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What is Graph Database mean?
In computing, a graph database (GDB) is a database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data. 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.
Graph databases are commonly referred to as a NoSQL database - implying that the approach to storing, querying and describing these data structures differs significantly from a traditional relational database. While the graph model explicitly lays out the dependencies between nodes of data, the relational model and other NoSQL database models link the data by implicit connections. In other words, relationships are a first-class citizen in a graph database and can be labelled, directed, and given properties. This is compared to relational approaches where these relationships are implied and must be reified at run-time. 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 and lack easy traversal over a chain of edges.
The underlying storage mechanism of graph databases can vary. 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 , 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. Graph databases attracted considerable attention in the 2000s, due to the successes of major technology corporations in using proprietary graph databases, 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.
referencePosted on 20 Oct 2024, this text provides information on Miscellaneous in Computing related to Computing. Please note that while accuracy is prioritized, the data presented might not be entirely correct or up-to-date. This information is offered for general knowledge and informational purposes only, and should not be considered as a substitute for professional advice.
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