Java 17: How to Efficiently Use the Stream API for Large Data Sets Without Memory Overhead?
I've searched everywhere and can't find a clear answer. I'm working on a project and hit a roadblock. I am currently using Java 17 and trying to process a large list of objects using the Stream API. The list contains around 1 million entries, and I noticed that when I try to perform operations such as filtering and mapping, the application runs out of memory. For example, I have the following code snippet: ```java List<MyObject> myList = // Assume this is populated with 1 million entries List<ProcessedObject> processedList = myList.stream() .filter(obj -> obj.getValue() > 10) .map(obj -> new ProcessedObject(obj.getId(), obj.getValue() * 2)) .collect(Collectors.toList()); ``` When I execute this code, I get an `OutOfMemoryError` and the program crashes. I tried increasing the heap size with `-Xmx2G`, but it still doesn't seem to help. I also considered using `parallelStream()` to improve performance, but I am not sure if that would alleviate the memory scenario or just make it worse. I've read that processing large datasets should be done in chunks or using an iterative approach instead of loading everything into memory at once. However, I'm not sure how to implement this effectively while still leveraging the Stream API. What would be the best approach to process this large dataset efficiently without running into memory issues? Any advice on optimizing this code or restructuring it to handle large amounts of data would be greatly appreciated! My development environment is Ubuntu. Has anyone else encountered this? This is happening in both development and production on macOS.