# Learned Indexes for a Google-scale Disk-based Database

arXiv: https://arxiv.org/abs/2012.12501 · HTML: https://mlsanity.com/paper/2012.12501/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2020.

## Built on by (most cited first, 9 of 9)

- [Transformer Memory as a Differentiable Search Index](https://mlsanity.com/paper/2202.06991/) (2022)
- [ELIAS: End-to-End Learning to Index and Search in Large Output Spaces](https://mlsanity.com/paper/2210.08410/) (2022)
- [A Survey of Learned Indexes for the Multi-dimensional Space](https://mlsanity.com/paper/2403.06456/) (2024)
- [Accelerating String-Key Learned Index Structures via Memoization-based Incremental Training](https://mlsanity.com/paper/2403.11472/) (2024)
- [Learned-Database Systems Security](https://mlsanity.com/paper/2212.10318/) (2022)
- [LSI: A Learned Secondary Index Structure](https://mlsanity.com/paper/2205.05769/) (2022)
- [CAMAL: Optimizing LSM-trees via Active Learning](https://mlsanity.com/paper/2409.15130/) (2024)
- [Testing the Robustness of Learned Index Structures](https://mlsanity.com/paper/2207.11575/) (2022)
- [LearnedKV: Integrating LSM and Learned Index for Superior Performance on Storage](https://mlsanity.com/paper/2406.18892/) (2024)

## Abstract (arXiv)

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.