  In this paper, we present an implementation of a cuckoo filter for membership
testing, optimized for distributed data stores operating in high workloads. In
large databases, querying becomes inefficient using traditional search methods.
To achieve optimal performance it is necessary to use probabilistic data
structures to test the membership of a given key, at the cost of getting false
positives while querying data. The widely used bloom filters can be used for
this, but they have limitations like no support for deletes. To improve upon
this we use a modified version of the cuckoo filter that gives better amortized
times for search, with less false positives.
