The following example illustrates an aggregate operation using * {@link Stream} and {@link IntStream}, computing the sum of the weights of the * red widgets: * *
{@code
* int sum = widgets.stream()
* .filter(w -> w.getColor() == RED)
* .mapToInt(w -> w.getWeight())
* .sum();
* }
*
* See the class documentation for {@link Stream} and the package documentation
* for java.util.stream for additional
* specification of streams, stream operations, stream pipelines, and
* parallelism.
*
* @since 1.8
* @see Stream
* @see java.util.stream
*/
public interface IntStream extends BaseStreamThis 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 */ IntStream filter(IntPredicate 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 mapper a non-interfering, * stateless * function to apply to each element * @return the new stream */ IntStream map(IntUnaryOperator mapper); /** * Returns an object-valued {@code 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 mapToObj(IntFunction 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(IntToLongFunction 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(IntToDoubleFunction 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. * * @param mapper a non-interfering, * stateless * function to apply to each element which produces an * {@code IntStream} of new values * @return the new stream * @see Stream#flatMap(Function) */ IntStream flatMap(IntFunction mapper); /** * Returns a stream consisting of the distinct elements of this stream. * *
This is a stateful * intermediate operation. * * @return the new stream */ IntStream distinct(); /** * Returns a stream consisting of the elements of this stream in sorted * order. * *
This is a stateful * intermediate operation. * * @return the new stream */ IntStream sorted(); /** * 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
* IntStream.of(1, 2, 3, 4)
* .filter(e -> e > 2)
* .peek(e -> System.out.println("Filtered value: " + e))
* .map(e -> e * e)
* .peek(e -> System.out.println("Mapped value: " + e))
* .sum();
* }
*
* @param action a
* non-interfering action to perform on the elements as
* they are consumed from the stream
* @return the new stream
*/
IntStream peek(IntConsumer 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(IntSupplier)}) 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 */ IntStream 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(IntSupplier)}) 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 */ IntStream skip(long n); /** * Returns, if this stream is ordered, a stream consisting of the longest * prefix of elements taken from this stream that match the given predicate. * Otherwise returns, if this stream is unordered, a stream consisting of a * subset of elements taken from this stream that match the given predicate. * *
If this stream is ordered then the longest prefix is a contiguous * sequence of elements of this stream that match the given predicate. The * first element of the sequence is the first element of this stream, and * the element immediately following the last element of the sequence does * not match the given predicate. * *
If this stream is unordered, and some (but not all) elements of this * stream match the given predicate, then the behavior of this operation is * nondeterministic; it is free to take any subset of matching elements * (which includes the empty set). * *
Independent of whether this stream is ordered or unordered if all * elements of this stream match the given predicate then this operation * takes all elements (the result is the same as the input), or if no * elements of the stream match the given predicate then no elements are * taken (the result is an empty stream). * *
This is a short-circuiting * stateful intermediate operation. * * @implSpec * The default implementation obtains the {@link #spliterator() spliterator} * of this stream, wraps that spliterator so as to support the semantics * of this operation on traversal, and returns a new stream associated with * the wrapped spliterator. The returned stream preserves the execution * characteristics of this stream (namely parallel or sequential execution * as per {@link #isParallel()}) but the wrapped spliterator may choose to * not support splitting. When the returned stream is closed, the close * handlers for both the returned and this stream are invoked. * * @apiNote * While {@code takeWhile()} is generally a cheap operation on sequential * stream pipelines, it can be quite expensive on ordered parallel * pipelines, since the operation is constrained to return not just any * valid prefix, but the longest prefix of elements in the encounter order. * Using an unordered stream source (such as {@link #generate(IntSupplier)}) * or removing the ordering constraint with {@link #unordered()} may result * in significant speedups of {@code takeWhile()} 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 takeWhile()} in parallel pipelines, switching to * sequential execution with {@link #sequential()} may improve performance. * * @param predicate a non-interfering, * stateless * predicate to apply to elements to determine the longest * prefix of elements. * @return the new stream * @since 9 */ default IntStream takeWhile(IntPredicate predicate) { Objects.requireNonNull(predicate); // Reuses the unordered spliterator, which, when encounter is present, // is safe to use as long as it configured not to split return StreamSupport.intStream( new WhileOps.UnorderedWhileSpliterator.OfInt.Taking(spliterator(), true, predicate), isParallel()).onClose(this::close); } /** * Returns, if this stream is ordered, a stream consisting of the remaining * elements of this stream after dropping the longest prefix of elements * that match the given predicate. Otherwise returns, if this stream is * unordered, a stream consisting of the remaining elements of this stream * after dropping a subset of elements that match the given predicate. * *
