Time series database

{{short description|Unordered set of n-time-series possibly of different lengths}} {{More citations needed|date=December 2018}} 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).<ref name = "Various, UCR," >{{ Cite book | url = https://www.cs.ucr.edu/~eamonn/EM.pdf | access-date = 31 July 2019 | first1 = Abdullah | last1 = Mueen | first2 = Eamonn | last2 = Keogh | first3 = Qiang | last3 = Zhu | first4 = Sydney | last4 = Cash | first5 = Brandon | last5 = Westover | title = Proceedings of the 2009 SIAM International Conference on Data Mining | chapter = Exact Discovery of Time Series Motifs | year = 2009 | volume = 2009 | pages = 473–484 | doi = 10.1137/1.9781611972795.41 | quote = Definition 2:A Time Series Database(D)is an unordered set of m time series possibly of different lengths. | pmid = 31656693 | pmc = 6814436 | isbn = 978-0-89871-682-5 | archive-url = https://web.archive.org/web/20100625200233/https://www.cs.ucr.edu/~eamonn/EM.pdf | archive-date = 25 June 2010 | df = dmy-all }}</ref> In some fields, ''time series'' may be called profiles, curves, traces or trends.<ref>{{cite journal |doi=10.1016/j.energy.2017.07.008 |title=Detection of non-technical losses in smart meter data based on load curve profiling and time series analysis |journal=Energy |volume=137 |pages=118–128 |year=2017 |last1=Villar-Rodriguez |first1=Esther |last2=Del Ser |first2=Javier |last3=Oregi |first3=Izaskun |last4=Bilbao |first4=Miren Nekane |last5=Gil-Lopez |first5=Sergio |bibcode=2017Ene...137..118V |hdl=20.500.11824/693 |hdl-access=free }}</ref> 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.<ref name="Gorilla">{{cite journal |doi=10.14778/2824032.2824078 |title=Gorilla |journal=Proceedings of the VLDB Endowment |volume=8 |issue=12 |pages=1816–1827 |year=2015 |last1=Pelkonen |first1=Tuomas |last2=Franklin |first2=Scott |last3=Teller |first3=Justin |last4=Cavallaro |first4=Paul |last5=Huang |first5=Qi |last6=Meza |first6=Justin |last7=Veeraraghavan |first7=Kaushik }}</ref><ref name="Lockerman 2020">{{cite web | last=Lockerman | first=Joshua | title=Time-series compression algorithms, explained | website=Timescale Blog | date=2020-04-22 | url=https://www.timescale.com/blog/time-series-compression-algorithms-explained/ | access-date=2022-10-07}}</ref> 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.<ref name = "Asay, TechRepublic, 2019" >{{ Cite web | url = https://www.techrepublic.com/article/why-time-series-databases-are-exploding-in-popularity/ | title = Why time series databases are exploding in popularity | access-date = 31 July 2019 | first = Matt | last = Asay | date = June 26, 2019 | website = TechRepublic | quote = 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. | archive-url = https://web.archive.org/web/20190626143018/https://www.techrepublic.com/article/why-time-series-databases-are-exploding-in-popularity/ | archive-date = 26 June 2019 | df = dmy-all }}</ref>

== Overview == Time series datasets are relatively large and uniform compared to other datasets―usually being composed of a timestamp and associated data.<ref name="Wayner2021">{{cite news |last1=Wayner |first1=Peter |title=Database trends: The rise of the time-series database |url=https://venturebeat.com/2021/01/15/database-trends-the-rise-of-the-time-series-database/ |access-date=7 July 2021 |work=VentureBeat |date=15 January 2021}}</ref> Time series datasets can also have fewer relationships between data entries in different tables and don't require indefinite storage of entries.<ref name="Wayner2021"/> 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.<ref name="Wayner2021"/> 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.<ref name="Wayner2021"/> Time series databases can also be configured to regularly delete (or downsample) old data, unlike regular databases which are designed to store data indefinitely.<ref name="Wayner2021"/> Special database indices can also provide boosts in query performance.<ref name="Wayner2021"/>

