A Comparison Of Relational, NoSQL and NewSQL Database Management Systems For The Persistence Of Time Series Data

Time series data is created in a variety of application areas such as sensors in cars, smartwatches or loT devices. This kind of data is often characterized by high resource demand due to the frequency the information is measured, with data points once a day, hour and even down to milliseconds. While real-time processing of such data is often sufficient, there are also many use cases, where batch processing and consequently the storage and managed access of measurements is required. For this reason, this work evaluates different database management systems in the context of storing time related data using different data models such as classical relational models, non-relational models using NoSQL database systems and the recently upcoming group of NewSQL databases. The evaluation shows that a highly optimized time series databases such as InftuxDB is able to outperform the other tested systems regarding write-throughput and RAM as well as disk utilization in a single server setup.