A Comparative Analysis of Database Management Systems for Time Series Data

Abstract: Time series data refers to data recorded over time, often periodically, and can rapidly accumulate into vast quantities. To effectively present, analyse, or conduct research on such data it must be stored in an accessible manner. For convenient storage, database management systems (DBMSs) are employed. There are numerous types of such systems, each with their own advantages and disadvantages, making different trade-offs between desired qualities. In this study we conduct a performance comparison between two contrasting DBMSs for time series data. The first system evaluated is PostgreSQL, a popular relational DBMS, equipped with the time series-specific extension TimescaleDB. The second comparand is MongoDB, one of the most well-known and widely used NoSQL systems, with out-of-the-box time series tailoring. We address the question of which out of these DBMSs is better suited for time series data by comparing their query execution times. This involves setting up two databases populated with sample time series data — in our case, publicly available weather data from the Swedish Meteorological and Hydrological Institute. Subsequently, a set of trial queries designed to mimic real-world use cases are executed against each database, while measuring their runtimes. The benchmark results are compared and analysed query-by-query, to identify relative performance differences. Our study finds considerable variation in the relative performance of the two systems, with PostgreSQL outperforming MongoDB in some queries (by up to more than two orders of magnitude) and MongoDB resulting in faster execution in others (by a factor of over 30 in one case). Based on these findings, we conclude that certain queries, and their corresponding real-world use cases, may be better suited for one of the two DBMSs due to the alignment between query structure and the strengths of that system. We further explore other possible explanations for our results, elaborating on factors impacting the efficiency with which each DBMS can execute the provided queries, and consider potential improvements.