🔧 Optimizing Stream API Usage in Java for Large Data Sets

Hi everyone,

I wanted to share a quick optimization tip for those working with large datasets in Java using the Stream API. I recently encountered a performance bottleneck in one of my projects and found that using parallelStream() made a significant difference.

Here's a basic example:

`**List data = getLargeDataSet();

// Before: Normal stream
List filteredData = data.stream()
.filter(s -> s.contains("keyword"))
.collect(Collectors.toList());

// After: Parallel stream for better performance on large datasets
List filteredData = data.parallelStream()
.filter(s -> s.contains("keyword"))
.collect(Collectors.toList());**`

By switching to parallelStream(), the processing time for filtering large datasets reduced significantly on multi-core processors. However, be cautious when using parallelStream() in scenarios where thread safety is a concern or when working with smaller data sets, as the overhead may not always justify the performance gain.

I'd love to hear your thoughts or other optimization suggestions when working with Java Streams!

Cheers!