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Surprise Benchmarking: The Why, What, and How

  • Lawrence Benson
    ,
  • Carsten Binnig
    ,
  • Jan-Micha Bodensohn
    ,
  • Federico Lorenzi
    ,
  • Jigao Luo
    ,
  • Danica Porobic
  • Technical University of Munich
    ,
  • Darmstadt University of Technology
    ,
  • TigerBeetle
    ,
  • Oracle Corporation
    ,
  • Hasso Plattner Institute
    ,
  • CrystalDB
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-review

Open access

Publication Information

Output type

Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 1-8 (8 pages)

Publication milestones

  • Published - 09/06/2024

Publication status

Published - 09/06/2024

Publisher

Association for Computing Machinery, United States
9798400706691

Publication IDs

  • Scopus: 85197238834

Host publication title

Proceedings of the Tenth International Workshop on Testing Database Systems, DBTest 2024, Santiago, Chile, 9 June 2024

Abstract

Standardized benchmarks are crucial to ensure a fair comparison of performance across systems. While extremely valuable, these benchmarks all use a setup where the workload is well-defined and known in advance. Unfortunately, this has led to overly-tuning data management systems for particular benchmark workloads such as TPC-H or TPC-C. As a result, benchmarking results frequently do not reflect the behavior of these systems in many real-world settings since workloads often significantly vary from the “known” benchmarking workloads. To address this issue, we present surprise benchmarking , a complementary approach to the current standardized benchmarking where “unknown” queries are exercised during the evaluation.

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Captures
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Citations
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Related Event

Title

International Workshop on Testing Database Systems

Event type

Workshop

Degree of recognition

International event

Date

09/06/2024

Location

SantiagoChile