{@code
* int sum = widgets.stream()
* .filter(w -> w.getColor() == RED)
* .mapToInt(w -> w.getWeight())
* .sum();
* }
*
* In this example, {@code widgets} is a {@code CollectionIn 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: *
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 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 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 This is an intermediate
* operation.
*
* @param 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:
* If {@code path} is the path to a file, then the following produces a
* stream of the {@code words} contained in that file:
* 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 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 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 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:
* 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 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 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:
* 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:
*
* 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 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 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:
* 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 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}:
* The following will take a stream of strings and concatenates them into a
* single string:
* 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:
* The following will classify {@code Person} objects by city:
* The following will classify {@code Person} objects by state and city,
* cascading two {@code Collector}s together:
* 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 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 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 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 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 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 {@code
* orders.flatMap(order -> order.getLineItems().stream())...
* }
*
* {@code
* Stream
* 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 {@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{@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{@code
* T result = identity;
* for (T element : this stream)
* result = accumulator.apply(result, element)
* return result;
* }
*
* but is not constrained to execute sequentially.
*
* {@code
* Integer sum = integers.reduce(0, (a, b) -> a+b);
* }
*
* or:
*
* {@code
* Integer sum = integers.reduce(0, Integer::sum);
* }
*
* {@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.
*
* {@code
* U result = identity;
* for (T element : this stream)
* result = accumulator.apply(result, element)
* return result;
* }
*
* but is not constrained to execute sequentially.
*
* {@code
* combiner.apply(u, accumulator.apply(identity, t)) == accumulator.apply(u, t)
* }
*
* {@code
* R result = supplier.get();
* for (T element : this stream)
* accumulator.accept(result, element);
* return result;
* }
*
* {@code
* List
*
* {@code
* String concat = stringStream.collect(StringBuilder::new, StringBuilder::append,
* StringBuilder::append)
* .toString();
* }
*
* @param {@code
* List
*
* {@code
* Map
*
* {@code
* Map
*
* @param {@code
* return mapToLong(e -> 1L).sum();
* }
*
* {@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