Query interactions in database workloads

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: Sanjay Agrawal , Eric Chu , Vivek Narasayya, Automatic physical design tuning: workload as a sequence, Proceedings of the 2006 ACM SIGMOD international conference on Management of data, June 27-29, 2006, Chicago, IL, USA [doi>10.1145/1142473.1142549]
• 2: Mumtaz Ahmad , Ashraf Aboulnaga , Shivnath Babu , Kamesh Munagala, Modeling and exploiting query interactions in database systems, Proceeding of the 17th ACM conference on Information and knowledge management, October 26-30, 2008, Napa Valley, California, USA [doi>10.1145/1458082.1458109]
• 3: Amazon Elastic Computing Cloud. http://aws.amazon.com/ec2/.
• 4: Nevil Brownlee , K. C. Claffy, Internet Measurement, IEEE Internet Computing, v.8 n.5, p.30-33, September 2004 [doi>10.1109/MIC.2004.41]
• 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.
• 6: Jeffrey Dean , Sanjay Ghemawat, MapReduce: simplified data processing on large clusters, Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation, p.10-10, December 06-08, 2004, San Francisco, CA
• 7: Archana Ganapathi , Harumi Kuno , Umeshwar Dayal , Janet L. Wiener , Armando Fox , Michael Jordan , David Patterson, Predicting Multiple Metrics for Queries: Better Decisions Enabled by Machine Learning, Proceedings of the 2009 IEEE International Conference on Data Engineering, p.592-603, March 29-April 02, 2009 [doi>10.1109/ICDE.2009.130]
• 8: C. R. Hicks and K. V. Turner. Fundamental Concepts in the Design of Experiments. Oxford University Press, 1999.
• 9: Michael Isard , Mihai Budiu , Yuan Yu , Andrew Birrell , Dennis Fetterly, Dryad: distributed data-parallel programs from sequential building blocks, Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007, March 21-23, 2007, Lisbon, Portugal
• 10: K. O'Gorman , A. El Abbadi , D. Agrawal, Multiple query optimization in middleware using query teamwork, Software—Practice & Experience, v.35 n.4, p.361-391, 10 April 2005 [doi>10.1002/spe.640]
• 11: Christopher Olston , Benjamin Reed , Utkarsh Srivastava , Ravi Kumar , Andrew Tomkins, Pig latin: a not-so-foreign language for data processing, Proceedings of the 2008 ACM SIGMOD international conference on Management of data, June 09-12, 2008, Vancouver, Canada [doi>10.1145/1376616.1376726]
• 12: Prasan Roy , S. Seshadri , S. Sudarshan , Siddhesh Bhobe, Efficient and extensible algorithms for multi query optimization, Proceedings of the 2000 ACM SIGMOD international conference on Management of data, p.249-260, May 15-18, 2000, Dallas, Texas, United States
• 13: T. J. Santner, B. J. Williams, and W. Notz. The Design and Analysis of Computer Experiments. Springer, first edition, July 2003.
• 14: Christopher Stewart , Terence Kelly , Alex Zhang, Exploiting nonstationarity for performance prediction, Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007, March 21-23, 2007, Lisbon, Portugal
• 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: Ian H. Witten , Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems), Morgan Kaufmann Publishers Inc., San Francisco, CA, 2005
• 18: Qi Zhang , Ludmila Cherkasova , Evgenia Smirni, A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications, Proceedings of the Fourth International Conference on Autonomic Computing, p.27, June 11-15, 2007 [doi>10.1109/ICAC.2007.1]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.7 Database Administration

Additional Classification:
  C. Computer Systems Organization
  C.4 PERFORMANCE OF SYSTEMS

General Terms:
Design, Performance, Reliability