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Computer Science > Databases

arXiv:2506.20010 (cs)
[Submitted on 24 Jun 2025 ]

Title: Near Data Processing in Taurus Database

Title: 猎户座数据库中的近数据处理

Authors:Shu Lin, Arunprasad P. Marathe, Per-Ȧke Larson, Chong Chen, Calvin Sun, Paul Lee, Weidong Yu
Abstract: Huawei's cloud-native database system GaussDB for MySQL (also known as Taurus) stores data in a separate storage layer consisting of a pool of storage servers. Each server has considerable compute power making it possible to push data reduction operations (selection, projection, and aggregation) close to storage. This paper describes the design and implementation of near data processing (NDP) in Taurus. NDP has several benefits: it reduces the amount of data shipped over the network; frees up CPU capacity in the compute layer; and reduces query run time, thereby enabling higher system throughput. Experiments with the TPCH benchmark (100 GB) showed that 18 out of 22 queries benefited from NDP; data shipped was reduced by 63 percent; and CPU time by 50 percent. On Q15 the impact was even higher: data shipped was reduced by 98 percent; CPU time by 91 percent; and run time by 80 percent.
Subjects: Databases (cs.DB)
ACM classes: H.2.4
Cite as: arXiv:2506.20010 [cs.DB]
  (or arXiv:2506.20010v1 [cs.DB] for this version)
  https://doi.org/10.48550/arXiv.2506.20010
Journal reference: 2022 IEEE 38th International Conference on Data Engineering (ICDE), Kuala Lumpur, Malaysia, 2022, pp. 1662-1674,
Related DOI: https://doi.org/10.1109/ICDE53745.2022.00170

Submission history

From: Per-Ake Larson [view email]
[v1] Tue, 24 Jun 2025 20:57:32 UTC (395 KB)
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