[![Build Status](https://travis-ci.org/greg7mdp/sparsepp.svg?branch=master)](https://travis-ci.org/greg7mdp/sparsepp) # Sparsepp: A fast, memory efficient hash map for C++ Sparsepp is derived from Google's excellent [sparsehash](https://github.com/sparsehash/sparsehash) implementation. It aims to achieve the following objectives: - A drop-in alternative for unordered_map and unordered_set. - **Extremely low memory usage** (typically about one byte overhead per entry). - **Very efficient**, typically faster than your compiler's unordered map/set or Boost's. - **C++11 support** (if supported by compiler). - **Single header** implementation - just copy `sparsepp.h` to your project and include it. - **Tested** on Windows (vs2010-2015, g++), linux (g++, clang++) and MacOS (clang++). We believe Sparsepp provides an unparalleled combination of performance and memory usage, and will outperform your compiler's unordered_map on both counts. Only Google's `dense_hash_map` is consistently faster, at the cost of much greater memory usage (especially when the final size of the map is not known in advance). For a detailed comparison of various hash implementations, including Sparsepp, please see our [write-up](bench.md). ## Example ```c++ #include #include #include using spp::sparse_hash_map; int main() { // Create an unordered_map of three strings (that map to strings) sparse_hash_map email = { { "tom", "tom@gmail.com"}, { "jeff", "jk@gmail.com"}, { "jim", "jimg@microsoft.com"} }; // Iterate and print keys and values for (const auto& n : email) std::cout << n.first << "'s email is: " << n.second << "\n"; // Add a new entry email["bill"] = "bg@whatever.com"; // and print it std::cout << "bill's email is: " << email["bill"] << "\n"; return 0; } ``` ## Installation Since the full Sparsepp implementation is contained in a single header file `sparsepp.h`, the installation consist in copying this header file wherever it will be convenient to include in your project(s). Optionally, a second header file `spp_utils.h` is provided, which implements only the spp::hash_combine() functionality. This is useful when we want to specify a hash function for a user-defined class in an header file, without including the full `sparsepp.h` header (this is demonstrated in [example 2](#example-2---providing-a-hash-function-for-a-user-defined-class) below). ## Warning - iterator invalidation on erase/insert 1. erasing elements is likely to invalidate iterators (for example when calling `erase()`) 2. inserting new elements is likely to invalidate iterators (iterator invalidation can also happen with std::unordered_map if rehashing occurs due to the insertion) ## Usage As shown in the example above, you need to include the header file: `#include ` This provides the implementation for the following classes: ```c++ namespace spp { template , class EqualKey = std::equal_to, class Alloc = libc_allocator_with_realloc>> class sparse_hash_map; template , class EqualKey = std::equal_to, class Alloc = libc_allocator_with_realloc> class sparse_hash_set; }; ``` These classes provide the same interface as std::unordered_map and std::unordered_set, with the following differences: - Calls to `erase()` may invalidate iterators. However, conformant to the C++11 standard, the position and range erase functions return an iterator pointing to the position immediately following the last of the elements erased. This makes it easy to traverse a sparse hash table and delete elements matching a condition. For example to delete odd values: ```c++ for (auto it = c.begin(); it != c.end(); ) if (it->first % 2 == 1) it = c.erase(it); else ++it; ``` As for std::unordered_map, the order of the elements that are not erased is preserved. - Since items are not grouped into buckets, Bucket APIs have been adapted: `max_bucket_count` is equivalent to `max_size`, and `bucket_count` returns the sparsetable size, which is normally at least twice the number of items inserted into the hash_map. ## Integer keys, and other hash function considerations. 1. For basic integer types, sparsepp provides a default hash function which does some mixing of the bits of the keys (see [Integer Hashing](http://burtleburtle.net/bob/hash/integer.html)). This prevents a pathological case where inserted keys are sequential (1, 2, 3, 4, ...), and the lookup on non-present keys becomes very slow. Of course, the user of sparsepp may provide its own hash function, as shown below: ```c++ #include struct Hash64 { size_t operator()(uint64_t k) const { return (k ^ 14695981039346656037ULL) * 1099511628211ULL; } }; struct Hash32 { size_t operator()(uint32_t k) const { return (k ^ 2166136261U) * 16777619UL; } }; int main() { spp::sparse_hash_map map; ... } ``` 2. When the user provides its own hash function, for example when inserting custom classes into a hash map, sometimes the resulting hash keys have similar low order bits and cause many collisions, decreasing the efficiency