A Comparative Study on the Disparities and Performance Metrics Between NoSQL Graph and Relational Databases

In the era of exponentially expanding data, particularly driven by social media development, effective data management and query processing have become critical challenges in application development. Graph databases, such as Neo4j, JanusGraph, ArangoDB, and OrientDB, offer significant advantages for applications requiring intensive processing of interconnected data, including social networks and recommendation systems. In this work, we focus on Neo4j as a representative of graph databases and MySQL as a representative of relational SQL databases for clarity and precision in data representation. We begin by introducing fundamental optimization techniques specific to each type of database. Subsequently, we concentrate on an experimental and investigative analysis of query performance on Neo4j and MySQL databases using original datasets and structures under consideration. The findings reveal that SQL databases outperform simpler queries, whereas graph databases excel in handling complex structures with multiple relationships. Moreover, the complexity of composing queries becomes apparent when addressing territories requiring table mergers (or node and relationship manipulation in graph databases). We also evaluate related research in this area, which further demonstrates that integrating graph and relational databases effectively can lead to optimal data management solutions, while utilizing both types of databases may offer combined advantages depending on the application requirements.