Trait rayon::iter::ParallelIterator
source · [−]pub trait ParallelIterator: Sized + Send {
type Item: Send;
Show 54 methods
fn drive_unindexed<C>(self, consumer: C) -> C::Result
where
C: UnindexedConsumer<Self::Item>;
fn for_each<OP>(self, op: OP)
where
OP: Fn(Self::Item) + Sync + Send,
{ ... }
fn for_each_with<OP, T>(self, init: T, op: OP)
where
OP: Fn(&mut T, Self::Item) + Sync + Send,
T: Send + Clone,
{ ... }
fn for_each_init<OP, INIT, T>(self, init: INIT, op: OP)
where
OP: Fn(&mut T, Self::Item) + Sync + Send,
INIT: Fn() -> T + Sync + Send,
{ ... }
fn try_for_each<OP, R>(self, op: OP) -> R
where
OP: Fn(Self::Item) -> R + Sync + Send,
R: Try<Ok = ()> + Send,
{ ... }
fn try_for_each_with<OP, T, R>(self, init: T, op: OP) -> R
where
OP: Fn(&mut T, Self::Item) -> R + Sync + Send,
T: Send + Clone,
R: Try<Ok = ()> + Send,
{ ... }
fn try_for_each_init<OP, INIT, T, R>(self, init: INIT, op: OP) -> R
where
OP: Fn(&mut T, Self::Item) -> R + Sync + Send,
INIT: Fn() -> T + Sync + Send,
R: Try<Ok = ()> + Send,
{ ... }
fn count(self) -> usize { ... }
fn map<F, R>(self, map_op: F) -> Map<Self, F>
where
F: Fn(Self::Item) -> R + Sync + Send,
R: Send,
{ ... }
fn map_with<F, T, R>(self, init: T, map_op: F) -> MapWith<Self, T, F>
where
F: Fn(&mut T, Self::Item) -> R + Sync + Send,
T: Send + Clone,
R: Send,
{ ... }
fn map_init<F, INIT, T, R>(
self,
init: INIT,
map_op: F
) -> MapInit<Self, INIT, F>
where
F: Fn(&mut T, Self::Item) -> R + Sync + Send,
INIT: Fn() -> T + Sync + Send,
R: Send,
{ ... }
fn cloned<'a, T>(self) -> Cloned<Self>
where
T: 'a + Clone + Send,
Self: ParallelIterator<Item = &'a T>,
{ ... }
fn copied<'a, T>(self) -> Copied<Self>
where
T: 'a + Copy + Send,
Self: ParallelIterator<Item = &'a T>,
{ ... }
fn inspect<OP>(self, inspect_op: OP) -> Inspect<Self, OP>
where
OP: Fn(&Self::Item) + Sync + Send,
{ ... }
fn update<F>(self, update_op: F) -> Update<Self, F>
where
F: Fn(&mut Self::Item) + Sync + Send,
{ ... }
fn filter<P>(self, filter_op: P) -> Filter<Self, P>
where
P: Fn(&Self::Item) -> bool + Sync + Send,
{ ... }
fn filter_map<P, R>(self, filter_op: P) -> FilterMap<Self, P>
where
P: Fn(Self::Item) -> Option<R> + Sync + Send,
R: Send,
{ ... }
fn flat_map<F, PI>(self, map_op: F) -> FlatMap<Self, F>
where
F: Fn(Self::Item) -> PI + Sync + Send,
PI: IntoParallelIterator,
{ ... }
fn flat_map_iter<F, SI>(self, map_op: F) -> FlatMapIter<Self, F>
where
F: Fn(Self::Item) -> SI + Sync + Send,
SI: IntoIterator,
SI::Item: Send,
{ ... }
fn flatten(self) -> Flatten<Self>
where
Self::Item: IntoParallelIterator,
{ ... }
fn flatten_iter(self) -> FlattenIter<Self>
where
Self::Item: IntoIterator,
<Self::Item as IntoIterator>::Item: Send,
{ ... }
fn reduce<OP, ID>(self, identity: ID, op: OP) -> Self::Item
where
OP: Fn(Self::Item, Self::Item) -> Self::Item + Sync + Send,
ID: Fn() -> Self::Item + Sync + Send,
{ ... }
fn reduce_with<OP>(self, op: OP) -> Option<Self::Item>
where
OP: Fn(Self::Item, Self::Item) -> Self::Item + Sync + Send,
{ ... }
fn try_reduce<T, OP, ID>(self, identity: ID, op: OP) -> Self::Item
where
OP: Fn(T, T) -> Self::Item + Sync + Send,
ID: Fn() -> T + Sync + Send,
Self::Item: Try<Ok = T>,
{ ... }
fn try_reduce_with<T, OP>(self, op: OP) -> Option<Self::Item>
where
OP: Fn(T, T) -> Self::Item + Sync + Send,
Self::Item: Try<Ok = T>,
{ ... }
fn fold<T, ID, F>(self, identity: ID, fold_op: F) -> Fold<Self, ID, F>
where
F: Fn(T, Self::Item) -> T + Sync + Send,
ID: Fn() -> T + Sync + Send,
T: Send,
{ ... }
fn fold_with<F, T>(self, init: T, fold_op: F) -> FoldWith<Self, T, F>
where
F: Fn(T, Self::Item) -> T + Sync + Send,
T: Send + Clone,
{ ... }
fn try_fold<T, R, ID, F>(
self,
identity: ID,
fold_op: F
) -> TryFold<Self, R, ID, F>
where
F: Fn(T, Self::Item) -> R + Sync + Send,
ID: Fn() -> T + Sync + Send,
R: Try<Ok = T> + Send,
{ ... }
fn try_fold_with<F, T, R>(
self,
init: T,
fold_op: F
) -> TryFoldWith<Self, R, F>
where
F: Fn(T, Self::Item) -> R + Sync + Send,
R: Try<Ok = T> + Send,
T: Clone + Send,
{ ... }
fn sum<S>(self) -> S
where
S: Send + Sum<Self::Item> + Sum<S>,
{ ... }
fn product<P>(self) -> P
where
P: Send + Product<Self::Item> + Product<P>,
{ ... }
fn min(self) -> Option<Self::Item>
where
Self::Item: Ord,
{ ... }
fn min_by<F>(self, f: F) -> Option<Self::Item>
where
F: Sync + Send + Fn(&Self::Item, &Self::Item) -> Ordering,
{ ... }
fn min_by_key<K, F>(self, f: F) -> Option<Self::Item>
where
K: Ord + Send,
F: Sync + Send + Fn(&Self::Item) -> K,
{ ... }
fn max(self) -> Option<Self::Item>
where
Self::Item: Ord,
{ ... }
fn max_by<F>(self, f: F) -> Option<Self::Item>
where
F: Sync + Send + Fn(&Self::Item, &Self::Item) -> Ordering,
{ ... }
fn max_by_key<K, F>(self, f: F) -> Option<Self::Item>
where
K: Ord + Send,
F: Sync + Send + Fn(&Self::Item) -> K,
{ ... }
fn chain<C>(self, chain: C) -> Chain<Self, C::Iter>
where
C: IntoParallelIterator<Item = Self::Item>,
{ ... }
fn find_any<P>(self, predicate: P) -> Option<Self::Item>
where
P: Fn(&Self::Item) -> bool + Sync + Send,
{ ... }
fn find_first<P>(self, predicate: P) -> Option<Self::Item>
where
P: Fn(&Self::Item) -> bool + Sync + Send,
{ ... }
fn find_last<P>(self, predicate: P) -> Option<Self::Item>
where
P: Fn(&Self::Item) -> bool + Sync + Send,
{ ... }
fn find_map_any<P, R>(self, predicate: P) -> Option<R>
where
P: Fn(Self::Item) -> Option<R> + Sync + Send,
R: Send,
{ ... }
fn find_map_first<P, R>(self, predicate: P) -> Option<R>
where
P: Fn(Self::Item) -> Option<R> + Sync + Send,
R: Send,
{ ... }
fn find_map_last<P, R>(self, predicate: P) -> Option<R>
where
P: Fn(Self::Item) -> Option<R> + Sync + Send,
R: Send,
{ ... }
fn any<P>(self, predicate: P) -> bool
where
P: Fn(Self::Item) -> bool + Sync + Send,
{ ... }
fn all<P>(self, predicate: P) -> bool
where
P: Fn(Self::Item) -> bool + Sync + Send,
{ ... }
fn while_some<T>(self) -> WhileSome<Self>
where
Self: ParallelIterator<Item = Option<T>>,
T: Send,
{ ... }
fn panic_fuse(self) -> PanicFuse<Self> { ... }
fn collect<C>(self) -> C
where
C: FromParallelIterator<Self::Item>,
{ ... }
fn unzip<A, B, FromA, FromB>(self) -> (FromA, FromB)
where
Self: ParallelIterator<Item = (A, B)>,
FromA: Default + Send + ParallelExtend<A>,
FromB: Default + Send + ParallelExtend<B>,
A: Send,
B: Send,
{ ... }
fn partition<A, B, P>(self, predicate: P) -> (A, B)
where
A: Default + Send + ParallelExtend<Self::Item>,
B: Default + Send + ParallelExtend<Self::Item>,
P: Fn(&Self::Item) -> bool + Sync + Send,
{ ... }
fn partition_map<A, B, P, L, R>(self, predicate: P) -> (A, B)
where
A: Default + Send + ParallelExtend<L>,
B: Default + Send + ParallelExtend<R>,
P: Fn(Self::Item) -> Either<L, R> + Sync + Send,
L: Send,
R: Send,
{ ... }
fn intersperse(self, element: Self::Item) -> Intersperse<Self>
where
Self::Item: Clone,
{ ... }
fn opt_len(&self) -> Option<usize> { ... }
}
Expand description
Parallel version of the standard iterator trait.
