The Rust Rand Book

This is the extended documentation for Rust's Random number library.

This book contains:

  1. Getting Started
  2. An overview of crates and functionality
  3. The Users' Guide
  4. Notes on Portability and Reproducibility. (Read this if you want reproducibility across builds.)
  5. Updating guides
  6. Contributor's guide

Outside this book, you may want:

Getting Started

Below we list a short example. For more, please refer to the API documentation or the guide.

Lets kick things off with an example (playground link):

extern crate rand;
// import commonly used items from the prelude:
use rand::prelude::*;

fn main() {
    // We can use random() immediately. It can produce values of many common types:
    let x: u8 = random();
    println!("{}", x);

    if random() { // generates a boolean

    // If we want to be a bit more explicit (and a little more efficient) we can
    // make a handle to the thread-local generator:
    let mut rng = thread_rng();
    if rng.gen() { // random bool
        let x: f64 = rng.gen(); // random number in range [0, 1)
        let y = rng.gen_range(-10.0..10.0);
        println!("x is: {}", x);
        println!("y is: {}", y);

    println!("Dice roll: {}", rng.gen_range(1..=6));
    println!("Number from 0 to 9: {}", rng.gen_range(0..10));
    // Sometimes it's useful to use distributions directly:
    let distr = rand::distributions::Uniform::new_inclusive(1, 100);
    let mut nums = [0i32; 3];
    for x in &mut nums {
        *x = rng.sample(distr);
    println!("Some numbers: {:?}", nums);

    // We can also interact with iterators and slices:
    let arrows_iter = "➡⬈⬆⬉⬅⬋⬇⬊".chars();
    println!("Lets go in this direction: {}", arrows_iter.choose(&mut rng).unwrap());
    let mut nums = [1, 2, 3, 4, 5];
    nums.shuffle(&mut rng);
    println!("I shuffled my {:?}", nums);

The first thing you may have noticed is that we imported everything from the prelude. This is the lazy way to use rand, and like the standard library's prelude, only imports the most common items. If you don't wish to use the prelude, remember to import the Rng trait!

The Rand library automatically initialises a secure, thread-local generator on demand. This can be accessed via the thread_rng and random functions. For more on this topic, see Random generators.

While the random function can only sample values in a Standard (type-dependent) manner, thread_rng gives you a handle to a generator. All generators implement the Rng trait, which provides the gen, gen_range and sample methods used above.

Rand provides functionality on iterators and slices via two more traits, IteratorRandom and SliceRandom.

Fixed seed RNGs

You may have noticed the use of thread_rng() above and wondered how to specify a fixed seed. To do so, you need to specify an RNG then use a method like seed_from_u64 or from_seed.

Note that seed_from_u64 is not suitable for cryptographic uses since a single u64 cannot provide sufficient entropy to securely seed an RNG. All cryptographic RNGs accept a more appropriate seed via from_seed.

We use ChaCha8Rng below because it is fast and portable with good quality. See the RNGs section for more RNGs, but avoid SmallRng and StdRng if you care about reproducible results.

extern crate rand;
extern crate rand_chacha;
use rand::{Rng, SeedableRng};

fn main() {
    let mut rng = rand_chacha::ChaCha8Rng::seed_from_u64(10);
    println!("Random f32: {}", rng.gen::<f32>());

Crates and features

The Rand library consists of a family of crates. The rand crate provides the main user-interface; where additional distributions are required, the rand_distr or statrs crate may be used in addition.

The library contains several building blocks: getrandom interfaces with the platform-dependent random number source, rand_core defines the API that generators must implement, and a number of crates like rand_chacha and rand_xoshiro provide pseudo-random generators.

getrandom ┐
          └ rand_core ┐
                      ├ rand_chacha ┐
                      ├ rand_hc     ┤
                      ├ rand_pcg    ┤
                      └─────────────┴ rand ┐
                                           ├ rand_distr
                                           └ statrs

Feature flags

Rand crates allow some configuration via feature flags. Check the READMEs of individual crates for details.

No-std support is available across most Rand crates by disabling default features: rand = { version = "0.7", default-features = false }. This is affected by the following flags:

  • std opts-in to functionality dependent on the std lib
  • alloc (implied by std) enables functionality requiring an allocator (when using this feature in no_std, Rand requires Rustc version 1.36 or greater)

Some Rand crates can be built with support for the following third-party crates:

  • log enables a few log messages via log
  • serde1 enables serialization via serde, version 1.0

Note that cryptographic RNGs do not support serialisation since this could be a security risk. If you need state-restore functionality on a cryptographic RNG, the ChaCha generator supports getting and setting the stream position, which, together with the seed, can be used to reconstruct the generator's state.

WASM support

Almost all Rand crates support WASM out of the box. However, when using the wasm32-unknown-unknown target, which doesn't make any assumptions about its operating environment by default, the getrandom crate may require enabling features for seeding entropy via the platform-provided APIs. Consequently, if you are using rand (or another Rand project crate) depending on getrandom, you may have to explicitly enable getrandom features for seeding to work. Alternatively, in case you are developing for a sandboxed or unknown WASM platform that can't depend on environment provided APIs, you might want to disable the rand crate's getrandom feature and seed the generator manually.

Rand and co

rand_core (API)

The rand_core crate defines the core traits implemented by RNGs. This exists as a separate crate with two purposes:

  • to provide a minimal API for defining and using RNGs
  • to provide tools to aid implementation of RNGs

The RngCore, SeedableRng, CryptoRng traits and Error type are all defined by this crate and re-exported by the rand crate.

rand (primary interface)

The rand crate is optimised for easy usage of common random-number functionality. This has several aspects:

  • the rngs module provides a few convenient generators
  • the distributions module concerns sampling of random values
  • the seq module concerns sampling from and shuffling sequences
  • the Rng trait provides a few convenience methods for generating random values
  • the random function provides convenient generation in a single call

Feature flags

Besides the common feature flags, several aspects are configurable:

  • small_rng enables the SmallRng generator (feature-gated since v0.7)
  • simd_support enables experimental (nightly-only) support for generating SIMD values

Note regarding SIMD: the above flag concerns explicit generation of SIMD types only and not optimisation. SIMD operations may be used internally regardless of this flag; e.g. the ChaCha generator has explicit support for SIMD operations internally.


The rand crate only implements sampling from the most common random number distributions: uniform and weighted sampling. For everything else,

  • rand_distr provides fast sampling from a variety of other distributions, including Normal (Gauss), Binomial, Poisson, UnitCircle, and many more
  • statrs is a port of the C# Math.NET library, implementing many of the same distributions (plus/minus a few), along with PDF and CDF functions, the error, beta, gamma and logistic special functions, plus a few utilities. (For clarity, statrs is not part of the Rand library.)

Random Generators


The getrandom crate provides a low-level API around platform-specific random-number sources, and is an important building block of rand and rand_core as well as a number of cryptography libraries. It is not intended for usage outside of low-level libraries.

In some cases, particularly when targeting WASM, end-users may need to configure this crate. Consult the getrandom documentation for the relevant version.

CPU Jitter

The rand_jitter crate implements a CPU-jitter-based entropy harvester which may be used to provide an alternative source of entropy where a high-resolution CPU timer is available.

It should be noted that CPU-jitter harvesters may be prone to side-channel attacks and that this implementation is quite slow (due to conservative estimates of entropy gained per step).

In prior versions of rand this was a direct dependency, used automatically when other sources of entropy failed. In current versions it is not a dependency (not even an optional one).

Deterministic generators

The following crates implement pseudo-random number generators (see Our RNGs):

  • rand_chacha provides generators using the ChaCha cipher
  • rand_hc implements a generator using the HC-128 cipher
  • rand_isaac implements the ISAAC generators
  • rand_pcg implements a small selection of PCG generators
  • rand_xoshiro implements the SplitMix and Xoshiro generators
  • rand_xorshift implements the basic Xorshift generator


This section attempts to explain some of the concepts used in this library.

  1. What is random data and what is randomness anyway?
  2. What kind of random generators are there?
  3. What random number generators does Rand provide?
  4. Turning random data into useful values
  5. Distributions: more control over random values
  6. Sequences
  7. Error handling

Importing items (prelude)

The most convenient way to import items from Rand is to use the prelude. This includes the most important parts of Rand, but only those unlikely to cause name conflicts.

Note that Rand 0.5 has significantly changed the module organization and contents relative to previous versions. Where possible old names have been kept (but are hidden in the documentation), however these will be removed in the future. We therefore recommend migrating to use the prelude or the new module organization in your imports.

Further examples

For some inspiration, see the example applications:

Cargo and features

The latest version of rand is available here:

On the same page, and also from the repository's README, you can see documentation of the Crate Features. In particular, you may want to use the small_rng feature to enable the SmallRng generator.

To add this dependency on Rand from the console, type:

cargo add rand --features small_rng

Or, add this to your Cargo.toml, under [dependencies]:

rand = { version = "0.8.5", features = ["small_rng"] }

Note: using version = "0.8.5" matches version 0.8.5, 0.8.6, etc., but not 0.9. Using instead version = "0.8" will match any 0.8.x version. In both cases, Cargo will normally install the latest matching version when the dependency is first added. Dependencies may be updated later via cargo update, and in some cases running cargo update can solve dependency problems.

For more on Cargo, see The Cargo Book.

Other crates

As noted on the Crates page, the Rand library consists of a family of crates. To run the examples in this guide, ensure you have the following dependencies in your Cargo.toml:

rand = { version = "0.8.5", features = ["small_rng"] }
rand_chacha = "0.3.1"
rand_distr = "0.4.3"
rand_pcg = "0.3.1"
rand_seeder = "0.2.3"

Random data

extern crate rand;
use rand::RngCore;
fn main() {
// get some random data:
let mut data = [0u8; 8];
rand::thread_rng().fill_bytes(&mut data);
println!("{:?}", data)

What is randomness?

What does random mean? Colloquially the word can mean simply unexpected or unknown, but we need to be a bit more precise than that. Wikipedia gives us a more specific definition:

Randomness is the lack of pattern or predictability in events.

We can take this further: lack of pattern implies there is no bias; in other words, all possible values are equally likely.

To understand what a random value is, we still need a context: what pool of numbers can our random value come from?

