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Mingqiang Zhuang

dblp:328/6412 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · unresolved

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
Distributed and cloud data management · 33% Transaction processing and concurrency control · 33% Indexing and storage engines · 33%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Distributed systems · 100%

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

TopicWeightPapersLastEvidence papers
Distributed and cloud data management › distributed database architecture
distributed relational database
0.612022
OceanBase: A 707 Million tpmC Distributed Relational Database System · Proc. VLDB Endow. 2022
Transaction processing and concurrency control
distributed transaction processing
0.612022
OceanBase: A 707 Million tpmC Distributed Relational Database System · Proc. VLDB Endow. 2022
Indexing and storage engines › storage management
storage engine
0.612022
OceanBase: A 707 Million tpmC Distributed Relational Database System · Proc. VLDB Endow. 2022
Distributed systems
fault tolerance
0.212022
OceanBase: A 707 Million tpmC Distributed Relational Database System · Proc. VLDB Endow. 2022

Methods — techniques the papers use, named apart from their topics

shared-nothing architecture · 1.1multi-tenancy · 1.1
YearPublicationVenuePosition
2022 OceanBase: A 707 Million tpmC Distributed Relational Database System
abstract
We have designed and developed OceanBase, a distributed relational database system from the very basics for a decade. Being a scale-out multi-tenant system, OceanBase is cross-region fault tolerant, which is based on the shared-nothing architecture. Besides sharing many similar goals with alternative distributed DBMS, such as horizontal scalability, fault-tolerance, etc., our design has been driven by the demands of typical RDBMS compatibility as well as both on-premise and off-premise deployments. OceanBase has fulfilled its design goal. It implements the salient features of certain mainstream classical RDBMS, and most applications on them can run on OceanBase, with or without a few minor modifications. Tens of thousands of OceanBase servers have been deployed in Alipay.com as well as many other commercial organizations. It has also successfully passed the TPC-C benchmark test and seized the first place with more than 707 million tpmC. This paper presents the goals, design criteria, infrastructure, and key components of OceanBase including its engines for storage and transaction processing. Further, it details how OceanBase achieves the above leading TPC-C benchmark in a distributed cluster with more than 1,500 servers from 3 zones. It also describes lessons what we have learnt in building OceanBase for more than a decade.
Chuanhui Yang, Fusheng Han, Mingqiang Zhuang, Zhifeng Yang, Xiaojun Cheng, Yuzhong Zhao, Wenhui Shi, Huafeng Xi, Huang Yu, Yi Pan 0001, Boxue Yin, Junquan Chen, Quanqing Xu
Proc. VLDB Endow.4