1 Revision History

From [P2760R0] to R1: Added output iterator enhancements and parallel algorithm support to Tier 1, following discussions in Kona.

2 Introduction

For the C++23 cycle, we set out to create a plan to prioritize what additions we wanted to make for Ranges [P2214R2]. We ended up adopting all of the proposals we originally labelled as Tier 1 (with the exception of some we deliberately dropped, see later), as well as some from Tier 2. Moreover, based on the questions we’ve seen in various contexts about how to solve certain problems with Ranges - a significant percentage of them can be answered with some new C++23 facility, which suggests that we prioritized the right tools.

To summarize, in C++23 we adopted the following facilities:

• General additions to ranges:
  • the ability to define first-class user-defined range adaptors ([P2387R3])
  • the ability to collect a range into a container, ranges::to ([P1206R7])
  • the ability to format ranges ([P2286R8])
• New range adaptors:
  • views::adjacent and views::adjacent_transform ([P2321R2])
  • views::as_const ([P2278R4])
  • views::as_rvalue ([P2446R2])
  • views::cartesian_product ([P2374R4])
  • views::chunk ([P2442R1])
  • views::chunk_by ([P2443R1])
  • views::enumerate ([P2164R9])
  • views::join_with ([P2441R2])
  • views::repeat ([P2474R2])
  • views::slide ([P2442R1])
  • views::stride ([P1899R3])
  • views::zip and views::zip_transform ([P2321R2])
• New (or improved) range algorithms:
  • allowing C++20 iterators to be used in C++17 algorithms ([P2408R5])
  • ranges::contains ([P2302R4])
  • ranges::fold and family ([P2322R6])
  • ranges::iota ([P2440R1])
  • ranges::shift_left and ranges::shift_right ([P2440R1])

There were also a bunch of smaller improvements that are not listed here.

But there’s still plenty more work to be done - both on the range adaptor and the range algorithm front. The goal of this paper is to do for the C++26 timeframe what our previous plan did for the C++23 one: express what we think is the right prioritization of work, while describing what some of the outstanding issues are so that we can start tackling them.

3 Views

As before, we’ll start by enumerating all the adaptors in range-v3 (and a few that aren’t), noting their status updated by C++23. Note that many of the adaptors here labelled C++20 or C++23 are in range-v3 also, we’re just using the status “range-v3” to indicate that an adaptor is in range-v3 only:

3.1 cache_last

One of the adaptors that we considered for C++23 but ended up not pursuing was what range-v3 calls cache1 and what we’d instead like to call something like cache_last. This is an adaptor which, as the name suggests, caches the last element. The reason for this is efficiency - specifically avoiding extra work that has to be done by iterator dereferencing.

The canonical example of this is transform(f) | filter(g), where if you then iterate over the subsequent range, f will be invoked twice for every element that satisfies g:

int main()
{
    std::vector<int> v = {1, 2, 3, 4, 5};

    auto even_squares = v
        | std::views::transform([](int i){
                std::print("transform: {}\n", i);
                return i * i;
            })
        | std::views::filter([](int i){
                std::print("filter: {}\n", i);
                return i % 2 == 0;
            });

    for (int i : even_squares) {
        std::print("Got: {}\n", i);
    }
}

prints the following (note that there are 7 invocations of transform):

transform: 1
filter: 1
transform: 2
filter: 4
transform: 2
Got: 4
transform: 3
filter: 9
transform: 4
filter: 16
transform: 4
Got: 16
transform: 5
filter: 25

The solution here is to add a layer of caching:

auto even_squares = v
    | views::transform(square)
    | views::cache_last
    | views::filter(is_even);

Which will ensure that square will only be called once per element.

The tricky part here is: how do you implement cache_last? Specifically: in what member function do you perform the caching?

