Efficient and extensible algorithms for multi query optimization

  • Authors:

  • Prasan Roy;S. Seshadri;S. Sudarshan;Siddhesh Bhobe

  • Affiliations:

  • I.I.T. Bombay;Bell Labs.;I.I.T. Bombay;PSPL Ltd. Pune

  • Venue:
  • SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
  • Year:
  • 2000

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Abstract

Complex queries are becoming commonplace, with the growing use of decision support systems. These complex queries often have a lot of common sub-expressions, either within a single query, or across multiple such queries run as a batch. Multiquery optimization aims at exploiting common sub-expressions to reduce evaluation cost. Multi-query optimization has hither-to been viewed as impractical, since earlier algorithms were exhaustive, and explore a doubly exponential search space.In this paper we demonstrate that multi-query optimization using heuristics is practical, and provides significant benefits. We propose three cost-based heuristic algorithms: Volcano-SH and Volcano-RU, which are based on simple modifications to the Volcano search strategy, and a greedy heuristic. Our greedy heuristic incorporates novel optimizations that improve efficiency greatly. Our algorithms are designed to be easily added to existing optimizers. We present a performance study comparing the algorithms, using workloads consisting of queries from the TPC-D benchmark. The study shows that our algorithms provide significant benefits over traditional optimization, at a very acceptable overhead in optimization time.