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Walking Recursive Data Structures Using Java 8 Streams

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Walking Recursive Data Structures Using Java 8 Streams

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The Streams API is a real gem in Java 8, and I keep finding more or less unexpected uses for them. I recently wrote about using them as ForkJoinPool facade. Here’s another interesting example: Walking recursive data structures.

Without much ado, have a look at the code:

class Tree {
    private int value;
    private List<Tree> children = new LinkedList<>();
    public Tree(int value, List<Tree> children) {
        this.value = value;
    public Tree(int value, Tree... children) {
        this(value, asList(children));
    public int getValue() {
        return value;
    public List<Tree> getChildren() {
        return Collections.unmodifiableList(children);
    public Stream<Tree> flattened() {
        return Stream.concat(

It’s pretty boring, except for the few highlighted lines.

Let’s say we want to be able to find elements matching some criteria in the tree or find particular element. One typical way to do it is a recursive function – but that has some complexity and is likely to need a mutable argument (e.g. a set where you can append matching elements). Another approach is iteration with a stack or a queue. They work fine, but take a few lines of code and aren’t so easy to generalize.

Here’s what we can do with this flattened function:

// Get all values in the tree:
// Get even values:
t.flattened().map(Tree::getValue).filter(v -> v % 2 == 0).collect(toList());
// Sum of even values:
t.flattened().map(Tree::getValue).filter(v -> v % 2 == 0).reduce((a, b) -> a + b);
// Does it contain 13?
t.flattened().anyMatch(t -> t.getValue() == 13);

I think this solution is pretty slick and versatile. One line of code (here split to 3 for readability on blog) is enough to flatten the tree to a straightforward stream that can be searched, filtered and whatnot.

It’s not perfect though: It is not lazy and flattened is called for each and every node in the tree every time. It probably could be improved using a Supplier. Anyway, it doesn’t matter for typical, reasonably small trees, especially in a business application on a very tall stack of libraries. But for very large trees, very frequent execution and tight time constraints the overhead might cause some trouble.

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