Eval

Eval is a data type for controlling synchronous evaluation. Its implementation is designed to provide stack-safety at all times using a technique called trampolining. There are two different factors that play into evaluation: memoization and laziness. Memoized evaluation evaluates an expression only once and then remembers (memoizes) that value. Lazy evaluation refers to when the expression is evaluated. We talk about eager evaluation if the expression is immediately evaluated when defined and about lazy evaluation if the expression is evaluated when it’s first used. For example, in Scala, a lazy val is both lazy and memoized, a method definition def is lazy, but not memoized, since the body will be evaluated on every call. A normal val evaluates eagerly and also memoizes the result. Eval is able to express all of these evaluation strategies and allows us to chain computations using its Monad instance.

Eval.now

First of the strategies is eager evaluation, we can construct an Eval eagerly using Eval.now:

import cats.Eval
// import cats.Eval

import cats.implicits._
// import cats.implicits._

val eager = Eval.now {
  println("Running expensive calculation...")
  1 + 2 * 3
}
// Running expensive calculation...
// eager: cats.Eval[Int] = Now(7)

We can run the computation using the given evaluation strategy anytime by using the value method. eager.value // res0: Int = 7 

val eagerEval = Eval.now {
  println("This is eagerly evaluated")
  1 :: 2 :: 3 :: Nil
}

//when/then
eagerEval.value shouldBe (resultList: List[Int])

Eval.later

If we want lazy evaluation, we can use Eval.later In this case

val lazyEval = Eval.later {
  println("Running expensive calculation...")
  1 + 2 * 3
}
// lazyEval: cats.Eval[Int] = cats.Later@6c2b03e9

lazyEval.value
// Running expensive calculation...
// res1: Int = 7

lazyEval.value
// res2: Int = 7

Notice that “Running expensive calculation” is printed only once, since the value was memoized internally. Meaning also that the resulted operation was only computed once. Eval.later is different to using a lazy val in a few different ways. First, it allows the runtime to perform garbage collection of the thunk after evaluation, leading to more memory being freed earlier. Secondly, when lazy vals are evaluated, in order to preserve thread-safety, the Scala compiler will lock the whole surrounding class, whereas Eval will only lock itself. 

val n = 2
var counter = 0
val lazyEval = Eval.later {
  println("This is lazyly evaluated with caching")
  counter = counter + 1
  (1 to n)
}

//when/then
List.fill(n)("").foreach(_ => lazyEval.value)
lazyEval.value shouldBe (resultList: List[Int])
counter shouldBe counterResult

Eval.always

If we want lazy evaluation, but without memoization akin to Function0, we can use Eval.always Here we can see, that the expression is evaluated every time we call .value.

val alwaysEval = Eval.always(println("Always evaluated"))
//Always evaluated
alwaysEval.eval
//Always evaluated
alwaysEval.eval
//Always evaluated
alwaysEval.eval
val n = 4
var counter = 0
val alwaysEval = Eval.always {
  println("This is lazyly evaluated without caching")
  counter = counter + 1
  (1 to n)
}

//when/then
List.fill(n)("").foreach(_ => alwaysEval.value)
counter shouldBe counterAfterListEval
alwaysEval.value shouldBe (resultList: List[Int])
counter shouldBe latestCounter

Eval.defer

Defer a computation which produces an Eval[A] value This is useful when you want to delay execution of an expression which produces an Eval[A] value. Like .flatMap, it is stack-safe. Because Eval guarantees stack-safety, we can chain a lot of computations together using flatMap without fear of blowing up the stack. 

val list = List.fill(3)(0)

//when
val deferedEval: Eval[List[Int]] = Eval.now(list).flatMap(e => Eval.defer(Eval.later(e)))

//then
Eval.defer(deferedEval).value shouldBe (resultList: List[Int])