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This paper presents new alternatives to the well-known Bloom filter datastructure. The Bloom filter, a compact data structure supporting set insertionand membership queries, has found wide application in databases, storagesystems, and networks. Because the Bloom filter performs frequent random readsand writes, it is used almost exclusively in RAM, limiting the size of the setsit can represent. This paper first describes the quotient filter, whichsupports the basic operations of the Bloom filter, achieving roughly comparableperformance in terms of space and time, but with better data locality.Operations on the quotient filter require only a small number of contiguousaccesses. The quotient filter has other advantages over the Bloom filter: itsupports deletions, it can be dynamically resized, and two quotient filters canbe efficiently merged. The paper then gives two data structures, the bufferedquotient filter and the cascade filter, which exploit the quotient filteradvantages and thus serve as SSD-optimized alternatives to the Bloom filter.The cascade filter has better asymptotic I/O performance than the bufferedquotient filter, but the buffered quotient filter outperforms the cascadefilter on small to medium data sets. Both data structures significantlyoutperform recently-proposed SSD-optimized Bloom filter variants, such as theelevator Bloom filter, buffered Bloom filter, and forest-structured Bloomfilter. In experiments, the cascade filter and buffered quotient filterperformed insertions 8.6-11 times faster than the fastest Bloom filter variantand performed lookups 0.94-2.56 times faster.

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