Key Techniques of Time Series Databases: A Survey

Liu Shuai; Qiao Ying; Luo Xiongfei; Zhao Yijing; Wang Hong’an

doi:10.7544/issn1000-1239.202330536

Journal of Computer Research and Development > 2024 > 61(3): 614-638. > DOI: 10.7544/issn1000-1239.202330536 CSTR: 32373.14.issn1000-1239.202330536

Liu Shuai, Qiao Ying, Luo Xiongfei, Zhao Yijing, Wang Hong’an. Key Techniques of Time Series Databases: A Survey[J]. Journal of Computer Research and Development, 2024, 61(3): 614-638. DOI: 10.7544/issn1000-1239.202330536

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Key Techniques of Time Series Databases: A Survey

Liu Shuai^{1, 2,},
Qiao Ying^{1, 2, ,},
Luo Xiongfei²,
Zhao Yijing^{1, 2},
Wang Hong’an^{1, 2}

1.
School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049
2.
Beĳing Key Laboratory of Human-computer Interaction (Institute of Software, Chinese Academy of Sciences), Beijing 100190

Funds: This work was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDC02030300).

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Author Bio:
Liu Shuai: born in 1992. PhD candidate. His main research interest includes database systems and technologies

Qiao Ying: born in 1973. PhD, professor. Her main research interests include real-time intelligence and real-time scheduling

Luo Xiongfei: born in 1977. PhD, senior engineer. His main research interest includes real-time data management and analysis technology

Zhao Yijing: born in 1994. PhD candidate. Her main research interests include intelligent database technology and data mining

Wang Hong’an: born in 1963. PhD, professor, PhD supervisor. Senior member of CCF. His main research interests include natural human-computer interaction and real-time intelligence
Received Date: June 20, 2023
Revised Date: November 30, 2023
Available Online: December 19, 2023

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Abstract

Abstract

With the continuous development of the industrial Internet of things (IIoT), an increasing number of devices and sensors are being connected to networks, resulting in a massive influx of time series data. The explosive growth of time series data presents new challenges for database management systems: continuous high-throughput data ingestion, low-latency multidimensional data queries, high-performance time series indexing, and cost-effective data storage. In recent years, time series database technology has become a hot research topic in the field of databases. Some scholars have conducted in-depth research on time series database technology, while specialized time series databases have emerged for managing time series data and have been applied in various fields. These databases have become essential components in IIoT. The existing reviews of time series databases primarily focus on the comparison of functionalities and performance, as well as providing selection recommendations for specific domains. There is a lack of research on key technologies such as data persistence, querying, computation, and indexing in time series stores. Additionally, these reviews appeared earlier and lacked research on modern time series database technologies. We conduct a comprehensive investigation and research analysis of both academic research on time series data storage and industrial time series databases. We take a deep dive into four key technologies in time series databases: 1) time series index optimization techniques; 2) in-memory data organization techniques; 3) high-throughput data ingestion and low-latency data query techniques; 4) cost-effective storage techniques for massive historical data. We also analyze and summarize existing TSDB benchmarks. Finally, we present future development directions for the key technologies in time series databases.
- industrial Internet of things,
- time series data,
- time series database,
- time series data compression,
- time series data store

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References (129)

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