mlsanityData to 13 Oct 2026

Learned Indexes for a Google-scale Disk-based Database

arXiv 2012.12501 ↗First posted 23 Dec 2020cs.DB cs.DC cs.LGcited by 9

Not on the map. Why?

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.

Key facts. Cited by 9 papers in the map (data through 13 Oct 2026). First posted on arXiv 23 Dec 2020.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2022: 5; 2023: 0; 2024: 4.

9 papers.

  1. 2022Transformer Memory as a Differentiable Search Indexcited by 243
  2. 2022ELIAS: End-to-End Learning to Index and Search in Large Output Spacescited by 14
  3. 2024A Survey of Learned Indexes for the Multi-dimensional Spacecited by 4
  4. 2024Accelerating String-Key Learned Index Structures via Memoization-based Incremental Trainingcited by 4
  5. 2022Learned-Database Systems Securitycited by 2
  6. 2022LSI: A Learned Secondary Index Structurecited by 1
  7. 2024CAMAL: Optimizing LSM-trees via Active Learningcited by 1
  8. 2022Testing the Robustness of Learned Index Structurescited by 0
  9. 2024LearnedKV: Integrating LSM and Learned Index for Superior Performance on Storagecited by 0