View source for Graph database

{{Short description|Database using graph structures for queries}}
{{Multiple issues|
{{original research|date=August 2016}}
{{primary sources|date=August 2016}}
{{more citations needed|date=August 2016}}
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A '''graph database''' ('''GDB''') is a [[database]] that uses [[Graph (data structure)|graph structures]] for [[Semantic query|semantic queries]] with [[Node (graph theory)|nodes]], [[Edge (graph theory)|edges]], and properties to represent and store data.<ref>{{cite book|first=Nikolaos G.|last=Bourbakis|url=https://books.google.com/books?id=mV3wxKLHlnwC&q=%22gdb%22+%22graph+database%22&pg=PA381|title=Artificial Intelligence and Automation|publisher=World Scientific|year=1998|isbn=9789810226374|page=381|access-date=2018-04-20}}</ref> A key concept of the system is the [[Graph (discrete mathematics)|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.<ref name=":0">{{cite journal|last1=Yoon|first1=Byoung-Ha|last2=Kim|first2=Seon-Kyu|last3=Kim|first3=Seon-Young|date=March 2017|title=Use of Graph Database for the Integration of Heterogeneous Biological Data|journal=Genomics & Informatics|volume=15|issue=1|pages=19–27|doi=10.5808/GI.2017.15.1.19|issn=1598-866X|pmc=5389944|pmid=28416946}}</ref>

Graph databases are commonly referred to as a [[NoSQL]] database. 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 (computer science)|abstraction]]<ref name="Gutierrez2">{{cite journal|last1=Angles|first1=Renzo|last2=Gutierrez|first2=Claudio|date=1 Feb 2008|title=Survey of graph database models|url=http://www.cse.iitk.ac.in/users/smitr/PhD%20Resources/Survey%20of%20Graph%20Databases%20Models.pdf|url-status=dead|journal=ACM Computing Surveys|volume=40|issue=1|pages=1–39|citeseerx=10.1.1.110.1072|doi=10.1145/1322432.1322433|archive-url=https://web.archive.org/web/20170815064527/https://www.cse.iitk.ac.in/users/smitr/PhD%20Resources/Survey%20of%20Graph%20Databases%20Models.pdf|archive-date=15 August 2017|access-date=28 May 2016|quote=network models [...] lack a good abstraction level: it is difficult to separate the db-model from the actual implementation|s2cid=207166126}}</ref> and lack easy [[Graph traversal|traversal]] over a chain of edges.<ref>{{cite book|last=Silberschatz|first=Avi|url=http://codex.cs.yale.edu/avi/db-book/db6/appendices-dir/d.pdf|title=Database System Concepts, Sixth Edition|date=28 January 2010|publisher=McGraw-Hill|isbn=978-0-07-352332-3|page=D-29}}</ref>

The underlying storage mechanism of graph databases can vary. Relationships are first-class citizens 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 (database)|table]] (although a table is a logical element, therefore this approach imposes a level of abstraction between the graph database management system and physical storage devices). Others use a [[Attribute–value pair|key–value store]] or [[document-oriented database]] for storage, making them inherently NoSQL structures.

{{As of|2021}}, no 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, many of which are tightly tied to one product. Some early standardization efforts led to multi-vendor query languages like [[Gremlin (programming language)|Gremlin]], [[SPARQL]], and [[Cypher Query Language|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). [[Graph Query Language|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 interface]]s (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.<ref>{{cite book |last=Robinson |first=Ian |date=2015-06-10 |title=Graph Databases: New Opportunities for Connected Data |url=https://books.google.com/books?id=RTvcCQAAQBAJ&pg=PA4 |publisher=O'Reilly Media, Inc. |page=4 |isbn=9781491930861}}</ref> Graph databases attracted considerable attention in the 2000s, due to the successes of major technology corporations in using proprietary graph databases,<ref>{{cite web|title=Graph Databases Burst into the Mainstream|url=https://www.kdnuggets.com/2018/02/graph-databases-burst-into-the-mainstream.html|access-date=2018-10-23|website=www.kdnuggets.com}}</ref> along with the introduction of [[Open-source software|open-source]] graph databases.

