//! Traits for writing parallel programs using an iterator-style interface //! //! You will rarely need to interact with this module directly unless you have //! need to name one of the iterator types. //! //! Parallel iterators make it easy to write iterator-like chains that //! execute in parallel: typically all you have to do is convert the //! first `.iter()` (or `iter_mut()`, `into_iter()`, etc) method into //! `par_iter()` (or `par_iter_mut()`, `into_par_iter()`, etc). For //! example, to compute the sum of the squares of a sequence of //! integers, one might write: //! //! ```rust //! use rayon::prelude::*; //! fn sum_of_squares(input: &[i32]) -> i32 { //! input.par_iter() //! .map(|i| i * i) //! .sum() //! } //! ``` //! //! Or, to increment all the integers in a slice, you could write: //! //! ```rust //! use rayon::prelude::*; //! fn increment_all(input: &mut [i32]) { //! input.par_iter_mut() //! .for_each(|p| *p += 1); //! } //! ``` //! //! To use parallel iterators, first import the traits by adding //! something like `use rayon::prelude::*` to your module. You can //! then call `par_iter`, `par_iter_mut`, or `into_par_iter` to get a //! parallel iterator. Like a [regular iterator][], parallel //! iterators work by first constructing a computation and then //! executing it. //! //! In addition to `par_iter()` and friends, some types offer other //! ways to create (or consume) parallel iterators: //! //! - Slices (`&[T]`, `&mut [T]`) offer methods like `par_split` and //! `par_windows`, as well as various parallel sorting //! operations. See [the `ParallelSlice` trait] for the full list. //! - Strings (`&str`) offer methods like `par_split` and `par_lines`. //! See [the `ParallelString` trait] for the full list. //! - Various collections offer [`par_extend`], which grows a //! collection given a parallel iterator. (If you don't have a //! collection to extend, you can use [`collect()`] to create a new //! one from scratch.) //! //! [the `ParallelSlice` trait]: ../slice/trait.ParallelSlice.html //! [the `ParallelString` trait]: ../str/trait.ParallelString.html //! [`par_extend`]: trait.ParallelExtend.html //! [`collect()`]: trait.ParallelIterator.html#method.collect //! //! To see the full range of methods available on parallel iterators, //! check out the [`ParallelIterator`] and [`IndexedParallelIterator`] //! traits. //! //! If you'd like to build a custom parallel iterator, or to write your own //! combinator, then check out the [split] function and the [plumbing] module. //! //! [regular iterator]: https://doc.rust-lang.org/std/iter/trait.Iterator.html //! [`ParallelIterator`]: trait.ParallelIterator.html //! [`IndexedParallelIterator`]: trait.IndexedParallelIterator.html //! [split]: fn.split.html //! [plumbing]: plumbing/index.html //! //! Note: Several of the `ParallelIterator` methods rely on a `Try` trait which //! has been deliberately obscured from the public API. This trait is intended //! to mirror the unstable `std::ops::Try` with implementations for `Option` and //! `Result`, where `Some`/`Ok` values will let those iterators continue, but //! `None`/`Err` values will exit early. //! //! A note about object safety: It is currently _not_ possible to wrap //! a `ParallelIterator` (or any trait that depends on it) using a //! `Box<dyn ParallelIterator>` or other kind of dynamic allocation, //! because `ParallelIterator` is **not object-safe**. //! (This keeps the implementation simpler and allows extra optimizations.) use self::plumbing::*; use self::private::Try; pub use either::Either; use std::cmp::{self, Ordering}; use std::iter::{Product, Sum}; use std::ops::{Fn, RangeBounds}; pub mod plumbing; #[cfg(test)] mod test; // There is a method to the madness here: // // - These modules are private but expose certain types to the end-user // (e.g., `enumerate::Enumerate`) -- specifically, the types that appear in the // public API surface of the `ParallelIterator` traits. // - In **this** module, those public types are always used unprefixed, which forces // us to add a `pub use` and helps identify if we missed anything. // - In contrast, items that appear **only** in the body of a method, // e.g. `find::find()`, are always used **prefixed**, so that they // can be readily distinguished. mod chain; mod chunks; mod cloned; mod collect; mod copied; mod empty; mod enumerate; mod extend; mod filter; mod filter_map; mod find; mod find_first_last; mod flat_map; mod flat_map_iter; mod flatten; mod flatten_iter; mod fold; mod fold_chunks; mod fold_chunks_with; mod for_each; mod from_par_iter; mod inspect; mod interleave; mod interleave_shortest; mod intersperse; mod len; mod map; mod map_with; mod multizip; mod noop; mod once; mod panic_fuse; mod par_bridge; mod positions; mod product; mod reduce; mod repeat; mod rev; mod skip; mod skip_any; mod skip_any_while; mod splitter; mod step_by; mod sum; mod take; mod take_any; mod take_any_while; mod try_fold; mod try_reduce; mod try_reduce_with; mod unzip; mod update; mod while_some; mod zip; mod zip_eq; pub use self::{ chain::Chain, chunks::Chunks, cloned::Cloned, copied::Copied, empty::{empty, Empty}, enumerate::Enumerate, filter::Filter, filter_map::FilterMap, flat_map::FlatMap, flat_map_iter::FlatMapIter, flatten::Flatten, flatten_iter::FlattenIter, fold::{Fold, FoldWith}, fold_chunks::FoldChunks, fold_chunks_with::FoldChunksWith, inspect::Inspect, interleave::Interleave, interleave_shortest::InterleaveShortest, intersperse::Intersperse, len::{MaxLen, MinLen}, map::Map, map_with::{MapInit, MapWith}, multizip::MultiZip, once::{once, Once}, panic_fuse::PanicFuse, par_bridge::{IterBridge, ParallelBridge}, positions::Positions, repeat::{repeat, repeatn, Repeat, RepeatN}, rev::Rev, skip::Skip, skip_any::SkipAny, skip_any_while::SkipAnyWhile, splitter::{split, Split}, step_by::StepBy, take::Take, take_any::TakeAny, take_any_while::TakeAnyWhile, try_fold::{TryFold, TryFoldWith}, update::Update, while_some::WhileSome, zip::Zip, zip_eq::ZipEq, }; /// `IntoParallelIterator` implements the conversion to a [`ParallelIterator`]. /// /// By implementing `IntoParallelIterator` for a type, you define how it will /// transformed into an iterator. This is a parallel version of the standard /// library's [`std::iter::IntoIterator`] trait. /// /// [`ParallelIterator`]: trait.ParallelIterator.html /// [`std::iter::IntoIterator`]: https://doc.rust-lang.org/std/iter/trait.IntoIterator.html pub trait IntoParallelIterator { /// The parallel iterator type that will be created. type Iter: ParallelIterator<Item = Self::Item>; /// The type of item that the parallel iterator will produce. type Item: Send; /// Converts `self` into a parallel iterator. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// /// println!("counting in parallel:"); /// (0..100).into_par_iter() /// .for_each(|i| println!("{}", i)); /// ``` /// /// This conversion is often implicit for arguments to methods like [`zip`]. /// /// ``` /// use rayon::prelude::*; /// /// let v: Vec<_> = (0..5).into_par_iter().zip(5..10).collect(); /// assert_eq!