Multiple-granularity interleaving for piggyback query processing

  • Authors:

  • Brian Dunkel;Qiang Zhu;Wing Lau;Suyun Chen

  • Affiliations:

  • Dept of Electrical Engineering and Computer Sci., The Univ. of Michigan, Ann Arbor, MI;Dept of Computer and Information Science, The Univ. of Michigan, Dearborn, MI;Dept of Computer and Information Science, The Univ. of Michigan, Dearborn, MI;IBM Toronto Lab, North York, Ontario, Canada

  • Venue:
  • CASCON '99 Proceedings of the 1999 conference of the Centre for Advanced Studies on Collaborative research
  • Year:
  • 1999

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Abstract

Piggyback query processing is a new technique, described in [24], intended to perform additional useful computation (e.g., database statistics collection) during normal query processing, taking full advantage of data resident in main memory. Different types of benecial piggybacking have been identifed and studied, but how to efficiently integrate piggyback operations with a given user query is still an open issue. In this paper, we propose a technique of multiple-granularity interleaving to efficiently integrate multiple piggyback operations with a given query at different levels of data granularity. We introduce an algebraic notation to capture the main characteristics of data flows in a database management system (DBMS), facilitating the study of piggybacking and enabling the automated integration of piggyback operations and user queries in a DBMS supporting the piggyback method. Various integration techniques are introduced to facilitate multiple-granularity interleaving including merging shared work, augmenting user queries, and downgrading piggyback operations. A set of transformations and heuristics are suggested that preserve the semantics of a user query, while efficiently interleaving the operations. Our preliminary experiments indicate that interleaving at proper levels of data granularity is key to the efficient implementation of the piggyback method.