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arXiv · 2012.12501

Learned Indexes for a Google-scale Disk-based Database

Hussam Abu-Libdeh·Deniz Altınbüken·Alex Beutel·Ed H. Chi·Lyric Doshi·Tim Kraska·Xiaozhou·Li·Andy Ly·Christopher Olston
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Abstract

There is great excitement about learned index structures, but understandable skepticism about the practicality of a new method uprooting decades of research on B-Trees. In this paper, we work to remove some of that uncertainty by demonstrating how a learned index can be integrated in a distributed, disk-based database system: Google's Bigtable. We detail several design decisions we made to integrate learned indexes in Bigtable. Our results show that integrating learned index significantly improves the end-to-end read latency and throughput for Bigtable.

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Hussam Abu-Libdeh, Deniz Altınbüken, Alex Beutel, Ed H. Chi, Lyric Doshi, Tim Kraska, Xiaozhou, Li, Andy Ly, Christopher Olston. 2020-12-23. Learned Indexes for a Google-scale Disk-based Database. https://arxiv.org/abs/2012.12501

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