* 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 an intermediate 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 {@link 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 an intermediate 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 {@link ArrayList}), no * additional synchronization is needed for a parallel reduction. *
** Note * 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 {@link Collector}s together: *
*{@code
* Map>> peopleByStateAndCity
* = personStream.collect(Collectors.groupingBy(Person::getState,
* Collectors.groupingBy(Person::getCity)));
* }
*
* @param {@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 an intermediate 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 {@link 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 {@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 an intermediate operation. *
* * Note 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 */ 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
* 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