Evaluation of Graph Databases Performance through Indexing Techniques

Abstract

The aim of this paper is to evaluate , through indexing techniques, the performance of Neo4j and OrientDB, both graph databases technologies and to come up with strength and weaknesses os each technology as a candidate for a storage mechanism of a graph structure. An index is a data structure that makes the searching faster for a specific node in concern of graph databases. The referred data structure is habitually a B-tree, however, can be a hash table or some other logic structure as well. The pivotal point of having an index is to speed up search queries, primarily by reducing the number of nodes in a graph or table to be examined. Graphs and graph databases are more commonly associated with social networking or “graph search” style recommendations. Thus, these technologies remarkably are a core technology platform for some Internet giants like Hi5, Facebook, Google, Badoo, Twitter and LinkedIn. The key to understanding graph database systems, in the social networking context, is they give equal prominence to storing both the data (users, favorites) and the relationships between them (who liked what, who ‘follows’ whom, which post was liked the most, what is the shortest path to ‘reach’ who). By a suitable application case study, in case a Twitter social networking of almost 5,000 nodes imported in local servers (Neo4j and Orient-DB), one queried to retrieval the node with the searched data, first without index (full scan), and second with index, aiming at comparing the response time (statement query time) of the aforementioned graph databases and find out which of them has a better performance (the speed of data or information retrieval) and in which case. Thereof, the main results are presented in the section 6.