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How to reduce/iterate multiple java streams of same size at once?

I have multiple Java streams of the same length, and I want to perform some operation on the corresponding elements of each stream. For e.g., add 1st elements of all stream, 2nd elements of all streams and 3rd elements of all streams and so on.
How can we do this without first reducing each of the streams?
for Minimum reproducible example, I have the following test snippet
#Test
void aggregateMultipleStreams() {
Stream<Integer> s1 = Stream.of(1, 2);
Stream<Integer> s2 = Stream.of(4, 5);
Stream<Integer> s3 = Stream.of(7, 8);
assertEquals(List.of(1 + 4 + 7, 2 + 5 + 8), aggregate(s1, s2, s3, 2));
}
I can write the aggregate method as follows, by reducing all the streams first.
private List<Integer> aggregate(Stream<Integer> s1, Stream<Integer> s2, Stream<Integer> s3, int streamSize) {
final List<List<Integer>> reduced = Stream.of(s1, s2, s3)
.map(s -> s.collect(Collectors.toList())).collect(Collectors.toList());
return IntStream.range(0, streamSize).mapToObj(n -> IntStream.range(0, reduced.size())
.map(v -> reduced.get(v).get(n)).sum()).collect(Collectors.toList());
}
But this could be a storage hassle, if each stream contains numerous records, for N records, we need 3N storage here.
can we accomplish the addition of corresponding elements in different streams without first reduction? can we reduce multiple streams at once in Java?
After implementing #jb_dk's solution below, the solution code snippet became:
private List<Integer> aggregate(Stream<Integer> s1, Stream<Integer> s2, Stream<Integer> s3, int streamSize) {
final List<Iterator<Integer>> iterators = Stream.of(s1, s2, s3)
.map(Stream::iterator).collect(Collectors.toList());
return IntStream.range(0, streamSize).mapToObj(n -> IntStream.range(0, iterators.size())
.map(v -> iterators.get(v).next()).sum()).collect(Collectors.toList());
}

Form an array of the input stream objects instead of 3 named variables, then create an output List or stream and use an outer loop over the stream length and an inner loop that iterates over the array of input streams and reads one element from each, adding to the output array elements.
Something like (code not tested, syntax errors may exist)
...
{
List<Integer> results; // NOT final, can be a stream builder instead
final List<Stream<Integer>> instrms = Stream.Of(s1, s2, s3);
Iterator<Integer>[] initers = new Iterator<Integer>[instrms.length]
// Get the iterator (not rewindable) for each stream
int i = 0;
for (Stream<Integer> instrm : instrms) {
initers[i++] = ((Iterator<Integer>)instrm::iterator);
}
// Actually loop over the stream elements, outputting one
// sum element for each element. Assumes all input streams
// are same length as the first one.
while(! initers[0].hasNext()) {
Integer res1 = 0;
for (Iterator<Integer> initer : initers) {
res1 += initer.next();
}
results.Add(res1);
}
return results; // results.build() if a stream builder
}

given an infinite sequence break it into intervals, and return a new infinite sequence with the average of each interval

i have to calculate the average of a Infinite Sequence using Stream API
Input:
Stream<Double> s = a,b,c,d ...
int interval = 3
Expected Result:
Stream<Double> result = avg(a,b,c), avg(d,e,f), ....
the result can be also an Iterator, or any other type
as long as it mantains the structure of an infinite list
of course what i written is pseudo code and doesnt run

There is a #Beta API termed mapWithIndex within Guava that could help here with certain assumption:
static Stream<Double> stepAverage(Stream<Double> stream, int step) {
return Streams.mapWithIndex(stream, (from, index) -> Map.entry(index, from))
.collect(Collectors.groupingBy(e -> (e.getKey() / step), TreeMap::new,
Collectors.averagingDouble(Map.Entry::getValue)))
.values().stream();
}
The assumption that it brings in is detailed in the documentation clearly(emphasized by me):
The resulting stream is efficiently splittable if and only if stream
was efficiently splittable and its underlying spliterator reported
Spliterator.SUBSIZED. This is generally the case if the underlying
stream comes from a data structure supporting efficient indexed random
access, typically an array or list.

