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SciTS: A Benchmark for Time-Series Databases in Scientific Experiments and Industrial Internet of Things

Mostafa, Jalal ORCID iD icon 1; Wehbi, Sara; Chilingaryan, Suren ORCID iD icon 1; Kopmann, Andreas ORCID iD icon 1
1 Institut für Prozessdatenverarbeitung und Elektronik (IPE), Karlsruher Institut für Technologie (KIT)


Time-series data has an increasingly growing usage in Industrial Internet of Things (IIoT) and large-scale scientific experiments. Managing time-series data needs a storage engine that can keep up with their constantly growing volumes while providing an acceptable query latency. While traditional ACID databases favor consistency over performance, many time-series databases with novel storage engines have been developed to provide better ingestion performance and lower query latency. To understand how the unique design of a time-series database affects its performance, we design SciTS, a highly extensible and parameterizable benchmark for time-series data. The benchmark studies the data ingestion capabilities of time-series databases especially as they grow larger in size. It also studies the latencies of 5 practical queries from the scientific experiments use case. We use SciTS to evaluate the performance of 4 databases of 4 distinct storage engines: ClickHouse, InfluxDB, TimescaleDB, and PostgreSQL.

Postprint §
DOI: 10.5445/IR/1000151117
Veröffentlicht am 24.08.2023
DOI: 10.1145/3538712.3538723
Zitationen: 5
Zitationen: 8
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Prozessdatenverarbeitung und Elektronik (IPE)
Institut für Theoretische Teilchenphysik (TTP)
KIT-Zentrum Elementarteilchen- und Astroteilchenphysik (KCETA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2022
Sprache Englisch
Identifikator ISBN: 978-1-4503-9667-7
KITopen-ID: 1000151117
HGF-Programm 54.12.02 (POF IV, LK 01) System Technologies
Erschienen in SSDBM 2022: 34th International Conference on Scientific and Statistical Database Management Copenhagen Denmark July 6 - 8, 2022. Ed.: E. Pourabbas
Veranstaltung 34th International Conference on Scientific and Statistical Database Management (SSDBM 2022), Kopenhagen, Dänemark, 06.07.2022 – 08.07.2022
Verlag Association for Computing Machinery (ACM)
Seiten Artikel-Nr.: 12
Vorab online veröffentlicht am 23.08.2022
Nachgewiesen in Scopus
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