  • To give a simple example, consider dice: they have values 1, 2, 3, 4, 5 and 6, and an unbiased (fair) die will make each number equally likely, with probability ⅙th.
  • Now lets take a silly example: the natural numbers (1, 2, 3, etc.). These numbers have no limit. So if you were to ask for an unbiased random natural number, 1, 5, 1000, 1 million, 1 trillion — all would be equally likely. In fact, for any natural number k, the numbers 1, 2, ..., k are an infinitely small fraction of all the natural numbers, which means the chance of picking a unbiased number from this range is effectively 1/∞ = 0. Put another way: for any natural number, we expect an unbiased random value to be bigger. This is impossible, so there cannot be any such thing as an unbiased random natural number.
  • Another example: real numbers between 0 and 1. Real numbers include all the fractions, irrational numbers like π and √2, and all multiples of those... there are infinitely many possibilities, even in a small range like (0, 1), so simply saying "all possibilities are equally likely" is not enough. Instead we interpret lack of pattern in a different way: every interval of equal size is equally likely; for example we could subdivide the interval 0,1 into 0,½ and ½,1 and toss a coin to decide which interval our random sample comes from. Say we pick ½,1 we can then toss another coin to decide between ½,¾ and ¾,1, restricting our random value to an interval of size ¼. We can repeat this as many times as necessary to pick a random value between 0 and 1 with as much precision as we want — although we should realise that we are not choosing an exact value but rather just a small interval.

What we have defined (or failed to define) above are uniform random number distributions, or simply uniform distributions. There are also non-uniform distributions, as we shall see later. It's also worth noting here that a uniform distribution does not imply that its samples will be evenly spread (try rolling six dice: you probably won't get 1, 2, 3, 4, 5, 6).

To bring us back to computing, we can now define what a uniformly distributed random value (an unbiased random value) is in several contexts:

  • u32: a random number between 0 and u32::MAX where each value is equally likely
  • BigInt: since this type has no upper bound, we cannot produce an unbiased random value (it would be infinitely large, and use infinite amounts of memory)
  • f64: we treat this as an approximation of the real numbers, and, by convention, restrict to the range 0 to 1 (if not otherwise specified). We will come back to the conversions used later; for now note that these produce 52-53 bits of precision (depending on which conversion is used, output will be in steps of ε or ε/2, where 1+ε is the smallest representable value greater than 1).

Random data

As seen above, the term "random number" is meaningless without context. "Random data" typically means a sequence of random bytes, where for each byte, each of the 256 possible values are equally likely.

RngCore::fill_bytes produces exactly this: a sequence of random bytes.

If a sequence of unbiased random bytes of the correct length is instead interpreted as an integer — say a u32 or u64 — the result is an unbiased integer. Since this conversion is trivial, RngCore::next_u32 and RngCore::next_u64 are part of the same trait. (In fact the conversion is often the other way around — algorithmic generators usually work with integers internally, which are then converted to whichever form of random data is required.)

Types of generators

The previous section introduced RngCore, the trait which all random data sources must implement. But what exactly is a random data source?

This section concerns theory; see also the chapter on random number generators.

extern crate rand;
extern crate rand_chacha;
use rand::{Rng, SeedableRng};

fn main() {
// prepare a non-deterministic random number generator:
let mut rng = rand::thread_rng();
println!("{}", rng.gen::<i32>()); // prints an unknown value

// prepare a deterministic generator:
let mut rng = rand_chacha::ChaCha8Rng::seed_from_u64(123);
println!("{}", rng.gen::<i32>()); // prints -416273517

True random number generators

A true random number generator (TRNG) is something which produces random numbers by observing some natural process, such as atomic decay or thermal noise. (Whether or not these things are truly random or are in fact deterministic — for example if the universe itself is a simulation — is besides the point here. For our purposes, it is sufficient that they are not distinguishable from true randomness.)

Note that these processes are often biased, thus some type of debiasing must be used to yield the unbiased random data we desire.

Pseudo-random number generators

CPUs are of course supposed to compute deterministically, yet it turns out they can do a pretty good job of emulating random processes. Most pseudo-random number generators are deterministic and can be defined by just:

  • some initial state
  • a function to compute a random value from the state
  • a function to advance to the next state
  • (optionally) a function to derive the initial state from a seed or key

The fact that these are deterministic can sometimes be very useful: it allows a simulation, randomised art work or game to be repeated exactly, producing a result which is a function of the seed. For more on this see the portability chapter (note that determinism alone isn't enough to guarantee reproducibility).

The other big attraction of PRNGs is their speed: some of these algorithms require only a few CPU operations per random value, and thus can produce random data on demand much more quickly than most TRNGs.

Note however that PRNGs have several limitations:

  • They are no stronger than their seed: if the seed is known or guessable, and the algorithm is known (or guessed), then only a small number of output sequences are likely.
  • Since the state size is usually fixed, only a finite number of output values are possible before the generator loops and repeats itself.
  • Several algorithms are easily predictable after seeing a few values, and with many other algorithms it is not clear whether they could be "cracked".

Cryptographically secure pseudo-random number generator

Cryptographically secure pseudo-random number generators (CSPRNGs) are the subset of PRNGs which are considered secure. That is:

  • their state is sufficiently large that a brute-force approach simply trying all initial values is not a feasible method of finding the initial state used to produce an observed sequence of output values,
  • and there is no other algorithm which is sufficiently better than the brute-force method which would make it feasible to predict the next output value.

Achieving secure generation requires not only a secure algorithm (CSPRNG), but also a secure and sufficiently large seed value (typically 256 bits), and protection against side-channel attacks (i.e. preventing attackers from reading the internal state).

Some CSPRNGs additionally satisfy a third property:

  • a CSPRNG is backtracking resistant if it is impossible for an attacker to calculate prior output values of the PRNG despite having discovered the value of the current internal state (implying that all future output is compromised).

Hardware random number generator

A hardware random number generator (HRNG) is theoretically an adaptor from some TRNG to digital information. In practice, these may use a PRNG to debias the TRNG. Even though an HRNG has some underlying TRNG, it is not guaranteed to be secure: the TRNG itself may produce insufficient entropy (i.e. be too predictable), or the signal amplification and debiasing process may be flawed.

An HRNG may be used to provide the seed for a PRNG, although usually this is not the only way to obtain a secure seed (see the next section). An HRNG might replace a PRNG altogether, although since we now have very fast and very strong software PRNGs, and since software implementations are easier to verify than hardware ones, this is often not the preferred solution.

Since a PRNG needs a random seed value to be secure, an HRNG may be used to provide that seed, or even replace the need for a PRNG. However, since the goal is usually "only" to produce unpredictable random values, there are acceptable alternatives to true random number generators (see next section).


As noted above, for a CSPRNG to be secure, its seed value must also be secure. The word entropy can be used in two ways:

  • as a measure of the amount of unknown information in some piece of data
  • as a piece of unknown data

Ideally, a random boolean or a coin flip has 1 bit of entropy, although if the value is biased, there will be less. Shannon Entropy attempts to measure this.

For example, a Unix time-stamp (seconds since the start of 1970) contains both high- and low-resolution data. This is typically a 32-bit number, but the amount of entropy will depend on how precisely a hypothetical attacker can guess the number. If an attacker can guess the number to the nearest minute, this may be approximately 6 bits (2^6 = 64); if an attacker can guess this to the second, this is 0 bits. JitterRng uses this concept to scavenge entropy without an HRNG (but using nanosecond resolution timers and conservatively assuming only a couple of bits entropy is available per time-stamp, after running several tests on the timer's quality).

Our RNGs

There are many kinds of RNGs, with different trade-offs. Rand provides some convenient generators in the rngs module. Often you can just use thread_rng, a function which automatically initializes an RNG in thread-local memory and returns a reference to it. It is fast, good quality, and (to the best of our knowledge) cryptographically secure.

Contents of this documentation:

  1. The generators
  2. Performance and size
  3. Quality and cycle length
  4. Security
  5. Extra features
  6. Further reading

The generators

Basic pseudo-random number generators (PRNGs)

The goal of "standard" non-cryptographic PRNGs is usually to find a good balance between simplicity, quality, memory usage and performance. Non-cryptographic generators pre-date cryptographic ones and are in some ways obsoleted by them, however non-cryptographic generators do have some advantages: a small state size, fast initialisation, simplicity, lower energy usage for embedded CPUs. (However, not all non-crypto PRNGs provide these benefits, e.g. the Mersenne Twister has a very large state despite being easy to predict).

These algorithms are very important to Monte Carlo simulations, and also suitable for several other problems such as randomized algorithms and games, where predictability is not an issue. (Note however that for gambling games predictability may be an issue and a cryptographic PRNG is recommended.)

The Rand project provides several non-cryptographic PRNGs. A sub-set of these are summarised below. You may wish to refer to the pcg-random and xoshiro websites.

namefull nameperformancememoryqualityperiodfeatures
SmallRng(unspecified)7 GB/s16 bytes★★★☆☆u32 * 264not portable
Pcg32PCG XSH RR 64/32 (LCG)3 GB/s16 bytes★★★☆☆u32 * 264
Pcg64PCG XSL 128/64 (LCG)4 GB/s32 bytes★★★☆☆u64 * 2128
Pcg64McgPCG XSL 128/64 (MCG)7 GB/s16 bytes★★★☆☆u64 * 2126
XorShiftRngXorshift 32/1285 GB/s16 bytes★☆☆☆☆u32 * 2128 - 1
Xoshiro256PlusPlusXoshiro256++7 GB/s32 bytes★★★☆☆u64 * 2256 - 1jump-ahead
Xoshiro256PlusXoshiro256+8 GB/s32 bytes★★☆☆☆u64 * 2256 - 1jump-ahead
SplitMix64splitmix648 GB/s8 bytes★☆☆☆☆u64 * 264
StepRngcounter51 GB/s16 bytes☆☆☆☆☆u64 * 264

Here, performance is measured roughly for u64 outputs on a 3.4GHz Haswell CPU (note that this will vary significantly by application; in general cryptographic RNGs do better with byte sequence output). Quality ratings are based on theory and observable defects, roughly as follows:

  • ★☆☆☆☆ = suitable for simple applications but with significant flaws
  • ★★☆☆☆ = good performance in most tests, some issues
  • ★★★☆☆ = good performance and theory, no major issues
  • ★★★★★ = cryptographic quality

Cryptographically secure pseudo-random number generators (CSPRNGs)

CSPRNGs have much higher requirements than basic PRNGs. The primary consideration is security. Performance and simplicity are also important, but in general CSPRNGs are more complex and slower than regular PRNGs. Quality is no longer a concern, as it is a requirement for a CSPRNG that the output is basically indistinguishable from true randomness since any bias or correlation makes the output more predictable.

