arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2105.04449cs.DBcs.DC
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G-Tran:使分布式图形事务更快

G-Tran: Making Distributed Graph Transactions Fast

Hongzhi Chen, Changji Li, Chenguang Zheng, Chenghuan Huang, Juncheng Fang, James Cheng, Jian Zhang

摘要:

图事务处理提出了许多独特的挑战,例如由于图结构的不规则性而导致的随机数据访问、由于图事务中相对较大的读/写集合而导致的低吞吐量和高中止率。为了解决这些挑战,我们提出了G-Tran --一个支持RDMA的分布式内存图形数据库,具有可序列化和快照隔离支持。首先,我们提出了一个图形原生的数据存储,以实现良好的数据本地性和快速的数据访问的事务更新和查询。其次,G-Tran采用完全分散的架构,利用RDMA以MPP模型处理分布式事务,可以利用所有计算资源实现高性能。此外,我们提出了一个新的MV-OCC实现与两个优化,以解决大的读/写集在图形事务的问题。大量的实验表明,G-Tran实现了竞争力的性能相比,其他流行的图数据库的基准工作负载。

英文摘要:

Graph transaction processing raises many unique challenges such as random data access due to the irregularity of graph structures, low throughput and high abort rate due to the relatively large read/write sets in graph transactions. To address these challenges, we present G-Tran -- an RDMA-enabled distributed in-memory graph database with serializable and snapshot isolation support. First, we propose a graph-native data store to achieve good data locality and fast data access for transactional updates and queries. Second, G-Tran adopts a fully decentralized architecture that leverages RDMA to process distributed transactions with the MPP model, which can achieve high performance by utilizing all computing resources. In addition, we propose a new MV-OCC implementation with two optimizations to address the issue of large read/write sets in graph transactions. Extensive experiments show that G-Tran achieves competitive performance compared with other popular graph databases on benchmark workloads.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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