The combinators on this trait are available on all parallel
iterators. Additional methods can be found on the
IndexedParallelIterator
trait: those methods are only
available for parallel iterators where the number of items is
known in advance (so, e.g., after invoking filter
, those methods
become unavailable).
For examples of using parallel iterators, see the docs on the
iter
module.
Associated Types
Required methods
fn drive_unindexed<C>(self, consumer: C) -> C::Result where
C: UnindexedConsumer<Self::Item>,
fn drive_unindexed<C>(self, consumer: C) -> C::Result where
C: UnindexedConsumer<Self::Item>,
Internal method used to define the behavior of this parallel iterator. You should not need to call this directly.
This method causes the iterator self
to start producing
items and to feed them to the consumer consumer
one by one.
It may split the consumer before doing so to create the
opportunity to produce in parallel.
See the README for more details on the internals of parallel iterators.
Provided methods
Executes OP
on each item produced by the iterator, in parallel.
Examples
use rayon::prelude::*;
(0..100).into_par_iter().for_each(|x| println!("{:?}", x));
Executes OP
on the given init
value with each item produced by
the iterator, in parallel.
The init
value will be cloned only as needed to be paired with
the group of items in each rayon job. It does not require the type
to be Sync
.
Examples
use std::sync::mpsc::channel;
use rayon::prelude::*;
let (sender, receiver) = channel();
(0..5).into_par_iter().for_each_with(sender, |s, x| s.send(x).unwrap());
let mut res: Vec<_> = receiver.iter().collect();
res.sort();
assert_eq!(&res[..], &[0, 1, 2, 3, 4])
Executes OP
on a value returned by init
with each item produced by
the iterator, in parallel.
The init
function will be called only as needed for a value to be
paired with the group of items in each rayon job. There is no
constraint on that returned type at all!
Examples
use rand::Rng;
use rayon::prelude::*;
let mut v = vec![0u8; 1_000_000];
v.par_chunks_mut(1000)
.for_each_init(
|| rand::thread_rng(),
|rng, chunk| rng.fill(chunk),
);
// There's a remote chance that this will fail...
for i in 0u8..=255 {
assert!(v.contains(&i));
}
Executes a fallible OP
on each item produced by the iterator, in parallel.
If the OP
returns Result::Err
or Option::None
, we will attempt to
stop processing the rest of the items in the iterator as soon as
possible, and we will return that terminating value. Otherwise, we will
return an empty Result::Ok(())
or Option::Some(())
. If there are
multiple errors in parallel, it is not specified which will be returned.
Examples
use rayon::prelude::*;
use std::io::{self, Write};
// This will stop iteration early if there's any write error, like
// having piped output get closed on the other end.
(0..100).into_par_iter()
.try_for_each(|x| writeln!(io::stdout(), "{:?}", x))
.expect("expected no write errors");
Executes a fallible OP
on the given init
value with each item
produced by the iterator, in parallel.
This combines the init
semantics of for_each_with()
and the
failure semantics of try_for_each()
.
Examples
use std::sync::mpsc::channel;
use rayon::prelude::*;
let (sender, receiver) = channel();
(0..5).into_par_iter()
.try_for_each_with(sender, |s, x| s.send(x))
.expect("expected no send errors");
let mut res: Vec<_> = receiver.iter().collect();
res.sort();
assert_eq!(&res[..], &[0, 1, 2, 3, 4])
Executes a fallible OP
on a value returned by init
with each item
produced by the iterator, in parallel.
This combines the init
semantics of for_each_init()
and the
failure semantics of try_for_each()
.
Examples
use rand::Rng;
use rayon::prelude::*;
let mut v = vec![0u8; 1_000_000];
v.par_chunks_mut(1000)
.try_for_each_init(
|| rand::thread_rng(),
|rng, chunk| rng.try_fill(chunk),
)
.expect("expected no rand errors");
// There's a remote chance that this will fail...
for i in 0u8..=255 {
assert!(v.contains(&i));
}
Counts the number of items in this parallel iterator.
Examples
use rayon::prelude::*;
let count = (0..100).into_par_iter().count();
assert_eq!(count, 100);
Applies map_op
to each item of this iterator, producing a new
iterator with the results.
Examples
use rayon::prelude::*;
let mut par_iter = (0..5).into_par_iter().map(|x| x * 2);
let doubles: Vec<_> = par_iter.collect();
assert_eq!(&doubles[..], &[0, 2, 4, 6, 8]);
Applies map_op
to the given init
value with each item of this
iterator, producing a new iterator with the results.
The init
value will be cloned only as needed to be paired with
the group of items in each rayon job. It does not require the type
to be Sync
.
Examples
use std::sync::mpsc::channel;
use rayon::prelude::*;
let (sender, receiver) = channel();
let a: Vec<_> = (0..5)
.into_par_iter() // iterating over i32
.map_with(sender, |s, x| {
s.send(x).unwrap(); // sending i32 values through the channel
x // returning i32
})
.collect(); // collecting the returned values into a vector
let mut b: Vec<_> = receiver.iter() // iterating over the values in the channel
.collect(); // and collecting them
b.sort();
assert_eq!(a, b);
Applies map_op
to a value returned by init
with each item of this
iterator, producing a new iterator with the results.
The init
function will be called only as needed for a value to be
paired with the group of items in each rayon job. There is no
constraint on that returned type at all!
Examples
use rand::Rng;
use rayon::prelude::*;
let a: Vec<_> = (1i32..1_000_000)
.into_par_iter()
.map_init(
|| rand::thread_rng(), // get the thread-local RNG
|rng, x| if rng.gen() { // randomly negate items
-x
} else {
x
},
).collect();
// There's a remote chance that this will fail...
assert!(a.iter().any(|&x| x < 0));
assert!(a.iter().any(|&x| x > 0));
Creates an iterator which clones all of its elements. This may be
useful when you have an iterator over &T
, but you need T
, and
that type implements Clone
. See also copied()
.
Examples
use rayon::prelude::*;
let a = [1, 2, 3];
let v_cloned: Vec<_> = a.par_iter().cloned().collect();
// cloned is the same as .map(|&x| x), for integers
let v_map: Vec<_> = a.par_iter().map(|&x| x).collect();
assert_eq!(v_cloned, vec![1, 2, 3]);
assert_eq!(v_map, vec![1, 2, 3]);
Creates an iterator which copies all of its elements. This may be
useful when you have an iterator over &T
, but you need T
, and
that type implements Copy
. See also cloned()
.
Examples
use rayon::prelude::*;
let a = [1, 2, 3];
let v_copied: Vec<_> = a.par_iter().copied().collect();
// copied is the same as .map(|&x| x), for integers
let v_map: Vec<_> = a.par_iter().map(|&x| x).collect();
assert_eq!(v_copied, vec![1, 2, 3]);
assert_eq!(v_map, vec![1, 2, 3]);
Applies inspect_op
to a reference to each item of this iterator,
producing a new iterator passing through the original items. This is
often useful for debugging to see what’s happening in iterator stages.
Examples
use rayon::prelude::*;
let a = [1, 4, 2, 3];
// this iterator sequence is complex.
let sum = a.par_iter()
.cloned()
.filter(|&x| x % 2 == 0)
.reduce(|| 0, |sum, i| sum + i);
println!("{}", sum);
// let's add some inspect() calls to investigate what's happening
let sum = a.par_iter()
.cloned()
.inspect(|x| println!("about to filter: {}", x))
.filter(|&x| x % 2 == 0)
.inspect(|x| println!("made it through filter: {}", x))
.reduce(|| 0, |sum, i| sum + i);
println!("{}", sum);
Mutates each item of this iterator before yielding it.
Examples
use rayon::prelude::*;
let par_iter = (0..5).into_par_iter().update(|x| {*x *= 2;});
let doubles: Vec<_> = par_iter.collect();
assert_eq!(&doubles[..], &[0, 2, 4, 6, 8]);
Applies filter_op
to each item of this iterator, producing a new
iterator with only the items that gave true
results.
Examples
use rayon::prelude::*;
let mut par_iter = (0..10).into_par_iter().filter(|x| x % 2 == 0);
let even_numbers: Vec<_> = par_iter.collect();
assert_eq!(&even_numbers[..], &[0, 2, 4, 6, 8]);
Applies filter_op
to each item of this iterator to get an Option
,
producing a new iterator with only the items from Some
results.
Examples
use rayon::prelude::*;
let mut par_iter = (0..10).into_par_iter()
.filter_map(|x| {
if x % 2 == 0 { Some(x * 3) }
else { None }
});
let even_numbers: Vec<_> = par_iter.collect();
assert_eq!(&even_numbers[..], &[0, 6, 12, 18, 24]);
Applies map_op
to each item of this iterator to get nested parallel iterators,
producing a new parallel iterator that flattens these back into one.
See also flat_map_iter
.
