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Scalable and adaptive log manager in distributed systems

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Abstract

On-line transaction processing (OLTP) systems rely on transaction logging and quorum-based consensus protocol to guarantee durability, high availability and strong consistency. This makes the log manager a key component of distributed database management systems (DDBMSs). The leader of DDBMSs commonly adopts a centralized logging method to writing log entries into a stable storage device and uses a constant log replication strategy to periodically synchronize its state to followers. With the advent of new hardware and high parallelism of transaction processing, the traditional centralized design of logging limits scalability, and the constant trigger condition of replication can not always maintain optimal performance under dynamic workloads.

In this paper, we propose a new log manager named Salmo with scalable logging and adaptive replication for distributed database systems. The scalable logging eliminates centralized contention by utilizing a highly concurrent data structure and speedy log hole tracking. The kernel of adaptive replication is an adaptive log shipping method, which dynamically adjusts the number of log entries transmitted between leader and followers based on the real-time workload. We implemented and evaluated Salmo in the open-sourced transaction processing systems Cedar and DBx1000. Experimental results show that Salmo scales well by increasing the number of working threads, improves peak throughput by 1.56× and reduces latency by more than 4× over log replication of Raft, and maintains efficient and stable performance under dynamic workloads all the time.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62002119, 61977026, 62072180, and 61772202) supported by the Fundamental Research Funds for the Central Universities, Southwest Minzu University (2021PTJS23), and supported by the Open Fund of Shanghai Engineering Research Center on Big Data Management System. We also thank FCS reviewers for valuable comments which greatly helped to improve this paper.

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Authors and Affiliations

  1. The Key Laboratory for Computer Systems of State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, 610041, China

    Huan Zhou & Wenrong Tan

  2. School of Data Science and Engineering, East China Normal University, Shanghai, 200062, China

    Weining Qian, Xuan Zhou, Qiwen Dong & Aoying Zhou

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  1. Huan Zhou
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  2. Weining Qian
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Correspondence to Huan Zhou.

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Huan Zhou is a lecturer of the School of Computer Science and Engineering, Southwest Minzu University (SWU), China. She received her BS in computer science and technology from Sichuan Normal University (SICNU), China in 2013 and her PhD in software engineering from East China Normal University (ECNU), China in 2019. Before she joined SWU in 2021, she had worked as a postdoctoral researcher in the School of Data Science and Engineering, ECNU, China. Her research interests include database management system and data mining.

Weining Qian is a professor and dean of the School of Data Science and Engineering, East China Normal University (ECNU), China. He received his BS, MS and PhD in computer science from Fudan University, China in 1998, 2001 and 2004, respectively. He is now serving as a standing committee member of Technical Committee on Databases of China Computer Federation (CCF), a member of Expert Group on Artificial Intelligence Science and Technology Innovation of MoE and a vice president of Data Science and Knowledge Systems Engineering Committee of Systems Engineering Society of China (SESC). His research interests include scalable transaction processing, benchmarking on big data systems, big data analysis and processing, big data application and computational education.

Xuan Zhou is a professor and a vice dean of the School of Data Science and Engineering, East China Normal University (ECNU), China. He obtained his BS from Fudan University, China in 2001 and his PhD from the National University of Singapore, Singapore in 2005, both in computer science. Since his graduation, he had worked as a scientist at the L3S Research Centre (Germany) and the CSIRO ICT Centre (Australia) until the end of 2010. Before he joined ECNU in 2017, he spent six years in Renmin University of China, as an associate professor. He is the winner of the Program for New Century Excellent Talents in University of Ministry of Education (MoE). His research interests include database system and information retrieval.

Qiwen Dong is a professor of the School of Data Science and Engineering, East China Normal University (ECNU), China. He received his BS and MS in aerospace engineering and mechanics from Harbin Institute of Technology, China in 2000 and 2002, respectively, and his PhD in computer science from Harbin Institute of Technology, China in 2008. He was a postdoctoral researcher in the School of Computer Science of Fudan University, China from 2008 to 2010, and subsequently served as an associate professor there until 2016. In the meantime, he visited the University of Michigan, USA. His research interests include bioinformatics, data mining, big-data. etc.

Aoying Zhou, Vice President of East China Normal University (ECNU), Vice President of Guizhou University (GZU), Founding Dean of School of Data Science and Engineering (DaSE) ECNU, Professor. He got his BS and MS in computer science from Sichuan University, China in 1985 and 1988, respectively, and he won his PhD from Fudan University, China in 1993. He is the winner of the National Science Fund for Distinguished Young Scholars supported by the National Natural Science Foundation of China (NSFC). He is CCF (China Computer Federation) Fellow and Associate Editor-in-Chief of Chinese Journal of Computer. He served General Chair of ER’2004, Vice PC Chair of ICDE’2009 and ICDE’2012, PC Co-chair of VLDB’2014. His research interests include Web data management, data management for data-intensive computing, inmemory cluster computing, distributed transaction processing, benchmarking for big data and performance.

Wenrong Tan is a professor and dean of the School of Computer Science and Engineering, Southwest Minzu University (SWU), China. She received her BS from Sichuan University, China in 1988 and her MS from Chongqing University, China in 1991, both in computer science. She is now serving as a vice president of Sichuan Province Computer Federation and a vice president of Sichuan Institute of Artificial Intelligence. Her research interests include internet of things and natural language processing.

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Zhou, H., Qian, W., Zhou, X. et al. Scalable and adaptive log manager in distributed systems. Front. Comput. Sci. 17, 172205 (2023). https://doi.org/10.1007/s11704-022-1357-5

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