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Cost Estimation

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Encyclopedia of Database Systems
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Definition

Execution costs, or simply costs, is a generic term to collectively refer to the various goals or objectives of database query optimization. Optimization aims at finding the “cheapest” (“best” or at least a “reasonably good”) query execution plan (QEP) among semantically equivalent alternative plans for the given query. Cost is used as a metric to compare plans. Depending on the application different types of costs are considered. Traditional optimization goals include minimizing response time (for the first answer or the complete result), minimizing resource consumption (like CPU time, I/O, network bandwidth, or amount of memory required), or maximizing throughput, i.e., the number of queries that the system can answer per time. Other, less obvious objectives – e.g., in a mobile environment – may be to minimize the power consumption needed to answer the query or the on-line time being connected to a remote database server.

Obviously, evaluating a QEP to measure its...

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Recommended Reading

  1. Blohsfeld B., Korus D., and Seeger B. A comparison of selectivity estimators for range queries on metric attributes. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1999, pp. 239–250.

    Google Scholar 

  2. Chakrabarti K., Garofalakis M.N., Rastogi R., and Shim K. Approximate query processing using wavelets. In Proc. 26th Int. Conf. on Very Large Data Bases, 2000, pp. 111–122.

    Google Scholar 

  3. Chaudhuri S., Motwani R., and Narasayya V.R. On random sampling over joins. In Proc. ACM SIGMOD Int. Conf. on Management of Data, Philadephia, PA, USA, June 1999, pp. 263–274.

    Google Scholar 

  4. Chen C.M. and Roussopoulos N. Adaptive selectivity estimation using query feedback. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1994, pp. 161–172.

    Google Scholar 

  5. Garcia-Molina H., Ullman J.D., and Widom J. Database Systems: The Complete Book. Prentice Hall, Englewood Cliffs, NJ, USA, 2002.

    Google Scholar 

  6. Gibbons P.B. and Matias Y. Synopsis data structures for massive data sets. In Proc. 10th Annual ACM-SIAM Symp. on Discrete Algorithms, 1999, pp. 909–910.

    Google Scholar 

  7. Gibbons P.B., Matias P.B., and Poosala V. Fast incremental maintenance of approximate histograms. In Proc. 23th Int. Conf. on Very Large Data Bases, 1997, pp. 466–475.

    Google Scholar 

  8. Haas P.J., Naughton J.F., Seshadri S., and Swami A.N. Selectivity and cost estimation for joins based on random sampling. J. Comput. Syst. Sci., 52(3):550–569, 1996.

    Article  MATH  MathSciNet  Google Scholar 

  9. Ioannidis Y.E. and Christodoulakis S. On the propagation of errors in the size of join results. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1991, pp. 268–277.

    Google Scholar 

  10. Ioannidis Y.E. and Poosala V. Histogram-based approximation of set-valued query-answers. In Proc. 25th Int. Conf. on Very Large Data Bases, 1999, pp. 174–185.

    Google Scholar 

  11. König A.C. and Weikum G. Combining histograms and parametric curve fitting for feedback-driven query result-size estimation. In Proc. 25th Int. Conf. on Very Large Data Bases, 1999, pp. 423–434.

    Google Scholar 

  12. König A.C. and Weikum G. Auto-tuned spline synopses for database statistics management. In Proc. Int. Conf. on Management of Data, 2000.

    Google Scholar 

  13. Korth H. and Silberschatz A. Database Systems Concepts. McGraw-Hill, Inc., New York, San Francisco, Washington, DC, USA, 1991.

    Google Scholar 

  14. Lu H., Tan K.L., and Shan M.C. Hash-based join algorithms for multiprocessor computers. In Proc. 16th Int. Conf. on Very Large Data Bases, 1990, pp. 198–209.

    Google Scholar 

  15. Manegold S. Understanding, Modeling, and Improving Main-Memory Database Performance. PhD Thesis, Universiteit van Amsterdam, Amsterdam, The Netherlands, December 2002.

    Google Scholar 

  16. Manegold S., Boncz P.A., and Kersten M.L. Generic database cost models for hierarchical memory systems. In Proc. 28th Int. Conf. on Very Large Data Bases, 2002, pp. 191–202.

    Google Scholar 

  17. Matias Y., Vitter J.S., and Wang M. Wavelet-based histograms for selectivity estimation. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1998, pp. 448–459.

    Google Scholar 

  18. Selinger P.G., Astrahan M.M., Chamberlin D.D., Lorie R.A., and Price T.G. Access path selection in a relational database management system. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1979, pp. 23–34.

    Google Scholar 

  19. Spiliopoulou M. and Freytag J.-C. Modelling resource utilization in pipelined query execution. In Proc. European Conference on Parallel Processing, 1996, pp. 872–880.

    Google Scholar 

  20. Sun W., Ling Y., Rishe N., and Deng Y. An instant and accurate size estimation method for joins and selection in a retrieval-intensive environment. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1993, pp. 79–88.

    Google Scholar 

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Manegold, S. (2009). Cost Estimation. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_857

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