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Graph Databases Comparison: Allegrograph, Arangodb, Infinitegraph, Neo4j, and Orientdb

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Graph Databases Comparison: Allegrograph, Arangodb, Infinitegraph, Neo4j, and Orientdb Graph Databases Comparison: AllegroGraph, ArangoDB, InfiniteGraph, Neo4J, and OrientDB Diogo Fernandes1 and Jorge Bernardino1,2 1Polytechnic of Coimbra - ISEC, Rua Pedro Nunes, Coimbra, Portugal 2CISUC – Centre for Informatics and Systems of the University of Coimbra, Portugal Keywords: Graph databases, NoSQL databases, AllegroGraph, ArangoDB, InfiniteGraph, Neo4J, OrientDB. Abstract: Graph databases are a very powerful solution for storing and searching for data designed for data rich in relationships, such as Facebook and Twitter. With data multiplication and data type diversity there has been a need to create new storage and analysis platforms that structure irregular data with a flexible schema, maintaining a high level of performance and ensuring data scalability effectively, which is a problem that relational databases cannot handle. In this paper, we analyse the most popular graph databases: AllegroGraph, ArangoDB, InfiniteGraph, Neo4J and OrientDB. We study the most important features for a complete and effective application, such as flexible schema, query language, sharding and scalability. 1 INTRODUCTION model relationships between objects. In this context, a graph is basically a structure, which is represented Nowadays, data volume is growing exponentially in by nodes, or also called vertices (the entities), by social networks, such as Facebook and Twitter, edges (the relations) that are the lines that connect which store and process massive amounts of data the various nodes and by properties (attributes). daily, where this data reaches petabytes of storage. Therefore, the graph databases can simply be Current relational databases are predominant in the described as a way of representing and storing data, market, but their adaptability is poor in processing using their structures: nodes, edges, and properties. connected data. Actual platforms must deal with For that reason, graph databases are optimized for huge amounts of data and related information. For storing and querying graphs. this reason, a special type of databases designated by The problem with graph databases is that they graph databases have appeared. are not particularly efficient in all desired operations A graph database is a non-relational database such as in the representation of data that are derived that provides an effective and efficient solution for from relational models. Therefore, they do not the storage of information in current scenarios, replace relational databases but are in fact an where data is increasingly interconnected. The efficient solution when dealing with huge volumes storage mechanisms of graph databases are of data that contain many related data. optimized for graphing, for the way they store The focus of this paper is the study of the main adjacent records linked by direct references. In this characteristics, advantages and use cases of graph adjacency list, each vertex maintains references to databases. We study five of the most popular graph their adjacent vertices, forming an index species for databases: AllegroGraph, ArangoDB, InfiniteGraph, the vertices on neighbourhood. This property is Neo4J and OrientDB. known as index-free adjacency (Robinson et al., The rest of this paper is structured as follows. 2015). Interest in graph models has been increasing Section 2 describes the advantages and uses of graph in recent years due to their applications in areas like databases. Sections 3 to 7 describe AllegroGraph, Semantic Web and Social Network Analysis ArangoDB, Infinite Graph, Neo4J and OrientDB, (Dietrich et al., 2008). This type of database is easy respectively. Section 8 presents the comparison of to understand because its concept is based on the the five graph databases. Finally, Section 9 presents theory of graphs. This theory is basically based on the main conclusions and future work. graphs, which are mathematical structures used to 373 Fernandes, D. and Bernardino, J. Graph Databases Comparison: AllegroGraph, ArangoDB, InfiniteGraph, Neo4J, and OrientDB. DOI: 10.5220/0006910203730380 In Proceedings of the 7th International Conference on Data Science, Technology and Applications (DATA 2018), pages 373-380 ISBN: 978-989-758-318-6 Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved DATA 2018 - 7th International Conference on Data Science, Technology and Applications 2 MAIN ADVANTAGES AND contrast, graph database performance stays constant even as our data grows year after year. USES OF GRAPH DATABASES Flexibility, because IT and data architect teams move at the speed of business because the structure Graph databases are getting popular as data and schema of a graph