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// Workers AI · dad joke modeWhy did the time series database go to therapy? It had a lot of past issues.

From Wikipedia, the free encyclopedia

A time series database (TSDB) is a software system optimized for handling time series data—a sequence of data points indexed in chronological order.[1] In various scientific and financial disciplines, these chronological sequences may be referred to as profiles, curves, traces, or trends.[2] Early iterations of these databases were primarily associated with industrial applications, where they functioned as data historians to efficiently record measurements from physical sensors; however, modern implementations now span a much wider range of uses. Due to the high ingestion rates and sequential nature of this data, TSDB repositories heavily utilize specialized compression algorithms to manage storage efficiently.[3] While time-series data can be stored across various database types, systems designed around a temporal primary index differ structurally from relational databases, which optimize for discrete relationships using referential models.

Overview

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Compared to general-purpose datasets, time series datasets are highly uniform and scale rapidly, typically consisting of sequential timestamps mapped to specific metric values.[4] Because individual entries rarely feature complex, cross-table relational dependencies, TSDBs do not require the overhead of traditional relational constraints. Furthermore, because these workloads seldom demand indefinite data retention, TSDBs can drastically reduce storage footprints and accelerate queries relative to general-purpose databases.[4]

For example, the sequential uniformity of temporal data allows specialized compression algorithms—such as delta encoding or Gorilla compression—to achieve significantly higher data density than standard compression techniques.[3] Additionally, while general-purpose systems are optimized for persistent data retention, TSDBs natively automate data lifecycle management by downsampling or dropping entire historical data partitions in bulk.[5] Finally, specialized temporal indexing strategies further isolate time windows to maximize query performance.

Workloads and design paradigms

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Time series databases are commonly designed for append-only workloads, where massive volumes of sequential observations or discrete events are continuously ingested alongside corresponding timestamps. Purpose-built series management systems are specifically designed to ingest and process data from infrastructure monitoring, system automation, the internet of things (IoT), and complex cyber-physical networks. In these environments, data streams are typically generated at extreme velocities and high volumes.[4][5]

Primary design paradigms focus on maximizing write throughput, ensuring low-latency range queries, utilizing highly efficient data compression, and natively supporting multi-window temporal aggregations or automated downsampling.[3][6] To facilitate multidimensional indexing, many implementations organize data using a schema composed of key-value tags, device IDs, or other metadata. This metadata structure allows applications to isolate and query telemetry based simultaneously on specific time intervals and source attributes.[6]

Applications and workloads

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Time series databases are deployed across diverse industries where continuous chronological data collection is critical. Common application areas include industrial automation monitoring, operational data historians, infrastructure and application performance monitoring (APM), IoT telemetry, smart energy grid management, financial market tick data tracking, and scientific experimental measurements.[5][7]

Within telemetry and systems monitoring, time series database systems must ingest and index massive streams of sensor or metric data in real time. To support downstream analytical workflows, these databases natively execute continuous queries and support analytical tasks such as data exploration, real-time anomaly detection, predictive trend analysis, and database gap-filling or missing-value recovery.[7]

Analysis and machine learning

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Time series databases frequently serve as the underlying storage and retrieval engines for downstream analytical pipelines, including forecasting, real-time anomaly detection, missing-value imputation, and pattern matching. To optimize performance and reduce data transfer overhead, certain modern time series systems support in-database analytics by embedding machine-learning functions directly into the database layer. This architecture allows operations to invoke forecasting or outlier detection algorithms natively through standard database query interfaces, such as specialized SQL extensions.[7][8]

Research in temporal data science increasingly utilizes large, pre-trained time series foundation models for complex forecasting and classification tasks. Within computer science literature, these deep learning architectures are classified strictly as algorithmic analysis methodologies applied to temporal datasets, rather than native, defining characteristics of the underlying database engine architecture.[9][10]

List of time series databases

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

NameLicenseLanguageReferences
Apache IoTDB Apache License 2.0 Java [6]
Apache KuduApache License 2.0C++[11]
Apache Pinot Apache License 2.0 Java [12]
ClickHouse Apache License 2.0 C++ [13]
CrateDB Apache License 2.0 Java [14][15]
eXtremeDB Commercial SQL, Python, C / C++, Java, and C# [4]
FAME (database)CommercialC[16]
InfluxDBMIT.[17] Chronograf AGPLv3, Clustering Commercial[18]Go (version 2), Rust (version 3)[19][20][21]
Informix TimeSeriesCommercialC / C++[20][22]
Kx kdb+CommercialQ[20]
MongoDB Server Side Public License (SSPL) v1.0 C++ [23]
PrometheusApache License 2.0Go[20]
Riak-TSApache License 2.0Erlang[20]
RRDtoolGPLv2C[20]
TimescaleDBApache License 2.0C[24]
Whisper (Graphite)Apache License 2.0Python[25]

