Time series database

Last updated

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] [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 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.

Amazon Timestream Commercial [7]
Apache Druid Apache License 2.0 Java N/A
Apache IoTDB Apache License 2.0 Java [8]
Apache Kudu Apache License 2.0 C++ [9]
Apache Pinot Apache License 2.0 Java [10]
CrateDB Apache License 2.0 Java [11] [12]
eXtremeDB CommercialSQL, Python, C / C++, Java, and C# [13]
InfluxDB MIT. [14] Chronograf AGPLv3, Clustering Commercial [15] Go [13] [16]
Informix TimeSeries Commercial C / C++ [13] [17]
Kx kdb+ Commercial Q [13]
MongoDB Server Side Public License C++, JavaScript, Python [18]
Prometheus Apache License 2.0 Go [13]
QuestDB Apache License 2.0 Java, C++ [19]
RedisTimeSeries BSD C [20]
Riak-TS Apache License 2.0 Erlang [13]
RRDtool GPLv2 C [13]
TimescaleDB Apache License 2.0 C [21]
Whisper (Graphite) Apache License 2.0 Python [22]

See also

Related Research Articles

<span class="mw-page-title-main">IBM Db2</span> Relational model database server

Db2 is a family of data management products, including database servers, developed by IBM. It initially supported the relational model, but was extended to support object–relational features and non-relational structures like JSON and XML. The brand name was originally styled as DB/2, then DB2 until 2017 and finally changed to its present form.

Online analytical processing, or OLAP, is an approach to answer multi-dimensional analytical (MDA) queries swiftly in computing. OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing and data mining. Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas, with new applications emerging, such as agriculture.

ISAM is a method for creating, maintaining, and manipulating computer files of data so that records can be retrieved sequentially or randomly by one or more keys. Indexes of key fields are maintained to achieve fast retrieval of required file records in Indexed files. IBM originally developed ISAM for mainframe computers, but implementations are available for most computer systems.

An XML database is a data persistence software system that allows data to be specified, and sometimes stored, in XML format. This data can be queried, transformed, exported and returned to a calling system. XML databases are a flavor of document-oriented databases which are in turn a category of NoSQL database.

<span class="mw-page-title-main">MonetDB</span>

MonetDB is an open-source column-oriented relational database management system (RDBMS) originally developed at the Centrum Wiskunde & Informatica (CWI) in the Netherlands. It is designed to provide high performance on complex queries against large databases, such as combining tables with hundreds of columns and millions of rows. MonetDB has been applied in high-performance applications for online analytical processing, data mining, geographic information system (GIS), Resource Description Framework (RDF), text retrieval and sequence alignment processing.

Bigtable is a fully managed wide-column and key-value NoSQL database service for large analytical and operational workloads as part of the Google Cloud portfolio.

A column-oriented DBMS or columnar DBMS is a database management system (DBMS) that stores data tables by column rather than by row. Benefits include more efficient access to data when only querying a subset of columns, and more options for data compression. However, they are typically less efficient for inserting new data.

A document-oriented database, or document store, is a computer program and data storage system designed for storing, retrieving and managing document-oriented information, also known as semi-structured data.

<span class="mw-page-title-main">Michael Stonebraker</span> American computer scientist (born 1943)

Michael Ralph Stonebraker is a computer scientist specializing in database systems. Through a series of academic prototypes and commercial startups, Stonebraker's research and products are central to many relational databases. He is also the founder of many database companies, including Ingres Corporation, Illustra, Paradigm4, StreamBase Systems, Tamr, Vertica and VoltDB, and served as chief technical officer of Informix. For his contributions to database research, Stonebraker received the 2014 Turing Award, often described as "the Nobel Prize for computing."

<span class="mw-page-title-main">Vertica</span> Software company

Vertica is an analytic database management software company. Vertica was founded in 2005 by the database researcher Michael Stonebraker with Andrew Palmer as the founding CEO. Ralph Breslauer and Christopher P. Lynch served as CEOs later on.

A NoSQL database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Such databases have existed since the late 1960s, but the name "NoSQL" was only coined in the early 21st century, triggered by the needs of Web 2.0 companies. NoSQL databases are increasingly used in big data and real-time web applications. NoSQL systems are also sometimes called Not only SQL to emphasize that they may support SQL-like query languages or sit alongside SQL databases in polyglot-persistent architectures.

