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A graph database (GDB) is a database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data.[1] A key concept of the system is the graph (or edge or relationship). The graph relates the data items in the store to a collection of nodes and edges, the edges representing the relationships between the nodes. The relationships allow data in the store to be linked together directly and, in many cases, retrieved with one operation. Graph databases hold the relationships between data as a priority. Querying relationships is fast because they are perpetually stored in the database. Relationships can be intuitively visualized using graph databases, making them useful for heavily inter-connected data.[2]
Graph databases are commonly referred to as a NoSQL. Graph databases are similar to 1970s network model databases in that both represent general graphs, but network-model databases operate at a lower level of abstraction[3] and lack easy traversal over a chain of edges.[4]
The underlying storage mechanism of graph databases can vary. Relationships are a first-class citizen in a graph database and can be labelled, directed, and given properties. Some depend on a relational engine and "store" the graph data in a table (although a table is a logical element, therefore this approach imposes another level of abstraction between the graph database, the graph database management system and the physical devices where the data is actually stored). Others use a key–value store or document-oriented database for storage, making them inherently NoSQL structures.
As of 2021[update], no universal graph query language has been adopted in the same way as SQL was for relational databases, and there are a wide variety of systems, most often tightly tied to one product. Some early standardization efforts lead to multi-vendor query languages like Gremlin, SPARQL, and Cypher. In September 2019 a proposal for a project to create a new standard graph query language (ISO/IEC 39075 Information Technology — Database Languages — GQL) was approved by members of ISO/IEC Joint Technical Committee 1(ISO/IEC JTC 1). GQL is intended to be a declarative database query language, like SQL. In addition to having query language interfaces, some graph databases are accessed through application programming interfaces (APIs).
Graph databases differ from graph compute engines. Graph databases are technologies that are translations of the relational online transaction processing (OLTP) databases. On the other hand, graph compute engines are used in online analytical processing (OLAP) for bulk analysis.[5] Graph databases attracted considerable attention in the 2000s, due to the successes of major technology corporations in using proprietary graph databases,[6] along with the introduction of open-source graph databases.
One study concluded that an RDBMS was "comparable" in performance to existing graph analysis engines at executing graph queries.[7]
- ^ Bourbakis, Nikolaos G. (1998). Artificial Intelligence and Automation. World Scientific. p. 381. ISBN 9789810226374. Retrieved 2018-04-20.
- ^ Yoon, Byoung-Ha; Kim, Seon-Kyu; Kim, Seon-Young (March 2017). "Use of Graph Database for the Integration of Heterogeneous Biological Data". Genomics & Informatics. 15 (1): 19–27. doi:10.5808/GI.2017.15.1.19. ISSN 1598-866X. PMC 5389944. PMID 28416946.
- ^ Angles, Renzo; Gutierrez, Claudio (1 Feb 2008). "Survey of graph database models" (PDF). ACM Computing Surveys. 40 (1): 1–39. CiteSeerX 10.1.1.110.1072. doi:10.1145/1322432.1322433. S2CID 207166126. Archived from the original (PDF) on 15 August 2017. Retrieved 28 May 2016.
network models [...] lack a good abstraction level: it is difficult to separate the db-model from the actual implementation
- ^ Silberschatz, Avi (28 January 2010). Database System Concepts, Sixth Edition (PDF). McGraw-Hill. p. D-29. ISBN 978-0-07-352332-3.
- ^ Robinson, Ian (2015-06-10). Graph Databases: New Opportunities for Connected Data. O'Reilly Media, Inc. p. 4. ISBN 9781491930861.
- ^ "Graph Databases Burst into the Mainstream". www.kdnuggets.com. Retrieved 2018-10-23.
- ^ Fan, Jing; Gerald, Adalbert (2014-12-25). The case against specialized graph analytics engines (PDF). Conference on Innovative Data Systems Research (CIDR).