DBMS metrology: Measuring query time

Sabah Currim, Richard T. Snodgrass, Young Kyoon Suh, Rui Zhang, Matthew Wong Johnson, Cheng Yi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

It is surprisingly hard to obtain accurate and precise measurements of the time spent executing a query. We review relevant process and overall measures obtainable from the Linux kernel and introduce a structural causal model relating these measures. A thorough correlational analysis provides strong support for this model. Using this model, we developed a timing protocol, which (1) performs sanity checks to ensure validity of the data, (2) drops some query executions via clearly motivated predicates, (3) drops some entire queries at a cardinality, again via clearly motivated predicates, (4) for those that remain, for each computes a single measured time by a carefully justified formula over the underlying measures of the remaining query executions, and (5) performs post-analysis sanity checks. The resulting query time measurement procedure, termed the Tucson Protocol, applies to proprietary and open-source DBMSes.

Original languageEnglish (US)
Title of host publicationSIGMOD 2013 - International Conference on Management of Data
Pages421-432
Number of pages12
DOIs
StatePublished - Jul 29 2013
Event2013 ACM SIGMOD Conference on Management of Data, SIGMOD 2013 - New York, NY, United States
Duration: Jun 22 2013Jun 27 2013

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Other

Other2013 ACM SIGMOD Conference on Management of Data, SIGMOD 2013
CountryUnited States
CityNew York, NY
Period6/22/136/27/13

Keywords

  • Accuracy
  • Repeatability
  • Tucson Protocol

ASJC Scopus subject areas

  • Software
  • Information Systems

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  • Cite this

    Currim, S., Snodgrass, R. T., Suh, Y. K., Zhang, R., Johnson, M. W., & Yi, C. (2013). DBMS metrology: Measuring query time. In SIGMOD 2013 - International Conference on Management of Data (pp. 421-432). (Proceedings of the ACM SIGMOD International Conference on Management of Data). https://doi.org/10.1145/2463676.2465331