A family of header-only, very fast and memory-friendly hashmap and btree containers.
A family of header-only, very fast and memory-friendly hashmap and btree containers.
We encourage phmap users to switch to gtl if possible. gtl provides the same functionality as this repository, but requires C++20 or above.
Eventually, support for issues and new features will move exclusively to gtl.
This repository aims to provide a set of excellent hash map implementations, as well as a btree alternative to std::map and std::set, with the following characteristics:
Header only: nothing to build, just copy the parallel_hashmap directory to your project and you are good to go.
drop-in replacement for std::unordered_map, std::unordered_set, std::map and std::set
Compiler with C++11 support required, C++14 and C++17 APIs are provided (such as try_emplace)
Very efficient, significantly faster than your compiler's unordered map/set or Boost's, or than sparsepp
Memory friendly: low memory usage, although a little higher than sparsepp
Supports heterogeneous lookup
Easy to forward declare: just include phmap_fwd_decl.h in your header files to forward declare Parallel Hashmap containers [note: this does not work currently for hash maps with pointer keys]
Dump/load feature: when a flat hash map stores data that is std::trivially_copyable, the table can be dumped to disk and restored as a single array, very efficiently, and without requiring any hash computation. This is typically about 10 times faster than doing element-wise serialization to disk, but it will use 10% to 60% extra disk space. See examples/serialize.cc. (flat hash map/set only)
Tested on Windows (vs2015 & vs2017, vs2019, vs2022, Intel compiler 18 and 19), linux (g++ 4.8, 5, 6, 7, 8, 9, 10, 11, 12, clang++ 3.9 to 16) and MacOS (g++ and clang++) - click on travis and appveyor icons above for detailed test status.
Automatic support for boost's hash_value() method for providing the hash function (see examples/hash_value.h). Also default hash support for std::pair and std::tuple.
natvis visualization support in Visual Studio (hash map/set only)
@byronhe kindly provided this Chinese translation of the README.md.
The observant among us may have noticed that I have two github repos, parallel-hashmap and gtl, which both provide very similar functionality. Indeed the hash tables in both are equivalent and the code mostly the same. The main difference is that parallel-hashmap only requires a C++11 compiler, while gtl requires a C++20 compiler.
My recommendation would be to use gtl if you are compiling with C++20 or higher, and parallel-hashmap otherwise. While the included hash maps are equivalent, gtl is where new development occurs, and it will include useful new classes.
Click here For a full writeup explaining the design and benefits of the Parallel Hashmap.
The hashmaps and btree provided here are built upon those open sourced by Google in the Abseil library. The hashmaps use closed hashing, where values are stored directly into a memory array, avoiding memory indirections. By using parallel SSE2 instructions, these hashmaps are able to look up items by checking 16 slots in parallel, allowing the implementation to remain fast even when the table is filled up to 87.5% capacity.
IMPORTANT: This repository borrows code from the abseil-cpp repository, with modifications, and may behave differently from the original. This repository is an independent work, with no guarantees implied or provided by the authors. Please visit abseil-cpp for the official Abseil libraries.
Copy the parallel_hashmap directory to your project. Update your include path. That's all.
If you are using Visual Studio, you probably want to add phmap.natvis to your projects. This will allow for a clear display of the hash table contents in the debugger.
A cmake configuration files (CMakeLists.txt) is provided for building the tests and examples. Command for building and running the tests is:
cmake -DPHMAP_BUILD_TESTS=ON -DPHMAP_BUILD_EXAMPLES=ON -B build
cmake --build build
ctest --test-dir build
…
The header parallel_hashmap/phmap.h provides the implementation for the following eight hash tables:
The header parallel_hashmap/btree.h provides the implementation for the following btree-based ordered containers:
The btree containers are direct ports from Abseil, and should behave exactly the same as the Abseil ones, modulo small differences (such as supporting std::string_view instead of absl::string_view, and being forward declarable).
When btrees are mutated, values stored within can be moved in memory. This means that pointers or iterators to values stored in btree containers can be invalidated when that btree is modified. This is a significant difference with std::map and std::set, as the std containers do offer a guarantee of pointer stability. The same is true for the 'flat' hash maps and sets.
The full types with template parameters can be found in the parallel_hashmap/phmap_fwd_decl.h header, which is useful for forward declaring the Parallel Hashmaps when necessary.
Key decision points for hash containers:
The flat hash maps will move the keys and values in memory. So if you keep a pointer to something inside a flat hash map, this pointer may become invalid when the map is mutated. The node hash maps don't, and should be used instead if this is a problem.
The flat hash maps will use less memory, and usually be faster than the node hash maps, so use them if you can. the exception is when the values inserted in the hash map are large (say more than 100 bytes [needs testing]) and costly to move.
The parallel hash maps are preferred when you have a few hash maps that will store a very large number of values. The non-parallel hash maps are preferred if you have a large number of hash maps, each storing a relatively small number of values.
The benefits of the parallel hash maps are:
a. reduced peak memory usage (when resizing), and
b. multithreading support (and inherent internal parallelism)
Key decision points for btree containers:
Btree containers are ordered containers, which can be used as alternatives to std::map and std::set. They store multiple values in each tree node, and are therefore more cache friendly and use significantly less memory.
Btree containers will usually be preferable to the default red-black trees of the STL, except when:
When an ordering is not needed, a hash container is typically a better choice than a btree one.
The default hash framework is std::hash, not absl::Hash. However, if you prefer the default to be the Abseil hash framework, include the Abseil headers before phmap.h and define the preprocessor macro PHMAP_USE_ABSL_HASH.
The erase(iterator) and erase(const_iterator) both return an iterator to the element following the removed element, as does the std::unordered_map. A non-standard void _erase(iterator) is provided in case the return value is not needed.
No new types, such as absl::string_view, are provided. All types with a std::hash<> implementation are supported by phmap tables (including std::string_view of course if your compiler provides it).
The Abseil hash tables internally randomize a hash seed, so that the table iteration order is non-deterministic. This can be useful to prevent Denial Of Service attacks when a hash table is used for a customer facing web service, but it can make debugging more difficult. The phmap hashmaps by default do not implement this randomization, but it can be enabled by adding #define PHMAP_NON_DETERMINISTIC 1 before including the header phmap.h (as is done in raw_hash_set_test.cc).
Unlike the Abseil hash maps, we do an internal mixing of the hash value provided. This prevents serious degradation of the hash table performance when the hash function provided by the user has poor entropy distribution. The cost in performance is very minimal, and this helps provide reliable performance even with imperfect hash functions. Disabling this mixing is possible by defining the preprocessor macro PHMAP_DISABLE_MIX=1 before phmap.h is included, but it is not recommended.
size() / bucket_count(). It varies between 0.4375 (just after the resize) to 0.875 (just before the resize). The size of the bucket array doubles at each resize.sizeof(void *) + 1, as the node hash maps store one pointer plus one byte of metadata for each entry in the bucket array.sizeof(C::value_type) + 1.0.03 roughly equal to 0.5 / 16The rules are the same as for std::unordered_map, and are valid for all the phmap hash containers:
Unlike for std::map and std::set, any mutating operation may invali