YA-Rand: Yet Another Rand

Provides simple and fast pseudo/crypto random number generation.

Usage

use ya_rand::*;

// **Correct** instantiation is easy.
// This seeds the RNG using operating system entropy,
// so you never have to worry about the quality of the
// initial state of RNG instances.
let mut rng = new_rng();

// Generate a random number with a given upper bound
let max: u64 = 420;
let val = rng.bound(max);
assert!(val < max);

// Generate a random number in a given range
let min: i64 = -69;
let max: i64 = 69;
let val = rng.range(min, max);
assert!(min <= val && val < max);

// Generate a random floating point value
let val = rng.f64();
assert!(0.0 <= val && val < 1.0);

// Generate a random ascii digit:
// '0' - '9' as a utf-8 character
let digit = rng.ascii_digit();
assert!(digit.is_ascii_digit());

See https://docs.rs/ya-rand/latest/ya_rand/ for full documentation.

lib.rs:

YA-Rand: Yet Another Rand

Provides simple and fast pseudo/crypto random number generation.

But why?

Because rand is very cool and powerful, but kind of an enormous fucking pain in the ass to use, and it's far too large and involved for someone who just a needs to flip a coin once every 7 minutes. But if you're doing some crazy black magic computational sorcery, it almost certainly has something you can use to complete your spell.

Other crates, like fastrand, tinyrand, or oorandom, fall somewhere between "I'm not sure I trust the backing RNG" to "this API is literally just rand but less powerful". Meaning their state size (how many internal bits they hold) is too small for comfort, or they lean into having a properly idiomatic Rust API instead of being straightforward to use. I wanted something easy, but also fast and statistically robust.

So here we are.

Usage

Glob import the contents of the library and use new_rng to create new RNGs wherever you need them. Then call whatever method you require on those instances. All methods available are directly accessible through any generator instance via the dot operator, and are named in a way that should make it easy to quickly identify what you need.

If you need cryptographic security, enable the secure library feature and use new_rng_secure instead.

"How do I access the thread-local RNG?" There isn't one, and unless Rust improves the performance and ergonomics of the TLS implementation, there probably won't ever be. Create a local instance when and where you need one and use it while you need it. If you need an RNG to stick around for awhile, passing it between functions or storing it in structs is a perfectly valid solution. The default RNG is only 32 bytes, so it shouldn't balloon your memory footprint.

use ya_rand::*;

// **Correct** instantiation is easy.
// This seeds the RNG using operating system entropy,
// so you never have to worry about the quality of the
// initial state of RNG instances.
let mut rng = new_rng();

// Generate a random number with a given upper bound
let max: u64 = 420;
let val = rng.bound(max);
assert!(val < max);

// Generate a random number in a given range
let min: i64 = -69;
let max: i64 = 69;
let val = rng.range(min, max);
assert!(min <= val && val < max);

// Generate a random floating point value
let val = rng.f64();
assert!(0.0 <= val && val < 1.0);

// Generate a random ascii digit:
// '0' - '9' as a utf-8 character
let digit = rng.ascii_digit();
assert!(digit.is_ascii_digit());

Features

• std - Enabled by default, but can be disabled for compatibility with no_std environments. Enables normal/exponential distributions and error type conversions for getrandom.
• inline - Marks each YARandGenerator::u64 implementation with #[inline]. Should generally increase runtime performance at the cost of binary size and maybe compile time. You'll have to test your specific use case to determine how much this feature will impact you.
• secure - Enables infrastructure for cryptographically secure random number generation via the chacha20 crate. Moderately increases compile time and binary size.

Details

This crate uses the xoshiro family of pseudo-random number generators. These generators are very fast, of very high statistical quality, and small. They aren't cryptograpically secure, but most users don't need their RNG to be secure, they just need it to be random and fast. The default generator is xoshiro256++, which should provide a large enough period for most users. The xoshiro512++ generator is also provided in case you need a longer period.

All generators output a distinct u64 value on each call, and the various methods used for transforming those outputs into more usable forms are all high-quality and well-understood. Placing an upper bound on these values uses Lemire's method. Doing this inclusively or within a given range are both applications of this same method with simple intermediary steps to alter the bound and apply shifts when needed. This approach is unbiased and quite fast, but for very large bounds performance might degrade slightly, since the algorithm may need to sample the underlying RNG more times to get an unbiased result. If your bound happens to be a power of 2, always use YARandGenerator::bits, since it's nothing more than a bitshift of the u64 provided by the RNG, and will always be as fast as possible.

Floating point values (besides the normal and exponential distributions) are uniformally distributed, with all the possible outputs being equidistant within the given interval. They are not maximally dense, if that's something you need you'll have to generate those values yourself. This approach is very fast, and endorsed by both Lemire and Vigna (the author of the RNGs used in this crate). The normal distribution implementation uses the Marsaglia polar method, returning pairs of independently sampled f64 values. Exponential variates are generated using this approach.

Security

If you're in the market for secure random number generation, you'll want to enable the secure feature, which provides SecureRng and the SecureYARandGenerator trait. It functions identically to the other provided RNGs, but with the addition of SecureYARandGenerator::fill_bytes. The current implementation uses ChaCha with 8 rounds via the chacha20 crate. In the future I'd like to look into doing a custom implementation of ChaCha, but no timeline on that. Why only 8 rounds? Because people who are very passionate about cryptography are convinced that's enough, and I have zero reason to doubt them, nor any capacity to prove them wrong. See the top of page 14 of the Too Much Crypto paper.

The security promises made to the user are identical to those made by ChaCha as an algorithm. It is up to you to determine if those guarantees meet the demands of your use case.

Safety

Generators are seeded using entropy from the underlying OS, and have the potential to fail during creation. But in practice this is extraordinarily unlikely, and isn't something the end-user should ever worry about. Modern Windows versions (10 and newer) have a crypto subsystem that will never fail during runtime, and the error branch should be optimized out.

In the pursuit of consistent performance and no runtime failures, there are no checks performed during runtime in release mode. This means that there are a few areas where the end-user is able to receive garbage after providing garbage. It is expected of the user to provide reasonable values where there is an input to be given: values shouldn't be on the verge of overflow and ranges should always have a max larger than their min. There is very little unsafe used, and it's all easily determined to have no ill side-effects.