A Case for Partitioned Bloom Filters

In a partitioned Bloom Filter (PBF) the bit vector is split into disjoint parts, one per hash function. Contrary to hardware designs, where they prevail, software implementations mostly ignore PBFs, considering them worse than standard filters (SBF), due slightly larger false positive rate (FPR). this paper, by performing an in-depth analysis, first we show that FPR advantage of SBFs smaller thought; more importantly, deriving per-element FPR, have weak spots in domain: elements test as positives much frequently expected. This relevant scenarios element tested against many filters. Moreover, are prone exhibit extremely if naive double hashing used, something occurring mainstream libraries. PBFs uniform distribution over domain, with no spots, even using hashing. Finally, survey beyond set membership testing, identifying advantages having designs SIMD techniques, for filter size reduction, disjointness, and duplicate detection streams. better, should replace SBFs, general purpose libraries base novel designs.