A Region Based Query Optimizer Through Cascades Query Optimizer Framework.

@article{DBLP:journals/debu/OzcanNKAD95,
  author    = {Fatma Ozcan and
               Sena Nural and
               Pinar Koksal and
               Mehmet Altinel and
               Asuman Dogac},
  title     = {A Region Based Query Optimizer Through Cascades Query Optimizer
               Framework},
  journal   = {IEEE Data Eng. Bull.},
  volume    = {18},
  number    = {3},
  year      = {1995},
  pages     = {30-40},
  ee        = {db/journals/debu/OzcanNKAD95.html},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

The Cascades Query Optimizer Framework is a tool to help the database implementor (DBI) in constructing a query optimizer for a DBMS. It is data model independent and allows to code a query optimizer by providing the implementations of the subclasses of predefined interface classes. When the implementations of the required classes are provided properly, the generated optimizer produces the optimum execution plans for the queries. Although providing the complete set of rules and thus finding the optimum execution plans are beneficial for most of the queries, the query optimization time increases unacceptably for certain types of queries, e.g., for star queries. Hence it is important to be able to limit the number of alternative plans considered by the optimizer for specific types of queries by using the proper heuristics for each type. This leads to the concept of region based query optimization, where different types of queries are optimized by using different search strategies in each region.

This paper describes our experiences in developing a region based query optimizer through Cascades. Cascades' guidance structures provide the facilities required. The performance comparisons between a region based query optimizer and an exhaustive ( complete rule set without heuristic guidance) query optimizer, both generated through Cascades, indicate that the region based optimizer has a superior performance. Both the sum of optimization and execution times, namely the response time, and the quality of the plans generated are investigated.

Copyright © 1995 by the author(s). Abstract used with permission.

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Referenced by

1. Joachim Kröger, Regina Illner, Steffen Rost, Andreas Heuer: Query Rewriting and Search in CROQUE. ADBIS 1999: 288-302
2. Clara Nippl, Bernhard Mitschang: TOPAZ: a Cost-Based, Rule-Driven, Multi-Phase Parallelizer. VLDB 1998: 251-262