Postgres vs mysql 202111/20/2023 We also evaluate how the lack of indexes affects the response time by performing a small number of experiments. ![]() Both systems were evaluated in a 5-node cluster setup as described in Section 4.3. The performance is measured using a set of spatio-temporal queries that mimic real case scenarios that performed in a dataset provided by MarineTraffic Footnote 1. PostGIS is a spatial extender that adds support for geographic objects. In particular, we compare the performance in terms of response time between a scalable document based NoSQL datastore-MongoDB and an open source object relational database system (ORDBMS)-PostgreSQL with the PostGIS extension. those that the response time in complex spatio-temporal queries is of high importance. This work aspires to contribute towards this direction by comparing two suchlike platforms for a particular class of requirements, i.e. It is imperative for the research community to contribute to the clarification of the purposes and highlight the pros and cons of certain distributed database platforms. The query and data characteristics only add to the confusion. The plethora of available systems and underlying technologies have left the researchers and practitioners alike puzzled as to what is the best option to employ in order to solve their big spatial data problem at hand. These systems are distinguished by two key-characteristics: a) system scalability: the underlying database system must be able to manage and store a huge amount of spatial data and to allow applications to efficiently retrieve it and, b) interactive performance: very fast response times to client requests. ĭistributed database systems have been proven instrumental in the effort to dealing with this data deluge. At the same time, numerous business applications are emerging by processing the 285 billion points regarding aircraft movements per year gathered from the Automatic Dependent Surveillance Broadcast (ADS-B) system and the 60Mb of AIS and weather data collected every second by MarineTraffic’s on-line monitoring service or the 4 millions geotagged tweets daily produced at Twitter. For example, mysteries are unravelled by harnessing the 1 TB of data that is generated per day from NASA’s Earth Observing System, or the more than 140 GB of raw science spatial data every week generated by space Hubble telescope. ![]() Managing and analyzing these data is becoming increasingly important, enabling novel applications that may transform science and society. The volumes of spatial data that modern-day systems are generating has met staggering growth during the last few years. Furthermore, the average response time is radically reduced with the use of indexes, especially in the case of MongoDB. The evaluation is based upon real, business scenarios and their subsequent queries as well as their underlying infrastructures and concludes in confirming the superiority of PostgreSQL in almost all cases with the exception of the polygon intersection queries. ![]() In particular, the work conducted, set to identify the most efficient data store system in terms of response times, comparing two of the most representative of the two categories (NoSQL and relational), i.e. This work is motivated by the question of which of those data storage systems is better suited to address the needs of industrial applications. The most prominent case is perhaps the data storage systems, that have developed a large number of functionalities to efficiently support spatio-temporal data operations. As such, in order to meet the application requirements, more and more systems are adapting to the specificities of those data. Several modern day problems need to deal with large amounts of spatio-temporal data.
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