How to efficiently handle large datasets using Scala's Akka Streams with backpressure?
I'm confused about This might be a silly question, but I'm maintaining legacy code that I've been banging my head against this for hours... I'm sure I'm missing something obvious here, but I'm working on a data processing application using Scala 2.13 and Akka Streams 2.6.15, and I'm running into issues with processing large datasets. My current setup involves reading data from a database and streaming it through a series of transformations before writing the results back to another database. However, I'm experiencing important performance optimization, and I suspect it might be related to backpressure handling. I've set up my flow like this: ```scala val source = DatabaseSource() // hypothetical database source val flow = Flow[Data] .map(data => transform(data)) // some transformation logic .buffer(100, OverflowStrategy.backpressure) val sink = DatabaseSink() // hypothetical database sink val graph = source.via(flow).to(sink) ``` When I run this, I see a lot of backpressure warnings in the logs, and occasionally, it fails with: ``` akka.stream.StreamTcpException: Stream failed because of "exceeded max buffer capacity" ``` I've tried experimenting with the buffer size and overflow strategies, but the performance doesn't seem to improve significantly. Additionally, I'm not sure if I'm correctly managing the backpressure signals coming from the sink. Could someone please provide insights on how to best optimize this flow for handling large datasets? Are there specific practices or patterns I should consider to improve throughput while ensuring that I handle backpressure effectively? Any examples would be appreciated! I'm working in a Ubuntu 20.04 environment. Could someone point me to the right documentation? I'm using Scala LTS in this project. Cheers for any assistance! I'm using Scala 3.11 in this project. What would be the recommended way to handle this? How would you solve this? The project is a desktop app built with Scala. Thanks for any help you can provide!