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In contrast, a graph database would search for all the users in "", then follow the backlinks through the subscriber relationship to find the subscriber users. This avoids several searches, look-ups, and the memory usage involved in holding all of the temporary data from multiple records needed to construct the output. The relative advantage of graph retrieval grows with the complexity of a query.

For example, one might want to know "that movie about submarines with the actor who was in that movie with that other actor that played the lead in Gone With the Wind ". This first requires the system to find the actors in Gone With the Wind , find all the movies they were in, find all the actors in all of those movies who were not the lead in Gone With the Wind , and then find all of the movies they were in, finally filtering that list to those with descriptions containing "submarine".

In a relational database, this would require several separate searches through the movies and actors tables, doing another search on submarine movies, finding all the actors in those movies, and then comparing the large collected results.

In contrast, the graph database would walk from Gone With the Wind to Clark Gable , gather the links to the movies he has been in, gather the links out of those movies to other actors, and then follow the links out of those actors back to the list of movies.

The resulting list of movies can then be searched for "submarine". All of this can be done via one search. Properties add another layer of abstraction to this structure that also improves many common queries. Properties are essentially labels that can be applied to any record, or in some cases, edges as well. For example, one might label Clark Gable as "actor", which would then allow the system to quickly find all the records that are actors, as opposed to director or camera operator.

If labels on edges are allowed, one could also label the relationship between Gone With the Wind and Clark Gable as "lead", and by performing a search on people that are "lead" "actor" in the movie Gone With the Wind , the database would produce Vivien Leigh , Olivia de Havilland and Clark Gable.

The equivalent SQL query would have to rely on added data in the table linking people and movies, adding more complexity to the query syntax.

These sorts of labels may improve search performance under certain circumstances, but are generally more useful in providing added semantic data for end users. Relational databases are very well suited to flat data layouts, where relationships between data is one or two levels deep. For example, an accounting database might need to look up all the line items for all the invoices for a given customer, a three-join query.

Graph databases are aimed at datasets that contain many more links. They are especially well suited to social networking systems, where the "friends" relationship is essentially unbounded. These properties make graph databases naturally suited to types of searches that are increasingly common in online systems, and in big data environments. For this reason, graph databases are becoming very popular for large online systems like Facebook , Google , Twitter , and similar systems with deep links between records.

In this case, searching for all of Jack's friends would result in the following SQL query. The above examples are a simple illustration of a basic relationship query. They condense the idea of relational models' query complexity that increases with the total amount of data.

In comparison, a graph database query is easily able to sort through the relationship graph to present the results. There are also results that indicate simple, condensed, and declarative queries of the graph databases do not necessarily provide good performance in comparison to the relational databases. While graph databases offer an intuitive representation of data, relational databases offer better results when set operations are needed.

The following is a list of notable graph databases:. From Wikipedia, the free encyclopedia. Database that uses mathematical graphs to store and search data.

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