I've written a couple of blog posts about how the Parallel type class has changed in Cats 2.0, but those posts don't really say much about why someone using Cats should care about Parallel in the first place. The name suggests that it has something to do with running computations at the same time, and while that's one of things you can do with it (via the instance for IO in cats-effect, for example), it has a much, much wider range of applications. This post will focus on a real-world use case for Parallel that at a glance might not seem to have much in common with running things in parallel: accumulating errors while validating form inputs.

Example problem🔗

This morning I remembered a Stack Overflow question about validation in Scala that I asked almost six years ago. In the question I give an example where I have a list of pairs of strings, and I want to parse all of the strings into integers, while also verifying that the second number in each pair is larger than the first. So the following would be valid input (in CSV form):

1 , 2 -100 , 100 200 , 3000

While this would not:

a , 1 b , c 1 , 0

I want to be able to present a complete list of problems to the person who submitted the data, so I need to be able to accumulate errors while parsing and validating. In the second data set, for example, there are four errors: three non-integer strings and one wrongly ordered pair.

Without Parallel🔗

Suppose I have some Scala code for parsing numbers and validating the pairs:

import scala.util.Try case class InvalidSizes ( x : Int , y : Int ) extends Exception ( s " Error: $x is not smaller than $y ! " ) def parseInt ( input : String ): Either [ Throwable , Int ] = Try(input.toInt).toEither def checkValues ( p : ( Int , Int )): Either [ InvalidSizes , ( Int , Int )] = if (p._1 >= p._2) Left(InvalidSizes(p._1, p._2)) else Right(p)

I can then compose these methods using operations from Cats type classes like Traverse (my original Stack Overflow question used Scalaz, but in this post I'll translate):

import cats.data.EitherNel import cats.instances.either. _ import cats.instances.list. _ import cats.syntax.apply. _ import cats.syntax.either. _ import cats.syntax.traverse. _ def checkParses ( p : ( String , String )): EitherNel [ Throwable , ( Int , Int )] = (parseInt(p._1).toValidatedNel, parseInt(p._2).toValidatedNel).tupled.toEither def parse ( input : List [( String , String )]): EitherNel [ Throwable , List [( Int , Int )]] = input.traverse( checkParses( _ ).flatMap(checkValues( _ ).toEitherNel).toValidated ).toEither

This solution works just fine:

scala> val badInput = List((" a ", " 1 "), (" b ", " c "), (" 1 ", " 0 ")) badInput: List [( String , String )] = List((a, 1 ), (b,c), ( 1 , 0 )) scala> parse(badInput).leftMap( _ .toList.foreach(println)) java.lang.NumberFormatException: For input string: " a " java.lang.NumberFormatException: For input string: " b " java.lang.NumberFormatException: For input string: " c " InvalidSizes: Error : 1 is not smaller than 0 !

The problem that I'm complaining about in this ancient Stack Overflow question is that all of these conversions between Either and Validated feel inelegant:

I bounce back and forth between ValidationNel and \/ [Scalaz's Either ] as appropriate depending on whether I need error accumulation or monadic binding.

I didn't know it at the time, but I was asking for Parallel , which as far as I can tell was first introduced into a library in a mainstream language a year later.

With Parallel🔗

The Parallel type class really isn't anything more than a way to generalize this process of going back and forth between monadic and applicative contexts. It allows us to rewrite our checkParses and parse methods without ever referring to Validated :

import cats.data.EitherNel import cats.instances.either. _ import cats.instances.list. _ import cats.instances.parallel. _ import cats.syntax.either. _ import cats.syntax.parallel. _ def checkParses ( p : ( String , String )): EitherNel [ Throwable , ( Int , Int )] = (parseInt(p._1).toEitherNel, parseInt(p._2).toEitherNel).parTupled def parse ( input : List [( String , String )]): EitherNel [ Throwable , List [( Int , Int )]] = input.parTraverse(checkParses( _ ).flatMap(checkValues( _ ).toEitherNel))

These do exactly the same thing as the versions above, but instead of having to convert Either to Validated manually when we want error accumulation (and then convert back to Either when we want monadic binding), all we have to do is use the par versions of tupled and traverse .

These parallelized operations are available here because Either[E, ?] has a Parallel instance (assuming we have a Semigroup for E ). This instance encodes the fact that we can convert Either values to Validated values and then use applicative operations on those values to get error accumulation, but it hides all of that from the user, who only has to know that somehow validation is being done in parallel instead of sequentially (where it would fail fast).