Module flow_sdk::algorithms::rand::rngs[−][src]

Expand description

Random number generators and adapters for common usage:

Background — Random number generators (RNGs)

Computers are inherently deterministic, so to get random numbers one either has to use a hardware generator or collect bits of entropy from various sources (e.g. event timestamps, or jitter). This is a relatively slow and complicated operation.

Generally the operating system will collect some entropy, remove bias, and use that to seed its own PRNG; OsRng provides an interface to this. JitterRng is an entropy collector included with Rand that measures jitter in the CPU execution time, and jitter in memory access time. EntropyRng is a wrapper that uses the best entropy source that is available.

Pseudo-random number generators

What is commonly used instead of “true” random number renerators, are pseudo-random number generators (PRNGs), deterministic algorithms that produce an infinite stream of pseudo-random numbers from a small random seed. PRNGs are faster, and have better provable properties. The numbers produced can be statistically of very high quality and can be impossible to predict. (They can also have obvious correlations and be trivial to predict; quality varies.)

There are two different types of PRNGs: those developed for simulations and statistics, and those developed for use in cryptography; the latter are called Cryptographically Secure PRNGs (CSPRNG or CPRNG). Both types can have good statistical quality but the latter also have to be impossible to predict, even after seeing many previous output values. Rand provides a good default algorithm from each class:

  • SmallRng is a PRNG chosen for low memory usage, high performance and good statistical quality.
  • StdRng is a CSPRNG chosen for good performance and trust of security (based on reviews, maturity and usage). The current algorithm is HC-128, which is one of the recommendations by ECRYPT’s eSTREAM project.

The above PRNGs do not cover all use-cases; more algorithms can be found in the prng module, as well as in several other crates. For example, you may wish a CSPRNG with significantly lower memory usage than StdRng while being less concerned about performance, in which case ChaChaRng is a good choice.

One complexity is that the internal state of a PRNG must change with every generated number. For APIs this generally means a mutable reference to the state of the PRNG has to be passed around.

A solution is ThreadRng. This is a thread-local implementation of StdRng with automatic seeding on first use. It is the best choice if you “just” want a convenient, secure, fast random number source. Use via the [thread_rng] function, which gets a reference to the current thread’s local instance.

Seeding

As mentioned above, PRNGs require a random seed in order to produce random output. This is especially important for CSPRNGs, which are still deterministic algorithms, thus can only be secure if their seed value is also secure. To seed a PRNG, use one of:

  • [FromEntropy::from_entropy]; this is the most convenient way to seed with fresh, secure random data.
  • [SeedableRng::from_rng]; this allows seeding from another PRNG or from an entropy source such as EntropyRng.
  • [SeedableRng::from_seed]; this is mostly useful if you wish to be able to reproduce the output sequence by using a fixed seed. (Don’t use StdRng or SmallRng in this case since different algorithms may be used by future versions of Rand; use an algorithm from the [prng] module.)

Conclusion

  • [thread_rng] is what you often want to use.
  • If you want more control, flexibility, or better performance, use StdRng, SmallRng or an algorithm from the [prng] module.
  • Use [FromEntropy::from_entropy] to seed new PRNGs.
  • If you need reproducibility, use [SeedableRng::from_seed] combined with a named PRNG.

More information and notes on cryptographic security can be found in the [prng] module.

Examples

Examples of seeding PRNGs:

use rand::prelude::*;

// StdRng seeded securely by the OS or local entropy collector:
let mut rng = StdRng::from_entropy();

// SmallRng seeded from thread_rng:
let mut rng = SmallRng::from_rng(thread_rng())?;

// SmallRng seeded by a constant, for deterministic results:
let seed = [1,2,3,4, 5,6,7,8, 9,10,11,12, 13,14,15,16]; // byte array
let mut rng = SmallRng::from_seed(seed);

Implementing custom RNGs

If you want to implement custom RNG, see the [rand_core] crate. The RNG will have to implement the [RngCore] trait, where the [Rng] trait is build on top of.

If the RNG needs seeding, also implement the [SeedableRng] trait.

[CryptoRng] is a marker trait cryptographically secure PRNGs can implement.

Modules

adapter

Wrappers / adapters forming RNGs

mock

Mock random number generator

Structs

EntropyRng

An interface returning random data from external source(s), provided specifically for securely seeding algorithmic generators (PRNGs).

JitterRng

A true random number generator based on jitter in the CPU execution time, and jitter in memory access time.

OsRng

A random number generator that retrieves randomness straight from the operating system.

SmallRng

An RNG recommended when small state, cheap initialization and good performance are required. The PRNG algorithm in SmallRng is chosen to be efficient on the current platform, without consideration for cryptography or security. The size of its state is much smaller than for StdRng.

StdRng

The standard RNG. The PRNG algorithm in StdRng is chosen to be efficient on the current platform, to be statistically strong and unpredictable (meaning a cryptographically secure PRNG).

ThreadRng

The type returned by thread_rng, essentially just a reference to the PRNG in thread-local memory.

Enums

TimerError

An error that can occur when JitterRng::test_timer fails.