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Scalable and quantitative contention generation for performance evaluation on OLTP databases

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Abstract

Massive scale of transactions with critical requirements become popular for emerging businesses, especially in E-commerce. One of the most representative applications is the promotional event running on Alibaba’s platform on some special dates, widely expected by global customers. Although we have achieved significant progress in improving the scalability of transactional database systems (OLTP), the presence of contention operations in workloads is still one of the fundamental obstacles to performance improving. The reason is that the overhead of managing conflict transactions with concurrency control mechanisms is proportional to the amount of contentions. As a consequence, generating contented workloads is urgent to evaluate performance of modern OLTP database systems. Though we have kinds of standard benchmarks which provide some ways in simulating contentions, e.g., skew distribution control of transactions, they can not control the generation of contention quantitatively; even worse, the simulation effectiveness of these methods is affected by the scale of data. So in this paper we design a scalable quantitative contention generation method with fine contention granularity control. We conduct a comprehensive set of experiments on popular opensourced DBMSs compared with the latest contention simulation method to demonstrate the effectiveness of our generation work.

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References

  1. Huang G, Cheng X, Wang J, Wang Y, He D, Zhang T, Li F, Wang S, Cao W, Li Q. X-engine: an optimized storage engine for large-scale ecommerce transaction processing. In: Proceedings of 2019 International Conference on Management of Data. 2019, 651–665

  2. Appuswamy R, Anadiotis A C, Porobic D, Iman M K, Ailamaki A. Analyzing the impact of system architecture on the scalability of OLTP engines for high-contention workloads. Proceedings of the VLDB Endowment, 2017, 11(2): 121–134

    Article  Google Scholar 

  3. Harding R, Van Aken D, Pavlo A, Stonebraker M. An evaluation of distributed concurrency control. Proceedings of the VLDB Endowment, 2017, 10(5): 553–564

    Article  Google Scholar 

  4. Lan H, Bao Z, Peng Y. A survey on advancing the DBMS query optimizer: cardinality estimation, cost model, and plan enumeration. Data Science and Engineering, 2021, 6(1): 86–101

    Article  Google Scholar 

  5. Ji Y, Chai Y, Zhou X, Ren L, Qin Y. Smart intra-query fault tolerance for massive parallel processing databases. Data Science And Engineering, 2020, 5(1): 65–79

    Article  Google Scholar 

  6. Yu X, Bezerra G, Pavlo A. Staring into the abyss: an evaluation of concurrency control with one thousand cores. Proceedings of the VLDB Endowment, 2014, 8(3): 209–220

    Article  Google Scholar 

  7. Johnson F R. Scalable storage managers for the multicore era. Carnegie Mellon University, Dissertation, 2010

  8. Faleiro J M, Abadi D J. FIT: a distributed database performance tradeoff. IEEE Data Engineering Bulletin, 2015, 38(1): 10–17

    Google Scholar 

  9. Gurajada A, Gala D, Zhou F, Pathak A, Ma Z F. BTrim: hybrid inmemory database architecture for extreme transaction processing. Proceedings of the VLDB Endowment, 2018, 11(12): 1889–1901

    Article  Google Scholar 

  10. Ren K, Faleiro J M, Abadi D J. Design principles for scaling multi-core OLTP under high contention. In: Proceedings of the 2016 International Conference on Management of Data. 2016, 1583–1598

  11. Zhou N, Zhou X, Zhang X, Du X, Wang S. Reordering transaction execution to boost high-frequency trading applications. Data Science and Engineering, 2017, 2(4): 301–315

    Article  Google Scholar 

  12. Chen S, Ailamaki A, Athanassoulis M, Gibbons P B, Johnson R, Pandis I, Stoica R. TPC-E vs. TPC-C: characterizing the new TPC-E benchmark via an I/O comparison study. Acm Sigmod Record, 2011, 39(3): 5–10