There is a close relationship between CSPRNGs and cryptographic ciphers. Any block cipher can be turned into a CSPRNG by encrypting a counter. Stream ciphers are basically a CSPRNG and a combining operation, usually XOR. This means that we can easily use any stream cipher as a CSPRNG.

This library provides the following CSPRNGs. We can make no guarantees of any security claims. This table omits the "quality" column from the previous table since CSPRNGs may not have observable defects.

namefull nameperformanceinitializationmemorysecurity (predictability)forward secrecy
StdRng(unspecified)1.5 GB/sfast136 byteswidely trustedno
ChaCha20RngChaCha201.8 GB/sfast136 bytesrigorously analysedno
ChaCha8RngChaCha82.2 GB/sfast136 bytessmall security marginno
Hc128RngHC-1282.1 GB/sslow4176 bytesrecommended by eSTREAMno
IsaacRngISAAC1.1 GB/sslow2072 bytesunknownunknown
Isaac64RngISAAC-642.2 GB/sslow4136 bytesunknownunknown

It should be noted that the ISAAC generators are only included for historical reasons: they have been with the Rust language since the very beginning. They have good quality output and no attacks are known, but have received little attention from cryptography experts.

Notes on generators


First it has to be said most PRNGs are very fast, and will rarely be a performance bottleneck.

Performance of basic PRNGs is a bit of a subtle thing. It depends a lot on the CPU architecture (32 vs. 64 bits), inlining, and also on the number of available registers. This often causes the performance to be affected by surrounding code due to inlining and other usage of registers.

When choosing a PRNG for performance it is important to benchmark your own application due to interactions between PRNGs and surrounding code and dependence on the CPU architecture as well as the impact of the size of data requested. Because of all this, we do not include performance numbers here but merely a qualitative rating.

CSPRNGs are a little different in that they typically generate a block of output in a cache, and pull outputs from the cache. This allows them to have good amortised performance, and reduces or completely removes the influence of surrounding code on the CSPRNG performance.

Worst-case performance

Simple PRNGs typically produce each random value on demand. In contrast, CSPRNGs usually produce a whole block at once, then read from this cache until it is exhausted, giving them much less consistent performance when drawing small quantities of random data.

Memory usage

Simple PRNGs often use very little memory, commonly only a few words, where a word is usually either u32 or u64. This is not true for all non-cryptographic PRNGs however, for example the historically popular Mersenne Twister MT19937 algorithm requires 2.5 kB of state.

CSPRNGs typically require more memory; since the seed size is recommended to be at least 192 bits and some more may be required for the algorithm, 256 bits would be approximately the minimum secure size. In practice, CSPRNGs tend to use quite a bit more, ChaChaRng is relatively small with 136 bytes of state.

Initialization time

The time required to initialize new generators varies significantly. Many simple PRNGs and even some cryptographic ones (including ChaChaRng) only need to copy the seed value and some constants into their state, and thus can be constructed very quickly. In contrast, CSPRNGs with large state require an expensive key-expansion.


Many basic PRNGs are not much more than a couple of bitwise and arithmetic operations. Their simplicity gives good performance, but also means there are small regularities hidden in the generated random number stream.

How much do those hidden regularities matter? That is hard to say, and depends on how the RNG gets used. If there happen to be correlations between the random numbers and the algorithm they are used in, the results can be wrong or misleading.

A random number generator can be considered good if it gives the correct results in as many applications as possible. The quality of PRNG algorithms can be evaluated to some extent analytically, to determine the cycle length and to rule out some correlations. Then there are empirical test suites designed to test how well a PRNG performs on a wide range of possible uses, the latest and most complete of which are TestU01 and PractRand.

CSPRNGs tend to be more complex, and have an explicit requirement to be unpredictable. This implies there must be no obvious correlations between output values.

Quality stars:

PRNGs with 3 stars or more should be good enough for most non-crypto applications. 1 or 2 stars may be good enough for typical apps and games, but do not work well with all algorithms.


The period or cycle length of a PRNG is the number of values that can be generated after which it starts repeating the same random number stream. Many PRNGs have a fixed-size period, while for others ("chaotic RNGs") the cycle length may depend on the seed and short cycles may exist.

Note that a long period does not imply high quality (e.g. a counter through u128 values provides a decently long period). Conversely, a short period may be a problem, especially when multiple RNGs are used simultaneously. In general, we recommend a period of at least 2128. (Alternatively, a PRNG with shorter period of at least 264 and support for multiple streams may be sufficient. Note however that in the case of PCG, its streams are closely correlated.)

Avoid reusing values! On today's hardware, a fast RNG with a cycle length of only 264 can be used sequentially for centuries before cycling. However, when multiple RNGs are used in parallel (each with a unique seed), there is a significant chance of overlap between the sequences generated. For a generator with a large period P, n independent generators, and a sequence of length L generated by each generator, the chance of any overlap between sequences can be approximated by Ln² / P when nL / P is close to zero. For more on this topic, please see these remarks by the Xoshiro authors.

Collisions and the birthday paradox! For a generator with outputs of equal size to its state, it is recommended not to use more than √P outputs. A generalisation for kw-bit state and w-bit generators is to ensure kL² < P. This requirement stems from the generalised birthday problem, asking how many unbiased samples from a set of size d = 2^w can be taken before the probability of a repeat is at least half. Note that for kL² > P a generator with kw-dimensional equidistribution cannot generate the expected number of repeated samples, however generators without this property are also not guaranteed to generate the expected number of repeats.



From the context of any PRNG, one can ask the question given some previous output from the PRNG, is it possible to predict the next output value? This is an important property in any situation where there might be an adversary.

Regular PRNGs tend to be predictable, although with varying difficulty. In some cases prediction is trivial, for example plain Xorshift outputs part of its state without mutation, and prediction is as simple as seeding a new Xorshift generator from four u32 outputs. Other generators, like PCG and truncated Xorshift* are harder to predict, but not outside the realm of common mathematics and a desktop PC.

The basic security that CSPRNGs must provide is the infeasibility to predict output. This requirement is formalized as the next-bit test; this is roughly stated as: given the first k bits of a random sequence, the sequence satisfies the next-bit test if there is no algorithm able to predict the next bit using reasonable computing power.

A further security that some CSPRNGs provide is forward secrecy: in the event that the CSPRNGs state is revealed at some point, it must be infeasible to reconstruct previous states or output. Note that many CSPRNGs do not have forward secrecy in their usual formulations.

Verifying security claims of an algorithm is a hard problem, and we are not able to provide any guarantees of the security of algorithms used or recommended by this project. We refer you to the NIST institute and ECRYPT network for recommendations.

State and seeding

It is worth noting that a CSPRNG's security relies absolutely on being seeded with a secure random key. Should the key be known or guessable, all output of the CSPRNG is easy to guess. This implies that the seed should come from a trusted source; usually either the OS or another CSPRNG. For this purpose, we recommend using the getrandom crate which interfaces the OS's secure random interface. SeedableRng::from_entropy is a wrapper around getrandom for convenience. Alternatively, using a user-space CSPRNG such as ThreadRng for seeding should be sufficient.

Further, it should be obvious that the internal state of a CSPRNG must be kept secret. With that in mind, our implementations do not provide direct access to most of their internal state, and Debug implementations do not print any internal state. This does not fully protect CSPRNG state; code within the same process may read this memory (and we allow cloning and serialisation of CSPRNGs for convenience). Further, a running process may be forked by the operating system, which may leave both processes with a copy of the same generator.

Not a crypto library

It should be emphasised that this is not a cryptography library; although Rand does take some measures to provide secure random numbers, it does not necessarily take all recommended measures. Further, cryptographic processes such as encryption and authentication are complex and must be implemented very carefully to avoid flaws and resist known attacks. It is therefore recommended to use specialized libraries where possible, for example openssl, ring and the RustCrypto libraries.

Extra features

Some PRNGs may provide extra features, like:

  • Support for multiple streams, which can help with parallel tasks.
  • The ability to jump or seek around in the random number stream; with a large period this can be used as an alternative to streams.

Further reading

There is quite a lot that can be said about PRNGs. The PCG paper is very approachable and explains more concepts.

Another good paper about RNG quality is "Good random number generators are (not so) easy to find" by P. Hellekalek.

Seeding RNGs

As we have seen, the output of pseudo-random number generators (PRNGs) is determined by their initial state.

Some PRNG definitions specify how the initial state should be generated from a key, usually specified as a byte-sequence for cryptographic generators or, for small PRNGs, often just a word. We formalise this for all our generators with the SeedableRng trait.

The Seed type

We require all seedable RNGs to define a Seed type satisfying AsMut<[u8]> + Default + Sized (usually [u8; N] for a fixed N). We recommend using [u8; 12] or larger for non-cryptographic PRNGs and [u8; 32] for cryptographic PRNGs.

PRNGs may be seeded directly from such a value with SeedableRng::from_seed.

Seeding from ...

Fresh entropy

Using a fresh seed (direct from the OS) is easy using SeedableRng::from_entropy:

extern crate rand;
extern crate rand_chacha;
use rand::prelude::*;
use rand_chacha::ChaCha20Rng;

fn main() {
    let mut rng = ChaCha20Rng::from_entropy();
    println!("{}", rng.gen_range(0..100));

Note that this requires rand_core has the feature getrandom enabled.

