Time series database

A time series database (TSDB) 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] 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.[4]

Overview

A time series database typically separates the set of fixed, discrete characteristics from its dynamic, continuous values into sets of points or 'tags.' An example is the storage of CPU utilization for performance monitoring: the fixed characteristics would include the name 'CPU Utilization' the units of measure '%' and a range '0 to 1'; and the dynamic values would store the utilization percentage and a timestamp. The separation is intended to efficiently store and index data for application purposes which can search through the set of points differently than the time-indexed values.

The databases vary significantly in their features, but most will enable features to create, read, update and delete the time-value pairs as well as the points to which they are associated. Additional features for calculations, interpolation, filtering, and analysis are commonly found, but are not commonly equivalent.

List of time series databases

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

NameLicenseLanguageReferences
Druid Apache License 2.0 Java [5]
eXtremeDB Commercial SQL, Python, C / C++, Java, and C# [5]
InfluxDBMIT.[6] Chronograf AGPLv3, Clustering Commercial[7]Go[5][8]
Informix TimeSeriesCommercialC / C++[5][9]
Kx kdb+CommercialQ[5]
KuduApache License 2.0C++[10]
PrometheusApache License 2.0Go[5]
Riak-TSApache License 2.0Erlang[5]
RRDtoolGPLv2C[5]
Whisper (Graphite)Apache 2Python[11]

See also

References
  1. Mueen, Abdullah; Keogh, Eamonn; Zhu, Qiang; Cash, Sydney; Westover, Brandon. "Exact Discovery of Time Series Motifs" (PDF). University of California, Riverside. p. 2. 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/j.energy.2017.07.008. 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. 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.
  5. Stephens, Rachel (2018-04-03). "State of the Time Series Database Market". Retrieved 2018-10-03.
  6. "influxdb license". GitHub. Retrieved 2016-08-14.
  7. "influxdb clustering". influxdata.com. Retrieved 2016-03-10.
  8. Anadiotis, George (2018-09-28). "Processing time series data: What are the options?". zdnet.com. Retrieved 2016-03-10.
  9. Dantale, Viabhav (2012-09-21). Solving Business Problems with Informix TimeSeries (PDF). IBM Redbooks. ISBN 9780738437231.
  10. "Benchmarking Time Series workloads on Apache Kudu using TSBS".
  11. Joshi, Nishes (May 23, 2012). Interoperability in monitoring and reporting systems (Thesis). hdl:10852/9085.
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