A Fast and Small Subsampled R-index

Dustin Cobas, Travis Gagie, and Gonzalo Navarro

The r-index (Gagie et al., JACM 2020) represented a breakthrough in compressed indexing of repetitive text collections, outperforming its alternatives by orders of magnitude. Its space usage, O(r) where r is the number of runs in the Burrows-Wheeler Transform of the text, is however larger than Lempel-Ziv and grammar-based indexes, and makes it uninteresting in various real-life scenarios of milder repetitiveness. In this paper we introduce the sr-index, a variant that limits a large fraction of the space to O(min(r,n/s)) for a text of length n and a given parameter s, at the expense of multiplying by s the time per occurrence reported. The sr-index is obtained by carefully subsampling the text positions indexed by the r-index, in a way that we prove is still able to support pattern matching with guaranteed performance. Our experiments demonstrate that the sr-index sharply outperforms virtually every other compressed index on repetitive texts, both in time and space, even matching the performance of the r-index while using 1.5-3.0 times less space. Only some Lempel-Ziv-based indexes achieve better compression than the sr-index, using about half the space, but they are an order of magnitude slower.