This should work fine using vanilla Java
I'm using Stream#mapMulti and a Set external to the Stream to aggregate the doubles
As you see, I also used DoubleSummaryStatistics to count the average.
I could have use the traditional looping and summing then dividing but I found this way more explicit
Update:
I changed the Collection used from Set to List as a Set could cause unexpected behaviour
int step = 3;
List<Double> list = new ArrayList<>();
Stream<Double> averagesStream =
infiniteStream.mapMulti(((Double aDouble, Consumer<Double> doubleConsumer) -> {
list.add(aDouble);
if (list.size() == step) {
DoubleSummaryStatistics doubleSummaryStatistics = new DoubleSummaryStatistics();
list.forEach(doubleSummaryStatistics::accept);
list.clear();
doubleConsumer.accept(doubleSummaryStatistics.getAverage());
}
}));

Java stream of optionals to optional stream

I need to convert Stream<Optional<Integer>> to Optional<Stream<Integer>>.
The output Optional<Stream<Integer>> should be an empty value when at least one value ofStream<Optional<Integer>> is empty.
Do you know any functional way to solve the problem? I tried to use collect method, but without success.

Well, the tricky thing here is that if you're just given a Stream, you can only use it once.
To be stateless and avoid redundant copying, one way is to just catch NoSuchElementException:
static <T> Optional<Stream<T>> invert(Stream<Optional<T>> stream) {
try {
return Optional.of(
stream.map(Optional::get)
.collect(Collectors.toList())
.stream());
} catch (NoSuchElementException e) {
return Optional.empty();
}
}
A simple inversion would be:
static <T> Optional<Stream<T>> invert(Stream<Optional<T>> stream) {
return Optional.of(stream.map(Optional::get));
}
But to find out if it contains an empty element, you need to actually traverse it which also consumes it.
If you're given the source of the stream, you can traverse it without collecting it:
static <T> Optional<Stream<T>> invert(
Supplier<Stream<Optional<T>>> supplier) {
// taking advantage of short-circuiting here
// instead of allMatch(Optional::isPresent)
return supplier.get().anyMatch(o -> !o.isPresent()) ?
Optional.empty() : Optional.of(supplier.get().map(Optional::get));
}
List<Optional<Integer>> myInts =
Arrays.asList(Optional.of(1), Optional.of(2), Optional.of(3));
Optional<Stream<Integer>> inverted = invert(myInts::stream);
That's probably a more interesting approach. (But it's prone to a race condition because the stream() is taken twice. If some other thread adds an empty element in between and gets away with it, we have a problem.)

Though this has already been answered yet to add to the list, with Java-9 introducing Optional.stream, this should be achievable as:
// initialized stream of optional
Stream<Optional<Integer>> so = Stream.empty();
// mapped stream of T
Stream<Integer> s = so.flatMap(Optional::stream);
// constructing optional from the stream
Optional<Stream<Integer>> os = Optional.of(s);

Similar to Radiodef's answer, though this one avoids the exception handling and the intermediate list.
private static <T> Optional<Stream<T>> invertOptional(Stream<Optional<T>> input) {
return input.map(integer -> integer.map(Stream::of))
.collect(Collectors.reducing((l, r) -> l.flatMap(lv -> r.map(rv -> Stream.concat(lv, rv)))))
.orElse(Optional.empty());
}
The way this works is it maps to a Stream of Optional Streams of T. The Optional.map is used in this case, so each one of the Optional<Stream<T>> items in the resultant stream is a either a Stream of 1, or an empty Optional.
Then it collects these streams by reducing them together. the l.flatMap will return an empty Optional if l is empty or the r.map returns an empty. if r.map isn't empty, it calls the Stream.concat, which combines the left and right stream values.
The whole collect reduction produces an Optional<Optional<Stream<T>>>, so we narrow that down with the .orElse(Optional.empty)
Note: Code is tested, and appears to work. The unspecified "edge case" of an empty input Stream is treated as an an empty Optional, but can be easily changed.

