ACM Home Page
Please provide us with feedback. Feedback
Configuration-parametric query optimization for physical design tuning
Full text PdfPdf (708 KB)
Source
International Conference on Management of Data archive
Proceedings of the 2008 ACM SIGMOD international conference on Management of data table of contents
Vancouver, Canada
SESSION: Research Session 20: Tuning and Probing table of contents
Pages 941-952  
Year of Publication: 2008
ISBN:978-1-60558-102-6
Authors
Nicolas Bruno  Microsoft Research, Redmond, WA, USA
Rimma V. Nehme  Purdue University, West Lafayette, IN, USA
Sponsors
ACM: Association for Computing Machinery
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 14,   Downloads (12 Months): 138,   Citation Count: 1
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1376616.1376710
What is a DOI?

ABSTRACT

Automated physical design tuning for database systems has recently become an active area of research and development. Existing tuning tools explore the space of feasible solutions by repeatedly optimizing queries in the input workload for several candidate configurations. This general approach, while scalable, often results in tuning sessions waiting for results from the query optimizer over 90% of the time. In this paper we introduce a novel approach, called Configuration-Parametric Query Optimization, that drastically improves the performance of current tuning tools. By issuing a single optimization call per query, we are able to generate a compact representation of the optimization space that can then produce very efficiently execution plans for the input query under arbitrary configurations. Our experiments show that our technique speeds-up query optimization by 30x to over 450x with virtually no loss in quality, and effectively eliminates the optimization bottleneck in existing tuning tools. Our techniques open the door for new, more sophisticated optimization strategies by eliminating the main bottleneck of current tuning tools.


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.

 
1
S. Agrawal et al. Database Tuning Advisor for Microsoft SQL Server 2005. In Proceedings of the International Conference on Very Large Databases (VLDB), 2004.
 
2
K. Billings. A TPC-D Model for Database Query Optimization in Cascades. Ms. thesis, Portland State University, 1996.
3
 
4
 
5
 
6
7
 
8
 
9
G. Graefe. The Cascades framework for query optimization. Data Engineering Bulletin, 18(3), 1995.
 
10
 
11
 
12
 
13
 
14
 
15


Collaborative Colleagues:
Nicolas Bruno: colleagues
Rimma V. Nehme: colleagues