Another RNG

Quite obviously, another RNG may be used to fill a seed. We provide a convenience method for this:

extern crate rand;
use rand::prelude::*;

fn main() {
    let mut rng = SmallRng::from_rng(thread_rng()).unwrap();
    println!("{}", rng.gen_range(0..100));

But, say you want to save a key and use it later. For that you need to be a little bit more explicit:

extern crate rand;
extern crate rand_chacha;
use rand::prelude::*;
use rand_chacha::ChaCha8Rng;

fn main() {
    let mut seed: <ChaCha8Rng as SeedableRng>::Seed = Default::default();
    thread_rng().fill(&mut seed);
    let mut rng = ChaCha8Rng::from_seed(seed);
    println!("{}", rng.gen_range(0..100));

Obligatory warning: a few simple PRNGs, notably XorShiftRng, behave badly when seeded from the same type of generator (in this case, Xorshift generates a clone). For cryptographic PRNGs this is not a problem; for others it is recommended to seed from a different type of generator. ChaCha8Rng is an excellent choice for a deterministic master generator (but for cryptographic uses, prefer the 12-round variant or higher).

A simple number

For some applications, especially simulations, all you want are a sequence of distinct, fixed random number seeds, e.g. 1, 2, 3, etc.

SeedableRng::seed_from_u64 is designed exactly for this use-case. Internally, it uses a simple PRNG to fill the bits of the seed from the input number while providing good bit-avalanche (so that two similar numbers such as 0 and 1 translate to very different seeds and independent RNG sequences).

extern crate rand;
extern crate rand_chacha;
use rand::prelude::*;
use rand_chacha::ChaCha8Rng;

fn main() {
    let mut rng = ChaCha8Rng::seed_from_u64(2);
    println!("{}", rng.gen_range(0..100));

Note that a number with 64-bits or less cannot be secure, so this should not be used for applications such as cryptography or gambling games.

A string, or any hashable data

Say you let users enter a string to seed the random number generator. Ideally, all parts of the string should influence the generator and making only a small change to the string should result in a fully independent generator sequence.

This can be achieved via use of a hash function to compress all input data down to a hash result, then using that result to seed a generator. The rand_seeder crate is designed for just this purpose.

use rand::prelude::*;
use rand_seeder::{Seeder, SipHasher};
use rand_pcg::Pcg64;

fn main() {
    // In one line:
    let mut rng: Pcg64 = Seeder::from("stripy zebra").make_rng();
    println!("{}", rng.gen::<char>());

    // If we want to be more explicit, first we create a SipRng:
    let hasher = SipHasher::from("a sailboat");
    let mut hasher_rng = hasher.into_rng();
    // (Note: hasher_rng is a full RNG and can be used directly.)

    // Now, we use hasher_rng to create a seed:
    let mut seed: <Pcg64 as SeedableRng>::Seed = Default::default();
    hasher_rng.fill(&mut seed);

    // And create our RNG from that seed:
    let mut rng = Pcg64::from_seed(seed);
    println!("{}", rng.gen::<char>());

Note that rand_seeder is not suitable for cryptographic usage. It is not a password hasher, for such applications a key-derivation function such as Argon2 must be used.

Parallel RNGs

Theory: multiple RNGs

If you want to use random generators in multiple worker threads simultaneously, then you will want to use multiple RNGs. A few suggested approaches:

  1. Use [thread_rng] in each worker thread. This is seeded automatically (lazily and uniquely) on each thread where it is used.
  2. Use [thread_rng] (or another master RNG) to seed a custom RNG on each worker thread. The main advantage here is flexibility over the RNG used.
  3. Use a custom RNG per work unit, not per worker thread. If these RNGs are seeded in a deterministic fashion, then deterministic results are possible. Unfortunately, seeding a new RNG for each work unit from a master generator cannot be done in parallel, thus may be slow.
  4. Use a single master seed. For each work unit, seed an RNG using the master seed and set the RNG's stream to the work unit number. This is potentially a faster than (3) while still deterministic, but not supported by all RNGs.

Note: do not simply clone RNGs for worker threads/units. Clones return the same sequence of output as the original. You may however use clones if you then set a unique stream on each.


Which RNG families support multiple streams?

  • ChaCha: the ChaCha RNGs support 256-bit seed, 64-bit stream and 64-bit counter (per 16-word block), thus supporting 264 streams of 268 words each.
  • Hc128 is a cryptographic RNG supporting a 256-bit seed; one could construct this seed from (e.g.) a smaller 192-bit key plus a 64-bit stream.

Note that the above approach of constructing the seed from a smaller key plus a stream counter can only be recommended with cryptographic PRNGs since simpler RNGs often have correlations in the RNG's output using two similar keys, and may also require "random looking" seeds to produce high quality output.

Non-cryptographic PRNGs may still support multiple streams, but likely with significant limitations (especially noting that a common recommendation with such PRNGs is not to consume more than the square root of the generator's period).

  • Xoshiro: the Xoshiro family of RNGs support jump and long_jump methods which may effectively be used to divide the output of a single RNG into multiple streams. In practice this is only useful with a small number of streams, since jump must be called n times to select the nth "stream".

  • Pcg: these RNGs support construction with state and stream parameters. Note, however, that the RNGs have been critiqued in that multiple streams using the same key are often strongly correlated. See the author's own comments.

    The PCG RNGs also support an fn advance(delta) method, which might be used to divide a single stream into multiple sub-streams as with Xoshiro's jump (but better since the offset can be specified).

Practice: non-deterministic multi-threaded

We use [Rayon]'s parallel iterators, using [map_init] to initialize an RNG in each worker thread. Note: this RNG may be re-used across multiple work units, which may be split between worker threads in non-deterministic fashion.

use rand::distributions::{Distribution, Uniform};
use rayon::prelude::*;

static SAMPLES: u64 = 1_000_000;

fn main() {
    let range = Uniform::new(-1.0f64, 1.0);

    let in_circle = (0..SAMPLES)
        .map_init(|| rand::thread_rng(), |rng, _| {
            let a = range.sample(rng);
            let b = range.sample(rng);
            if a * a + b * b <= 1.0 {
            } else {
        .reduce(|| 0usize, |a, b| a + b);

    // prints something close to 3.14159...
        "π is approximately {}",
        4. * (in_circle as f64) / (SAMPLES as f64)

Practice: deterministic multi-threaded

We use approach (4) above to achieve a deterministic result: initialize all RNGs from a single seed, but using multiple streams. We use [ChaCha8Rng::set_stream] to achieve this.

Note further that we manually batch multiple work-units according to BATCH_SIZE. This is important since the cost of initializing an RNG is large compared to the cost of our work unit (generating two random samples plus some trivial calculations). Manual batching could improve performance of the above non-deterministic simulation too.

(Note: this example is

use rand::distributions::{Distribution, Uniform};
use rand_chacha::{rand_core::SeedableRng, ChaCha8Rng};
use rayon::prelude::*;

static SEED: u64 = 0;
static BATCH_SIZE: u64 = 10_000;
static BATCHES: u64 = 1000;

fn main() {
    let range = Uniform::new(-1.0f64, 1.0);

    let in_circle = (0..BATCHES)
        .map(|i| {
            let mut rng = ChaCha8Rng::seed_from_u64(SEED);
            let mut count = 0;
            for _ in 0..BATCH_SIZE {
                let a = range.sample(&mut rng);
                let b = range.sample(&mut rng);
                if a * a + b * b <= 1.0 {
                    count += 1;
        .reduce(|| 0usize, |a, b| a + b);

    // prints 3.1409052 (determinstic and reproducible result)
        "π is approximately {}",
        4. * (in_circle as f64) / ((BATCH_SIZE * BATCHES) as f64)

Random values

Now that we have a way of producing random data, how can we convert it to the type of value we want?

This is a trick question: we need to know both the range we want and the type of distribution of this value (which is what the next section is all about).

The Rng trait

For convenience, all generators automatically implement the Rng trait, which provides short-cuts to a few ways of generating values. This has several convenience functions for producing uniformly distributed values:

  • Rng::gen generates an unbiased (uniform) random value from a range appropriate for the type. For integers this is normally the full representable range (e.g. from 0u32 to std::u32::MAX), for floats this is between 0 and 1, and some other types are supported, including arrays and tuples.

    This method is a convenience wrapper around the Standard distribution, as documented in the next section.

  • Rng::gen_range generates an unbiased random value in the given range

  • Rng::fill and Rng::try_fill are optimised functions for filling any byte or integer slice with random values

It also has convenience functions for producing non-uniform boolean values:

  • Rng::gen_bool generates a boolean with the given probability
  • Rng::gen_ratio also generates a boolean, where the probability is defined via a fraction

Finally, it has a function to sample from arbitrary distributions:


extern crate rand;
use rand::Rng;
fn main() {
let mut rng = rand::thread_rng();

// an unbiased integer over the entire range:
let i: i32 = rng.gen();
println!("i = {i}");

// a uniformly distributed value between 0 and 1:
let x: f64 = rng.gen();
println!("x = {x}");

// simulate rolling a die:
println!("roll = {}", rng.gen_range(1..=6));

Additionally, the random function is a short-cut to Rng::gen on the thread_rng:

extern crate rand;
use rand::Rng;
fn main() {
println!("Tossing a coin...");
if rand::random() {
    println!("We got lucky!");

Custom random types

Notice from the above that rng.gen() yields a different distribution of values depending on the type:

  • i32 values are sampled from i32::MIN ..= i32::MAX uniformly
  • f32 values are sampled from 0.0 .. 1.0 uniformly

This is the Standard distribution. [Distribution]s are the topic of the next chapter, but given the importance of the Standard distribution we introduce it here. As usual, standards are somewhat arbitrary, but chosen according to reasonable logic:

  • Values are sampled uniformly: given any two sub-ranges of equal size, each has an equal chance of containing the next sampled value
  • Usually, the whole range of the target type is used
  • For f32 and f64 the range 0.0 .. 1.0 is used (exclusive of 1.0), for two reasons: (a) this is common practice for random-number generators and (b) because for many purposes having a uniform distribution of samples (along the Real number line) is important, and this is only possible for floating-point representations by restricting the range.

