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Consider adding LazyIO and friends #525

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@sobolevn

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@sobolevn

The thing about IO in multiple languages / libraries is that it is lazy.

Our IO is not lazy by design. It is done, so Python developers can use it like so: impure(print)(1) # prints "1"
But, we also need to think about other problems as well:

  1. Retries, with proper lazy IO one can retry an operation as many times as one wishes: p = impure_lazy(print)(1); p(); p() # prints "1" twice
  2. Semantical identity to Future, currently it is not similar to regular IO, because Futures are lazy: they don't run until they are executed properly
  3. New users will find the similar data-type they already know from other languages / libraries

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sobolevn

sobolevn commented on Jul 31, 2020

@sobolevn
MemberAuthor

LazyIO(x) is basically just IO(lambda: x).

But, when talking about composition, we need to give it a helping hand:

Lazy = Callable[[], _ValueType]

class LazyIO(...):
    inner_value: Lazy[IO[_ValueType]]

    def __init__(self, inner_value: Lazy[IO[_ValueType]]) -> None: ...

    def __call__(self) -> IO[_ValueType]: ...

    def map(self, function: Callable[[_ValueType], _NewValueType]) -> LazyIO[_NewValueType]:  ...

def lazy(inner_value: _ValueType) -> Lazy[_ValueType]: ...

def lazy_impure(function: Callable[..., _ValueType]) -> Callable[..., LazyIO[_ValueType]: ...

It is much better than working with IO(lambda: x)

thepabloaguilar

thepabloaguilar commented on Sep 4, 2020

@thepabloaguilar
Member

This can be useful to create something like lazy_cond that accepts lazy objects as the inner values:

def example(arg: str) -> Result[int, str]:
    return lazy_cond(Result, arg.isnumeric(), Lazy(int), 'Is not a number')

assert example('42') == Success(42)
assert example('string') == Failure('string')

Today is impossible to make something similar and simple

sobolevn

sobolevn commented on Jan 27, 2021

@sobolevn
MemberAuthor

Well, I was wrong. LazyIO is not IO(lambda: x) it is lambda: IO(x)

efagerberg

efagerberg commented on Apr 25, 2021

@efagerberg

I know the documentation mentions IOs are not lazy because it would be more familiar to existing python developers and help with using the impure decorator. However I am skeptical that this is as useful in practice. Most people using this library I assume are starting with some traditional understanding of the IO monad.

Taking my personal experience as an example, I learned explicitly that the IO monad is supposed to be something like a grenade that the caller pulls the pin from when they are ready, and not just a wrapper to tell the user the data came from some nondeterministic world. This common form of IO in my reading has also been used to build on the ideas of functional composition and equational reasoning.

In my example I started in earnest with reading https://github.com/MostlyAdequate/mostly-adequate-guide, maybe my experience is not representative of everyone's. I am just not as sure the value of a simple IO container is compared to the traditional definition. The traditional definition helps more to represent a value is non deterministic but still returns a deterministic value, an IO with some function inside of it.

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          Consider adding LazyIO and friends · Issue #525 · dry-python/returns