(v, [(0, 5), (1, 6), (2, 7), (3, 8), (4, 9)]); /// ``` /// /// [`zip`]: trait.IndexedParallelIterator.html#method.zip fn into_par_iter(self) -> Self::Iter; } /// `IntoParallelRefIterator` implements the conversion to a /// [`ParallelIterator`], providing shared references to the data. /// /// This is a parallel version of the `iter()` method /// defined by various collections. /// /// This trait is automatically implemented /// `for I where &I: IntoParallelIterator`. In most cases, users /// will want to implement [`IntoParallelIterator`] rather than implement /// this trait directly. /// /// [`ParallelIterator`]: trait.ParallelIterator.html /// [`IntoParallelIterator`]: trait.IntoParallelIterator.html pub trait IntoParallelRefIterator<'data> { /// The type of the parallel iterator that will be returned. type Iter: ParallelIterator<Item = Self::Item>; /// The type of item that the parallel iterator will produce. /// This will typically be an `&'data T` reference type. type Item: Send + 'data; /// Converts `self` into a parallel iterator. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// /// let v: Vec<_> = (0..100).collect(); /// assert_eq!(v.par_iter().sum::<i32>(), 100 * 99 / 2); /// /// // `v.par_iter()` is shorthand for `(&v).into_par_iter()`, /// // producing the exact same references. /// assert!(v.par_iter().zip(&v) /// .all(|(a, b)| std::ptr::eq(a, b))); /// ``` fn par_iter(&'data self) -> Self::Iter; } impl<'data, I: 'data + ?Sized> IntoParallelRefIterator<'data> for I where &'data I: IntoParallelIterator, { type Iter = <&'data I as IntoParallelIterator>::Iter; type Item = <&'data I as IntoParallelIterator>::Item; fn par_iter(&'data self) -> Self::Iter { self.into_par_iter() } } /// `IntoParallelRefMutIterator` implements the conversion to a /// [`ParallelIterator`], providing mutable references to the data. /// /// This is a parallel version of the `iter_mut()` method /// defined by various collections. /// /// This trait is automatically implemented /// `for I where &mut I: IntoParallelIterator`. In most cases, users /// will want to implement [`IntoParallelIterator`] rather than implement /// this trait directly. /// /// [`ParallelIterator`]: trait.ParallelIterator.html /// [`IntoParallelIterator`]: trait.IntoParallelIterator.html pub trait IntoParallelRefMutIterator<'data> { /// The type of iterator that will be created. type Iter: ParallelIterator<Item = Self::Item>; /// The type of item that will be produced; this is typically an /// `&'data mut T` reference. type Item: Send + 'data; /// Creates the parallel iterator from `self`. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// /// let mut v = vec![0usize; 5]; /// v.par_iter_mut().enumerate().for_each(|(i, x)| *x = i); /// assert_eq!(v, [0, 1, 2, 3, 4]); /// ``` fn par_iter_mut(&'data mut self) -> Self::Iter; } impl<'data, I: 'data + ?Sized> IntoParallelRefMutIterator<'data> for I where &'data mut I: IntoParallelIterator, { type Iter = <&'data mut I as IntoParallelIterator>::Iter; type Item = <&'data mut I as IntoParallelIterator>::Item; fn par_iter_mut(&'data mut self) -> Self::Iter { self.into_par_iter() } } /// 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][iter]. /// /// [iter]: index.html /// [`IndexedParallelIterator`]: trait.IndexedParallelIterator.html pub trait ParallelIterator: Sized + Send { /// The type of item that this parallel iterator produces. /// For example, if you use the [`for_each`] method, this is the type of /// item that your closure will be invoked with. /// /// [`for_each`]: #method.for_each type Item: Send; /// 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)); /// ``` fn for_each<OP>(self, op: OP) where OP: Fn(Self::Item) + Sync + Send, { for_each::for_each(self, &op) } /// 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]) /// ``` fn for_each_with<OP, T>(self, init: T, op: OP) where OP: Fn(&mut T, Self::Item) + Sync + Send, T: Send + Clone, { self.map_with(init, op).collect() } /// 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)); /// } /// ``` 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, { self.map_init(init, op).collect() } /// 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"); /// ``` fn try_for_each<OP, R>(self, op: OP) -> R where OP: Fn(Self::Item) -> R + Sync + Send, R: Try<Output = ()> + Send, { fn ok<R: Try<Output = ()>>(_: (), _: ()) -> R { R::from_output(()) } self.map(op).try_reduce(<()>::default, ok) } /// 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()`]. /// /// [`for_each_with()`]: #method.for_each_with /// [`try_for_each()`]: #method.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]) /// ``` 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<Output = ()> + Send, { fn ok<R: Try<Output = ()>>(_: (), _: ()) -> R { R::from_output(()) } self.map_with(init, op).try_reduce(<()>::default, ok) } /// 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()`]. /// /// [`for_each_init()`]: #method.for_each_init /// [`try_for_each()`]: #method.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)); /// } /// ``` 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<Output = ()> + Send, { fn ok<R: Try<Output = ()>>(_: (), _: ()) -> R { R::from_output(()) } self.map_init(init, op).try_reduce(<()>::default, ok) } /// 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); /// ``` fn count(self) -> usize { fn one<T>(_: T) -> usize { 1 } self.map(one).sum() } /// 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]); /// ``` fn map<F, R>(self, map_op: F) -> Map<Self, F> where F: Fn(Self::Item) -> R + Sync + Send, R: Send, { Map::new(self, map_op) } /// 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); /// ``` 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, { MapWith::new(self, init, map_op) } /// 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)); /// ``` 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, { MapInit::new(self, init, map_op) } /// 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()`]. /// /// [`copied()`]: #method.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]); /// ``` fn cloned<'a, T>(self) -> Cloned<Self> where T: 'a + Clone + Send, Self: ParallelIterator<Item = &'a T>, { Cloned::new(self) } /// 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()`]. /// /// [`cloned()`]: #method.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]); /// ``` fn copied<'a, T>(self) -> Copied<Self> where T: 'a + Copy + Send, Self: ParallelIterator<Item = &'a T>, { Copied::new(self) } /// 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); /// ``` fn inspect<OP>(self, inspect_op: OP) -> Inspect<Self, OP> where OP: Fn(&Self::Item) + Sync + Send, { Inspect::new(self, inspect_op) } /// 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]); /// ``` fn update<F>(self, update_op: F) -> Update<Self, F> where F: Fn(&mut Self::Item) + Sync + Send, { Update::new(self, update_op) } /// 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]); /// ``` fn filter<P>(self, filter_op: P) -> Filter<Self, P> where P: Fn(&Self::Item) -> bool + Sync + Send, { Filter::new(self, filter_op) } /// 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]); /// ``` fn filter_map<P, R>(self, filter_op: P) -> FilterMap<Self, P> where P: Fn(Self::Item) -> Option<R> + Sync + Send, R: Send, { FilterMap::new(self, filter_op) } /// 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`](#method.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<F, PI>(self, map_op: F) -> FlatMap<Self, F> where F: Fn(Self::Item) -> PI + Sync + Send, PI: IntoParallelIterator, { FlatMap::new(self, map_op) } /// 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. /// /// [`flat_map`]: #method.flat_map /// /// # 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 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, { FlatMapIter::new(self, map_op) } /// An adaptor that flattens parallel-iterable `Item`s into one large iterator. /// /// See also [`flatten_iter`](#method.