API Documentation: NonEmptyList


We start with two examples of NonEmptyLists

Usage in Validated and Ior

If you have had the opportunity of taking a look to Validated or Ior, you'll find that a common case is to use NonEmptyList with one of these data structures.

Why? Because it fits nicely in the error reporting cases. As stated by its name, NonEmptyList is a specialized data type that has at least one element. Otherwise it behaves like a normal List. For sum types like Validated (and Ior), it does not make sense to have a Invalid with no errors: no errors means it is a Valid! By using NonEmptyList, we explicitly say in the type that:

If it is a Invalid, then there is at least one error.

This is much more precise and we don't have to wonder whether the list of errors might be empty when reporting them later on.

Avoiding Option by demanding more specific arguments

As functional programmers, we naturally shy away from partial functions that can throw exceptions like the famous head method on, e.g., List.

Let's take as an example a method that calculates the average:

def average(xs: List[Int]): Double = {
  xs.sum / xs.length.toDouble

Clearly, this is not a valid definition for empty lists, because division by zero will throw an exception. To fix this, one way is to return an Option instead of a Double right away:

def average(xs: List[Int]): Option[Double] = if (xs.isEmpty) {
} else {
  Some(xs.sum / xs.length.toDouble)

That works and is safe, but this only masks the problem of accepting invalid input. By using Option, we extend the average function with the logic to handle empty lists. Additionally, all callers have to handle the Option cases, maybe over and over again. While better than failing with an exception, this is far from perfect.

Instead what we would like to express is that average does not make sense at all for an empty list. Luckily, cats defines the NonEmptyList. As the name says, this represents a list that cannot, by construction, be empty. So given a NonEmptyList[A] you can be sure that there is at least one A in there.

Let's see how that impacts your average method:

def average(xs: NonEmptyList[Int]): Double = {
  xs.reduceLeft(_+_) / xs.length.toDouble

With that, average is free of any "domain invariant validation" and instead can focus on the actual logic of computing the average of the list. This ties in nicely with the recommendation of shifting your validation to the very borders of your program, where the input enters your system.

Structure of a NonEmptyList

NonEmptyList is defined as follows:

final case class NonEmptyList[+A](head: A, tail: List[A]) {
  // Implementation elided

The head of the NonEmptyList will be non-empty. Meanwhile, the tail can have zero or more elements contained in a List.

Defined for all its elements

An important trait of NonEmptyList is the totality. For List specifically, both head and tail are partial: they are only well-defined if it has at least one element.

NonEmptyList on the other hand, guarantees you that operations like head and tail are defined, because constructing an empty NonEmptyList is simply not possible!

Constructing a NonEmptyList

To construct a NonEmptyList you have different possibilities.

Using one

If you want to construct a collection with only one argument, use
// res0: NonEmptyList[Int] = NonEmptyList(head = 42, tail = List())

Using of

The NonEmptyList.of method on the companion of NonEmptyList as the signature:

def of[A](head: A, tail: A*): NonEmptyList[A]

It accepts an argument list with at least one A followed by a varargs argument for the tail. Call it like this:

// res1: NonEmptyList[Int] = NonEmptyList(head = 1, tail = List())
NonEmptyList.of(1, 2)
// res2: NonEmptyList[Int] = NonEmptyList(head = 1, tail = List(2))
NonEmptyList.of(1, 2, 3, 4)
// res3: NonEmptyList[Int] = NonEmptyList(head = 1, tail = List(2, 3, 4))

There also is ofInitLast which takes a normal List[A] for the prefix and a last element:

NonEmptyList.ofInitLast(List(), 4)
// res4: NonEmptyList[Int] = NonEmptyList(head = 4, tail = List())
NonEmptyList.ofInitLast(List(1,2,3), 4)
// res5: NonEmptyList[Int] = NonEmptyList(head = 1, tail = List(2, 3, 4))

Using fromList

There is also NonEmptyList.fromList which returns an Option[NonEmptyList[A]]:

// res6: Option[NonEmptyList[Nothing]] = None
// res7: Option[NonEmptyList[Int]] = Some(
//   value = NonEmptyList(head = 1, tail = List(2, 3))
// )

Last but not least, there is .toNel if you import the syntax for list:

import cats.syntax.list._
// res8: Option[NonEmptyList[Int]] = Some(
//   value = NonEmptyList(head = 1, tail = List(2, 3))
// )

Using fromFoldable and fromReducible

Even more general, you can use NonEmptyList.fromFoldable and NonEmptyList.fromReducible. The difference between the two is that fromReducible can avoid the Option in the return type, because it is only available for non-empty datastructures.

Here are some examples:

import cats.syntax.all._

// res9: Option[NonEmptyList[Nothing]] = None
// res10: Option[NonEmptyList[Int]] = Some(
//   value = NonEmptyList(head = 1, tail = List(2, 3))
// )

// res11: Option[NonEmptyList[Int]] = Some(
//   value = NonEmptyList(head = 42, tail = List())
// )
// res12: Option[NonEmptyList[Int]] = Some(
//   value = NonEmptyList(head = 42, tail = List())
// )

// Everything that has a Foldable instance!
NonEmptyList.fromFoldable(Either.left[String, Int]("Error"))
// res13: Option[NonEmptyList[Int]] = None
NonEmptyList.fromFoldable(Either.right[String, Int](42))
// res14: Option[NonEmptyList[Int]] = Some(
//   value = NonEmptyList(head = 42, tail = List())
// )

// Avoid the Option for things with a `Reducible` instance
NonEmptyList.fromReducible(NonEmptyVector.of(1, 2, 3))
// res15: NonEmptyList[Int] = NonEmptyList(head = 1, tail = List(2, 3))