Sorting an array in parallel using Streams

Question

I have written a program that sorts an array with multiple threads by splitting the array into equal chunks and sorting in individual threads with a bubble sort. I have then used a merging algorithm to combine the two arrays.

I would like to compare this program to one which sorts an array using Streams. My question is if I pass an array into a stream how would I go about splitting, sorting and merging to execute the sort in parallel but through the use of parallel streams rather than creating my own threads/runnables etc.

Any ideas?

Answer 1

I assume that your problem is purely educational and experimental without any practical application as there are much more efficient ways to sort elements in Java. If you want to utilize the Stream API here, you may create a spliterator which performs bubble sorting and collector which performs merge sorting in combiner.

Here's spliterator:

static class BubbleSpliterator<T> implements Spliterator<T> {
    private final Comparator<? super T> cmp;
    private final Spliterator<T> source;
    private T[] data;
    private int offset;

    public BubbleSpliterator(Spliterator<T> source, Comparator<? super T> cmp) {
        this.source = source;
        this.cmp = cmp;
    }

    @SuppressWarnings("unchecked")
    private void init() {
        if (data != null)
            return;
        Stream.Builder<T> buf = Stream.builder();
        source.forEachRemaining(buf);
        data = (T[]) buf.build().toArray();
        bubble(data, cmp);
    }

    private static <T> void bubble(T[] data, Comparator<? super T> cmp) {
        for (int i = 0; i < data.length - 1; i++)
            for (int j = i + 1; j < data.length; j++) {
                if (cmp.compare(data[i], data[j]) > 0) {
                    T tmp = data[i];
                    data[i] = data[j];
                    data[j] = tmp;
                }
            }
    }

    @Override
    public boolean tryAdvance(Consumer<? super T> action) {
        init();
        if (offset >= data.length)
            return false;
        action.accept(data[offset++]);
        return true;
    }

    @Override
    public void forEachRemaining(Consumer<? super T> action) {
        init();
        for (int i = offset; i < data.length; i++)
            action.accept(data[i]);
        offset = data.length;
    }

    @Override
    public Spliterator<T> trySplit() {
        if (data != null)
            return null;
        Spliterator<T> prefix = source.trySplit();
        return prefix == null ? null : new BubbleSpliterator<>(prefix, cmp);
    }

    @Override
    public long estimateSize() {
        if (data != null)
            return data.length - offset;
        return source.estimateSize();
    }

    @Override
    public int characteristics() {
        return source.characteristics();
    }

    public static <T> Stream<T> stream(Stream<T> source, 
                                       Comparator<? super T> comparator) {
        Spliterator<T> spltr = source.spliterator();
        return StreamSupport.stream(new BubbleSpliterator<>(spltr, comparator), 
               source.isParallel()).onClose(source::close);
    }
}

It takes source, delegates splitting to the source, but when elements are requested it dumps the source elements to array and performs a bubble sorting for them. You may check it like this:

int[] data = new Random(1).ints(100, 0, 1000).toArray();
Comparator<Integer> comparator = Comparator.naturalOrder();
List<Integer> list = BubbleSpliterator.stream(Arrays.stream(data).parallel().boxed(), comparator).collect(
    Collectors.toList());
System.out.println(list);

The result depends on number of hardware threads on your machine and may look like this:

[254, 313, 588, 847, 904, 985, 434, 473, 569, 606, 748, 978, 234, 262, 263, 317, 562, 592, 99, 189, 310,...]

Here you can see that output consists of several sorted sequences. The number of such sequences corresponds to the number of parallel tasks Stream API creates.

Now to combine sorted sequences via merge sorting you may write a special collector like this:

static <T> List<T> merge(List<T> l1, List<T> l2, Comparator<? super T> cmp) {
    List<T> result = new ArrayList<>(l1.size()+l2.size());
    int i=0, j=0;
    while(i < l1.size() && j < l2.size()) {
        if(cmp.compare(l1.get(i), l2.get(j)) <= 0) {
            result.add(l1.get(i++));
        } else {
            result.add(l2.get(j++));
        }
    }
    result.addAll(l1.subList(i, l1.size()));
    result.addAll(l2.subList(j, l2.size()));
    return result;
}

static <T> Collector<T, ?, List<T>> mergeSorting(Comparator<? super T> cmp) {
    return Collector.<T, List<T>> of(ArrayList::new, List::add, 
                                     (l1, l2) -> merge(l1, l2, cmp));
}

In sequential more it works just like the Collectors.toList() , but in parallel it performs merge sorting assuming that both input lists are already sorted. My mergeSorting implementation is probably suboptimal, you may write something better.

So to sort everything via Stream API, you can use BubbleSpliterator and mergeSorting collector together:

int[] data = new Random(1).ints(100, 0, 1000).toArray();
Comparator<Integer> comparator = Comparator.naturalOrder();
List<Integer> list = BubbleSpliterator.stream(Arrays.stream(data).parallel().boxed(), comparator).collect(
    mergeSorting(comparator));
System.out.println(list);

The result will be completely sorted.

This implementation performs unnecessary copying of input data several times, so I guess, custom bubble+merge implementation could beat this one in terms of performance.

Answer 2

if you want to sort an array in parallel using Java 8 Stream API, this may help you :

IntStream randomIntegers = ThreadLocalRandom.current().ints(100, 0, 100);
int[] sortedArray = randomIntegers
        .parallel() // (1)
        .sorted() // (2)
        .toArray();
System.out.println(Arrays.toString(sortedArray));

no matter what type of Stream you have, just invoke parallel() and then sorted() . (the order of invocation is not important)

by tracing the code we find that :

final class SortedOps {

    private static final class OfInt extends IntPipeline.StatefulOp<Integer> {
        //...

        @Override
        public <P_IN> Node<Integer> opEvaluateParallel(PipelineHelper<Integer> helper,
                                                       Spliterator<P_IN> spliterator,
                                                       IntFunction<Integer[]> generator) {
            if (StreamOpFlag.SORTED.isKnown(helper.getStreamAndOpFlags())) {
                return helper.evaluate(spliterator, false, generator);
            } else {
                Node.OfInt n = (Node.OfInt) helper.evaluate(spliterator, true, generator);

                int[] content = n.asPrimitiveArray();
                Arrays.parallelSort(content); // <== this

                return Nodes.node(content);
            }
        }
    }

    private static final class OfRef<T> extends ReferencePipeline.StatefulOp<T, T> {
        //...

        @Override
        public <P_IN> Node<T> opEvaluateParallel(PipelineHelper<T> helper,
                                                 Spliterator<P_IN> spliterator,
                                                 IntFunction<T[]> generator) {
            // If the input is already naturally sorted and this operation
            // naturally sorts then collect the output
            if (StreamOpFlag.SORTED.isKnown(helper.getStreamAndOpFlags()) && isNaturalSort) {
                return helper.evaluate(spliterator, false, generator);
            }
            else {
                // @@@ Weak two-pass parallel implementation; parallel collect, parallel sort
                T[] flattenedData = helper.evaluate(spliterator, true, generator).asArray(generator);
                Arrays.parallelSort(flattenedData, comparator); // <== this
                return Nodes.node(flattenedData);
            }
        }
    }

}

Arrays.parallelSort() is used to sort the backing array.