The Suitability of Graph Databases for Big Data Analysis: A Benchmark
| Authors | |
|---|---|
| Year of publication | 2020 |
| Type | Article in Proceedings |
| Conference | Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS |
| MU Faculty or unit | |
| Citation | |
| Web | https://www.scitepress.org/PublicationsDetail.aspx?ID=qc6Zz7Qsgn0=&t=1 |
| Doi | http://dx.doi.org/10.5220/0009350902130220 |
| Keywords | Big Data; Benchmark; Graph Database; Neo4j; PostgreSQL |
| Description | Digitalization of our society brings various new digital ecosystems (e.g., Smart Cities, Smart Buildings, Smart Mobility), which rely on the collection, storage, and processing of Big Data. One of the recently popular advancements in Big Data storage and processing are the graph databases. A graph database is specialized to handle highly connected data, which can be, for instance, found in the cross-domain setting where various levels of data interconnection take place. Existing works suggest that for data with many relationships, the graph databases perform better than non-graph databases. However, it is not clear where are the borders for specific query types, for which it is still efficient to use a graph database. In this paper, we design and perform tests that examine these borders. We perform the tests in a cluster of three machines so that we explore the database behavior in Big Data scenarios concerning the query. We specifically work with Neo4j as a representative of graph databases and PostgreSQL as a representative of non-graph databases. |
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