👀 Views: 349 💬 Answers: 1 📅 Created: 2025-06-03

java stream performance parallel-processing Java

I'm prototyping a solution and I keep running into I'm facing significant performance issues when using parallel streams in Java 11 for processing large datasets. My code snippet looks like this: ```java List<Data> largeDataset = ...; // Assume this is initialized with a large dataset List<Result> results = largeDataset.parallelStream() .map(data -> process(data)) .collect(Collectors.toList()); ``` The `process(data)` function involves some computational logic and is not I/O bound. However, when I run this code, I see that the processing time actually increases compared to using a sequential stream. Specifically, I noticed that for a dataset of about 100,000 records, the parallel stream takes around 5 seconds, while the sequential stream takes about 3 seconds. I tried tuning the number of available processors using `System.setProperty("java.util.concurrent.ForkJoinPool.common.parallelism", "4");`, but it didn't seem to improve the situation. I also considered the overhead of splitting tasks, but in my case, the `process` method is computationally intensive, so I expected some gains from parallel processing. Moreover, I'm concerned about potential thread contention since `process(data)` modifies some shared state (a `Map` that tracks counts). Could this be causing the slowdown? I used `ConcurrentHashMap` for thread safety, but I'm wondering if the contention is still affecting performance. Is there a way to optimize the parallel stream for better performance in this scenario? Should I avoid parallel streams altogether when modifying shared state, or are there alternative approaches that could help? I'm working in a Ubuntu 20.04 environment. Has anyone else encountered this? For reference, this is a production CLI tool. This is part of a larger mobile app I'm building. This is for a microservice running on CentOS. Any feedback is welcome!