We present context-aware benchmarking and performance engineering of a mature TByte-scale air quality database system which was created by the Tropospheric Ozone Assessment Report (TOAR) and contains one of the world’s largest collections of near-surface air quality measurements. A special feature of our data service https://join.fz-juelich.de is on-demand processing of several air quality metrics directly from the TOAR database. As a service that is used by more than 350 users of the international air quality research community, our web service must be easily accessible and functionally flexible, while delivering good performance. The current on-demand calculations of air quality metrics outside the database together with the necessary transfer of large volume raw data are identified as the major performance bottleneck. In this study, we therefore explore and benchmark in-database approaches for the statistical processing, which results in performance enhancements of up to 32%.

我们介绍了成熟的 TByte 级空气质量数据库系统的上下文感知基准和性能工程，该系统由对流层臭氧评估报告 (TOAR) 创建，包含世界上最大的近地表空气质量测量集合之一。我们的数据服务 https://join.fz-juelich.de 的一个特殊功能是直接从 TOAR 数据库按需处理多个空气质量指标。作为一项被国际空气质量研究社区的 350 多名用户使用的服务，我们的 Web 服务必须易于访问且功能灵活，同时提供良好的性能。当前在数据库之外按需计算空气质量指标以及必要的大量原始数据传输被认为是主要的性能瓶颈。在这项研究中，