research-article

Query interactions in database workloads

Authors:

Ashraf Aboulnaga,

Shivnath BabuAuthors Info & Claims

DBTest '09: Proceedings of the Second International Workshop on Testing Database Systems

Article No.: 11, Pages 1 - 6

https://doi.org/10.1145/1594156.1594170

Published: 29 June 2009 Publication History

Abstract

Database workloads consist of mixes of queries that run concurrently and interact with each other. In this paper, we demonstrate that query interactions can have a significant impact on database system performance. Hence, we argue that it is important to take these interactions into account when characterizing workloads, designing test cases, or developing performance tuning algorithms for database systems. To capture and model query interactions, we propose using an experimental approach that is based on sampling the space of possible interactions and fitting statistical models to the sampled data. We discuss using such an approach for database testing and tuning, and we present some opportunities and research challenges.

References

[1]

S. Agrawal, E. Chu, and V. R. Narasayya. Automatic physical design tuning: Workload as a sequence. In SIGMOD, 2006.

Digital Library

[2]

M. Ahmad, A. Aboulnaga, S. Babu, and K. Munagala. Modeling and exploiting query interactions in database systems. In CIKM, 2008.

Digital Library

[3]

Amazon Elastic Computing Cloud. http://aws.amazon.com/ec2/.

[4]

N. Brownlee and K. C. Claffy. Internet measurement. IEEE Internet Computing, 8(5), 2004.

Digital Library

[5]

R. Chaiken, B. Jenkins, P. Larson, B. Ramsey, D. Shakib, S. Weaver, and J. Zhou. SCOPE: Easy and efficient parallel processing of massive data sets. In VLDB, 2008.

Digital Library

[6]

J. Dean and S. Ghemawat. MapReduce: Simplified data processing on large clusters. In OSDI, 2004.

Digital Library

[7]

A. Ganapathi, H. Kuno, U. Dayal, J. Wiener, A. Fox, M. Jordan, and D. Patterson. Predicting multiple metrics for queries: Better decisions enabled by machine learning. In ICDE, 2009.

Digital Library

[8]

C. R. Hicks and K. V. Turner. Fundamental Concepts in the Design of Experiments. Oxford University Press, 1999.

[9]

M. Isard, M. Budiu, Y. Yu, A. Birrell, and D. Fetterly. Dryad: Distributed data-parallel programs from sequential building blocks. In EuroSys, 2007.

Digital Library

[10]

K. O'Gorman, A. E. Abbadi, and D. Agrawal. Multiple query optimization in middleware using query teamwork. Software - Practice and Experience, 35(4), 2005.

Digital Library

[11]

C. Olston, B. Reed, U. Srivastava, R. Kumar, and A. Tomkins. Pig Latin: A not-so-foreign language for data processing. In SIGMOD, 2008.

Digital Library

[12]

P. Roy, S. Seshadri, S. Sudarshan, and S. Bhobe. Efficient and extensible algorithms for multi query optimization. In SIGMOD, 2008.

Digital Library

[13]

T. J. Santner, B. J. Williams, and W. Notz. The Design and Analysis of Computer Experiments. Springer, first edition, July 2003.

[14]

C. Stewart, T. Kelly, and A. Zhang. Exploiting nonstationarity for performance prediction. In EuroSys, 2007.

Digital Library

[15]

Transaction processing performance council (TPC). http://www.tpc.org/.

[16]

Weka 3: Data mining software in Java. http://www.cs.waikato.ac.nz/ml/weka/.

[17]

I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, second edition, June 2005.

Digital Library

[18]

Q. Zhang, L. Cherkasova, and E. Smirni. A regression-based analytic model for dynamic resource provisioning of multi-tier applications. In ICAC, 2007.

