# Performance Study of Time Series Databases

arXiv: https://arxiv.org/abs/2208.13982 · HTML: https://mlsanity.com/paper/2208.13982/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2022.

## Built on by (most cited first, 2 of 2)

- [Multi-Modal Financial Time-Series Retrieval Through Latent Space Projections](https://mlsanity.com/paper/2309.16741/) (2023)
- [Context-aware LLM-based AI Agents for Human-centered Energy Management Systems in Smart Buildings](https://mlsanity.com/paper/2512.25055/) (2025)

## Abstract (arXiv)

The growth of big-data sectors such as the Internet of Things (IoT) generates enormous volumes of data. As IoT devices generate a vast volume of time-series data, the Time Series Database (TSDB) popularity has grown alongside the rise of IoT. Time series databases are developed to manage and analyze huge amounts of time series data. However, it is not easy to choose the best one from them. The most popular benchmarks compare the performance of different databases to each other but use random or synthetic data that applies to only one domain. As a result, these benchmarks may not always accurately represent real-world performance. It is required to comprehensively compare the performance of time series databases with real datasets. The experiment shows significant performance differences for data injection time and query execution time when comparing real and synthetic datasets. The results are reported and analyzed.