Poly Base: Adapting to Data Affinity Changes in Geo-Replicated Database via Row-Level Consensus-Group Affiliation Re-Assignment
Ruan, Chaoyi Zhang, Yingqiang Zhang, Juncheng Li, Cheng Ma, Xiaosong Chen, Hao Zhou, Jie Li, Feifei Yang, Xinjun
Ruan, Chaoyi
Zhang, Yingqiang
Zhang, Juncheng
Li, Cheng
Ma, Xiaosong
Chen, Hao
Zhou, Jie
Li, Feifei
Yang, Xinjun
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Department
Computer Science
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Journal article
Date
2025
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Language
English
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Abstract
Transaction performance in geo-replicated databases heavily relies on the request location: when not issued by the primary region, transactions are forced to involve costly wide-area communication. While existing systems distribute primary roles across regions, such assignment typically occurs at the shard level, making it difficult to align with geographically dispersed access to individual records.
This paper introduces PolyBase, a pioneering architecture to address such misalignment, leveraging the widely adopted Paxos-based log replication mechanisms. It enables flexible row-level consensus group affiliation, which runs on an unchanged Paxos protocol, but dynamically re-assigns database rows between Paxos log replication groups, whose leaders become the primary region, enjoying faster writes and up-to-date versions for reads. With carefully designed data structures and protocols, PolyBase significantly reduces wide-area RTTs without compromising transaction or log replication consistency or reliability guarantees. We implemented PolyBase with optimized re-assignment policies and integrated it into two popular databases (RocksDB and MySQL). Our evaluation on AWS, using a production e-commerce workload and microbench-marks confirms that PolyBase offers significantly higher transaction throughput and lower average/tail latency compared to baselines.
Citation
C. Ruan et al., “Poly Base: Adapting to Data Affinity Changes in Geo-Replicated Database via Row-Level Consensus-Group Affiliation Re-Assignment,” Proceedings of the VLDB Endowment, vol. 18, no. 3, pp. 702–714, Nov. 2025, doi: 10.14778/3712221.3712236
Source
Proceedings of the VLDB Endowment
Conference
51st International Conference on Very Large Data Bases, VLDB 2025
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Source
51st International Conference on Very Large Data Bases, VLDB 2025
Publisher
VLDB Endowment