Digital Library

Cited By

Das JGhosh SGhosh SBuyya R(2022)LYRIC: Deadline and Budget Aware Spatio-Temporal Query Processing in CloudIEEE Transactions on Services Computing10.1109/TSC.2021.307300615:5(2869-2882)Online publication date: 1-Sep-2022
https://doi.org/10.1109/TSC.2021.3073006
Yan ZLu JChainani NLin C(2021)Workload-Aware Performance Tuning for Autonomous DBMSs2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00267(2365-2368)Online publication date: Apr-2021
https://doi.org/10.1109/ICDE51399.2021.00267
Bellatreche LKerkad A(2015)Query Interaction Based Approach for Horizontal Data PartitioningInternational Journal of Data Warehousing and Mining10.4018/ijdwm.201504010311:2(44-61)Online publication date: 1-Apr-2015
https://doi.org/10.4018/ijdwm.2015040103
Show More Cited By

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Query interactions in database workloads

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cover image ACM Conferences

DBTest '09: Proceedings of the Second International Workshop on Testing Database Systems

June 2009

79 pages

ISBN:9781605587066

DOI:10.1145/1594156

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOD: ACM Special Interest Group on Management of Data

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 June 2009

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SIGMOD/PODS '09

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SIGMOD

SIGMOD/PODS '09: International Conference on Management of Data

June 29, 2009

Rhode Island, Providence

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Overall Acceptance Rate 31 of 56 submissions, 55%

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Cited By

Das JGhosh SGhosh SBuyya R(2022)LYRIC: Deadline and Budget Aware Spatio-Temporal Query Processing in CloudIEEE Transactions on Services Computing10.1109/TSC.2021.307300615:5(2869-2882)Online publication date: 1-Sep-2022
https://doi.org/10.1109/TSC.2021.3073006
Yan ZLu JChainani NLin C(2021)Workload-Aware Performance Tuning for Autonomous DBMSs2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00267(2365-2368)Online publication date: Apr-2021
https://doi.org/10.1109/ICDE51399.2021.00267
Bellatreche LKerkad A(2015)Query Interaction Based Approach for Horizontal Data PartitioningInternational Journal of Data Warehousing and Mining10.4018/ijdwm.201504010311:2(44-61)Online publication date: 1-Apr-2015
https://doi.org/10.4018/ijdwm.2015040103
Mian RMartin PZulkernine FVazquez-Poletti J(2015)Cost-Effective Resource Configurations for Multi-Tenant Database Systems in Public CloudsInternational Journal of Cloud Applications and Computing10.4018/IJCAC.20150401015:2(1-22)Online publication date: 1-Apr-2015
https://dl.acm.org/doi/10.4018/IJCAC.2015040101
Li HHou XChen MDai ZZhu MHuang M(2015)Performance Prediction for Concurrent Workloads in Distributed Database SystemsAlgorithms and Architectures for Parallel Processing10.1007/978-3-319-27140-8_43(626-639)Online publication date: 16-Dec-2015
https://doi.org/10.1007/978-3-319-27140-8_43
Getta JHandoko (2015)On Transformation of Query Scheduling Strategies in Distributed and Heterogeneous Database SystemsIntelligent Information and Database Systems10.1007/978-3-319-15702-3_14(139-148)Online publication date: 17-Mar-2015
https://doi.org/10.1007/978-3-319-15702-3_14
Mian RMartin PZulkernine FVazquez-Poletti JTůma PCasale GField TAmaral J(2013)Towards building performance models for data-intensive workloads in public cloudsProceedings of the 4th ACM/SPEC International Conference on Performance Engineering10.1145/2479871.2479908(259-270)Online publication date: 21-Apr-2013
https://dl.acm.org/doi/10.1145/2479871.2479908
Qing-Feng ZJing XShan-Shan LQing-Ju W(2013)Enhancing Query Performance by Avoiding Negative Interactions2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing10.1109/HPCC.and.EUC.2013.148(1047-1053)Online publication date: Nov-2013
https://doi.org/10.1109/HPCC.and.EUC.2013.148
Zhang QXu JLi SWang Q(2013)Enhancing Query Performance by Avoiding Negative InteractionsProceedings of the 2013 Fourth International Conference on Emerging Intelligent Data and Web Technologies10.1109/EIDWT.2013.37(184-190)Online publication date: 9-Sep-2013
https://dl.acm.org/doi/10.1109/EIDWT.2013.37
Ahmad MBowman IGraefe GSalem K(2011)Predicting system performance for multi-tenant database workloadsProceedings of the Fourth International Workshop on Testing Database Systems10.1145/1988842.1988848(1-6)Online publication date: 13-Jun-2011
https://dl.acm.org/doi/10.1145/1988842.1988848
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