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It turned out that many folks were unclear on the difference between laziness and delayed computation:

  • Delayed Computation is when you have a value like answer : () -> Int that you can evaluate later. You only do all the computation when you say answer () at some later time. If you call answer () four times, you do the computation four times.

  • Laziness is an optimization on top of delayed computation. It is just like having answer : () -> Int but when this function is evaluated for the first time, the results are saved. So if you call answer () four times, you do the computation one time.

In all the cases in Elm that I have heard of that use this library, folks only really needed delayed computation and ended up with simpler code when they went that way.

So in the end, there are two major reasons to stop supporting laziness:

  1. It is overkill for all the scenarios I have seen in Elm.
  2. It allows the creation of cyclic data, significantly complicating GC.

With laziness you can create a list like ones = 1 :: ones that refers to itself. Without laziness, there is no way to create cyclic data in Elm. That means we can use a naive reference counting approach to collect garbage if we wanted. So although people have dreamed up data structures that use laziness in interesting ways, I do not feel these cases are compelling enough to commit to the collateral complications.

What follows is some of content from the old README.


Laziness + Time — Over time, laziness can become a bad strategy. As a very simple example, think of a timer that counts down from 10 minutes, decrementing every second. Each step is very cheap to compute. You subtract one from the current time and store the new time in memory, so each step has a constant cost and memory usage is constant. Great! If you are lazy, you say “here is how you would subtract one” and store that entire computation in memory. This means our memory usage grows linearly as each second passes. When we finally need the result, we might have 10 minutes of computation to run all at once. In the best case, this introduces a delay that no one really notices. In the worst case, this computation is actually too big to run all at once and crashes. Just like with dishes or homework, being lazy over time can be quite destructive.

Laziness + Concurrency — When you add concurrency into the mix, you need to be even more careful with laziness. As an example, say we are running expensive computations on three worker threads, and the results are sent to a fourth thread just for rendering. If our three worker threads are doing their work lazily, they “finish” super quick and pass the entire workload onto the render thread. All the work we put into designing this concurrent system is wasted, everything is run sequentially on the render thread! It is just like working on a team with lazy people. You have to pay the cost of coordinating with them, but you end up doing all the work anyway. You are better off making things single threaded!

Learn More

One of the most delightful uses of laziness is to create infinite streams of values. Hopefully we can get a set of interesting challenges together so you can run through them and get comfortable.

For a deeper dive, Chris Okasaki's book Purely Functional Data Structures and thesis have interesting examples of data structures that get great benefits from laziness, and hopefully it will provide some inspiration for the problems you face in practice.

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