Skip to main content

Adaptive Query Processing: A Survey

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2405))

Abstract

In wide-area database systems, which may be running on unpredictable and volatile environments (such as computational grids), it is difficult to produce efficient database query plans based on information available solely at compile time. A solution to this problem is to exploit information that becomes available at query runtime and adapt the query plan to changing conditions during execution. This paper presents a survey on adaptive query processing techniques, examining the opportunities they offer to modify a plan dynamically and classifying them into categories according to the problem they focus on, their objectives, the nature of feedback they collect from the environment, the frequency at which they can adapt, their implementation environment and which component is responsible for taking the adaptation decisions.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. L. Amsaleg, M. Franklin, and A. Tomasic. Dynamic query operator scheduling for wide-area remote access. Distributed and Parallel Databases, 6(3):217–246, 1998.

    Article  Google Scholar 

  2. L. Amsaleg, M. Franklin, A. Tomasic, and T. Urhan. Scrambling query plans to cope with unexpected delays. In Proc. of the Fourth International Conference on Parallel and Distributed Information Systems, pages 208–219. IEEE Computer Society, 1996.

    Google Scholar 

  3. R. Arpaci-Dusseau, E. Anderson, N. Treuhaft, D. Culler, J. Hellerstein, D. Patterson, and K. Yelick. Cluster I/O with River: Making the fast case common. In Proceedings of the Sixth Workshop on Input/Output in Parallel and Distributed Systems, pages 10–22, Atlanta, GA, 1999. ACM Press.

    Google Scholar 

  4. R. Avnur and J. Hellerstein. Continuous query optimization. Technical Report CSD-99-1078, University of California, Berkeley, 1999.

    Google Scholar 

  5. R. Avnur and J. Hellerstein. Eddies: Continuously adaptive query processing. In Proc. of ACMSIGMOD 2000, pages 261–272, 2000.

    Google Scholar 

  6. L. Bouganim, F. Fabret, C. Mohan, and P. Valduriez. A dynamic query processing architecture for data integration systems. IEEE Data Engineering Bulletin, 23(2):42–48, 2000.

    Google Scholar 

  7. L. Bouganim, F. Fabret, C. Mohan, and P. Valduriez. Dynamic query scheduling in data integration systems. In Proc. of ICDE 2000, pages 425–434, 2000.

    Google Scholar 

  8. I. Foster and C. Kesselman. The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann Publishers Inc., 1999.

    Google Scholar 

  9. P. Haas and J. Hellerstein. Ripple joins for online aggregation. In Proc. of ACM SIGMOD 1999, pages 287–298, 1999.

    Google Scholar 

  10. A. Hameurlain and F. Morvan. An overview of parallel query optimization in relational databases. In 11th International Workshop on Database and Expert Systems Application (DEXA 2000). IEEE Computer Society, 2000.

    Google Scholar 

  11. J. Hellerstein, M. Franklin, S. Chandrasekaran, A. Deshpande, K. Hildrum, S. Madden, V. Raman, and M. Shah. Adaptive query processing: Technology in evolution. IEEE Data Engineering Bulletin, 23(2):7–18, 2000.

    Google Scholar 

  12. Y. Ioannidis. Query optimization. ACM Computing Surveys, 28(1):121–123, 1996.

    Article  Google Scholar 

  13. Z. Ives, D. Florescu, M. Friedman, A. Levy, and D. Weld. An adaptive query execution system for data integration. In Proc. of ACM SIGMOD, pages 299–310, 1999.

    Google Scholar 

  14. Z. Ives, A. Levy, D. Weld, D. Florescu, and M. Friedman. Adaptive query processing for internet applications. IEEE Data Engineering Bulletin, 23(2):19–26, 2000.

    Google Scholar 

  15. N. Kabra and D. DeWitt. Efficient mid-query re-optimization of sub-optimal query execution plans. In Proc. of ACM SIGMOD 1998, pages 106–117, 1998.

    Google Scholar 

  16. K. Ng, Z. Wang, and R. Muntz. Dynamic reconfiguration of sub-optimal parallel query execution plans. Technical Report CSD-980033, UCLA, 1998.

    Google Scholar 

  17. K. Ng, Z. Wang, R. Muntz, and S. Nittel. Dynamic query re-optimization. In Proc. of 11th International Conference on Statistical and Scientific Database Management, pages 264–273. IEEE Computer Society, 1999.

    Google Scholar 

  18. F. Ozcan, S. Nural, P. Koksal, C. Evrendilek, and A. Dogac. Dynamic query optimization in multidatabases. Data Engineering Bulletin, 20(3):38–45, 1997.

    Google Scholar 

  19. H. Pang, M. Carey, and M. Livny. Memory-adaptive external sorting. The VLDB Journal, pages 618–629, 1993.

    Google Scholar 

  20. H. Pang, M. Carey, and M. Livny. Partially preemptible hash joins. In Proc. of ACM SIGMOD 1993, pages 59–68, 1993.

    Google Scholar 

  21. B. Plale and K. Schwan. dquob: Managing large data flows using dynamic embedded queries. In IEEE International High Performance Distributed Computing (HPDC), pages 263–270, 2000.

    Google Scholar 

  22. B. Plale and K. Schwan. Optimizations enabled by a relational data model view to querying data streams. In IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2001.

    Google Scholar 

  23. V. Raman, B. Raman, and J. Hellerstein. Online dynamic reordering. The VLDB Journal, pages 247–260, 2000.

    Google Scholar 

  24. T. Urhan and M. Franklin. XJoin: A reactively-scheduled pipelined join operator. IEEE Data Engineering Bulletin, 23(2):27–33, 2000.

    Google Scholar 

  25. T. Urhan and M. Franklin. Dynamic pipeline scheduling for improving interactive query performance. The VLDB Journal, pages 501–510, 2001.

    Google Scholar 

  26. T. Urhan, M. Franklin, and Laurent Amsaleg. Cost-based query scrambling for initial delays. In Proc. of ACMSIGMOD 1998, pages 130–141, 1998.

    Google Scholar 

  27. W. Zhang and P. Larson. Dynamic memory adjustment for external mergesort. The VLDB Journal, pages 376–385, 1997.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Gounaris, A., Paton, N.W., Fernandes, A.A.A., Sakellariou, R. (2002). Adaptive Query Processing: A Survey. In: Eaglestone, B., North, S., Poulovassilis, A. (eds) Advances in Databases. BNCOD 2002. Lecture Notes in Computer Science, vol 2405. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45495-0_2

Download citation

  • DOI: https://doi.org/10.1007/3-540-45495-0_2

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43905-9

  • Online ISBN: 978-3-540-45495-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics