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Near Data Processing in Taurus Database

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arxiv 2506.20010 v1 pith:N4E3NYSV submitted 2025-06-24 cs.DB

Near Data Processing in Taurus Database

Shu Lin , Arunprasad P. Marathe , Per-\.Ake Larson , Chong Chen , Calvin Sun , Paul Lee , Weidong Yu This is my paper
classification cs.DB
keywords datapercenttimeshippedstoragetauruscomputedatabase
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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