The robin-map library is a C++ implementation of a fast hash map and hash set using open-addressing and linear robin hood hashing with backward shift deletion to resolve collisions.
Four classes are provided: tsl::robin_map, tsl::robin_set, tsl::robin_pg_map and tsl::robin_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::robin_map against other hash maps may be found here. 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 add the include directory to your include path and you are ready to go. If you use CMake, you can also use the
tsl::robin_mapexported target from the CMakeLists.txt. - Fast hash table, check the benchmark for some numbers.
- Support for move-only and non-default constructible key/value.
- Support for heterogeneous lookups allowing the usage of
findwith a type different thanKey(e.g. if you have a map that usesstd::unique_ptr<foo>as key, you can use afoo*or astd::uintptr_tas key parameter tofindwithout constructing astd::unique_ptr<foo>, see example). - No need to reserve any sentinel value from the keys.
- Possibility to store the hash value alongside the stored key-value for faster rehash and lookup if the hash or the key equal functions are expensive to compute. Note that hash may be stored even if not asked explicitly when the library can detect that it will have no impact on the size of the structure in memory due to alignment. See the StoreHash template parameter for details.
- If the hash is known before a lookup, it is possible to pass it as parameter to speed-up the lookup (see
precalculated_hashparameter in API). - Support for efficient serialization and deserialization (see example and the
serialize/deserializemethods in the API for details). - The library can be used with exceptions disabled (through
-fno-exceptionsoption on Clang and GCC, without an/EHoption on MSVC or simply by definingTSL_NO_EXCEPTIONS).std::terminateis used in replacement of thethrowinstruction when exceptions are disabled. - API closely similar to
std::unordered_mapandstd::unordered_set.
tsl::robin_map tries to have an interface similar to std::unordered_map, but some differences exist.
- The strong exception guarantee only holds if the following statement is true
std::is_nothrow_swappable<value_type>::value && std::is_nothrow_move_constructible<value_type>::value(wherevalue_typeisKeyfortsl::robin_setandstd::pair<Key, T>fortsl::robin_map). Otherwise if an exception is thrown during the swap or the move, the structure may end up in a undefined state. Note that per the standard, avalue_typewith a noexcept copy constructor and no move constructor also satisfies this condition and will thus guarantee the strong exception guarantee for the structure (see API for details). - The type
Key, and alsoTin case of map, must be swappable. They must also be copy and/or move constructible. - 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 of
tsl::robin_map,operator*()andoperator->()return a reference and a pointer toconst std::pair<Key, T>instead ofstd::pair<const Key, T>making the valueTnot modifiable. To modify the value you have to call thevalue()method of the iterator to get a mutable reference. Example:
tsl::robin_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::robin_set.
Thread-safety guarantees are the same as std::unordered_map/set (i.e. possible to have multiple readers with no writer).
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::rh::power_of_two_growth_policy. Default policy used by
tsl::robin_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 ofhash % 2n, it useshash & (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::rh::prime_growth_policy. Default policy used by
tsl::robin_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 thantsl::rh::power_of_two_growth_policybut more secure. - tsl::rh::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 and rehash, min_bucket_count_in_out is the minimum buckets
// that the hash table needs. The policy can change it to a higher number of buckets if needed
// and the hash table will use this value as bucket count. If 0 bucket is asked, then the value
// must stay at 0.
explicit custom_policy(std::size_t& min_bucket_count_in_out);
// Return the bucket [0, bucket_count()) to which the hash belongs.
// If bucket_count() is 0, it must always return 0.
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;
// Reset the growth policy as if the policy was created with a bucket count of 0.
// After a clear, the policy must always return 0 when bucket_for_hash() is called.
void clear() noexcept;
}To use robin-map, just add the include directory to your include path. It is a header-only library.
If you use CMake, you can also use the tsl::robin_map exported target from the CMakeLists.txt with target_link_libraries.
# Example where the robin-map project is stored in a third-party directory
add_subdirectory(third-party/robin-map)
target_link_libraries(your_target PRIVATE tsl::robin_map) If the project has been installed through make install, you can also use find_package(tsl-robin-map REQUIRED) instead of add_subdirectory.
The library is available in vcpkg and conan. It's also present in Debian, Ubuntu and Fedora package repositories.
The code should work with any C++17 standard-compliant compiler.
To run the tests you will need the Boost Test library and CMake.
git clone https://github.com/Tessil/robin-map.git
cd robin-map/tests
mkdir build
cd build
cmake ..
cmake --build .
