Bloom filters are space efficient data structures that support Recovery - Training Equipment approximate membership queries.They are easily extensible but incur significant overheads when extended to support additional functionality, such as removals or counting.This paper shows that fingerprint-based hash tables offer a much better tradeoff between accuracy and space.We present TinyTable that supports set membership, removals, and multiplicity queries.
TinyTable reduces the required memory by as much as 28% compared to Bloom filter-based variants Egg Chalk for the set membership and by as much as 60% for counting and statistics.It is more compact than Bloom filters as long as the false positive ratio is less than 1%.