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A Versatile Framework for Painless Benchmarking of Database Management Systems

  • Lexi Brent
  • Alan FeketeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11393)

Abstract

Benchmarking is a crucial aspect of evaluating database management systems. Researchers, developers, and users utilise industry-standard benchmarks to assist with their research, development, or purchase decisions, respectively. Despite this ubiquity, benchmarking is usually a difficult process involving laborious tasks such as writing and debugging custom testbed scripts, or extracting and transforming output into useful formats. To date, there are only a limited number of comprehensive benchmarking frameworks designed to tackle these usability and efficiency challenges directly.

In this paper we propose a new versatile benchmarking framework. Our design, not yet implemented, is based on exploration of the benchmarking practices of individuals in the database community. Through user interviews, we identify major pain points these people encountered during benchmarking, and map these onto a pipeline of processes representative of a typical benchmarking workflow. We explain how our proposed framework would target each process in this pipeline, potentiating significant overall usability and efficiency improvements. We also contrast the responses of engineers working in industry with those of researchers, and examine how database benchmarking requirements differ between these two groups. The framework we propose is based around traditional synthetic workloads, would be simple to configure, highly extensible, could support any benchmark, and write output to any well-defined data format. It would collect and track all generated events, data, and relationships from the benchmark and underlying systems, and offer simple reproducibility. Complex scenarios such as distributed-client and multi-tenant benchmarks would be simplified by the framework’s workload partitioning, client coordination, and output collation capabilities.

Keywords

Benchmark TPCC YCSB DBMS Performance evaluation 

References

  1. 1.
    Ameri, P., Schlitter, N., Mayer, J., Streit, A.: NoWog: a workload generator for database performance benchmarking. In: 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, 14th International Conference on Pervasive Intelligence and Computing, 2nd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress, DASC/PiCom/DataCom/CyberSciTech 2016, Auckland, New Zealand, 8–12 August 2016, pp. 666–673 (2016)Google Scholar
  2. 2.
    Barahmand, S., Ghandeharizadeh, S.: D-Zipfian: a decentralized implementation of Zipfian. In: Proceedings of the Sixth International Workshop on Testing Database Systems, DBTest 2013, pp. 6:1–6:6. ACM, New York (2013)Google Scholar
  3. 3.
    Bermbach, D., Kuhlenkamp, J., Dey, A., Ramachandran, A., Fekete, A., Tai, S.: BenchFoundry: a benchmarking framework for cloud storage services. In: Maximilien, M., Vallecillo, A., Wang, J., Oriol, M. (eds.) ICSOC 2017. LNCS, vol. 10601, pp. 314–330. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69035-3_22CrossRefGoogle Scholar
  4. 4.
    Bermbach, D., Kuhlenkamp, J., Dey, A., Sakr, S., Nambiar, R.: Towards an extensible middleware for database benchmarking. In: Nambiar, R., Poess, M. (eds.) TPCTC 2014. LNCS, vol. 8904, pp. 82–96. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-15350-6_6CrossRefGoogle Scholar
  5. 5.
    Bermbach, D., Wittern, E., Tai, S.: Cloud Service Benchmarking. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-55483-9CrossRefGoogle Scholar
  6. 6.
    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, SoCC 2010, pp. 143–154. ACM, New York (2010)Google Scholar
  7. 7.
    Dey, A., Fekete, A., Nambiar, R., Rohm, U.: YCSB+T: benchmarking web-scale transactional databases. In: Proceedings - International Conference on Data Engineering, pp. 223–230 (2014)Google Scholar
  8. 8.
    Difallah, D., Pavlo, A.: OLTP-bench: an extensible testbed for benchmarking relational databases. Proc. VLDB Endow. 7(4), 277–288 (2013)CrossRefGoogle Scholar
  9. 9.
    Ghazal, A., et al.: BigBench: towards an industry standard benchmark for big data analytics. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2013, New York, NY, USA, 22–27 June 2013, pp. 1197–1208 (2013). https://doi.acm.org/10.1145/2463676.2463712
  10. 10.
    Hoag, J.E., Thompson, C.W.: A parallel general-purpose synthetic data generator. SIGMOD Rec. 36(1), 19–24 (2007)CrossRefGoogle Scholar
  11. 11.
    Lu, J.: Towards benchmarking multi-model databases. In: 8th Biennial Conference on Innovative Data Systems Research, CIDR 2017, Chaminade, CA, USA, 8–11 January 2017, Online Proceedings (2017)Google Scholar
  12. 12.
    Rabl, T., Frank, M., Sergieh, H.M., Kosch, H.: A data generator for cloud-scale benchmarking. In: Nambiar, R., Poess, M. (eds.) TPCTC 2010. LNCS, vol. 6417, pp. 41–56. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-18206-8_4CrossRefGoogle Scholar
  13. 13.
    Rabl, T., Poess, M., Danisch, M., Jacobsen, H.A.: Rapid development of data generators using meta generators in PDGF. In: Proceedings of the Sixth International Workshop on Testing Database Systems, DBTest 2013, pp. 5:1–5:6. ACM, New York (2013)Google Scholar
  14. 14.
    Sakr, S., Casati, F.: Liquid benchmarks: towards an online platform for collaborative assessment of computer science research results. In: Nambiar, R., Poess, M. (eds.) TPCTC 2010. LNCS, vol. 6417, pp. 10–24. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-18206-8_2CrossRefGoogle Scholar
  15. 15.
    Seybold, D.: Towards a framework for orchestrated distributed database evaluation in the cloud. In: Proceedings of the 18th Doctoral Symposium of the 18th International Middleware Conference, Middleware 2017, pp. 13–14. ACM, New York (2017)Google Scholar
  16. 16.
    Stephens, J.M., Poess, M.: MUDD: a multi-dimensional data generator. SIGSOFT Softw. Eng. Notes 29(1), 104–109 (2004)CrossRefGoogle Scholar
  17. 17.
    Transaction Processing Performance Council (TPC): TPC-Homepage V5 (2016). http://www.tpc.org/
  18. 18.
    Van Aken, D., Difallah, D.E., Pavlo, A., Curino, C., Cudré-Mauroux, P.: BenchPress: dynamic workload control in the OLTP-bench testbed. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, SIGMOD 2015, pp. 1069–1073. ACM, New York (2015)Google Scholar
  19. 19.
    Yoon, D.D.Y.: Database Performance Evaluation Framework. Ph.D. thesis, The University of Sydney (2008)Google Scholar
  20. 20.
    van der Zijden, W., Hiemstra, D., van Keulen, M.: MTCB: a multi-tenant customizable database benchmark. In: Proceedings of the 9th International Conference on Information Management and Engineering, ICIME 2017, pp. 17–23. ACM, New York (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.The University of SydneySydneyAustralia

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