🚀 ankerl::unordered_dense::{map, set}

A fast & densely stored hashmap and hashset based on robin-hood backward shift deletion for C++17 and later.

The classes ankerl::unordered_dense::map and ankerl::unordered_dense::set are (almost) drop-in replacements of std::unordered_map and std::unordered_set. While they don't have as strong iterator / reference stability guaranties, they are typically much faster.

Additionally, there are ankerl::unordered_dense::segmented_map and ankerl::unordered_dense::segmented_set with lower peak memory usage. and stable iterator/references on insert.

1. Overview
2. Installation
- 2.1. Installing using cmake
3. Usage
4. segmented_map and segmented_set
5. Design
6. Real World Usage

1. Overview

The chosen design has a few advantages over std::unordered_map:

Perfect iteration speed - Data is stored in a std::vector, all data is contiguous!
Very fast insertion & lookup speed, in the same ballpark as absl::flat_hash_map
Low memory usage
Full support for std::allocators, and polymorphic allocators. There are ankerl::unordered_dense::pmr typedefs available
Customizeable storage type: with a template parameter you can e.g. switch from std::vector to boost::interprocess::vector or any other compatible random-access container.
Better debugging: the underlying data can be easily seen in any debugger that can show an std::vector.

There's no free lunch, so there are a few disadvantages:

Deletion speed is relatively slow. This needs two lookups: one for the element to delete, and one for the element that is moved onto the newly empty spot.
no const Key in std::pair<Key, Value>
Iterators and references are not stable on insert or erase.

2. Installation

The default installation location is /usr/local.

2.1. Installing using cmake

Clone the repository and run these commands in the cloned folder:

mkdir build && cd build
cmake ..
cmake --build . --target install

Consider setting an install prefix if you do not want to install unordered_dense system wide, like so:

mkdir build && cd build
cmake -DCMAKE_INSTALL_PREFIX:PATH=${HOME}/unordered_dense_install ..
cmake --build . --target install

To make use of the installed library, add this to your project:

find_package(unordered_dense CONFIG REQUIRED)
target_link_libraries(your_project_name unordered_dense::unordered_dense)

3. Usage

3.1. Modules

ankerl::unordered_dense supports c++20 modules. Simply compile src/ankerl.unordered_dense.cpp and use the resulting module, e.g. like so:

clang++ -std=c++20 -I include --precompile -x c++-module src/ankerl.unordered_dense.cpp
clang++ -std=c++20 -c ankerl.unordered_dense.pcm

To use the module with e.g. in module_test.cpp, use

import ankerl.unordered_dense;

and compile with e.g.

clang++ -std=c++20 -fprebuilt-module-path=. ankerl.unordered_dense.o module_test.cpp -o main

A simple demo script can be found in test/modules.

3.2. Hash

ankerl::unordered_dense::hash is a fast and high quality hash, based on wyhash. The ankerl::unordered_dense map/set differentiates between hashes of high quality (good avalanching effect) and bad quality. Hashes with good quality contain a special marker:

using is_avalanching = void;

This is the cases for the specializations bool, char, signed char, unsigned char, char8_t, char16_t, char32_t, wchar_t, short, unsigned short, int, unsigned int, long, long long, unsigned long, unsigned long long, T*, std::unique_ptr<T>, std::shared_ptr<T>, enum, std::basic_string<C>, and std::basic_string_view<C>.

Hashes that do not contain such a marker are assumed to be of bad quality and receive an additional mixing step inside the map/set implementation.

3.2.1. Simple Hash

Consider a simple custom key type:

struct id {
    uint64_t value{};

    auto operator==(id const& other) const -> bool {
        return value == other.value;
    }
};

The simplest implementation of a hash is this:

struct custom_hash_simple {
    auto operator()(id const& x) const noexcept -> uint64_t {
        return x.value;
    }
};

This can be used e.g. with

auto ids = ankerl::unordered_dense::set<id, custom_hash_simple>();

Since custom_hash_simple doesn't have a using is_avalanching = void; marker it is considered to be of bad quality and additional mixing of x.value is automatically provided inside the set.

3.2.2. High Quality Hash

Back to the id example, we can easily implement a higher quality hash:

struct custom_hash_avalanching {
    using is_avalanching = void;

    auto operator()(id const& x) const noexcept -> uint64_t {
        return ankerl::unordered_dense::detail::wyhash::hash(x.value);
    }
};

We know wyhash::hash is of high quality, so we can add using is_avalanching = void; which makes the map/set directly use the returned value.