Given that, we can implement the Standard distribution for our own types:

extern crate rand;
use rand::Rng;
use rand::distributions::{Distribution, Standard, Uniform};
use std::f64::consts::TAU; // = 2π

/// Represents an angle, in radians
pub struct Angle(f64);
impl Angle {
    pub fn from_degrees(degrees: f64) -> Self {
        Angle(degrees * (std::f64::consts::TAU / 360.0))

impl Distribution<Angle> for Standard {
    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Angle {
        // It would be correct to write:
        // Angle(rng.gen::<f64>() * TAU)

        // However, the following is preferred:
        Angle(Uniform::new(0.0, TAU).sample(rng))

fn main() {
    let mut rng = rand::thread_rng();
    let angle: Angle = rng.gen();
    println!("Random angle: {angle:?}");

Random distributions

For maximum flexibility when producing random values, we define the Distribution trait:

use rand::{Rng, distributions::DistIter};
// a producer of data of type T:
pub trait Distribution<T> {
    // the key function:
    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> T;

    // a convenience function defined using sample:
    fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T>
        Self: Sized,
        R: Rng,
        // [has a default implementation]

Implementations of Distribution are probability distribution: mappings from events to probabilities (e.g. for a die roll P(x = i) = ⅙ or for a Normal distribution with mean μ=0, P(x > 0) = ½).

Note that although probability distributions all have properties such as a mean, a Probability Density Function, and can be sampled by inverting the Cumulative Density Function, here we only concern ourselves with sampling random values. If you require use of such properties you may prefer to use the statrs crate.

Rand provides implementations of many different distributions; we cover the most common of these here, but for full details refer to the distributions module and the rand_distr crate.

Uniform distributions

The most obvious type of distribution is the one we already discussed: one where each equally-sized sub-range has equal chance of containing the next sample. This is known as uniform.

Rand actually has several variants of this, representing different ranges:

  • Standard requires no parameters and samples values uniformly according to the type. Rng::gen provides a short-cut to this distribution.
  • Uniform is parametrised by Uniform::new(low, high) (including low, excluding high) or Uniform::new_inclusive(low, high) (including both), and samples values uniformly within this range. Rng::gen_range is a convenience method defined over Uniform::sample_single, optimised for single-sample usage.
  • Alphanumeric is uniform over the char values 0-9A-Za-z.
  • Open01 and OpenClosed01 are provide alternate sampling ranges for floating-point types (see below).

Uniform sampling by type

Lets go over the distributions by type:

  • For bool, Standard samples each value with probability 50%.

  • For Option<T>, the Standard distribution samples None with probability 50%, otherwise Some(value) is sampled, according to its type.

  • For integers (u8 through to u128, usize, and i* variants), Standard samples from all possible values while Uniform samples from the parameterised range.

  • For NonZeroU8 and other "non-zero" types, Standard samples uniformly from all non-zero values (rejection method).

  • Wrapping<T> integer types are sampled as for the corresponding integer type by the Standard distribution.

  • For floats (f32, f64),

    • Standard samples from the half-open range [0, 1) with 24 or 53 bits of precision (for f32 and f64 respectively)
    • OpenClosed01 samples from the half-open range (0, 1] with 24 or 53 bits of precision
    • Open01 samples from the open range (0, 1) with 23 or 52 bits of precision
    • Uniform samples from a given range with 23 or 52 bits of precision
  • For the char type, the Standard distribution samples from all available Unicode code points, uniformly; many of these values may not be printable (depending on font support). The Alphanumeric samples from only a-z, A-Z and 0-9 uniformly.

  • For tuples and arrays, each element is sampled as above, where supported. The Standard and Uniform distributions each support a selection of these types (up to 12-tuples and 32-element arrays). This includes the empty tuple () and array. When using rustc ≥ 1.51, enable the min_const_gen feature to support arrays larger than 32 elements.

  • For SIMD types, each element is sampled as above, for Standard and Uniform (for the latter, low and high parameters are also SIMD types, effectively sampling from multiple ranges simultaneously). SIMD support requires using the simd_support feature flag and nightly rustc.

  • For enums, you have to implement uniform sampling yourself. For example, you could use the following approach:

    fn main() {
    use rand::{Rng, distributions::{Distribution, Standard}};
    pub enum Food {
    impl Distribution<Food> for Standard {
        fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Food {
            let index: u8 = rng.gen_range(0..3);
            match index {
                0 => Food::Burger,
                1 => Food::Pizza,
                2 => Food::Kebab,
                _ => unreachable!(),

Non-uniform distributions

The rand crate provides only two non-uniform distributions:

  • The Bernoulli distribution simply generates a boolean where the probability of sampling true is some constant (Bernoulli::new(0.5)) or ratio (Bernoulli::from_ratio(1, 6)).
  • The WeightedIndex distribution may be used to sample from a sequence of weighted values. See the Sequences section.

Many more non-uniform distributions are provided by the rand_distr crate.


The Binomial distribution is related to the Bernoulli in that it models running n independent trials each with probability p of success, then counts the number of successes.

Note that for large n the Binomial distribution's implementation is much faster than sampling n trials individually.

The Poisson distribution expresses the expected number of events occurring within a fixed interval, given that events occur with fixed rate λ. Poisson distribution sampling generates Float values because Floats are used in the sampling calculations, and we prefer to defer to the user on integer types and the potentially lossy and panicking associated conversions. For example, u64 values can be attained with rng.sample(Poisson) as u64.

Note that out of range float to int conversions with as result in undefined behavior for Rust <1.45 and a saturating conversion for Rust >=1.45.

Continuous non-uniform distributions

Continuous distributions model samples drawn from the real number line ℝ, or in some cases a point from a higher dimension (ℝ², ℝ³, etc.). We provide implementations for f64 and for f32 output in most cases, although currently the f32 implementations simply reduce the precision of an f64 sample.

The exponential distribution, Exp, simulates time until decay, assuming a fixed rate of decay (i.e. exponential decay).

The Normal distribution (also known as Gaussian) simulates sampling from the Normal distribution ("Bell curve") with the given mean and standard deviation. The LogNormal is related: for sample X from the log-normal distribution, log(X) is normally distributed; this "skews" the normal distribution to avoid negative values and to have a long positive tail.

The UnitCircle and UnitSphere distributions simulate uniform sampling from the edge of a circle or surface of a sphere.

The Cauchy distribution (also known as the Lorentz distribution) is the distribution of the x-intercept of a ray from point (x0, γ) with uniformly distributed angle.

The Beta distribution is a two-parameter probability distribution, whose output values lie between 0 and 1. The Dirichlet distribution is a generalisation to any positive number of parameters.

Random processes

You may have noticed that the Distribution trait does not allow mutation of self (no &mut self methods). This is by design: a probability distribution is defined as a mapping from events to probabilities.

In contrast, a Stochastic Process concerns a family of variables (or state) which mutate in a random manner.

We do not attempt to define a general API covering random processes or to provide direct support for modelling them. Here we merely discuss some.

Sampling without replacement

Given, for example, a bag of 10 red marbles and 30 green marbles, the initial probability that a marble sampled from the bag is red is 10/(10 + 30) = ¼ = 0.25. If the first marble is red and is not replaced, then the probability that the second marble sampled from the bag is red is 9/(9 + 30) = 3/13 ≅ 0.23.

The rand crate does not provide any system supporting step-wise sampling without replacement. What it does provide is support for sampling multiple distinct values from a sequence in a single step: IteratorRandom::choose_multiple and SliceRandom::choose_multiple.

If you wish to implement step-wise sampling yourself, here are a few ideas:

  • Place all elements in a Vec. Each step sample and remove one value. Note that if the set of all possible elements is large this is inefficient since Vec::remove is O(n) and since all elements must be constructed.
  • Place all elements in a Vec and shuffle. Each step simply take the next element.
  • Construct a method of sampling values from the initial distribution plus an empty HashSet representing "taken" values. Each step, sample a value; if it is in the HashSet then reject the value and sample again, otherwise place a copy in a HashSet and return. Note that this method is inefficient unless the number of samples taken is much smaller than the number of available elements.
  • Investigate src/seq/ several sampling algorithms are used which may be adjusted to this application.


Rand implements a few common random operations on sequences via the IteratorRandom and SliceRandom traits.

Generating indices

To sample:


To shuffle a slice:


The following provide a convenient way of sampling a value from a slice or iterator:

Note that operating on an iterator is often less efficient than operating on a slice.

Weighted sampling

For example, weighted sampling could be used to model the colour of a marble sampled from a bucket containing 5 green, 15 red and 80 blue marbles.

With replacement

Sampling with replacement implies that any sampled values (marbles) are replaced (thus, the probability of sampling each variant is not affected by the action of sampling).

This is implemented by the following distributions:

  • WeightedIndex has fast setup and O(log N) sampling
  • WeightedAliasIndex has slow setup and O(1) sampling, thus may be faster with a large number of samples

For convenience, you may use:

Without replacement

Sampling without replacement implies that the action of sampling modifies the distribution. Since the Distribution trait is built around the idea of immutable distributions, we offer the following:

Error handling

Error handling in Rand is a compromise between simplicity and necessity. Most RNGs and sampling functions will never produce errors, and making these able to handle errors would add significant overhead (to code complexity and ergonomics of usage at least, and potentially also performance, depending on the approach). However, external RNGs can fail, and being able to handle this is important.

It has therefore been decided that most methods should not return a Result type, but with a few important exceptions, namely:

Most functions consuming random values will not attempt any error handling, and reduce to calls to RngCore's "infallible" methods. Since most RNGs cannot fail anyway this is usually not a problem, but the few generators which can may be forced to fail in this case:

  • OsRng is a wrapper over getrandom. From the latter's documentation: "In general, on supported platforms, failure is highly unlikely, though not impossible." OsRng will forward errors through RngCore::try_fill_bytes while other methods panic on error.
  • thread_rng seeds itself via OsRng on first use and periodically thereafter, thus can potentially fail, though unlikely. If initial seeding fails, a panic will result. If a failure happens during reseeding (less likely) then the RNG continues without reseeding; a log message (warning) is emitted if logging is enabled.



Given fixed inputs, all items (should) fall into one of three categories:

  • Output is non-deterministic, thus never reproducible
  • Output is deterministic, but not considered portable
  • Output is deterministic and portable

In general, functionality is considered deterministic and portable unless it is clearly non-deterministic (e.g. getrandom, ThreadRng) or it is documented as being unportable (e.g. StdRng, SmallRng).

Crate versions

We try to follow semver rules regarding API-breaking changes and MAJOR.MINOR.PATCH versions:

  • New patch versions should not include API-breaking changes or major new features
  • Before 1.0, minor versions may include API breaking changes. After 1.0 they should not.