./tsl_robin_map_testsThe 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/robin_map.h>
#include <tsl/robin_set.h>
int main() {
tsl::robin_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;
}
if(map.find("a") != map.end()) {
std::cout << "Found \"a\"." << std::endl;
}
const std::size_t precalculated_hash = std::hash<std::string>()("a");
// If we already know the hash beforehand, we can pass it in parameter to speed-up lookups.
if(map.find("a", precalculated_hash) != map.end()) {
std::cout << "Found \"a\" with hash " << precalculated_hash << "." << std::endl;
}
/*
* Calculating the hash and comparing two std::string may be slow.
* We can store the hash of each std::string in the hash map to make
* the inserts and lookups faster by setting StoreHash to true.
*/
tsl::robin_map<std::string, int, std::hash<std::string>,
std::equal_to<std::string>,
std::allocator<std::pair<std::string, int>>,
true> map2;
map2["a"] = 1;
map2["b"] = 2;
// {a, 1} {b, 2}
for(const auto& key_value : map2) {
std::cout << "{" << key_value.first << ", " << key_value.second << "}" << std::endl;
}
tsl::robin_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::robin_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/robin_map.h>
struct employee {
employee(int id, std::string name) : m_id(id), m_name(std::move(name)) {
}
// Either we include the comparators in the class and we use `std::equal_to<>`...
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;
};
// ... or we implement a separate class to compare employees.
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;
}
};
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);
}
};
int main() {
// Use std::equal_to<> which will automatically deduce and forward the parameters
tsl::robin_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::robin_map<employee, int, hash_employee, equal_employee> map2;
map2.insert({employee(4, "Johnny Doe"), 2004});
// 2004
std::cout << map2.at(4) << std::endl;
} The library provides an efficient way to serialize and deserialize a map or a set so that it can be saved to a file or send through the network. To do so, it requires the user to provide a function object for both serialization and deserialization.
struct serializer {
// Must support the following types for U: std::int16_t, std::uint32_t,
// std::uint64_t, float and std::pair<Key, T> if a map is used or Key for
// a set.
template<typename U>
void operator()(const U& value);
};struct deserializer {
// Must support the following types for U: std::int16_t, std::uint32_t,
// std::uint64_t, float and std::pair<Key, T> if a map is used or Key for
// a set.
template<typename U>
U operator()();
};Note that the implementation leaves binary compatibility (endianness, float binary representation, size of int, ...) of the types it serializes/deserializes in the hands of the provided function objects if compatibility is required.
More details regarding the serialize and deserialize methods can be found in the API.
#include <cassert>
#include <cstdint>
#include <fstream>
#include <type_traits>
#include <tsl/robin_map.h>
class serializer {
public:
serializer(const char* file_name) {
m_ostream.exceptions(m_ostream.badbit | m_ostream.failbit);
m_ostream.open(file_name, std::ios::binary);
}
template<class T,
typename std::enable_if<std::is_arithmetic<T>::value>::type* = nullptr>
void operator()(const T& value) {
m_ostream.write(reinterpret_cast<const char*>(&value), sizeof(T));
}
void operator()(const std::pair<std::int64_t, std::int64_t>& value) {
(*this)(value.first);
(*this)(value.second);
}
private:
std::ofstream m_ostream;
};
class deserializer {
public:
deserializer(const char* file_name) {
m_istream.exceptions(m_istream.badbit | m_istream.failbit | m_istream.eofbit);
m_istream.open(file_name, std::ios::binary);
}
template<class T>
T operator()() {
T value;
deserialize(value);
return value;
}
private:
template<class T,
typename std::enable_if<std::is_arithmetic<T>::value>::type* = nullptr>
void deserialize(T& value) {
m_istream.read(reinterpret_cast<char*>(&value), sizeof(T));
}
void deserialize(std::pair<std::int64_t, std::int64_t>& value) {
deserialize(value.first);
deserialize(value.second);
}
private:
std::ifstream m_istream;
};
int main() {
const tsl::robin_map<std::int64_t, std::int64_t> map = {{1, -1}, {2, -2}, {3, -3}, {4, -4}};
const char* file_name = "robin_map.data";
{
serializer serial(file_name);
map.serialize(serial);
}
{
deserializer dserial(file_name);
auto map_deserialized = tsl::robin_map<std::int64_t, std::int64_t>::deserialize(dserial);
assert(map == map_deserialized);
}
{
deserializer dserial(file_name);
/**
* If the serialized and deserialized map are hash compatibles (see conditions in API),
* setting the argument to true speed-up the deserialization process as we don't have
* to recalculate the hash of each key. We also know how much space each bucket needs.
