<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Databases are a basic component of every GIS system and many geoinformation applications. They also hold a prominent place in the tool kit of any cartographer. Solutions based on the relational model have been the standard for a long time, but there is a new increasingly popular technological trend – solutions based on the NoSQL database which have many advantages in the context of processing of large data sets. This paper compares the performance of selected spatial relational and NoSQL databases executing queries with selected spatial operators. It has been hypothesised that a non-relational solution will prove to be more effective, which was confirmed by the results of the study. The same spatial data set was loaded into PostGIS and MongoDB databases, which ensured standardisation of data for comparison purposes. Then, SQL queries and JavaScript commands were used to perform specific spatial analyses. The parameters necessary to compare the performance were measured at the same time. The study’s results have revealed which approach is faster and utilises less computer resources. However, it is difficult to clearly identify which technology is better because of a number of other factors which have to be considered when choosing the right tool.</p></abstract>

Databases are a basic component of every GIS system and many geoinformation applications. They also hold a prominent place in the tool kit of any cartographer. Solutions based on the relational model have been the standard for a long time, but there is a new increasingly popular technological trend – solutions based on the NoSQL database which have many advantages in the context of processing of large data sets. This paper compares the performance of selected spatial relational and NoSQL databases executing queries with selected spatial operators. It has been hypothesised that a non-relational solution will prove to be more effective, which was confirmed by the results of the study. The same spatial data set was loaded into PostGIS and MongoDB databases, which ensured standardisation of data for comparison purposes. Then, SQL queries and JavaScript commands were used to perform specific spatial analyses. The parameters necessary to compare the performance were measured at the same time. The study’s results have revealed which approach is faster and utilises less computer resources. However, it is difficult to clearly identify which technology is better because of a number of other factors which have to be considered when choosing the right tool.

Analysis of performance of selected geospatial analyses implemented on the basis of relational and NoSQL databases

Faculty of Geodesy and Cartography, Department of Cartography

Polish Cartographical Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Databases are a basic component of every GIS system and many geoinformation applications. They also hold a prominent place in the tool kit of any cartographer....

Analysis of performance of selected geospatial analyses implemented on the basis of relational and NoSQL databases

Data publikacji: 01 lut 2020

Zakres stron: 167 - 179

Otrzymano: 14 paź 2019

Przyjęty: 25 lis 2019

DOI: https://doi.org/10.2478/pcr-2019-0014

Słowa kluczowe
spatial analyses, spatial data, MongoDB, PostGIS, efficiency

© 2019 Marcin Pietroń, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Analysis of performance of selected geospatial analyses implemented on the basis of relational and NoSQL databases

Data publikacji: 01 lut 2020

Zakres stron: 167 - 179

Otrzymano: 14 paź 2019

Przyjęty: 25 lis 2019

DOI: https://doi.org/10.2478/pcr-2019-0014

Słowa kluczowespatial analyses, spatial data, MongoDB, PostGIS, efficiency

© 2019 Marcin Pietroń, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
spatial analyses, spatial data, MongoDB, PostGIS, efficiency