Near-Optimal Per-Key Streaming Quantile Estimation
Summary: KLL-Polymer is the first near-optimal per-key heavy-hitter quantile sketch, aggregating keys during KLL hierarchical sampling rather than keeping a sketch per key. It achieves ε error with failure probability δ in O((θε)⁻¹ log²log(1/δ)) space and accelerates per-key quantile queries in RocksDB. (summarized by gpt-6-luna on Oct 22 2026)
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Authors
- 1. Jiarui Guo (Peking University)
- 2. Feiyu Wang (Peking University)
- 3. Zhuochen Fan (Peng Cheng Laboratory)
- 4. Tong Yang (Peking University)
- 5. Xiaolin Wang (Peking University)
BibTeX Citation
@inproceedings{guo_sigmod27,
title = {{Near-Optimal Per-Key Streaming Quantile Estimation}},
author = {Guo, Jiarui and Wang, Feiyu and Fan, Zhuochen and Yang, Tong and Wang, Xiaolin},
series = {{SIGMOD} '27},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3837118},
url = {https://dl.acm.org/doi/10.1145/3837118},
year = {2027}
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