Advertisement

Using parallelism and pipeline for the optimisation of join queries

  • Maria Spiliopoulou
  • Michalis Hatzopoulos
  • Costas Vassilakis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 605)

Abstract

In this study we present a technique for the parallel optimisation of join queries, that uses the offered coarse-grain parallelism of the underlying architecture in order to reduce the CPU-bound optimisation overhead. The optimisation technique performs an almost exhaustive search of the solution space for small join queries and gradually, as the number of joins increases, it diverges towards iterative improvement. This technique has been developed on a low-parallelism transputer-based architecture, where its behaviour is studied for the optimisation of queries with many tenths of joins.

Keywords

Assure Sorting Helios DICI 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    D.J.DeWitt, J.Gray “Parallel Database Systems: The Future of Database Processing or a Passing Fad?”, ACM-SIGMOD, Vol.19/4, 104–112, Dec. 1990Google Scholar
  2. 2.
    Y.E.Ioannidis, E.Wong “Query optimization by simulated annealing”, Proc. of the ACM-SIGMOD Intl. Conf. on Management of Data (San Francisco, CA), 9–22,1987Google Scholar
  3. 3.
    Y.E.Ioannidis, Y.C.Kang “Randomized Algorithms for Optimizing Large Join Queries”, Proc. of the ACM-SIGMOD Intl. Conf. on Management of Data (Atlantic City, NJ), 312–321, 1990Google Scholar
  4. 4.
    Y.E.Ioannidis, Y.C.Kang “Left-deep vs. Bushy Trees: An Analysis of Strategy Spaces and its Implications on Query Optimization”, Proc. of the ACM-SIGMOD Intl. Conf. on Management of Data (Denver, Colorado), 168–177, 1991Google Scholar
  5. 5.
    W.Kim “On optimizing an SQL-like nested query”, ACM-TODS, Vol.7/3, 443–469, Sept. 1982CrossRefGoogle Scholar
  6. 6.
    K.Mikkilineni, S.Su “An Evaluation of Relational Join Algorithms in a Pipelined Query Processing Environment”, IEEE Transactions on Software Engineering, Vol. 14/6, 838–848, June 1988CrossRefGoogle Scholar
  7. 7.
    P.G.Selinger et al “Access Path Selection in a Relational Database Management System”, Proc. of ACM-SIGMOD Int. Conf. on Management of Data (Boston, Mass.), 23–34, 1979Google Scholar
  8. 8.
    L.D.Shapiro “Join Processing in Database Systems with Large Main Memories”, ACM-TODS, Vol.11/3, 239–264, Sept. 1986CrossRefGoogle Scholar
  9. 9.
    M.Spiliopoulou, M.Hatzopoulos “Parallel Optimisation of Large Join Queries with Set Operators and Aggregates in a Parallel Environment Supporting Pipeline”, Submitted for publicationGoogle Scholar
  10. 10.
    M.Spiliopoulou, M.Hatzopoulos, C.Vassilakis “Cost and Behaviour of Nested and Canonical SQL-queries in a Parallel Environment Supporting Pipeline”, Submitted for publicationGoogle Scholar
  11. 11.
    M.Spiliopoulou, M.Hatzopoulos “Translation of SQL Queries into a Graph Structure: Query Transformations and Pre-optimization Issues in a Pipeline Multiprocesor Environment”, To appear in Information Systems, Vol. 17/2, 1992Google Scholar
  12. 12.
    A.Swami, A.Gupta “Optimization of large join queries”, Proc. of the ACM-SIGMOD Intl. Conf. on Management of Data (Chicago, Illinois), 8–17, Sept. 1988Google Scholar
  13. 13.
    A.Swami “Optimization of large join queries: Combining heuristics and combinatorial techniques”, Proc. of the ACM-SIGMOD Intl. Conf. on Management of Data (Portland, Oregon), 367–376, June 1989Google Scholar

Copyright information

© Springer-Verlag 1992

Authors and Affiliations

  • Maria Spiliopoulou
    • 1
  • Michalis Hatzopoulos
    • 1
  • Costas Vassilakis
    • 1
  1. 1.Department of InformaticsUniversity of AthensIlisia

Personalised recommendations