model adjusts itself as increases in scalability and relationships become the applications and industries change. Rather than first choice over the relational databases. Ironically, exhaustively modeling a domain ahead of time, data legacy relational database management systems teams can add to the existing graph structure without (RDBMS) are poor at handling relationships endangering current functionality. between data points. Their tabular data models and Finally, agility because developing with graph rigid schemas make it difficult to add new or databases aligns perfectly with today’s agile, test- different kinds of connections. Graphs are the future. driven development practices, allowing graph Not only do graph databases effectively store the database to evolve in step with the rest of the relationships between data points, but they are also application and any changing business requirements. flexible in adding new kinds of relationships or However, as any new technology replacing old adapting a data model to new business requirements technology, there are still obstacles in adopting (Webber et al, 2015). Graph databases did not see a graph databases. One is that there are fewer qualified greater advantage over relational databases until developers in the job market than the SQL recent years, when frequent schema changes, developers. Another is the non-standardization of the managing enormous volume of data, real-time query graph database query language (Wu, 2017). response time and more intelligent data activation In the next sections, we analyze five of the most requirements, make people realize the advantages of popular graph databases. the graph model. This technology is disrupting many areas, such as supply chain management, e-commerce recommendations, security, fraud detection and 3 AllegroGraph many other areas in advanced data analytics. The main advantages of a graph database are the AllegroGraph is a modern, consistent and persistent following (Guia et al., 2017) and (Neo4J, 2018): Resource Description Framework (RDF) graph database with high performance that is currently in . Better optimization on gathering information use in open source, commercial, and US Department than relational databases; of Defense projects. AllegroGraph is characterized . ACID rules support; by the efficient use of memory by combining disk . Support of the data storage in the order of storage, making it possible to scale up to one billion petabytes (1015); nodes, always maintaining top performance. Allow for new types of data; Basically, it provides services including vision . Easily expandable due to its structural model - building, rapid prototyping and proof-of-concept graph model; development, complete enterprise technology . Suitable for complex and irregular data, solution stack, and best practices to maximize value normally involved in real world; from semantic technologies. Excellent for data mining operations; AllegroGraph provides an architecture through . A high performance while querying deep data the REST protocol, which is an architectural style when compared to relational databases; that consists of a coordinated set of constraints . There is no need to declare the data type for the applied to components, connectors, and data nodes or edges, unlike relational model; elements within a distributed system. Developers . They are very agile in development, since they have developed adapters for the various supported can be easily adapted over time; languages, Sesame Java, Sesame Jena, Python, using . Combination of multiple dimensions to manage proprietary Sesame and Lisp signatures. big data, including time series, geo-dimensions, AllegroGraph’s competitive advantages are the and hierarchies on different dimensions. following (Ranking of Graph DBMS, 2018): In short, the three key advantages are: . Suited to support adhoc queries through performance, flexibility, and agility. SPARQL, Prolog and languages like Performance, because with traditional databases JavaScript; relationship queries will come to a grinding halt as . Sorted quintuple indices that will index every the number and depth of relationships increase. In primary and non-primary field so users never 374 Graph Databases Comparison: AllegroGraph, ArangoDB, InfiniteGraph, Neo4J, and OrientDB have to worry about whether a certain field is tolerance, huge amount of storage memory and indexed or not; lower cost than other databases. This software has . It is possible to mix Geospatial, Temporal, two versions: Community and Enterprise editions. Social Network Analytics, and Reasoning, all The ArangoDB cluster architecture is a master / in the same query (SPARQL or Prolog); master CP (Consistent and Partition) (Mehra, 2017) . Triple Level Security with Security Filters; model without a single point of failure. With "CP", . Gruff - Graph Visualization,
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