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The sparse-map library is a C++ implementation of a memory efficient hash map and hash set. It uses open-addressing with quadratic probing. The goal of the library is to be the most memory efficient possible, even at low load factor, while keeping reasonable performances.

Four classes are provided: tsl::sparse_map, tsl::sparse_set, tsl::sparse_pg_map and tsl::sparse_pg_set. The first two are faster and use a power of two growth policy, the last two use a prime growth policy instead and are able to cope better with a poor hash function. Use the prime version if there is a chance of repeating patterns in the lower bits of your hash (e.g. you are storing pointers with an identity hash function). See GrowthPolicy for details.

A benchmark of tsl::sparse_map against other hash maps may be found here. The benchmark notably includes google::sparse_hash_map and spp::sparse_hash_map to which tsl::sparse_map is an alternative. This page also gives some advices on which hash table structure you should try for your use case (useful if you are a bit lost with the multiple hash tables implementations in the tsl namespace).

• Header-only library, just include the project to your include path and you are ready to go.
• Memory efficient while keeping good lookup speed, see the benchmark for some numbers.
• Support for heterogeneous lookups (e.g. if you have a map that uses std::unique_ptr<int> as key, you could use an int* or a std::uintptr_t as key parameter to find, see example).
• No need to reserve any sentinel value from the keys.
• If the hash is known before a lookup, it is possible to pass it as parameter to speed-up the lookup.
• Possibility to control the balance between insertion speed and memory usage with the Sparsity template parameter. A high sparsity means less memory but longer insertion times, and vice-versa for low sparsity. The default medium sparsity offers a good compromise (see API for details). For reference, with simple integers as keys and values, a low sparsity offers ~15% faster insertions times but uses ~12% more memory. Nothing change regarding lookup speed.
• API closely similar to std::unordered_map and std::unordered_set.

tsl::sparse_map tries to have an interface similar to std::unordered_map, but some differences exist.

• By default only the basic exception safety is guaranteed which mean that all resources used by the hash map will be freed (no memory leaks). It is the same guarantee that the one provided by google::sparse_hash_map and spp::sparse_hash_map. If you need the strong exception guarantee, check the ExceptionSafety template parameter (see API for details).
• Iterator invalidation doesn't behave in the same way, any operation modifying the hash table invalidate them (see API for details).
• References and pointers to keys or values in the map are invalidated in the same way as iterators to these keys-values.
• For iterators, operator*() and operator->() return a reference and a pointer to const std::pair<Key, T> instead of std::pair<const Key, T> making the value T not modifiable. To modify the value you have to call the value() method of the iterator to get a mutable reference. Example:

tsl::sparse_map<int, int> map = {{1, 1}, {2, 1}, {3, 1}};
for(auto it = map.begin(); it != map.end(); ++it) {
    //it->second = 2; // Illegal
    it.value() = 2; // Ok
}

• No support for some buckets related methods (like bucket_size, bucket, ...).

These differences also apply between std::unordered_set and tsl::sparse_set.

Thread-safety guarantees are the same as std::unordered_map/set (i.e. possible to have multiple readers with no writer).

The library relies heavily on the popcount operation.

With Clang and GCC, the library uses the __builtin_popcount function which will use the fast CPU instruction POPCNT when the library is compiled with -mpopcnt. Using the POPCNT instruction offers an improvement of ~15% to ~30% on lookups. So if you are compiling your code for a specific architecture that support the operation, don't forget the -mpopcnt (or -march=native) flag of your compiler.

On Windows with MSVC, the detection is done at runtime.

Make sure that your key Key and potential value T have a noexcept move constructor. The library will work without it but insertions will be slower.

The library supports multiple growth policies through the GrowthPolicy template parameter. Three policies are provided by the library but you can easily implement your own if needed.

• tsl::sh::power_of_two_growth_policy. Default policy used by tsl::sparse_map/set. This policy keeps the size of the bucket array of the hash table to a power of two. This constraint allows the policy to avoid the usage of the slow modulo operation to map a hash to a bucket, instead of hash % 2n, it uses hash & (2n - 1) (see fast modulo). Fast but this may cause a lot of collisions with a poor hash function as the modulo with a power of two only masks the most significant bits in the end.
• tsl::sh::prime_growth_policy. Default policy used by tsl::sparse_pg_map/set. The policy keeps the size of the bucket array of the hash table to a prime number. When mapping a hash to a bucket, using a prime number as modulo will result in a better distribution of the hash across the buckets even with a poor hash function. To allow the compiler to optimize the modulo operation, the policy use a lookup table with constant primes modulos (see API for details). Slower than tsl::sh::power_of_two_growth_policy but more secure.
• tsl::sh::mod_growth_policy. The policy grows the map by a customizable growth factor passed in parameter. It then just use the modulo operator to map a hash to a bucket. Slower but more flexible.

