When should you prefer parallel streams and when should you avoid it?

Java's parallel streams are a powerful tool for handling data processing tasks efficiently by utilizing multiple threads. However, knowing when to use them is crucial for maximizing performance and ensuring code clarity.

When to Prefer Parallel Streams

• Large Data Sets: When dealing with significant amounts of data, parallel streams can significantly speed up processing time.
• Independent Operations: If the operations performed on data items are independent, parallel streams can be very effective.
• CPU-bound Tasks: Parallel streams are suited for tasks that are CPU-intensive, as they can distribute processing load across multiple cores.

When to Avoid Parallel Streams

• Small Data Sets: For small collections, the overhead of managing multiple threads may outweigh the benefits.
• Stateful Operations: If your operations rely on shared mutable state, parallel streams can lead to concurrency issues and bugs.
• Order of Processing: When the order of operations is crucial, using parallel streams may not guarantee the correct sequence of execution.

Example Code

        List numbers = Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
        // Using parallel stream
        int sum = numbers.parallelStream()
                          .filter(n -> n % 2 == 0)
                          .mapToInt(Integer::intValue)
                          .sum();
        System.out.println("Sum of even numbers: " + sum);