A Benchmark for Performance Evaluation of a Multi-Model Database vs. Polyglot Persistence

Abstract

As the need for handling data from various sources becomes crucial for making optimal decisions, managing multi-model data has become a key area of research. Currently, it is challenging to strike a balance between two methods: polyglot persistence and multi-model databases. Moreover, existing studies suggest that current benchmarks are not completely suitable for comparing these two methods, whether in terms of test datasets, workloads, or metrics. To address this issue, the authors introduce MDBench, an end-to-end benchmark tool. Based on the multi-model dataset and proposed workloads, the experiments reveal that ArangoDB is superior at insertion operations of graph data, while the polyglot persistence instance is better at handling the deletion operations of document data. When it comes to multi-thread and associated queries to multiple tables, the polyglot persistence outperforms ArangoDB in both execution time and resource usage. However, ArangoDB has the edge over MongoDB and Neo4j regarding reliability and availability.