3.2.3. Specialize `ankerl::unordered_dense::hash`

Instead of creating a new class you can also specialize ankerl::unordered_dense::hash:

template <>
struct ankerl::unordered_dense::hash<id> {
    using is_avalanching = void;

    [[nodiscard]] auto operator()(id const& x) const noexcept -> uint64_t {
        return detail::wyhash::hash(x.value);
    }
};

3.2.4. Heterogeneous Overloads using `is_transparent`

This map/set supports heterogeneous overloads as described in P2363 Extending associative containers with the remaining heterogeneous overloads which is targeted for C++26. This has overloads for find, count, contains, equal_range (see P0919R3), erase (see P2077R2), and try_emplace, insert_or_assign, operator[], at, and insert & emplace for sets (see P2363R3).

For heterogeneous overloads to take affect, both hasher and key_equal need to have the attribute is_transparent set.

Here is an example implementation that's usable with any string types that is convertible to std::string_view (e.g. char const* and std::string):

struct string_hash {
    using is_transparent = void; // enable heterogeneous overloads
    using is_avalanching = void; // mark class as high quality avalanching hash

    [[nodiscard]] auto operator()(std::string_view str) const noexcept -> uint64_t {
        return ankerl::unordered_dense::hash<std::string_view>{}(str);
    }
};

To make use of this hash you'll need to specify it as a type, and also a key_equal with is_transparent like std::equal_to<>:

auto map = ankerl::unordered_dense::map<std::string, size_t, string_hash, std::equal_to<>>();

For more information see the examples in test/unit/transparent.cpp.

3.2.5. Automatic Fallback to `std::hash`

When an implementation for std::hash of a custom type is available, this is automatically used and assumed to be of bad quality (thus std::hash is used, but an additional mixing step is performed).

3.2.6. Hash the Whole Memory

When the type has a unique object representation (no padding, trivially copyable), one can just hash the object's memory. Consider a simple class

struct point {
    int x{};
    int y{};

    auto operator==(point const& other) const -> bool {
        return x == other.x && y == other.y;
    }
};

A fast and high quality hash can be easily provided like so:

struct custom_hash_unique_object_representation {
    using is_avalanching = void;

    [[nodiscard]] auto operator()(point const& f) const noexcept -> uint64_t {
        static_assert(std::has_unique_object_representations_v<point>);
        return ankerl::unordered_dense::detail::wyhash::hash(&f, sizeof(f));
    }
};

3.3. Container API

In addition to the standard std::unordered_map API (see https://en.cppreference.com/w/cpp/container/unordered_map) we have additional API that is somewhat similar to the node API, but leverages the fact that we're using a random access container internally:

3.3.1. `auto extract() && -> value_container_type`

Extracts the internally used container. *this is emptied.

3.3.2. `extract()` single Elements

Similar to erase() I have an API call extract(). It behaves exactly the same as erase, except that the return value is the moved element that is removed from the container:

auto extract(const_iterator it) -> value_type
auto extract(Key const& key) -> std::optional<value_type>
template <class K> auto extract(K&& key) -> std::optional<value_type>

Note that the extract(key) API returns an std::optional<value_type> that is empty when the key is not found.

3.3.3. `[[nodiscard]] auto values() const noexcept -> value_container_type const&`

Exposes the underlying values container.

3.3.4. `auto replace(value_container_type&& container)`

Discards the internally held container and replaces it with the one passed. Non-unique elements are removed, and the container will be partly reordered when non-unique elements are found.

3.4. Custom Container Types

unordered_dense accepts a custom allocator, but you can also specify a custom container for that template argument. That way it is possible to replace the internally used std::vector with e.g. std::deque or any other container like boost::interprocess::vector. This supports fancy pointers (e.g. offset_ptr), so the container can be used with e.g. shared memory provided by boost::interprocess.

3.5. Custom Bucket Types

The map/set supports two different bucket types. The default should be good for pretty much everyone.

3.5.1. `ankerl::unordered_dense::bucket_type::standard`

Up to 2^32 = 4.29 billion elements.
8 bytes overhead per bucket.

3.5.2. `ankerl::unordered_dense::bucket_type::big`

up to 2^63 = 9223372036854775808 elements.
12 bytes overhead per bucket.

