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%).
For details about the algorithm and citations please use this article for now
"Cuckoo Filter: Better Than Bloom" by Bin Fan, Dave Andersen and Michael Kaminsky
```rust extern crate cuckoofilter;
...
let value: &str = "hello world";
//Create cuckoo filter wit max capacity of 1000000 items let cf = cuckoofilter::new(1000000);
// Add data to the filter let success = cf.add(&value.as_bytes()); // success ==> true
// Lookup if data is in the filter let success = cf.lookup(&value.as_bytes()); // success ==> true
// Test and add to the filter (if data does not exists then add) let success = cf.testandadd(&value.as_bytes()); // success ==> false
// Test and add to the filter (if data does not exists then add) let success = cf.delete(&value.as_bytes()); // success ==> true
// Lookup if data is in the filter let success = cf.lookup(&value.as_bytes()); // success ==> false ```
This implementation uses a a static bucket size of 4 fingerprints and a fingerprint size of 1 byte based on my understanding of an optimal bucket/fingerprint/size ratio from the aforementioned paper.