[swift-evolution] [Proposal] Random Unification

[Chris Lattner]

> On Sep 11, 2017, at 9:43 PM, Brent Royal-Gordon <brent at architechies.com> wrote:
> 
>> On Sep 9, 2017, at 10:31 PM, Chris Lattner via swift-evolution <swift-evolution at swift.org <mailto:swift-evolution at swift.org>> wrote:
>> 
>> - I’d love to see several of the most common random kinds supported, and I agree it would be nice (but not required IMO) for the default to be cryptographically secure.
> 
> I would be very careful about choosing a "simple" solution. There is a log, sad history of languages trying to provide a "simple" random number generator and accidentally providing a powerful footgun instead. But:
> 
>> - We should avoid the temptation to nuke this mosquito with a heavy handed solution designed to solve all of the world’s problems: For example, the C++ random number stuff is crazily over-general.  The stdlib should aim to solve (e.g.) the top 3 most common cases, and let a more specialized external library solve the fully general problem (e.g. seed management, every distribution imaginable, etc).
> 
> 
> That's not to say we need to have seven engines and twenty distributions like C++ does. The standard library is not a statistics package; it exists to provide basic abstractions and fundamental functionality. I don't think it should worry itself with distributions at all. I think it needs to provide:
> 
> 	1. The abstraction used to plug in different random number generators (i.e. an RNG protocol of some kind).
> 
> 	2. APIs on existing standard library types which perform basic randomness-related functions correctly—essentially, encapsulating Knuth. (Specifically, I think selecting a random element from a collection (which also covers generating a random integer in a range), shuffling a mutable collection, and generating a random float will do the trick.)
> 
> 	3. A default RNG with a conservative design that will sometimes be too slow, but will never be insufficiently random.
> 
> If you want to pick elements with a Poisson distribution, go get a statistics framework; if you want repeatable random numbers for testing, use a seedable PRNG from XCTest or some other test tools package. These can leverage the standard library's RNG protocol to work with existing random number generators or random number consumers.

+1 to this general plan!

-Chris


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