Assessing Query Execution Time and Implementational Complexity in Different Databases for Time Series Data

Sammanfattning

Traditional database management systems are designed for general purpose data handling, and fail to work efficiently with time-series data due to characteristics like high volume, rapid ingestion rates, and a focus on temporal relationships. However, what is a best solution is not a trivial question to answer. Hence, this thesis aims to analyze four different Database Management Systems (DBMS) to determine their suitability for managing time series data, with a specific focus on Internet of Things (IoT) applications. The DBMSs examined include PostgreSQL, TimescaleDB, ClickHouse, and InfluxDB. This thesis evaluates query performance across varying dataset sizes and time ranges, as well as the implementational complexity of each DBMS. The benchmarking results indicate that InfluxDB consistently delivers the best performance, though it involves higher implementational complexity and time consumption. ClickHouse emerges as a strong alternative with the second-best performance and the simplest implementation. The thesis also identifies potential biases in benchmarking tools and suggests that TimescaleDB's performance may have been affected by configuration errors. The findings provide significant insights into the performance metrics and implementation challenges of the selected DBMSs. Despite limitations in fully addressing the research questions, this thesis offers a valuable overview of the examined DBMSs in terms of performance and implementational complexity. These results should be considered alongside additional research when selecting a DBMS for time series data.