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Xiaojian Fang

dblp:325/9679 · 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 · 70% Transaction processing and concurrency control · 23% Database system architecture and tuning · 7%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Distributed and cloud data management › cloud database
cloud-native database
0.612022
PolarDB-X: An Elastic Distributed Relational Database for Cloud-Native Applications · ICDE 2022
Distributed and cloud data management › distributed database architecture
distributed relational database
0.612022
PolarDB-X: An Elastic Distributed Relational Database for Cloud-Native Applications · ICDE 2022
Transaction processing and concurrency control › isolation levels
snapshot isolation
0.612022
PolarDB-X: An Elastic Distributed Relational Database for Cloud-Native Applications · ICDE 2022
Database system architecture and tuning
hybrid transactional and analytical processing
0.212022
PolarDB-X: An Elastic Distributed Relational Database for Cloud-Native Applications · ICDE 2022

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

paxos · 0.6hybrid logical clock · 0.6MPP engine · 0.6
YearPublicationVenuePosition
2022 PolarDB-X: An Elastic Distributed Relational Database for Cloud-Native Applications
abstract
Cloud computing is on the rise, which promotes new breeds of database systems to accommodate the cloud environment. The development of cloud-native databases reveals three trends. One is the adoption of multi-datacenter (DC) deployment to survive the downtime of any single site. Another is the separation of computation and storage resources to achieve higher elasticity and scalability. The last is the support of HTAP to eliminate data redundancy and system complexity from heterogeneous databases. To cater to these trends, we design a distributed relational database called PolarDB-X, which is built on top of the cloud-native database PolarDB. It hence inherits many cloud-native features, such as multi-datacenter deployment and elasticity. To achieve cross-DC capability, it leverages Paxos and hybrid logical clock to achieve durability and snapshot-isolation consistency with low coordination costs. For resource elasticity, since the underlying PolarDB supports rapid migration of tenants between nodes, PolarDB-X can quickly scale the cluster to cope with a sudden traffic increase. For HTAP support, with the help of read replicas and a HTAP executor, PolarDB-X can improve the latency and parallelism of analytical queries without impacting concurrently-running TP workloads. Using its MPP engine and an in-memory column index, the efficiency of analytical queries can be further enhanced. PolarDB-X is now a cloud database service at Alibaba Cloud. We have learned many useful lessons from its development and operation, and have incorporated those into our design and analysis.
Wei Cao 0006, Feifei Li 0001, Gui Huang, Jianghang Lou, Dengcheng He, Mengshi Sun, Yingqiang Zhang, Sheng Wang 0011, Xueqiang Wu, Han Liao, Zilin Chen, Xiaojian Fang, Chenghui Liang, Yanxin Luo, Huanming Wang, Songlei Wang, Zhanfeng Ma, Xinjun Yang, Yubin Ruan, Qingda Hu, Junbin Kang
ICDE13