The range-v3 implementation looks roughly like this:

template <view V>
struct cache_last_view {
    V base_;
    bool dirty_ = true;
    non-propagating-cache<range_value_t<V>> cache_;

    struct iterator {
        cache_last_view* parent_;
        iterator_t<V> cur_;

        auto operator*() const -> range_value_t<V>&& {
            if (parent_->dirty_) {
                parent_->cache_.emplace(iter_move(cur_));
                parent_->dirty_ = false;
            }
            return std::move(*parent_->cache_);
        }

        auto operator++() -> iterator& {
            ++cur_;
            parent_->dirty_ = true;
        }
    };
};

But there’s a problem here: 16.4.6.10 [res.on.data.races] says that const member functions are not allowed to introduce data races. While everything here is const-correct (there isn’t even a mutable), iterator dereference here does introduce a data race: two threads were both dereferencing an iterator into a dirty cache_last_view.

There are four potential solutions to this problem, presented in our order of preference:

1. We could carve out an exception to [res.on.data.races] for all input iterators. Even some standard library implementations of input iterators (like std::istreambuf_iterator<char>) already don’t satisfy this, and using input iterators in multi-threaded contexts is already kind of interesting. This makes the above implementation valid.
2. We could require synchronization on operator*() const. This probably isn’t terrible expensive in this context, but adding synchronization to an adaptor whose primary purpose is to improve performance seems a bit heavy-handed, especially since that synchronization will almost never be actually necessary.
3. We could move the updating of the cached value from operator*() const to operator++(), which is already a mutable member function. This has the downside of requiring calculating more elements than necessary - since r | cache_last | stride(2) will still have to cache every element, even if only every other one is necessary.
4. We could allow input iterators to have mutable operator*(), since some of them clearly need it. A mutable operator*() makes the concepts even more awkward, and adds more work for every range adaptor. It theoretically is sensible, but seems extremely impractical.

The other issue is what the reference type of the range should be. range-v3 uses range_value_t<V>&&, but this somewhat defeats the purpose of caching if you can so easily invalidate it. range_value_t<V>& is probably a better choice.

3.2 istream<T>

views::istream<T> was one of the original C++20 range factories, modified slightly since then to be a bit more user-friendly. But there’s an interesting issue with it as pointed out in [P2406R5] and even before that in [range-v3#57]: views::istream<T>(stream) | views::take(N) will extract N+1 elements from stream. Barry did a CppNow talk on this example (video).

There are, potentially, two approaches to implementing views::istream<T>:

template <class Val>
class istream_view {
  istream* stream;
  Val value;

  struct iterator {
    istream_view* parent;

    auto operator++() -> iterator& {
      parent->extract();
      return *this;
    }

    auto operator*() const -> Val& {
      return parent->value;
    }

    auto operator==(default_sentinel_t) const -> bool {
      return not *parent->stream;
    }
  };

  auto extract() -> void {
    *stream >> value;
  }

public:
  auto begin() -> iterator {
    extract();
    return iterator{this};
  }
  auto end() -> default_sentinel_t {
    return default_sentinel;
  }
};

template <class Val>
class istream_view {
  istream* stream;
  Val value;

  struct iterator {
    istream_view* parent;
    mutable bool dirty = true;

    auto prime() const -> void {
      if (dirty) {
        *parent->stream >> parent->value;
        dirty = false;
      }
    }

    auto operator++() -> iterator& {
      prime();
      dirty = true;
      return *this;
    }

    auto operator*() const -> Val& {
      prime();
      return parent->value;
    }

    auto operator==(default_sentinel_t) const -> bool {
      prime();
      return not *parent->stream;
    }
  };

public:
  auto begin() -> iterator {
    return iterator{this};
  }

  auto end() -> default_sentinel_t {
    return default_sentinel;
  }
};

This alternative implementation ensures that consuming views::istream<T>(stream) | views::take(N) extracts exactly N elements from stream, including for N == 0. It does, however, require doing work in two different const member functions: both operator*() and operator==(). Neither of these violate the semantic guarantees of those functions - repeated invocations of either will give you the same result every time, until you increment again. But they do violate 16.4.6.10 [res.on.data.races].