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(self) -> Flatten<Self> where Self::Item: IntoParallelIterator, { Flatten::new(self) } /// An adaptor that flattens serial-iterable `Item`s into one large iterator. /// /// See also [`flatten`](#method.flatten) and the analogous comparison of /// [`flat_map_iter` versus `flat_map`](#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]); /// ``` fn flatten_iter(self) -> FlattenIter<Self> where Self::Item: IntoIterator, <Self::Item as IntoIterator>::Item: Send, { FlattenIter::new(self) } /// 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. /// /// [associative]: https://en.wikipedia.org/wiki/Associative_property 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, { reduce::reduce(self, identity, op) } /// 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. /// /// [associative]: https://en.wikipedia.org/wiki/Associative_property fn reduce_with<OP>(self, op: OP) -> Option<Self::Item> where OP: Fn(Self::Item, Self::Item) -> Self::Item + Sync + Send, { fn opt_fold<T>(op: impl Fn(T, T) -> T) -> impl Fn(Option<T>, T) -> Option<T> { move |opt_a, b| match opt_a { Some(a) => Some(op(a, b)), None => Some(b), } } fn opt_reduce<T>(op: impl Fn(T, T) -> T) -> impl Fn(Option<T>, Option<T>) -> Option<T> { move |opt_a, opt_b| match (opt_a, opt_b) { (Some(a), Some(b)) => Some(op(a, b)), (Some(v), None) | (None, Some(v)) => Some(v), (None, None) => None, } } self.fold(<_>::default, opt_fold(&op)) .reduce(<_>::default, opt_reduce(&op)) } /// Reduces the items in the iterator into one item using a fallible `op`. /// The `identity` argument is used the same way as in [`reduce()`]. /// /// [`reduce()`]: #method.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); /// ``` 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<Output = T>, { try_reduce::try_reduce(self, identity, op) } /// 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`. /// /// [`reduce_with()`]: #method.reduce_with /// [`try_reduce()`]: #method.try_reduce /// /// For instance, with `Option` items, the return value may be: /// - `None`, the iterator was empty /// - `Some(None)`, we stopped after encountering `None`. /// - `Some(Some(x))`, the entire iterator reduced to `x`. /// /// With `Result` items, the nesting is more obvious: /// - `None`, the iterator was empty /// - `Some(Err(e))`, we stopped after encountering an error `e`. /// - `Some(Ok(x))`, the entire iterator reduced to `x`. /// /// # 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"); /// ``` fn try_reduce_with<T, OP>(self, op: OP) -> Option<Self::Item> where OP: Fn(T, T) -> Self::Item + Sync + Send, Self::Item: Try<Output = T>, { try_reduce_with::try_reduce_with(self, op) } /// 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 we 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: /// /// ```notrust /// 22 3 77 89 46 /// | | /// 102 135 /// ``` /// /// Or perhaps these three numbers: /// /// ```notrust /// 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 /// ``` 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, { Fold::new(self, identity, fold_op) } /// 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 /// ``` 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, { FoldWith::new(self, init, fold_op) } /// 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. /// /// [`fold()`]: #method.fold /// [`try_reduce()`]: #method.try_reduce /// /// # 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<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<Output = T> + Send, { TryFold::new(self, identity, fold_op) } /// 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()`]. /// /// [`fold_with()`]: #method.fold_with /// [`try_fold()`]: #method.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 /// ``` 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<Output = T> + Send, T: Clone + Send, { TryFoldWith::new(self, init, fold_op) } /// 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. /// /// [associative]: https://en.wikipedia.org/wiki/Associative_property /// /// 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); /// ``` fn sum<S>(self) -> S where S: Send + Sum<Self::Item> + Sum<S>, { sum::sum(self) } /// 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. /// /// [associative]: https://en.wikipedia.org/wiki/Associative_property /// /// 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); /// ``` fn product<P>(self) -> P where P: Send + Product<Self::Item> + Product<P>, { product::product(self) } /// 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); /// ``` fn min(self) -> Option<Self::Item> where Self::Item: Ord, { self.reduce_with(cmp::min) } /// 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)); /// ``` fn min_by<F>(self, f: F) -> Option<Self::Item> where F: Sync + Send + Fn(&Self::Item, &Self::Item) -> Ordering, { fn min<T>(f: impl Fn(&T, &T) -> Ordering) -> impl Fn(T, T) -> T { move |a, b| match f(&a, &b) { Ordering::Greater => b, _ => a, } } self.reduce_with(min(f)) } /// 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)); /// ``` fn min_by_key<K, F>(self, f: F) -> Option<Self::Item> where K: Ord + Send, F: Sync + Send + Fn(&Self::Item) -> K, { fn key<T, K>(f: impl Fn(&T) -> K) -> impl Fn(T) -> (K, T) { move |x| (f(&x), x) } fn min_key<T, K: Ord>(a: (K, T), b: (K, T)) -> (K, T) { match (a.0).cmp(&b.0) { Ordering::Greater => b, _ => a, } } let (_, x) = self.map(key(f)).reduce_with(min_key)?; Some(x) } /// 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); /// ``` fn max(self) -> Option<Self::Item> where Self::Item: Ord, { self.reduce_with(cmp::max) } /// 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)); /// ``` fn max_by<F>(self, f: F) -> Option<Self::Item> where F: Sync + Send + Fn(&Self::Item, &Self::Item) -> Ordering, { fn max<T>(f: impl Fn(&T, &T) -> Ordering) -> impl Fn(T, T) -> T { move |a, b| match f(&a, &b) { Ordering::Greater => a, _ => b, } } self.reduce_with(max(f)) } /// 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)); /// ``` fn max_by_key<K, F>(self, f: F) -> Option<Self::Item> where K: Ord + Send, F: Sync + Send + Fn(&Self::Item) -> K, { fn key<T, K>(f: impl Fn(&T) -> K) -> impl Fn(T) -> (K, T) { move |x| (f(&x), x) } fn max_key<T, K: Ord>(a: (K, T), b: (K, T)) -> (K, T) { match (a.0).cmp(&b.0) { Ordering::Greater => a, _ => b, } } let (_, x) = self.map(key(f)).reduce_with(max_key)?; Some(x) } /// 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]); /// ``` fn chain<C>(self, chain: C) -> Chain<Self, C::Iter> where C: IntoParallelIterator<Item = Self::Item>, { Chain::new(self, chain.into_par_iter()) } /// 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][find] 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). /// /// [find]: https://doc.rust-lang.org/std/iter/trait.Iterator.html#method.find /// /// # 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); /// ``` fn find_any<P>(self, predicate: P) -> Option<Self::Item> where P: Fn(&Self::Item) -> bool + Sync + Send, { find::find(self, predicate) } /// 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); /// ``` fn find_first<P>(self, predicate: P) -> Option<Self::Item> where P: Fn(&Self::Item) -> bool + Sync + Send, { find_first_last::find_first(self, predicate) } /// 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); /// ``` fn find_last<P>(self, predicate: P) -> Option<Self::Item> where P: Fn(&Self::Item) -> bool + Sync + Send, { find_first_last::find_last(self, predicate) } /// 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)); /// ``` fn find_map_any<P, R>(self, predicate: P) -> Option<R> where P: Fn(Self::Item) -> Option<R> + Sync + Send, R: Send, { fn yes<T>(_: &T) -> bool { true } self.filter_map(predicate).find_any(yes) } /// 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)); /// ``` fn find_map_first<P, R>(self, predicate: P) -> Option<R> where P: Fn(Self::Item) -> Option<R> + Sync + Send, R: Send, { fn yes<T>(_: &T) -> bool { true } self.filter_map(predicate).find_first(yes) } /// 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)); /// ``` fn find_map_last<P, R>(self, predicate: P) -> Option<R> where P: Fn(Self::Item) -> Option<R> + Sync + Send, R: Send, { fn yes<T>(_: &T) -> bool { true } self.filter_map(predicate).find_last(yes) } #[doc(hidden)] #[deprecated(note = "parallel `find` does not search in order -- use `find_any`, \\ `find_first`, or `find_last`")] fn find<P>(self, predicate: P) -> Option<Self::Item> where P: Fn(&Self::Item) -> bool + Sync + Send, { self.find_any(predicate) } /// 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); /// ``` fn any<P>(self, predicate: P) -> bool where P: Fn(Self::Item) -> bool + Sync + Send, { self.map(predicate).find_any(bool::clone).is_some() } /// 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 all<P>(self, predicate: P) -> bool where P: Fn(Self::Item) -> bool + Sync + Send, { #[inline] fn is_false(x: &bool) -> bool { !x } self.map(predicate).find_any(is_false).is_none() } /// 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 while_some<T>(self) -> WhileSome<Self> where Self: ParallelIterator<Item = Option<T>>, T: Send, { WhileSome::new(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. /// /// [`join`]: ../fn.join.html#panics /// /// # 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. /// /// ```should_panic /// 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 panic_fuse(self) -> PanicFuse<Self> { PanicFuse::new(self) } /// 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. /// /// [`IndexedParallelIterator`]: trait.IndexedParallelIterator.html /// [`collect_into_vec()`]: /// trait.IndexedParallelIterator.html#method.collect_into_vec /// /// # 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`](#method.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`](#method.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 collect<C>(self) -> C where C: FromParallelIterator<Self::Item>, { C::from_par_iter(self) } /// 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]); /// ``` 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, { unzip::unzip(self) } /// 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 inference 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]); /// ``` 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, { unzip::partition(self, predicate) } /// 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 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, { unzip::partition_map(self, predicate) } /// 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]); /// ``` fn intersperse(self, element: Self::Item) -> Intersperse<Self> where Self::Item: Clone, { Intersperse::new(self, element) } /// Creates an iterator that yields `n` elements from *anywhere* in the original iterator. /// /// This is similar to [`IndexedParallelIterator::take`] without being /// constrained to the "first" `n` of the original iterator order. The /// taken items will still maintain their relative order where that is /// visible in `collect`, `reduce`, and similar outputs. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// /// let result: Vec<_> = (0..100) /// .into_par_iter() /// .filter(|&x| x % 2 == 0) /// .take_any(5) /// .collect(); /// /// assert_eq!(result.len(), 5); /// assert!(result.windows(2).all(|w| w[0] < w[1])); /// ``` fn take_any(self, n: usize) -> TakeAny<Self> { TakeAny::new(self, n) } /// Creates an iterator that skips `n` elements from *anywhere* in the original iterator. /// /// This is similar to [`IndexedParallelIterator::skip`] without being /// constrained to the "first" `n` of the original iterator order. The /// remaining items will still maintain their relative order where that is /// visible in `collect`, `reduce`, and similar outputs. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// /// let result: Vec<_> = (0..100) /// .into_par_iter() /// .filter(|&x| x % 2 == 0) /// .skip_any(5) /// .collect(); /// /// assert_eq!(result.len(), 45); /// assert!(result.windows(2).all(|w| w[0] < w[1])); /// ``` fn skip_any(self, n: usize) -> SkipAny<Self> { SkipAny::new(self, n) } /// Creates an iterator that takes elements from *anywhere* in the original iterator /// until the given `predicate` returns `false`. /// /// The `predicate` may be anything -- e.g. it could be checking a fact about the item, a /// global condition unrelated to the item itself, or some combination thereof. /// /// If parallel calls to the `predicate` race and give different results, then the /// `true` results will still take those particular items, while respecting the `false` /// result from elsewhere to skip any further items. /// /// This is similar to [`Iterator::take_while`] without being constrained to the original /// iterator order. The taken items will still maintain their relative order where that is /// visible in `collect`, `reduce`, and similar outputs. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// /// let result: Vec<_> = (0..100) /// .into_par_iter() /// .take_any_while(|x| *x < 50) /// .collect(); /// /// assert!(result.len() <= 50); /// assert!(result.windows(2).all(|w| w[0] < w[1])); /// ``` /// /// ``` /// use rayon::prelude::*; /// use std::sync::atomic::AtomicUsize; /// use std::sync::atomic::Ordering::Relaxed; /// /// // Collect any group of items that sum <= 1000 /// let quota = AtomicUsize::new(1000); /// let result: Vec<_> = (0_usize..100) /// .into_par_iter() /// .take_any_while(|&x| { /// quota.fetch_update(Relaxed, Relaxed, |q| q.checked_sub(x)) /// .is_ok() /// }) /// .collect(); /// /// let sum = result.iter().sum::<usize>(); /// assert!(matches!(sum, 902..=1000)); /// ``` fn take_any_while<P>(self, predicate: P) -> TakeAnyWhile<Self, P> where P: Fn(&Self::Item) -> bool + Sync + Send, { TakeAnyWhile::new(self, predicate) } /// Creates an iterator that skips elements from *anywhere* in the original iterator /// until the given `predicate` returns `false`. /// /// The `predicate` may be anything -- e.g. it could be checking a fact about the item, a /// global condition unrelated to the item itself, or some combination thereof. /// /// If parallel calls to the `predicate` race and give different results, then the /// `true` results will still skip those particular items, while respecting the `false` /// result from elsewhere to skip any further items. /// /// This is similar to [`Iterator::skip_while`] without being constrained to the original /// iterator order. The remaining items will still maintain their relative order where that is /// visible in `collect`, `reduce`, and similar outputs. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// /// let result: Vec<_> = (0..100) /// .into_par_iter() /// .skip_any_while(|x| *x < 50) /// .collect(); /// /// assert!(result.len() >= 50); /// assert!