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RCBench: an RDMA-enabled transaction framework for analyzing concurrency control algorithms

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Abstract

Distributed transaction processing over the TCP/IP network suffers from the weak transaction scalability problem, i.e., its performance drops significantly when the number of involved data nodes per transaction increases. Although quite a few of works over the high-performance RDMA-capable network are proposed, they mainly focus on accelerating distributed transaction processing, rather than solving the weak transaction scalability problem. In this paper, we propose RCBench, an RDMA-enabled transaction framework, which serves as a unified evaluation tool for assessing the transaction scalability of various concurrency control algorithms. The usability and advancement of RCBench primarily come from the proposed concurrency control primitives , which facilitate the convenient implementation of RDMA-enabled concurrency control algorithms. Various optimization principles are proposed to ensure that concurrency control algorithms in RCBench can fully benefit from the advantages offered by RDMA-capable networks. We conduct extensive experiments to evaluate the scalability of mainstream concurrency control algorithms. The results show that by exploiting the capabilities of RDMA, concurrency control algorithms in RCBench can obtain 42X performance improvement, and transaction scalability can be achieved in RCBench.

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Notes

  1. https://github.com/dbiir/RCBench.

  2. https://github.com/dbiir/RCBench/blob/master/RCBench.pdf.

  3. Specifically, for any read of T, we abort T if \(T^l.bts < X^m.wts\), and for any write of T, we abort T if \(T^l.bts < X^m.rts\) or \(T^l.bts < X^m.wts\).

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Acknowledgements

The paper is supported by the National Natural Science Foundation of China under Grant No. 61972403, and the paper is supported by Public Computing Cloud, Renmin University of China.

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Zhao, H., Li, J., Lu, W. et al. RCBench: an RDMA-enabled transaction framework for analyzing concurrency control algorithms. The VLDB Journal 33, 543–567 (2024). https://doi.org/10.1007/s00778-023-00821-0

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