final Stream<Optional<Integer>> streamOfInts = Stream.of(Optional.of(1), Optional.of(2), Optional.of(3), Optional.of(4), Optional.of(5));
// false - list of Optional.empty(); true -> list of Optional.of(Integer)
final Map<Boolean, List<Optional<Integer>>> collect = streamOfInts.collect(Collectors.partitioningBy(Optional::isPresent));
final Function<List<Optional<Integer>>, Stream<Integer>> mapToStream = List->List.stream().filter(o->o.isPresent()).map(o->o.get());
Optional<Stream<Integer>> result = Optional
.of(Optional.of(collect.get(false)).filter(list->list.size()>0).orElse(collect.get(true)))
.filter(list->list.size()>0)
.filter(list->list.get(0).isPresent())
.map(mapToStream)
.map(Optional::of)
.orElse(Optional.empty());

Implement counting variable with lambda expression java

I have a question about lambda expressions. I have a class Pair which should hold a String and an int.
Pair gets the String out of a file.
and the int is representiv for the line number.
So far I have this:
Stream<String> lineNumbers = Files.lines(Paths.get(fileName));
List<Integer> posStream = Stream.iterate(0, x -> x + 1).limit(lineNumbers.count()).collect(Collectors.toList());
lineNumbers.close();
Stream<String> line = Files.lines(Paths.get(fileName));
List<Pair> pairs = line.map((f) -> new Pair<>(f,1))
.collect(Collectors.toList());
pairs.forEach(f -> System.out.println(f.toString()));
line.close();
How can I now input the file numbers to the pairs?
Is there a lambda expression which can perform this? Or do I need something else?

There are a few ways to do this. The counter technique suggested by Saloparenator's answer could be implemented as follows, using an AtomicInteger as the mutable counter object and assuming the obvious Pair class:
List<Pair> getPairs1() throws IOException {
AtomicInteger counter = new AtomicInteger(0);
try (Stream<String> lines = Files.lines(Paths.get(FILENAME))) {
return lines.parallel()
.map(line -> new Pair(line, counter.incrementAndGet()))
.collect(toList());
}
}
The problem is that if the stream is run in parallel, the counter won't be incremented in the same order as the lines are read! This will occur if your file has several thousand lines. The Files.lines stream source will batch up bunches of lines and dispatch them to several threads, which will then number their batches in parallel, interleaving their calls to incrementAndGet(). Thus, the lines won't be numbered sequentially. It will work if you can guarantee that your stream will never run in parallel, but it's often a bad idea to write streams that are likely to return different results sequentially vs. in parallel.
Here's another approach. Since you're reading all the lines into memory no matter what, just read them all into a list. Then use a stream to number them:
static List<Pair> getPairs2() throws IOException {
List<String> lines = Files.readAllLines(Paths.get(FILENAME));
return IntStream.range(0, lines.size())
.parallel()
.mapToObj(i -> new Pair(lines.get(i), i+1))
.collect(toList());
}

Another functional way would be to ZIP your list of stream with a integer generator
(I can see java8 dont have it yet but it is essentially merging every cell of two list in a list of pair, so it is easy to implement)
You can see example of generator in java8 here

int cnt = 1;
List<Pair> pairs = line.map((f) -> new Pair<>(f,cnt++))
.collect(Collectors.toList());
I have not tried it yet but may work.

Can you split a stream into two streams?

I have a data set represented by a Java 8 stream:
Stream<T> stream = ...;
I can see how to filter it to get a random subset - for example
Random r = new Random();
PrimitiveIterator.OfInt coin = r.ints(0, 2).iterator();
Stream<T> heads = stream.filter((x) -> (coin.nextInt() == 0));
I can also see how I could reduce this stream to get, for example, two lists representing two random halves of the data set, and then turn those back into streams.
But, is there a direct way to generate two streams from the initial one? Something like
(heads, tails) = stream.[some kind of split based on filter]
Thanks for any insight.

