Is there a way to zip two streams?

Question

This question arrose from the answer to another question where map and reduce were suggested to calculate a sum concurrently.

In that question there's a complexCalculation(e), but now I was wondering how to parallellise even more, by splitting the calculation in two parts, so that complexCalculation(e) = part1(e) * part2(e). I wonder whether it would be possible to calculate part1 and part2 on a collection concurrently (using map() again) and then zip the two resulting streams so that the ith element of both streams is combined with the function * so that the resulting stream equals the stream that can be gotten by mapping complexCalculation(e) on that collection. In code this would look like:

Stream map1 = bigCollection.parallelStream().map(e -> part1(e));
Stream map2 = bigCollection.parallelStream().map(e -> part2(e));
// preferably map1 and map2 are computed concurrently...
Stream result = map1.zip(map2, (e1, e2) -> e1 * e2);

result.equals(bigCollection.map(e -> complexCalculation(e))); //should be true

So my question is: does there exist some functionality like the zip function I tried to describe here?

Answer 1

parallelStream() is guarenteed to complete in the order submitted. This means you cannot assume that two parallelStreams can be zipped together like this.

Your original bigCollection.map(e -> complexCalculation(e)) is likely to be faster unless your collection is actually smaller than the number of CPUs you have.

Answer 2

If you really want to parallelize part1 and part2 (for example your bigCollection has very few elements, less than CPU cores), you can do the following trick. Suppose you have two methods part1 and part2 in the current class:

public long part1(Type t) { ... }
public long part2(Type t) { ... }

Create a stream of two functions created from these methods and process it in parallel like this:

bigCollection.parallelStream()
    .map(e -> Stream.<ToLongFunction<Type>>of(this::part1, this::part2)
            .parallel()
            .mapToLong(fn -> fn.applyAsLong(e)).reduce(1, (a, b) -> a*b))
    .// continue the outer stream operations

However it's very rare case. As @PeterLawrey noted if your outer collection is big enough, no need to parallelize part1 and part2. Instead you will handle separate elements in parallel.