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Efficient Mid-Query Re-Optimization of Sub-Optimal Query Execution Plans.

Navin Kabra, David J. DeWitt: Efficient Mid-Query Re-Optimization of Sub-Optimal Query Execution Plans. SIGMOD Conference 1998: 106-117
@inproceedings{DBLP:conf/sigmod/KabraD98,
  author    = {Navin Kabra and
               David J. DeWitt},
  editor    = {Laura M. Haas and
               Ashutosh Tiwary},
  title     = {Efficient Mid-Query Re-Optimization of Sub-Optimal Query Execution
               Plans},
  booktitle = {SIGMOD 1998, Proceedings ACM SIGMOD International Conference
               on Management of Data, June 2-4, 1998, Seattle, Washington, USA},
  publisher = {ACM Press},
  year      = {1998},
  isbn      = {0-89791-995-5},
  pages     = {106-117},
  ee        = {http://doi.acm.org/10.1145/276304.276315, db/conf/sigmod/KabraD98.html},
  crossref  = {DBLP:conf/sigmod/98},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

For a number of reasons, even the best query optimizers can very often produce sub-optimal query execution plans, leading to a significant degradation of performance. This is especially true in databases used for complex decision support queries and/or object-relational databases. In this paper, we describe an algorithm that detects sub-optimality of a query execution plan during query execution and attempts to correct the problem. The basic idea is to collect statistics at key points during the execution of a complex query. These statistics are then used to optimize the execution of the query, either by improving the resource allocation for that query, or by changing the execution plan for the remainder of the query. To ensure that this does not significantly slow down the normal execution of a query, the Query Optimizer carefully chooses what statistics to collect, when to collect them, and the circumstances under which to re-optimize the query. We describe an implementation of this algorithm in the Paradise Database System, and we report on performance studies, which indicate that this can result in significant improvements in the performance of complex queries.

Copyright © 1998 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.


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Laura M. Haas, Ashutosh Tiwary (Eds.): SIGMOD 1998, Proceedings ACM SIGMOD International Conference on Management of Data, June 2-4, 1998, Seattle, Washington, USA. ACM Press 1998, ISBN 0-89791-995-5 BibTeX , SIGMOD Record 27(2), June 1998
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BibTeX

Referenced by

  1. Ron Avnur, Joseph M. Hellerstein: Eddies: Continuously Adaptive Query Processing. SIGMOD Conference 2000: 261-272
  2. Peter A. Boncz, Martin L. Kersten: MIL Primitives for Querying a Fragmented World. VLDB J. 8(2): 101-119(1999)
  3. Arnd Christian König, Gerhard Weikum: Combining Histograms and Parametric Curve Fitting for Feedback-Driven Query Result-size Estimation. VLDB 1999: 423-434
  4. Zachary G. Ives, Daniela Florescu, Marc Friedman, Alon Y. Levy, Daniel S. Weld: An Adaptive Query Execution System for Data Integration. SIGMOD Conference 1999: 299-310
  5. Ashraf Aboulnaga, Surajit Chaudhuri: Self-tuning Histograms: Building Histograms Without Looking at Data. SIGMOD Conference 1999: 181-192
  6. Francis C. Chu, Joseph Y. Halpern, Praveen Seshadri: Least Expected Cost Query Optimization: An Exercise in Utility. PODS 1999: 138-147
  7. William O'Connell, Felipe Cariño, G. Linderman: Optimizer and Parallel Engine Extensions for Handling Expensive Methods Based on Large Objects. ICDE 1999: 304-313
  8. Daniela Florescu, Alon Y. Levy, Alberto O. Mendelzon: Database Techniques for the World-Wide Web: A Survey. SIGMOD Record 27(3): 59-74(1998)
BibTeX
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