Additionally, we must also consider value-breaking changes and portability. When given fixed inputs,

  • For non-deterministic items, implementations may change in any release
  • For deterministic unportable items, output should be preserved in patch releases, but may change in any minor release (including after 1.0)
  • For portable items, any change of output across versions is considered equivalent to an API breaking change.


We expect all pseudo-random algorithms to test the value-stability of their output, where possible:

  • PRNGs should be compared with a reference vector (example)
  • Other algorithms should include their own test vectors within a value_stability test or similar (example)


Portability of usize

There is unfortunately one non-portable item baked into the heart of the Rust language: usize (and isize). For example, the size of an empty Vec will differ on 32-bit and 64-bit targets. For most purposes this is not an issue, but when it comes to generating random numbers in a portable manner it does matter.

A simple rule follows: if portability is required, never sample a usize or isize value directly.

Within Rand we adhere to this rule whenever possible. All sequence-related code requiring a bounded usize value will sample a u32 value unless the upper bound exceeds u32::MAX. (Note that this actually improves benchmark performance in many cases.)

Portability of floats

The results of floating point arithmetic depend on rounding modes and implementation details. Especially the results of transcendental functions vary from platform to platform. Due to this, the distributions in rand_distr are not always portable for f32 and f64. However, we strive to make them as portable as possible.


This guide is intended to facilitate upgrading to the next minor or major version of Rand. Note that updating to the next patch version (e.g. 0.5.1 to 0.5.2) should never require code changes.

This guide gives a few more details than the changelog, in particular giving guidance on how to use new features and migrate away from old ones.

Updating to 0.5

The 0.5 release has quite significant changes over the 0.4 release; as such, it may be worth reading through the following coverage of breaking changes. This release also contains many optimisations, which are not detailed below.


We have a new crate: rand_core! This crate houses some important traits, RngCore, BlockRngCore, SeedableRng and CryptoRng, the error types, as well as two modules with helpers for implementations: le and impls. It is recommended that implementations of generators use the rand_core crate while other users use only the rand crate, which re-exports most parts of rand_core.

The rand_derive crate has been deprecated due to very low usage and deprecation of Rand.


Several new Cargo feature flags have been added:

  • alloc, used without std, allows use of Box and Vec
  • serde1 adds serialization support to some PRNGs
  • log adds logging in a few places (primarily to OsRng and JitterRng)

Rng and friends (core traits)

Rng trait has been split into two traits, a "back end" RngCore (implemented by generators) and a "front end" Rng implementing all the convenient extension methods.

Implementations of generators must impl RngCore instead. Usage of rand_core for implementations is encouraged; the rand_core::{le, impls} modules may prove useful.

Users of Rng who don't need to implement it won't need to make so many changes; often users can forget about RngCore and only import Rng. Instead of RngCore::next_u32() / next_u64() users should prefer Rng::gen(), and instead of RngCore::fill_bytes(dest), Rng::fill(dest) can be used.

Rng / RngCore methods

To allow error handling from fallible sources (e.g. OsRng), a new RngCore::try_fill_bytes method has been added; for example EntropyRng uses this mechanism to fall back to JitterRng if OsRng fails, and various handlers produce better error messages. As before, the other methods will panic on failure, but since these are usually used with algorithmic generators which are usually infallible, this is considered an appropriate compromise.

A few methods from the old Rng have been removed or deprecated:

  • next_f32 and next_f64; these are no longer implementable by generators; use gen instead
  • gen_iter; users may instead use standard iterators with closures: ::std::iter::repeat(()).map(|()| rng.gen())
  • gen_ascii_chars; use repeat as above and rng.sample(Alphanumeric)
  • gen_weighted_bool(n); use gen_bool(1.0 / n) instead

Rng has a few new methods:

  • sample(distr) is a shortcut for distr.sample(rng) for any Distribution
  • gen_bool(p) generates a boolean with probability p of being true
  • fill and try_fill, corresponding to fill_bytes and try_fill_bytes respectively (i.e. the only difference is error handling); these can fill and integer slice / array directly, and provide better performance than gen()

Constructing PRNGs

New randomly-initialised PRNGs

A new trait has been added: FromEntropy. This is automatically implemented for any type supporting SeedableRng, and provides construction from fresh, strong entropy:

fn main() {
use rand_0_5::{ChaChaRng, FromEntropy};

let mut rng = ChaChaRng::from_entropy();

Seeding PRNGs

The SeedableRng trait has been modified to include the seed type via an associated type (SeedableRng::Seed) instead of a template parameter (SeedableRng<Seed>). Additionally, all PRNGs now seed from a byte-array ([u8; N] for some fixed N). This allows generic handling of PRNG seeding which was not previously possible.

PRNGs are no longer constructed from other PRNGs via Rand support / gen(), but through SeedableRng::from_rng, which allows error handling and is intentionally explicit.

SeedableRng::reseed has been removed since it has no utility over from_seed and its performance advantage is questionable.

Implementations of SeedableRng may need to change their Seed type to a byte-array; this restriction has been made to ensure portable handling of Endianness. Helper functions are available in rand_core::le to read u32 and u64 values from byte arrays.

Block-based PRNGs

rand_core has a new helper trait, BlockRngCore, and implementation, BlockRng. These are for use by generators which generate a block of random data at a time instead of word-sized values. Using this trait and implementation has two advantages: optimised RngCore methods are provided, and the PRNG can be used with ReseedingRng with very low overhead.

Cryptographic RNGs

A new trait has been added: CryptoRng. This is purely a marker trait to indicate which generators should be suitable for cryptography, e.g. fn foo<R: Rng + CryptoRng>(rng: &mut R). Suitability for cryptographic use cannot be guaranteed.

Error handling

A new Error type has been added, designed explicitly for no-std compatibility, simplicity, and enough flexibility for our uses (carrying a cause when possible):

pub struct Error {
    pub kind: ErrorKind,
    pub msg: &'static str,
    // some fields omitted

The associated ErrorKind allows broad classification of errors into permanent, unexpected, transient and not-yet-ready kinds.

The following use the new error type:

  • RngCore::try_fill_bytes
  • Rng::try_fill
  • OsRng::new
  • JitterRng::new

External generators

We have a new generator, EntropyRng, which wraps OsRng and JitterRng (preferring to use the former, but falling back to the latter if necessary). This allows easy construction with fallback via SeedableRng::from_rng, e.g. IsaacRng::from_rng(EntropyRng::new())?. This is equivalent to using FromEntropy except for error handling.

It is recommended to use EntropyRng over OsRng to avoid errors on platforms with broken system generator, but it should be noted that the JitterRng fallback is very slow.


Pseudo-Random Number Generators (i.e. deterministic algorithmic generators) have had a few changes since 0.4, and are now housed in the prng module (old names remain temporarily available for compatibility; eventually these generators will likely be housed outside the rand crate).

All PRNGs now do not implement Copy to prevent accidental copying of the generator's state (and thus repetitions of generated values). Explicit cloning via Clone is still available. All PRNGs now have a custom implementation of Debug which does not print any internal state; this helps avoid accidentally leaking cryptographic generator state in log files. External PRNG implementations are advised to follow this pattern (see also doc on RngCore).

SmallRng has been added as a wrapper, currently around XorShiftRng (but likely another algorithm soon). This is for uses where small state and fast initialisation are important but cryptographic strength is not required. (Actual performance of generation varies by benchmark; depending on usage this may or may not be the fastest algorithm, but will always be fast.)


The ReseedingRng wrapper has been significantly altered to reduce overhead. Unfortunately the new ReseedingRng is not compatible with all RNGs, but only those using BlockRngCore.


The method ChaChaRng::set_counter has been replaced by two new methods, set_word_pos and set_stream. Where necessary, the behaviour of the old method may be emulated as follows:

extern crate rand;
extern crate rand_chacha;
use rand::prelude::*;
use rand_chacha::ChaChaRng;
fn main() {
let lower = 88293;
let higher = 9300932;

// previously:
// let mut rng = rand::ChaChaRng::new_unseeded();
// rng.set_counter(lower, higher);

// now:
let mut rng = ChaChaRng::from_seed([0u8; 32]);
rng.set_word_pos(lower << 4);

assert_eq!(4060232610, rng.next_u32());
assert_eq!(2786236710, rng.next_u32());


The IsaacRng and Isaac64Rng PRNGs now have an additional construction method: new_from_u64(seed). 64 bits of state is insufficient for cryptography but may be of use in simulations and games. This will likely be superseded by a method to construct any PRNG from any hashable object in the future.


This is a new cryptographic generator, selected as one of the "stream ciphers suitable for widespread adoption" by eSTREAM. This is now the default cryptographic generator, used by StdRng and thread_rng().

Helper functions/traits

The Rand trait has been deprecated. Instead, users are encouraged to use Standard which is a real distribution and supports the same sampling as Rand. Rng::gen() now uses Standard and should work exactly as before. See the documentation of the distributions module on how to implement Distribution<T> for Standard for user types T

weak_rng() has been deprecated; use SmallRng::from_entropy() instead.


The Sample and IndependentSample traits have been replaced by a single trait, Distribution. This is largely equivalent to IndependentSample, but with ind_sample replaced by just sample. Support for mutable distributions has been dropped; although it appears there may be a few genuine uses, these are not used widely enough to justify the existence of two independent traits or of having to provide mutable access to a distribution object. Both Sample and IndependentSample are still available, but deprecated; they will be removed in a future release.

Distribution::sample (as well as several other functions) can now be called directly on type-erased (unsized) RNGs.

RandSample has been removed (see Rand deprecation and new Standard distribution).

The Closed01 wrapper has been removed, but OpenClosed01 has been added.

Uniform distributions

Two new distributions are available:

  • Standard produces uniformly-distributed samples for many different types, and acts as a replacement for Rand
  • Alphanumeric samples chars from the ranges a-z A-Z 0-9


The Range distribution has been heavily adapted, and renamed to Uniform:

  • Uniform::new(low, high) remains (half open [low, high))
  • Uniform::new_inclusive(low, high) has been added, including high in the sample range
  • Uniform::sample_single(low, high, rng) is a faster variant for single usage sampling from [low, high)

Uniform can now be implemented for user-defined types; see the uniform module.

