HIRE: A Hybrid Learned Index for Robust and Efficient Performance under Mixed Workloads

Summary: HIRE: hybrid learned in-memory index targeting robust mixed-workload performance, combining adaptive leaf nodes, model-accelerated internal nodes, and non-blocking recalibration to retain stability under updates. Aims to fix learned-index pain points—tail latency, range queries, and workload sensitivity—while outperforming classic indexes on throughput. (summarized by gpt-5-mini on Apr 11 2026)

Paper ID: h3c929863a3910aea
Venue: SIGMOD
Year: 2026
Pagerank: 4.9605343e-05
Overall Rank: 10,720 | 28.15%
DOI: 10.1145/3786657
PDF: Download (CC BY 4.0)

Incoming Non-self Citations Over Time

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Authors

1. Xinyi Zhang (Hong Kong Baptist University)
2. Liang Liang (EPFL)
3. Anastasia Ailamaki (EPFL)
4. Jianliang Xu (Hong Kong Baptist University)

BibTeX Citation

@inproceedings{zhang_sigmod26,
        title = {{HIRE: A Hybrid Learned Index for Robust and Efficient Performance under Mixed Workloads}},
        author = {Zhang, Xinyi and Liang, Liang and Ailamaki, Anastasia and Xu, Jianliang},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3786657},
        url = {https://dl.acm.org/doi/10.1145/3786657},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 24 of 24 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	0.00046249672
427	ALEX: An Updatable Adaptive Learned Index	2020	SIGMOD	0.00018429918
461	The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds	2020	VLDB	0.00017835351
774	FITing-Tree: A Data-aware Index Structure	2019	SIGMOD	0.00014063511
834	Benchmarking Learned Indexes	2021	VLDB	0.00013539098
1,529	Updatable Learned Index with Precise Positions	2021	VLDB	0.00010320909
2,254	FINEdex: A Fine-grained Learned Index Scheme for Scalable and Concurrent Memory Systems	2022	VLDB	8.7429699e-05
2,590	Are Updatable Learned Indexes Ready?	2022	VLDB	8.2368464e-05
2,870	APEX: A High-Performance Learned Index on Persistent Memory	2022	VLDB	7.9033232e-05
3,632	Learned Index: A Comprehensive Experimental Evaluation	2023	VLDB	7.1274345e-05
3,716	CARMI: A Cache-Aware Learned Index with a Cost-based Construction Algorithm	2022	VLDB	7.0541948e-05
4,389	DILI: A Distribution-Driven Learned Index	2023	VLDB	6.6111249e-05
6,250	Predicate Caching: Query-Driven Secondary Indexing for Cloud Data Warehouses	2024	SIGMOD	5.818039e-05
6,622	Making In-Memory Learned Indexes Efficient on Disk	2024	SIGMOD	5.7136886e-05
6,802	SALI: A Scalable Adaptive Learned Index Framework based on Probability Models	2023	SIGMOD	5.6623052e-05
7,103	LITS: An Optimized Learned Index for Strings	2024	VLDB	5.5824915e-05
7,314	Hyper: A High-Performance and Memory-Efficient Learned Index via Hybrid Construction	2024	SIGMOD	5.5425359e-05
8,401	Tuning Hierarchical Learned Indexes on Disk and Beyond	2022	SIGMOD	5.3233821e-05
8,558	AirIndex: Versatile Index Tuning Through Data and Storage	2023	SIGMOD	5.3016395e-05
8,638	Limousine: Blending Learned and Classical Indexes to Self-Design Larger-than-Memory Cloud Storage Engines	2024	SIGMOD	5.2825944e-05
8,782	A New Paradigm in Tuning Learned Indexes: A Reinforcement Learning Enhanced Approach	2025	SIGMOD	5.2647219e-05
8,903	Why Are Learned Indexes So Effective but Sometimes Ineffective?	2025	VLDB	5.238416e-05
9,627	Can Learned Indexes be Built Efficiently? A Deep Dive into Sampling Trade-offs	2024	SIGMOD	5.1369088e-05
10,222	SWIX: A Memory-efficient Sliding Window Learned Index	2024	SIGMOD	5.0460607e-05

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