If this stream is ordered then the longest prefix is a contiguous * sequence of elements of this stream that match the given predicate. The * first element of the sequence is the first element of this stream, and * the element immediately following the last element of the sequence does * not match the given predicate. * *
If this stream is unordered, and some (but not all) elements of this * stream match the given predicate, then the behavior of this operation is * nondeterministic; it is free to drop any subset of matching elements * (which includes the empty set). * *
Independent of whether this stream is ordered or unordered if all * elements of this stream match the given predicate then this operation * drops all elements (the result is an empty stream), or if no elements of * the stream match the given predicate then no elements are dropped (the * result is the same as the input). * *
This is a stateful * intermediate operation. * * @implSpec * The default implementation obtains the {@link #spliterator() spliterator} * of this stream, wraps that spliterator so as to support the semantics * of this operation on traversal, and returns a new stream associated with * the wrapped spliterator. The returned stream preserves the execution * characteristics of this stream (namely parallel or sequential execution * as per {@link #isParallel()}) but the wrapped spliterator may choose to * not support splitting. When the returned stream is closed, the close * handlers for both the returned and this stream are invoked. * * @apiNote * While {@code dropWhile()} is generally a cheap operation on sequential * stream pipelines, it can be quite expensive on ordered parallel * pipelines, since the operation is constrained to return not just any * valid prefix, but the longest prefix of elements in the encounter order. * Using an unordered stream source (such as {@link #generate(IntSupplier)}) * or removing the ordering constraint with {@link #unordered()} may result * in significant speedups of {@code dropWhile()} 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 dropWhile()} in parallel pipelines, switching to * sequential execution with {@link #sequential()} may improve performance. * * @param predicate a non-interfering, * stateless * predicate to apply to elements to determine the longest * prefix of elements. * @return the new stream * @since 9 */ default IntStream dropWhile(IntPredicate predicate) { Objects.requireNonNull(predicate); // Reuses the unordered spliterator, which, when encounter is present, // is safe to use as long as it configured not to split return StreamSupport.intStream( new WhileOps.UnorderedWhileSpliterator.OfInt.Dropping(spliterator(), true, predicate), isParallel()).onClose(this::close); } /** * Performs an action for each element of this stream. * *
This is a terminal * operation. * *
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(IntConsumer action); /** * Performs an action for each element of this stream, guaranteeing that * each element is processed in encounter order for streams that have a * defined encounter order. * *
This is a terminal * operation. * * @param action a * non-interfering action to perform on the elements * @see #forEach(IntConsumer) */ void forEachOrdered(IntConsumer action); /** * Returns an array containing the elements of this stream. * *
This is a terminal * operation. * * @return an array containing the elements of this stream */ int[] toArray(); /** * 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
* int result = identity;
* for (int element : this stream)
* result = accumulator.applyAsInt(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 x}, * {@code accumulator.apply(identity, x)} is equal to {@code x}. * The {@code accumulator} function must be an * associative function. * *
This is a terminal * operation. * * @apiNote Sum, min, max, and average are all special cases of reduction. * Summing a stream of numbers can be expressed as: * *
{@code
* int sum = integers.reduce(0, (a, b) -> a+b);
* }
*
* or more compactly:
*
* {@code
* int 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 op an associative, * non-interfering, * stateless * function for combining two values * @return the result of the reduction * @see #sum() * @see #min() * @see #max() * @see #average() */ int reduce(int identity, IntBinaryOperator op); /** * Performs a reduction on the * elements of this stream, using an * associative accumulation * function, and returns an {@code OptionalInt} describing the reduced value, * if any. This is equivalent to: *
{@code
* boolean foundAny = false;
* int result = null;
* for (int element : this stream) {
* if (!foundAny) {
* foundAny = true;
* result = element;
* }
* else
* result = accumulator.applyAsInt(result, element);
* }
* return foundAny ? OptionalInt.of(result) : OptionalInt.empty();
* }
*
* but is not constrained to execute sequentially.
