Revisiting ETL Benchmarking: The Case for Hybrid Flows

  • Alkis Simitsis
  • Kevin Wilkinson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7755)

Abstract

Modern business intelligence systems integrate a variety of data sources using multiple data execution engines. A common example is the use of Hadoop to analyze unstructured text and merging the results with relational database queries over a data warehouse. These analytic data flows are generalizations of ETL flows. We refer to multi-engine data flows as hybrid flows. In this paper, we present our benchmark infrastructure for hybrid flows and illustrate its use with an example hybrid flow. We then present a collection of parameters to describe hybrid flows. Such parameters are needed to define and run a hybrid flows benchmark. An inherent difficulty in benchmarking ETL flows is the diversity of operators offered by ETL engines. However, a commonality for all engines is extract and load operations, operations which rely on data and function shipping. We propose that by focusing on these two operations for hybrid flows, it may be feasible to revisit the ETL benchmark effort and thus, enable comparison of flows for modern business intelligence applications. We believe our framework may be a useful step toward an industry standard benchmark for ETL flows.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Callahan, S.P., Freire, J., Santos, E., Scheidegger, C.E., Silva, C.T., Vo, H.T.: Managing the evolution of dataflows with VisTrails. In: ICDE Workshops, p. 71 (2006)Google Scholar
  2. 2.
    Dayal, U.: Processing queries over generalization hierarchies in a multidatabase system. In: VLDB, pp. 342–353 (1983)Google Scholar
  3. 3.
    Du, W., Krishnamurthy, R., Shan, M.C.: Query optimization in a heterogeneous DBMS. In: VLDB, pp. 277–291 (1992)Google Scholar
  4. 4.
    Ewen, S., Ortega-Binderberger, M., Markl, V.: A learning optimizer for a federated database management system. In: BTW, pp. 87–106 (2005)Google Scholar
  5. 5.
    Gardarin, G., Sha, F., Tang, Z.H.: Calibrating the query optimizer cost model of IRO-DB, an object-oriented federated database system. In: VLDB, pp. 378–389 (1996)Google Scholar
  6. 6.
    Informatica: PowerCenter Pushdown Optimization Option Datasheet (2011)Google Scholar
  7. 7.
    Naacke, H., Tomasic, A., Valduriez, P.: Validating mediator cost models with disco. Networking and Information Systems Journal 2(5) (2000)Google Scholar
  8. 8.
    Roth, M.T., Arya, M., Haas, L.M., Carey, M.J., Cody, W.F., Fagin, R., Schwarz, P.M., Thomas II, J., Wimmers, E.L.: The Garlic project. In: SIGMOD, p. 557 (1996)Google Scholar
  9. 9.
    Simitsis, A., Vassiliadis, P., Dayal, U., Karagiannis, A., Tziovara, V.: Benchmarking ETL Workflows. In: Nambiar, R., Poess, M. (eds.) TPCTC 2009. LNCS, vol. 5895, pp. 199–220. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Simitsis, A., Vassiliadis, P., Sellis, T.K.: State-space optimization of ETL workflows. IEEE Trans. Knowl. Data Eng. 17(10), 1404–1419 (2005)CrossRefGoogle Scholar
  11. 11.
    Simitsis, A., Wilkinson, K., Castellanos, M., Dayal, U.: Optimizing analytic data flows for multiple execution engines. In: SIGMOD Conference, pp. 829–840 (2012)Google Scholar
  12. 12.
    TPC Council: TPC Benchmark DS (April 2012), http://www.tpc.org/tpcds/
  13. 13.
    TPC Council: TPC Benchmark H (April 2012), http://www.tpc.org/tpch/
  14. 14.
    Wyatt, L., Caufield, B., Pol, D.: Principles for an ETL Benchmark. In: Nambiar, R., Poess, M. (eds.) TPCTC 2009. LNCS, vol. 5895, pp. 183–198. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alkis Simitsis
    • 1
  • Kevin Wilkinson
    • 1
  1. 1.HP LabsPalo AltoUSA

Personalised recommendations