One study concluded that an RDBMS was "comparable" in performance to existing graph analysis engines at executing graph queries.<ref>{{cite conference|last1=Fan|first1=Jing|last2=Gerald|first2=Adalbert|date=2014-12-25|title=The case against specialized graph analytics engines|url=http://cidrdb.org/cidr2015/Papers/CIDR15_Paper20.pdf|conference=Conference on Innovative Data Systems Research (CIDR)}}</ref>

== History ==
In the mid-1960s, [[navigational database]]s such as [[IBM]]'s [[IBM Information Management System|IMS]] supported [[Tree (data structure)|tree]]-like structures in its [[Hierarchical database model|hierarchical model]], but the strict [[tree structure]] could be circumvented with virtual records.<ref>{{cite book |last=Silberschatz |first=Avi |date=28 January 2010 |title=Database System Concepts, Sixth Edition |url=http://codex.cs.yale.edu/avi/db-book/db6/appendices-dir/e.pdf |publisher=McGraw-Hill |page=E-20 |isbn=978-0-07-352332-3}}</ref><ref>{{cite web |url=http://www.people.vcu.edu/~lparker/IMS.html |title=IMS Notes |last1=Parker |first1=Lorraine |website=vcu.edu |access-date=31 May 2016}}</ref>

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.

[[Graph labeling|Labeled graphs]] could be represented in graph databases from the mid-1980s, such as the Logical Data Model.<ref name="Gutierrez">{{cite journal|last1=Angles|first1=Renzo|last2=Gutierrez|first2=Claudio|date=1 Feb 2008|title=Survey of graph database models|url=http://www.cse.iitk.ac.in/users/smitr/PhD%20Resources/Survey%20of%20Graph%20Databases%20Models.pdf|url-status=dead|journal=ACM Computing Surveys|volume=40|issue=1|pages=1–39|citeseerx=10.1.1.110.1072|doi=10.1145/1322432.1322433|archive-url=https://web.archive.org/web/20170815064527/https://www.cse.iitk.ac.in/users/smitr/PhD%20Resources/Survey%20of%20Graph%20Databases%20Models.pdf|archive-date=15 August 2017|access-date=28 May 2016|quote=network models [...] lack a good abstraction level: it is difficult to separate the db-model from the actual implementation|s2cid=207166126}}</ref><ref>{{cite thesis |last=Kuper |first=Gabriel M. |date=1985 |title=The Logical Data Model: A New Approach to Database Logic |type=Ph.D. |docket=STAN-CS-85-1069 |url=http://apps.dtic.mil/dtic/tr/fulltext/u2/a323935.pdf |archive-url=https://web.archive.org/web/20160630154240/http://www.dtic.mil/dtic/tr/fulltext/u2/a323935.pdf |url-status=live |archive-date=June 30, 2016 |access-date=31 May 2016}}</ref>

Commercial [[object database]]s (ODBMSs) emerged in the early 1990s. In 2000, the [[Object Data Management Group]] published a standard language for defining object and relationship (graph) structures in their ODMG'93 publication.

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

In the mid-to-late 2000s, commercial graph databases with [[ACID]] guarantees such as [[Neo4j]] and [[Oracle Spatial and Graph]] became available.

In the 2010s, commercial ACID graph databases that could be [[Scalability#Horizontal and vertical scaling|scaled horizontally]] became available. Further, [[SAP HANA]] brought [[In-memory database|in-memory]] and [[Column-oriented DBMS|columnar]] technologies to graph databases.<ref>{{cite web|url=http://www.dbta.com/Editorial/News-Flashes/SAP-Announces-New-Capabilities-in-the-Cloud-with-HANA-100186.aspx|title=SAP Announces New Capabilities in the Cloud with HANA|date=2014-10-22|access-date=2016-07-07}}</ref> Also in the 2010s, [[multi-model database]]s 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. Also during the decade, [[Cloud computing|cloud]]-based graph databases such as [[Amazon Neptune]] and [[Neo4j#Licensing and editions|Neo4j AuraDB]] became available.

== Background ==

Graph databases portray the data as it is viewed conceptually. This is accomplished by transferring the data into nodes and its relationships into edges.

A graph database is a database that is based on [[graph theory]]. It consists of a set of objects, which can be a node or an edge.

* '''Nodes''' represent entities or instances such as people, businesses, accounts, or any other item to be tracked. They are roughly the equivalent of a record, relation, or [[row (database)|row]] in a relational database, or a document in a document-store database.
* '''Edges''', also termed ''graphs'' or ''relationships'', are the lines that connect nodes to other nodes; representing the relationship between them. Meaningful patterns emerge when examining the connections and interconnections of nodes, properties and edges. The edges can either be directed or undirected. In an undirected graph, an edge connecting two nodes has a single meaning. In a directed graph, the edges connecting two different nodes have different meanings, depending on their direction. Edges are the key concept in graph databases, representing an abstraction that is not directly implemented in a [[relational model]] or a [[Document-oriented database|document-store model]]'''.'''
* '''Properties''' are information associated to nodes. For example, if ''Wikipedia'' were one of the nodes, it might be tied to properties such as ''website'', ''reference material'', or ''words that starts with the letter w'', depending on which aspects of ''Wikipedia'' are germane to a given database.

