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diff --git a/rand/src/rngs/mod.rs b/rand/src/rngs/mod.rs deleted file mode 100644 index 847fc94..0000000 --- a/rand/src/rngs/mod.rs +++ /dev/null @@ -1,182 +0,0 @@ -// Copyright 2018 Developers of the Rand project. -// -// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or -// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license -// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your -// 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 -//! - [`EntropyRng`], [`OsRng`] and [`JitterRng`] as entropy sources -//! - [`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. -//! [`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::*; -//! # 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. -//! -//! -// This module: -//! [`ThreadRng`]: struct.ThreadRng.html -//! [`StdRng`]: struct.StdRng.html -//! [`SmallRng`]: struct.SmallRng.html -//! [`EntropyRng`]: struct.EntropyRng.html -//! [`OsRng`]: struct.OsRng.html -//! [`JitterRng`]: struct.JitterRng.html -// Other traits and functions: -//! [`rand_core`]: https://crates.io/crates/rand_core -//! [`prng` module]: ../prng/index.html -//! [`CryptoRng`]: ../trait.CryptoRng.html -//! [`FromEntropy`]: ../trait.FromEntropy.html -//! [`FromEntropy::from_entropy`]: ../trait.FromEntropy.html#tymethod.from_entropy -//! [`RngCore`]: ../trait.RngCore.html -//! [`Rng`]: ../trait.Rng.html -//! [`SeedableRng`]: ../trait.SeedableRng.html -//! [`SeedableRng::from_rng`]: ../trait.SeedableRng.html#tymethod.from_rng -//! [`SeedableRng::from_seed`]: ../trait.SeedableRng.html#tymethod.from_seed -//! [`thread_rng`]: ../fn.thread_rng.html -//! [`mock::StepRng`]: mock/struct.StepRng.html -//! [`adapter::ReadRng`]: adapter/struct.ReadRng.html -//! [`adapter::ReseedingRng`]: adapter/struct.ReseedingRng.html -//! [`ChaChaRng`]: ../../rand_chacha/struct.ChaChaRng.html - -pub mod adapter; - -#[cfg(feature="std")] mod entropy; -mod jitter; -pub mod mock; // Public so we don't export `StepRng` directly, making it a bit - // more clear it is intended for testing. -mod small; -mod std; -#[cfg(feature="std")] pub(crate) mod thread; - - -pub use self::jitter::{JitterRng, TimerError}; -#[cfg(feature="std")] pub use self::entropy::EntropyRng; - -pub use self::small::SmallRng; -pub use self::std::StdRng; -#[cfg(feature="std")] pub use self::thread::ThreadRng; - -#[cfg(feature="rand_os")] -pub use rand_os::OsRng; |