A piggyback method to collect statistics for query optimization in database management systems

  • Authors:

  • Qiang Zhu;Brian Dunkel;Nandit Soparkar;Suyun Chen;Berni Schiefer;Tony Lai

  • Affiliations:

  • Department of Computer and Information Science, The University of Michigan, Dearborn, MI;Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor, MI;Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor, MI;IBM Toronto Laboratory, North York, Ontario, M3C 1H7, Canada;IBM Toronto Laboratory, North York, Ontario, M3C 1H7, Canada;IBM Toronto Laboratory, North York, Ontario, M3C 1H7, Canada

  • Venue:
  • CASCON '98 Proceedings of the 1998 conference of the Centre for Advanced Studies on Collaborative research
  • Year:
  • 1998

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Abstract

A database management system (DBMS) performs query optimization based on statistical information about data in the underlying data-base. Out-of-date statistics may lead to inefficient query processing in the system. Existing solutions to this problem have some drawbacks such as heavy administrative burden, high system load, and tardy updates. To overcome these drawbacks, our new approach, called the piggyback method, is proposed in this paper. The key idea is to piggyback some additional retrievals during the processing of a user query in order to collect more up-to-date statistics. The collected statistics are used to optimize the processing of subsequent queries. To specify the piggybacked queries, basic piggybacking operators are defined in this paper. Using the operators, several types of piggybacking such as vertical, horizontal, mixed vertical and horizontal, and multi-query piggybacking are introduced. Statistics that can be obtained from different access methods by applying piggyback analysis during query processing are also studied. In order to meet users' different requirements for the associated overhead, several piggybacking levels are suggested. Other related issues including initial statistics, piggybacking time, and parallelism are also discussed. Our analysis shows that the piggyback method is promising in improving the quality of query optimization in a DBMS as well as in reducing the user's administrative burden for maintaining an efficient DBMS.