Choosing and developing performant database solutions helps organizations optimize their operational practices and decision-making. Since graph data is becoming more common, it is crucial to develop and use them in big data with complex relationships with high and consistent performance. However, legacy database technologies such as MySQL are tailored to store relational databases and need to perform more complex queries to retrieve graph data. Previous research has dealt with performance aspects such as CPU and memory usage. In contrast, energy usage and temperature of the servers are lacking. Thus, this paper evaluates and compares state-of-the-art graphs and relational databases from the performance aspects to allow a more informed selection of technologies. Graph-based big data applications benefit from informed selection database technologies for data retrieval and analytics problems. The results show that Neo4j performs faster in querying connected data than MySQL and ArangoDB, and energy, CPU, and memory usage performances are reported in this paper.

翻译：选择并开发高性能数据库解决方案有助于组织优化其运营实践与决策制定。随着图数据日益普及，在具有复杂关系的大数据场景中高效且稳定地运用此类数据库变得至关重要。然而，MySQL等传统数据库技术是为存储关系型数据而设计的，检索图数据时需执行更复杂的查询操作。已有研究关注了CPU与内存使用等性能指标，但服务器能耗与温度方面的研究仍显不足。因此，本文从多维度性能角度评估并对比当前最先进的图数据库与关系数据库，以支持更明智的技术选型。基于图的大数据应用在数据检索与分析问题上可从数据库技术的审慎选择中获益。实验结果表明：Neo4j在关联数据查询速度上优于MySQL与ArangoDB，本文同时报告了能耗、CPU及内存使用性能的实测数据。