An Efficient NoSQL-Based Storage Schema for Large-Scale Time Series Data

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Author(s): Ruizhe Ma (University of Massachusetts, Lowell, USA), Weiwei Zhou (Nanjing University of Aeronautics and Astronautics, China)and Zongmin Ma (Nanjing University of Aeronautics and Astronautics, China)
Copyright: 2024
Volume: 35
Issue: 1
Pages: 21
Source title: Journal of Database Management (JDM)
Editor(s)-in-Chief: Keng Siau (City University of Hong Kong, Hong Kong SAR)
DOI: 10.4018/JDM.339915

Keywords: Data Mining and Databases / Databases / Information Science Reference / Library & Information Science

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Abstract

In IoT (internet of things), most data from the connected devices change with time and have sampling intervals, which are called time-series data. It is challenging to design a time series storage model that can write massive time-series data in a short time and can query and analyze the persistent time-series data for a long time. This paper constructs the RHTSDB (Redis-HBase Time Series Database) storage model based on Redis and HBase. RHTSDB uses the memory database Redis (Remote Dictionary Server) to cache massive time-series data, providing efficient data storage and query functions. HBase is used in RHTSDB for long-term storage of time-series data to realize their persistence. The paper designs a cold and hot separation mechanism for time-series data, where the infrequently accessed cold data are stored in HBase, and the frequently accessed and latest data are stored in Redis. Experiments verify that RHTSDB has apparent advantages over Apache IoTDB and HBase in data intake and query efficiency.

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An Efficient NoSQL-Based Storage Schema for Large-Scale Time Series Data

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