2016•Syracuse University Libraries (Syracuse University)Open access

Models for Storing Relationships: Relational vs. Graph Databases

Dylan Hantula

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Abstract

Relational databases have been the universal industry standard for almost as long as databases have existed. While relational databases are undoubtedly useful for storing tabular data that fits into a pre-defined schema of rows and columns, they are not very accommodating of interconnections within a data set. Forcing a highly connected data set into a relational database commonly results in severe performance issues in query return time. With the recent rise of social networks and other modern technological advancements, data is quickly becoming more connected and thus less suitable for relational databases. As a result, a new type of database, called a graph database, has emerged to store relationship-oriented data naturally and efficiently using nodes and edges. Deciding which database is more suitable for the task at hand is not always trivial, however. This paper sheds light on the differences between the two databases and delves into why one database might be more advantageous in certain situations.

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Relational databases have been the universal industry standard for almost as long as databases have existed. While relational databases are undoubtedly useful for storing tabular data that fits into a pre-defined schema of rows and columns, they are not very accommodating of interconnections within a data set. Forcing a highly connected data set into a relational database commonly results in severe performance issues in query return time. With the recent rise of social networks and other modern technological advancements, data is quickly becoming more connected and thus less suitable for relational databases. As a result, a new type of database, called a graph database, has emerged to store relationship-oriented data naturally and efficiently using nodes and edges. Deciding which database is more suitable for the task at hand is not always trivial, however. This paper sheds light on the differences between the two databases and delves into why one database might be more advantageous in certain situations.

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Available abstract

Relational databases have been the universal industry standard for almost as long as databases have existed. While relational databases are undoubtedly useful for storing tabular data that fits into a pre-defined schema of rows and columns, they are not very accommodating of interconnections within a data set. Forcing a highly connected data set into a relational database commonly results in severe performance issues in query return time. With the recent rise of social networks and other modern technological advancements, data is quickly becoming more connected and thus less suitable for relational databases. As a result, a new type of database, called a graph database, has emerged to store relationship-oriented data naturally and efficiently using nodes and edges. Deciding which database is more suitable for the task at hand is not always trivial, however. This paper sheds light on the differences between the two databases and delves into why one database might be more advantageous in certain situations.

Key concepts: Relational database, Database, Computer science, Graph, Graph database, Relational model, Information retrieval, Theoretical computer science

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