Add an element to the filter
The element to add
Allocate a CountingBloomFilter with a target maximum capacity and error rate
The maximum capacity of the filter
The error rate of the filter
A new CountingBloomFilter
Check if another Counting Bloom Filter is equal to this one
True if they are equal, false otherwise
Build a new Bloom Filter from an iterable with a fixed error rate
Iterable used to populate the filter
The error rate of the filter
A new Bloom Filter filled with the iterable's elements
Load an Object from a provided JSON object
the JSON object to load
Return the Object loaded from the provided JSON object
Test an element for membership
The element to look for in the filter
False if the element is definitively not in the filter, True is the element might be in the filter
Return a next random seeded int32 integer
Get the current false positive rate (or error rate) of the filter
The current false positive rate of the filter
Remove an element from the filter
The element to delete
Save the current structure as a JSON
Constructor
The size of the filter
The number of hash functions
Get the number of elements currently in the filter
Get a function used to draw random number
A factory function used to draw random integer
Get the seed used in this structure
Set the seed for this structure
the new seed that will be used in this structure
Get the optimal size of the filter
Generated using TypeDoc
A Counting Bloom filter works in a similar manner as a regular Bloom filter; however, it is able to keep track of insertions and deletions. In a counting Bloom filter, each entry in the Bloom filter is a small counter associated with a basic Bloom filter bit.
Reference: F. Bonomi, M. Mitzenmacher, R. Panigrahy, S. Singh, and G. Varghese, βAn Improved Construction for Counting Bloom Filters,β in 14th Annual European Symposium on Algorithms, LNCS 4168, 2006, pp. 684β695.
Thomas Minier & Arnaud Grall