Abstract
Modern business intelligence requires data processing not only across a huge variety of domains but also across different paradigms, such as relational, stream, and graph models. This variety is a challenge for existing systems that typically only support a single or few different data models. Polystores were proposed as a solution for this challenge and received wide attention both in academia and in industry. These are systems that integrate different specialized data processing engines to enable fast processing of a large variety of data models. Yet, there is no standard to assess the performance of polystores. The goal of this work is to develop the first benchmark for polystores. To capture the flexibility of polystores, we focus on high level features in order to enable an execution of our benchmark suite on a large set of polystore solutions.
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Notes
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We partially benefitted from the library https://github.com/codemaniac/sopex.
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References
Apache Arrow: a cross-language development platform for in-memory data. https://arrow.apache.org/. Accessed 24 Feb 2018
Query modeling and optimization in the BigDAWG polystore system. http://istc-bigdata.org/index.php/query-modeling-and-optimization-in-the-bigdawg-polystore-system/. Accessed 10 Mar 2018
Avery, C.: Giraph: large-scale graph processing infrastructure on Hadoop. In: Proceedings of the Hadoop Summit, Santa Clara, vol. 11, pp. 5–9 (2011)
Bondiombouy, C., Kolev, B., Levchenko, O., Valduriez, P.: Integrating big data and relational data with a functional SQL-like query language. In: Chen, Q., Hameurlain, A., Toumani, F., Wagner, R., Decker, H. (eds.) DEXA 2015. LNCS, vol. 9261, pp. 170–185. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-22849-5_13
Chaudhuri, S., Narasayya, V.: Self-tuning database systems: a decade of progress. In: Proceedings of the 33rd International Conference on Very Large Data Bases, pp. 3–14. VLDB Endowment (2007)
Chen, Y., Xu, C., Rao, W., Min, H., Su, G.: Octopus: hybrid big data integration engine. In: 2015 IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom), pp. 462–466. IEEE (2015)
Duggan, J., et al.: The BigDAWG polystore system. ACM SIGMOD Rec. 44(2), 11–16 (2015)
Dziedzic, A., Elmore, A.J., Stonebraker, M.: Data transformation and migration in polystores. In: 2016 IEEE High Performance Extreme Computing Conference (HPEC), pp. 1–6. IEEE (2016)
Gadepally, V., et al.: The BigDAWG polystore system and architecture. In: 2016 IEEE High Performance Extreme Computing Conference (HPEC), pp. 1–6. IEEE (2016)
Ghazal, A., et al.: BigBench: towards an industry standard benchmark for big data analytics. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pp. 1197–1208. ACM (2013)
Haynes, B., Cheung, A., Balazinska, M.: PipeGen: data pipe generator for hybrid analytics. In: Proceedings of the Seventh ACM Symposium on Cloud Computing, pp. 470–483. ACM (2016)
Jovanovic, P., Simitsis, A., Wilkinson, K.: Engine independence for logical analytic flows. In: 2014 IEEE 30th International Conference on Data Engineering (ICDE), pp. 1060–1071. IEEE (2014)
Kolev, B., Pau, R., Levchenko, O., Valduriez, P., Jiménez-Peris, R., Pereira, J.: Benchmarking polystores: the cloudMdsQL experience. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 2574–2579. IEEE (2016)
Kolev, B., Valduriez, P., Bondiombouy, C., Jiménez-Peris, R., Pau, R., Pereira, J.: CloudMdsQL: querying heterogeneous cloud data stores with a common language. Distrib. Parallel Databases 34(4), 463–503 (2016)
LeFevre, J., Sankaranarayanan, J., Hacigumus, H., Tatemura, J., Polyzotis, N., Carey, M.J.: MISO: souping up big data query processing with a multistore system. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 1591–1602. ACM (2014)
Leskovec, J., Sosič, R.: SNAP: a general-purpose network analysis and graph-mining library. ACM Trans. Intell. Syst. Technol. (TIST) 8(1), 1 (2016)
Lu, J.: Towards benchmarking multi-model databases. In: CIDR (2017)
Lu, J., Holubová, I.: Multi-model data management: what’s new and what’s next? In: EDBT, pp. 602–605 (2017)
Lu, J., Liu, Z.H., Xu, P., Zhang, C.: UDBMS: road to unification for multi-model data management. arXiv preprint arXiv:1612.08050 (2016)
Palkar, S., et al.: Weld: a common runtime for high performance data analytics. In: Conference on Innovative Data Systems Research (CIDR) (2017)
Simitsis, A., Wilkinson, K., Castellanos, M., Dayal, U.: Optimizing analytic data flows for multiple execution engines. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, pp. 829–840. ACM (2012)
Stonebraker, M., Cetintemel, U.: “One size fits all”: an idea whose time has come and gone. In: Proceedings of 21st International Conference on Data Engineering, ICDE 2005, pp. 2–11. IEEE (2005)
Sun, N., Morris, J., Xu, J., Zhu, X., Xie, M.: ICARE: a framework for big data-based banking customer analytics. IBM J. Res. Dev. 58(5/6), 4:1–4:9 (2014)
Valduriez, P.: Parallel database systems: open problems and new issues. Distrib. Parallel Databases 1(2), 137–165 (1993)
Xu, C., Chen, Y., Liu, Q., Rao, W., Min, H., Su, G.: A unified computation engine for big data analytics. In: 2015 IEEE/ACM 2nd International Symposium on Big Data Computing (BDC), pp. 73–77. IEEE (2015)
Yu, K., Gadepally, V., Stonebraker, M.: Database engine integration and performance analysis of the BigDAWG polystore system. In: 2017 IEEE High Performance Extreme Computing Conference (HPEC), pp. 1–7. IEEE (2017)
Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: HotCloud, vol. 10, no. 10–10, p. 95 (2010)
Acknowledgments
This work has been supported by the European Commission through Proteus (ref. 687691) and Streamline (ref. 688191) and by the German Ministry for Education and Research as Berlin Big Data Center BBDC (funding mark 01IS14013A).
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Karimov, J., Rabl, T., Markl, V. (2019). PolyBench: The First Benchmark for Polystores. In: Nambiar, R., Poess, M. (eds) Performance Evaluation and Benchmarking for the Era of Artificial Intelligence. TPCTC 2018. Lecture Notes in Computer Science(), vol 11135. Springer, Cham. https://doi.org/10.1007/978-3-030-11404-6_3
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