* Consumer<java.lang.reflect.Method> consumer = m -> {
* try {
* m.invoke(o, args);
* } catch (Throwable t) {
* throw Functions.rethrow(t);
* }
* };
* stream.forEach(consumer);
*
*
* Using a {@link FailableStream}, this can be rewritten as follows:
*
* * Streams.failable(stream).forEach((m) -> m.invoke(o, args)); ** * Obviously, the second version is much more concise and the spirit of Lambda * expressions is met better than in the first version. * * @see Stream * @see Functions * @since 3.10 * @deprecated Use {@link org.apache.commons.lang3.stream.Streams}. */ @Deprecated public class Streams { /** * A reduced, and simplified version of a {@link Stream} with failable method * signatures. * * @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
*/
public FailableStream
* 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
*/
public void forEach(final FailableConsumer
* If the underlying stream is parallel, and the {@code Collector} is
* concurrent, and either the stream is unordered or the collector is 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.
*
* Note 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:
*
*
* Like {@link #reduce(Object, BinaryOperator)}, {@code collect} operations can
* be parallelized without requiring additional synchronization.
*
*
* This is a terminal operation.
*
* Note 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:
*
*
* 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.
*
* Note 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
*/
public O reduce(final O identity, final BinaryOperator
* This is an intermediate operation.
*
* @param
* This is a short-circuiting terminal operation.
*
* Note This method evaluates the universal quantification of the
* predicate over the elements of the stream (for all x P(x)). If the stream is
* empty, the quantification is said to be vacuously satisfied and is
* always {@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}.
*/
public boolean allMatch(final FailablePredicate
* This is a short-circuiting terminal operation.
*
* Note 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}
*/
public boolean anyMatch(final FailablePredicate
* {
* @code
* List
*
*
* {
* @code
* Map
*
*
* {
* @code
* Map
*
* @param
* {@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
* 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);
* }
*
*
*
* final List<O> list;
* final Method m;
* final Function<O, String> mapper = (o) -> {
* try {
* return (String) m.invoke(o);
* } catch (Throwable t) {
* throw Functions.rethrow(t);
* }
* };
* final List<String> strList = list.stream().map(mapper).collect(Collectors.toList());
*
*
* as follows:
*
*
* final List<O> list;
* final Method m;
* final List<String> strList = Functions.stream(list.stream()).map((o) -> (String) m.invoke(o))
* .collect(Collectors.toList());
*
*
* While the second version may not be quite as efficient (because it
* depends on the creation of additional, intermediate objects, of type
* FailableStream), it is much more concise, and readable, and meets the spirit
* of Lambdas better than the first version.
*
* @param
* final List<O> list;
* final Method m;
* final Function<O, String> mapper = (o) -> {
* try {
* return (String) m.invoke(o);
* } catch (Throwable t) {
* throw Functions.rethrow(t);
* }
* };
* final List<String> strList = list.stream().map(mapper).collect(Collectors.toList());
*
*
* as follows:
*
*
* final List<O> list;
* final Method m;
* final List<String> strList = Functions.stream(list.stream()).map((o) -> (String) m.invoke(o))
* .collect(Collectors.toList());
*
*
* While the second version may not be quite as efficient (because it
* depends on the creation of additional, intermediate objects, of type
* FailableStream), it is much more concise, and readable, and meets the spirit
* of Lambdas better than the first version.
*
* @param