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Plover: parallel logging for replication systems

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Abstract

Logging and replication are commonly used recovery approaches in database systems. To guarantee that the database state is not corrupted due to system crash, database systems rely on a centralized logging method to persist log entries into a stable storage device; to prevent data loss due to device failure, a primary server in the database system periodically replicates its state to backup servers by copying log entries over networks. As the transaction execution in a modern database system is highly parallelized, the centralized logging with a single I/O channel tends to inhibit the scalability of the system. Meanwhile, log entries generated at high speed make a network with limited bandwidth a potential bottleneck for replication.

In this paper, we propose an in-memory transaction engine named Plover with parallel logging and speedy replication for primary-backup replication systems. The parallel logging enables concurrent execution of logging by utilizing multiple log buffers associated with multiple stable storages. All log entries in the log buffers maintain a global sequence number (GSN), which ensures a partial order among transactions. The kernel of the speedy replication is an adaptive shipping method, which allows to transfer data increments instead of log entries to backups under heavy workloads. Experimental results using the YCSB and TPC-C benchmarks show that Plover scales well with the increasing number of worker threads and stable storage devices. And our adaptive shipping requires only one fifth network bandwidth of the conventional log shipping.

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Acknowledgements

This work was partially supported by National Key R&D Program of China (2018YFB1003303), the National Natural Science Foundation of China (Grant Nos. 61672232 and 61772202). Youth Foundation of National Science Foundation (61702189). Youth Science and Technology — “Yang Fan” Program of Shanghai (17YF1427800).

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Authors and Affiliations

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

    Huan Zhou, Jinwei Guo, Huiqi Hu, Weining Qian, Xuan Zhou & Aoying Zhou

Authors
  1. Huan Zhou
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  2. Jinwei Guo
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  3. Huiqi Hu
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  4. Weining Qian
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  5. Xuan Zhou
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  6. Aoying Zhou
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Corresponding author

Correspondence to Huiqi Hu.

Additional information

Huan Zhou is a PhD candidate in the School of Data Science and Engineering, East China Normal University, China. She received her BS in Computer Science and Technology from Sichuan Normal University, China in 2013. Her research interests include transaction processing in database management systems and replication in distributed systems.

Jinwei Guo is a PhD candidate in School of Data Science and Engineering from East China Normal University (ECNU), China. He received his bachelor degree in Computer Science and Technology from Qufu Normal University, China in 2010, and his master degree from Guizhou University, China in 2014. His research interests include transaction processing in database management systems and high availability in distributed systems.

Huiqi Hu is currently a lecture in the School of Data Science and Engineering, East China Normal University, China. He received his PhD Degree in Tsinghua University, China. His research interests mainly include database system theory and implementation, query optimization.

Weining Qian is a professor and dean of the School of Data Science and Engineering, East China Normal University, China. He received his MS and PhD in computer science from Fudan University, China in 2001 and 2004, respectively. He is now serving as a standing committee member of Database Technology Committee of China Computer Federation, and committee member of ACM SIGMOD China Chapter. His research interests include scalable transaction processing, benchmarking big data systems, and management and analysis of massive datasets.

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 BSc from Fudan University, China and his PhD from the National University of Singapore, both in Computer Science. Since his graduation in 2005, 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. Xuan’s research interests include database system and information retrieval.

Aoying Zhou, Vice President of East China Normal University, Founding Dean of School of Data Science and Engineering (DaSE), Professor. He got his master and bachelor degree in Computer Science from Sichuan University, China in 1988 and 1985 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) and the professorship appointment under Changjiang Scholars Program of Ministry of Education (MoE). He is CCF (China Computer Federation) Fellow, the Vice Director of Database Technology Committee of CCF, 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, in-memory cluster computing, distributed transaction processing, benchmarking for big data and performance.

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Zhou, H., Guo, J., Hu, H. et al. Plover: parallel logging for replication systems. Front. Comput. Sci. 14, 144606 (2020). https://doi.org/10.1007/s11704-019-8314-y

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