不幸的是，Stream API 创建您自己的短路操作的能力有限。不太干净的解决方案是扔一个RuntimeException并抓住它。这是实现IntStream，但它也可以推广到其他流类型：
public static int reduceWithCancelEx(IntStream stream, int identity,
IntBinaryOperator combiner, IntPredicate cancelCondition) {
class CancelException extends RuntimeException {
private final int val;
CancelException(int val) {
this.val = val;
}
}
try {
return stream.reduce(identity, (a, b) -> {
int res = combiner.applyAsInt(a, b);
if(cancelCondition.test(res))
throw new CancelException(res);
return res;
});
} catch (CancelException e) {
return e.val;
}
}
使用示例：
int product = reduceWithCancelEx(
IntStream.of(2, 3, 4, 5, 0, 7, 8).peek(System.out::println),
1, (a, b) -> a * b, val -> val == 0);
System.out.println("Result: "+product);
Output:
2
3
4
5
0
Result: 0
请注意，即使它适用于并行流，也不能保证一旦其中一个并行任务抛出异常，其他并行任务就会完成。已经开始的子任务可能会一直运行到完成，因此您可能会处理比预期更多的元素。
Update：替代解决方案更长，但更并行友好。它基于自定义 spliterator，最多返回一个元素，该元素是所有底层元素累积的结果）。当您在顺序模式下使用它时，它会以单一方式完成所有工作tryAdvance称呼。当您拆分它时，每个部分都会生成相应的单个部分结果，这些结果由 Stream 引擎使用组合器函数进行缩减。这是通用版本，但原始专业化也是可能的。
final static class CancellableReduceSpliterator<T, A> implements Spliterator<A>,
Consumer<T>, Cloneable {
private Spliterator<T> source;
private final BiFunction<A, ? super T, A> accumulator;
private final Predicate<A> cancelPredicate;
private final AtomicBoolean cancelled = new AtomicBoolean();
private A acc;
CancellableReduceSpliterator(Spliterator<T> source, A identity,
BiFunction<A, ? super T, A> accumulator, Predicate<A> cancelPredicate) {
this.source = source;
this.acc = identity;
this.accumulator = accumulator;
this.cancelPredicate = cancelPredicate;
}
@Override
public boolean tryAdvance(Consumer<? super A> action) {
if (source == null || cancelled.get()) {
source = null;
return false;
}
while (!cancelled.get() && source.tryAdvance(this)) {
if (cancelPredicate.test(acc)) {
cancelled.set(true);
break;
}
}
source = null;
action.accept(acc);
return true;
}
@Override
public void forEachRemaining(Consumer<? super A> action) {
tryAdvance(action);
}
@Override
public Spliterator<A> trySplit() {
if(source == null || cancelled.get()) {
source = null;
return null;
}
Spliterator<T> prefix = source.trySplit();
if (prefix == null)
return null;
try {
@SuppressWarnings("unchecked")
CancellableReduceSpliterator<T, A> result =
(CancellableReduceSpliterator<T, A>) this.clone();
result.source = prefix;
return result;
} catch (CloneNotSupportedException e) {
throw new InternalError();
}
}
@Override
public long estimateSize() {
// let's pretend we have the same number of elements
// as the source, so the pipeline engine parallelize it in the same way
return source == null ? 0 : source.estimateSize();
}
@Override
public int characteristics() {
return source == null ? SIZED : source.characteristics() & ORDERED;
}
@Override
public void accept(T t) {
this.acc = accumulator.apply(this.acc, t);
}
}
方法类似于Stream.reduce(identity, accumulator, combiner) https://docs.oracle.com/javase/8/docs/api/java/util/stream/Stream.html#reduce-U-java.util.function.BiFunction-java.util.function.BinaryOperator- and Stream.reduce(identity, combiner) https://docs.oracle.com/javase/8/docs/api/java/util/stream/Stream.html#reduce-T-java.util.function.BinaryOperator-，但与cancelPredicate:
public static <T, U> U reduceWithCancel(Stream<T> stream, U identity,
BiFunction<U, ? super T, U> accumulator, BinaryOperator<U> combiner,
Predicate<U> cancelPredicate) {
return StreamSupport
.stream(new CancellableReduceSpliterator<>(stream.spliterator(), identity,
accumulator, cancelPredicate), stream.isParallel()).reduce(combiner)
.orElse(identity);
}
public static <T> T reduceWithCancel(Stream<T> stream, T identity,
BinaryOperator<T> combiner, Predicate<T> cancelPredicate) {
return reduceWithCancel(stream, identity, combiner, combiner, cancelPredicate);
}
让我们测试这两个版本并计算实际处理了多少个元素。让我们把0接近尾声。异常版本：
AtomicInteger count = new AtomicInteger();
int product = reduceWithCancelEx(
IntStream.range(-1000000, 100).filter(x -> x == 0 || x % 2 != 0)
.parallel().peek(i -> count.incrementAndGet()), 1,
(a, b) -> a * b, x -> x == 0);
System.out.println("product: " + product + "/count: " + count);
Thread.sleep(1000);
System.out.println("product: " + product + "/count: " + count);
典型输出：
product: 0/count: 281721
product: 0/count: 500001
因此，虽然仅处理某些元素时返回结果，但任务继续在后台工作并且计数器仍在增加。这是分离器版本：
AtomicInteger count = new AtomicInteger();
int product = reduceWithCancel(
IntStream.range(-1000000, 100).filter(x -> x == 0 || x % 2 != 0)
.parallel().peek(i -> count.incrementAndGet()).boxed(),
1, (a, b) -> a * b, x -> x == 0);
System.out.println("product: " + product + "/count: " + count);
Thread.sleep(1000);
System.out.println("product: " + product + "/count: " + count);
典型输出：
product: 0/count: 281353
product: 0/count: 281353
当结果返回时，所有任务实际上已经完成。
