Graph database

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 within a graph database is fast because they are perpetually stored within the database itself. Relationships can be intuitively visualized using graph databases, making them useful for heavily inter-connected data. Graph databases are part of the NoSQL databases created to address the limitations of the existing relational databases. 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. Graph databases, by design, allow simple and fast retrieval of complex hierarchical structures that are difficult to model in relational systems. 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. Most graph databases based on non-relational storage engines also add the concept of tags or properties, which are essentially relationships having a pointer to another document. This allows data elements to be categorized for easy retrieval en masse. Retrieving data from a graph database requires a query language other than SQL, which was designed for the manipulation of data in a relational system and therefore cannot “elegantly” handle traversing a graph. As of 2017, no single graph query language has been universally 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 standardization efforts have occurred, leading to multi-vendor query languages like Gremlin, SPARQL, and Cypher. 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 OLTP databases. On the other hand, graph compute engines are utilized in OLAP for bulk analysis. Graph databases have attracted considerable attention in the 2000s, due to the successes of major technology corporations in using proprietary graph databases, and the introduction of open-source graph databases.