SQL Query Volume Performance Estimation Tool
Pages 165 - 166
Abstract
Typically, applications are tested on small data size for both functional and non functional requirements. However, in production environment, the applications, having SQL queries, may experience performance violations due to increase in data volume. There is need to have tool which could test SQL query performance for large data sizes without elongating application testing phase. In this paper, we have presented a tool for estimating SQL query execution time for large data sizes without actually generating and loading the large volume of data. The model behind the working of the tool has been validated with TPC-H benchmarks and industry applications to predict within 10% average prediction error. The tool is built using underlying popular open source project CoDD with better project management and user interfaces.
References
[1]
I. N. Rakshit S. Trivedi and J. R. Haritsa. Codd: Constructing dataless databases. In Proceedings of DBTest, 2012.
[2]
R. Singhal and M. Nambiar. Extrapolation of sql query elapsed response time at application development stage. In Proceedings of INDICON IEEE Proceedings, 2012.
[3]
R. Singhal and M. Nambiar. Measurement based model to study the effect of increase in data size on query response time. InIn Proceedings of Peformance and Capacity CMG, 2013.
[4]
R. Singhal and M. Nambiar. Predicting sql query execution time for large data volume. In Proceedings of IDEAS ACM Proceedings, 2016.
Index Terms
- SQL Query Volume Performance Estimation Tool
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Predicting SQL Query Execution Time for Large Data Volume
IDEAS '16: Proceedings of the 20th International Database Engineering & Applications SymposiumIn a production system, increase in data size will increase the execution time of the application's SQL queries and degrade its performance. Tuning SQL queries in production requires additional efforts and cost. Time constraints during application ...
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Information & Contributors
Information
Published In
April 2017
450 pages
ISBN:9781450344043
DOI:10.1145/3030207
- General Chairs:
- Walter Binder,
- Vittorio Cortellessa,
- Program Chairs:
- Anne Koziolek,
- Evgenia Smirni,
- Meikel Poess
Copyright © 2017 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 17 April 2017
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Conference
ICPE '17
Sponsor:
ICPE '17: ACM/SPEC International Conference on Performance Engineering
April 22 - 26, 2017
L'Aquila, Italy
Acceptance Rates
ICPE '17 Paper Acceptance Rate 27 of 83 submissions, 33%;
Overall Acceptance Rate 252 of 851 submissions, 30%
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