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Bounding the Last Mile: Efficient Learned String Indexing

29 November 2021
Benjamin Spector
Andreas Kipf
Kapil Vaidya
Chi Wang
U. F. Minhas
Tim Kraska
ArXiv (abs)PDFHTML
Abstract

We introduce the RadixStringSpline (RSS) learned index structure for efficiently indexing strings. RSS is a tree of radix splines each indexing a fixed number of bytes. RSS approaches or exceeds the performance of traditional string indexes while using 7-70×\times× less memory. RSS achieves this by using the minimal string prefix to sufficiently distinguish the data unlike most learned approaches which index the entire string. Additionally, the bounded-error nature of RSS accelerates the last mile search and also enables a memory-efficient hash-table lookup accelerator. We benchmark RSS on several real-world string datasets against ART and HOT. Our experiments suggest this line of research may be promising for future memory-intensive database applications.

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