#Nosql vs postgresql generator
However, although the synthetic data usually favor the evaluation of queries regarding numeric and string characters, the synthetic geometries generated differ from real-world data, because the randomness of the generator can produce too-square polygons and unnatural landforms. The study of also focuses on comparing geographical queries using synthetic data.
#Nosql vs postgresql update
Among these works, there are and, which focus on comparing the performance in the operations insert, select, update and delete. One of the main motivations that led us to conduct this research was the scarce number of works comparing PostgreSQL’s and MongoDB’s performance. Lastly, both are open code DBMS and Geoserver gives them support because in its version 2.11.4, Geoserver included a data connexion and publication component from MongoDB. We chose MongoDB because, to date, it was the only document-based NoSQL database that supports line intersection and point containment queries. On the other hand, there are currently over 225 NoSQL databases with only a reduced number supporting geographical data operations, among which Neo4j, CouchDB, MongoDB and ArangoDB outstand in this area. PostgreSQL’s extension, PostGIS,, is highly optimized for spatial queries and its large quantity of spatial functions make it relevant for this research project. Since PostgreSQL was one of the first databases to address spatial issues, we selected it to construct the hybrid and distributed database. The aim was to choose appropriately the DBMS (Data Base Management System) to be employed by the Web Map Service (WMS) implemented by Geoserver.
However, while developing the project, it became necessary to establish if there was any difference in the response times of PostgreSQL and MongoDB, according to the type of query and use of geographical indexes. Ĭurrently, a hybrid and distributed database that uses PostgreSQL and MongoDB database engines is being developed for future implementation in the Geoserver base architecture to evaluate whether this modification enhances its performance. Hybrid databases work as an abstraction layer over SQL and NoSQL databases. Īs a response to the issue raised above, work has been conducted on producing hybrids between the relational (SQL) and non-relational (NoSQL) database paradigms. While using it, users also participate actively in the generation of geographical data, in the sense that most people have currently become a mobile sensor that registers and records large volumes of data that require higher computing capability and more advanced and efficient processing and analyzing methods. The demand for geographical information has grown considerably in recent years and citizens are among the main generators of this geographical explosion.
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