Java 8 One Stream To Multiple Map

Question

Lets say I have huge webserver log file that does not fit in memory. I need to stream this file to a mapreduce method and save to database. I do this using Java 8 stream api. For example, I get a list after the mapreduce process such as, consumption by client, consumption by ip, consumption by content. But, my needs are not that like that given in my example. Since I cannot share code, I just want to give basic example.

By Java 8 Stream Api, I want to read file exactly once, get 3 lists at the same time, while I am streaming file, parallel or sequential. But parallel would be good. Is there any way to do that?

Answer 1

Generally collecting to anything other than standard API's gives you is pretty easy via a custom Collector. In your case collecting to 3 lists at a time (just a small example that compiles, since you can't share your code either):

private static <T> Collector<T, ?, List<List<T>>> to3Lists() {
    class Acc {

        List<T> left = new ArrayList<>();

        List<T> middle = new ArrayList<>();

        List<T> right = new ArrayList<>();

        List<List<T>> list = Arrays.asList(left, middle, right);

        void add(T elem) {
            // obviously do whatever you want here
            left.add(elem);
            middle.add(elem);
            right.add(elem);
        }

        Acc merge(Acc other) {

            left.addAll(other.left);
            middle.addAll(other.middle);
            right.addAll(other.right);

            return this;
        }

        public List<List<T>> finisher() {
            return list;
        }

    }
    return Collector.of(Acc::new, Acc::add, Acc::merge, Acc::finisher);
}

And using it via:

Stream.of(1, 2, 3)
      .collect(to3Lists());

Obviously this custom collector does not do anything useful, but just an example of how you could work with it.

Answer 2

I have adapted the answer to this question to your case. The custom Spliterator will "split" the stream into multiple streams that collect by different properties:

@SafeVarargs
public static <T> long streamForked(Stream<T> source, Consumer<Stream<T>>... consumers)
{
    return StreamSupport.stream(new ForkingSpliterator<>(source, consumers), false).count();
}

public static class ForkingSpliterator<T>
    extends AbstractSpliterator<T>
{
    private Spliterator<T>         sourceSpliterator;

    private List<BlockingQueue<T>> queues = new ArrayList<>();

    private boolean                sourceDone;

    @SafeVarargs
    private ForkingSpliterator(Stream<T> source, Consumer<Stream<T>>... consumers)
    {
        super(Long.MAX_VALUE, 0);

        sourceSpliterator = source.spliterator();

        for (Consumer<Stream<T>> fork : consumers)
        {
            LinkedBlockingQueue<T> queue = new LinkedBlockingQueue<>();
            queues.add(queue);
            new Thread(() -> fork.accept(StreamSupport.stream(new ForkedConsumer(queue), false))).start();
        }
    }

    @Override
    public boolean tryAdvance(Consumer<? super T> action)
    {
        sourceDone = !sourceSpliterator.tryAdvance(t -> queues.forEach(queue -> queue.offer(t)));
        return !sourceDone;
    }

    private class ForkedConsumer
        extends AbstractSpliterator<T>
    {
        private BlockingQueue<T> queue;

        private ForkedConsumer(BlockingQueue<T> queue)
        {
            super(Long.MAX_VALUE, 0);
            this.queue = queue;
        }

        @Override
        public boolean tryAdvance(Consumer<? super T> action)
        {
            while (queue.peek() == null)
            {
                if (sourceDone)
                {
                    // element is null, and there won't be no more, so "terminate" this sub stream
                    return false;
                }
            }

            // push to consumer pipeline
            action.accept(queue.poll());

            return true;
        }
    }
}

You can use it as follows:

streamForked(Stream.of(new Row("content1", "client1", "location1", 1),
                       new Row("content2", "client1", "location1", 2),
                       new Row("content1", "client1", "location2", 3),
                       new Row("content2", "client2", "location2", 4),
                       new Row("content1", "client2", "location2", 5)),
             rows -> System.out.println(rows.collect(Collectors.groupingBy(Row::getClient,
                                                                           Collectors.groupingBy(Row::getContent,
                                                                                                 Collectors.summingInt(Row::getConsumption))))),
             rows -> System.out.println(rows.collect(Collectors.groupingBy(Row::getClient,
                                                                           Collectors.groupingBy(Row::getLocation,
                                                                                                 Collectors.summingInt(Row::getConsumption))))),
             rows -> System.out.println(rows.collect(Collectors.groupingBy(Row::getContent,
                                                                           Collectors.groupingBy(Row::getLocation,
                                                                                                 Collectors.summingInt(Row::getConsumption))))));

// Output
// {client2={location2=9}, client1={location1=3, location2=3}}
// {client2={content2=4, content1=5}, client1={content2=2, content1=4}}
// {content2={location1=2, location2=4}, content1={location1=1, location2=8}}

Note that you can do pretty much anything you want with your the copies of the stream. As per your example, I used a stacked groupingBy collector to group the rows by two properties and then summed up the int property. So the result will be a Map<String, Map<String, Integer>>. But you could also use it for other scenarios:

rows -> System.out.println(rows.count())
rows -> rows.forEach(row -> System.out.println(row))
rows -> System.out.println(rows.anyMatch(row -> row.getConsumption() > 3))