== Graph models ==

=== Labeled-property graph ===
[[File:GraphDatabase PropertyGraph.svg|right|thumb|308px|An example of a Labeled-property graph]]
A labeled-property graph model is represented by a set of nodes, relationships, properties, and labels. Both nodes of data and their relationships are named and can store properties represented by [[attribute–value pair|key–value pairs]]. Nodes can be labelled to be grouped. The edges representing the relationships have two qualities: they always have a start node and an end node, and are directed;<ref>{{cite web|url=http://www.dataversity.net/property-graphs-swiss-army-knife-data-modeling/|title=Property Graphs|last=Frisendal|first=Thomas|date=2017-09-22|website=graphdatamodeling.com|access-date=2018-10-23}}</ref> making the graph a [[directed graph]]. Relationships can also have properties. This is useful in providing additional metadata and semantics to relationships of the nodes.<ref>{{cite journal|last1=Das|first1=S|last2=Srinivasan|first2=J|last3=Perry|first3=Matthew|last4=Chong|first4=Eugene|last5=Banerjee|first5=Jay|date=2014-03-24|title=A Tale of Two Graphs: Property Graphs as RDF in Oracle|url=https://www.researchgate.net/publication/264702160}}</ref> Direct storage of relationships allows a [[Time complexity#Constant time|constant-time]] [[Graph traversal|traversal]].<ref name=":32">{{cite journal|last1=Have|first1=Christian Theil|last2=Jensen|first2=Lars Juhl|date=2013-10-17|title=Are graph databases ready for bioinformatics?|journal=Bioinformatics|volume=29|issue=24|pages=3107–3108|doi=10.1093/bioinformatics/btt549|issn=1460-2059|pmc=3842757|pmid=24135261}}</ref>

=== Resource Description Framework (RDF) ===
{{main|RDF (computer science)}}
[[File:Rdf-graph3.png|thumb|493x493px|An example RDF graph]]

In an [[RDF (computer science)|RDF]] graph model, each addition of information is represented with a separate node. For example, imagine a scenario where a user has to add a name property for a person represented as a distinct node in the graph. In a labeled-property graph model, this would be done with an addition of a name property into the node of the person. However, in an RDF, the user has to add a separate node called <code>hasName</code> connecting it to the original person node. Specifically, an RDF graph model is composed of nodes and arcs. An RDF graph notation or a statement is represented by: a node for the subject, a node for the object, and an arc for the predicate. A node may be left blank, a [[Literal (computer programming)|literal]] and/or be identified by a [[Uniform Resource Identifier|URI]]. An arc may also be identified by a URI. A literal for a node may be of two types: plain (untyped) and typed. A plain literal has a lexical form and optionally a language tag. A typed literal is made up of a string with a URI that identifies a particular datatype. A blank node may be used to accurately illustrate the state of the data when the data does not have a [[Uniform Resource Identifier|URI]].<ref>{{cite web|url=https://www.w3.org/TR/rdf-concepts/#section-Overview|title=Resource Description Framework (RDF): Concepts and Abstract Syntax|website=www.w3.org|access-date=2018-10-24}}</ref>

== Properties ==
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).

Graphs are flexible, meaning it allows the user to insert new data into the existing graph without loss of application functionality. There is no need for the designer of the database to plan out extensive details of the database's future use cases.

=== Storage ===
The underlying storage mechanism of graph databases can vary. Some depend on a relational engine and "store" the graph data in a [[Table (database)|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 [[attribute–value pair|key–value store]] or [[document-oriented database]] for storage, making them inherently [[NoSQL]] structures. A node would be represented as any other document store, but edges that link two different nodes hold special attributes inside its document; a _from and _to attributes.

=== Index-free adjacency ===
Data lookup performance is dependent on the access speed from one particular node to another. Because [[Database index|index]]-free adjacency enforces the nodes to have direct physical [[Random-access memory|RAM]] addresses and physically point to other adjacent nodes, it results in a fast retrieval. A native graph system with index-free adjacency does not have to move through any other type of data structures to find links between the nodes. Directly related nodes in a graph are stored in the [[Cache (computing)|cache]] once one of the nodes are retrieved, making the data lookup even faster than the first time a user fetches a node. However, such advantage comes at a cost. Index-free adjacency sacrifices the efficiency of queries that do not use [[graph traversal]]s. Native graph databases use index-free adjacency to process [[CRUD]] operations on the stored data.

