Streams in Scala provide a lazy evaluation mechanism where elements are computed on-demand rather than being eagerly evaluated and stored in memory. This allows for efficient memory utilization, especially when dealing with large datasets or potentially infinite sequences of data. By Aniefiok Akpan.
There are many reasons for using a stream-processing approach when writing software. In this blog post I’m going to focus on just one of those reasons: Memory Management.
The article covers the following topics:
- Why Streams
- Scala Stream
- Call-by-name ( CBN )
- A Simple use-case of Scala Stream
- Alternative Libraries that implement Streams
// With LazyList, the content of the files is not loaded into memory
files.map {
case (file) =>
Source.fromFile(file).getLines().to(LazyList)
.filter(_.contains("Success"))
.take(10)
}
The author highlights the advantages of processing elements one at a time and retaining only the necessary elements in memory. With streams, you can confidently tackle memory-intensive tasks, knowing that the memory footprint is optimized, leading to more stable and scalable applications.There is also GitHub repo provided showcasing stream use. Good read!
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