Examples
use rayon::prelude::*;
let a = [[1, 2], [3, 4], [5, 6], [7, 8]];
let par_iter = a.par_iter().cloned().flat_map(|a| a.to_vec());
let vec: Vec<_> = par_iter.collect();
assert_eq!(&vec[..], &[1, 2, 3, 4, 5, 6, 7, 8]);
fn flat_map_iter<F, SI>(self, map_op: F) -> FlatMapIter<Self, F> where
F: Fn(Self::Item) -> SI + Sync + Send,
SI: IntoIterator,
SI::Item: Send,
fn flat_map_iter<F, SI>(self, map_op: F) -> FlatMapIter<Self, F> where
F: Fn(Self::Item) -> SI + Sync + Send,
SI: IntoIterator,
SI::Item: Send,
Applies map_op
to each item of this iterator to get nested serial iterators,
producing a new parallel iterator that flattens these back into one.
flat_map_iter
versus flat_map
These two methods are similar but behave slightly differently. With flat_map
,
each of the nested iterators must be a parallel iterator, and they will be further
split up with nested parallelism. With flat_map_iter
, each nested iterator is a
sequential Iterator
, and we only parallelize between them, while the items
produced by each nested iterator are processed sequentially.
When choosing between these methods, consider whether nested parallelism suits the
potential iterators at hand. If there’s little computation involved, or its length
is much less than the outer parallel iterator, then it may perform better to avoid
the overhead of parallelism, just flattening sequentially with flat_map_iter
.
If there is a lot of computation, potentially outweighing the outer parallel
iterator, then the nested parallelism of flat_map
may be worthwhile.
Examples
use rayon::prelude::*;
use std::cell::RefCell;
let a = [[1, 2], [3, 4], [5, 6], [7, 8]];
let par_iter = a.par_iter().flat_map_iter(|a| {
// The serial iterator doesn't have to be thread-safe, just its items.
let cell_iter = RefCell::new(a.iter().cloned());
std::iter::from_fn(move || cell_iter.borrow_mut().next())
});
let vec: Vec<_> = par_iter.collect();
assert_eq!(&vec[..], &[1, 2, 3, 4, 5, 6, 7, 8]);
fn flatten(self) -> Flatten<Self> where
Self::Item: IntoParallelIterator,
fn flatten(self) -> Flatten<Self> where
Self::Item: IntoParallelIterator,
An adaptor that flattens parallel-iterable Item
s into one large iterator.
See also flatten_iter
.
Examples
use rayon::prelude::*;
let x: Vec<Vec<_>> = vec![vec![1, 2], vec![3, 4]];
let y: Vec<_> = x.into_par_iter().flatten().collect();
assert_eq!(y, vec![1, 2, 3, 4]);
fn flatten_iter(self) -> FlattenIter<Self> where
Self::Item: IntoIterator,
<Self::Item as IntoIterator>::Item: Send,
fn flatten_iter(self) -> FlattenIter<Self> where
Self::Item: IntoIterator,
<Self::Item as IntoIterator>::Item: Send,
An adaptor that flattens serial-iterable Item
s into one large iterator.
See also flatten
and the analagous comparison of
flat_map_iter
versus flat_map
.
Examples
use rayon::prelude::*;
let x: Vec<Vec<_>> = vec![vec![1, 2], vec![3, 4]];
let iters: Vec<_> = x.into_iter().map(Vec::into_iter).collect();
let y: Vec<_> = iters.into_par_iter().flatten_iter().collect();
assert_eq!(y, vec![1, 2, 3, 4]);
Reduces the items in the iterator into one item using op
.
The argument identity
should be a closure that can produce
“identity” value which may be inserted into the sequence as
needed to create opportunities for parallel execution. So, for
example, if you are doing a summation, then identity()
ought
to produce something that represents the zero for your type
(but consider just calling sum()
in that case).
Examples
// Iterate over a sequence of pairs `(x0, y0), ..., (xN, yN)`
// and use reduce to compute one pair `(x0 + ... + xN, y0 + ... + yN)`
// where the first/second elements are summed separately.
use rayon::prelude::*;
let sums = [(0, 1), (5, 6), (16, 2), (8, 9)]
.par_iter() // iterating over &(i32, i32)
.cloned() // iterating over (i32, i32)
.reduce(|| (0, 0), // the "identity" is 0 in both columns
|a, b| (a.0 + b.0, a.1 + b.1));
assert_eq!(sums, (0 + 5 + 16 + 8, 1 + 6 + 2 + 9));
Note: unlike a sequential fold
operation, the order in
which op
will be applied to reduce the result is not fully
specified. So op
should be associative or else the results
will be non-deterministic. And of course identity()
should
produce a true identity.
Reduces the items in the iterator into one item using op
.
If the iterator is empty, None
is returned; otherwise,
Some
is returned.
This version of reduce
is simple but somewhat less
efficient. If possible, it is better to call reduce()
, which
requires an identity element.
Examples
use rayon::prelude::*;
let sums = [(0, 1), (5, 6), (16, 2), (8, 9)]
.par_iter() // iterating over &(i32, i32)
.cloned() // iterating over (i32, i32)
.reduce_with(|a, b| (a.0 + b.0, a.1 + b.1))
.unwrap();
assert_eq!(sums, (0 + 5 + 16 + 8, 1 + 6 + 2 + 9));
Note: unlike a sequential fold
operation, the order in
which op
will be applied to reduce the result is not fully
specified. So op
should be associative or else the results
will be non-deterministic.
Reduces the items in the iterator into one item using a fallible op
.
The identity
argument is used the same way as in reduce()
.
If a Result::Err
or Option::None
item is found, or if op
reduces
to one, we will attempt to stop processing the rest of the items in the
iterator as soon as possible, and we will return that terminating value.
Otherwise, we will return the final reduced Result::Ok(T)
or
Option::Some(T)
. If there are multiple errors in parallel, it is not
specified which will be returned.
Examples
use rayon::prelude::*;
// Compute the sum of squares, being careful about overflow.
fn sum_squares<I: IntoParallelIterator<Item = i32>>(iter: I) -> Option<i32> {
iter.into_par_iter()
.map(|i| i.checked_mul(i)) // square each item,
.try_reduce(|| 0, i32::checked_add) // and add them up!
}
assert_eq!(sum_squares(0..5), Some(0 + 1 + 4 + 9 + 16));
// The sum might overflow
assert_eq!(sum_squares(0..10_000), None);
// Or the squares might overflow before it even reaches `try_reduce`
assert_eq!(sum_squares(1_000_000..1_000_001), None);
Reduces the items in the iterator into one item using a fallible op
.
Like reduce_with()
, if the iterator is empty, None
is returned;
otherwise, Some
is returned. Beyond that, it behaves like
try_reduce()
for handling Err
/None
.
For instance, with Option
items, the return value may be:
None
, the iterator was emptySome(None)
, we stopped after encounteringNone
.Some(Some(x))
, the entire iterator reduced tox
.
With Result
items, the nesting is more obvious:
None
, the iterator was emptySome(Err(e))
, we stopped after encountering an errore
.Some(Ok(x))
, the entire iterator reduced tox
.
Examples
use rayon::prelude::*;
let files = ["/dev/null", "/does/not/exist"];
// Find the biggest file
files.into_par_iter()
.map(|path| std::fs::metadata(path).map(|m| (path, m.len())))
.try_reduce_with(|a, b| {
Ok(if a.1 >= b.1 { a } else { b })
})
.expect("Some value, since the iterator is not empty")
.expect_err("not found");
Parallel fold is similar to sequential fold except that the
sequence of items may be subdivided before it is
folded. Consider a list of numbers like 22 3 77 89 46
. If
you used sequential fold to add them (fold(0, |a,b| a+b)
,
you would wind up first adding 0 + 22, then 22 + 3, then 25 +
77, and so forth. The parallel fold works similarly except
that it first breaks up your list into sublists, and hence
instead of yielding up a single sum at the end, it yields up
multiple sums. The number of results is nondeterministic, as
is the point where the breaks occur.
So if did the same parallel fold (fold(0, |a,b| a+b)
) on
our example list, we might wind up with a sequence of two numbers,
like so:
22 3 77 89 46
| |
102 135
Or perhaps these three numbers:
22 3 77 89 46
| | |
102 89 46
In general, Rayon will attempt to find good breaking points that keep all of your cores busy.
Fold versus reduce
The fold()
and reduce()
methods each take an identity element
and a combining function, but they operate rather differently.
reduce()
requires that the identity function has the same
type as the things you are iterating over, and it fully
reduces the list of items into a single item. So, for example,
imagine we are iterating over a list of bytes bytes: [128_u8, 64_u8, 64_u8]
. If we used bytes.reduce(|| 0_u8, |a: u8, b: u8| a + b)
, we would get an overflow. This is because 0
,
a
, and b
here are all bytes, just like the numbers in the
list (I wrote the types explicitly above, but those are the
only types you can use). To avoid the overflow, we would need
to do something like bytes.map(|b| b as u32).reduce(|| 0, |a, b| a + b)
, in which case our result would be 256
.
In contrast, with fold()
, the identity function does not
have to have the same type as the things you are iterating
over, and you potentially get back many results. So, if we
continue with the bytes
example from the previous paragraph,
we could do bytes.fold(|| 0_u32, |a, b| a + (b as u32))
to
convert our bytes into u32
. And of course we might not get
back a single sum.
There is a more subtle distinction as well, though it’s
actually implied by the above points. When you use reduce()
,
your reduction function is sometimes called with values that
were never part of your original parallel iterator (for
example, both the left and right might be a partial sum). With
fold()
, in contrast, the left value in the fold function is
always the accumulator, and the right value is always from
your original sequence.