A collector can be used for this.
For two categories, use Collectors.partitioningBy() factory.
This will create a Map<Boolean, List>, and put items in one or the other list based on a Predicate.
Note: Since the stream needs to be consumed whole, this can't work on infinite streams. And because the stream is consumed anyway, this method simply puts them in Lists instead of making a new stream-with-memory. You can always stream those lists if you require streams as output.
Also, no need for the iterator, not even in the heads-only example you provided.
Binary splitting looks like this:
Random r = new Random();
Map<Boolean, List<String>> groups = stream
.collect(Collectors.partitioningBy(x -> r.nextBoolean()));
System.out.println(groups.get(false).size());
System.out.println(groups.get(true).size());
For more categories, use a Collectors.groupingBy() factory.
Map<Object, List<String>> groups = stream
.collect(Collectors.groupingBy(x -> r.nextInt(3)));
System.out.println(groups.get(0).size());
System.out.println(groups.get(1).size());
System.out.println(groups.get(2).size());
In case the streams are not Stream, but one of the primitive streams like IntStream, then this .collect(Collectors) method is not available. You'll have to do it the manual way without a collector factory. It's implementation looks like this:
[Example 2.0 since 2020-04-16]
IntStream intStream = IntStream.iterate(0, i -> i + 1).limit(100000).parallel();
IntPredicate predicate = ignored -> r.nextBoolean();
Map<Boolean, List<Integer>> groups = intStream.collect(
() -> Map.of(false, new ArrayList<>(100000),
true , new ArrayList<>(100000)),
(map, value) -> map.get(predicate.test(value)).add(value),
(map1, map2) -> {
map1.get(false).addAll(map2.get(false));
map1.get(true ).addAll(map2.get(true ));
});
In this example I initialize the ArrayLists with the full size of the initial collection (if this is known at all). This prevents resize events even in the worst-case scenario, but can potentially gobble up 2NT space (N = initial number of elements, T = number of threads). To trade-off space for speed, you can leave it out or use your best educated guess, like the expected highest number of elements in one partition (typically just over N/2 for a balanced split).
I hope I don't offend anyone by using a Java 9 method. For the Java 8 version, look at the edit history.

I stumbled across this question to my self and I feel that a forked stream has some use cases that could prove valid. I wrote the code below as a consumer so that it does not do anything but you could apply it to functions and anything else you might come across.
class PredicateSplitterConsumer<T> implements Consumer<T>
{
private Predicate<T> predicate;
private Consumer<T> positiveConsumer;
private Consumer<T> negativeConsumer;
public PredicateSplitterConsumer(Predicate<T> predicate, Consumer<T> positive, Consumer<T> negative)
{
this.predicate = predicate;
this.positiveConsumer = positive;
this.negativeConsumer = negative;
}
#Override
public void accept(T t)
{
if (predicate.test(t))
{
positiveConsumer.accept(t);
}
else
{
negativeConsumer.accept(t);
}
}
}
Now your code implementation could be something like this:
personsArray.forEach(
new PredicateSplitterConsumer<>(
person -> person.getDateOfBirth().isPresent(),
person -> System.out.println(person.getName()),
person -> System.out.println(person.getName() + " does not have Date of birth")));

Unfortunately, what you ask for is directly frowned upon in the JavaDoc of Stream:
A stream should be operated on (invoking an intermediate or terminal
stream operation) only once. This rules out, for example, "forked"
streams, where the same source feeds two or more pipelines, or
multiple traversals of the same stream.
You can work around this using peek or other methods should you truly desire that type of behaviour. In this case, what you should do is instead of trying to back two streams from the same original Stream source with a forking filter, you would duplicate your stream and filter each of the duplicates appropriately.
However, you may wish to reconsider if a Stream is the appropriate structure for your use case.

You can get two Streams out of one
since Java 12 with teeing
counting heads and tails in 100 coin flips
Random r = new Random();
PrimitiveIterator.OfInt coin = r.ints(0, 2).iterator();
List<Long> list = Stream.iterate(0, i -> coin.nextInt())
.limit(100).collect(teeing(
filtering(i -> i == 1, counting()),
filtering(i -> i == 0, counting()),
(heads, tails) -> {
return(List.of(heads, tails));
}));
System.err.println("heads:" + list.get(0) + " tails:" + list.get(1));
gets eg.: heads:51 tails:49

