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Efficient and Extensible Algorithms for Multi Query Optimization
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Prasan Roy,
S. Seshadri,
S. Sudarshan, and
Siddhesh Bhobe
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Abstract
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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. Multi-query 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.
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References
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BIBTEX
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@inproceedings{DBLP:conf/sigmod/RoySSB00,
author = {Prasan Roy and
S. Seshadri and
S. Sudarshan and
Siddhesh Bhobe},
editor = {Weidong Chen and
Jeffrey F. Naughton and
Philip A. Bernstein},
title = {Efficient and Extensible Algorithms for Multi Query Optimization},
booktitle = {Proceedings of the 2000 ACM SIGMOD International Conference on
Management of Data, May 16-18, 2000, Dallas, Texas, USA},
journal = {SIGMOD Record},
publisher = {ACM},
volume = {29},
number = {2},
year = {2000},
isbn = {1-58113-218-2},
pages = {249-260},
crossref = {DBLP:conf/sigmod/2000},
bibsource = {DBLP, http://dblp.uni-trier.de} } },
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