== Applications ==
Multiple categories of graphs by kind of data have been recognised. Gartner suggests the five broad categories of graphs:<ref>{{cite web|url=https://www.gartner.com/doc/2081316/competitive-dynamics-consumer-web-graphs|title=The Competitive Dynamics of the Consumer Web: Five Graphs Deliver a Sustainable Advantage|website=www.gartner.com|access-date=2018-10-23}}</ref>

* [[Social graph]]: this is about the connections between people; examples include [[Facebook]], [[Twitter]], and the idea of [[six degrees of separation]]
* Intent graph: this deals with reasoning and motivation. 
* Consumption graph: also known as the "payment graph", the consumption graph is heavily used in the retail industry. E-commerce companies such as Amazon, eBay and Walmart use consumption graphs to track the consumption of individual customers.
* [[Interest graph]]: this maps a person's interests and is often complemented by a social graph. It has the potential to follow the previous revolution of web organization by mapping the web by interest rather than indexing webpages.
* Mobile graph: this is built from mobile data. Mobile data in the future may include data from the web, applications, digital wallets, GPS, and [[Internet of things|Internet of Things]] (IoT) devices.

== Comparison with relational databases ==
Since [[Edgar F. Codd]]'s 1970 paper on the [[relational model]],<ref name=":2">{{cite journal|last=Codd|first=E. F.|date=1970-06-01|title=A relational model of data for large shared data banks|journal=Communications of the ACM|volume=13|issue=6|pages=377–387|doi=10.1145/362384.362685|s2cid=207549016|issn=0001-0782|doi-access=free}}</ref> [[relational databases]] have been the de facto industry standard for large-scale data storage systems. Relational models require a strict schema and [[data normalization]] which separates data into many tables and removes any duplicate data within the database. Data is normalized in order to preserve [[data consistency]] and support [[ACID (computer science)|ACID transactions]]. However this imposes limitations on how relationships can be queried.

One of the relational model's design motivations was to achieve a fast row-by-row access.<ref name=":2" /> Problems arise when there is a need to form complex relationships between the stored data. Although relationships can be analyzed with the relational model, complex queries performing many join operations on many different attributes over several tables are required. In working with relational models, [[foreign key]] constraints should also be considered when retrieving relationships, causing additional overhead.

Compared with [[relational database]]s, graph databases are often faster for associative data sets{{Citation needed|date=August 2013}} and map more directly to the structure of [[object-oriented programming|object-oriented]] applications. They can scale more naturally{{Citation needed|date=August 2017}} to large datasets as they do not typically need [[Join (SQL)|join]] operations, which can often be expensive. As they depend less on a rigid schema, they are marketed as more suitable to manage ad hoc and changing data with evolving schemas.

Conversely, relational database management systems are typically faster at performing the same operation on large numbers of data elements, permitting the manipulation of the data in its natural structure. Despite the graph databases' advantages and recent popularity over {{Citation needed|date=May 2021}} relational databases, it is recommended the graph model itself should not be the sole reason to replace an existing relational database. A graph database may become relevant if there is an evidence for performance improvement by orders of magnitude and lower latency.<ref name=":12">{{cite news|url=https://www.safaribooksonline.com/library/view/graph-databases-2nd/9781491930885/|title=Graph Databases, 2nd Edition|work=O’Reilly {{!}} Safari|access-date=2018-10-23}}</ref>

=== Examples ===
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 [[Table (database)|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 [[database index|index]]es, which allow data to be stored in a smaller sub-table, containing only the selected data and a [[unique key]] (or primary key) of the record. 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. Usually, a table is stored in a way that allows a lookup via a key to be very fast.<ref name="from">{{cite web|url=http://neo4j.com/developer/graph-db-vs-rdbms/#_from_relational_to_graph_databases|title=From Relational to Graph Databases|website=Neo4j}}</ref>

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 <code>userpk</code>, 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 <code>userpk</code> 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 (SQL)|join]], can be computationally expensive. Depending on the complexity of the query, the number of joins, and indexing various keys, the system may have to search through multiple tables and indexes and then sort it all to match it together.<ref name="from" />

