Trait bonsaidb::local::argon2::password_hash::rand_core::SeedableRng
source · pub trait SeedableRng: Sized {
type Seed: Sized + Default + AsMut<[u8]>;
// Required method
fn from_seed(seed: Self::Seed) -> Self;
// Provided methods
fn seed_from_u64(state: u64) -> Self { ... }
fn from_rng<R>(rng: R) -> Result<Self, Error>
where R: RngCore { ... }
fn from_entropy() -> Self { ... }
}Expand description
A random number generator that can be explicitly seeded.
This trait encapsulates the low-level functionality common to all pseudo-random number generators (PRNGs, or algorithmic generators).
Required Associated Types§
sourcetype Seed: Sized + Default + AsMut<[u8]>
type Seed: Sized + Default + AsMut<[u8]>
Seed type, which is restricted to types mutably-dereferenceable as u8
arrays (we recommend [u8; N] for some N).
It is recommended to seed PRNGs with a seed of at least circa 100 bits,
which means an array of [u8; 12] or greater to avoid picking RNGs with
partially overlapping periods.
For cryptographic RNG’s a seed of 256 bits is recommended, [u8; 32].
Implementing SeedableRng for RNGs with large seeds
Note that the required traits core::default::Default and
core::convert::AsMut<u8> are not implemented for large arrays
[u8; N] with N > 32. To be able to implement the traits required by
SeedableRng for RNGs with such large seeds, the newtype pattern can be
used:
use rand_core::SeedableRng;
const N: usize = 64;
pub struct MyRngSeed(pub [u8; N]);
pub struct MyRng(MyRngSeed);
impl Default for MyRngSeed {
fn default() -> MyRngSeed {
MyRngSeed([0; N])
}
}
impl AsMut<[u8]> for MyRngSeed {
fn as_mut(&mut self) -> &mut [u8] {
&mut self.0
}
}
impl SeedableRng for MyRng {
type Seed = MyRngSeed;
fn from_seed(seed: MyRngSeed) -> MyRng {
MyRng(seed)
}
}Required Methods§
sourcefn from_seed(seed: Self::Seed) -> Self
fn from_seed(seed: Self::Seed) -> Self
Create a new PRNG using the given seed.
PRNG implementations are allowed to assume that bits in the seed are
well distributed. That means usually that the number of one and zero
bits are roughly equal, and values like 0, 1 and (size - 1) are unlikely.
Note that many non-cryptographic PRNGs will show poor quality output
if this is not adhered to. If you wish to seed from simple numbers, use
seed_from_u64 instead.
All PRNG implementations should be reproducible unless otherwise noted:
given a fixed seed, the same sequence of output should be produced
on all runs, library versions and architectures (e.g. check endianness).
Any “value-breaking” changes to the generator should require bumping at
least the minor version and documentation of the change.
It is not required that this function yield the same state as a reference implementation of the PRNG given equivalent seed; if necessary another constructor replicating behaviour from a reference implementation can be added.
PRNG implementations should make sure from_seed never panics. In the
case that some special values (like an all zero seed) are not viable
seeds it is preferable to map these to alternative constant value(s),
for example 0xBAD5EEDu32 or 0x0DDB1A5E5BAD5EEDu64 (“odd biases? bad
seed”). This is assuming only a small number of values must be rejected.
Provided Methods§
sourcefn seed_from_u64(state: u64) -> Self
fn seed_from_u64(state: u64) -> Self
Create a new PRNG using a u64 seed.
This is a convenience-wrapper around from_seed to allow construction
of any SeedableRng from a simple u64 value. It is designed such that
low Hamming Weight numbers like 0 and 1 can be used and should still
result in good, independent seeds to the PRNG which is returned.
This is not suitable for cryptography, as should be clear given that the input size is only 64 bits.
Implementations for PRNGs may provide their own implementations of this function, but the default implementation should be good enough for all purposes. Changing the implementation of this function should be considered a value-breaking change.
sourcefn from_rng<R>(rng: R) -> Result<Self, Error>where
R: RngCore,
fn from_rng<R>(rng: R) -> Result<Self, Error>where
R: RngCore,
Create a new PRNG seeded from another Rng.
This may be useful when needing to rapidly seed many PRNGs from a master PRNG, and to allow forking of PRNGs. It may be considered deterministic.
The master PRNG should be at least as high quality as the child PRNGs.
When seeding non-cryptographic child PRNGs, we recommend using a
different algorithm for the master PRNG (ideally a CSPRNG) to avoid
correlations between the child PRNGs. If this is not possible (e.g.
forking using small non-crypto PRNGs) ensure that your PRNG has a good
mixing function on the output or consider use of a hash function with
from_seed.
Note that seeding XorShiftRng from another XorShiftRng provides an
extreme example of what can go wrong: the new PRNG will be a clone
of the parent.
PRNG implementations are allowed to assume that a good RNG is provided
for seeding, and that it is cryptographically secure when appropriate.
As of rand 0.7 / rand_core 0.5, implementations overriding this
method should ensure the implementation satisfies reproducibility
(in prior versions this was not required).
sourcefn from_entropy() -> Self
fn from_entropy() -> Self
Creates a new instance of the RNG seeded via getrandom.
This method is the recommended way to construct non-deterministic PRNGs since it is convenient and secure.
In case the overhead of using getrandom to seed many PRNGs is an
issue, one may prefer to seed from a local PRNG, e.g.
from_rng(thread_rng()).unwrap().
Panics
If getrandom is unable to provide secure entropy this method will panic.