In today’s high-tech world the amount of data and especially spatial data is growing from day to day. Databases are one of the best ways to store data. Several database concepts exist for storing data, while the most used is the relational database concept. Relational databases are very well in use for the storage of spatial and non-spatial data. But for example in social media, like Facebook or Twitter, relational databases often reach their limit of performance. For huge amounts of data and frequent data changes, NoSQL-databases can be used. The paper gives an overview of selected SQL and NoSQL-databases according to their geo-functionalities. In addition, it compares two document-based NoSQL-databases with a relational database by several performance tests.
NoSQL, Databases, SQL, MongoDB, CouchBase, PostgreSQL
A characteristic of today’s high-tech world is the vast amount of stored data. According to Kolb, 90% of the worldwide generated data until 2012, which is 2.5 Exabytes per day, was generated within the previous two years . Different database systems can be used for the storage, use, and analysis of this growing amount of data. According to Paul Campaniello from MySQL-Scalability mostly relational database management systems are used .
For the satisfaction of the users, significant characteristics of a database like a scalability, performance, and latency play a crucial role. Especially social media projects, like Facebook and google+, with high user-traffic, use other database management systems like Apache Cassandra or Google BigTable. Instead of the relational approach, a Not-only-SQL (NoSQL) approach is used. NoSQL-databases are increasingly used to deal with simultaneously high read and write requests related to large datasets.
In many fields, spatial data fulfills the criteria of fast-changing, large datasets that make continuous indexing of the data necessary. It might be expected that future data storage concepts for spatial data are more often based on NoSQL-databases more often. Besides querying the growing amount of spatial data, there is a need for performant analysis.
The paper focuses on the current status of NoSQL-databases for their usability in geo-applications. Therefore it presents a comparison between two document-based NoSQL-databases (MongoDB, CouchBase) and a relational database (PostgreSQL). This mainly includes an overview of existing geo-functionalities as well as several performance tests.
Outline: Section 2 introduces two NoSQL-databases with a common definition for NoSQL-databases and a classification based on their main characteristics. Section 3 compares the differences between the relational data model and the NoSQL-approach. It describes storage concepts and compares the geo-functionality between a relational database and the selected NoSQL-databases. Section 4 describes the test setup and test procedure for the performance tests. Further, it analyzes the test results. Section 5 summarizes the paper and gives an outlook on future work.
NoSQL databases are a relatively new technology in the field of geoinformation. There are several different NoSQL-concepts available. The paper pointed out that there is still a lack of geo-functionalities within document-oriented NoSQL-databases. The currently implemented geo-functions support only very basic operations. Relational databases are still far superior if the user needs to calculate geoinformation on the database level.
In direct comparison to the performance test of the two test cases, the results show that queries with the use of geo-functions take longer than queries on attribute-information, which was expectable. For requests purely on the attribute, information NoSQL-databases are very fast and are superior compared to relational databases.
For requests with geo-functions NoSQL-databases also perform very constant. The measured response times vary only about some seconds for an increasing amount of data. But for small datasets with complex geometry, the relational database performed better.
In future work, it needs to be investigated how the performance of the NoSQL-databases can be optimized. An optimization can be achieved by:
– Improvement of the indices
– Enhancement of the JSON schema
– General database improvements (i.e. Cache)
MongoDB usually is optimized for a shared setup over several servers. This possibility was not investigated in the tests but may still lead to some performance improvement.
Another important aspect is the investigation of NoSQL-databases as a basis for Geo Web Services. This includes different Web Services like WMS, WFS, and WCS.
The results presented in the paper are only valid for the chosen database settings but they clearly show that No-SQL databases are a possible alternative, at least for querying attribute information.
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FULL Paper PDF file:Performance investigation of selected SQL and NoSQL databases
Stephan Schmid,University of the BundeswehrWerner-Heisenberg-Weg 39Neubiberg, Germany ,email@example.com
Eszter Galicz ,University of the Bundeswehr,Werner-Heisenberg-Weg 39 Neubiberg, Germany, firstname.lastname@example.org
Wolfgang Reinhardt ,University of the Bundeswehr,Werner-Heisenberg-Weg 39 Neubiberg, Germanywolfgang.email@example.com
Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.