Fold vs Map/Reduce
Fold makes sense if you have some operation where it is cheaper to create groups of elements at a time. For example, imagine collecting characters into a string. If you were going to use map/reduce, you might try this:
use rayon::prelude::*;
let s =
['a', 'b', 'c', 'd', 'e']
.par_iter()
.map(|c: &char| format!("{}", c))
.reduce(|| String::new(),
|mut a: String, b: String| { a.push_str(&b); a });
assert_eq!(s, "abcde");
Because reduce produces the same type of element as its input,
you have to first map each character into a string, and then
you can reduce them. This means we create one string per
element in our iterator – not so great. Using fold
, we can
do this instead:
use rayon::prelude::*;
let s =
['a', 'b', 'c', 'd', 'e']
.par_iter()
.fold(|| String::new(),
|mut s: String, c: &char| { s.push(*c); s })
.reduce(|| String::new(),
|mut a: String, b: String| { a.push_str(&b); a });
assert_eq!(s, "abcde");
Now fold
will process groups of our characters at a time,
and we only make one string per group. We should wind up with
some small-ish number of strings roughly proportional to the
number of CPUs you have (it will ultimately depend on how busy
your processors are). Note that we still need to do a reduce
afterwards to combine those groups of strings into a single
string.
You could use a similar trick to save partial results (e.g., a cache) or something similar.
Combining fold with other operations
You can combine fold
with reduce
if you want to produce a
single value. This is then roughly equivalent to a map/reduce
combination in effect:
use rayon::prelude::*;
let bytes = 0..22_u8;
let sum = bytes.into_par_iter()
.fold(|| 0_u32, |a: u32, b: u8| a + (b as u32))
.sum::<u32>();
assert_eq!(sum, (0..22).sum()); // compare to sequential
Applies fold_op
to the given init
value with each item of this
iterator, finally producing the value for further use.
This works essentially like fold(|| init.clone(), fold_op)
, except
it doesn’t require the init
type to be Sync
, nor any other form
of added synchronization.
Examples
use rayon::prelude::*;
let bytes = 0..22_u8;
let sum = bytes.into_par_iter()
.fold_with(0_u32, |a: u32, b: u8| a + (b as u32))
.sum::<u32>();
assert_eq!(sum, (0..22).sum()); // compare to sequential
Performs a fallible parallel fold.
This is a variation of fold()
for operations which can fail with
Option::None
or Result::Err
. The first such failure stops
processing the local set of items, without affecting other folds in the
iterator’s subdivisions.
Often, try_fold()
will be followed by try_reduce()
for a final reduction and global short-circuiting effect.
Examples
use rayon::prelude::*;
let bytes = 0..22_u8;
let sum = bytes.into_par_iter()
.try_fold(|| 0_u32, |a: u32, b: u8| a.checked_add(b as u32))
.try_reduce(|| 0, u32::checked_add);
assert_eq!(sum, Some((0..22).sum())); // compare to sequential
fn try_fold_with<F, T, R>(self, init: T, fold_op: F) -> TryFoldWith<Self, R, F> where
F: Fn(T, Self::Item) -> R + Sync + Send,
R: Try<Ok = T> + Send,
T: Clone + Send,
fn try_fold_with<F, T, R>(self, init: T, fold_op: F) -> TryFoldWith<Self, R, F> where
F: Fn(T, Self::Item) -> R + Sync + Send,
R: Try<Ok = T> + Send,
T: Clone + Send,
Performs a fallible parallel fold with a cloneable init
value.
This combines the init
semantics of fold_with()
and the failure
semantics of try_fold()
.
use rayon::prelude::*;
let bytes = 0..22_u8;
let sum = bytes.into_par_iter()
.try_fold_with(0_u32, |a: u32, b: u8| a.checked_add(b as u32))
.try_reduce(|| 0, u32::checked_add);
assert_eq!(sum, Some((0..22).sum())); // compare to sequential
Sums up the items in the iterator.
Note that the order in items will be reduced is not specified,
so if the +
operator is not truly associative (as is the
case for floating point numbers), then the results are not
fully deterministic.
Basically equivalent to self.reduce(|| 0, |a, b| a + b)
,
except that the type of 0
and the +
operation may vary
depending on the type of value being produced.
Examples
use rayon::prelude::*;
let a = [1, 5, 7];
let sum: i32 = a.par_iter().sum();
assert_eq!(sum, 13);
Multiplies all the items in the iterator.
Note that the order in items will be reduced is not specified,
so if the *
operator is not truly associative (as is the
case for floating point numbers), then the results are not
fully deterministic.
Basically equivalent to self.reduce(|| 1, |a, b| a * b)
,
except that the type of 1
and the *
operation may vary
depending on the type of value being produced.
Examples
use rayon::prelude::*;
fn factorial(n: u32) -> u32 {
(1..n+1).into_par_iter().product()
}
assert_eq!(factorial(0), 1);
assert_eq!(factorial(1), 1);
assert_eq!(factorial(5), 120);
Computes the minimum of all the items in the iterator. If the
iterator is empty, None
is returned; otherwise, Some(min)
is returned.
Note that the order in which the items will be reduced is not
specified, so if the Ord
impl is not truly associative, then
the results are not deterministic.
Basically equivalent to self.reduce_with(|a, b| cmp::min(a, b))
.
Examples
use rayon::prelude::*;
let a = [45, 74, 32];
assert_eq!(a.par_iter().min(), Some(&32));
let b: [i32; 0] = [];
assert_eq!(b.par_iter().min(), None);
Computes the minimum of all the items in the iterator with respect to
the given comparison function. If the iterator is empty, None
is
returned; otherwise, Some(min)
is returned.
Note that the order in which the items will be reduced is not specified, so if the comparison function is not associative, then the results are not deterministic.
Examples
use rayon::prelude::*;
let a = [-3_i32, 77, 53, 240, -1];
assert_eq!(a.par_iter().min_by(|x, y| x.cmp(y)), Some(&-3));
Computes the item that yields the minimum value for the given
function. If the iterator is empty, None
is returned;
otherwise, Some(item)
is returned.
Note that the order in which the items will be reduced is not
specified, so if the Ord
impl is not truly associative, then
the results are not deterministic.
Examples
use rayon::prelude::*;
let a = [-3_i32, 34, 2, 5, -10, -3, -23];
assert_eq!(a.par_iter().min_by_key(|x| x.abs()), Some(&2));
Computes the maximum of all the items in the iterator. If the
iterator is empty, None
is returned; otherwise, Some(max)
is returned.
Note that the order in which the items will be reduced is not
specified, so if the Ord
impl is not truly associative, then
the results are not deterministic.
Basically equivalent to self.reduce_with(|a, b| cmp::max(a, b))
.
Examples
use rayon::prelude::*;
let a = [45, 74, 32];
assert_eq!(a.par_iter().max(), Some(&74));
let b: [i32; 0] = [];
assert_eq!(b.par_iter().max(), None);
Computes the maximum of all the items in the iterator with respect to
the given comparison function. If the iterator is empty, None
is
returned; otherwise, Some(min)
is returned.
Note that the order in which the items will be reduced is not specified, so if the comparison function is not associative, then the results are not deterministic.
Examples
use rayon::prelude::*;
let a = [-3_i32, 77, 53, 240, -1];
assert_eq!(a.par_iter().max_by(|x, y| x.abs().cmp(&y.abs())), Some(&240));
Computes the item that yields the maximum value for the given
function. If the iterator is empty, None
is returned;
otherwise, Some(item)
is returned.
Note that the order in which the items will be reduced is not
specified, so if the Ord
impl is not truly associative, then
the results are not deterministic.
Examples
use rayon::prelude::*;
let a = [-3_i32, 34, 2, 5, -10, -3, -23];
assert_eq!(a.par_iter().max_by_key(|x| x.abs()), Some(&34));
Takes two iterators and creates a new iterator over both.
Examples
use rayon::prelude::*;
let a = [0, 1, 2];
let b = [9, 8, 7];
let par_iter = a.par_iter().chain(b.par_iter());
let chained: Vec<_> = par_iter.cloned().collect();
assert_eq!(&chained[..], &[0, 1, 2, 9, 8, 7]);
Searches for some item in the parallel iterator that
matches the given predicate and returns it. This operation
is similar to find
on sequential iterators but
the item returned may not be the first one in the parallel
sequence which matches, since we search the entire sequence in parallel.
Once a match is found, we will attempt to stop processing
the rest of the items in the iterator as soon as possible
(just as find
stops iterating once a match is found).
Examples
use rayon::prelude::*;
let a = [1, 2, 3, 3];
assert_eq!(a.par_iter().find_any(|&&x| x == 3), Some(&3));
assert_eq!(a.par_iter().find_any(|&&x| x == 100), None);
Searches for the sequentially first item in the parallel iterator that matches the given predicate and returns it.
Once a match is found, all attempts to the right of the match will be stopped, while attempts to the left must continue in case an earlier match is found.
Note that not all parallel iterators have a useful order, much like
sequential HashMap
iteration, so “first” may be nebulous. If you
just want the first match that discovered anywhere in the iterator,
find_any
is a better choice.
Examples
use rayon::prelude::*;
let a = [1, 2, 3, 3];
assert_eq!(a.par_iter().find_first(|&&x| x == 3), Some(&3));
assert_eq!(a.par_iter().find_first(|&&x| x == 100), None);
Searches for the sequentially last item in the parallel iterator that matches the given predicate and returns it.
