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-reviewOpen access
Publication Information
Output type
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-reviewOriginal language
EnglishPages 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 StatesISBN (Print)
9798400706691Publication IDs
- Scopus: 85197238834
Host publication title
Proceedings of the Tenth International Workshop on Testing Database Systems, DBTest 2024, Santiago, Chile, 9 June 2024Abstract
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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Related Event
Title
International Workshop on Testing Database Systems
Event type
WorkshopDegree of recognition
International eventDate
09/06/2024 Location
SantiagoChile
