Hyper: A High-Performance and Memory-Efficient Learned Index via Hybrid Construction

Summary: Hyper: hybrid learned index construction to break the usual performance/memory trade-off in dynamic settings. Bottom-up leaves + top-down internals yield concurrent write support and strong speedups with drastically lower memory than prior learned indexes. (summarized by gpt-5.4-mini on May 24 2026)

Paper ID: h4b305f11385d3d8e
Venue: SIGMOD
Year: 2024
Pagerank: 5.5609237e-05
Overall Rank: 7,297 | 50.96%
DOI: 10.1145/3654948

Incoming Non-self Citations Over Time

Authors

1. Shunkang Zhang (Hong Kong University of Science and Technology)
2. Ji Qi (Institute of Software, Chinese Academy of Sciences)
3. Xin Yao (Huawei Theory Lab)
4. André Brinkmann (Johannes Gutenberg University Mainz)

BibTeX Citation

@inproceedings{zhang_sigmod24,
        title = {{Hyper: A High-Performance and Memory-Efficient Learned Index via Hybrid Construction}},
        author = {Zhang, Shunkang and Qi, Ji and Yao, Xin and Brinkmann, André},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3654948},
        url = {https://dl.acm.org/doi/10.1145/3654948},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 7 of 7 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
8,032	NeurDB: On the Design and Implementation of an AI-powered Autonomous Database	2025	CIDR	5.4017794e-05
9,782	Understanding Robustness Issues of Updatable Learned Indexes: [Experiments & Analysis]	2026	SIGMOD	5.129066e-05
10,350	Replace or Augment: Rethinking the Role of Learned Indexes in Relational Databases	2027	VLDB	4.9769913e-05
10,486	LINE: A Learned Index with Group-Enhanced Leaves and Cache-Optimized Inner Tree	2026	SIGMOD	4.9769913e-05
10,586	High Performance or Low Memory? An Updatable Learned Index Framework for Time-Space Tradeoff	2026	SIGMOD	4.9769913e-05
10,659	HIRE: A Hybrid Learned Index for Robust and Efficient Performance under Mixed Workloads	2026	SIGMOD	4.9769913e-05
11,245	FB+-tree: A Memory-Optimized B+-tree with Latch-Free Update	2025	VLDB	4.9769913e-05

Outgoing Citations (Sorted by Pagerank)

Showing 19 of 19 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
40	The Case for Learned Index Structures	2018	SIGMOD	0.00046363107
61	Integrating Compression and Execution in Column-Oriented Database Systems	2006	SIGMOD	0.00039236924
277	FAST: Fast Architecture Sensitive Tree Search on Modern CPUs and GPUs	2010	SIGMOD	0.00022320139
422	ALEX: An Updatable Adaptive Learned Index	2020	SIGMOD	0.00018488849
458	The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds	2020	VLDB	0.00017880664
513	Tree Indexing on Solid State Drives	2010	VLDB	0.00017040424
768	FITing-Tree: A Data-aware Index Structure	2019	SIGMOD	0.00014107655
904	HOT: A Height Optimized Trie Index for Main-Memory Database Systems	2018	SIGMOD	0.00013170142
1,188	Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads	2021	VLDB	0.00011598149
1,525	Updatable Learned Index with Precise Positions	2021	VLDB	0.00010355133
2,235	Efficiently Searching In-Memory Sorted Arrays: Revenge of the Interpolation Search?	2019	SIGMOD	8.787006e-05
2,270	FINEdex: A Fine-grained Learned Index Scheme for Scalable and Concurrent Memory Systems	2022	VLDB	8.7166469e-05
2,582	Are Updatable Learned Indexes Ready?	2022	VLDB	8.2641447e-05
3,198	CDFShop: Exploring and Optimizing Learned Index Structures	2020	SIGMOD	7.5422544e-05
3,621	Learned Index: A Comprehensive Experimental Evaluation	2023	VLDB	7.1510804e-05
4,366	DILI: A Distribution-Driven Learned Index	2023	VLDB	6.6330579e-05
5,388	Order-Preserving Key Compression for In-Memory Search Trees	2020	SIGMOD	6.1520776e-05
6,787	SALI: A Scalable Adaptive Learned Index Framework based on Probability Models	2023	SIGMOD	5.6810904e-05
8,179	Hyperion: Building the Largest In-memory Search Tree	2019	SIGMOD	5.3816264e-05

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