Multi-Perspective Evaluation of Relational and Graph Databases

How to store data is an enduring topic in the computer science field, and traditional relational databases have done this well and are still widely used today. However, with the growth of non-relational data and the challenges in the big data era, a series of NoSQL databases have come into view. Thus, comparing, evaluating, and choosing a better database has become a worthy topic of research. In this thesis, an experiment that can store the same data set and execute the same tasks or workload on the relational, graph and multi-model databases is designed. The investigation proposes how to adapt relational data, tables on a graph database and, conversely, store graph data on a relational database. Similarly, the tasks performed are unified across query languages. We conducted exhaustive experiments to compare and report the performance of the three databases. In addition, we propose a workload classification method to analyze the performance of the databases and compare multiple aspects of the database from an end-user perspective. We have selected PostgreSQL, ArangoDB, Neo4j as representatives. The comparison in terms of task execution time does not have any database that completely wins. The results show that relational databases have performance advantages for tasks such as data import, but the execution of multi-table join tasks is slow and graph algorithm support is lacking. The multi-model databases have impressive support for simultaneous storage of multiple data formats and unified language queries, but the performance is not outstanding. The graph database has strong graph algorithm support and intuitive support for graph query language, but it is also important to consider whether the format and interrelationships of the original data, etc. can be well converted into graph format