Bloom filters are an efficient probabilistic data structure used to verify membership of an element inside of a set. There is diminishing marginal value for inserting each additional element into a Bloom filter, and so steps must be taken to maintain scalability. One such option is to create a secondary hash set for a particular hash set in a Bloom filter that has become full, known as an overflow area. At this time, there are no implementations of a Bloom filter that implement this overflow system while maintaining concurrency. In this paper, we demonstrate the creation of a concurrent overflow system for Bloom filters. We use the base Bloom filter presented in recent literature and replace their method of dynamically resizing the Bloom filters with our overflow table implementation, as outlined in one of their suggested areas for future exploration. We then compare the results of our Bloom filter with those from the previously mentioned implementation as well as a standard Bloom filter.
These Are Not the K-mers You Are Looking For: Efficient Online K-mer Counting Using a Probabilistic Data Structure