Once a match is found, all attempts to the left of the match will be stopped, while attempts to the right must continue in case a later match is found.
Note that not all parallel iterators have a useful order, much like
sequential HashMap
iteration, so “last” may be nebulous. When the
order doesn’t actually matter to you, find_any
is a better choice.
Examples
use rayon::prelude::*;
let a = [1, 2, 3, 3];
assert_eq!(a.par_iter().find_last(|&&x| x == 3), Some(&3));
assert_eq!(a.par_iter().find_last(|&&x| x == 100), None);
Applies the given predicate to the items in the parallel iterator and returns any non-None result of the map operation.
Once a non-None value is produced from the map operation, we will attempt to stop processing the rest of the items in the iterator as soon as possible.
Note that this method only returns some item in the parallel iterator that is not None from the map predicate. The item returned may not be the first non-None value produced in the parallel sequence, since the entire sequence is mapped over in parallel.
Examples
use rayon::prelude::*;
let c = ["lol", "NaN", "5", "5"];
let found_number = c.par_iter().find_map_any(|s| s.parse().ok());
assert_eq!(found_number, Some(5));
Applies the given predicate to the items in the parallel iterator and returns the sequentially first non-None result of the map operation.
Once a non-None value is produced from the map operation, all attempts to the right of the match will be stopped, while attempts to the left must continue in case an earlier match is found.
Note that not all parallel iterators have a useful order, much like
sequential HashMap
iteration, so “first” may be nebulous. If you
just want the first non-None value discovered anywhere in the iterator,
find_map_any
is a better choice.
Examples
use rayon::prelude::*;
let c = ["lol", "NaN", "2", "5"];
let first_number = c.par_iter().find_map_first(|s| s.parse().ok());
assert_eq!(first_number, Some(2));
Applies the given predicate to the items in the parallel iterator and returns the sequentially last non-None result of the map operation.
Once a non-None value is produced from the map operation, all attempts to the left of the match will be stopped, while attempts to the right must continue in case a later match is found.
Note that not all parallel iterators have a useful order, much like
sequential HashMap
iteration, so “first” may be nebulous. If you
just want the first non-None value discovered anywhere in the iterator,
find_map_any
is a better choice.
Examples
use rayon::prelude::*;
let c = ["lol", "NaN", "2", "5"];
let last_number = c.par_iter().find_map_last(|s| s.parse().ok());
assert_eq!(last_number, Some(5));
Searches for some item in the parallel iterator that matches the given predicate, and if so returns true. Once a match is found, we’ll attempt to stop process the rest of the items. Proving that there’s no match, returning false, does require visiting every item.
Examples
use rayon::prelude::*;
let a = [0, 12, 3, 4, 0, 23, 0];
let is_valid = a.par_iter().any(|&x| x > 10);
assert!(is_valid);
Tests that every item in the parallel iterator matches the given predicate, and if so returns true. If a counter-example is found, we’ll attempt to stop processing more items, then return false.
Examples
use rayon::prelude::*;
let a = [0, 12, 3, 4, 0, 23, 0];
let is_valid = a.par_iter().all(|&x| x > 10);
assert!(!is_valid);
fn while_some<T>(self) -> WhileSome<Self> where
Self: ParallelIterator<Item = Option<T>>,
T: Send,
fn while_some<T>(self) -> WhileSome<Self> where
Self: ParallelIterator<Item = Option<T>>,
T: Send,
Creates an iterator over the Some
items of this iterator, halting
as soon as any None
is found.
Examples
use rayon::prelude::*;
use std::sync::atomic::{AtomicUsize, Ordering};
let counter = AtomicUsize::new(0);
let value = (0_i32..2048)
.into_par_iter()
.map(|x| {
counter.fetch_add(1, Ordering::SeqCst);
if x < 1024 { Some(x) } else { None }
})
.while_some()
.max();
assert!(value < Some(1024));
assert!(counter.load(Ordering::SeqCst) < 2048); // should not have visited every single one
fn panic_fuse(self) -> PanicFuse<Self>
fn panic_fuse(self) -> PanicFuse<Self>
Wraps an iterator with a fuse in case of panics, to halt all threads as soon as possible.
Panics within parallel iterators are always propagated to the caller,
but they don’t always halt the rest of the iterator right away, due to
the internal semantics of join
. This adaptor makes a greater effort
to stop processing other items sooner, with the cost of additional
synchronization overhead, which may also inhibit some optimizations.
Examples
If this code didn’t use panic_fuse()
, it would continue processing
many more items in other threads (with long sleep delays) before the
panic is finally propagated.
use rayon::prelude::*;
use std::{thread, time};
(0..1_000_000)
.into_par_iter()
.panic_fuse()
.for_each(|i| {
// simulate some work
thread::sleep(time::Duration::from_secs(1));
assert!(i > 0); // oops!
});
fn collect<C>(self) -> C where
C: FromParallelIterator<Self::Item>,
fn collect<C>(self) -> C where
C: FromParallelIterator<Self::Item>,
Creates a fresh collection containing all the elements produced by this parallel iterator.
You may prefer collect_into_vec()
implemented on
IndexedParallelIterator
, if your underlying iterator also implements
it. collect_into_vec()
allocates efficiently with precise knowledge
of how many elements the iterator contains, and even allows you to reuse
an existing vector’s backing store rather than allocating a fresh vector.
Examples
use rayon::prelude::*;
let sync_vec: Vec<_> = (0..100).into_iter().collect();
let async_vec: Vec<_> = (0..100).into_par_iter().collect();
assert_eq!(sync_vec, async_vec);
You can collect a pair of collections like unzip
for paired items:
use rayon::prelude::*;
let a = [(0, 1), (1, 2), (2, 3), (3, 4)];
let (first, second): (Vec<_>, Vec<_>) = a.into_par_iter().collect();
assert_eq!(first, [0, 1, 2, 3]);
assert_eq!(second, [1, 2, 3, 4]);
Or like partition_map
for Either
items:
use rayon::prelude::*;
use rayon::iter::Either;
let (left, right): (Vec<_>, Vec<_>) = (0..8).into_par_iter().map(|x| {
if x % 2 == 0 {
Either::Left(x * 4)
} else {
Either::Right(x * 3)
}
}).collect();
assert_eq!(left, [0, 8, 16, 24]);
assert_eq!(right, [3, 9, 15, 21]);
You can even collect an arbitrarily-nested combination of pairs and Either
:
use rayon::prelude::*;
use rayon::iter::Either;
let (first, (left, right)): (Vec<_>, (Vec<_>, Vec<_>))
= (0..8).into_par_iter().map(|x| {
if x % 2 == 0 {
(x, Either::Left(x * 4))
} else {
(-x, Either::Right(x * 3))
}
}).collect();
assert_eq!(first, [0, -1, 2, -3, 4, -5, 6, -7]);
assert_eq!(left, [0, 8, 16, 24]);
assert_eq!(right, [3, 9, 15, 21]);
All of that can also be combined with short-circuiting collection of
Result
or Option
types:
use rayon::prelude::*;
use rayon::iter::Either;
let result: Result<(Vec<_>, (Vec<_>, Vec<_>)), _>
= (0..8).into_par_iter().map(|x| {
if x > 5 {
Err(x)
} else if x % 2 == 0 {
Ok((x, Either::Left(x * 4)))
} else {
Ok((-x, Either::Right(x * 3)))
}
}).collect();
let error = result.unwrap_err();
assert!(error == 6 || error == 7);
fn unzip<A, B, FromA, FromB>(self) -> (FromA, FromB) where
Self: ParallelIterator<Item = (A, B)>,
FromA: Default + Send + ParallelExtend<A>,
FromB: Default + Send + ParallelExtend<B>,
A: Send,
B: Send,
fn unzip<A, B, FromA, FromB>(self) -> (FromA, FromB) where
Self: ParallelIterator<Item = (A, B)>,
FromA: Default + Send + ParallelExtend<A>,
FromB: Default + Send + ParallelExtend<B>,
A: Send,
B: Send,
Unzips the items of a parallel iterator into a pair of arbitrary
ParallelExtend
containers.
You may prefer to use unzip_into_vecs()
, which allocates more
efficiently with precise knowledge of how many elements the
iterator contains, and even allows you to reuse existing
vectors’ backing stores rather than allocating fresh vectors.
Examples
use rayon::prelude::*;
let a = [(0, 1), (1, 2), (2, 3), (3, 4)];
let (left, right): (Vec<_>, Vec<_>) = a.par_iter().cloned().unzip();
assert_eq!(left, [0, 1, 2, 3]);
assert_eq!(right, [1, 2, 3, 4]);
Nested pairs can be unzipped too.
use rayon::prelude::*;
let (values, (squares, cubes)): (Vec<_>, (Vec<_>, Vec<_>)) = (0..4).into_par_iter()
.map(|i| (i, (i * i, i * i * i)))
.unzip();
assert_eq!(values, [0, 1, 2, 3]);
assert_eq!(squares, [0, 1, 4, 9]);
assert_eq!(cubes, [0, 1, 8, 27]);
Partitions the items of a parallel iterator into a pair of arbitrary
ParallelExtend
containers. Items for which the predicate
returns
true go into the first container, and the rest go into the second.
