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)
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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}
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