How well a multi-model database performs against its single-model variants: Benchmarking OrientDB with Neo4j and MongoDB
| Authors | |
|---|---|
| Year of publication | 2020 |
| Type | Article in Proceedings |
| Conference | Proceedings of the 2020 Federated Conference on Computer Science and Information Systems |
| MU Faculty or unit | |
| Citation | |
| Web | https://annals-csis.org/proceedings/2020/drp/76.html |
| Doi | http://dx.doi.org/10.15439/2020F76 |
| Keywords | Big Data; Benchmark; Multi-model Database; OrientDB; Neo4j; MongoDB |
| Description | Digitalization is currently the key factor for progress, with a rising need for storing, collecting, and processing large amounts of data. In this context, NoSQL databases have become a popular storage solution, each specialized on a specific type of data. Next to that, the multi-model approach is designed to combine benefits from different types of databases, supporting several models for data. Despite its versatility, a multi-model database might not always be the best option, due to the risk of worse performance comparing to the single-model variants. It is hence crucial for software engineers to have access to benchmarks comparing the performance of multi-model and single-model variants. Moreover, in the current Big Data era, it is important to have cluster infrastructure considered within the benchmarks. In this paper, we aim to examine how the multi-model approach performs compared to its single-model variants. To this end, we compare the OrientDB multi-model database with the Neo4j graph database and the MongoDB document store. We do so in the cluster setup, to enhance state of the art in database benchmarks, which is not yet giving much insight into cluster-operating database performance. |
| Related projects: |