*/
const bool hash_compatible = true;
auto map_deserialized =
tsl::robin_map<std::int64_t, std::int64_t>::deserialize(dserial, hash_compatible);
assert(map == map_deserialized);
}
} It is possible to use a serialization library to avoid the boilerplate.
The following example uses Boost Serialization with the Boost zlib compression stream to reduce the size of the resulting serialized file. The example requires C++20 due to the usage of the template parameter list syntax in lambdas, but it can be adapted to less recent versions.
#include <boost/archive/binary_iarchive.hpp>
#include <boost/archive/binary_oarchive.hpp>
#include <boost/iostreams/filter/zlib.hpp>
#include <boost/iostreams/filtering_stream.hpp>
#include <boost/serialization/split_free.hpp>
#include <boost/serialization/utility.hpp>
#include <cassert>
#include <cstdint>
#include <fstream>
#include <tsl/robin_map.h>
namespace boost { namespace serialization {
template<class Archive, class Key, class T>
void serialize(Archive & ar, tsl::robin_map<Key, T>& map, const unsigned int version) {
split_free(ar, map, version);
}
template<class Archive, class Key, class T>
void save(Archive & ar, const tsl::robin_map<Key, T>& map, const unsigned int /*version*/) {
auto serializer = [&ar](const auto& v) { ar & v; };
map.serialize(serializer);
}
template<class Archive, class Key, class T>
void load(Archive & ar, tsl::robin_map<Key, T>& map, const unsigned int /*version*/) {
auto deserializer = [&ar]<typename U>() { U u; ar & u; return u; };
map = tsl::robin_map<Key, T>::deserialize(deserializer);
}
}}
int main() {
tsl::robin_map<std::int64_t, std::int64_t> map = {{1, -1}, {2, -2}, {3, -3}, {4, -4}};
const char* file_name = "robin_map.data";
{
std::ofstream ofs;
ofs.exceptions(ofs.badbit | ofs.failbit);
ofs.open(file_name, std::ios::binary);
boost::iostreams::filtering_ostream fo;
fo.push(boost::iostreams::zlib_compressor());
fo.push(ofs);
boost::archive::binary_oarchive oa(fo);
oa << map;
}
{
std::ifstream ifs;
ifs.exceptions(ifs.badbit | ifs.failbit | ifs.eofbit);
ifs.open(file_name, std::ios::binary);
boost::iostreams::filtering_istream fi;
fi.push(boost::iostreams::zlib_decompressor());
fi.push(ifs);
boost::archive::binary_iarchive ia(fi);
tsl::robin_map<std::int64_t, std::int64_t> map_deserialized;
ia >> map_deserialized;
assert(map == map_deserialized);
}
}Two potential performance pitfalls involving tsl::robin_map and
tsl::robin_set are noteworthy:
-
Bad hashes. Hash functions that produce many collisions can lead to the following surprising behavior: when the number of collisions exceeds a certain threshold, the hash table will automatically expand to fix the problem. However, in degenerate cases, this expansion might have no effect on the collision count, causing a failure mode where a linear sequence of insertion leads to exponential storage growth.
This case has mainly been observed when using the default power-of-two growth strategy with the default STL
std::hash<T>for arithmetic typesT, which is often an identity! See issue #39 for an example. The solution is simple: use a better hash function and/ortsl::robin_pg_set/tsl::robin_pg_map. -
Element erasure and low load factors.
tsl::robin_mapandtsl::robin_setmirror the STL map/set API, which exposes aniterator erase(iterator)method that removes an element at a certain position, returning a valid iterator that points to the next element.Constructing this new iterator object requires walking to the next nonempty bucket in the table, which can be a expensive operation when the hash table has a low load factor (i.e., when
capacity()is much larger thensize()).The
erase()method furthermore never shrinks & re-hashes the table as this is not permitted by the specification of this function. A linear sequence of random removals without intermediate insertions can then lead to a degenerate case with quadratic runtime cost.In such cases, an iterator return value is often not even needed, so the cost is entirely unnecessary. Both
tsl::robin_setandtsl::robin_maptherefore provide an alternative erasure methodvoid erase_fast(iterator)that does not return an iterator to avoid having to find the next element.
The code is licensed under the MIT license, see the LICENSE file for details.