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Efficient exploitation of similar subexpressions for query processing
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International Conference on Management of Data archive
Proceedings of the 2007 ACM SIGMOD international conference on Management of data table of contents
Beijing, China
SESSION: Query processing table of contents
Pages: 533 - 544  
Year of Publication: 2007
ISBN:978-1-59593-686-8
Authors
Jingren Zhou  Microsoft Research, Redmond, WA
Per-Ake Larson  Microsoft Research, Redmond, WA
Johann-Christoph Freytag  Humboldt-University zu Berlin, Berlin, Germany
Wolfgang Lehner  Dresden University of Technology, Dresden, Germany
Sponsors
ACM: Association for Computing Machinery
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 17,   Downloads (12 Months): 110,   Citation Count: 6
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ABSTRACT

Complex queries often contain common or similar subexpressions, either within a single query or among multiple queries submitted as a batch. If so, query execution time can be improved by evaluating a common subexpression once and reusing the result in multiple places. However, current query optimizers do not recognize and exploit similar subexpressions, even within the same query.

We present an efficient, scalable, and principled solution to this long-standing optimization problem. We introduce a light-weight and effective mechanism to detect potential sharing opportunities among expressions. Candidate covering subexpressions are constructed and optimization is resumed to determine which, if any, such subexpressions to include in the final query plan. The chosen subexpression(s) are computed only once and the results are reused to answer other parts of queries. Our solution automatically applies to optimization of query batches, nested queries, and maintenance of multiple materialized views. It is the first comprehensive solution covering all aspects of the problem: detection, construction, and cost-based optimization. Experiments on Microsoft SQL Server show significant performance improvements with minimal overhead.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Jingren Zhou: colleagues
Per-Ake Larson: colleagues
Johann-Christoph Freytag: colleagues
Wolfgang Lehner: colleagues