We have the same potential four options here as we described with cache_last, but we could also just keep the existing implementation of views::istream<T>. Changing this range does have observable effects, but we think we should seriously consider doing so. LEWG seemed very willing to change counted_iterator<I> and views::take in order to address this issue before, so we think serious consideration should be given to changing views::istream<T>.

Additionally, this would set a precedent for how to write these kinds of input ranges. So it’s important to get right.

Separately, there is also views::getlines. In the say way that views::istream<T>(is) is a factory that produces elements of type T on demand by way of is >> obj, views::getlines is a factory that produces elements of type std::string on demand by way of std::getline(is, obj). Note that both could nearly be implemented in terms of views::generate:

template <class T>
inline constexpr auto istream = [](std::istream& is){
  return views::generate([&is, obj=T()]() mutable -> T& {
    is >> obj;
    return obj;
  });
});

inline constexpr auto getlines = [](std::istream& is, char delim = '\n'){
  return views::generate(
    [&is, delim, obj=std::string()]() mutable -> std::string& {
      std::getline(is, obj);
      return obj;
    });
});

Almost because neither of these terminates, and we eventually do need some kind of termination condition. Which might call for some kind of views::generate_until.

3.3 scan

If you want to take a range of elements and get a new range that is applying f to every element, that’s transform(f). But there are many cases where you need a transform to that is stateful. That is, rather than have the input to f be the current element (and require that f be regular_invocable), have the input to f be both the current element and the current state.

For instance, given the range [1, 2, 3, 4, 5], if you want to produce the range [1, 3, 6, 10, 15] - you can’t get there with transform. Instead, you need to use scan using + as the binary operator. The special case of scan over + is partial_sum.

One consideration here is how to process the first element. You might want [1, 3, 6, 10, 15] and you might want [0, 1, 3, 6, 10, 15] (with one extra element), the latter could be called a prescan.

3.4 generate

C++23 has std::generator<T>. There are two very closely related range factories in range-v3, which are basically:

template <class F>
    requires std::invocable<F&>
auto generate(F f) -> std::generator<std::invoke_result_t<F&>> {
    while (true) {
        co_yield f();
    }
}

template <class F>
    requires std::invocable<F&>
auto generate_n(F f, int n) -> std::generator<std::invoke_result_t<F&>> {
    for (int i = 0; i != n; ++i) {
        co_yield f();
    }
}

Note that the constraint here is invocable, not regular_invocable. The latter wouldn’t be very interesting - that’s views::repeat(f()). These factories are somewhat related to scan (in the sense that we have a mutable function that we’re repeatedly invoking) and also somewhat related to cache_latest (in the sense that the range-v3 implementation of both also violate [res.on.data.races]).

Since with views::repeat, we just used the same name for the infinite and finite versions, we should probably end up with just the one name for views::generate.

A similar factory in this vein is one that Haskell calls iterate:

template <class F, class T>
auto iterate(F f, T x) -> std::generator<T> {
    while (true) {
        co_yield x;
        x = f(x);
    }
}

Whereas generate(f) is the sequence [f(), f(), f(), f(), ...], iterate(f, x) is the sequence [x, f(x), f(f(x)), f(f(f(x))), ...]

Yet another factory, following the theme, is one that Dlang calls recurrence (implementation). Although maybe this one is too cute:

auto main() -> int {
    // fibonacci: [1, 1, 2, 3, 5, 8, 13, 21, 34, 55]
    print("fibonacci: {}\n",
        recurrence([](auto a, int n){ return a[n-1] + a[n-2]; }, 1, 1)
        | views::take(10)
    );

    // factorial: [1, 1, 2, 6, 24, 120, 720, 5040, 40320, 362880]
    print("factorial: {}\n",
        recurrence([](auto a, int n){ return a[n-1] * n; }, 1)
        | views::take(10)
    );
}

3.5 as_input

We added two fairly simply adaptors in C++23: views::as_const and views::as_rvalue, both of which are specialized versions of views::transform. Well, views::as_const is conceptually simple anyway - even as it is remarkably complex.