Note: unlike the standard Iterator::partition
, this allows distinct
collection types for the left and right items. This is more flexible,
but may require new type annotations when converting sequential code
that used type inferrence assuming the two were the same.
Examples
use rayon::prelude::*;
let (left, right): (Vec<_>, Vec<_>) = (0..8).into_par_iter().partition(|x| x % 2 == 0);
assert_eq!(left, [0, 2, 4, 6]);
assert_eq!(right, [1, 3, 5, 7]);
Partitions and maps the items of a parallel iterator into a pair of
arbitrary ParallelExtend
containers. Either::Left
items go into
the first container, and Either::Right
items go into the second.
Examples
use rayon::prelude::*;
use rayon::iter::Either;
let (left, right): (Vec<_>, Vec<_>) = (0..8).into_par_iter()
.partition_map(|x| {
if x % 2 == 0 {
Either::Left(x * 4)
} else {
Either::Right(x * 3)
}
});
assert_eq!(left, [0, 8, 16, 24]);
assert_eq!(right, [3, 9, 15, 21]);
Nested Either
enums can be split as well.
use rayon::prelude::*;
use rayon::iter::Either::*;
let ((fizzbuzz, fizz), (buzz, other)): ((Vec<_>, Vec<_>), (Vec<_>, Vec<_>)) = (1..20)
.into_par_iter()
.partition_map(|x| match (x % 3, x % 5) {
(0, 0) => Left(Left(x)),
(0, _) => Left(Right(x)),
(_, 0) => Right(Left(x)),
(_, _) => Right(Right(x)),
});
assert_eq!(fizzbuzz, [15]);
assert_eq!(fizz, [3, 6, 9, 12, 18]);
assert_eq!(buzz, [5, 10]);
assert_eq!(other, [1, 2, 4, 7, 8, 11, 13, 14, 16, 17, 19]);
fn intersperse(self, element: Self::Item) -> Intersperse<Self> where
Self::Item: Clone,
fn intersperse(self, element: Self::Item) -> Intersperse<Self> where
Self::Item: Clone,
Intersperses clones of an element between items of this iterator.
Examples
use rayon::prelude::*;
let x = vec![1, 2, 3];
let r: Vec<_> = x.into_par_iter().intersperse(-1).collect();
assert_eq!(r, vec![1, -1, 2, -1, 3]);
Internal method used to define the behavior of this parallel iterator. You should not need to call this directly.
Returns the number of items produced by this iterator, if known
statically. This can be used by consumers to trigger special fast
paths. Therefore, if Some(_)
is returned, this iterator must only
use the (indexed) Consumer
methods when driving a consumer, such
as split_at()
. Calling UnindexedConsumer::split_off_left()
or
other UnindexedConsumer
methods – or returning an inaccurate
value – may result in panics.
This method is currently used to optimize collect
for want
of true Rust specialization; it may be removed when
specialization is stable.
Implementors
sourceimpl<'a> ParallelIterator for rayon::string::Drain<'a>
impl<'a> ParallelIterator for rayon::string::Drain<'a>
sourceimpl<'a, K: Ord + Sync + 'a, V: Send + 'a> ParallelIterator for rayon::collections::btree_map::IterMut<'a, K, V>
impl<'a, K: Ord + Sync + 'a, V: Send + 'a> ParallelIterator for rayon::collections::btree_map::IterMut<'a, K, V>
sourceimpl<'a, K: Ord + Sync + 'a, V: Sync + 'a> ParallelIterator for rayon::collections::btree_map::Iter<'a, K, V>
impl<'a, K: Ord + Sync + 'a, V: Sync + 'a> ParallelIterator for rayon::collections::btree_map::Iter<'a, K, V>
sourceimpl<'a, K: Hash + Eq + Sync + 'a, V: Send + 'a> ParallelIterator for rayon::collections::hash_map::IterMut<'a, K, V>
impl<'a, K: Hash + Eq + Sync + 'a, V: Send + 'a> ParallelIterator for rayon::collections::hash_map::IterMut<'a, K, V>
sourceimpl<'a, K: Hash + Eq + Sync + 'a, V: Sync + 'a> ParallelIterator for rayon::collections::hash_map::Iter<'a, K, V>
impl<'a, K: Hash + Eq + Sync + 'a, V: Sync + 'a> ParallelIterator for rayon::collections::hash_map::Iter<'a, K, V>
sourceimpl<'a, T, I> ParallelIterator for Cloned<I> where
I: ParallelIterator<Item = &'a T>,
T: 'a + Clone + Send + Sync,
impl<'a, T, I> ParallelIterator for Cloned<I> where
I: ParallelIterator<Item = &'a T>,
T: 'a + Clone + Send + Sync,
type Item = T
sourceimpl<'a, T, I> ParallelIterator for Copied<I> where
I: ParallelIterator<Item = &'a T>,
T: 'a + Copy + Send + Sync,
impl<'a, T, I> ParallelIterator for Copied<I> where
I: ParallelIterator<Item = &'a T>,
T: 'a + Copy + Send + Sync,
type Item = T
sourceimpl<'a, T: Ord + Send> ParallelIterator for rayon::collections::binary_heap::Drain<'a, T>
impl<'a, T: Ord + Send> ParallelIterator for rayon::collections::binary_heap::Drain<'a, T>
type Item = T
sourceimpl<'a, T: Ord + Sync + 'a> ParallelIterator for rayon::collections::binary_heap::Iter<'a, T>
impl<'a, T: Ord + Sync + 'a> ParallelIterator for rayon::collections::binary_heap::Iter<'a, T>
sourceimpl<'a, T: Ord + Sync + 'a> ParallelIterator for rayon::collections::btree_set::Iter<'a, T>
impl<'a, T: Ord + Sync + 'a> ParallelIterator for rayon::collections::btree_set::Iter<'a, T>
sourceimpl<'a, T: Hash + Eq + Sync + 'a> ParallelIterator for rayon::collections::hash_set::Iter<'a, T>
impl<'a, T: Hash + Eq + Sync + 'a> ParallelIterator for rayon::collections::hash_set::Iter<'a, T>
sourceimpl<'a, T: Send + 'a> ParallelIterator for rayon::collections::linked_list::IterMut<'a, T>
impl<'a, T: Send + 'a> ParallelIterator for rayon::collections::linked_list::IterMut<'a, T>
sourceimpl<'a, T: Send + 'a> ParallelIterator for rayon::collections::vec_deque::IterMut<'a, T>
impl<'a, T: Send + 'a> ParallelIterator for rayon::collections::vec_deque::IterMut<'a, T>
sourceimpl<'a, T: Send + 'a> ParallelIterator for rayon::option::IterMut<'a, T>
impl<'a, T: Send + 'a> ParallelIterator for rayon::option::IterMut<'a, T>
sourceimpl<'a, T: Send + 'a> ParallelIterator for rayon::result::IterMut<'a, T>
impl<'a, T: Send + 'a> ParallelIterator for rayon::result::IterMut<'a, T>
sourceimpl<'a, T: Send> ParallelIterator for rayon::collections::vec_deque::Drain<'a, T>
impl<'a, T: Send> ParallelIterator for rayon::collections::vec_deque::Drain<'a, T>
type Item = T
sourceimpl<'a, T: Sync + 'a> ParallelIterator for rayon::collections::linked_list::Iter<'a, T>
impl<'a, T: Sync + 'a> ParallelIterator for rayon::collections::linked_list::Iter<'a, T>
sourceimpl<'a, T: Sync + 'a> ParallelIterator for rayon::collections::vec_deque::Iter<'a, T>
impl<'a, T: Sync + 'a> ParallelIterator for rayon::collections::vec_deque::Iter<'a, T>
sourceimpl<'ch> ParallelIterator for Bytes<'ch>
impl<'ch> ParallelIterator for Bytes<'ch>
sourceimpl<'ch> ParallelIterator for CharIndices<'ch>
impl<'ch> ParallelIterator for CharIndices<'ch>
sourceimpl<'ch> ParallelIterator for Chars<'ch>
impl<'ch> ParallelIterator for Chars<'ch>
sourceimpl<'ch> ParallelIterator for EncodeUtf16<'ch>
impl<'ch> ParallelIterator for EncodeUtf16<'ch>
sourceimpl<'ch> ParallelIterator for Lines<'ch>
impl<'ch> ParallelIterator for Lines<'ch>
sourceimpl<'ch> ParallelIterator for SplitWhitespace<'ch>
impl<'ch> ParallelIterator for SplitWhitespace<'ch>
sourceimpl<'ch, P: Pattern> ParallelIterator for MatchIndices<'ch, P>
impl<'ch, P: Pattern> ParallelIterator for MatchIndices<'ch, P>
sourceimpl<'ch, P: Pattern> ParallelIterator for Matches<'ch, P>
impl<'ch, P: Pattern> ParallelIterator for Matches<'ch, P>
sourceimpl<'ch, P: Pattern> ParallelIterator for rayon::str::Split<'ch, P>
impl<'ch, P: Pattern> ParallelIterator for rayon::str::Split<'ch, P>
sourceimpl<'ch, P: Pattern> ParallelIterator for SplitTerminator<'ch, P>
impl<'ch, P: Pattern> ParallelIterator for SplitTerminator<'ch, P>
sourceimpl<'data, T, P> ParallelIterator for rayon::slice::Split<'data, T, P> where
P: Fn(&T) -> bool + Sync + Send,
T: Sync,
impl<'data, T, P> ParallelIterator for rayon::slice::Split<'data, T, P> where
P: Fn(&T) -> bool + Sync + Send,
T: Sync,
type Item = &'data [T]
sourceimpl<'data, T, P> ParallelIterator for SplitMut<'data, T, P> where
P: Fn(&T) -> bool + Sync + Send,
T: Send,
impl<'data, T, P> ParallelIterator for SplitMut<'data, T, P> where
