# [−][src]Module rand::rngs

## Background: Random number generators (RNGs)

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

• "True" random number generators (TRNGs) use hard-to-predict data sources (e.g. the high-resolution parts of event timings and sensor jitter) to harvest random bit-sequences, apply algorithms to remove bias and estimate available entropy, then combine these bits into a byte-sequence or an entropy pool. This job is usually done by the operating system or a hardware generator (HRNG).
• "Pseudo"-random number generators (PRNGs) use algorithms to transform a seed into a sequence of pseudo-random numbers. These generators can be fast and produce well-distributed unpredictable random numbers (or not). They are usually deterministic: given algorithm and seed, the output sequence can be reproduced. They have finite period and eventually loop; with many algorithms this period is fixed and can be proven sufficiently long, while others are chaotic and the period depends on the seed.
• "Cryptographically secure" pseudo-random number generators (CSPRNGs) are the sub-set of PRNGs which are secure. Security of the generator relies both on hiding the internal state and using a strong algorithm.

## 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:

• `from_seed` accepts a type specific to the PRNG
• `from_rng` allows a PRNG to be seeded from any other RNG
• `seed_from_u64` allows any PRNG to be seeded from a `u64` insecurely
• `from_entropy` securely seeds a PRNG from fresh entropy

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.

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.
 EntropyRng DeprecatedAn interface returning random data from external source(s), provided specifically for securely seeding algorithmic generators (PRNGs). OsRng A random number generator that retrieves randomness from from the operating system. SmallRng A small-state, fast non-crypto PRNG 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.