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Java Academy · Lesson

Spliterator: Splitting for Parallelism

Implement a custom Spliterator to expose domain data as a splittable stream source.

Spliterator: Splitting for Parallelism is a free Java Academy lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Java Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

What Is a Spliterator?

A Spliterator (Splittable Iterator) is the core mechanism behind streams. It iterates elements and can split itself into two parts for parallel processing.

Key Spliterator Methods

Four core methods: tryAdvance (process one element), forEachRemaining (process all remaining), trySplit (split into two), estimateSize (element count estimate).

Spliterator<Integer> sp = List.of(1,2,3,4,5,6).spliterator();
Spliterator<Integer> half = sp.trySplit(); // splits off first ~half
half.forEachRemaining(System.out::println); // 1 2 3
sp.forEachRemaining(System.out::println);   // 4 5 6

Spliterator Characteristics

Spliterators declare their characteristics with bit flags: SIZED, ORDERED, DISTINCT, SORTED, NONNULL, IMMUTABLE, CONCURRENT, SUBSIZED.

Spliterator<String> sp = List.of("a","b","c").spliterator();
System.out.println(Integer.toBinaryString(sp.characteristics()));
// Characteristics include ORDERED, SIZED, SUBSIZED

How trySplit Works

trySplit() returns a new Spliterator covering roughly half the remaining elements. The original covers the other half. Return null if splitting is not possible.

// ArrayList Spliterator splits efficiently at midpoint:
// [0,1,2,3,4,5] -> [0,1,2] (new) + [3,4,5] (original)

Building a Custom Spliterator

Implement Spliterator<T> to expose a custom data structure as a stream source. Define trySplit to enable parallelism.

public class RangeSpliterator implements Spliterator<Integer> {
    private int start, end;
    public RangeSpliterator(int start, int end) { this.start=start; this.end=end; }
    public boolean tryAdvance(Consumer<? super Integer> action) {
        if (start >= end) return false;
        action.accept(start++); return true;
    }
    public Spliterator<Integer> trySplit() {
        int mid = (start + end) / 2;
        if (mid <= start) return null;
        RangeSpliterator prefix = new RangeSpliterator(start, mid);
        this.start = mid; return prefix;
    }
    public long estimateSize() { return end - start; }
    public int characteristics() { return ORDERED | SIZED | SUBSIZED | IMMUTABLE; }
}

Creating a Stream from Spliterator

Use StreamSupport.stream(spliterator, parallel) to create a stream from any spliterator — the bridge between custom data sources and the Stream API.

Spliterator<Integer> sp = new RangeSpliterator(0, 1_000_000);
Stream<Integer> stream = StreamSupport.stream(sp, true); // true = parallel
long count = stream.filter(n -> n % 2 == 0).count();
System.out.println(count); // 500000

Spliterator for a Binary Tree

Custom Spliterators enable parallelism on non-list structures like trees. trySplit returns the left subtree spliterator and keeps the right.

Parallel Efficiency and Split Quality

Parallel streams split recursively until chunks are small enough for a single thread. Good splitting requires estimateSize to be accurate and splits to be roughly equal.

forEachRemaining for Bulk Processing

If no splitting is needed, override forEachRemaining for bulk processing that avoids per-element overhead of tryAdvance in a loop.

@Override
public void forEachRemaining(Consumer<? super Integer> action) {
    for (int i = start; i < end; i++) action.accept(i);
    start = end; // mark as exhausted
}

Spliterator vs Iterator

Iterator: sequential only, no size hint, no splitting. Spliterator: parallel-capable, provides characteristics and size estimate, designed for the Stream API.

Quick Check

What does trySplit() return when splitting is not possible?

Recap

Spliterator is the engine of parallel streams. Implement tryAdvance, trySplit, estimateSize, and characteristics to expose custom data structures as streams. Use StreamSupport.stream(sp, true) to go parallel.

Frequently asked questions

Is the “Spliterator: Splitting for Parallelism” lesson free?

Yes — the full text of “Spliterator: Splitting for Parallelism” is free to read here on the web, and the Java Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Java Academy course, upgrade to CoddyKit PRO.

What will I learn in “Spliterator: Splitting for Parallelism”?

Implement a custom Spliterator to expose domain data as a splittable stream source. You practise Java Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start Java Academy?

No prior experience is required. Java Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Spliterator: Splitting for Parallelism” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this Java Academy lesson?

Yes. Every Java Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. flatMap for Nested Collections
  2. Parallel Streams: Performance and Pitfalls
  3. Spliterator: Splitting for Parallelism
  4. Infinite Streams with iterate and generate
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