There’s a third adaptor in this family that we should consider adding: views::as_input(r). This is an adaptor that all it does is reduce r’s category to input and force it to be non-common. Otherwise: same value type, same reference type, same sized-ness, same borrowed-ness, same const-iterability.

Why would anybody want such a thing? Performance.

Range adaptors typically provide the maximum possible iterator category - in order to maximize functionality. But sometimes it takes work to do so. A few examples:

• views::join(r) is common when r is, which means it provides two iterators. The iterator comparison for join does two iterator comparisons, for both the outer and the inner iterator, which is definitely necessary when comparing two iterators. But if all you want to do is compare it == end, you could’ve gotten away with one iterator comparison. As such, iterating over a common join_view is more expensive than an uncommon one.
• vews::chunk(r, n) has a different algorithm for input vs forward. For forward+, you get a range of views::take(n) - if you iterate through every element, then advancing from one chunk to the next chunk requires iterating through all the elements of that chunk again. For input, you can only advance element at a time.

The added cost that views::chunk adds when consuming all elements for forward+ can be necessary if you need the forward iterator guarantees. But if you don’t need it, like if you’re just going to consume all the elements in order one time. Or, worse, the next adaptor in the chain reduces you down to input anyway, this is unnecessary.

In this way, r | views::chunk(n) | views::join can be particularly bad, since you’re paying additional cost for chunk that you can’t use anyway, since views::join here would always be an input range. r | views::as_input | views::chunk(n) | views::join would alleviate this problem. It would be a particularly nice way to alleviate this problem if users didn’t have to write the views::as_input part!

This situation was originally noted in [range-v3#704].

3.6 Simple Adaptor Compositions

Many adaptors have to have their own dedicated implementation. Some are merely more convenient spellings of existing ones (like keys for elements<0> and pairwise for adjacent<2>). Still others could be just compositions of existing range adaptors.

One such is what most of the rest of the world calls flat_map: this is a combination of map and then flatten. In C++ terms, we could very simply provide such an adaptor:

inline constexpr auto transform_join = []<class F>(F&& f){
    return transform((F&&)f) | join;
};

Well, the actual implementation is slightly more involved in order to be able to also support views::transform_join(r, f) in addition to r | views::transform_join(f), but not dramatically so. Importantly, there really isn’t much benefit to providing a bespoke transform_join as opposed to simply implementing it in terms of these two existing adaptors. But this is such a common piece of functionality that it probably merits direct addition into the standard library.

In slide-ware, it probably doesn’t make that much of a difference. But in real code that uses namespaces, it really does:

r | transform(f) | join
r | transform_join(f)

r | std::views::transform(f) | std::views::join
r | std::views::transform_join(f)

A few other common patterns worth considering:

• views::replace(old_val, new_val) and views::replace_if(pred, new_val) are kinds of views::transform
• views::remove(val) and views::remove_if(pred) are kinds of views::filter, the latter being just filter(not_fn(pred))
• views::upto(n) is just views::iota(decltype(n){}, n), which is useful not just because it’s terser and a better name, but also because a fairly typical use is views::iota(0, r.size()) - or at least it would be, but that doesn’t compile when r.size() is unsigned.
• For the algorithms discussed later, ranges::sum and ranges::product are just special cases of ranges::reduce.

But it is not always the case that just writing one algorithm in terms of others is optimal. It is tempting to define views::tail as simply views::drop(1), but a dedicated tail could be more efficient (it does not need to store the count or cache begin()). It’s unfortunate that the relative difference in specification is so high though.

