Bloom filters are used to quickly check whether an element is part of a set. Xor filters and binary fuse filters are faster and more concise alternative to Bloom filters. Furthermore, unlike Bloom filters, xor and binary fuse filters are naturally compressible using standard techniques (gzip, zstd, etc.). They are also smaller than cuckoo filters. They are used in production systems.
- Thomas Mueller Graf, Daniel Lemire, Binary Fuse Filters: Fast and Smaller Than Xor Filters, Journal of Experimental Algorithmics (to appear). DOI: 10.1145/3510449
- Thomas Mueller Graf, Daniel Lemire, Xor Filters: Faster and Smaller Than Bloom and Cuckoo Filters, Journal of Experimental Algorithmics 25 (1), 2020. DOI: 10.1145/3376122
This is a simple C header-only library. It implements both binary fuse and xor filters.
To use the state-of-the-art binary fuse filters, simply add (for example) the binaryfusefilter.h file to your project. It is made available under the business-friendly Apache license.
For a simple application built on this library, see https://github.com/FastFilter/FilterPassword
We are assuming that your set is made of 64-bit integers. If you have a set of strings or other data structures, you need to hash them first to a 64-bit integer. It is not important to have a good hash function, but collisions should be unlikely (~1/2^64). A few collisions are acceptable, but we expect that your initial set should have no duplicated entry.
The basic version works with 8-bit word and has a false-positive probability of 1/256 (or 0.4%).
uint64_t *big_set = ...
binary_fuse8_t filter;
bool is_ok = binary_fuse8_allocate(size, &filter);
if(! is_ok ) {
// do something (you have run out of memory)
}
is_ok = binary_fuse8_populate(big_set, size, &filter);
if(! is_ok ) {
// do something (you have run out of memory)
}
binary_fuse8_contain(big_set[0], &filter); // will be true
binary_fuse8_contain(somerandomvalue, &filter); // will be false with high probability
binary_fuse8_free(&filter);We also have a 16-bit version which uses about twice the memory,
but has a far lower false-positive probability (256 times smaller):
about 0.0015%. The type is binary_fuse16_t and you may use it with
functions such as binary_fuse16_allocate, binary_fuse16_populate,
binary_fuse8_contain and binary_fuse8_free.
You may serialize the data as follows:
size_t buffer_size = binary_fuse16_serialization_bytes(&filter);
char *buffer = (char*)malloc(buffer_size);
binary_fuse16_serialize(&filter, buffer);
binary_fuse16_free(&filter);
binary_fuse16_deserialize(&filter, buffer);
free(buffer);The serialization does not handle endianess: it is expected that you will serialize and deserialize on the little endian systems. (Big endian systems are vanishingly rare.)
If you want a C++ version, we recommend binfuse by Oliver Schönrock.
You can also roll your own:
#include "binaryfusefilter.h"
class BinaryFuse {
public:
explicit BinaryFuse(const size_t size) {
if (!binary_fuse8_allocate(size, &filter)) {
throw ::std::runtime_error("Allocation failed.");
}
}
~BinaryFuse() {
binary_fuse8_free(&filter);
}
bool AddAll(uint64_t* data, const size_t start, const size_t end) {
return binary_fuse8_populate(data + start, end - start, &filter);
}
inline bool Contain(uint64_t &item) const {
return binary_fuse8_contain(item, &filter);
}
inline size_t SizeInBytes() const {
return binary_fuse8_size_in_bytes(&filter);
}
BinaryFuse(BinaryFuse && o) : filter(o.filter) {
o.filter.Fingerprints = nullptr; // we take ownership for the data
}
binary_fuse8_t filter;
private:
BinaryFuse(const BinaryFuse & o) = delete;
};The construction of a binary fuse filter is fast but it needs a fair amount of temporary memory: plan for about 24 bytes of memory per set entry. It is possible to construct a binary fuse filter with almost no temporary memory, but the construction is then somewhat slower.
The data structure of a binary_fuse8_t instance quite simple. Thus you can easily save it to disk or memory-map it. E.g., we have
typedef struct binary_fuse8_s {
uint64_t Seed;
uint32_t SegmentLength;
uint32_t SegmentLengthMask;
uint32_t SegmentCount;
uint32_t SegmentCountLength;
uint32_t ArrayLength;
uint8_t *Fingerprints; // points to ArrayLength bytes
} binary_fuse8_t;To run tests: make test.
$ make test
$ ./unit
./unit
testing binary fuse8
fpp 0.00392 (estimated)
bits per entry 9.04
bits per entry 7.99 (theoretical lower bound)
efficiency ratio 1.131
....
To run construction benchmarks:
$ make bench
$ ./bench
❯ ./bench
testing binary fuse8 size = 10000000
It took 0.358196 seconds to build an index over 10000000 values.
It took 0.355775 seconds to build an index over 10000000 values.
It took 0.367437 seconds to build an index over 10000000 values.
It took 0.358578 seconds to build an index over 10000000 values.
It took 0.358220 seconds to build an index over 10000000 values.
...