<span class="mw-page-title-main">Redis</span> Open-source in-memory key–value database

Redis is an in-memory data structure store, used as a distributed, in-memory key–value database, cache and message broker, with optional durability. Redis supports different kinds of abstract data structures, such as strings, lists, maps, sets, sorted sets, HyperLogLogs, bitmaps, streams, and spatial indices. The project was developed and maintained by Salvatore Sanfilippo, starting in 2009. From 2015 until 2020, he led a project core team sponsored by Redis Labs. Salvatore Sanfilippo left Redis as the maintainer in 2020. It is open-source software released under a BSD 3-clause license. In 2021, not long after the original author and main maintainer left, Redis Labs dropped the Labs from its name and now is known simply as "Redis".

Voldemort is a distributed data store that was designed as a key-value store used by LinkedIn for highly-scalable storage. It is named after the fictional Harry Potter villain Lord Voldemort.

H-Store is an experimental database management system (DBMS). It was designed for online transaction processing applications. H-Store was developed by a team at Brown University, Carnegie Mellon University, the Massachusetts Institute of Technology, and Yale University in 2007 by researchers Michael Stonebraker, Sam Madden, Andy Pavlo and Daniel Abadi.

<span class="mw-page-title-main">SingleStore</span>

SingleStore is a proprietary, cloud-native database designed for data-intensive applications. A distributed, relational, SQL database management system (RDBMS) that features ANSI SQL support, it is known for speed in data ingest, transaction processing, and query processing.

NewSQL is a class of relational database management systems that seek to provide the scalability of NoSQL systems for online transaction processing (OLTP) workloads while maintaining the ACID guarantees of a traditional database system.

<span class="mw-page-title-main">Martin L. Kersten</span> Dutch computer scientist (born 1953)

Martin L. Kersten was a computer scientist with research focus on database architectures, query optimization and their use in scientific databases. He was an architect of the MonetDB system, an open-source column store for data warehouses, online analytical processing (OLAP) and geographic information systems (GIS). He has been (co-) founder of several successful spin-offs of the Centrum Wiskunde & Informatica (CWI).

<span class="mw-page-title-main">Feature engineering</span> Machine learning data process

Feature engineering or feature extraction or feature discovery is the process of using domain knowledge to extract features from raw data. The motivation is to use these extra features to improve the quality of results from a machine learning process, compared with supplying only the raw data to the machine learning process.

<span class="mw-page-title-main">YugabyteDB</span> Transactional distributed SQL database

YugabyteDB is a high-performance transactional distributed SQL database for cloud-native applications, developed by Yugabyte.

Apache IoTDB is a column-oriented open-source, time-series database (TSDB) management system written in Java. It has both edge and cloud versions, provides an optimized columnar file format for efficient time-series data storage, and TSDB with high ingestion rate, low latency queries and data analysis support. It is specially optimized for time-series oriented operations like aggregations query, downsampling and sub-sequence similarity search. The name IoTDB comes from Internet of Things (IoT) Database, which means it was designed as an IoT-native TSDB that resolves the pain points of the typical IoT scenarios, including massive data generation, high frequency sampling, out-of-order data, specific analytics requirements, high costs of storage and operation & maintenance, low computational power of IoT devices.


  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/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. 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. 1 2 3 4 5 6 Wayner, Peter (15 January 2021). "Database trends: The rise of the time-series database". VentureBeat . Retrieved 7 July 2021.
  7. "Time Series Database – Amazon Timestream – Amazon Web Services" . Retrieved 12 June 2023.
  8. 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.
  9. "Benchmarking Time Series workloads on Apache Kudu using TSBS". 18 March 2020.
  10. Fu, Yupeng; Soman, Chinmay (9 June 2021). "Real-time Data Infrastructure at Uber". Proceedings of the 2021 International Conference on Management of Data: 2503–2516. arXiv: 2104.00087 . doi:10.1145/3448016.3457552. ISBN   9781450383431. S2CID   232478317.
  11. "DB-Engines Ranking". DB-Engines. Retrieved 2023-01-22.
  12. "Anforderungen für Zeitreihendatenbanken im industriellen IoT". springerprofessional.de (in German). Retrieved 2023-01-22.
  13. 1 2 3 4 5 6 7 Stephens, Rachel (2018-04-03). "State of the Time Series Database Market" . Retrieved 2018-10-03.
  14. "influxdb license". GitHub. Retrieved 2016-08-14.
  15. "influxdb clustering". influxdata.com. Retrieved 2016-03-10.
  16. Anadiotis, George (2018-09-28). "Processing time series data: What are the options?". zdnet.com. 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. QuestDB. "Introduction | QuestDB". questdb.io. Retrieved 2023-07-05.
  20. "RedisTimeSeries | A NoSQL Time Series Database". 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.