A comparison of a graph database and a relational database

TLDR

Relational databases have long dominated data‑intensive applications with efficient SQL queries, yet they become less effective when data contain many interrelated entities, spurring interest in NoSQL systems such as BigTable and Cassandra. This study compares the NoSQL graph database Neo4j to the relational database MySQL for use in a software system that records and queries data provenance information. The authors benchmarked Neo4j and MySQL by implementing a provenance data model and measuring storage, retrieval, and query performance relevant to the system.

Abstract

Relational databases have been around for many decades and are the database technology of choice for most traditional data-intensive storage and retrieval applications. Retrievals are usually accomplished using SQL, a declarative query language. Relational database systems are generally efficient unless the data contains many relationships requiring joins of large tables. Recently there has been much interest in data stores that do not use SQL exclusively, the so-called NoSQL movement. Examples are Google's BigTable and Facebook's Cassandra. This paper reports on a comparison of one such NoSQL graph database called Neo4j with a common relational database system, MySQL, for use as the underlying technology in the development of a software system to record and query data provenance information.