(result.windows(2).all(|w| w[0] < w[1])); /// ``` fn skip_any_while<P>(self, predicate: P) -> SkipAnyWhile<Self, P> where P: Fn(&Self::Item) -> bool + Sync + Send, { SkipAnyWhile::new(self, predicate) } /// 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. /// /// [README]: https://github.com/rayon-rs/rayon/blob/master/src/iter/plumbing/README.md 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. /// /// 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. fn opt_len(&self) -> Option<usize> { None } } impl<T: ParallelIterator> IntoParallelIterator for T { type Iter = T; type Item = T::Item; fn into_par_iter(self) -> T { self } } /// An iterator that supports "random access" to its data, meaning /// that you can split it at arbitrary indices and draw data from /// those points. /// /// **Note:** Not implemented for `u64`, `i64`, `u128`, or `i128` ranges // Waiting for `ExactSizeIterator::is_empty` to be stabilized. See rust-lang/rust#35428 #[allow(clippy::len_without_is_empty)] pub trait IndexedParallelIterator: ParallelIterator { /// Collects the results of the iterator into the specified /// vector. The vector is always truncated before execution /// begins. If possible, reusing the vector across calls can lead /// to better performance since it reuses the same backing buffer. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// /// // any prior data will be truncated /// let mut vec = vec![-1, -2, -3]; /// /// (0..5).into_par_iter() /// .collect_into_vec(&mut vec); /// /// assert_eq!(vec, [0, 1, 2, 3, 4]); /// ``` fn collect_into_vec(self, target: &mut Vec<Self::Item>) { collect::collect_into_vec(self, target); } /// Unzips the results of the iterator into the specified /// vectors. The vectors are always truncated before execution /// begins. If possible, reusing the vectors across calls can lead /// to better performance since they reuse the same backing buffer. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// /// // any prior data will be truncated /// let mut left = vec![42; 10]; /// let mut right = vec![-1; 10]; /// /// (10..15).into_par_iter() /// .enumerate() /// .unzip_into_vecs(&mut left, &mut right); /// /// assert_eq!(left, [0, 1, 2, 3, 4]); /// assert_eq!(right, [10, 11, 12, 13, 14]); /// ``` fn unzip_into_vecs<A, B>(self, left: &mut Vec<A>, right: &mut Vec<B>) where Self: IndexedParallelIterator<Item = (A, B)>, A: Send, B: Send, { collect::unzip_into_vecs(self, left, right); } /// Iterates over tuples `(A, B)`, where the items `A` are from /// this iterator and `B` are from the iterator given as argument. /// Like the `zip` method on ordinary iterators, if the two /// iterators are of unequal length, you only get the items they /// have in common. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// /// let result: Vec<_> = (1..4) /// .into_par_iter() /// .zip(vec!['a', 'b', 'c']) /// .collect(); /// /// assert_eq!(result, [(1, 'a'), (2, 'b'), (3, 'c')]); /// ``` fn zip<Z>(self, zip_op: Z) -> Zip<Self, Z::Iter> where Z: IntoParallelIterator, Z::Iter: IndexedParallelIterator, { Zip::new(self, zip_op.into_par_iter()) } /// The same as `Zip`, but requires that both iterators have the same length. /// /// # Panics /// Will panic if `self` and `zip_op` are not the same length. /// /// ```should_panic /// use rayon::prelude::*; /// /// let one = [1u8]; /// let two = [2u8, 2]; /// let one_iter = one.par_iter(); /// let two_iter = two.par_iter(); /// /// // this will panic /// let zipped: Vec<(&u8, &u8)> = one_iter.zip_eq(two_iter).collect(); /// /// // we should never get here /// assert_eq!(1, zipped.len()); /// ``` #[track_caller] fn zip_eq<Z>(self, zip_op: Z) -> ZipEq<Self, Z::Iter> where Z: IntoParallelIterator, Z::Iter: IndexedParallelIterator, { let zip_op_iter = zip_op.into_par_iter(); assert_eq!( self.len(), zip_op_iter.len(), "iterators must have the same length" ); ZipEq::new(self, zip_op_iter) } /// Interleaves elements of this iterator and the other given /// iterator. Alternately yields elements from this iterator and /// the given iterator, until both are exhausted. If one iterator /// is exhausted before the other, the last elements are provided /// from the other. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// let (x, y) = (vec![1, 2], vec![3, 4, 5, 6]); /// let r: Vec<i32> = x.into_par_iter().interleave(y).collect(); /// assert_eq!(r, vec![1, 3, 2, 4, 5, 6]); /// ``` fn interleave<I>(self, other: I) -> Interleave<Self, I::Iter> where I: IntoParallelIterator<Item = Self::Item>, I::Iter: IndexedParallelIterator<Item = Self::Item>, { Interleave::new(self, other.into_par_iter()) } /// Interleaves elements of this iterator and the other given /// iterator, until one is exhausted. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// let (x, y) = (vec![1, 2, 3, 4], vec![5, 6]); /// let r: Vec<i32> = x.into_par_iter().interleave_shortest(y).collect(); /// assert_eq!(r, vec![1, 5, 2, 6, 3]); /// ``` fn interleave_shortest<I>(self, other: I) -> InterleaveShortest<Self, I::Iter> where I: IntoParallelIterator<Item = Self::Item>, I::Iter: IndexedParallelIterator<Item = Self::Item>, { InterleaveShortest::new(self, other.into_par_iter()) } /// Splits an iterator up into fixed-size chunks. /// /// Returns an iterator that returns `Vec`s of the given number of elements. /// If the number of elements in the iterator is not divisible by `chunk_size`, /// the last chunk may be shorter than `chunk_size`. /// /// See also [`par_chunks()`] and [`par_chunks_mut()`] for similar behavior on /// slices, without having to allocate intermediate `Vec`s for the chunks. /// /// [`par_chunks()`]: ../slice/trait.ParallelSlice.html#method.par_chunks /// [`par_chunks_mut()`]: ../slice/trait.ParallelSliceMut.html#method.par_chunks_mut /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// let a = vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10]; /// let r: Vec<Vec<i32>> = a.into_par_iter().chunks(3).collect(); /// assert_eq!(r, vec![vec![1,2,3], vec![4,5,6], vec![7,8,9], vec![10]]); /// ``` #[track_caller] fn chunks(self, chunk_size: usize) -> Chunks<Self> { assert!(chunk_size != 0, "chunk_size must not be zero"); Chunks::new(self, chunk_size) } /// Splits an iterator into fixed-size chunks, performing a sequential [`fold()`] on /// each chunk. /// /// Returns an iterator that produces a folded result for each chunk of items /// produced by this iterator. /// /// This works essentially like: /// /// ```text /// iter.chunks(chunk_size) /// .map(|chunk| /// chunk.into_iter() /// .fold(identity, fold_op) /// ) /// ``` /// /// except there is no per-chunk allocation overhead. /// /// [`fold()`]: std::iter::Iterator#method.fold /// /// **Panics** if `chunk_size` is 0. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// let nums = vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10]; /// let chunk_sums = nums.into_par_iter().fold_chunks(2, || 0, |a, n| a + n).collect::<Vec<_>>(); /// assert_eq!(chunk_sums, vec![3, 7, 11, 15, 19]); /// ``` #[track_caller] fn fold_chunks<T, ID, F>( self, chunk_size: usize, identity: ID, fold_op: F, ) -> FoldChunks<Self, ID, F> where ID: Fn() -> T + Send + Sync, F: Fn(T, Self::Item) -> T + Send + Sync, T: Send, { assert!(chunk_size != 0, "chunk_size must not be zero"); FoldChunks::new(self, chunk_size, identity, fold_op) } /// Splits an iterator into fixed-size chunks, performing a sequential [`fold()`] on /// each chunk. /// /// Returns an iterator that produces a folded result for each chunk of items /// produced by this iterator. /// /// This works essentially like `fold_chunks(chunk_size, || init.clone(), fold_op)`, /// except it doesn't require the `init` type to be `Sync`, nor any other form of /// added synchronization. /// /// [`fold()`]: std::iter::Iterator#method.fold /// /// **Panics** if `chunk_size` is 0. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// let nums = vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10]; /// let chunk_sums = nums.into_par_iter().fold_chunks_with(2, 0, |a, n| a + n).collect::<Vec<_>>(); /// assert_eq!