Buffered Count-Min Sketch on SSD: Theory and Experiments

Frequency estimation data structures such as the count-min sketch (CMS) have found numerous applications in databases, networking, computational biology and other domains. Many applications that use the count-min sketch process massive and rapidly evolving data sets. For data-intensive applications that aim to keep the overestimate error low, the count-min sketch becomes too large to store in available RAM and may have to migrate to external storage (e.g., SSD.) Due to the random-read/write nature of hash operations of the count-min sketch, simply placing it on SSD stifles the performance of time-critical applications, requiring about 4-6 random reads/writes to SSD per estimate (lookup) and update (insert) operation. In this paper, we expand on the preliminary idea of the buffered count-min sketch (BCMS) {[Eydi et al., 2017]}, an SSD variant of the count-min sketch, that uses hash localization to scale efficiently out of RAM while keeping the total error bounded. We describe the design and implementation of the buffered count-min sketch, and empirically show that our implementation achieves 3.7 x-4.7 x speedup on update and 4.3 x speedup on estimate operations compared to the traditional count-min sketch on SSD. Our design also offers an asymptotic improvement in the external-memory model over the original data structure: r random I/Os are reduced to 1 I/O for the estimate operation. For a data structure that uses k blocks on SSD, w as the word/counter size, r as the number of rows, M as the number of bits in the main memory, our data structure uses kwr/M amortized I/Os for updates, or, if kwr/M > 1, 1 I/O in the worst case. In typical scenarios, kwr/M is much smaller than 1. This is in contrast to O(r) I/Os incurred for each update in the original data structure. Lastly, we mathematically show that for the buffered count-min sketch, the error rate does not substantially degrade over the traditional count-min sketch. Specifically, we prove that for any query q, our data structure provides the guarantee: Pr[Error(q) >= n epsilon (1+o(1))] <= delta + o(1), which, up to o(1) terms, is the same guarantee as that of a traditional count-min sketch

Don't Thrash: How to Cache Your Hash on Flash

This paper presents new alternatives to the well-known Bloom filter data structure. The Bloom filter, a compact data structure supporting set insertion and membership queries, has found wide application in databases, storage systems, and networks. Because the Bloom filter performs frequent random reads and writes, it is used almost exclusively in RAM, limiting the size of the sets it can represent. This paper first describes the quotient filter, which supports the basic operations of the Bloom filter, achieving roughly comparable performance in terms of space and time, but with better data locality. Operations on the quotient filter require only a small number of contiguous accesses. The quotient filter has other advantages over the Bloom filter: it supports deletions, it can be dynamically resized, and two quotient filters can be efficiently merged. The paper then gives two data structures, the buffered quotient filter and the cascade filter, which exploit the quotient filter advantages and thus serve as SSD-optimized alternatives to the Bloom filter. The cascade filter has better asymptotic I/O performance than the buffered quotient filter, but the buffered quotient filter outperforms the cascade filter on small to medium data sets. Both data structures significantly outperform recently-proposed SSD-optimized Bloom filter variants, such as the elevator Bloom filter, buffered Bloom filter, and forest-structured Bloom filter. In experiments, the cascade filter and buffered quotient filter performed insertions 8.6-11 times faster than the fastest Bloom filter variant and performed lookups 0.94-2.56 times faster.Comment: VLDB201

Don&apos;t Thrash: How to Cache your Hash on Flash Appears in the proceedings of the 3rd USENIX Workshop in Hot Topics in Storage and File Systems (HotStorage 2011)

Abstract Many large storage systems use approximatemembership-query (AMQ) data structures to deal with the massive amounts of data that they process. An AMQ data structure is a dictionary that trades off space for a false positive rate on membership queries. It is designed to fit into small, fast storage, and it is used to avoid I/Os on slow storage. The Bloom filter is a well-known example of an AMQ data structure. Bloom filters, however, do not scale outside of main memory. This paper describes the Cascade Filter TM , an AMQ data structure that scales beyond main memory, supporting over half a million insertions/deletions per second and over 500 lookups per second on a commodity flashbased SSD