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Well-tuned, production-ready cuckoo filter that performs best in class for low false positive rates (at around 0.01%). For details, see full evaluation.
Cuckoo filter is a Bloom filter replacement for approximated set-membership queries. While Bloom filters are well-known space-efficient data structures to serve queries like "if item x is in a set?", they do not support deletion. Their variances to enable deletion (like counting Bloom filters) usually require much more space.
Cuckoo filters provide the flexibility to add and remove items dynamically. A cuckoo filter is based on cuckoo hashing (and therefore named as cuckoo filter). It is essentially a cuckoo hash table storing each key's fingerprint. Cuckoo hash tables can be highly compact, thus a cuckoo filter could use less space than conventional Bloom filters, for applications that require low false positive rates (< 3%).
"Cuckoo Filter: Better Than Bloom" by Bin Fan, Dave Andersen and Michael Kaminsky
The paper cited above leaves several parameters to choose. In this implementation
1 and 2 are suggested to be the optimum by the authors. The choice of 3 comes down to the desired false positive rate. Given a target false positive rate of r and a bucket size b, they suggest choosing the fingerprint size f using
f >= log2(2b/r) bits
With the 64 bit fingerprint size in this repository, you can expect r ~= 4.12 * 10âťÂšâš.
Other implementations use 8 bit, which correspond to a false positive rate of r ~= 0.03.
import (
"fmt"
cuckoo "github.com/xorium/cuckoofilter"
)
func Example() {
cf := cuckoo.NewFilter(1000)
cf.Insert([]byte("pizza"))
cf.Insert([]byte("tacos"))
cf.Insert([]byte("tacos")) // Re-insertion is possible.
fmt.Println(cf.Lookup([]byte("pizza")))
fmt.Println(cf.Lookup([]byte("missing")))
cf.Reset()
fmt.Println(cf.Lookup([]byte("pizza")))
// Output:
// true
// false
// false
}
For more examples, see the example tests. Operations on a filter are not thread safe by default. See this example for using the filter concurrently.
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