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arXiv · 2111.14905

Bounding the Last Mile: Efficient Learned String Indexing

Benjamin Spector·Andreas Kipf·Kapil Vaidya·Chi Wang·Umar Farooq Minhas·Tim Kraska
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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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Benjamin Spector, Andreas Kipf, Kapil Vaidya, Chi Wang, Umar Farooq Minhas, Tim Kraska. 2021-11-29. Bounding the Last Mile: Efficient Learned String Indexing. https://arxiv.org/abs/2111.14905

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