Performance Study of Time Series Databases

TL;DR

This paper compares the performance of various time series databases using real datasets to provide more accurate insights, revealing significant differences in data injection and query times.

Contribution

It offers a comprehensive performance comparison of TSDBs using real-world data, addressing limitations of synthetic data benchmarks.

Findings

Significant performance differences between real and synthetic datasets.

Variations in data injection times across databases.

Differences in query execution times among databases.

Abstract

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.