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Database Tuning using Online Algorithms

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Encyclopedia of Database Systems

Definition

A self-managing database system needs to gracefully handle variations in input workloads by adapting its internal structures and representation to changes in the environment. One approach to cope with evolving workloads is to periodically obtain the best possible configuration for a hypothetical “average” scenario. Unfortunately, this approach might be arbitrarily suboptimal for instances that lie outside the previously determined average case. An alternative approach is to require the database system to continuously tune its internal parameters in response to changes in the workload. This is the online tuning paradigm. Although solutions for different problems share the same underlying philosophy, the specific details are usually domain-specific. In the context of database systems, online tuning has been successfully applied to issues such as buffer pool management, statistics construction and maintenance, and physical design.

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Recommended Reading

  1. Aboulnaga A. and Chaudhuri S. Self-tuning histograms: building histograms without looking at data. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1999.

    Google Scholar 

  2. Brown K.P., Mehta M., Carey M.J., and Livny M. Towards automated performance tuning for complex workloads. In Proc. 20th Int. Conf. on Very Large Data Bases, 1994, pp. 72–84.

    Google Scholar 

  3. Bruno N. and Chaudhuri S. An online approach to physical design tuning. In Proc. 23rd Int. Conf. on Data Engineering, 2007.

    Google Scholar 

  4. Bruno N., Chaudhuri S., and Gravano L. STHoles: a multidimensional workload-aware histogram. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 2001.

    Google Scholar 

  5. Chaudhuri S. and Narasayya V.R. Self-tuning database systems: a decade of progress. In Proc. 33rd Int. Conf. on Very Large Data Bases, 2007.

    Google Scholar 

  6. 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 

  7. Dageville B. and Zait M. SQL memory management in Oracle9i. In Proc. 28th Int. Conf. on Very Large Data Bases, 2002.

    Google Scholar 

  8. Diao Y., Hellerstein J.L., Parekh S.S., Griffith R., Kaiser G.E., and Phung D.B. Self-managing systems: a control theory foundation. In Proc. 12th IEEE Int. Conf. Engineering of Computer-Based Systems, 2005, pp. 441–448.

    Google Scholar 

  9. Markl V., Haas P.J., Kutsch M., Megiddo N., Srivastava U., and Tran T.M., Consistent selectivity estimation via maximum entropy. VLDB J., 16(1):55–76, 2007.

    Google Scholar 

  10. Srivastava U. et al. ISOMER: consistent histogram construction using query feedback. In Proc. 22nd Int. Conf. on Data Engineering, 2006.

    Google Scholar 

  11. Stillger M., Lohman G.M., Markl V., and Kandil M. LEO - DB2’s LEarning Optimizer. In Proc. 27th Int. Conf. on Very Large Data Bases, 2001, pp. 19–28.

    Google Scholar 

  12. Weikum G., König A.C., Kraiss A., and Sinnwell M. Towards self-tuning memory management for data servers. IEEE Data Eng. Bull., 22(2):3–11, 1999.

    Google Scholar 

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Bruno, N., Chaudhuri, S., Weikum, G. (2009). Database Tuning using Online Algorithms. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_335

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