(chunk_sums, vec![3, 7, 11, 15, 19]); /// ``` #[track_caller] fn fold_chunks_with<T, F>( self, chunk_size: usize, init: T, fold_op: F, ) -> FoldChunksWith<Self, T, F> where T: Send + Clone, F: Fn(T, Self::Item) -> T + Send + Sync, { assert!(chunk_size != 0, "chunk_size must not be zero"); FoldChunksWith::new(self, chunk_size, init, fold_op) } /// Lexicographically compares the elements of this `ParallelIterator` with those of /// another. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// use std::cmp::Ordering::*; /// /// let x = vec![1, 2, 3]; /// assert_eq!(x.par_iter().cmp(&vec![1, 3, 0]), Less); /// assert_eq!(x.par_iter().cmp(&vec![1, 2, 3]), Equal); /// assert_eq!(x.par_iter().cmp(&vec![1, 2]), Greater); /// ``` fn cmp<I>(self, other: I) -> Ordering where I: IntoParallelIterator<Item = Self::Item>, I::Iter: IndexedParallelIterator, Self::Item: Ord, { #[inline] fn ordering<T: Ord>((x, y): (T, T)) -> Ordering { Ord::cmp(&x, &y) } #[inline] fn inequal(&ord: &Ordering) -> bool { ord != Ordering::Equal } let other = other.into_par_iter(); let ord_len = self.len().cmp(&other.len()); self.zip(other) .map(ordering) .find_first(inequal) .unwrap_or(ord_len) } /// Lexicographically compares the elements of this `ParallelIterator` with those of /// another. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// use std::cmp::Ordering::*; /// use std::f64::NAN; /// /// let x = vec![1.0, 2.0, 3.0]; /// assert_eq!(x.par_iter().partial_cmp(&vec![1.0, 3.0, 0.0]), Some(Less)); /// assert_eq!(x.par_iter().partial_cmp(&vec![1.0, 2.0, 3.0]), Some(Equal)); /// assert_eq!(x.par_iter().partial_cmp(&vec![1.0, 2.0]), Some(Greater)); /// assert_eq!(x.par_iter().partial_cmp(&vec![1.0, NAN]), None); /// ``` fn partial_cmp<I>(self, other: I) -> Option<Ordering> where I: IntoParallelIterator, I::Iter: IndexedParallelIterator, Self::Item: PartialOrd<I::Item>, { #[inline] fn ordering<T: PartialOrd<U>, U>((x, y): (T, U)) -> Option<Ordering> { PartialOrd::partial_cmp(&x, &y) } #[inline] fn inequal(&ord: &Option<Ordering>) -> bool { ord != Some(Ordering::Equal) } let other = other.into_par_iter(); let ord_len = self.len().cmp(&other.len()); self.zip(other) .map(ordering) .find_first(inequal) .unwrap_or(Some(ord_len)) } /// Determines if the elements of this `ParallelIterator` /// are equal to those of another fn eq<I>(self, other: I) -> bool where I: IntoParallelIterator, I::Iter: IndexedParallelIterator, Self::Item: PartialEq<I::Item>, { #[inline] fn eq<T: PartialEq<U>, U>((x, y): (T, U)) -> bool { PartialEq::eq(&x, &y) } let other = other.into_par_iter(); self.len() == other.len() && self.zip(other).all(eq) } /// Determines if the elements of this `ParallelIterator` /// are unequal to those of another fn ne<I>(self, other: I) -> bool where I: IntoParallelIterator, I::Iter: IndexedParallelIterator, Self::Item: PartialEq<I::Item>, { !self.eq(other) } /// Determines if the elements of this `ParallelIterator` /// are lexicographically less than those of another. fn lt<I>(self, other: I) -> bool where I: IntoParallelIterator, I::Iter: IndexedParallelIterator, Self::Item: PartialOrd<I::Item>, { self.partial_cmp(other) == Some(Ordering::Less) } /// Determines if the elements of this `ParallelIterator` /// are less or equal to those of another. fn le<I>(self, other: I) -> bool where I: IntoParallelIterator, I::Iter: IndexedParallelIterator, Self::Item: PartialOrd<I::Item>, { let ord = self.partial_cmp(other); ord == Some(Ordering::Equal) || ord == Some(Ordering::Less) } /// Determines if the elements of this `ParallelIterator` /// are lexicographically greater than those of another. fn gt<I>(self, other: I) -> bool where I: IntoParallelIterator, I::Iter: IndexedParallelIterator, Self::Item: PartialOrd<I::Item>, { self.partial_cmp(other) == Some(Ordering::Greater) } /// Determines if the elements of this `ParallelIterator` /// are less or equal to those of another. fn ge<I>(self, other: I) -> bool where I: IntoParallelIterator, I::Iter: IndexedParallelIterator, Self::Item: PartialOrd<I::Item>, { let ord = self.partial_cmp(other); ord == Some(Ordering::Equal) || ord == Some(Ordering::Greater) } /// Yields an index along with each item. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// /// let chars = vec!['a', 'b', 'c']; /// let result: Vec<_> = chars /// .into_par_iter() /// .enumerate() /// .collect(); /// /// assert_eq!(result, [(0, 'a'), (1, 'b'), (2, 'c')]); /// ``` fn enumerate(self) -> Enumerate<Self> { Enumerate::new(self) } /// Creates an iterator that steps by the given amount /// /// # Examples /// /// ``` ///use rayon::prelude::*; /// /// let range = (3..10); /// let result: Vec<i32> = range /// .into_par_iter() /// .step_by(3) /// .collect(); /// /// assert_eq!(result, [3, 6, 9]) /// ``` fn step_by(self, step: usize) -> StepBy<Self> { StepBy::new(self, step) } /// Creates an iterator that skips the first `n` elements. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// /// let result: Vec<_> = (0..100) /// .into_par_iter() /// .skip(95) /// .collect(); /// /// assert_eq!(result, [95, 96, 97, 98, 99]); /// ``` fn skip(self, n: usize) -> Skip<Self> { Skip::new(self, n) } /// Creates an iterator that yields the first `n` elements. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// /// let result: Vec<_> = (0..100) /// .into_par_iter() /// .take(5) /// .collect(); /// /// assert_eq!(result, [0, 1, 2, 3, 4]); /// ``` fn take(self, n: usize) -> Take<Self> { Take::new(self, n) } /// Searches for **some** item in the parallel iterator that /// matches the given predicate, and returns its index. Like /// `ParallelIterator::find_any`, the parallel search will not /// necessarily find the **first** match, and once a match is /// found we'll attempt to stop processing any more. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// /// let a = [1, 2, 3, 3]; /// /// let i = a.par_iter().position_any(|&x| x == 3).expect("found"); /// assert!(i == 2 || i == 3); /// /// assert_eq!(a.par_iter().position_any(|&x| x == 100), None); /// ``` fn position_any<P>(self, predicate: P) -> Option<usize> where P: Fn(Self::Item) -> bool + Sync + Send, { #[inline] fn check(&(_, p): &(usize, bool)) -> bool { p } let (i, _) = self.map(predicate).enumerate().find_any(check)?; Some(i) } /// Searches for the sequentially **first** item in the parallel iterator /// that matches the given predicate, and returns its index. /// /// Like `ParallelIterator::find_first`, 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, /// `position_any` is a better choice. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// /// let a = [1, 2, 3, 3]; /// /// assert_eq!(a.par_iter().position_first(|&x| x == 3), Some(2)); /// /// assert_eq!(a.par_iter().position_first(|&x| x == 100), None); /// ``` fn position_first<P>(self, predicate: P) -> Option<usize> where P: Fn(Self::Item) -> bool + Sync + Send, { #[inline] fn check(&(_, p): &(usize, bool)) -> bool { p } let (i, _) = self.map(predicate).enumerate().find_first(check)?; Some(i) } /// Searches for the sequentially **last** item in the parallel iterator /// that matches the given predicate, and returns its index. /// /// Like `ParallelIterator::find_last`, 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, `position_any` is a better /// choice. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// /// let a = [1, 2, 3, 3]; /// /// assert_eq!(a.par_iter().position_last(|&x| x == 3), Some(3)); /// /// assert_eq!