4. `segmented_map` and `segmented_set`

ankerl::unordered_dense provides a custom container implementation that has lower memory requirements than the default std::vector. Memory is not contiguous, but it can allocate segments without having to reallocate and move all the elements. In summary, this leads to

Much smoother memory usage, memory usage increases continuously.
No high peak memory usage.
Faster insertion because elements never need to be moved to new allocated blocks
Slightly slower indexing compared to std::vector because an additional indirection is needed.

Here is a comparison against absl::flat_hash_map and the ankerl::unordered_dense::map when inserting 10 million entries

Abseil is fastest for this simple inserting test, taking a bit over 0.8 seconds. It's peak memory usage is about 430 MB. Note how the memory usage goes down after the last peak; when it goes down to ~290MB it has finished rehashing and could free the previously used memory block.

ankerl::unordered_dense::segmented_map doesn't have these peaks, and instead has a smooth increase of memory usage. Note there are still sudden drops & increases in memory because the indexing data structure needs still needs to increase by a fixed factor. But due to holding the data in a separate container we are able to first free the old data structure, and then allocate a new, bigger indexing structure; thus we do not have peaks.

5. Design

The map/set has two data structures:

std::vector<value_type> which holds all data. map/set iterators are just std::vector<value_type>::iterator!
An indexing structure (bucket array), which is a flat array with 8-byte buckets.

5.1. Inserts

Whenever an element is added it is emplace_back to the vector. The key is hashed, and an entry (bucket) is added at the corresponding location in the bucket array. The bucket has this structure:

struct Bucket {
    uint32_t dist_and_fingerprint;
    uint32_t value_idx;
};

Each bucket stores 3 things:

The distance of that value from the original hashed location (3 most significant bytes in dist_and_fingerprint)
A fingerprint; 1 byte of the hash (lowest significant byte in dist_and_fingerprint)
An index where in the vector the actual data is stored.

This structure is especially designed for the collision resolution strategy robin-hood hashing with backward shift deletion.

5.2. Lookups

The key is hashed and the bucket array is searched if it has an entry at that location with that fingerprint. When found, the key in the data vector is compared, and when equal the value is returned.

5.3. Removals

Since all data is stored in a vector, removals are a bit more complicated:

First, lookup the element to delete in the index array.
When found, replace that element in the vector with the last element in the vector.
Update two locations in the bucket array: First remove the bucket for the removed element
Then, update the value_idx of the moved element. This requires another lookup.

6. Real World Usage

On 2023-09-10 I did a quick search on github to see if this map is used in any popular open source projects. Here are some of the projects I found. Please send me a note if you want on that list!

PruaSlicer - G-code generator for 3D printers (RepRap, Makerbot, Ultimaker etc.)
Kismet: Wi-Fi, Bluetooth, RF, and more. Kismet is a sniffer, WIDS, and wardriving tool for Wi-Fi, Bluetooth, Zigbee, RF, and more, which runs on Linux and macOS
Rspamd - Fast, free and open-source spam filtering system.
kallisto - Near-optimal RNA-Seq quantification
Slang - Slang is a shading language that makes it easier to build and maintain large shader codebases in a modular and extensible fashion.
CyberFSR2 - Drop-in DLSS replacement with FSR 2.0 for various games such as Cyberpunk 2077.
ossia score - A free, open-source, cross-platform intermedia sequencer for precise and flexible scripting of interactive scenarios.
HiveWE - A Warcraft III World Editor (WE) that focusses on speed and ease of use.
opentxs - The Open-Transactions project is a collaborative effort to develop a robust, commercial-grade, fully-featured, free-software toolkit implementing the OTX protocol as well as a full-strength financial cryptography library, API, GUI, command-line interface, and prototype notary server.
LuisaCompute - High-Performance Rendering Framework on Stream Architectures
Lethe - Lethe (pronounced /ˈliːθiː/) is open-source computational fluid dynamics (CFD) software which uses high-order continuous Galerkin formulations to solve the incompressible Navier–Stokes equations (among others).
PECOS - PECOS is a versatile and modular machine learning (ML) framework for fast learning and inference on problems with large output spaces, such as extreme multi-label ranking (XMR) and large-scale retrieval.
Operon - A modern C++ framework for symbolic regression that uses genetic programming to explore a hypothesis space of possible mathematical expressions in order to find the best-fitting model for a given regression target.
MashMap - A fast approximate aligner for long DNA sequences
minigpt4.cpp - Port of MiniGPT4 in C++ (4bit, 5bit, 6bit, 8bit, 16bit CPU inference with GGML)

glebm/unordered_dense