Add an element to the InvertibleBloomFilter
The element to insert
Create an Invertible Bloom filter optimal for an expected size and error rate.
Number of items expected to insert into the IBLT
Expected error rate
A new Invertible Bloom filter optimal for the given parameters.
Decode an InvertibleBloomFilter based on its substracted version
The results of the deconding process
Test if two InvertibleBloomFilters are equals
The filter to compare with
True if the two filters are equals, False otherwise
Create an Invertible Bloom filter from a set of Buffer and optimal for an error rate.
An iterable to yield Buffers to be inserted into the filter
Expected error rate
A new Invertible Bloom filter filled with the iterable's items.
Load an Object from a provided JSON object
the JSON object to load
Return the Object loaded from the provided JSON object
Test if an item is in the filter.
The element to test
False if the element is not in the filter, true if "may be" in the filter.
List all entries from the filter using a Generator. The generator ends with True if the operation has not failed, False otheriwse. It is not recommended to modify an IBLT while listing its entries!
A generator that yields all filter's entries.
Return a next random seeded int32 integer
Remove an element from the filter
The element to remove
True if the element has been removed, False otheriwse
Save the current structure as a JSON
Substract the filter with another InvertibleBloomFilter, and returns the resulting filter.
A new InvertibleBloomFilter which is the XOR of the local and remote one
Construct an Invertible Bloom Lookup Table
The number of cells in the InvertibleBloomFilter. It should be set to d * alpha, where d is the number of difference and alpha is a constant
The number of hash functions used (empirically studied to be 3 or 4 in most cases)
Return the cells used to store elements in this InvertibleBloomFilter
Return the number of hash functions used
Get the number of elements added in the filter Complexity in time: O(alpha*d)
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 number of cells of the filter
Generated using TypeDoc
An Invertible Bloom Lookup Table is a space-efficient and probabilistic data-structure for solving the set-difference problem efficiently without the use of logs or other prior context. It computes the set difference with communication proportional to the size of the difference between the sets being compared. They can simultaneously calculate D(A−B) and D(B−A) using O(d) space. This data structure encodes sets in a fashion that is similar in spirit to Tornado codes’ construction [6], in that it randomly combines elements using the XOR function Reference: Eppstein, D., Goodrich, M. T., Uyeda, F., & Varghese, G. (2011). What's the difference?: efficient set reconciliation without prior context. ACM SIGCOMM Computer Communication Review, 41(4), 218-229.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.220.6282&rep=rep1&type=pdf for more details about Invertible Bloom Lookup Tables
Arnaud Grall
Thomas Minier