Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Cost Estimation

  • Stefan Manegold
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_857

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...
This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Blohsfeld B, Korus D, Seeger B. A comparison of selectivity estimators for range queries on metric attributes. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1999. p. 239–50.Google Scholar
  2. 2.
    Chakrabarti K, Garofalakis MN, Rastogi R, Shim K. Approximate query processing using wavelets. In: Proceedings of the 26th International Conference on Very Large Data Bases; 2000. p. 111–22.Google Scholar
  3. 3.
    Chaudhuri S, Motwani R, Narasayya VR. On random sampling over joins. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Philadephia; 1999. p. 263–74.Google Scholar
  4. 4.
    Chen CM, Roussopoulos N. Adaptive selectivity estimation using query feedback. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1994. p. 161–72.Google Scholar
  5. 5.
    Garcia-Molina H, Ullman JD, Widom J. Database systems: the complete book. Englewood Cliffs: Prentice Hall; 2002.Google Scholar
  6. 6.
    Gibbons PB, Matias Y. Synopsis data structures for massive data sets. In: Proceedings of the 10th Annual ACM-SIAM Symposium on Discrete Algorithms; 1999. p. 909–10.Google Scholar
  7. 7.
    Gibbons PB, Matias PB, Poosala V. Fast incremental maintenance of approximate histograms. In: Proceedings of the 23th International Conference on Very Large Data Bases; 1997. p. 466–75.Google Scholar
  8. 8.
    Haas PJ, Naughton JF, Seshadri S, Swami AN. Selectivity and cost estimation for joins based on random sampling. J Comput Syst Sci. 1996;52(3):550–69.MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Ioannidis YE, Christodoulakis S. On the propagation of errors in the size of join results. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1991. p. 268–77.Google Scholar
  10. 10.
    Ioannidis YE, Poosala V. Histogram-based approximation of set-valued query-answers. In: Proceedings of the 25th International Conference on Very Large Data Bases; 1999. p. 174–85.Google Scholar
  11. 11.
    König AC, Weikum G. Combining histograms and parametric curve fitting for feedback-driven query result-size estimation. In: Proceedings of the 25th International Conference on Very Large Data Bases; 1999. p. 423–34.Google Scholar
  12. 12.
    König AC, Weikum G. Auto-tuned spline synopses for database statistics management. In: Proceedings of the International Conference on Management of Data; 2000.Google Scholar
  13. 13.
    Korth H, Silberschatz A. Database systems concepts. New York/San Francisco/Washington, DC: McGraw-Hill; 1991.zbMATHGoogle Scholar
  14. 14.
    Lu H, Tan KL, Shan MC. Hash-based join algorithms for multiprocessor computers. In: Proceedings of the 16th International Conference on Very Large Data Bases; 1990. p. 198–209.Google Scholar
  15. 15.
    Manegold S. Understanding, modeling, and improving main-memory database performance. PhD thesis, Universiteit van Amsterdam, Amsterdam; 2002.Google Scholar
  16. 16.
    Manegold S., Boncz PA, Kersten ML. Generic database cost models for hierarchical memory systems. In: Proceedings of the 28th International Conference on Very Large Data Bases; 2002. p. 191–202.CrossRefGoogle Scholar
  17. 17.
    Matias Y, Vitter JS, Wang M. Wavelet-based histograms for selectivity estimation. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1998. p. 448–59.CrossRefGoogle Scholar
  18. 18.
    Selinger PG, Astrahan MM, Chamberlin DD, Lorie RA, Price TG. Access path selection in a relational database management system. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1979. p. 23–34.Google Scholar
  19. 19.
    Spiliopoulou M, Freytag J-C. Modelling resource utilization in pipelined query execution. In: Proceedings of the European Conference on Parallel Processing; 1996. p. 872–80.Google Scholar
  20. 20.
    Sun W, Ling Y, Rishe N, Deng Y. An instant and accurate size estimation method for joins and selection in a retrieval-intensive environment. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1993. p. 79–88.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.CWIAmsterdamThe Netherlands

Section editors and affiliations

  • Evaggelia Pitoura
    • 1
  1. 1.Dept. of Computer ScienceUniv. of IoanninaIoanninaGreece