*
* The {@code accumulator} function must be an * associative function. * *
This is a terminal * operation. * * @param op an associative, * non-interfering, * stateless * function for combining two values * @return the result of the reduction * @see #reduce(int, IntBinaryOperator) */ OptionalInt reduce(IntBinaryOperator op); /** * 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 (int element : this stream)
* accumulator.accept(result, element);
* return result;
* }
*
* Like {@link #reduce(int, IntBinaryOperator)}, {@code collect} operations * can be parallelized without requiring additional synchronization. * *
This is a terminal
* operation.
*
* @param This is a terminal
* operation.
*
* @return the sum of elements in this stream
*/
int sum();
/**
* Returns an {@code OptionalInt} describing the minimum element of this
* stream, or an empty optional if this stream is empty. This is a special
* case of a reduction
* and is equivalent to:
* This is a terminal operation.
*
* @return an {@code OptionalInt} containing the minimum element of this
* stream, or an empty {@code OptionalInt} if the stream is empty
*/
OptionalInt min();
/**
* Returns an {@code OptionalInt} describing the maximum element of this
* stream, or an empty optional if this stream is empty. This is a special
* case of a reduction
* and is equivalent to:
* This is a terminal
* operation.
*
* @return an {@code OptionalInt} containing the maximum element of this
* stream, or an empty {@code OptionalInt} if the stream is empty
*/
OptionalInt max();
/**
* Returns the count of elements in this stream. This is a special case of
* a reduction and is
* equivalent to:
* This is a terminal operation.
*
* @apiNote
* An implementation may choose to not execute the stream pipeline (either
* sequentially or in parallel) if it is capable of computing the count
* directly from the stream source. In such cases no source elements will
* be traversed and no intermediate operations will be evaluated.
* Behavioral parameters with side-effects, which are strongly discouraged
* except for harmless cases such as debugging, may be affected. For
* example, consider the following stream:
* This is a terminal
* operation.
*
* @return an {@code OptionalDouble} containing the average element of this
* stream, or an empty optional if the stream is empty
*/
OptionalDouble average();
/**
* Returns an {@code IntSummaryStatistics} describing various
* summary data about the elements of this stream. This is a special
* case of a reduction.
*
* This is a terminal
* operation.
*
* @return an {@code IntSummaryStatistics} describing various summary data
* about the elements of this stream
*/
IntSummaryStatistics summaryStatistics();
/**
* 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(IntPredicate 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(IntPredicate 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(IntPredicate predicate);
/**
* Returns an {@link OptionalInt} describing the first element of this
* stream, or an empty {@code OptionalInt} 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 OptionalInt} describing the first element of this stream,
* or an empty {@code OptionalInt} if the stream is empty
*/
OptionalInt findFirst();
/**
* Returns an {@link OptionalInt} describing some element of the stream, or
* an empty {@code OptionalInt} 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 OptionalInt} describing some element of this stream, or
* an empty {@code OptionalInt} if the stream is empty
* @see #findFirst()
*/
OptionalInt findAny();
/**
* Returns a {@code LongStream} consisting of the elements of this stream,
* converted to {@code long}.
*
* This is an intermediate
* operation.
*
* @return a {@code LongStream} consisting of the elements of this stream,
* converted to {@code long}
*/
LongStream asLongStream();
/**
* Returns a {@code DoubleStream} consisting of the elements of this stream,
* converted to {@code double}.
*
* This is an intermediate
* operation.
*
* @return a {@code DoubleStream} consisting of the elements of this stream,
* converted to {@code double}
*/
DoubleStream asDoubleStream();
/**
* Returns a {@code Stream} consisting of the elements of this stream,
* each boxed to an {@code Integer}.
*
* This is an intermediate
* operation.
*
* @return a {@code Stream} consistent of the elements of this stream,
* each boxed to an {@code Integer}
*/
Stream The first element (position {@code 0}) in the {@code IntStream} 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 seed the initial element
* @param f a function to be applied to the previous element to produce
* a new element
* @return A new sequential {@code IntStream}
*/
public static IntStream iterate(final int seed, final IntUnaryOperator f) {
Objects.requireNonNull(f);
final PrimitiveIterator.OfInt iterator = new PrimitiveIterator.OfInt() {
int t = seed;
@Override
public boolean hasNext() {
return true;
}
@Override
public int nextInt() {
int v = t;
t = f.applyAsInt(t);
return v;
}
};
return StreamSupport.intStream(Spliterators.spliteratorUnknownSize(
iterator,
Spliterator.ORDERED | Spliterator.IMMUTABLE | Spliterator.NONNULL), false);
}
/**
* Returns an infinite sequential unordered stream where each element is
* generated by the provided {@code IntSupplier}. This is suitable for
* generating constant streams, streams of random elements, etc.
