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Time series database

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Template:More citations needed A time series database is a software system that is optimized for storing and serving time series through associated pairs of time(s) and value(s).[1] In some fields, time series may be called profiles, curves, traces or trends.[2] Several early time series databases are associated with industrial applications which could efficiently store measured values from sensory equipment (also referred to as data historians), but now are used in support of a much wider range of applications.

In many cases, the repositories of time-series data will utilize compression algorithms to manage the data efficiently.[3][4] Although it is possible to store time-series data in many different database types, the design of these systems with time as a key index is distinctly different from relational databases which reduce discrete relationships through referential models.[5]


Time series datasets are relatively large and uniform compared to other datasets―usually being composed of a timestamp and associated data.[6] Time series datasets can also have fewer relationships between data entries in different tables and don't require indefinite storage of entries.[6] The unique properties of time series datasets mean that time series databases can provide significant improvements in storage space and performance over general purpose databases.[6] For instance, due to the uniformity of time series data, specialized compression algorithms can provide improvements over regular compression algorithms designed to work on less uniform data.[6] Time series databases can also be configured to regularly delete (or downsample) old data, unlike regular databases which are designed to store data indefinitely.[6] Special database indices can also provide boosts in query performance.[6]

List of time series databases

The following database systems have functionality optimized for handling time series data.

Name License Language References
Apache IoTDB Apache License 2.0 Java [7]
Apache Kudu Apache License 2.0 C++ [8]
Apache Pinot Apache License 2.0 Java [9]
CrateDB Apache License 2.0 Java [10][11]
eXtremeDB Commercial SQL, Python, C / C++, Java, and C# [12]
InfluxDB MIT.[13] Chronograf AGPLv3, Clustering Commercial[14] Go (version 2), Rust (version 3)[15] [12][16]
Informix TimeSeries Commercial C / C++ [12][17]
Kx kdb+ Commercial Q [12]
MongoDB Server Side Public License C++, JavaScript, Python [18]
Prometheus Apache License 2.0 Go [12]
RedisTimeSeries RSALv2/SSPLv1[19] C [20]
Riak-TS Apache License 2.0 Erlang [12]
RRDtool GPLv2 C [12]
TimescaleDB Apache License 2.0 C [21]
Whisper (Graphite) Apache License 2.0 Python [22]

See also


  1. Mueen, Abdullah; Keogh, Eamonn; Zhu, Qiang; Cash, Sydney; Westover, Brandon (2009). "Exact Discovery of Time Series Motifs" (PDF). University of California, Riverside. 2009: 473–484. doi:10.1137/1.9781611972795.41. ISBN 978-0-89871-682-5. PMC 6814436. PMID 31656693. Archived from the original (PDF) on 25 June 2010. Retrieved 31 July 2019. Definition 2:A Time Series Database(D)is an unordered set of m time series possibly of different lengths.
  2. Villar-Rodriguez, Esther; Del Ser, Javier; Oregi, Izaskun; Bilbao, Miren Nekane; Gil-Lopez, Sergio (2017). "Detection of non-technical losses in smart meter data based on load curve profiling and time series analysis". Energy. 137: 118–128. doi:10.1016/ hdl:20.500.11824/693.
  3. Pelkonen, Tuomas; Franklin, Scott; Teller, Justin; Cavallaro, Paul; Huang, Qi; Meza, Justin; Veeraraghavan, Kaushik (2015). "Gorilla". Proceedings of the VLDB Endowment. 8 (12): 1816–1827. doi:10.14778/2824032.2824078.
  4. Lockerman, Joshua (2020-04-22). "Time-series compression algorithms, explained". Timescale Blog. Retrieved 2022-10-07.
  5. Asay, Matt (26 June 2019). "Why time series databases are exploding in popularity". TechRepublic. Archived from the original on 26 June 2019. Retrieved 31 July 2019. Relational databases and NoSQL databases can be used for time series data, but arguably developers will get better performance from purpose-built time series databases, rather than trying to apply a one-size-fits-all database to specific workloads.
  6. a b c d e f Wayner, Peter (15 January 2021). "Database trends: The rise of the time-series database". VentureBeat. Retrieved 7 July 2021.
  7. Wang, Chen; Huang, Xiangdong; Qiao, Jialin; Jiang, Tian; Rui, Lei; Zhang, Jinrui; Kang, Rong; Feinauer, Julian; McGrail, Kevin A.; Wang, Peng; Luo, Diaohan; Yuan, Jun; Wang, Jianmin; Sun, Jiaguang (August 2020). "Apache IoTDB: time-series database for internet of things". Proceedings of the VLDB Endowment. 13 (12): 2901–2904. doi:10.14778/3415478.3415504. ISSN 2150-8097. S2CID 221352039.
  8. "Benchmarking Time Series workloads on Apache Kudu using TSBS". 18 March 2020.
  9. Fu, Yupeng; Soman, Chinmay (9 June 2021). "Real-time Data Infrastructure at Uber". Proceedings of the 2021 International Conference on Management of Data. pp. 2503–2516. arXiv:2104.00087. doi:10.1145/3448016.3457552. ISBN 9781450383431. S2CID 232478317.
  10. "DB-Engines Ranking". DB-Engines. Retrieved 2023-01-22.
  11. "Anforderungen für Zeitreihendatenbanken im industriellen IoT". (in Deutsch). Retrieved 2023-01-22.
  12. a b c d e f g Stephens, Rachel (2018-04-03). "State of the Time Series Database Market". Retrieved 2018-10-03.
  13. "influxdb license". GitHub. Retrieved 2016-08-14.
  14. "influxdb clustering". Retrieved 2016-03-10.
  15. Wachtel, Jessica (2023-07-06). "Meet the Founders Who Rewrote in Rust". InfluxData. Retrieved 2023-10-05.
  16. Anadiotis, George (2018-09-28). "Processing time series data: What are the options?". Retrieved 2016-03-10.
  17. Dantale, Viabhav (2012-09-21). Solving Business Problems with Informix TimeSeries (PDF). IBM Redbooks. ISBN 9780738437231.
  18. "MongoDB's New Time Series Collections".
  19. "RedisTimeSeries/LICENSE.txt at master · RedisTimeSeries/RedisTimeSeries". GitHub. Retrieved 2023-10-05.
  20. "RedisTimeSeries". Redis. Retrieved 12 June 2023.
  21. Design Recommendations for Intelligent Tutoring Systems: Volume 8 - Data Visualization. Army Research Laboratory. December 29, 2020. p. 50. ISBN 9780997725780.
  22. Joshi, Nishes (May 23, 2012). Interoperability in monitoring and reporting systems (Thesis). hdl:10852/9085.