Near Data Processing in Taurus Database

Shu Lin,Arunprasad P. Marathe,Per-Åke Larson,Chong Chen,Calvin Sun,Paul Lee,Weidong Yu, Jianwei Li, Juncai Meng, Roulin Lin, Xiaoyang Chen, Qingping Zhu

2022 IEEE 38TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2022)(2022)

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摘要
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 TPC-H benchmark (100 GB) showed that 18 out of 22 queries benefited from NDP; data shipped was reduced by 63%; and CPU time by 50%. On Q15 the impact was even higher: data shipped was reduced by 98%; CPU time by 91%; and run time by 80%.
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关键词
cloud DBMS systems, query processing, storage virtualization, selection pushdown, early data reduction, online analytical processing, database engine architecture
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