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Bulletin of the Technical Committee on Data Engineering March 2009
| Content Provider | Semantic Scholar |
|---|---|
| Author | Lomet, David B. Ooi, Beng Chin Srinivasan Parthasarathy Abadi, Daniel J. Salem, Kenneth Soror, Ahmed A. Minhas, Umar Farooq Kokosielis, Peter Kamath, Sunil Beyer, Kevin S. Ercegovac, Vuk Krishnamurthy, Rajasekar Sriram Raghavan Rao, Jun Zhu, Huaiyu Baldeschwieler, Eric Kistler, James J. Chuck Neerdaels Negrin, Toby Ramakrishnan, Raghu Paton, Norman W. Aragão, Marcelo A. T. Lee, Kevin Sakellariou, Rizos Ioannidis, Yannis |
| Copyright Year | 2008 |
| Abstract | Tuning database system configuration parameters to proper v alues according to the expected query workload plays a very important role in determining DBMS per formance. However, the number of configuration parameters in a DBMS is very large. Furthermor e, typical query workloads have a large number of constituent queries, which makes tuning very time and effort intensive. To reduce tuning time and effort, database administrators rely on their experien ce and some rules of thumb to select a set of important configuration parameters for tuning. Nonetheles s, as a statistically rigorous methodology is not used, time and effort may be wasted by tuning those parame ters which may have no or marginal effects on the DBMS performance for the given query workload . Database administrators also use compressed query workloads to reduce tuning time. If not car efully selected, the compressed query workload may fail to include a query which may reveal importa nt performance bottleneck parameters. In this article, we provide a systematic approach to help the database administrators in tuning activities. We achieve our goals through two phases. First, we estimate t he ffects of the configuration parameters for each workload query. The effects are estimated through a design of experiments-based PLACKETT & BURMAN design methodology where the number of experiments require d is linearly proportional to the number of input parameters. Second, we exploit the estimate d eff cts to: 1) rank DBMS configuration parameters for a given query workload based on their impact o n the DBMS performance, and 2) select a compressed query workload that preserves the fidelity of th e original workload. Experimental results using PostgreSQL and TPC-H query workload show that our meth odologies are working correctly. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://sites.computer.org/debull/A09mar/A09MAR-CD.pdf |
| Alternate Webpage(s) | http://www.lamsade.dauphine.fr/~litwin/cours98/CoursBD/doc/doc/DataEngBullMarch09CloudComputing.pdf |
| Alternate Webpage(s) | http://sites.computer.org/debull/A13mar/A13MAR-CD.pdf |
| Alternate Webpage(s) | http://sites.computer.org/debull/A08mar/A08MAR-CD.pdf |
| Alternate Webpage(s) | http://sites.computer.org/debull/A17dec/A17DEC-CD.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Chimeric antigen receptor Database Design of experiments Experiment HL7PublishingSubSection |
| Content Type | Text |
| Resource Type | Article |
