# Updatable Learned Indexes Meet Disk-Resident DBMS -- From Evaluations to Design Choices

arXiv: https://arxiv.org/abs/2305.01237 · HTML: https://mlsanity.com/paper/2305.01237/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2023.

## Built on by (most cited first, 6 of 6)

- [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)
- [DobLIX: A Dual-Objective Learned Index for Log-Structured Merge Trees](https://mlsanity.com/paper/2502.05369/) (2025)
- [LearnedKV: Integrating LSM and Learned Index for Superior Performance on Storage](https://mlsanity.com/paper/2406.18892/) (2024)
- [Sig2Model: A Boosting-Driven Model for Updatable Learned Indexes](https://mlsanity.com/paper/2509.20781/) (2025)
- [Towards Anticipatory Databases Through Shared Data and Workload Semantics](https://mlsanity.com/paper/2609.14255/) (2026)

## Abstract (arXiv)

Although many updatable learned indexes have been proposed in recent years, whether they can outperform traditional approaches on disk remains unknown. In this study, we revisit and implement four state-of-the-art updatable learned indexes on disk, and compare them against the B+-tree under a wide range of settings. Through our evaluation, we make some key observations: 1) Overall, the B+-tree performs well across a range of workload types and datasets. 2) A learned index could outperform B+-tree or other learned indexes on disk for a specific workload. For example, PGM achieves the best performance in write-only workloads while LIPP significantly outperforms others in lookup-only workloads. We further conduct a detailed performance analysis to reveal the strengths and weaknesses of these learned indexes on disk. Moreover, we summarize the observed common shortcomings in five categories and propose four design principles to guide future design of on-disk, updatable learned indexes: (1) reducing the index's tree height, (2) better data structures to lower operation overheads, (3) improving the efficiency of scan operations, and (4) more efficient storage layout.