaves as if:
* {@code
* accept(t)
* return this;
* }
*
* @param t the element to add
* @return {@code this} builder
* @throws IllegalStateException if the builder has already transitioned to
* the built state
*/
default Builder add(T t) {
accept(t);
return this;
}
/**
* Builds the stream, transitioning this builder to the built state.
* An {@code IllegalStateException} is thrown if there are further attempts
* to operate on the builder after it has entered the built state.
*
* @return the built stream
* @throws IllegalStateException if the builder has already transitioned to
* the built state
*/
Stream build();
}
public static Stream of(T... values) {
return Arrays.asList(values).stream();
}
}