Not exactly. You can't get two Streams out of one; this doesn't make sense -- how would you iterate over one without needing to generate the other at the same time? A stream can only be operated over once.
However, if you want to dump them into a list or something, you could do
stream.forEach((x) -> ((x == 0) ? heads : tails).add(x));

This is against the general mechanism of Stream. Say you can split Stream S0 to Sa and Sb like you wanted. Performing any terminal operation, say count(), on Sa will necessarily "consume" all elements in S0. Therefore Sb lost its data source.
Previously, Stream had a tee() method, I think, which duplicate a stream to two. It's removed now.
Stream has a peek() method though, you might be able to use it to achieve your requirements.

not exactly, but you may be able to accomplish what you need by invoking Collectors.groupingBy(). you create a new Collection, and can then instantiate streams on that new collection.

This was the least bad answer I could come up with.
import org.apache.commons.lang3.tuple.ImmutablePair;
import org.apache.commons.lang3.tuple.Pair;
public class Test {
public static <T, L, R> Pair<L, R> splitStream(Stream<T> inputStream, Predicate<T> predicate,
Function<Stream<T>, L> trueStreamProcessor, Function<Stream<T>, R> falseStreamProcessor) {
Map<Boolean, List<T>> partitioned = inputStream.collect(Collectors.partitioningBy(predicate));
L trueResult = trueStreamProcessor.apply(partitioned.get(Boolean.TRUE).stream());
R falseResult = falseStreamProcessor.apply(partitioned.get(Boolean.FALSE).stream());
return new ImmutablePair<L, R>(trueResult, falseResult);
}
public static void main(String[] args) {
Stream<Integer> stream = Stream.iterate(0, n -> n + 1).limit(10);
Pair<List<Integer>, String> results = splitStream(stream,
n -> n > 5,
s -> s.filter(n -> n % 2 == 0).collect(Collectors.toList()),
s -> s.map(n -> n.toString()).collect(Collectors.joining("|")));
System.out.println(results);
}
}
This takes a stream of integers and splits them at 5. For those greater than 5 it filters only even numbers and puts them in a list. For the rest it joins them with |.
outputs:
([6, 8],0|1|2|3|4|5)
Its not ideal as it collects everything into intermediary collections breaking the stream (and has too many arguments!)

I stumbled across this question while looking for a way to filter certain elements out of a stream and log them as errors. So I did not really need to split the stream so much as attach a premature terminating action to a predicate with unobtrusive syntax. This is what I came up with:
public class MyProcess {
/* Return a Predicate that performs a bail-out action on non-matching items. */
private static <T> Predicate<T> withAltAction(Predicate<T> pred, Consumer<T> altAction) {
return x -> {
if (pred.test(x)) {
return true;
}
altAction.accept(x);
return false;
};
/* Example usage in non-trivial pipeline */
public void processItems(Stream<Item> stream) {
stream.filter(Objects::nonNull)
.peek(this::logItem)
.map(Item::getSubItems)
.filter(withAltAction(SubItem::isValid,
i -> logError(i, "Invalid")))
.peek(this::logSubItem)
.filter(withAltAction(i -> i.size() > 10,
i -> logError(i, "Too large")))
.map(SubItem::toDisplayItem)
.forEach(this::display);
}
}

Shorter version that uses Lombok
import java.util.function.Consumer;
import java.util.function.Predicate;
import lombok.RequiredArgsConstructor;
/**
* Forks a Stream using a Predicate into postive and negative outcomes.
*/
#RequiredArgsConstructor
#FieldDefaults(makeFinal = true, level = AccessLevel.PROTECTED)
public class StreamForkerUtil<T> implements Consumer<T> {
Predicate<T> predicate;
Consumer<T> positiveConsumer;
Consumer<T> negativeConsumer;
#Override
public void accept(T t) {
(predicate.test(t) ? positiveConsumer : negativeConsumer).accept(t);
}
}

How about:
Supplier<Stream<Integer>> randomIntsStreamSupplier =
() -> (new Random()).ints(0, 2).boxed();
Stream<Integer> tails =
randomIntsStreamSupplier.get().filter(x->x.equals(0));
Stream<Integer> heads =
randomIntsStreamSupplier.get().filter(x->x.equals(1));