Java 17: implementing performance optimization when using Streams in a large data processing application
I'm working on a personal project and I've searched everywhere and can't find a clear answer. I'm stuck on something that should probably be simple. I'm working with important performance optimization when using Java Streams for processing large datasets in a Java 17 application. I've implemented a pipeline to read, filter, and transform a collection of over a million records, but the processing time is unacceptably high, often taking several minutes to complete. Here's a simplified version of my code: ```java List<Record> records = fetchRecordsFromDatabase(); List<ProcessedRecord> processedRecords = records.stream() .filter(record -> record.getValue() > 100) // filtering condition .map(record -> new ProcessedRecord(record.getId(), record.getValue() * 2)) // transformation .collect(Collectors.toList()); ``` Although the logic seems straightforward, I've noticed that the performance degrades significantly with larger datasets. Running a profiler, I found that a lot of time is spent in the filtering step. I tried increasing the parallelism with `.parallelStream()`, but it didn't yield the expected performance improvement and sometimes even made it worse due to thread contention. Additionally, I've checked the JVM flags and heap size, ensuring there’s enough memory allocated. I also tried optimizing the `fetchRecordsFromDatabase()` method by using pagination, but it didn’t resolve the scenario. What changes can I make to improve the performance of this stream processing? Are there specific patterns or techniques that I should consider for handling large datasets effectively? Any insights or best practices for using Streams in Java 17 would be greatly appreciated! This is part of a larger CLI tool I'm building. I'd really appreciate any guidance on this. Is there a better approach? I've been using Java for about a year now. Thanks in advance!