Month: January 2026

The Java Spliterator, introduced in Java 8, powers the Stream API by providing sophisticated traversal and partitioning capabilities. This enables both sequential and parallel stream processing with optimal performance across diverse data sources.

What Is a Spliterator?

A Spliterator (split + iterator) traverses elements while supporting data partitioning for concurrent processing. Unlike traditional Iterator, its trySplit() method divides data sources into multiple Spliterators, making it perfect for parallel streams.

Spliterator's Role in Stream API

Stream API methods like collection.stream() and collection.parallelStream() internally call the collection's spliterator() method. The StreamSupport.stream(spliterator, parallel) factory creates the stream pipeline.

Enabling Parallel Processing

The Fork/Join framework uses trySplit() to recursively partition data across threads. Each split creates smaller Spliterators processed independently, then results merge efficiently.

Core Spliterator Methods

Spliterator Characteristics

Characteristics describe data source properties, optimizing stream execution:

These flags enable Stream API optimizations like skipping redundant operations based on source properties.

Custom Spliterator Example: Square Generator

Here's a production-ready custom Spliterator that generates squares of numbers in a range, with full parallel execution support:

import java.util.Spliterator;
import java.util.function.Consumer;
import java.util.stream.StreamSupport;

/**
 * A Spliterator that generates squares of numbers in a range.
 * This implementation properly supports parallel execution because
 * each element can be computed independently without shared mutable state.
 */
public class SquareSpliterator implements Spliterator<Integer> {
    private int start;
    private final int end;

    public SquareSpliterator(int start, int end) {
        this.start = start;
        this.end = end;
    }

    @Override
    public boolean tryAdvance(Consumer<? super Integer> action) {
        if (start >= end) {
            return false;
        }
        int value = start * start;
        action.accept(value);
        start++;
        return true;
    }

    @Override
    public Spliterator<Integer> trySplit() {
        int remaining = end - start;

        // Only split if we have at least 2 elements
        if (remaining < 2) {
            return null;
        }

        // Split the range in half
        int mid = start + remaining / 2;
        int oldStart = start;
        start = mid;

        // Return a new spliterator for the first half
        return new SquareSpliterator(oldStart, mid);
    }

    @Override
    public long estimateSize() {
        return end - start;
    }

    @Override
    public int characteristics() {
        return IMMUTABLE | SIZED | SUBSIZED | NONNULL | ORDERED;
    }

    public static void main(String[] args) {
        System.out.println("=== Sequential Execution ===");
        var sequentialStream = StreamSupport.stream(new SquareSpliterator(1, 11), false);
        sequentialStream.forEach(n -> System.out.println(
            Thread.currentThread().getName() + ": " + n
        ));

        System.out.println("\n=== Parallel Execution ===");
        var parallelStream = StreamSupport.stream(new SquareSpliterator(1, 11), true);
        parallelStream.forEach(n -> System.out.println(
            Thread.currentThread().getName() + ": " + n
        ));

        System.out.println("\n=== Computing Sum in Parallel ===");
        long sum = StreamSupport.stream(new SquareSpliterator(1, 101), true)
                .mapToLong(Integer::longValue)
                .sum();
        System.out.println("Sum of squares from 1² to 100²: " + sum);

        System.out.println("\n=== Finding Max in Parallel ===");
        int max = StreamSupport.stream(new SquareSpliterator(1, 51), true)
                .max(Integer::compareTo)
                .orElse(0);
        System.out.println("Max square (1-50): " + max);

        System.out.println("\n=== Filtering Even Squares in Parallel ===");
        long countEvenSquares = StreamSupport.stream(new SquareSpliterator(1, 21), true)
                .filter(n -> n % 2 == 0)
                .count();
        System.out.println("Count of even squares (1-20): " + countEvenSquares);
    }
}

Key Features Demonstrated:

• Perfect parallel splitting via balanced trySplit()
• Thread-independent computation (no shared mutable state)
• Rich characteristics enabling Stream API optimizations
• Real-world stream operations: sum, max, filter, count

Sample Output shows different threads processing different ranges, proving effective parallelization.

Why Spliterators Matter

Spliterators provide complete control over stream data sources. They enable:

• Custom data generation (ranges, algorithms, files, networks)
• Optimal parallel processing with balanced workload distribution
• Metadata-driven performance tuning through characteristics

This architecture makes Java Stream API uniquely scalable, from simple collections to complex distributed data processing pipelines.