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

Summary: Disk-resident updatable learned indexes vs B+-tree; four SOTA methods implemented. Findings: B+-tree robust on disk; learned indexes beat it only on select workloads; design principles: lower height, lean structures, faster scans, compact storage. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6642
Venue: SIGMOD
Year: 2023
Pagerank: 5.0589805e-05
Overall Rank: 6,445 | 55.17%
DOI: 10.1145/3589284

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 10 of 10 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
5,762	Oasis: An Optimal Disjoint Segmented Learned Range Filter	2024	VLDB	5.3377299e-05
7,390	Making In-Memory Learned Indexes Efficient on Disk	2024	SIGMOD	4.7431654e-05
7,869	SALI: A Scalable Adaptive Learned Index Framework based on Probability Models	2023	SIGMOD	4.6315248e-05
8,076	Accelerating String-key Learned Index Structures via Memoization-based Incremental Training	2024	VLDB	4.5917398e-05
9,346	Can Learned Indexes be Built Efficiently? A Deep Dive into Sampling Trade-offs	2024	SIGMOD	4.3532026e-05
10,087	High Performance or Low Memory? An Updatable Learned Index Framework for Time-Space Tradeoff	2026	SIGMOD	4.1945683e-05
10,396	VEGA: An Active-tuning Learned Index with Group-Wise Learning Granularity	2025	SIGMOD	4.1945683e-05
10,484	Femur: A Flexible Framework for Fast and Secure Querying from Public Key-Value Store	2025	SIGMOD	4.1945683e-05
10,712	DobLIX: A Dual-Objective Learned Index for Log-Structured Merge Trees	2025	VLDB	4.1945683e-05
11,021	SeLeP: Learning Based Semantic Prefetching for Exploratory Database Workloads	2024	VLDB	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
102	The Case for Learned Index Structures	2018	SIGMOD	0.00049545203
826	ALEX: An Updatable Adaptive Learned Index	2020	SIGMOD	0.00016224841
857	The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds	2020	VLDB	0.00015882892
1,375	FITing-Tree: A Data-aware Index Structure	2019	SIGMOD	0.00012303141
1,460	Benchmarking Learned Indexes	2021	VLDB	0.00011887068
1,478	Learning Multi-dimensional Indexes	2020	SIGMOD	0.00011762542
1,889	Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads	2021	VLDB	0.00010200865
2,115	LISA: A Learned Index Structure for Spatial Data	2020	SIGMOD	9.5257379e-05
2,552	Updatable Learned Index with Precise Positions	2021	VLDB	8.5530411e-05
2,678	Effectively Learning Spatial Indices	2020	VLDB	8.3252088e-05
3,131	FINEdex: A Fine-grained Learned Index Scheme for Scalable and Concurrent Memory Systems	2022	VLDB	7.4985793e-05
4,128	Are Updatable Learned Indexes Ready?	2022	VLDB	6.4292373e-05
4,646	CARMI: A Cache-Aware Learned Index with a Cost-based Construction Algorithm	2022	VLDB	6.0250374e-05
5,642	NFL: Robust Learned Index via Distribution Transformation	2022	VLDB	5.3929294e-05

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Overall Rank	Paper	Year	Venue	Pagerank
102	The Case for Learned Index Structures	2018	SIGMOD	0.00049545203
826	ALEX: An Updatable Adaptive Learned Index	2020	SIGMOD	0.00016224841
10,038	Understanding Robustness Issues of Updatable Learned Indexes: [Experiments & Analysis]	2026	SIGMOD	4.1945683e-05
9,746	Why Are Learned Indexes So Effective but Sometimes Ineffective?	2025	VLDB	4.2897489e-05
1,460	Benchmarking Learned Indexes	2021	VLDB	0.00011887068
8,811	Tuning Hierarchical Learned Indexes on Disk and Beyond	2022	SIGMOD	4.4441574e-05
5,074	Learned Index: A Comprehensive Experimental Evaluation	2023	VLDB	5.7175726e-05
4,128	Are Updatable Learned Indexes Ready?	2022	VLDB	6.4292373e-05
2,552	Updatable Learned Index with Precise Positions	2021	VLDB	8.5530411e-05
7,390	Making In-Memory Learned Indexes Efficient on Disk	2024	SIGMOD	4.7431654e-05