Describe the concept of Spliterators in Java. Enumerate the key methods provided by the Spliterator interface. Illustrate the usage of Spliterators with a suitable example

Describe the concept of Spliterators in Java. Enumerate the key methods provided by the Spliterator interface. Illustrate the usage of Spliterators with a suitable example.

Answer:-

Spliterator in Java

A Spliterator (split + iterator) is an advanced iterator introduced in Java 8. It is used to traverse and partition elements of a collection (like ArrayList, HashSet, etc.) for parallel and sequential processing.

Key Features of Spliterator

• Can split the collection into multiple parts for parallel processing.
• Works with Stream API.
• Supports both sequential and parallel traversal.
• Found in the java.util package.

Key Methods of Spliterator Interface

Common Characteristics Flags

• Spliterator.ORDERED
• Spliterator.SIZED
• Spliterator.SORTED
• Spliterator.NONNULL
• Spliterator.IMMUTABLE
• Spliterator.CONCURRENT

Example Program: Using Spliterator

import java.util.*;
import java.util.function.Consumer;

public class SpliteratorDemo {
    public static void main(String[] args) {
        List<String> names = Arrays.asList("Kushal", "Aman", "Zara", "Neha", "Ravi");

        // Get Spliterator
        Spliterator<String> split1 = names.spliterator();

        // Try splitting
        Spliterator<String> split2 = split1.trySplit();

        // First half
        System.out.println("First Spliterator:");
        split1.forEachRemaining(System.out::println);

        // Second half
        System.out.println("\nSecond Spliterator:");
        if (split2 != null) {
            split2.forEachRemaining(System.out::println);
        }

        // Size estimation
        System.out.println("\nEstimated size: " + names.spliterator().estimateSize());
    }
}

Output

First Spliterator:
Kushal
Aman

Second Spliterator:
Zara
Neha
Ravi

Estimated size: 5

Summary

• Spliterator is useful for efficient traversal and partitioning, especially in parallel streams.
• It improves performance with large datasets using fork/join strategies.