To implement your own policy, you have to implement the following interface.

struct custom_policy {
    // Called on hash table construction, min_bucket_count_in_out is the minimum size
    // that the hash table needs. The policy can change it to a higher bucket count if needed
    custom_policy(std::size_t& min_bucket_count_in_out);
    
    // Return the bucket for the corresponding hash
    std::size_t bucket_for_hash(std::size_t hash) const noexcept;
    
    // Return the number of buckets that should be used on next growth
    std::size_t next_bucket_count() const;
    
    // Maximum number of buckets supported by the policy
    std::size_t max_bucket_count() const;
}

To use sparse-map, just add the project to your include path. It is a header-only library.

The code should work with any C++11 standard-compliant compiler and has been tested with GCC 4.8.4, Clang 3.5.0 and Visual Studio 2015.

To run the tests you will need the Boost Test library and CMake.

git clone https://github.com/Tessil/sparse-map.git
cd sparse-map
mkdir build
cd build
cmake ..
make
./test_sparse_map

The API can be found here.

All methods are not documented yet, but they replicate the behavior of the ones in std::unordered_map and std::unordered_set, except if specified otherwise.

#include <cstdint>
#include <iostream>
#include <string>
#include <tsl/sparse_map.h>
#include <tsl/sparse_set.h>

int main() {
    tsl::sparse_map<std::string, int> map = {{"a", 1}, {"b", 2}};
    map["c"] = 3;
    map["d"] = 4;
    
    map.insert({"e", 5});
    map.erase("b");
    
    for(auto it = map.begin(); it != map.end(); ++it) {
        //it->second += 2; // Not valid.
        it.value() += 2;
    }
    
    // {d, 6} {a, 3} {e, 7} {c, 5}
    for(const auto& key_value : map) {
        std::cout << "{" << key_value.first << ", " << key_value.second << "}" << std::endl;
    }
    
    
    
    
    tsl::sparse_set<int> set;
    set.insert({1, 9, 0});
    set.insert({2, -1, 9});
    
    // {0} {1} {2} {9} {-1}
    for(const auto& key : set) {
        std::cout << "{" << key << "}" << std::endl;
    }
} 

Heterogeneous overloads allow the usage of other types than Key for lookup and erase operations as long as the used types are hashable and comparable to Key.

To activate the heterogeneous overloads in tsl::sparse_map/set, the qualified-id KeyEqual::is_transparent must be valid. It works the same way as for std::map::find. You can either use std::equal_to<> or define your own function object.

Both KeyEqual and Hash will need to be able to deal with the different types.

#include <functional>
#include <iostream>
#include <string>
#include <tsl/sparse_map.h>


struct employee {
    employee(int id, std::string name) : m_id(id), m_name(std::move(name)) {
    }
    
    friend bool operator==(const employee& empl, int empl_id) {
        return empl.m_id == empl_id;
    }
    
    friend bool operator==(int empl_id, const employee& empl) {
        return empl_id == empl.m_id;
    }
    
    friend bool operator==(const employee& empl1, const employee& empl2) {
        return empl1.m_id == empl2.m_id;
    }
    
    
    int m_id;
    std::string m_name;
};

struct hash_employee {
    std::size_t operator()(const employee& empl) const {
        return std::hash<int>()(empl.m_id);
    }
    
    std::size_t operator()(int id) const {
        return std::hash<int>()(id);
    }
};

struct equal_employee {
    using is_transparent = void;
    
    bool operator()(const employee& empl, int empl_id) const {
        return empl.m_id == empl_id;
    }
    
    bool operator()(int empl_id, const employee& empl) const {
        return empl_id == empl.m_id;
    }
    
    bool operator()(const employee& empl1, const employee& empl2) const {
        return empl1.m_id == empl2.m_id;
    }
};


int main() {
    // Use std::equal_to<> which will automatically deduce and forward the parameters
    tsl::sparse_map<employee, int, hash_employee, std::equal_to<>> map; 
    map.insert({employee(1, "John Doe"), 2001});
    map.insert({employee(2, "Jane Doe"), 2002});
    map.insert({employee(3, "John Smith"), 2003});

    // John Smith 2003
    auto it = map.find(3);
    if(it != map.end()) {
        std::cout << it->first.m_name << " " << it->second << std::endl;
    }

    map.erase(1);



    // Use a custom KeyEqual which has an is_transparent member type
    tsl::sparse_map<employee, int, hash_employee, equal_employee> map2;
    map2.insert({employee(4, "Johnny Doe"), 2004});

    // 2004
    std::cout << map2.at(4) << std::endl;
}   

The code is licensed under the MIT license, see the LICENSE file for details.