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#![allow(dead_code)] //! The `ParallelIterator` module makes it easy to write parallel //! programs using an iterator-style interface. To get access to all //! the methods you want, the easiest is to write `use //! rayon::prelude::*;` at the top of your module, which will import //! the various traits and methods you need. //! //! The submodules of this module mostly just contain implementaton //! details of little interest to an end-user. If you'd like to read //! the code itself, the `internal` module and `README.md` file are a //! good place to start. use std::cmp::{self, Ordering}; use std::iter::{Sum, Product}; use std::ops::Fn; use self::internal::*; // There is a method to the madness here: // // - Most of 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 find; mod find_first_last; mod chain; pub use self::chain::Chain; mod collect; mod enumerate; pub use self::enumerate::Enumerate; mod filter; pub use self::filter::Filter; mod filter_map; pub use self::filter_map::FilterMap; mod flat_map; pub use self::flat_map::FlatMap; mod from_par_iter; pub mod internal; mod for_each; mod fold; pub use self::fold::Fold; mod reduce; mod skip; pub use self::skip::Skip; mod splitter; pub use self::splitter::{split, Split}; mod take; pub use self::take::Take; mod map; pub use self::map::{Map, MapOp, MapFn, MapCloned, MapInspect}; mod weight; pub use self::weight::Weight; mod zip; pub use self::zip::Zip; mod noop; mod rev; pub use self::rev::Rev; mod len; pub use self::len::{MinLen, MaxLen}; mod sum; mod product; #[cfg(test)] mod test; pub trait IntoParallelIterator { type Iter: ParallelIterator<Item = Self::Item>; type Item: Send; fn into_par_iter(self) -> Self::Iter; } pub trait IntoParallelRefIterator<'data> { type Iter: ParallelIterator<Item = Self::Item>; type Item: Send + 'data; 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() } } pub trait IntoParallelRefMutIterator<'data> { type Iter: ParallelIterator<Item = Self::Item>; type Item: Send + 'data; 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() } } /// The `ParallelIterator` interface. pub trait ParallelIterator: Sized { type Item: Send; /// Deprecated. If the adaptive algorithms don't split appropriately, try /// `IndexedParallelIterator::with_min_len()` or `with_max_len()` instead. #[deprecated(since = "v0.7.0", note = "try `with_min_len` or `with_max_len` instead")] fn weight(self, _scale: f64) -> Weight<Self> { weight::new(self) } /// Deprecated. If the adaptive algorithms don't split appropriately, try /// `IndexedParallelIterator::with_min_len()` or `with_max_len()` instead. #[deprecated(since = "v0.7.0", note = "try `with_min_len` or `with_max_len` instead")] fn weight_max(self) -> Weight<Self> { weight::new(self) } /// Executes `OP` on each item produced by the iterator, in parallel. fn for_each<OP>(self, op: OP) where OP: Fn(Self::Item) + Sync { for_each::for_each(self, &op) } /// Counts the number of items in this parallel iterator. fn count(self) -> usize { self.map(|_| 1).sum() } /// Applies `map_op` to each item of this iterator, producing a new /// iterator with the results. fn map<F, R>(self, map_op: F) -> Map<Self, MapFn<F>> where F: Fn(Self::Item) -> R + Sync, R: Send { map::new(self, MapFn(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`. fn cloned<'a, T>(self) -> Map<Self, MapCloned> where T: 'a + Clone + Send, Self: ParallelIterator<Item = &'a T> { map::new(self, MapCloned) } /// 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. fn inspect<OP>(self, inspect_op: OP) -> Map<Self, MapInspect<OP>> where OP: Fn(&Self::Item) + Sync { map::new(self, MapInspect(inspect_op)) } /// Applies `filter_op` to each item of this iterator, producing a new /// iterator with only the items that gave `true` results. fn filter<P>(self, filter_op: P) -> Filter<Self, P> where P: Fn(&Self::Item) -> bool + Sync { 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. fn filter_map<P, R>(self, filter_op: P) -> FilterMap<Self, P> where P: Fn(Self::Item) -> Option<R> + Sync, R: Send { filter_map::new(self, filter_op) } /// Applies `map_op` to each item of this iterator to get nested iterators, /// producing a new iterator that flattens these back into one. fn flat_map<F, PI>(self, map_op: F) -> FlatMap<Self, F> where F: Fn(Self::Item) -> PI + Sync, PI: IntoParallelIterator { flat_map::new(self, map_op) } /// 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). /// /// Example: /// /// ``` /// // 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, ID: Fn() -> Self::Item + Sync { reduce::reduce(self, &reduce::ReduceWithIdentityOp::new(&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. /// /// **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 { self.map(Some).reduce(|| None, |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, }) } /// Deprecated. Use `reduce()` instead. #[deprecated(since = "v0.5.0", note = "call `reduce` instead")] fn reduce_with_identity<OP>(self, identity: Self::Item, op: OP) -> Self::Item where OP: Fn(Self::Item, Self::Item) -> Self::Item + Sync, Self::Item: Clone + Sync { self.reduce(|| identity.clone(), 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 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 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 ou 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; // series of u8 bytes /// 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::Fold<Self, ID, F> where F: Fn(T, Self::Item) -> T + Sync, ID: Fn() -> T + Sync, T: Send { fold::fold(self, identity, 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. fn sum<S>(self) -> S where S: Send + Sum<Self::Item> + Sum { 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. fn product<P>(self) -> P where P: Send + Product<Self::Item> + Product { product::product(self) } /// DEPRECATED #[deprecated(since = "v0.6.0", note = "name changed to `product()` to match sequential iterators")] fn mul(self) -> Self::Item where Self::Item: Product { 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))`. fn min(self) -> Option<Self::Item> where Self::Item: Ord { self.reduce_with(cmp::min) } /// 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. fn min_by_key<K, F>(self, f: F) -> Option<Self::Item> where K: Ord + Send, F: Sync + Fn(&Self::Item) -> K { self.map(|x| (f(&x), x)) .reduce_with(|a, b| match (a.0).cmp(&b.0) { Ordering::Greater => b, _ => a, }) .map(|(_, x)| 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))`. fn max(self) -> Option<Self::Item> where Self::Item: Ord { self.reduce_with(cmp::max) } /// 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. fn max_by_key<K, F>(self, f: F) -> Option<Self::Item> where K: Ord + Send, F: Sync + Fn(&Self::Item) -> K { self.map(|x| (f(&x), x)) .reduce_with(|a, b| match (a.0).cmp(&b.0) { Ordering::Greater => a, _ => b, }) .map(|(_, x)| x) } /// Takes two iterators and creates a new iterator over both. 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 fn find_any<P>(self, predicate: P) -> Option<Self::Item> where P: Fn(&Self::Item) -> bool + Sync { find::find(self, predicate) } /// Searches for the **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. fn find_first<P>(self, predicate: P) -> Option<Self::Item> where P: Fn(&Self::Item) -> bool + Sync { find_first_last::find_first(self, predicate) } /// Searches for the **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. fn find_last<P>(self, predicate: P) -> Option<Self::Item> where P: Fn(&Self::Item) -> bool + Sync { find_first_last::find_last(self, predicate) } #[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 { 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. fn any<P>(self, predicate: P) -> bool where P: Fn(Self::Item) -> bool + Sync { self.map(predicate).find_any(|&p| p).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. fn all<P>(self, predicate: P) -> bool where P: Fn(Self::Item) -> bool + Sync { self.map(predicate).find_any(|&p| !p).is_none() } /// Create a fresh collection containing all the element produced /// by this parallel iterator. /// /// You may prefer to use `collect_into()`, which allocates more /// 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. fn collect<C>(self) -> C where C: FromParallelIterator<Self::Item> { C::from_par_iter(self) } /// 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]: 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(&mut self) -> Option<usize> { None } } impl<T: ParallelIterator> IntoParallelIterator for T { type Iter = T; type Item = T::Item; fn into_par_iter(self) -> T { self } } /// A trait for parallel iterators items where the precise number of /// items is not known, but we can at least give an upper-bound. These /// sorts of iterators result from filtering. pub trait BoundedParallelIterator: ParallelIterator { fn upper_bound(&mut 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]: