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Roulin Lin

dblp:325/9433 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Database system architecture and tuning · 50% Query processing and optimization · 50%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Cloud and datacenter computing · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Database system architecture and tuning › hardware acceleration for data management
near-data processing		 0.612022
Near Data Processing in Taurus Database · ICDE 2022
Query processing and optimization › query optimization › transformation-based optimization
query pushdown		 0.612022
Near Data Processing in Taurus Database · ICDE 2022
Cloud and datacenter computing › cloud data management
cloud-native database		 0.212022
Near Data Processing in Taurus Database · ICDE 2022
YearPublicationVenuePosition
2022 Near Data Processing in Taurus Database
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 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%.
Arunprasad P. Marathe, Per-Åke Larson, Calvin Sun, Paul Lee, Juncai Meng, Roulin Lin, Qingping Zhu
 ICDE10