Comparison of SQL, NoSQL and TSDB database systems for smart buildings and smart metering applications.

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Bibliographic Details
Title: Comparison of SQL, NoSQL and TSDB database systems for smart buildings and smart metering applications.
Alternate Title: Porównanie systemów baz danych SQL, NoSQL i TSDB dla zastosowań w inteligentnych budynkach i smart metering. (Polish)
Authors: WŁOSTOWSKA, Sandra, SZABELA, Julia, CHOJECKI, Adrian, BORKOWSKI, Piotr
Source: Przegląd Elektrotechniczny; 2023, Vol. 2023 Issue 11, p7-12, 6p
Subject Terms: DATABASES, NONRELATIONAL databases, SMART meters, PROGRAMMING languages, RELATIONAL databases, INTELLIGENT buildings, TIME series analysis, PYTHON programming language, SQL
Abstract (English): The article compares different types of databases for storing data sourced from smart building and smart metering systems. The differences between non-relational (NoSQL - NoRDBS), time-series (TSDB) and the most popular relational database (SQL - RDBS) were highlighted. Then, the possibilities of practical usage of them for gathering data from typical sensors used in buildings and the integration with the most popular installation standards (KNX, LCN) and communication protocols (Modbus, MQTT, MBus) were examined. For examination, the most popular software under open-source licenses were selected: PostgreSQL as a relational database; MongoDB as a non-relational document database; and InfluxDB as a TSDB database. Installation and configuration methods, the possibility of integration with other systems and interfaces to popular programming languages (C#, Python, Java, C++) were compared. In the empirical part, the following were studied: access times and increase in used disk space in cooperation with a real sensor to reach 1 million records in each of the databases. In the conclusions, the results are presented and the authors' suggestions for potential applications for the databases in the fields mentioned in the title are provided. [ABSTRACT FROM AUTHOR]
Abstract (Polish): W artykule porównano różne typy baz danych na potrzeby przechowywania danych pochodzących z systemów inteligentnego budynku i smart-meteringu. Zwrócono uwage na różnice pomiędzy bazami nierelacyjna (NoSQL - NoRDBS), oparta o szeregi czasowe (TSDB) i najpopularniejsza baza relacyjna (SQL - RDBS). Następnie przeanalizowano możliwosci ich praktycznego wykorzystania na potrzeby gromadzenia danych z typowych czujników stosowanych w inteligentnym budownictwie oraz integracji z najpopularniejszymi standardami instalacji (KNX, LCN) i protokołów komunikacyjnych (Modbus, MQTT, MBus). Do przebadania wybrano najpopularniejsze systemy na licencjach open-source: PostgreSQL jako baza relacyjna; MongoDB jako baza nierelacyjna dokumentowa oraz InfluxDB jako baza typu TSDB. Porównano sposoby instalacji i konfiguracji, możliwość integracji z innymi systemami oaz interfejsy dostępowe dla popularnych języków programowania (C#, Python, Java, C++), a w cześci empirycznej czasy dostępu i przyrost zajetej przestrzeni dyskowej we współpracy z rzeczywistym czujnikiem do osiągniecia 1 miliona rekordów w kazdej z baz. We wnioskach przedstawiono wyniki oraz zebrano sugestie autorów dotyczące potencjalnych zastosowan dla przetestowanych systemów baz danych w dziedzinach zawartych w tytule. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:The article compares different types of databases for storing data sourced from smart building and smart metering systems. The differences between non-relational (NoSQL - NoRDBS), time-series (TSDB) and the most popular relational database (SQL - RDBS) were highlighted. Then, the possibilities of practical usage of them for gathering data from typical sensors used in buildings and the integration with the most popular installation standards (KNX, LCN) and communication protocols (Modbus, MQTT, MBus) were examined. For examination, the most popular software under open-source licenses were selected: PostgreSQL as a relational database; MongoDB as a non-relational document database; and InfluxDB as a TSDB database. Installation and configuration methods, the possibility of integration with other systems and interfaces to popular programming languages (C#, Python, Java, C++) were compared. In the empirical part, the following were studied: access times and increase in used disk space in cooperation with a real sensor to reach 1 million records in each of the databases. In the conclusions, the results are presented and the authors' suggestions for potential applications for the databases in the fields mentioned in the title are provided. [ABSTRACT FROM AUTHOR]
ISSN:00332097
DOI:10.15199/48.2023.11.02