(a.par_iter().position_last(|&x| x == 100), None); /// ``` fn position_last<P>(self, predicate: P) -> Option<usize> where P: Fn(Self::Item) -> bool + Sync + Send, { #[inline] fn check(&(_, p): &(usize, bool)) -> bool { p } let (i, _) = self.map(predicate).enumerate().find_last(check)?; Some(i) } #[doc(hidden)] #[deprecated( note = "parallel `position` does not search in order -- use `position_any`, \\ `position_first`, or `position_last`" )] fn position<P>(self, predicate: P) -> Option<usize> where P: Fn(Self::Item) -> bool + Sync + Send, { self.position_any(predicate) } /// Searches for items in the parallel iterator that match the given /// predicate, and returns their indices. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// /// let primes = vec![2, 3, 5, 7, 11, 13, 17, 19, 23, 29]; /// /// // Find the positions of primes congruent to 1 modulo 6 /// let p1mod6: Vec<_> = primes.par_iter().positions(|&p| p % 6 == 1).collect(); /// assert_eq!(p1mod6, [3, 5, 7]); // primes 7, 13, and 19 /// /// // Find the positions of primes congruent to 5 modulo 6 /// let p5mod6: Vec<_> = primes.par_iter().positions(|&p| p % 6 == 5).collect(); /// assert_eq!(p5mod6, [2, 4, 6, 8, 9]); // primes 5, 11, 17, 23, and 29 /// ``` fn positions<P>(self, predicate: P) -> Positions<Self, P> where P: Fn(Self::Item) -> bool + Sync + Send, { Positions::new(self, predicate) } /// Produces a new iterator with the elements of this iterator in /// reverse order. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// /// let result: Vec<_> = (0..5) /// .into_par_iter() /// .rev() /// .collect(); /// /// assert_eq!(result, [4, 3, 2, 1, 0]); /// ``` fn rev(self) -> Rev<Self> { Rev::new(self) } /// Sets the minimum length of iterators desired to process in each /// rayon job. Rayon will not split any smaller than this length, but /// of course an iterator could already be smaller to begin with. /// /// Producers like `zip` and `interleave` will use greater of the two /// minimums. /// Chained iterators and iterators inside `flat_map` may each use /// their own minimum length. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// /// let min = (0..1_000_000) /// .into_par_iter() /// .with_min_len(1234) /// .fold(|| 0, |acc, _| acc + 1) // count how many are in this segment /// .min().unwrap(); /// /// assert!(min >= 1234); /// ``` fn with_min_len(self, min: usize) -> MinLen<Self> { MinLen::new(self, min) } /// Sets the maximum length of iterators desired to process in each /// rayon job. Rayon will try to split at least below this length, /// unless that would put it below the length from `with_min_len()`. /// For example, given min=10 and max=15, a length of 16 will not be /// split any further. /// /// Producers like `zip` and `interleave` will use lesser of the two /// maximums. /// Chained iterators and iterators inside `flat_map` may each use /// their own maximum length. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// /// let max = (0..1_000_000) /// .into_par_iter() /// .with_max_len(1234) /// .fold(|| 0, |acc, _| acc + 1) // count how many are in this segment /// .max().unwrap(); /// /// assert!(max <= 1234); /// ``` fn with_max_len(self, max: usize) -> MaxLen<Self> { MaxLen::new(self, max) } /// Produces an exact count of how many items this iterator will /// produce, presuming no panic occurs. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// /// let par_iter = (0..100).into_par_iter().zip(vec![0; 10]); /// assert_eq!(par_iter.len(), 10); /// /// let vec: Vec<_> = par_iter.collect(); /// assert_eq!(vec.len(), 10); /// ``` fn len(&self) -> usize; /// 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. If a split does happen, it /// will inform the consumer of the index where the split should /// occur (unlike `ParallelIterator::drive_unindexed()`). /// /// See the [README] for more details on the internals of parallel /// iterators. /// /// [README]: https://github.com/rayon-rs/rayon/blob/master/src/iter/plumbing/README.md fn drive<C: Consumer<Self::Item>>(self, consumer: C) -> C::Result; /// Internal method used to define the behavior of this parallel /// iterator. You should not need to call this directly. /// /// This method converts the iterator into a producer P and then /// invokes `callback.callback()` with P. Note that the type of /// this producer is not defined as part of the API, since /// `callback` must be defined generically for all producers. This /// allows the producer type to contain references; it also means /// that parallel iterators can adjust that type without causing a /// breaking change. /// /// See the [README] for more details on the internals of parallel /// iterators. /// /// [README]: https://github.com/rayon-rs/rayon/blob/master/src/iter/plumbing/README.md fn with_producer<CB: ProducerCallback<Self::Item>>(self, callback: CB) -> CB::Output; } /// `FromParallelIterator` implements the creation of a collection /// from a [`ParallelIterator`]. By implementing /// `FromParallelIterator` for a given type, you define how it will be /// created from an iterator. /// /// `FromParallelIterator` is used through [`ParallelIterator`]'s [`collect()`] method. /// /// [`ParallelIterator`]: trait.ParallelIterator.html /// [`collect()`]: trait.ParallelIterator.html#method.collect /// /// # Examples /// /// Implementing `FromParallelIterator` for your type: /// /// ``` /// use rayon::prelude::*; /// use std::mem; /// /// struct BlackHole { /// mass: usize, /// } /// /// impl<T: Send> FromParallelIterator<T> for BlackHole { /// fn from_par_iter<I>(par_iter: I) -> Self /// where I: IntoParallelIterator<Item = T> /// { /// let par_iter = par_iter.into_par_iter(); /// BlackHole { /// mass: par_iter.count() * mem::size_of::<T>(), /// } /// } /// } /// /// let bh: BlackHole = (0i32..1000).into_par_iter().collect(); /// assert_eq!(bh.mass, 4000); /// ``` pub trait FromParallelIterator<T> where T: Send, { /// Creates an instance of the collection from the parallel iterator `par_iter`. /// /// If your collection is not naturally parallel, the easiest (and /// fastest) way to do this is often to collect `par_iter` into a /// [`LinkedList`] or other intermediate data structure and then /// sequentially extend your collection. However, a more 'native' /// technique is to use the [`par_iter.fold`] or /// [`par_iter.fold_with`] methods to create the collection. /// Alternatively, if your collection is 'natively' parallel, you /// can use `par_iter.for_each` to process each element in turn. /// /// [`LinkedList`]: https://doc.rust-lang.org/std/collections/struct.LinkedList.html /// [`par_iter.fold`]: trait.ParallelIterator.html#method.fold /// [`par_iter.fold_with`]: trait.ParallelIterator.html#method.fold_with /// [`par_iter.for_each`]: trait.ParallelIterator.html#method.for_each fn from_par_iter<I>(par_iter: I) -> Self where I: IntoParallelIterator<Item = T>; } /// `ParallelExtend` extends an existing collection with items from a [`ParallelIterator`]. /// /// [`ParallelIterator`]: trait.ParallelIterator.html /// /// # Examples /// /// Implementing `ParallelExtend` for your type: /// /// ``` /// use rayon::prelude::*; /// use std::mem; /// /// struct BlackHole { /// mass: usize, /// } /// /// impl<T: Send> ParallelExtend<T> for BlackHole { /// fn par_extend<I>(&mut self, par_iter: I) /// where I: IntoParallelIterator<Item = T> /// { /// let par_iter = par_iter.into_par_iter(); /// self.mass += par_iter.count() * mem::size_of::<T>(); /// } /// } /// /// let mut bh = BlackHole { mass: 0 }; /// bh.par_extend(0i32..1000); /// assert_eq!(bh.mass, 4000); /// bh.par_extend(0i64..10); /// assert_eq!