    Article  Google Scholar 

  13. Cooper B F, Silberstein A, Tam E, Ramakrishnan R, Sears R. Benchmarking cloud serving systems with YCSB. In: Proceedings of the 1st ACM Symposium on Cloud Computing. 2010. 143–154

  14. Zhang C, Li Y, Zhang R, Qian W, Zhou A. Benchmarking on intensive transaction processing. Frontiers of Computer Science, 2020, 14(5): 145204

    Article  Google Scholar 

  15. Bodik P, Fox A, Franklin M J, Jordan M I, David A. Patterson: characterizing, modeling, and generating workload spikes for stateful services. In: Proceedings of the 1st ACM Symposium on Cloud Computing. 2010, 241–252

  16. Wolski A. TATP benchmark description. See Tatpbenchmark.source-forge.net/website, 2009

  17. Stonebraker M. A measure of transaction processing power. In: Stonebraker M, ed. Readings in Database Systems. 2nd ed. San Francisco: Morgan Kaufmann Publishers Inc. 1994, 442–454

    Google Scholar 

  18. Cahill M J, Röhm U, Fekete A D. Fekete: serializable isolation for snapshot databases. ACM Transactions on Database Systems, 2009, 34(4): 20

    Article  Google Scholar 

  19. Difallah D E, Pavlo A, Curino C, Cudre-Mauroux P. OLTP-bench: an extensible testbed for benchmarking relational databases. Proceedings of the VLDB Endowment, 2013, 7(4): 277–288

    Article  Google Scholar 

  20. MySQL database. See Mysql.com website, 2021

  21. PostgreSQL database. See Postgresql.org website, 2021

Download references

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Grant No. 62072179), ECNU-OceanBase Joint Lab of Distributed Database System and 2020 the Key Software Adaptation and Verification Project (Database).

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Authors and Affiliations

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

    Chunxi Zhang, Yuming Li, Rong Zhang, Weining Qian & Aoying Zhou

Authors
  1. Chunxi Zhang
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  2. Yuming Li
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  3. Rong Zhang
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  4. Weining Qian
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  5. Aoying Zhou
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Corresponding author

Correspondence to Rong Zhang.

Additional information

Chunxi Zhang is a doctoral student of East China Normal University, China under the supervision of Prof. Rong Zhang. She received her bachelor degree in software engineering from Shandong University of Science and Technology, China in 2011. Her current research interest is performance evaluation of databases, including intensive workload generation, contention simulation and isolation validation.

Yuming Li is a doctoral student of East China Normal University, China under the supervision of Profs. Aoying Zhou and Rong Zhang. He received his bachelor degree in computer science from Northeastern University, China in 2014. His research interests include applicationoriented database performance testing, automated database testing and distributed data management.

Rong Zhang is a member of China Computer Federation. She received her PhD degree in computer science from Fudan University, China in 2007. She joined East China Normal University, China since 2011 and is currently a professor in the university. From 2007 to 2010, she worked as an expert researcher in NICT, Japan. Her current research interests include knowledge management, distributed data management and database benchmarking.

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 degrees 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.

Aoying Zhou is vice President of East China Normal University (ECNU), China, professor of School of Data Science and Engineering, and doctoral supervisor. Before joining ECNU in 2008, Aoying worked for Fudan University at the Computer Science Department for 15 years. He is now acting as a vicedirector of ACM SIGMOD China and Database Technology Committee of China Computer Federation. He is serving as a member of the editorial boards VLDB Journal, WWW Journal, and etc. His research interests include data management, in-memory cluster computing, big data benchmarking and performance optimization.

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Zhang, C., Li, Y., Zhang, R. et al. Scalable and quantitative contention generation for performance evaluation on OLTP databases. Front. Comput. Sci. 17, 172202 (2023). https://doi.org/10.1007/s11704-022-1056-2

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  • DOI: https://doi.org/10.1007/s11704-022-1056-2

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