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AZDBLab: a laboratory information system for large-scale empirical DBMS studies

Published: 01 August 2014 Publication History

Abstract

In the database field, while very strong mathematical and engineering work has been done, the scientific approach has been much less prominent. The deep understanding of query optimizers obtained through the scientific approach can lead to better engineered designs. Unlike other domains, there have been few DBMS-dedicated laboratories, focusing on such scientific investigation.
In this demonstration, we present a novel DBMS-oriented research infrastructure, called Arizona Database Laboratory (AZDBLab), to assist database researchers in conducting a large-scale empirical study across multiple DBMSes. For them to test their hypotheses on the behavior of query optimizers, AZDBLab can run and monitor a large-scale experiment with thousands (or millions) of queries on different DBMSes. Furthermore, AZDBLab can help users automatically analyze these queries. In the demo, the audience will interact with AZDBLab through the stand-alone application and the mobile app to conduct such a large-scale experiment for a study. The audience will then run a Tucson Timing Protocol analysis on the finished experiment and then see the analysis (data sanity check and timing) results.

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Published In

cover image Proceedings of the VLDB Endowment
Proceedings of the VLDB Endowment  Volume 7, Issue 13
August 2014
466 pages
ISSN:2150-8097
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VLDB Endowment

Publication History

Published: 01 August 2014
Published in PVLDB Volume 7, Issue 13

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  • (2024)Identifying the Root Causes of DBMS SuboptimalityACM Transactions on Database Systems10.1145/363642549:1(1-40)Online publication date: 28-Feb-2024
  • (2021)Have query optimizers hit the wall?The VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-021-00689-y31:1(181-200)Online publication date: 20-Sep-2021
  • (2017)An empirical study of transaction throughput thrashing across multiple relational DBMSesInformation Systems10.1016/j.is.2016.12.00466:C(119-136)Online publication date: 1-Jun-2017
  • (2017)EMP: execution time measurement protocol for compute‐bound programsSoftware: Practice and Experience10.1002/spe.247647:4(559-597)Online publication date: 23-Feb-2017
  • (2016)DBMS MetrologyACM Transactions on Database Systems10.1145/299645442:1(1-42)Online publication date: 9-Nov-2016

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