Build Notes

scala-cli

I found using the resource folder directive to be a convienient and portable way of working with CSV files

//> using scala 3.7.1
//> using dep io.github.quafadas::scautable:{{latest}}
//> using resourceDir ./csvs

import io.github.quafadas.table.*

@main def checkCsv =
  def csv = CSV.resource("cereals.csv")

  csv.take(10).toSeq.ptbln

Mill

Is my preferred build tool. Note that something like this;

def csv : CsvIterator[("col1", "col2", "col3")] = CSV.resource("simple.csv")

May (confusingly) throw a compile error at you. Remember: scautable asks the compiler to analyze the CSV file.

Mill seperates compile resources and run resources. From the compilers point of view, "simple.csv" is indeed not a resource by default in mill.

Now that we know this, it's easy enough to work around in a few ways. Here's one way that adds the runtime resources to the compilers resource path - thus ensuring that the CSV file is available to the compiler, at compile time.

trait ShareCompileResources extends ScalaModule {
  override def compileResources = super.compileResources() ++ resources()
}

Memory

Scautable makes the compiler do some heavy lifting. For large CSV files you may need to increase the memory available to the compiler.

In mill you can do this by adding JVM options to your build.mill file like so:

//| mill-jvm-opts: ["-Xmx1g", "-Xms256m", "-Xss2m"]

In other tools, such as scala-cli, you may be able do this by setting the JAVA_OPTS environment variable - we need to make sure, that the compiler is getting the extra memory, not just the execution of the program - be sure to check your tools documentation for how to do this.