See also

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References

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  1. ↑ Mueen, Abdullah; Keogh, Eamonn; Zhu, Qiang; Cash, Sydney; Westover, Brandon (2009). "Exact Discovery of Time Series Motifs". Proceedings of the 2009 SIAM International Conference on Data Mining (PDF). Vol. 2009. pp. 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. Bibcode:2017Ene...137..118V. doi:10.1016/j.energy.2017.07.008. hdl:20.500.11824/693.
  3. 1 2 3 Pelkonen, Tuomas; Franklin, Scott; Teller, Justin; Cavallaro, Paul; Huang, Qi; Meza, Justin; Veeraraghavan, Kaushik (2015-08-01). "Gorilla: a fast, scalable, in-memory time series database". Proceedings of the VLDB Endowment. 8 (12): 1816–1827. doi:10.14778/2824032.2824078. ISSN 2150-8097.
  4. 1 2 3 4 Bader, Andreas; Kopp, Oliver; Mitschang, Bernhard (July 2017). "A Survey on Time Series Databases". Proceedings of the 13th International Conference on Advances in Databases, Knowledge, and Data Applications: 23–32.
  5. 1 2 3 Jensen, Søren Kejser; Pedersen, Torben Bach; Thomsen, Christian (2017). "Time Series Management Systems: A Survey". IEEE Transactions on Knowledge and Data Engineering. 29 (11): 2581–2600. doi:10.1109/TKDE.2017.2740932.
  6. 1 2 3 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" (PDF). Proceedings of the VLDB Endowment. 13 (12): 2901–2904. doi:10.14778/3415478.3415504. ISSN 2150-8097. S2CID 221352039.
  7. 1 2 3 Khelifati, Abdelouahab; Khayati, Mourad; Dignös, Anton; Difallah, Djellel; Cudré-Mauroux, Philippe (2023). "TSM-Bench: Benchmarking Time Series Database Systems for Monitoring Applications" (PDF). Proceedings of the VLDB Endowment. 16 (11): 3363–3376. doi:10.14778/3611479.3611532.
  8. ↑ Shen, Chunhui; Ouyang, Qianyu; Li, Feibo; Liu, Zhipeng; Zhu, Longcheng; Zou, Yujie; Su, Qing; Yu, Tianhuan; Yi, Yi; Hu, Jianhong; Zheng, Cen; Wen, Bo; Zheng, Hanbang; Xu, Lunfan; Pan, Sicheng; Wu, Bin; He, Xiao; Li, Ye; Tan, Jian; Wang, Sheng; Pei, Dan; Zhang, Wei; Li, Feifei (2023). "Lindorm TSDB: A Cloud-native Time-series Database for Large-scale Monitoring Systems" (PDF). Proceedings of the VLDB Endowment. 16 (12): 3715–3727. doi:10.14778/3611540.3611559.
  9. ↑ Jin, Ming; Wen, Qingsong; Liang, Yuxuan; Zhang, Chaoli; Xue, Siqiao; Wang, Zue; Zhang, Jianbin; Wang, Yi; Chen, Sheng; Li, Hao; Pan, Shirui; Larson, Vincent S.; Yin, Yidong; Ma, William K.; Wang, Jia; Huang, Xiyuan; Meggars, Charles; Khayati, Mourad; Cudré-Mauroux, Philippe; Torres, Rodrigo; Deng, Simeng; Subrahmanya, Suhas; Bhattacharjee, Sanyam; Su, Yuan-Fang; Radivojac, Predrag; Kalyanaraman, Yasant; Sun, Jian; Ke, Sheng; Yang, Zheng; Januschowski, Tim; Li, Yanyan; Giles, C. Lee; Alahi, Alexandre; Yi, Jinfeng; Spitz, Andreas; Dietterich, Thomas G. (January 2024). "Position Paper: What Can Large Language Models Do for Time Series?". ACM SIGKDD Explorations Newsletter. 25 (2): 1–12. doi:10.1145/3648354.3648356.
  10. ↑ Liu, Yong; Zhang, Haoran; Li, Chenyu; Huang, Xiangdong; Wang, Jianmin; Long, Mingsheng (2024). "Timer: Generative Pre-trained Transformers Are Large Time Series Models". Proceedings of the 41st International Conference on Machine Learning. Proceedings of Machine Learning Research. Vol. 235. PMLR. pp. 32369–32399.
  11. ↑ Apache Kudu Documentation.
  12. ↑ Apache Pinot Documentation.
  13. ↑ ClickHouse Core Documentation.
  14. ↑ Gupta, Saurabh; Pathak, Jyoti (2020). "SmartBench: A Benchmark For Data Management In Smart Spaces". Proceedings of the VLDB Endowment. 13 (12): 1807–1810.
  15. ↑ Kopp, Oliver; Mitschang, Bernhard (2021). "Big Sequence Management: Scaling up and Out". Proceedings of the 24th International Conference on Extending Database Technology (EDBT). pp. 305–310.
  16. ↑ Bank for International Settlements: Central Banks' Use of Time Series Products (FAME Analysis).
  17. ↑ "influxdb license". GitHub. Retrieved 2016-08-14.
  18. ↑ "influxdb clustering". influxdata.com. Retrieved 2016-03-10.
  19. ↑ Wachtel, Jessica (2023-07-06). "Meet the Founders Who Rewrote in Rust". InfluxData. Retrieved 2023-10-05.
  20. 1 2 3 4 5 6 Stephens, Rachel (2018-04-03). "State of the Time Series Database Market". Retrieved 2018-10-03.
  21. ↑ Anadiotis, George (2018-09-28). "Processing time series data: What are the options?". ZDNet. Retrieved 2016-03-10.
  22. ↑ Dantale, Viabhav (2012-09-21). Solving Business Problems with Informix TimeSeries (PDF). IBM Redbooks. ISBN 9780738437231.
  23. ↑ "Server Side Public License (SSPL)". MongoDB. Retrieved 2026-04-17.
  24. ↑ "TimescaleDB Architecture Docs". timescale.com. Retrieved 2026-07-09.
  25. ↑ "Official Graphite Whisper Repository Documentation". github.com. Retrieved 2026-07-09.
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