Prairie: A rule specification framework for query optimizers

Prairie: A rule specification framework for query optimizers (1995)

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by Dinesh Das , Don Batory

Venue:	In Proceedings 11th International Conference on Data Engineering
Citations:	13 - 6 self

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Datum	Value	Source
TITLE	Prairie: A rule specification framework for query optimizers	INFERENCE
AUTHOR NAME	Dinesh Das	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Sciences; The University of Texas at Austin	SVM HeaderParse 0.2
AUTHOR ADDR	Austin, Texas 78712–1188	SVM HeaderParse 0.2
AUTHOR NAME	Don Batory	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Sciences; The University of Texas at Austin	SVM HeaderParse 0.2
AUTHOR ADDR	Austin, Texas 78712–1188	SVM HeaderParse 0.2
ABSTRACT	From our experience, current rule-based query optimizers do not provide a very intuitive and well-defined framework to define rules and actions. To remedy this situation, we propose an extensible and structured algebraic framework called Prairie for specifying rules. Prairie facilitates rule-writing by enabling a user to write rules and actions more quickly, correctly and in an easy-to-understand and easy-to-debug manner. Query optimizers consist of three major parts: a search space, a cost model and a search strategy. The approach we take is only to develop the algebra which defines the search space and the cost model; we do not propose a search engine (i.e., search strategy) to drive the rules. We have chosen the Volcano optimizer generator as our search engine, because it is publicly available, and also because it has an efficient branch-and-bound search strategy. Using Prairie as a front-end, we translate Prairie rules to Volcano to validate our claim that Prairie makes it easier to write rules. We describe our algebra and present experimental results which show that using a high-level framework like Prairie to design large-scale optimizers does not sacrifice efficiency. 1	SVM HeaderParse 0.2
YEAR	1995	INFERENCE
VENUE	In Proceedings 11th International Conference on Data Engineering	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	201--210	INFERENCE
CITATIONS	18 found	ParsCit 1.0