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Apache IoTDB

Authors :
Rong Kang
Huang Xiangdong
Julian Feinauer
Zhang Jinrui
Chen Wang
Qiao Jialin
Peng Wang
Lei Rui
Jun Yuan
Sun Jiaguang
Kevin A. McGrail
Jiang Tian
Diaohan Luo
Jianmin Wang
Source :
Proceedings of the VLDB Endowment. 13:2901-2904
Publication Year :
2020
Publisher :
Association for Computing Machinery (ACM), 2020.

Abstract

The amount of time-series data that is generated has exploded due to the growing popularity of Internet of Things (IoT) devices and applications. These applications require efficient management of the time-series data on both the edge and cloud side that support high throughput ingestion, low latency query and advanced time series analysis. In this demonstration, we present Apache IoTDB managing time-series data to enable new classes of IoT applications. IoTDB has both edge and cloud versions, provides an optimized columnar file format for efficient time-series data storage, and time-series database with high ingestion rate, low latency queries and data analysis support. It is specially optimized for time-series oriented operations like aggregations query, down-sampling and sub-sequence similarity search. An edge-to-cloud time-series data management application is chosen to demonstrate how IoTDB handles time-series data in real-time and supports advanced analytics by integrating with Hadoop and Spark. An end-to-end IoT data management solution is shown by integrating IoTDB with PLC4x, Calcite, and Grafana.

Details

ISSN :
21508097
Volume :
13
Database :
OpenAIRE
Journal :
Proceedings of the VLDB Endowment
Accession number :
edsair.doi...........26cb9e2f42891fc5f30383302fbeebf1
Full Text :
https://doi.org/10.14778/3415478.3415504