P: Fn(&T) -> bool + Sync + Send,
T: Send,
type Item = &'data mut [T]
sourceimpl<'data, T: Send + 'data> ParallelIterator for ChunksExactMut<'data, T>
impl<'data, T: Send + 'data> ParallelIterator for ChunksExactMut<'data, T>
type Item = &'data mut [T]
sourceimpl<'data, T: Send + 'data> ParallelIterator for ChunksMut<'data, T>
impl<'data, T: Send + 'data> ParallelIterator for ChunksMut<'data, T>
type Item = &'data mut [T]
sourceimpl<'data, T: Send + 'data> ParallelIterator for rayon::slice::IterMut<'data, T>
impl<'data, T: Send + 'data> ParallelIterator for rayon::slice::IterMut<'data, T>
type Item = &'data mut T
sourceimpl<'data, T: Send> ParallelIterator for rayon::vec::Drain<'data, T>
impl<'data, T: Send> ParallelIterator for rayon::vec::Drain<'data, T>
type Item = T
sourceimpl<'data, T: Sync + 'data> ParallelIterator for rayon::slice::Chunks<'data, T>
impl<'data, T: Sync + 'data> ParallelIterator for rayon::slice::Chunks<'data, T>
type Item = &'data [T]
sourceimpl<'data, T: Sync + 'data> ParallelIterator for ChunksExact<'data, T>
impl<'data, T: Sync + 'data> ParallelIterator for ChunksExact<'data, T>
type Item = &'data [T]
sourceimpl<'data, T: Sync + 'data> ParallelIterator for rayon::slice::Iter<'data, T>
impl<'data, T: Sync + 'data> ParallelIterator for rayon::slice::Iter<'data, T>
sourceimpl<'data, T: Sync + 'data> ParallelIterator for Windows<'data, T>
impl<'data, T: Sync + 'data> ParallelIterator for Windows<'data, T>
type Item = &'data [T]
sourceimpl<A> ParallelIterator for MultiZip<(A,)> where
A: IndexedParallelIterator,
impl<A> ParallelIterator for MultiZip<(A,)> where
A: IndexedParallelIterator,
sourceimpl<A, B> ParallelIterator for Chain<A, B> where
A: ParallelIterator,
B: ParallelIterator<Item = A::Item>,
impl<A, B> ParallelIterator for Chain<A, B> where
A: ParallelIterator,
B: ParallelIterator<Item = A::Item>,
sourceimpl<A, B> ParallelIterator for MultiZip<(A, B)> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
impl<A, B> ParallelIterator for MultiZip<(A, B)> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
sourceimpl<A, B> ParallelIterator for Zip<A, B> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
impl<A, B> ParallelIterator for Zip<A, B> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
sourceimpl<A, B> ParallelIterator for ZipEq<A, B> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
impl<A, B> ParallelIterator for ZipEq<A, B> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
sourceimpl<A, B, C> ParallelIterator for MultiZip<(A, B, C)> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
C: IndexedParallelIterator,
impl<A, B, C> ParallelIterator for MultiZip<(A, B, C)> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
C: IndexedParallelIterator,
sourceimpl<A, B, C, D> ParallelIterator for MultiZip<(A, B, C, D)> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
C: IndexedParallelIterator,
D: IndexedParallelIterator,
impl<A, B, C, D> ParallelIterator for MultiZip<(A, B, C, D)> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
C: IndexedParallelIterator,
D: IndexedParallelIterator,
sourceimpl<A, B, C, D, E> ParallelIterator for MultiZip<(A, B, C, D, E)> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
C: IndexedParallelIterator,
D: IndexedParallelIterator,
E: IndexedParallelIterator,
impl<A, B, C, D, E> ParallelIterator for MultiZip<(A, B, C, D, E)> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
C: IndexedParallelIterator,
D: IndexedParallelIterator,
E: IndexedParallelIterator,
sourceimpl<A, B, C, D, E, F> ParallelIterator for MultiZip<(A, B, C, D, E, F)> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
C: IndexedParallelIterator,
D: IndexedParallelIterator,
E: IndexedParallelIterator,
F: IndexedParallelIterator,
impl<A, B, C, D, E, F> ParallelIterator for MultiZip<(A, B, C, D, E, F)> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
C: IndexedParallelIterator,
D: IndexedParallelIterator,
E: IndexedParallelIterator,
F: IndexedParallelIterator,
sourceimpl<A, B, C, D, E, F, G> ParallelIterator for MultiZip<(A, B, C, D, E, F, G)> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
C: IndexedParallelIterator,
D: IndexedParallelIterator,
E: IndexedParallelIterator,
F: IndexedParallelIterator,
G: IndexedParallelIterator,
impl<A, B, C, D, E, F, G> ParallelIterator for MultiZip<(A, B, C, D, E, F, G)> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
C: IndexedParallelIterator,
D: IndexedParallelIterator,
E: IndexedParallelIterator,
F: IndexedParallelIterator,
G: IndexedParallelIterator,
sourceimpl<A, B, C, D, E, F, G, H> ParallelIterator for MultiZip<(A, B, C, D, E, F, G, H)> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
C: IndexedParallelIterator,
D: IndexedParallelIterator,
E: IndexedParallelIterator,
F: IndexedParallelIterator,
G: IndexedParallelIterator,
H: IndexedParallelIterator,
impl<A, B, C, D, E, F, G, H> ParallelIterator for MultiZip<(A, B, C, D, E, F, G, H)> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
C: IndexedParallelIterator,
D: IndexedParallelIterator,
E: IndexedParallelIterator,
F: IndexedParallelIterator,
G: IndexedParallelIterator,
H: IndexedParallelIterator,
sourceimpl<A, B, C, D, E, F, G, H, I> ParallelIterator for MultiZip<(A, B, C, D, E, F, G, H, I)> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
C: IndexedParallelIterator,
D: IndexedParallelIterator,
E: IndexedParallelIterator,
F: IndexedParallelIterator,
G: IndexedParallelIterator,
H: IndexedParallelIterator,
I: IndexedParallelIterator,
impl<A, B, C, D, E, F, G, H, I> ParallelIterator for MultiZip<(A, B, C, D, E, F, G, H, I)> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
C: IndexedParallelIterator,
D: IndexedParallelIterator,
E: IndexedParallelIterator,
F: IndexedParallelIterator,
G: IndexedParallelIterator,
H: IndexedParallelIterator,
I: IndexedParallelIterator,
sourceimpl<A, B, C, D, E, F, G, H, I, J> ParallelIterator for MultiZip<(A, B, C, D, E, F, G, H, I, J)> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
C: IndexedParallelIterator,
D: IndexedParallelIterator,
E: IndexedParallelIterator,
F: IndexedParallelIterator,
G: IndexedParallelIterator,
H: IndexedParallelIterator,
I: IndexedParallelIterator,
J: IndexedParallelIterator,
impl<A, B, C, D, E, F, G, H, I, J> ParallelIterator for MultiZip<(A, B, C, D, E, F, G, H, I, J)> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
C: IndexedParallelIterator,
D: IndexedParallelIterator,
E: IndexedParallelIterator,
F: IndexedParallelIterator,
G: IndexedParallelIterator,
H: IndexedParallelIterator,
I: IndexedParallelIterator,
J: IndexedParallelIterator,
sourceimpl<A, B, C, D, E, F, G, H, I, J, K> ParallelIterator for MultiZip<(A, B, C, D, E, F, G, H, I, J, K)> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
C: IndexedParallelIterator,
D: IndexedParallelIterator,
E: IndexedParallelIterator,
F: IndexedParallelIterator,
G: IndexedParallelIterator,
H: IndexedParallelIterator,
I: IndexedParallelIterator,
J: IndexedParallelIterator,
K: IndexedParallelIterator,
impl<A, B, C, D, E, F, G, H, I, J, K> ParallelIterator for MultiZip<(A, B, C, D, E, F, G, H, I, J, K)> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
C: IndexedParallelIterator,
D: IndexedParallelIterator,
E: IndexedParallelIterator,
F: IndexedParallelIterator,
G: IndexedParallelIterator,
H: IndexedParallelIterator,
I: IndexedParallelIterator,
J: IndexedParallelIterator,
K: IndexedParallelIterator,
sourceimpl<A, B, C, D, E, F, G, H, I, J, K, L> ParallelIterator for MultiZip<(A, B, C, D, E, F, G, H, I, J, K, L)> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
C: IndexedParallelIterator,
D: IndexedParallelIterator,