3.7 Extending conditionally borrowed

In [P2017R1], we made some range adaptors conditionally borrowed. But we didn’t touch adaptors that had callables - like views::transform. It turns out to be very useful to have a borrowable version of views::transform. Indeed, [P2728R6] even adds a dedicated new range adaptor (views::project) which is simply a version of views::transform that can be borrowed (because its callable must be a constant).

But rather than add a dedicated view for this specific case, which requires a new name but really only helps views::transform, we can generalize views::transform to address the use-case in a way that would also help all the other range adaptors that take callables. At the very least, in views::transform(r, f) if r is borrowed and f is empty, an implementation can simply put f in the transform_view<R, F>::iterator directly (rather than a transform_view<R, F>*) which would allow it to be borrowed. The same could be said for other range adaptors that take callables as well, which seems like a more useful approach as well as not requiring new names for every adaptor.

The main question then is what the criteria should be for when transform_view<R, F> should be a borrowed range (when R is):

• is_empty_v<F> (range-v3 already does this - not for conditionally borrowed, but just to decide whether to store the callable by value in the iterator)
• sizeof(F) <= sizeof(void*) and is_trivially_copyable_v<F> (this means that when transforming with a function pointer, the function pointer itself can live in the iterator - which takes the same amount of space as the parent pointer, except with one less indirection)
• something else?

This question is a little simpler for views::transform (which only needs to potentially store f in the adapted iterator) than it is for views::filter (which would need not only the predicate but also the underlying sentinel, so this may not be worthwhile). This would need to be carefully considered.

4 View Adjuncts

In the C++23 plan, we listed several facilities that would greatly improve the usability of views: the ability for users to define first class pipe support, the ability to collect into a container (ranges::to), and formatting.

There are some other operations that we’ve seen come up regularly - operations that are not themselves views or algorithms, but would improve the quality of life around using the standard library (and other) range adpators.

4.1 More Function Objects

The standard library has a lot of function objects, but there are still plenty of common ones that are missing.

Some unary operators have no associated function object:

• indirection: *_1
• addressof: &_1 (except if we add a function object for this, it should do std::addressof)
• prefix and postfix increment: ++_1 or _1++
• prefix and postfix decrement: --_1 or _1--

range-v3 has views::indirect, for instance, which is basically an over-constrained views::transform(*_1).

Some binary operators have no associated function object:

• the shifts: _1 << _2 and _1 >> _2
• all the compound assignments: _1 += _2, etc.

The various language cases also have no associated function object. The most common of these is static_cast<T>(_1).

It is also worth considering whether we should actually add function objects for these, like std::indirect (or std::ranges::indirect?) or whether we should try to bring back one of the earlier proposals that added nicer syntax for passing operators as function objects:

4.2 More Function Adaptors

The standard library doesn’t have very many function adaptors. There are two particularly notable ones that seem to come up frequently.

• function composition: an adaptor compose such that compose(f, g)(x...) == f(g(x...))
• function projection: an adaptor proj such that proj(p, f)(x...) == f(p(x)...)

If we had a proj adaptor, people wouldn’t need to ask for range adaptors to support projections - they could just provide one.

The difficulty with these is that both are syntactically heavy in C++, because our lambdas are verbose and we have difficulties passing functions around (see the two papers noted in the previous section).

The other problem is that these adaptors don’t really have obvious ordering. Should compose(f, g)(x) mean f(g(x)) or g(f(x))? There’s good arguments for either. The same is true for proj (which is sometimes also called on).

5 Algorithms

We improved the Ranges story on algorithms quite a bit in C++23 - both in terms of new and existing algorithms. But there’s a few more pretty interesting ones left on the table.

5.1 reduce

We talked about reduce in [P2214R2]. ranges::reduce is a version of ranges::fold_left ([P2322R6]) that is parallelizable. It requires the binary operation to be associative (to allow chunks of the range to be reduced in praallel) and commutative (to allow those chunks to be arbitrarily combined). So we will need to figure out what constraints to add on this algorithm (see [P1813R0]) as well as how we determine what the return type is (see this section discussing the same problem for ranges::fold_left).