(bh.mass, 4080); /// ``` pub trait ParallelExtend<T> where T: Send, { /// Extends an instance of the collection with the elements drawn /// from the parallel iterator `par_iter`. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// /// let mut vec = vec![]; /// vec.par_extend(0..5); /// vec.par_extend((0..5).into_par_iter().map(|i| i * i)); /// assert_eq!(vec, [0, 1, 2, 3, 4, 0, 1, 4, 9, 16]); /// ``` fn par_extend<I>(&mut self, par_iter: I) where I: IntoParallelIterator<Item = T>; } /// `ParallelDrainFull` creates a parallel iterator that moves all items /// from a collection while retaining the original capacity. /// /// Types which are indexable typically implement [`ParallelDrainRange`] /// instead, where you can drain fully with `par_drain(..)`. /// /// [`ParallelDrainRange`]: trait.ParallelDrainRange.html pub trait ParallelDrainFull { /// The draining parallel iterator type that will be created. type Iter: ParallelIterator<Item = Self::Item>; /// The type of item that the parallel iterator will produce. /// This is usually the same as `IntoParallelIterator::Item`. type Item: Send; /// Returns a draining parallel iterator over an entire collection. /// /// When the iterator is dropped, all items are removed, even if the /// iterator was not fully consumed. If the iterator is leaked, for example /// using `std::mem::forget`, it is unspecified how many items are removed. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// use std::collections::{BinaryHeap, HashSet}; /// /// let squares: HashSet<i32> = (0..10).map(|x| x * x).collect(); /// /// let mut heap: BinaryHeap<_> = squares.iter().copied().collect(); /// assert_eq!( /// // heaps are drained in arbitrary order /// heap.par_drain() /// .inspect(|x| assert!(squares.contains(x))) /// .count(), /// squares.len(), /// ); /// assert!(heap.is_empty()); /// assert!(heap.capacity() >= squares.len()); /// ``` fn par_drain(self) -> Self::Iter; } /// `ParallelDrainRange` creates a parallel iterator that moves a range of items /// from a collection while retaining the original capacity. /// /// Types which are not indexable may implement [`ParallelDrainFull`] instead. /// /// [`ParallelDrainFull`]: trait.ParallelDrainFull.html pub trait ParallelDrainRange<Idx = usize> { /// The draining parallel iterator type that will be created. type Iter: ParallelIterator<Item = Self::Item>; /// The type of item that the parallel iterator will produce. /// This is usually the same as `IntoParallelIterator::Item`. type Item: Send; /// Returns a draining parallel iterator over a range of the collection. /// /// When the iterator is dropped, all items in the range are removed, even /// if the iterator was not fully consumed. If the iterator is leaked, for /// example using `std::mem::forget`, it is unspecified how many items are /// removed. /// /// # Examples /// /// ``` /// use rayon::prelude::*; /// /// let squares: Vec<i32> = (0..10).map(|x| x * x).collect(); /// /// println!("RangeFull"); /// let mut vec = squares.clone(); /// assert!(vec.par_drain(..) /// .eq(squares.par_iter().copied())); /// assert!(vec.is_empty()); /// assert!(vec.capacity() >= squares.len()); /// /// println!("RangeFrom"); /// let mut vec = squares.clone(); /// assert!(vec.par_drain(5..) /// .eq(squares[5..].par_iter().copied())); /// assert_eq!(&vec[..], &squares[..5]); /// assert!(vec.capacity() >= squares.len()); /// /// println!("RangeTo"); /// let mut vec = squares.clone(); /// assert!(vec.par_drain(..5) /// .eq(squares[..5].par_iter().copied())); /// assert_eq!(&vec[..], &squares[5..]); /// assert!(vec.capacity() >= squares.len()); /// /// println!("RangeToInclusive"); /// let mut vec = squares.clone(); /// assert!(vec.par_drain(..=5) /// .eq(squares[..=5].par_iter().copied())); /// assert_eq!(&vec[..], &squares[6..]); /// assert!(vec.capacity() >= squares.len()); /// /// println!("Range"); /// let mut vec = squares.clone(); /// assert!(vec.par_drain(3..7) /// .eq(squares[3..7].par_iter().copied())); /// assert_eq!(&vec[..3], &squares[..3]); /// assert_eq!(&vec[3..], &squares[7..]); /// assert!(vec.capacity() >= squares.len()); /// /// println!("RangeInclusive"); /// let mut vec = squares.clone(); /// assert!(vec.par_drain(3..=7) /// .eq(squares[3..=7].par_iter().copied())); /// assert_eq!(&vec[..3], &squares[..3]); /// assert_eq!(&vec[3..], &squares[8..]); /// assert!(vec.capacity() >= squares.len()); /// ``` fn par_drain<R: RangeBounds<Idx>>(self, range: R) -> Self::Iter; } /// We hide the `Try` trait in a private module, as it's only meant to be a /// stable clone of the standard library's `Try` trait, as yet unstable. mod private { use std::convert::Infallible; use std::ops::ControlFlow::{self, Break, Continue}; use std::task::Poll; /// Clone of `std::ops::Try`. /// /// Implementing this trait is not permitted outside of `rayon`. pub trait Try { private_decl! {} type Output; type Residual; fn from_output(output: Self::Output) -> Self; fn from_residual(residual: Self::Residual) -> Self; fn branch(self) -> ControlFlow<Self::Residual, Self::Output>; } impl<B, C> Try for ControlFlow<B, C> { private_impl! {} type Output = C; type Residual = ControlFlow<B, Infallible>; fn from_output(output: Self::Output) -> Self { Continue(output) } fn from_residual(residual: Self::Residual) -> Self { match residual { Break(b) => Break(b), Continue(_) => unreachable!(), } } fn branch(self) -> ControlFlow<Self::Residual, Self::Output> { match self { Continue(c) => Continue(c), Break(b) => Break(Break(b)), } } } impl<T> Try for Option<T> { private_impl! {} type Output = T; type Residual = Option<Infallible>; fn from_output(output: Self::Output) -> Self { Some(output) } fn from_residual(residual: Self::Residual) -> Self { match residual { None => None, Some(_) => unreachable!(), } } fn branch(self) -> ControlFlow<Self::Residual, Self::Output> { match self { Some(c) => Continue(c), None => Break(None), } } } impl<T, E> Try for Result<T, E> { private_impl! {} type Output = T; type Residual = Result<Infallible, E>; fn from_output(output: Self::Output) -> Self { Ok(output) } fn from_residual(residual: Self::Residual) -> Self { match residual { Err(e) => Err(e), Ok(_) => unreachable!(), } } fn branch(self) -> ControlFlow<Self::Residual, Self::Output> { match self { Ok(c) => Continue(c), Err(e) => Break(Err(e)), } } } impl<T, E> Try for Poll<Result<T, E>> { private_impl! {} type Output = Poll<T>; type Residual = Result<Infallible, E>; fn from_output(output: Self::Output) -> Self { output.map(Ok) } fn from_residual(residual: Self::Residual) -> Self { match residual { Err(e) => Poll::Ready(Err(e)), Ok(_) => unreachable!(), } } fn branch(self) -> ControlFlow<Self::Residual, Self::Output> { match self { Poll::Pending => Continue(Poll::Pending), Poll::Ready(Ok(c)) => Continue(Poll::Ready(c)), Poll::Ready(Err(e)) => Break(Err(e)), } } } impl<T, E> Try for Poll<Option<Result<T, E>>> { private_impl! {} type Output = Poll<Option<T>>; type Residual = Result<Infallible, E>; fn from_output(output: Self::Output) -> Self { match output { Poll::Ready(o) => Poll::Ready(o.map(Ok)), Poll::Pending => Poll::Pending, } } fn from_residual(residual: Self::Residual) -> Self { match residual { Err(e) => Poll::Ready(Some(Err(e))), Ok(_) => unreachable!(), } } fn branch(self) -> ControlFlow<Self::Residual, Self::Output> { match self { Poll::Pending => Continue(Poll::Pending), Poll::Ready(None) => Continue(Poll::Ready(None)), Poll::Ready(Some(Ok(c))) => Continue(Poll::Ready(Some(c))), Poll::Ready(Some(Err(e))) => Break(Err(e)), } } } }