*
* @param s the {@code IntSupplier} for generated elements
* @return a new infinite sequential unordered {@code IntStream}
*/
public static IntStream generate(IntSupplier s) {
Objects.requireNonNull(s);
return StreamSupport.intStream(
new StreamSpliterators.InfiniteSupplyingSpliterator.OfInt(Long.MAX_VALUE, s), false);
}
/**
* Returns a sequential ordered {@code IntStream} from {@code startInclusive}
* (inclusive) to {@code endExclusive} (exclusive) by an incremental step of
* {@code 1}.
*
* @apiNote
* An equivalent sequence of increasing values can be produced
* sequentially using a {@code for} loop as follows:
* An equivalent sequence of increasing values can be produced
* sequentially using a {@code for} loop as follows:
* 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 stream whose elements are the elements that were added to the
* stream builder, in the order they were added.
*
* @see IntStream#builder()
* @since 1.8
*/
public interface Builder extends IntConsumer {
/**
* Adds an element to the stream being built.
*
* @throws IllegalStateException if the builder has already transitioned
* to the built state
*/
@Override
void accept(int t);
/**
* Adds an element to the stream being built.
*
* @implSpec
* The default implementation behaves as if:
* {@code
* return reduce(0, Integer::sum);
* }
*
* {@code
* return reduce(Integer::min);
* }
*
* {@code
* return reduce(Integer::max);
* }
*
* {@code
* return mapToLong(e -> 1L).sum();
* }
*
* {@code
* IntStream s = IntStream.of(1, 2, 3, 4);
* long count = s.peek(System.out::println).count();
* }
* The number of elements covered by the stream source is known and the
* intermediate operation, {@code peek}, does not inject into or remove
* elements from the stream (as may be the case for {@code flatMap} or
* {@code filter} operations). Thus the count is 4 and there is no need to
* execute the pipeline and, as a side-effect, print out the elements.
*
* @return the count of elements in this stream
*/
long count();
/**
* Returns an {@code OptionalDouble} describing the arithmetic mean of elements of
* this stream, or an empty optional if this stream is empty. This is a
* special case of a
* reduction.
*
* {@code
* for (int i = startInclusive; i < endExclusive ; i++) { ... }
* }
*
* @param startInclusive the (inclusive) initial value
* @param endExclusive the exclusive upper bound
* @return a sequential {@code IntStream} for the range of {@code int}
* elements
*/
public static IntStream range(int startInclusive, int endExclusive) {
if (startInclusive >= endExclusive) {
return empty();
} else {
return StreamSupport.intStream(
new Streams.RangeIntSpliterator(startInclusive, endExclusive, false), false);
}
}
/**
* Returns a sequential ordered {@code IntStream} from {@code startInclusive}
* (inclusive) to {@code endInclusive} (inclusive) by an incremental step of
* {@code 1}.
*
* @apiNote
* {@code
* for (int i = startInclusive; i <= endInclusive ; i++) { ... }
* }
*
* @param startInclusive the (inclusive) initial value
* @param endInclusive the inclusive upper bound
* @return a sequential {@code IntStream} for the range of {@code int}
* elements
*/
public static IntStream rangeClosed(int startInclusive, int endInclusive) {
if (startInclusive > endInclusive) {
return empty();
} else {
return StreamSupport.intStream(
new Streams.RangeIntSpliterator(startInclusive, endInclusive, true), 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 StackOverflowError}.
*
* @param a the first stream
* @param b the second stream
* @return the concatenation of the two input streams
*/
public static IntStream concat(IntStream a, IntStream b) {
Objects.requireNonNull(a);
Objects.requireNonNull(b);
Spliterator.OfInt split = new Streams.ConcatSpliterator.OfInt(
a.spliterator(), b.spliterator());
IntStream stream = StreamSupport.intStream(split, a.isParallel() || b.isParallel());
return stream.onClose(Streams.composedClose(a, b));
}
/**
* A mutable builder for an {@code IntStream}.
*
* {@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(int 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
*/
IntStream build();
}
}