How to optimize the performance of a large Scala collection transformation using Cats and parallelism?
I'm currently working on a Scala application that processes a substantial dataset of approximately 10 million records, stored in a List. The transformation involves filtering and mapping the data, and I'm using Cats for functional programming paradigms. However, I've noticed that the operation is considerably slow, taking around 30 seconds on my local machine. I've tried using `parallelCollections` to speed things up, but I'm not achieving the expected performance gains. Here's a simplified version of my code: ```scala import cats.implicits._ import scala.collection.parallel.CollectionConverters._ val records: List[Record] = getRecords() // Assume this fetches a List of records val processedRecords = records.par.filter(_.isValid).map(record => processRecord(record)) ``` While this does run on multiple threads, it doesn't seem to significantly reduce the processing time. I've also explored using `Future`, but given the sheer volume of data, it seems to introduce its own overhead. The result is that I end up with large memory usage and occasional `OutOfMemoryError` exceptions. Is there a more efficient way to handle this data transformation in Scala, while leveraging Cats and ensuring optimal performance? Any insights on tuning parallel collections or alternative approaches would be greatly appreciated. I'm running Scala 2.13 and Cats 2.6.0.