Module rand::rngs[][src]

Random number generators and adapters

Background: Random number generators (RNGs)

Computers cannot produce random numbers from nowhere. We classify random number generators as follows:

Traits and functionality

All RNGs implement the RngCore trait, as a consequence of which the Rng extension trait is automatically implemented. Secure RNGs may additionally implement the CryptoRng trait.

All PRNGs require a seed to produce their random number sequence. The SeedableRng trait provides three ways of constructing PRNGs:

Use the rand_core crate when implementing your own RNGs.

Our generators

This crate provides several random number generators:

The algorithms selected for StdRng and [SmallRng] may change in any release and may be platform-dependent, therefore they should be considered not reproducible.

Additional generators

TRNGs: The rdrand crate provides an interface to the RDRAND and RDSEED instructions available in modern Intel and AMD CPUs. The rand_jitter crate provides a user-space implementation of entropy harvesting from CPU timer jitter, but is very slow and has security issues.

PRNGs: Several companion crates are available, providing individual or families of PRNG algorithms. These provide the implementations behind StdRng and [SmallRng] but can also be used directly, indeed should be used directly when reproducibility matters. Some suggestions are: rand_chacha, rand_pcg, rand_xoshiro. A full list can be found by searching for crates with the rng tag.

Modules

adapter

Wrappers / adapters forming RNGs

mock

Mock random number generator

Structs

OsRng

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

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

A reference to the thread-local generator