Java: Trouble with Lambda Expressions in Parallel Streams Causing Unexpected Results
I'm having a hard time understanding I've been banging my head against this for hours... I'm stuck trying to After trying multiple solutions online, I still can't figure this out. I'm relatively new to this, so bear with me. I am currently working with an scenario when using parallel streams in Java to process a list of integers and apply a lambda expression. My goal is to filter out even numbers and then square the remaining odd numbers. However, I am experiencing inconsistent results when executing the operation in parallel compared to sequential execution. Here’s the code I am using: ```java import java.util.Arrays; import java.util.List; import java.util.stream.Collectors; public class ParallelStreamExample { public static void main(String[] args) { List<Integer> numbers = Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9, 10); List<Integer> result = numbers.parallelStream() .filter(n -> n % 2 != 0) .map(n -> n * n) .collect(Collectors.toList()); System.out.println(result); } } ``` When I run this code, sometimes I get the expected output like `[1, 9, 25, 49, 81]`, but other times I see unexpected values in the results, including duplicates and even numbers appearing in the output. I have also tried using `forEach` instead of `collect` to debug, but it didn't reveal any useful insights. I suspect that the scenario might be related to the way parallel streams handle the order of operations, but I am unsure how to resolve it. I've read some articles suggesting that shared mutable state can lead to unpredictable results, but I am not modifying any shared state here. Is there something I am missing? Should I enforce sequential processing instead, or is there a way to safely use parallel streams in this scenario? Any insights would be greatly appreciated. Am I missing something obvious? What are your experiences with this? For context: I'm using Java on Windows 10. I've been using Java for about a year now. I'd really appreciate any guidance on this. This is happening in both development and production on Linux.