SEER: An End-to-End Toolkit for Benchmarking Time Series Database Systems in Monitoring Applications

Luca Althaus, Mourad Khayati, Abdelouahab Khelifati, Anton Dignös, Djellel Difallah, Philippe Cudré-Mauroux

Research output: Contribution to journalConference articlepeer-review

Abstract

Time series database systems (TSDBs) are prevalent in many applications ranging from monitoring and IoT devices to scientific research. Those systems are specifically designed to efficiently manage data indexed by time. Because of the variety of workloads, the diversity of time series features, and the sophistication of existing TSDBs, there is no clear way to pick the most suitable system. In this demo, we introduce SEER, an automated, configurable, and interactive toolkit to evaluate TSDBs. SEER is based on TSM-Bench, a benchmark tailored for time series database systems used in monitoring applications. It implements an end-to-end pipeline for database benchmarking from data generation and feature contamination to workload evaluation. Users can define their portfolios by configuring and parameterizing custom queries, specifying their frequencies, controlling the type and level of data features, and indicating the type of workloads. Moreover, they can deploy new systems and/or reconfigure the pre-installed ones. SEER would process users' requests and gracefully recommend the best system on a use-case basis.

Original languageEnglish (US)
Pages (from-to)4361-4364
Number of pages4
JournalProceedings of the VLDB Endowment
Volume17
Issue number12
DOIs
StatePublished - 2024
Event50th International Conference on Very Large Data Bases, VLDB 2024 - Guangzhou, China
Duration: Aug 25 2024Aug 29 2024

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • General Computer Science

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