Order-Preserving Key Compression for In-Memory Search Trees
Summary: HOPE is a fast, order-preserving dictionary encoder for in-memory search trees, compressing arbitrary keys with a compact dictionary. A theory-driven model selects six schemes, evaluated on SuRF, ART, HOT, B+Tree, and Prefix B+Tree, delivering up to 40% latency reduction and 30% memory savings for string workloads. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Huanchen Zhang (Carnegie Mellon University)
- 2. Xiaoxuan Liu (Carnegie Mellon University)
- 3. David G. Andersen (Carnegie Mellon University)
- 4. Michael Kaminsky (BrdgAI)
- 5. Kimberly Keeton (Hewlett Packard Enterprise)
- 6. Andrew Pavlo (Carnegie Mellon University)
BibTeX Citation
@inproceedings{zhang_sigmod20,
title = {{Order-Preserving Key Compression for In-Memory Search Trees}},
author = {Zhang, Huanchen and Liu, Xiaoxuan and Andersen, David G. and Kaminsky, Michael and Keeton, Kimberly and Pavlo, Andrew},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3380583},
url = {https://dl.acm.org/doi/10.1145/3318464.3380583},
year = {2020}
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