TimescaleDB
TimescaleDB is an open-source time series database developed by Timescale Inc. (renamed "TigerData" on June 17, 2025). It is written in C and extends PostgreSQL. TimescaleDB is a relational database and supports standard SQL queries. Additional SQL functions and table structures provide support for time series data oriented towards storage, performance, and analysis facilities for data-at-scale. One of the key features of TimescaleDB is its performance, which has been compared to InfluxDB. Time-based data partitioning via hypertables provides for improved query execution and performance when used for time oriented applications. More granular partition definition is achieved through the use of user defined attributes. TimescaleDB is offered as open source software under the Apache 2.0 license. Additional features are offered in a community edition as source available software under the Timescale License Agreement (TLS).
| TimescaleDB | |
|---|---|
| Developer | Timescale Inc (renamed Tiger Data)[1] |
| Initial release | 1 November 2018 |
| Stable release | 2.24.0
/ 2 September 2025[2] |
| Written in | C |
| Operating system | Cross-platform |
| Type | Time series database |
| License | Apache 2.0 |
| Website | tigerdata.com |
| Repository | https://github.com/timescale/timescaledb |
TimescaleDB is an open-source time series database[3][4][5] developed by Timescale Inc. (renamed "TigerData" on June 17, 2025).[6] It is written in C and extends PostgreSQL.[7][8] TimescaleDB is a relational database[9] and supports standard SQL queries. Additional SQL functions and table structures provide support for time series data oriented towards storage, performance, and analysis facilities for data-at-scale.[10]
One of the key features of TimescaleDB is its performance, which has been compared to InfluxDB.[11] Time-based data partitioning via hypertables provides for improved query execution and performance when used for time oriented applications.[12] More granular partition definition is achieved through the use of user defined attributes.[13]
TimescaleDB is offered as open source software under the Apache 2.0 license. Additional features are offered in a community edition as source available software under the Timescale License Agreement (TLS).[14]
History
[edit]Timescale was founded by Ajay Kulkarni (CEO)[15] and Michael J. Freedman (CTO) in response to their need for a database solution to support internet of things workloads.[16]
References
[edit]- ^ "Tiger Data Homepage". Retrieved 1 October 2025.
- ^ "TimescaleDB v2.24.0 release notes". Retrieved 3 December 2025.
- ^ Design Recommendations for Intelligent Tutoring Systems: Volume 8 - Data Visualization. Army Research Laboratory. December 29, 2020. p. 50. ISBN 9780997725780.
For example, the emergence of reliable, open-source, timeseries databases, such as InfluxDB and TimescaleDB, have made them indispensable tools upon which to build metric-driven workflows (Assay, 2019).The value in these specialized data-stores is in their singular focus around ingesting and vending massive amounts of tagged measurements at specific points in time. The optimization and specificity to common issues related to measurement in general --- as well as their open-source licenses --- have made them indispensable solutions for a wide range of needs requiring measurement at scale.
- ^ Dr. Quan Ha Le; Marcelo Diaz (13 August 2021). Developing Modern Database Applications with PostgreSQL. Packt Publishing. p. 162. ISBN 9781838641061.
TimescaleDB is an open source database for time series data; we first heard about TimescaleDB when we investigated standalone PostgREST because it had TimescaleDB as a built-in extension.
- ^ Nathan Liefting; Brian van Baekel (26 February 2021). Zabbix 5 IT Infrastructure Monitoring Cookbook. Packt Publishing. p. 358. ISBN 9781800208452.
TimescaleDB is an open source relational PostgreSQL database for time-based series data.
- ^ "Timescale Becomes TigerData, Defining a New Standard as the Fastest PostgreSQL Platform for Modern Applications". Retrieved 17 June 2025.
- ^ Baer, Tony (June 17, 2021). "Timescale scales out and sets its sights on analytics". ZDNet.
Thus, TimescaleDB joins what is literally a crowd in the PostgreSQL community, but it is unique in being one of the few, if not the only, PostgreSQL variants that have been specifically designed for time series data.
- ^ Grzesik, Piotr; Mrozek, Dariusz (2020-05-25). "Comparative Analysis of Time Series Databases in the Context of Edge Computing for Low Power Sensor Networks". Computational Science – ICCS 2020. Lecture Notes in Computer Science. Vol. 12141. pp. 371–383. doi:10.1007/978-3-030-50426-7_28. ISBN 978-3-030-50425-0. PMC 7302557.
- ^ Struckov, Alexey; Yufa, Semen; Visheratin, Alexander A.; Nasonov, Denis (2019-01-01). "Evaluation of modern tools and techniques for storing time-series data". Procedia Computer Science. 156: 19–28. doi:10.1016/j.procs.2019.08.125. ISSN 1877-0509.
- ^ "High Volume Space Exploration Time-Series Data Storage in PostgreSQL". InfoQ. Retrieved 2021-08-04.
- ^ Jowanza Joseph (December 6, 2021). Mastering Apache Pulsar (Ebook). O'Reilly Media. ISBN 9781492084853.
TimescaleDB shares some philosophical and performance characteristics with InfluxDB, a database supported out of the box by Pulsar IO.
- ^ Martin, Steven J. (2018-09-01). "Cray Advanced Power Management Updates" (PDF). CUG Proceedings 2018. Retrieved 2021-08-04.
- ^ Jinka, Preetam (2018-12-05). "Time Series at ShiftLeft". Medium. Retrieved 2021-08-04.
- ^ "TimescaleDB License Agreement". 2020-09-24. Retrieved 2021-08-09.
- ^ David G. Cohen; Brad Feld (July 2020). Do More Faster India. Wiley. ISBN 9781119698913.
Founded by Andrew Cheung and Ajay Kulkarni, Sensobi was a mobile address book that enabled users to record notes, set follow-up reminders, stay connected, and manage relationships. Sensobi received a small amount of funding and was acquired by GroupMe in 2011, when the app was relaunched as a mobile chat platform. Andrew is currently a technical lead at Signal Services and Ajay is cofounder and CEO of TimescaleDB.
- ^ "A scalable time-series database that supports SQL". O'Reilly Data Show Podcast. 2017-06-22. Retrieved 2021-08-09.