One thing is clear: ranges::reduce should not take a default binary operation nor a default initial parameter. The user needs to supply both.

However, for convenience, we do propose providing ranges::sum(r) as ranges::reduce(r, plus{}, range_value_t<R>()) and ranges::product(r) as ranges::reduce(r, multiplies{}, range_value_t<R>(1)).

Note that naming is a problem here: some languages (Rust, Scala, Kotlin) have an algorithm that takes an initial value named fold and an algorithm that takes no initial value and returns and optional reduce. In C++23, we called these fold_left and fold_left_first since we’ve already had std::reduce since C++17.

But since our reduce differs from our fold not based on initial element but rather on operation requirements, it also leaves open the question for whether there should be a reduce_first. A good example there might be using std::max as the reduction operator - which is both associative and commutative, but for some types may not have an obvious choice for the minimum.

5.2 distance and advance

We have ranges::size(E), which gives you the size of a range in constant time. For non-sized ranges, if you want to know the size you have to use ranges::distance(E). For non-sized ranges though, ranges::distance has to iterate over the entire range, element by element, counting the number of iterator increments until the sentinel is reached.

For many ranges, that’s really the best you can do anyway. But for some, you could do better. Consider views::join. You could, potentially, do much better on distance in some cases: if I’m joining a range of sized ranges (like vector<vector<T>>, although the outer one need not be sized, so even forward_list<vector<T>>), you could compute the size of the overall range by summing the size() of each element. That’s still not O(1), so ranges::size cannot do this, but it would be substantially more efficient than the naive ranges::distance implementation.

A similar argument holds for ranges::advance for non-random-access iterators. Implementations already do provide special-case overloads for std::advance in some cases, though they cannot do so for ranges::advance. For instance, libstdc++ provides a custom implementation for std::istreambuf_iterator<Char>. You cannot provide it + n, because that cannot necessarily be constant time, but advance doesn’t have to be constant - it just has to get there (reduced for brevity):

template<typename _CharT, typename _Distance>
advance(istreambuf_iterator<_CharT>& __i, _Distance __n)
{
    if (__n == 0)
        return;

    using traits_type = /* ... */;
    const traits_type::int_type __eof = traits_type::eof();

    streambuf_type* __sb = __i._M_sbuf;
    while (__n > 0) {
        streamsize __size = __sb->egptr() - __sb->gptr();
        if (__size > __n) {
            __sb->_M_in_cur += __n;
            break;
        }

        __sb->_M_in_cur += __size;
        __n -= __size;
        if (traits_type::eq_int_type(__sb->underflow(), __eof)) {
            break;
        }
    }

    __i._M_c = __eof;
}

The advance here is that if we want to advance(it, 10), we can simply right away check if there are at least 10 characters in the get area. If there are, we just advance by 10 and we’re done. If not, we have to go pull more characters. Either way, we end up significantly reducing the number of times that we have to go back to the stream - we’re not pulling one character at a time, we’re potentially consuming the entire get buffer at a time, for a significant reduction in the number of branches.

This is more efficient for the same reason that the hypothetical implementation of ranges::distance for a join_view could be more efficient.

Currently, none of the non-constant-time algorithms (like distance, advance, and next) are customizable - but there could be clear benefits to making them so. Unfortunately, there are very clear costs to making them so: even more work that every range and iterator adaptor has to do.

6 Output Iterators

There are two kinds of output iterators: those that are also input iterators (like int*) and those are that are not. This section is dedicated to output-only iterators. The one of these that people are probably most familiar with is std::back_insert_iterator<C>.

Output-only iterators are important, yet severely underpowered. The problem with them ultimately is they are shoe-horned into the same syntax as input iterators, despite not really have anything to do with iterators.

