KairosDB is an open-source time-series database written in Java that is designed to scale to massive levels, making it ideal for storing metrics and other time-series data. It offers high performance and scalability, allowing users to efficiently manage and analyze large volumes of timestamped data. KairosDB provides flexible data aggregation across different time intervals, enabling users to query and visualize time-series data effectively.
Databases such as KairosDB fall under the category of Time-Series Databases. These specialized databases are optimized for handling data that is indexed by time, making them especially suitable for applications that deal with metrics, sensor data, and IoT (Internet of Things) data. Time-series databases excel in efficiently storing, querying, and analyzing data points that are associated with timestamps, allowing users to derive valuable insights from temporal data patterns.
KairosDB was founded in 2012 by a team of developers with a vision to create a robust time-series database that could address the growing need for managing time-stamped data at scale. Motivated by the increasing demand for handling metrics and sensor data in real-time applications, the creators of KairosDB set out to build a solution that could meet the performance and scalability requirements of modern time-series data use cases.
In terms of current market share, KairosDB has carved a niche for itself within the Time-Series Database category, particularly favored by users looking for an open-source solution with strong scaling capabilities. While its market share may not be as dominant as some of the more established players in the database market, KairosDB continues to attract users who value its performance and flexibility for time-series data storage and analysis. With the increasing adoption of IoT technologies and the growing importance of real-time analytics, it is anticipated that KairosDB's market share is poised to see gradual growth in the future as organizations seek specialized solutions for managing time-series data effectively.
