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: 6643
Venue: SIGMOD
Year: 2023
Pagerank: 5.0541252e-05
Overall Rank: 6,442 | 55.23%
DOI: 10.1145/3589284

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 11 of 11 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
5,769	Oasis: An Optimal Disjoint Segmented Learned Range Filter	2024	VLDB	5.3326049e-05
7,389	Making In-Memory Learned Indexes Efficient on Disk	2024	SIGMOD	4.7386163e-05
7,870	SALI: A Scalable Adaptive Learned Index Framework based on Probability Models	2023	SIGMOD	4.6271153e-05
8,079	Accelerating String-key Learned Index Structures via Memoization-based Incremental Training	2024	VLDB	4.5873372e-05
9,351	Can Learned Indexes be Built Efficiently? A Deep Dive into Sampling Trade-offs	2024	SIGMOD	4.3490308e-05
10,087	High Performance or Low Memory? An Updatable Learned Index Framework for Time-Space Tradeoff	2026	SIGMOD	4.1905499e-05
10,282	Toward Drift-Aware Database Benchmarking	2026	VLDB	4.1905499e-05
10,407	VEGA: An Active-tuning Learned Index with Group-Wise Learning Granularity	2025	SIGMOD	4.1905499e-05
10,494	Femur: A Flexible Framework for Fast and Secure Querying from Public Key-Value Store	2025	SIGMOD	4.1905499e-05
10,719	DobLIX: A Dual-Objective Learned Index for Log-Structured Merge Trees	2025	VLDB	4.1905499e-05
11,024	SeLeP: Learning Based Semantic Prefetching for Exploratory Database Workloads	2024	VLDB	4.1905499e-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
101	The Case for Learned Index Structures	2018	SIGMOD	0.00049778866
819	ALEX: An Updatable Adaptive Learned Index	2020	SIGMOD	0.00016237497
844	The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds	2020	VLDB	0.00015964123
1,365	FITing-Tree: A Data-aware Index Structure	2019	SIGMOD	0.00012379754
1,438	Benchmarking Learned Indexes	2021	VLDB	0.00011965956
1,464	Learning Multi-dimensional Indexes	2020	SIGMOD	0.0001184772
1,887	Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads	2021	VLDB	0.00010201938
2,108	LISA: A Learned Index Structure for Spatial Data	2020	SIGMOD	9.5283642e-05
2,550	Updatable Learned Index with Precise Positions	2021	VLDB	8.5569576e-05
2,676	Effectively Learning Spatial Indices	2020	VLDB	8.326321e-05
3,136	FINEdex: A Fine-grained Learned Index Scheme for Scalable and Concurrent Memory Systems	2022	VLDB	7.4926368e-05
4,056	Are Updatable Learned Indexes Ready?	2022	VLDB	6.4905689e-05
4,830	CARMI: A Cache-Aware Learned Index with a Cost-based Construction Algorithm	2022	VLDB	5.8884997e-05
5,655	NFL: Robust Learned Index via Distribution Transformation	2022	VLDB	5.3877506e-05

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Overall Rank	Paper	Year	Venue	Pagerank
101	The Case for Learned Index Structures	2018	SIGMOD	0.00049778866
819	ALEX: An Updatable Adaptive Learned Index	2020	SIGMOD	0.00016237497
10,038	Understanding Robustness Issues of Updatable Learned Indexes: [Experiments & Analysis]	2026	SIGMOD	4.1905499e-05
9,745	Why Are Learned Indexes So Effective but Sometimes Ineffective?	2025	VLDB	4.2856385e-05
1,438	Benchmarking Learned Indexes	2021	VLDB	0.00011965956
8,811	Tuning Hierarchical Learned Indexes on Disk and Beyond	2022	SIGMOD	4.4398976e-05
5,072	Learned Index: A Comprehensive Experimental Evaluation	2023	VLDB	5.7121108e-05
4,056	Are Updatable Learned Indexes Ready?	2022	VLDB	6.4905689e-05
2,550	Updatable Learned Index with Precise Positions	2021	VLDB	8.5569576e-05
7,389	Making In-Memory Learned Indexes Efficient on Disk	2024	SIGMOD	4.7386163e-05