If we take an algorithm like std::copy, it’s implemented something like this:

template <typename InputIt, typename OutputIt>
void copy(InputIt first, InputIt last, OutputIt out) {
    for (; first != last; ++first) {
        *out++ = *first;
    }
}

In order to provide std::back_insert_iterator<C>, it has to meet that syntax. So we end up with something like:

template <typename C>
class back_inserter {
    C* cont_;

public:
    explicit back_inserter(C& c) : cont_(&c) { }

    // these do nothing
    auto operator*() -> back_inserter& { return *this; }
    auto operator++() -> back_inserter& { return *this; }
    auto operator++(int) -> back_inserter { return *this; }

    // this one does something
    auto operator=(typename C::value_type const& val) -> back_inserter& {
        cont_->push_back(val);
        return *this;
    }

    // same
    auto operator=(typename C::value_type&& val) -> back_inserter& {
};

There are two problems with this approach. First, it’s a really awkward API to go about implementing an output iterator. You have to write three no-op functions and one useful function, whose spelling doesn’t really convey any meaning. An output-only iterator is a function call, yet it cannot be implemented as such, which is an annoying loss in convenience since you cannot simply use a lambda as an output iterator. Sure, it’s not a huge task to implement a function_output_iterator<F> - you can find such a thing in Boost too - but there really shouldn’t be a need for this.

But more importantly, it’s very inefficient. An output-only iterator gets one element at a time, even when the algorithm knows it’s producing more. A common use of back_insert_iterator is doing something like this:

std::vector<T> vec;
std::ranges::copy(r, std::back_inserter(vec));

That will compile into N calls to vec.push_back. Maybe r is an unsized input range and that’s the best you can do anyway. But if r is sized, that’s pretty wasteful - vector has a range insertion API which does the right thing, it can be much more efficient to simply call:

std::vector<T> vec;
vec.append_range(r);

Indeed, 2.7x faster in this simple benchmark.

This is a known problem, to the point where libraries try to detect and work around this pessimization. The {fmt} formatting library, now <format> since C++20, is entirely output-iterator based. But, because of type erasure, the typical output iterator that you will interact with is an output-only iterator, not an input iterator. So what happens when you try to write a std::string_view through that output iterator (a not-especially-uncommon operation when it comes to formatting)?

{fmt} has an internal helper named copy_str, whose default implementation is pretty familiar:

template <typename Char, typename InputIt, typename OutputIt>
FMT_CONSTEXPR auto copy_str(InputIt begin, InputIt end, OutputIt out)
    -> OutputIt {
  while (begin != end) *out++ = static_cast<Char>(*begin++);
  return out;
}

But there’s this other important overload too:

template <typename Char, typename InputIt>
auto copy_str(InputIt begin, InputIt end, appender out) -> appender {
  get_container(out).append(begin, end);
  return out;
}

For most of the operations in {fmt}, the implementation-defined type-erased iterator is appender, so this would be the overload used. And appender is a back_insert_iterator into a buffer<char>, which is a growable buffer (not unlike vector<char>) which has a dedicated append for this case:

template <typename T>
template <typename U>
void buffer<T>::append(const U* begin, const U* end) {
  while (begin != end) {
    auto count = to_unsigned(end - begin);
    try_reserve(size_ + count);
    auto free_cap = capacity_ - size_;
    if (free_cap < count) count = free_cap;
    std::uninitialized_copy_n(begin, count, make_checked(ptr_ + size_, count));
    size_ += count;
    begin += count;
  }
}

So here, we know that std::copy and std::ranges::copy would be inefficient, so the library provides (and internally uses) a way to special case that algorithm for its particular output iterator.

This kind of thing really shouldn’t be QoI. Output-only iterators that can support efficient range-based operations should be able to do so.

