[manjalyc]

Starting to program with Java streams is weird because you are utilizing functional constructs in a language that historically had little notion of them. When I first started using them it felt worthless when I could achieve the same thing in classic OOP faster (and often run it faster too). But after a while you get a feel for the fluid style programming streams enable (and imo cleaner code). These days with ChatGPT, its probably a lot easier to get started.

With that said, you should almost always write a stream thinking only sequentially first, then identify steps which can benefit from .parallel() and only parallelize those steps. Its leveraging .parallel() efficiently that provides an advantage at run-time and why I tend to use it.

[vbezhenar]

I guess I was unclear, but I wrote specifically about parallel streams. Of course I use ordinary streams on every day basis. But using parallel stream in a server application which processes dozens of other requests simultaneously and runs on a server with dozen of other applications (very typical use-case for Java) just makes very little sense, because CPUs are already loaded and it'll just result with more context switches. I could imagine use-case for that (very urgent request which must be completed at expense of other requests and includes heavy collection processing), but I've yet to encounter it.

[manjalyc]

Yea, I don't imagine native stream parallelism will help when CPUs are already loaded. Presumably you're using Spring or Rx, in which case you probably can leverage reactive streams and/or Managed Blockers, but thats really just taking advantage of async patterns and not necessarily parallelism. The only case I could envision having a concrete benefit is if you used your own fork-join pool leveraging virtual threads instead of the global fork-join pool to prevent platform threads from hogging CPU, and then used reactive streams that leveraged the virtual threads. Although this would (theoretically) raise responsiveness, it would almost certainly come at the cost of throughput. All that is to say, parallelism generally only provides as much value as you have idle CPU cores.