Graph database

Description

Graph databases are based on graph theory, and employ nodes, edges, and properties.

The relational model gathers data together using information in the data. For example, one might look for all the "users" whose phone number contains the area code "311". This would be done by searching selected datastores, or tables, looking in the selected phone number fields for the string "311". This can be a time consuming process in large tables, so relational databases offer the concept of a database index, which allows data like this to be stored in a smaller subtable, containing only the selected data and a unique key (or primary key) of the record it is part of. If the phone numbers are indexed, the same search would occur in the smaller index table, gathering the keys of matching records, and then looking in the main data table for the records with those keys. Generally, the tables are physically stored so that lookups on these keys are fast.

Relational databases do not inherently contain the idea of fixed relationships between records. Instead, related data is linked to each other by storing one record's unique key in another record's data. For example, a table containing email addresses for users might hold a data item called userpk, which contains the primary key of the user record it is associated with. In order to link users and their email addresses, the system first looks up the selected user records primary keys, looks for those keys in the userpk column in the email table (or more likely, an index of them), extracts the email data, and then links the user and email records to make composite records containing all the selected data. This operation, termed a join, can be computationally costly. Depending on the complexity of the query, the number of joins, and the indexing of the various keys, the system may have to search through multiple tables and indexes, gather lots of information, and then sort it all to match it together.

In contrast, graph databases directly store the relationships between records. Instead of an email address being found by looking up its user's key in the userpk column, the user record has a pointer directly to the email address record. That is, having selected a user, the pointer can be followed directly to the email records, there is no need to search the email table to find the matching records. This can eliminate the costly join operations. For example, if one searches for all of the email addresses for users in area code "311", the engine would first perform a conventional search to find the users in "311", but then retrieve the email addresses by following the links found in those records. A relational database would first find all the users in "311", extract a list of the pk's, perform another search for any records in the email table with those pk's, and link the matching records together. For these types of common operations, a graph database (in theory at least) is significantly faster.

The true value of the graph approach becomes evident when one performs searches that are more than one level deep. For example, consider a search for users who have "subscribers" (a table linking users to other users) in the "311" area code. In this case a relational database has to first look for all the users with an area code in "311", then look in the subscribers table for any of those users, and then finally look in the users table to retrieve the matching users. In contrast, a graph database would look for all the users in "311", then follow the back-links through the subscriber relationship to find the subscriber users. This avoids several searches, lookups and the memory involved in holding all of the temporary data from multiple records needed to construct the output. Technically, this sort of lookup is completed in O(log(n)) + O(1) time, that is, roughly relative to the logarithm of the size of the data. In contrast, the relational version would be multiple O(log(n)) lookups, plus more time to join all the data.

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 will require several separate searches through the movies and actors tables, doing another search on submarine movies, finding all the actors in those movies, and the comparing the (large) collected results. In contrast, the graph database would simply 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 also. 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.

Properties

Compared with relational databases, graph databases are often faster for associative data sets and map more directly to the structure of object-oriented applications. They can scale more naturally to large data sets as they do not typically need costly join operations. As they depend less on a rigid schema, they are more suitable to manage ad hoc and changing data with evolving schemas. Conversely, relational databases are typically faster at performing the same operation on large numbers of data elements.

Graph databases are a powerful tool for graph-like queries. For example, computing the shortest path between two nodes in the graph. Other graph-like queries can be performed over a graph database in a natural way (for example graph's diameter computations or community detection).

History

In the pre-history of graph databases, in the mid-1960s Navigational databases such as IBM's IMS supported tree-like structures in its hierarchical model, but the strict tree structure could be circumvented with virtual records.

Graph structures could be represented in network model databases from the late 1960s. CODASYL, which had defined COBOL in 1959, defined the Network Database Language in 1969.

Labeled graphs could be represented in graph databases from the mid-1980s, such as the Logical Data Model.

Several improvements to graph databases appeared in the early 1990s, accelerating in the late 1990s with endeavors to index web pages.

In the mid-late 2000s, commercial atomicity, consistency, isolation, durability (ACID) graph databases such as Neo4j and Oracle Spatial and Graph became available.

In the 2010s, commercial ACID graph databases that could be scaled horizontally became available. Further, SAP HANA brought in-memory and columnar technologies to graph databases. Also in the 2010s, multi-model databases that supported graph models (and other models such as relational database or document-oriented database) became available, such as OrientDB, ArangoDB, and MarkLogic (starting with its 7.0 version). During this time, graph databases of various types have become especially popular with social network analysis with the advent of social media companies.

List of graph databases

The following is a list of notable graph databases:

APIs and graph query-programming languages