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Improve rngs module doc; add OsRng example
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@dhardy
dhardy committed Apr 4, 2019
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11 changes: 11 additions & 0 deletions rand_os/src/lib.rs
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Expand Up @@ -54,6 +54,17 @@ use rand_core::{CryptoRng, RngCore, Error, ErrorKind, impls};
/// The implementation is provided by the [getrandom] crate. Refer to
/// [getrandom] documentation for details.
///
/// ## Example
///
/// ```
/// use rand::rngs::{StdRng, OsRng};
/// use rand::{RngCore, SeedableRng};
///
/// println!("Random number, straight from the OS: {}", OsRng.next_u32());
/// let mut rng = StdRng::from_rng(OsRng).unwrap();
/// println!("Another random number: {}", rng.next_u32());
/// ```
///
/// [getrandom]: https://crates.io/crates/getrandom
#[derive(Clone, Copy, Debug)]
pub struct OsRng;
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210 changes: 84 additions & 126 deletions src/rngs/mod.rs
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Expand Up @@ -6,142 +6,100 @@
// option. This file may not be copied, modified, or distributed
// except according to those terms.

//! Random number generators and adapters for common usage:
//!
//! - [`ThreadRng`], a fast, secure, auto-seeded thread-local generator
//! - [`StdRng`] and [`SmallRng`], algorithms to cover typical usage
//! - [`OsRng`] as an entropy source
//! - [`mock::StepRng`] as a simple counter for tests
//! - [`adapter::ReadRng`] to read from a file/stream
//! - [`adapter::ReseedingRng`] to reseed a PRNG on clone / process fork etc.
//!
//! # 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.
//! Some alternatives are available although not normally required; see the
//! [`rdrand`] and [`rand_jitter`] crates.
//!
//! ## 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`][crate::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`]
//! (from the `rand_chacha` crate) 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 [`OsRng`].
//! - [`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::*;
//! # use rand::Error;
//!
//! // StdRng seeded securely by the OS or local entropy collector:
//! let mut rng = StdRng::from_entropy();
//! # let v: u32 = rng.gen();
//!
//! // SmallRng seeded from thread_rng:
//! # fn try_inner() -> Result<(), Error> {
//! let mut rng = SmallRng::from_rng(thread_rng())?;
//! # let v: u32 = rng.gen();
//! # Ok(())
//! # }
//! # try_inner().unwrap();
//!
//! // 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);
//! # let v: u32 = rng.gen();
//! ```
//!
//!
//! # 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.
//! Random number generators and adapters
//!
//! ## 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
//!
//! Additionally, [`FromEntropy::from_entropy`] is a shortcut for seeding from
//! [`OsRng`].
//!
//! Use the [`rand_core`] crate when implementing your own RNGs.
//!
//! ## Our generators
//!
//! This crate provides several random number generators:
//!
//! - [`OsRng`] is an interface to the operating system's random number
//! source. Typically the operating system uses a CSPRNG with entropy
//! provided by a TRNG and some type of on-going re-seeding.
//! - [`ThreadRng`], provided by the [`thread_rng`] function, is a handle to a
//! thread-local CSPRNG with periodic seeding from [`OsRng`]. Because this
//! is local, it is typically much faster than [`OsRng`]. It should be
//! secure, though the paranoid may prefer [`OsRng`].
//! - [`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.
//! [`StdRng`] provides the algorithm used by [`ThreadRng`] but without
//! periodic reseeding.
//! - [`SmallRng`] is an **insecure** PRNG designed to be fast, simple, require
//! little memory, and have good output quality.
//!
//! 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](https://github.com/rust-random/rand/issues/699).
//!
//! **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].
//!
//! [`SmallRng`]: rngs::SmallRng
//! [`StdRng`]: rngs::StdRng
//! [`OsRng`]: rngs::OsRng
//! [`ThreadRng`]: rngs::ThreadRng
//! [`mock::StepRng`]: rngs::mock::StepRng
//! [`adapter::ReadRng`]: rngs::adapter::ReadRng
//! [`adapter::ReseedingRng`]: rngs::adapter::ReseedingRng
//! [`ChaChaRng`]: ../../rand_chacha/struct.ChaChaRng.html
//! [`rdrand`]: https://crates.io/crates/rdrand
//! [`rand_jitter`]: https://crates.io/crates/rand_jitter
//! [`rand_chacha`]: https://crates.io/crates/rand_chacha
//! [`rand_pcg`]: https://crates.io/crates/rand_pcg
//! [`rand_xoshiro`]: https://crates.io/crates/rand_xoshiro
//! [`rng` tag]: https://crates.io/keywords/rng
pub mod adapter;

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11 changes: 11 additions & 0 deletions src/rngs/os.rs
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Expand Up @@ -19,6 +19,17 @@ use rand_core::{CryptoRng, RngCore, Error, ErrorKind, impls};
/// The implementation is provided by the [getrandom] crate. Refer to
/// [getrandom] documentation for details.
///
/// ## Example
///
/// ```
/// use rand::rngs::{StdRng, OsRng};
/// use rand::{RngCore, SeedableRng};
///
/// println!("Random number, straight from the OS: {}", OsRng.next_u32());
/// let mut rng = StdRng::from_rng(OsRng).unwrap();
/// println!("Another random number: {}", rng.next_u32());
/// ```
///
/// [getrandom]: https://crates.io/crates/getrandom
#[derive(Clone, Copy, Debug)]
pub struct OsRng;
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