Non-uniform distributions

Two distributions have been added:

  • Poisson, modeling the number of events expected from a constant-rate source within a fixed time interval (e.g. nuclear decay)
  • Binomial, modeling the outcome of a fixed number of yes-no trials

The sampling methods are based on those in "Numerical Recipes in C".

Exponential and Normal distributions

The main Exp and Normal distributions are unchanged, however the "standard" versions, Exp1 and StandardNormal are no longer wrapper types, but full distributions. Instead of writing let Exp1(x) = rng.gen(); you now write let x = rng.sample(Exp1);.

Updating to 0.6

During the 0.6 cycle, Rand found a new home under the rust-random project. We already feel at home, but if you'd like to help us decorate, a new logo would be appreciated!

We also found a new home for user-centric documentation — this book!


All PRNGs in our old PRNG module have been moved to new crates. We also added an additional crate with the PCG algorithms, and an external crate with Xoshiro / Xoroshiro algorithms:


This update, we switched the algorithm behind SmallRng from Xorshift to a PCG algorithm (either Pcg64Mcg aka XSL 128/64 MCG, or Pcg32 aka XSH RR 64/32 LCG aka the standard PCG algorithm).


The seq module has been completely re-written, and the choose and shuffle methods have been removed from the Rng trait. Most functionality can now be found in the IteratorRandom and SliceRandom traits.

Weighted choices

The WeightedChoice distribution has now been replaced with WeightedIndex, solving a few issues by making the functionality more generic.

For convenience, the SliceRandom::choose_weighted method (and _mut variant) allow a WeightedIndex sample to be applied directly to a slice.

Other features

SIMD types

Rand now has rudimentary support for generating SIMD types, gated behind the simd_support feature flag.

i128 / u128 types

Since these types are now available on stable compilers, these types are supported automatically (with recent enough Rust version). The i128_support feature flag still exists to avoid breakage, but no longer does anything.

Updating to 0.7

Since the 0.6 release, rust-random gained a logo and a new crate: getrandom!


Rand crates now require rustc version 1.32.0 or later. This allowed us to remove all files for faster compilation.

The Rand crate now has fewer dependencies overall, though with some new ones.


As mentioned above, we have a new crate: getrandom, delivering a minimal API around platform-independent access to fresh entropy. This replaces the previous implementation in OsRng, which is now merely a wrapper.

Core features

The FromEntropy trait has now been removed. Fear not though, its from_entropy method continues to provide easy initialisation from its new home in the SeedableRng trait (this requires that rand_core has the std or getrandom feature enabled):

fn main() {
use rand::{SeedableRng, rngs::StdRng};
let mut rng = StdRng::from_entropy();

The SeedableRng::from_rng method is now considered value-stable: implementations should have portable results.

The Error type of rand_core and rand has seen a major redesign; direct usage of this type is likely to need adjustment.


These have seen less change than in the previous release, but noteworthy is:

  • rand_chacha has been rewritten for much better performance (via SIMD instructions)
  • StdRng and ThreadRng now use the ChaCha algorithm. This is a value-breaking change for StdRng.
  • SmallRng is now gated behind the small_rng feature flag.
  • The xoshiro crate is now rand_xoshiro.
  • rand_pcg now includes Pcg64.


For the most widely used distributions (Standard and Uniform), there have been no significant changes. But for most of the rest...

  • We added a new crate, rand_distr, to house the all distributions (including re-exporting those still within rand::distributions). If you previously used rand::distributions::Normal, now you use rand_distr::Normal.
  • Constructors for many distributions changed in order to return a Result instead of panicking on error.
  • Many distributions are now generic over their parameter type (in most cases supporting f32 and f64). This aids usage with generic code, and allows reduced size of parameterised distributions. Currently the more complex algorithms always use f64 internally.
  • Standard can now sample NonZeroU* values

We also added several distributions:


To aid portability, all random samples of type usize now instead sample a u32 value when the upper-bound is less than u32::MAX. This means that upgrading to 0.7 is a value-breaking change for use of seq functionality, but that after upgrading to 0.7 results should be consistent across CPU architectures.

Updating to 0.8

In the following, instructions are provided for porting your code from rand 0.7 and rand_distr 0.2 to rand 0.8 and rand_distr 0.3.


Rand crates now require rustc version 1.36.0 or later. This allowed us to remove some unsafe code and simplify the internal cfg logic.

The dependency on getrandom was bumped to version 0.2. While this does not affect Rand's API, you may be affected by some of the breaking changes even if you use getrandom only as a dependency:

  • You may have to update the getrandom features you are using. The following features are now available:
    • "rdrand": Use the RDRAND instruction on no_std x86/x86_64 targets.
    • "js": Use JavaScript calls on wasm32-unknown-unknown. This replaces the stdweb and wasm-bindgen features, which are removed.
    • "custom": Allows you to specify a custom implementation.
  • Unsupported targets no longer compile. If you require the previous behavior (panicking at runtime instead of failing to compile), you can use the custom feature to provide a panicking implementation.
  • Windows XP and stdweb are, as of getrandom version 0.2.1, no longer supported. If you require support for either of these platforms you may add a dependency on getrandom = "=0.2.0" to pin this version.
  • Hermit, L4Re and UEFI are no longer officially supported. You can use the rdrand feature on these platforms.
  • The minimum supported Linux kernel version is now 2.6.32.

If you are using getrandom's API directly, there are further breaking changes that may affect you. See its changelog.

Serde has been re-added as an optional dependency (use the serde1 feature flag), supporting many types (where appropriate). StdRng and SmallRng are deliberately excluded since these types are not portable.

Core features


ThreadRng no longer implements Copy. This was necessary to fix a possible use-after-free in its thread-local destructor. Any code relying on ThreadRng being copied must be updated to use a mutable reference instead. For example,

use rand_0_7::distributions::{Distribution, Standard};
let rng = rand_0_7::thread_rng();
let a: u32 = Standard.sample_iter(rng).next().unwrap();
let b: u32 = Standard.sample_iter(rng).next().unwrap();

can be replaced with the following code:

extern crate rand;
use rand::prelude::*;
use rand::distributions::Standard;
fn main () {
let mut rng = thread_rng();
let a: u32 = Standard.sample_iter(&mut rng).next().unwrap();
let b: u32 = Standard.sample_iter(&mut rng).next().unwrap();


Rng::gen_range now takes a Range instead of two numbers. Thus, replace gen_range(a, b) with gen_range(a..b). We suggest using the following regular expression to search-replace in all files:

  • replace gen_range\(([^,]*),\s*([^)]*)\)
  • with gen_range(\1..\2)
  • or with gen_range($1..$2) (if your tool does not support backreferences)

Most IDEs support search-replace-across-files or similar; alternatively an external tool such as Regexxer may be used.

This change has a couple of other implications:

  • inclusive ranges are now supported, e.g. gen_range(1..=6) or gen_range('A'..='Z')
  • it may be necessary to explicitly dereference some parameters
  • SIMD types are no longer supported (Uniform types may still be used directly)


The AsByteSliceMut trait was replaced with the Fill trait. This should only affect code implementing AsByteSliceMut on user-defined types, since the Rng::fill and Rng::try_fill retain support for previously-supported types.

Fill supports some additional slice types which could not be supported with AsByteSliceMut: [bool], [char], [f32], [f64].


The entire rand::rngs::adapter module is now restricted to the std feature. While this is technically a breaking change, it should only affect no_std code using ReseedingRng, which is unlikely to exist in the wild.


StdRng has switched from the 20-round ChaCha20 to ChaCha12 for improved performance. This is a reduction in complexity but the 12-round variant is still considered secure: see rand#932. This is a value-breaking change for StdRng.

SmallRng now uses the Xoshiro128++ and Xoshiro256++ algorithm on 32-bit and 64-bit platforms respectively. This reduces correlations of random data generated from similar seeds and improves performance. It is a value-breaking change.

We now implement PartialEq and Eq for StdRng, SmallRng, and StepRng.


Several smaller changes occurred to rand distributions:

  • The Uniform distribution now additionally supports the char type, so for example rng.gen_range('a'..='f') is now supported.
  • UniformSampler::sample_single_inclusive was added.
  • The Alphanumeric distribution now samples bytes instead of chars. This more closely reflects the internally used type, but old code likely has to be adapted to perform the conversion from u8 to char. For example, with Rand 0.7 you could write:
    use rand_0_7::{distributions::Alphanumeric, Rng};
    let mut rng = rand_0_7::thread_rng();
    let chars: String = std::iter::repeat(())
        .map(|()| rng.sample(Alphanumeric))
    With Rand 0.8, this is equivalent to the following:
    extern crate rand;
    use rand::{distributions::Alphanumeric, Rng};
    fn main() {
    let mut rng = rand::thread_rng();
    let chars: String = std::iter::repeat(())
        .map(|()| rng.sample(Alphanumeric))
    println!("chars = \"{chars}\"");
  • The alternative implementation of WeightedIndex employing the alias method was moved from rand to rand_distr::weighted_alias::WeightedAliasIndex. The alias method is faster for large sizes, but it suffers from a slow initialization, making it less generally useful.

In rand_distr v0.4, more changes occurred (since v0.2):

  • rand_distr::weighted_alias::WeightedAliasIndex was added (moved from the rand crate)
  • rand_distr::InverseGaussian and rand_distr::NormalInverseGaussian were added
  • The Geometric and Hypergeometric distributions are now supported.
  • A different algorithm is used for the Beta distribution, improving both performance and accuracy. This is a value-breaking change.
  • The Normal and LogNormal distributions now support a from_mean_cv constructor method and from_zscore sampler method.
  • rand_distr::Dirichlet now uses boxed slices internally instead of Vec. Therefore, the weights are taken as a slice instead of a Vec as input. For example, the following rand_distr 0.2 code
    use rand_distr_0_2::Dirichlet;
    Dirichlet::new(vec![1.0, 2.0, 3.0]).unwrap();
    can be replaced with the following rand_distr 0.3 code:
    use rand_distr::Dirichlet;
    Dirichlet::new(&[1.0, 2.0, 3.0]).unwrap();
  • rand_distr::Poisson does no longer support sampling u64 values directly. Old code may have to be updated to perform the conversion from f64 explicitly.
  • The custom Float trait in rand_distr was replaced with num_traits::Float. Any implementations of Float for user-defined types have to be migrated. Thanks to the math functions from num_traits::Float, rand_distr now supports no_std.

