README
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xorfilter: Go library implementing xor filters
Bloom filters are used to quickly check whether an element is part of a set. Xor filters are a faster and more concise alternative to Bloom filters. They are also smaller than cuckoo filters.
Reference: Xor Filters: Faster and Smaller Than Bloom and Cuckoo Filters, Journal of Experimental Algorithmics (to appear).
We are assuming that your set is made of 64-bit integers. If you have 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 collision should be unlikely (~1/2^64).
The current implementation has a false positive rate of about 0.3% and a memory usage of less than 9 bits per entry for sizeable sets.
You construct the filter as follows starting from a slice of 64-bit integers:
filter := xorfilter.Populate(keys) // keys is of type []uint64
It returns an object of type Xor8. The 64-bit integers would typically be hash values of your objects.
You can then query it as follows:
filter.Contains(v) // v is of type uint64
It will always return true if v was part of the initial construction (Populate) and almost always
return false otherwise.
An xor filter is immutable, it is concurrent. The expectation is that you build it once and use it many times.
Though the filter itself does not use much memory, the construction of the filter needs many bytes of memory per set entry.
For persistence, you only need to serialize the following data structure:
type Xor8 struct {
Seed uint64
BlockLength uint32
Fingerprints []uint8
}
Implements of xor filters in other programming languages
Documentation
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Source Files