This is an implementation of a Bloom Filter in C++ for my own learning and teaching purposes. A bloom filter is a probabilistic filter that can tell you that something is probably in a set, or definitely not in a set. It works by hashing the input data (Mermer2 hash is popular) several times with different seeds, and then mapping those hashes to a bitmap (array of bits). It flips those bits to 1's. Then when checking if something is in the set, it hashes the input again, and if the hash all matches the 1's in the bitmap, it is probably there, but if it gets any 0's then it definitely isn't there.
This implementation accepts the maximum number of items expected to be put into the filter and the desired error probability, and will automatically size itself.
This implementation is a fixed size determined by one of the pre made sizes shown below. This is ideal when you don't know the upper limit and instead want a fixed size of memory for the bloom filter.
class FixedSize {
public :
constexpr static std::size_t TINY = 65535; // 8K
constexpr static std::size_t SMALL = 1048576; // 128K
constexpr static std::size_t MEDIUM = 8388608; // 1024K
constexpr static std::size_t LARGE = 67108864; // 8M
constexpr static std::size_t XL = 536870912; // 64M
constexpr static std::size_t XXL = 1073741824; // 128M
constexpr static std::size_t XXXL = 2147483648; // 256M
constexpr static std::size_t LARGEST = 4294967296; // 512M
};
Of course, your free to add any sizes you want.
Copyright (c) 2020 James Slocum (jamesslocum.com)
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