Additionally, there were some minor improvements:

  • The treatment of rounding errors and NaN was improved for the WeightedIndex distribution.
  • The rand_distr::Exp distribution now supports the lambda = 0 parametrization.


Weighted sampling without replacement is now supported, see rand::seq::index::sample_weighted and SliceRandom::choose_multiple_weighted.

There have been value-breaking changes to IteratorRandom::choose, improving accuracy and performance. Furthermore, IteratorRandom::choose_stable was added to provide an alternative that sacrifices performance for independence of iterator size hints.

Feature flags

StdRng is now gated behind a new feature flag, std_rng. This is enabled by default.

The nightly feature no longer implies the simd_support feature. If you were relying on this for SIMD support, you will have to use simd_support feature directly.


Value-stability tests were added for all distributions (rand#786), helping enforce our rules regarding value-breaking changes (see Portability section).


Thank you for your interest in contributing to Rand!

We are open to all contributors, but please consider that we have limited resources, usually have other on-going work within the project, and that even accepting complete PRs costs us time (review and potentially on-going support), thus we may take considerable time to get back to you.

All contributions

  • Scope: please consider whether your "issue" falls within the existing scope of the project or is an enhancement. Note that whether something is considered a defect may depend on your point of view. We may choose to reject contributions to avoid increasing our workload.

    If you wish to expand the scope of the project (e.g. new platforms or additional CI testing) then please be prepared to provide on-going support.

  • Fixes: if you can easily fix this yourself, please consider making a PR instead of opening an issue. On the other hand if it's less easy or looks like it may conflict with other work, don't hesitate to open an issue.

Pull Requests

  • Changelog: unless your change is trivial, please include a note in the changelog ( of each crate affected, under the [Unreleased] heading at the top (add if necessary). Please include the PR number (this implies the note must be added after opening a PR).

  • Commits: if contributing large changes, consider splitting these over multiple commits, if possible such that each commit at least compiles. Rebasing commits may be appropriate when making significant changes.

  • Documentation: we require documentation of all public items. Short examples may be included where appropriate.

  • Maintainability: it is important to us that code is easy to read and understand and not hard to review for correctness.

  • Performance: we always aim for good performance and sometimes do considerable extra work to get there, however we must also make compromises for the sake of maintainability, and consider whether a minor efficiency gain is worth the extra code complexity. Use benchmarks.

  • Style: make it neat. Usually limit length to 80 chars.

  • Unsafe: use it where necessary, not if there is a good alternative. Ensure unsafe code is easy to review for correctness.

  • License and attribution: this project is freely licenced under the MIT and Apache Public Licence v2. We assume that all contributions are made under these licence grants. Copyrights are retained by their contributors.

    Our works are attributed to "The Rand Project Developers". This is not a formal entity but merely the collection of all contributors to this project. For more, see the COPYRIGHT file.

  • Thank you!



All documentation is in English, but no particular dialect is preferred.

The documentation should be accessible to multiple audiences: both seasoned Rustaceans and relative newcomers, those with experience in statistical modelling or cryptography, as well as those new to the subjects. Since it is often impossible to write appropriate one-size-fits-all documentation, we prefer concise technical documentation with reference to extended articles aimed at more specific audiences.

API documentation

Rand crates

It is recommended to use nightly Rust for correct link handling.

To build all API documentation for all crates in the rust-random/rand repository, run:

# Build doc for all modules:
cargo doc --all --no-deps

# And open it:
xdg-open target/doc/rand/index.html

On Linux, it is easy to set up automatic rebuilds after any edit:

while inotifywait -r -e close_write src/ rand_*/; do cargo doc; done

After editing API documentation, we recommend testing examples:

cargo test --doc

Rand API docs are automatically built and hosted at for the latest code in master.

Getrandom crate

The rust-random/getrandom repository contains only a single crate, hence a simple cargo doc will suffice.

Cross-crate links

When referring to another crate, we prefer linking to the crate page on since (a) this includes the README documenting the purpose of the crate and (b) this links directly to both the repository and the API documentation. Example:

// Link to the crate page:
//! [`rand_chacha`]:

When referring to an item from within another crate,

  1. if that item is accessible via a crate dependency (even if not via the public API), use the Rust item path
  2. when linking to another crate within the rust-random/rand repository, relative paths within the generated documentation files (under target/doc) can be used; these work on but not currently on (see docs#204)
  3. if neither of the above are applicable, use an absolute link
  4. consider revising documentation, e.g. refer to the crate instead


//! We depend on rand_core, therefore can use the Rust path:
//! [`BlockRngCore`]: rand_core::block::BlockRngCore

//! rand_chacha is not a dependency, but is within the same repository:
//! [`ChaCha20Rng`]: ../../rand_chacha/struct.ChaCha20Rng.html

//! Link directly to, with major & minor but no patch version:
//! [`getrandom`]:

Auxiliary documentation

README files

README files contain a brief introduction to the crate, shield badges, useful links, feature-flag documentation, licence information, and potentially an example.

For the most part these files do not have any continuous testing. Where examples are included (currently only for the rand_jitter crate), we enable continuous testing via doc_comment (see onwards).


Changelog formats are based on the Keep a Changelog format.

All significant changes merged since the last release should be listed under an [Unreleased] section at the top of log.

The book

The source to this book is contained in the rust-random/book repository. It is built using mdbook, which makes building and testing easy:

cargo install mdbook --version "^0.2"

mdbook build --open
mdbook test

# To automatically rebuild after any changes:
mdbook watch

Note that links in the book are relative and designed to work in the published book. If you build the book locally, you might want to set up a symbolic link pointing to your build of the API documentation:

ln -s ../rand/target/doc rand


Over time, the scope of the project has grown, and Rand has moved from using a monolithic crate to using a "main" crate plus multiple single-purpose crates. For new functionality, one must consider where, and whether, it fits within the Rand project.

Small, focused crates may be used for a few reasons, but we aim not to maximally divide functionality into small crates. Valid reasons for using a separate crate for a feature are therefore:

  • to allow a clear dependency hierarchy (rand_core)
  • to make the feature available in a stand-alone fashion (e.g. getrandom)
  • to remove little-used features with non-trivial amounts of code from widely used crates (e.g. rand_jitter and rand_distr both extracted functionality from rand)
  • to allow choice, without including large amounts of unused code for all users, but also without producing an enormous number of new crates (RNG family crates like rand_xoshiro and rand_isaac)

Traits, basics and UI

The main user interface to the Rand project remains the central rand crate. Goals for this crate are:

  • ease of use
  • expose commonly used functionality in a single place
  • permit usage of additional randomness sources and distribution samplers

To allow better modularity, the core traits have been moved to the rand_core crate. Goals of this crate are:

  • expose the core traits with minimal dependencies
  • provide common tools needed to implement various randomness sources

External random sources

The main (and usually only) external source of randomness is the Operating System, interfaced via the getrandom crate. This crate also supports usage of RDRAND on a few no_std targets.

Support for other no_std targets has been discussed but with little real implementation effort. See getrandom#4.

The rand_jitter crate provides an implementation of a CPU Jitter entropy harvester, and is only included in Rand for historical reasons.

Pseudo-random generators

The Rand library includes several pseudo-random number generators, for the following reasons:

  • to implement the StdRng and SmallRng generators
  • to provide a few high-quality alternative generators
  • historical usage

These are implemented within "family" crates, e.g. rand_chacha, rand_pcg, rand_xoshiro.

We have received several requests to adopt new algorithms into the library; when evaluating such requests we must consider several things:

  • purpose for inclusion within Rand
  • whether the PRNG is cryptographically secure, and if so, how trustworthy such claims are
  • statistical quality of output
  • performance and features of the generator
  • reception and third-party review of the algorithm


The Distribution trait is provided by Rand, along with commonly-used distributions (mostly linear ones).

Additional distributions are packaged within the rand_distr crate, which depends on rand and re-exports all of its distributions.


Rand has a number of unit tests, though these are not comprehensive or perfect (improvements welcome). We prefer to have tests for all new functionality.

The first line of testing is simply to run cargo test from the appropriate directory. Since Rand supports no_std (core-only), core+alloc and std environments, it is important to test all three (depending on which features are applicable to the code in question):

# Test using std:
cargo test
# Test using only core:
cargo test --tests --no-default-features
# Test using core + alloc (requires nightly):
cargo +nightly test --tests --no-default-features --features=alloc

It may also be worth testing with other feature flags:

cargo test --all-features

Note that this only tests the current package (i.e. the main Rand lib when run from the repo's top level). To test another lib, cd to its directory.

We do not recommend using Cargo's --package option due to its surprising interactions with --feature options and failure when multiple versions of the same package are in the build tree. The CI instead uses --manifest-path to select packages; while developing, using cd is easier.

Writing tests

Tests may be unit tests within a test sub-module, documentation examples, example applications (examples dir), integration tests (tests dir), or benchmarks (benches dir).

Note that only unit tests and integration tests are expected to pass in no_std (core only) and core+alloc configurations. This is a deliberate choice; example code should only need to target the common case (std).

Random Number Generators

Often test code needs some RNG to test with, but does not need any particular RNG. In this case, we prefer use of ::test::rng which is simple, fast to initialise and deterministic:

let mut rng = ::test::rng(528); // just pick some number

Various tests concern properties which are probably true, but not definitely. We prefer that such tests are deterministic to avoid spurious failures.


We already have many benchmarks:

cargo +nightly bench

# In a few cases, nightly features may use different code paths:
cargo +nightly bench --features=nightly

Benchmarks for distributions now live in the rand_distr crate; all other benchmarks (including all our RNGs) live in the main rand crate (hence the many dev-dependencies).

A lot of code in Rand is performance sensitive, most of it is expected to be used in hot loops in some libraries/applications. If you change code in rand_core, in PRNG crates, or in the rngs or distributions modules (especially when an 'obvious cleanup'), make sure the benchmarks do not regress.

Please report before-and-after results for any affected benchmarks. If you are optimising something previously not benchmarked, please add new benchmarks first, then add your changes in a separate commit (to make before-and-after benchmarking easy).