6.1 Potential Design

Barry laid out an approach in a blog post [improve.output] based on the model the D library uses, using two customization point objects: one for single elements and one for a range of elements:

ranges::put(out, e) could be the first valid expression of:

1. out.put(e)
2. *out++ = e;
3. out(e);

ranges::put_range(out, r) could be the first valid expression of:

1. out.put_range(r)
2. ranges::for_each(r, bind_front(ranges::put, out))

This isn’t quite what D does, but it’s more suited for C++, and would allow output-only iterators to be as efficient (and easy to implement) as they should be.

If we had the above, the implementation of back_insert_iterator would become:

template <typename C>
class back_inserter {
    C* cont_;

public:
    explicit back_inserter(C& c) : cont_(&c) { }

    auto put(typename C::value_type const& val) -> void {
        cont_->push_back(val);
    }
    auto put(typename C::value_type&& val) -> void {
        cont_->push_back(std::move(val));
    }


    template <ranges::input_range R>
      requires std::convertible_to<ranges::range_reference_t<R>, typename C::value_type>
    auto put_range(R&& r) -> void
    {
        if constexpr (requires { cont_->append_range(r); }) {
            cont_->append_range(r);
        } else if constexpr (requires { cont_->insert(cont_->end(), ranges::begin(r), ranges::end(r)); }) {
            cont_->insert(cont_->end(), ranges::begin(r), ranges::end(r));
        } else {
            for (auto&& e : r) {
                cont_->push_back(FWD(e));
            }
        }
    }
};

Sure, put_range is mildly complicated, but it’s much more efficient than the original implementation, and we no longer have functions that do nothing.

Now, the issue here is that this is a fairly large redesign of the output iterator model with minimal implementation experience (unless you count D or the blog post). So this approach needs more time, but we do think it’s worth doing.

7 Parallel Algorithms

C++17 added parallel versions of standard library algorithms, but we never added any ranges versions thereof. There is no std::ranges parallel for_each, etc. In the same way that we want to add a ranges version of reduce, we also more broadly want to add ranges versions of all the parallel algorithms.

Here, there are a lot of questions to consider, especially when it comes to tying into [P2300R7]. [P2500R2] is attempting to solve that gap - both by adding range-based parallel algorithms and also by tying it with the intended C++26 senders/receivers model. We think that is also a high priority item for C++26, although it also needs a lot of design consideration.

8 Plan Summary

As previously, we want to triage a lot of outstanding views, algorithms, and other utilities into three tiers based on our opinions of their importance. While ideally we could just add everything into C++26, we realize that this is not realistic with the amount of available LWG bandwidth, so our tier 1 here is trying to be as small as possible while still hitting as many major pain points as possible.

8.1 Tier 1

• Range Adaptors:
  • views::concat ([P2542R2])
  • Take/Drop Family:
    • views::drop_last and views::take_last
    • views::drop_last_while and views::take_last_while
    • views::drop_exactly and views::take_exactly
    • views::slice
  • Simple Adaptor Compositions:
    • views::transform_join
    • views::replace and views::replace_if
    • views::remove and views::remove_if
    • views::upto
  • views::as_input
  • views::cache_last
  • views::chunk_on
  • views::cycle
  • views::delimit and views::c_str
  • making more adaptors (e.g. views::transform) conditionally borrowed in more complex circumstances
  • Generators:
    • views::scan
    • views::generate and views::generate_n
• Algorithms:
  • ranges::reduce
  • ranges::sum
  • ranges::product
• Improving Output Iterators
• Parallelizing Algorithms ([P2500R2])

8.2 Tier 2

• Range Adaptors:
  • views::adjacent_filter
  • views::adjacent_remove_if
  • views::head
  • views::intersperse
  • views::iterate
  • views::split_when
  • views::tail
  • views::trim
  • views::unique

8.3 Tier 3

• Range Adaptors:
  • views::linear_distribute
  • views::sample
  • Set Adaptors
    • views::set_difference
    • views::set_intersection
    • views::set_symmetric_difference
    • views::set_union

9 References