README.md fn drive<'c, C: Consumer<Self::Item>>(self, consumer: C) -> C::Result; } /// A trait for parallel iterators items where the precise number of /// items is known. This occurs when e.g. iterating over a /// vector. Knowing precisely how many items will be produced is very /// useful. pub trait ExactParallelIterator: BoundedParallelIterator { /// Produces an exact count of how many items this iterator will /// produce, presuming no panic occurs. fn len(&mut self) -> usize; /// 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. fn collect_into(self, target: &mut Vec<Self::Item>) { collect::collect_into(self, target); } } /// An iterator that supports "random access" to its data, meaning /// that you can split it at arbitrary indices and draw data from /// those points. pub trait IndexedParallelIterator: ExactParallelIterator { /// Iterate 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. 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()) } /// Lexicographically compares the elements of this `ParallelIterator` with those of /// another. fn cmp<I>(mut self, other: I) -> Ordering where I: IntoParallelIterator<Item = Self::Item>, I::Iter: IndexedParallelIterator, Self::Item: Ord { let mut other = other.into_par_iter(); let ord_len = self.len().cmp(&other.len()); self.zip(other) .map(|(x, y)| Ord::cmp(&x, &y)) .find_first(|&ord| ord != Ordering::Equal) .unwrap_or(ord_len) } /// Lexicographically compares the elements of this `ParallelIterator` with those of /// another. fn partial_cmp<I>(mut self, other: I) -> Option<Ordering> where I: IntoParallelIterator, I::Iter: IndexedParallelIterator, Self::Item: PartialOrd<I::Item> { let mut other = other.into_par_iter(); let ord_len = self.len().cmp(&other.len()); self.zip(other) .map(|(x, y)| PartialOrd::partial_cmp(&x, &y)) .find_first(|&ord| ord != Some(Ordering::Equal)) .unwrap_or(Some(ord_len)) } /// Determines if the elements of this `ParallelIterator` /// are equal to those of another fn eq<I>(mut self, other: I) -> bool where I: IntoParallelIterator, I::Iter: IndexedParallelIterator, Self::Item: PartialEq<I::Item> { let mut other = other.into_par_iter(); self.len() == other.len() && self.zip(other).all(|(x, y)| x.eq(&y)) } /// 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. fn enumerate(self) -> Enumerate<Self> { enumerate::new(self) } /// Creates an iterator that skips the first `n` elements. fn skip(self, n: usize) -> Skip<Self> { skip::new(self, n) } /// Creates an iterator that yields the first `n` elements. 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. fn position_any<P>(self, predicate: P) -> Option<usize> where P: Fn(Self::Item) -> bool + Sync { self.map(predicate) .enumerate() .find_any(|&(_, p)| p) .map(|(i, _)| i) } /// Searches for the **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. fn position_first<P>(self, predicate: P) -> Option<usize> where P: Fn(Self::Item) -> bool + Sync { self.map(predicate) .enumerate() .find_first(|&(_, p)| p) .map(|(i, _)| i) } /// Searches for the **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. fn position_last<P>(self, predicate: P) -> Option<usize> where P: Fn(Self::Item) -> bool + Sync { self.map(predicate) .enumerate() .find_last(|&(_, p)| p) .map(|(i, _)| 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 { self.position_any(predicate) } /// Produces a new iterator with the elements of this iterator in /// reverse order. fn rev(self) -> Rev<Self> { rev::new(self) } /// Sets the minimum length of iterators desired to process in each /// thread. Rayon will not split any smaller than this length, but /// of course an iterator could already be smaller to begin with. fn with_min_len(self, min: usize) -> MinLen<Self> { len::new_min_len(self, min) } /// Sets the maximum length of iterators desired to process in each /// thread. 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. fn with_max_len(self, max: usize) -> MaxLen<Self> { len::new_max_len(self, max) } /// 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]: README.md fn with_producer<CB: ProducerCallback<Self::Item>>(self, callback: CB) -> CB::Output; } /// `FromParallelIterator` implements the conversion from a [`ParallelIterator`]. /// By implementing `FromParallelIterator` for a 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 pub trait FromParallelIterator<T> where T: Send { fn from_par_iter<I>(par_iter: I) -> Self where I: IntoParallelIterator<Item = T>; }