of the hash map. To address this use case, sparsepp provides an optional 'mixing' of the hash key (see [Integer Hash Function](https://gist.github.com/badboy/6267743) which can be enabled by defining the proprocessor macro: SPP_HASH_MIX. ## Example 2 - providing a hash function for a user-defined class In order to use a sparse_hash_set or sparse_hash_map, a hash function should be provided. Even though a the hash function can be provided via the HashFcn template parameter, we recommend injecting a specialization of `std::hash` for the class into the "std" namespace. For example: ```c++ #include #include #include #include "sparsepp.h" using std::string; struct Person { bool operator==(const Person &o) const { return _first == o._first && _last == o._last; } string _first; string _last; }; namespace std { // inject specialization of std::hash for Person into namespace std // ---------------------------------------------------------------- template<> struct hash { std::size_t operator()(Person const &p) const { std::size_t seed = 0; spp::hash_combine(seed, p._first); spp::hash_combine(seed, p._last); return seed; } }; } int main() { // As we have defined a specialization of std::hash() for Person, // we can now create sparse_hash_set or sparse_hash_map of Persons // ---------------------------------------------------------------- spp::sparse_hash_set persons = { { "John", "Galt" }, { "Jane", "Doe" } }; for (auto& p: persons) std::cout << p._first << ' ' << p._last << '\n'; } ``` The `std::hash` specialization for `Person` combines the hash values for both first and last name using the convenient spp::hash_combine function, and returns the combined hash value. spp::hash_combine is provided by the header `sparsepp.h`. However, class definitions often appear in header files, and it is desirable to limit the size of headers included in such header files, so we provide the very small header `spp_utils.h` for that purpose: ```c++ #include #include "spp_utils.h" using std::string; struct Person { bool operator==(const Person &o) const { return _first == o._first && _last == o._last && _age == o._age; } string _first; string _last; int _age; }; namespace std { // inject specialization of std::hash for Person into namespace std // ---------------------------------------------------------------- template<> struct hash { std::size_t operator()(Person const &p) const { std::size_t seed = 0; spp::hash_combine(seed, p._first); spp::hash_combine(seed, p._last); spp::hash_combine(seed, p._age); return seed; } }; } ``` ## Example 3 - serialization sparse_hash_set and sparse_hash_map can easily be serialized/unserialized to a file or network connection. This support is implemented in the following APIs: ```c++ template bool serialize(Serializer serializer, OUTPUT *stream); template bool unserialize(Serializer serializer, INPUT *stream); ``` The following example demontrates how a simple sparse_hash_map can be written to a file, and then read back. The serializer we use read and writes to a file using the stdio APIs, but it would be equally simple to write a serialized using the stream APIS: ```c++ #include #include "sparsepp.h" using spp::sparse_hash_map; using namespace std; class FileSerializer { public: // serialize basic types to FILE // ----------------------------- template bool operator()(FILE *fp, const T& value) { return fwrite((const void *)&value, sizeof(value), 1, fp) == 1; } template bool operator()(FILE *fp, T* value) { return fread((void *)value, sizeof(*value), 1, fp) == 1; } // serialize std::string to FILE // ----------------------------- bool operator()(FILE *fp, const string& value) { const size_t size = value.size(); return (*this)(fp, size) && fwrite(value.c_str(), size, 1, fp) == 1; } bool operator()(FILE *fp, string* value) { size_t size; if (!(*this)(fp, &size)) return false; char* buf = new char[size]; if (fread(buf, size, 1, fp) != 1) { delete [] buf; return false; } new (value) string(buf, (size_t)size); delete[] buf; return true; } // serialize std::pair to FILE - needed for maps // --------------------------------------------------------- template bool operator()(FILE *fp, const std::pair& value) { return (*this)(fp, value.first) && (*this)(fp, value.second); } template bool operator()(FILE *fp, std::pair *value) { return (*this)(fp, (A *)&value->first) && (*this)(fp, &value->second); } }; int main(int argc, char* argv[]) { sparse_hash_map age{ { "John", 12 }, {"Jane", 13 }, { "Fred", 8 } }; // serialize age hash_map to "ages.dmp" file FILE *out = fopen("ages.dmp", "wb"); age.serialize(FileSerializer(), out); fclose(out); sparse_hash_map age_read; // read from "ages.dmp" file into age_read hash_map FILE *input = fopen("ages.dmp", "rb"); age_read.unserialize(FileSerializer(), input); fclose(input); // print out contents of age_read to verify correct serialization for (auto& v : age_read) printf("age_read: %s -> %d\n", v.first.c_str(), v.second); } ```