E: IndexedParallelIterator,
F: IndexedParallelIterator,
G: IndexedParallelIterator,
H: IndexedParallelIterator,
I: IndexedParallelIterator,
J: IndexedParallelIterator,
K: IndexedParallelIterator,
L: IndexedParallelIterator,
impl<A, B, C, D, E, F, G, H, I, J, K, L> ParallelIterator for MultiZip<(A, B, C, D, E, F, G, H, I, J, K, L)> where
A: IndexedParallelIterator,
B: IndexedParallelIterator,
C: IndexedParallelIterator,
D: IndexedParallelIterator,
E: IndexedParallelIterator,
F: IndexedParallelIterator,
G: IndexedParallelIterator,
H: IndexedParallelIterator,
I: IndexedParallelIterator,
J: IndexedParallelIterator,
K: IndexedParallelIterator,
L: IndexedParallelIterator,
sourceimpl<D, S> ParallelIterator for rayon::iter::Split<D, S> where
D: Send,
S: Fn(D) -> (D, Option<D>) + Sync + Send,
impl<D, S> ParallelIterator for rayon::iter::Split<D, S> where
D: Send,
S: Fn(D) -> (D, Option<D>) + Sync + Send,
type Item = D
sourceimpl<I> ParallelIterator for rayon::iter::Chunks<I> where
I: IndexedParallelIterator,
impl<I> ParallelIterator for rayon::iter::Chunks<I> where
I: IndexedParallelIterator,
sourceimpl<I> ParallelIterator for Enumerate<I> where
I: IndexedParallelIterator,
impl<I> ParallelIterator for Enumerate<I> where
I: IndexedParallelIterator,
sourceimpl<I> ParallelIterator for Flatten<I> where
I: ParallelIterator,
I::Item: IntoParallelIterator,
impl<I> ParallelIterator for Flatten<I> where
I: ParallelIterator,
I::Item: IntoParallelIterator,
type Item = <I::Item as IntoParallelIterator>::Item
sourceimpl<I> ParallelIterator for FlattenIter<I> where
I: ParallelIterator,
I::Item: IntoIterator,
<I::Item as IntoIterator>::Item: Send,
impl<I> ParallelIterator for FlattenIter<I> where
I: ParallelIterator,
I::Item: IntoIterator,
<I::Item as IntoIterator>::Item: Send,
type Item = <I::Item as IntoIterator>::Item
sourceimpl<I> ParallelIterator for Intersperse<I> where
I: ParallelIterator,
I::Item: Clone + Send,
impl<I> ParallelIterator for Intersperse<I> where
I: ParallelIterator,
I::Item: Clone + Send,
sourceimpl<I> ParallelIterator for MaxLen<I> where
I: IndexedParallelIterator,
impl<I> ParallelIterator for MaxLen<I> where
I: IndexedParallelIterator,
sourceimpl<I> ParallelIterator for MinLen<I> where
I: IndexedParallelIterator,
impl<I> ParallelIterator for MinLen<I> where
I: IndexedParallelIterator,
sourceimpl<I> ParallelIterator for PanicFuse<I> where
I: ParallelIterator,
impl<I> ParallelIterator for PanicFuse<I> where
I: ParallelIterator,
sourceimpl<I> ParallelIterator for Rev<I> where
I: IndexedParallelIterator,
impl<I> ParallelIterator for Rev<I> where
I: IndexedParallelIterator,
sourceimpl<I> ParallelIterator for Skip<I> where
I: IndexedParallelIterator,
impl<I> ParallelIterator for Skip<I> where
I: IndexedParallelIterator,
sourceimpl<I> ParallelIterator for StepBy<I> where
I: IndexedParallelIterator,
impl<I> ParallelIterator for StepBy<I> where
I: IndexedParallelIterator,
sourceimpl<I> ParallelIterator for Take<I> where
I: IndexedParallelIterator,
impl<I> ParallelIterator for Take<I> where
I: IndexedParallelIterator,
sourceimpl<I, F> ParallelIterator for Inspect<I, F> where
I: ParallelIterator,
F: Fn(&I::Item) + Sync + Send,
impl<I, F> ParallelIterator for Inspect<I, F> where
I: ParallelIterator,
F: Fn(&I::Item) + Sync + Send,
sourceimpl<I, F> ParallelIterator for Update<I, F> where
I: ParallelIterator,
F: Fn(&mut I::Item) + Send + Sync,
impl<I, F> ParallelIterator for Update<I, F> where
I: ParallelIterator,
F: Fn(&mut I::Item) + Send + Sync,
sourceimpl<I, F, PI> ParallelIterator for FlatMap<I, F> where
I: ParallelIterator,
F: Fn(I::Item) -> PI + Sync + Send,
PI: IntoParallelIterator,
impl<I, F, PI> ParallelIterator for FlatMap<I, F> where
I: ParallelIterator,
F: Fn(I::Item) -> PI + Sync + Send,
PI: IntoParallelIterator,
sourceimpl<I, F, R> ParallelIterator for Map<I, F> where
I: ParallelIterator,
F: Fn(I::Item) -> R + Sync + Send,
R: Send,
impl<I, F, R> ParallelIterator for Map<I, F> where
I: ParallelIterator,
F: Fn(I::Item) -> R + Sync + Send,
R: Send,
sourceimpl<I, F, SI> ParallelIterator for FlatMapIter<I, F> where
I: ParallelIterator,
F: Fn(I::Item) -> SI + Sync + Send,
SI: IntoIterator,
SI::Item: Send,
impl<I, F, SI> ParallelIterator for FlatMapIter<I, F> where
I: ParallelIterator,
F: Fn(I::Item) -> SI + Sync + Send,
SI: IntoIterator,
SI::Item: Send,
sourceimpl<I, INIT, T, F, R> ParallelIterator for MapInit<I, INIT, F> where
I: ParallelIterator,
INIT: Fn() -> T + Sync + Send,
F: Fn(&mut T, I::Item) -> R + Sync + Send,
R: Send,
impl<I, INIT, T, F, R> ParallelIterator for MapInit<I, INIT, F> where
I: ParallelIterator,
INIT: Fn() -> T + Sync + Send,
F: Fn(&mut T, I::Item) -> R + Sync + Send,
R: Send,
type Item = R
sourceimpl<I, J> ParallelIterator for Interleave<I, J> where
I: IndexedParallelIterator,
J: IndexedParallelIterator<Item = I::Item>,
impl<I, J> ParallelIterator for Interleave<I, J> where
I: IndexedParallelIterator,
J: IndexedParallelIterator<Item = I::Item>,
sourceimpl<I, J> ParallelIterator for InterleaveShortest<I, J> where
I: IndexedParallelIterator,
J: IndexedParallelIterator<Item = I::Item>,
impl<I, J> ParallelIterator for InterleaveShortest<I, J> where
I: IndexedParallelIterator,
J: IndexedParallelIterator<Item = I::Item>,
sourceimpl<I, P> ParallelIterator for Filter<I, P> where
I: ParallelIterator,
P: Fn(&I::Item) -> bool + Sync + Send,
impl<I, P> ParallelIterator for Filter<I, P> where
I: ParallelIterator,
P: Fn(&I::Item) -> bool + Sync + Send,
sourceimpl<I, P> ParallelIterator for Positions<I, P> where
I: IndexedParallelIterator,
P: Fn(I::Item) -> bool + Sync + Send,
impl<I, P> ParallelIterator for Positions<I, P> where
I: IndexedParallelIterator,
P: Fn(I::Item) -> bool + Sync + Send,
sourceimpl<I, P, R> ParallelIterator for FilterMap<I, P> where
I: ParallelIterator,
P: Fn(I::Item) -> Option<R> + Sync + Send,
R: Send,
impl<I, P, R> ParallelIterator for FilterMap<I, P> where
I: ParallelIterator,
P: Fn(I::Item) -> Option<R> + Sync + Send,
R: Send,
type Item = R
sourceimpl<I, T> ParallelIterator for WhileSome<I> where
I: ParallelIterator<Item = Option<T>>,
T: Send,
impl<I, T> ParallelIterator for WhileSome<I> where
I: ParallelIterator<Item = Option<T>>,
T: Send,
type Item = T
sourceimpl<I, T, F, R> ParallelIterator for MapWith<I, T, F> where
I: ParallelIterator,
T: Send + Clone,
F: Fn(&mut T, I::Item) -> R + Sync + Send,
R: Send,
impl<I, T, F, R> ParallelIterator for MapWith<I, T, F> where
I: ParallelIterator,
T: Send + Clone,
F: Fn(&mut T, I::Item) -> R + Sync + Send,
R: Send,
type Item = R
sourceimpl<Iter: Iterator + Send> ParallelIterator for IterBridge<Iter> where
Iter::Item: Send,
impl<Iter: Iterator + Send> ParallelIterator for IterBridge<Iter> where
Iter::Item: Send,
sourceimpl<K: Ord + Send, V: Send> ParallelIterator for rayon::collections::btree_map::IntoIter<K, V>
impl<K: Ord + Send, V: Send> ParallelIterator for rayon::collections::btree_map::IntoIter<K, V>
sourceimpl<K: Hash + Eq + Send, V: Send> ParallelIterator for rayon::collections::hash_map::Drain<'_, K, V>
impl<K: Hash + Eq + Send, V: Send> ParallelIterator for rayon::collections::hash_map::Drain<'_, K, V>
sourceimpl<K: Hash + Eq + Send, V: Send> ParallelIterator for rayon::collections::hash_map::IntoIter<K, V>
impl<K: Hash + Eq + Send, V: Send> ParallelIterator for rayon::collections::hash_map::IntoIter<K, V>
sourceimpl<L, R> ParallelIterator for Either<L, R> where
L: ParallelIterator,
R: ParallelIterator<Item = L::Item>,
impl<L, R> ParallelIterator for Either<L, R> where
L: ParallelIterator,
R: ParallelIterator<Item = L::Item>,
Either<L, R>
is a parallel iterator if both L
and R
are parallel iterators.