== List of time series databases ==  The following database systems have functionality optimized for handling time series data. {| class="wikitable sortable " |+ |- ! Name !! License !! Language !! References |- |Apache IoTDB |Apache License 2.0 |Java |<ref>{{Cite journal |last1=Wang |first1=Chen |last2=Huang |first2=Xiangdong |last3=Qiao |first3=Jialin |last4=Jiang |first4=Tian |last5=Rui |first5=Lei |last6=Zhang |first6=Jinrui |last7=Kang |first7=Rong |last8=Feinauer |first8=Julian |last9=McGrail |first9=Kevin A. |last10=Wang |first10=Peng |last11=Luo |first11=Diaohan |last12=Yuan |first12=Jun |last13=Wang |first13=Jianmin |last14=Sun |first14=Jiaguang |date=August 2020 |title=Apache IoTDB: time-series database for internet of things |url=https://dl.acm.org/doi/10.14778/3415478.3415504 |journal=Proceedings of the VLDB Endowment |language=en |volume=13 |issue=12 |pages=2901–2904 |doi=10.14778/3415478.3415504 |s2cid=221352039 |issn=2150-8097|url-access=subscription }}</ref> |- | Apache Kudu || Apache License 2.0 || C++ || <ref>{{Cite web|url=https://blog.cloudera.com/benchmarking-time-series-workloads-on-apache-kudu-using-tsbs/|title=Benchmarking Time Series workloads on Apache Kudu using TSBS|date=18 March 2020}}</ref> |- | Apache Pinot | Apache License 2.0 | Java | <ref>{{cite book |last1=Fu |first1=Yupeng |last2=Soman |first2=Chinmay |title=Proceedings of the 2021 International Conference on Management of Data |chapter=Real-time Data Infrastructure at Uber |date=9 June 2021 |pages=2503–2516 |doi=10.1145/3448016.3457552 |arxiv=2104.00087 |isbn=9781450383431 |s2cid=232478317 }}</ref> |- | ClickHouse | Apache License 2.0 | C++ | <ref>{{Cite journal |last1=Schulze |first1=Robert |last2=Schreiber |first2=Tom |last3=Yatsishin |first3=Ilya |last4=Dahimene |first4=Ryadh |last5=Milovidov |first5=Alexey |date=August 2024 |title=ClickHouse - Lightning Fast Analytics for Everyone |url=https://www.vldb.org/pvldb/vol17/p3731-schulze.pdf |journal=Proceedings of the VLDB Endowment |language=en |volume=17 |issue=12 |pages=3731–3744 |doi=10.14778/3685800.3685802}}</ref> |- | CrateDB | Apache License 2.0 | Java | <ref>{{Cite web |title=DB-Engines Ranking |url=https://db-engines.com/en/ranking/time+series+dbms |access-date=2023-01-22 |website=DB-Engines |language=en}}</ref><ref>{{Cite web |title=Anforderungen für Zeitreihendatenbanken im industriellen IoT |url=https://www.springerprofessional.de/anforderungen-fuer-zeitreihendatenbanken-im-industriellen-iot/19119282 |access-date=2023-01-22 |website=springerprofessional.de |language=de}}</ref> |- |eXtremeDB |Commercial |SQL, Python, C / C++, Java, and C# |<ref name="redmonk">{{Cite web|url=https://redmonk.com/rstephens/2018/04/03/the-state-of-the-time-series-database-market/|title=State of the Time Series Database Market|last=Stephens|first=Rachel|access-date=2018-10-03|date=2018-04-03}}</ref> |- | FAME (database) || Commercial || C || |- | InfluxDB || MIT.<ref name="MITgithub">{{Cite web|url=https://github.com/influxdata/influxdb/blob/master/LICENSE|title=influxdb license|website=GitHub|access-date=2016-08-14}}</ref> Chronograf AGPLv3, Clustering Commercial<ref>{{Cite web|url=https://www.influxdata.com/influxdb-clustering/|title=influxdb clustering|last=|first=|date=|website=influxdata.com|access-date=2016-03-10}}</ref> || Go (version 2), Rust (version 3)<ref>{{Cite web |first=Jessica |last=Wachtel |date=2023-07-06 |title=Meet the Founders Who Rewrote in Rust |url=https://www.influxdata.com/blog/meet-founders-who-rewrote-in-rust/ |access-date=2023-10-05 |website=InfluxData}}</ref>|| <ref name="redmonk"/><ref name="processing time series data">{{Cite web|url=https://www.zdnet.com/article/processing-time-series-data-what-are-the-options/|title=Processing time series data: What are the options?|last=Anadiotis|first=George|date=2018-09-28|website=ZDNet|access-date=2016-03-10}}</ref> |- | Informix TimeSeries || Commercial || C / C++ || <ref name="redmonk"/><ref>{{cite book |last1=Dantale |first1=Viabhav |title=Solving Business Problems with Informix TimeSeries |publisher=IBM Redbooks |isbn=9780738437231 |url=http://www.redbooks.ibm.com/redbooks/pdfs/sg248021.pdf|date=2012-09-21 }}</ref> |- | Kx kdb+ || Commercial || Q || <ref name="redmonk"/> |- |MongoDB |Server Side Public License (SSPL) v1.0 |C++ |<ref>{{Cite web |title=Server Side Public License (SSPL) |url=https://www.mongodb.com/legal/licensing/server-side-public-license |access-date=2026-04-17 |website=MongoDB |language=en-us}}</ref> |- | Prometheus || Apache License 2.0 || Go || <ref name="redmonk"/> |- | Riak-TS || Apache License 2.0 || Erlang || <ref name="redmonk"/> |- | RRDtool || GPLv2 || C || <ref name="redmonk"/> |- | TimescaleDB || Apache License 2.0 || C || <ref>{{cite book |title=Design Recommendations for Intelligent Tutoring Systems: Volume 8 - Data Visualization |date=December 29, 2020 |publisher=Army Research Laboratory |isbn=9780997725780 |page=50 |url=https://books.google.com/books?id=TxY6EAAAQBAJ&dq=%22TimescaleDB%22+-wikipedia&pg=PA50}}</ref> |- | VictoriaMetrics || Apache License 2.0 || Go || <ref name="redmonk"/> |- | Whisper (Graphite) || Apache License 2.0 || Python ||<ref name=Joshi>{{cite book |last1=Joshi |first1=Nishes |hdl=10852/9085 |title=Interoperability in monitoring and reporting systems |date=May 23, 2012 |type=Thesis }}</ref> |}

== See also == * Operational historian * Delta encoding ** Differential backup

== References == {{Reflist}} Category:Time series software