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 <code>userpk</code> column, the user record contains a pointer that directly refers 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 primary keys, perform another search for any records in the email table with those primary keys, and link the matching records together. For these types of common operations, graph databases would theoretically be faster.<ref name="from" />

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 search for all the users with an area code in "311", then search the subscribers table for any of those users, and then finally search the users table to retrieve the matching users. In contrast, a graph database would search for all the users in "311", then follow the [[backlink]]s 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. In terms of [[big O notation]], this query would be <math>O(\log n) + O(1)</math> time – i.e., proportional to the logarithm of the size of the data. In contrast, the relational version would be multiple <math>O(\log n)</math> lookups, plus the <math>O(n)</math> time needed to join all of the data records.<ref name="from" />

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 (film)|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.<ref name="examples">{{citation|title=Examples where Graph databases shine: Neo4j edition|url=https://zeroturnaround.com/rebellabs/examples-where-graph-databases-shine-neo4j-edition/2/|website=ZeroTurnaround}}</ref>

''Properties'' add another layer of [[Abstraction (computer science)|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.<ref name="examples" />

Relational databases are very well suited to flat data layouts, where relationships between data are only 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.

To further illustrate, imagine a relational model with two tables: a <code>people</code> table (which has a <code>person_id</code> and <code>person_name</code> column) and a <code>friend</code> table (with <code>friend_id</code> and <code>person_id</code>, which is a [[foreign key]] from the <code>people</code> table). In this case, searching for all of Jack's friends would result in the following SQL query.

<syntaxhighlight lang="sql">
SELECT p2.person_name 
FROM people p1 
JOIN friend ON (p1.person_id = friend.person_id)
JOIN people p2 ON (p2.person_id = friend.friend_id)
WHERE p1.person_name = 'Jack';
</syntaxhighlight>

The same query may be translated into --

* [[Cypher Query Language|Cypher]], a graph database [[query language]]<syntaxhighlight lang="cypher">
MATCH (p1:person {name: 'Jack'})-[:FRIEND_WITH]-(p2:person)
RETURN p2.name
</syntaxhighlight>

* [[SPARQL]], an RDF graph database [[query language]] standardized by [[W3C]] and used in multiple RDF [[Triplestore|Triple]] and [[Named graph|Quad]] stores
** Long form <syntaxhighlight lang="sparql">
PREFIX foaf: <http://xmlns.com/foaf/0.1/>

SELECT ?name
WHERE { ?s a          foaf:Person . 
        ?s foaf:name  "Jack" . 
        ?s foaf:knows ?o . 
        ?o foaf:name  ?name . 
      }
</syntaxhighlight>
** Short form <syntaxhighlight lang="sparql">
PREFIX foaf: <http://xmlns.com/foaf/0.1/>

SELECT ?name
WHERE { ?s foaf:name     "Jack" ;
           foaf:knows    ?o .
           ?o foaf:name  ?name .
      }

</syntaxhighlight>
* SPASQL, a hybrid database query language, that extends [[SQL]] with [[SPARQL]]<syntaxhighlight lang="sparql">
SELECT people.name
FROM (
       SPARQL PREFIX foaf: <http://xmlns.com/foaf/0.1/>
              SELECT ?name
              WHERE { ?s foaf:name  "Jack" ; 
                         foaf:knows ?o .
                      ?o foaf:name  ?name .
                    }
    ) AS people ;
</syntaxhighlight>

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.<ref name=":32"/>

==List of graph databases==
<!--Additions to this list should have a Wiki-article or be referenced to an independent reliable source (not press releases, advertorials or similar PR publications).-->
The following is a list of [[WP:GNG|notable]] graph databases:
{|class="wikitable sortable"
!name!!current<br/>version!!latest<br/>release<br/>date<br/><small>{{Nowrap|(YYYY-MM-DD)}}</small>!![[Software license|software<br/>license]]!![[programming language]]!!description
|-
|[[Aerospike (database)|Aerospike]]
|7.0
|2023-11-15
|Proprietary
|C
|Aerospike Graph is a highly scalable, low-latency property graph database built on Aerospike’s proven real-time data platform. Aerospike Graph combines the enterprise capabilities of the Aerospike Database - the most scalable real-time NoSQL database - with the property graph data model via the Apache Tinkerpop graph compute engine. Developers will enjoy native support for the Gremlin query language, which enables them to write powerful business processes directly.
|-
|[[AgensGraph]]<ref>{{cite web | url=https://bitnine.net/agensgraph/ | title=AgensGraph | website=bitnine.net | access-date=2025-02-19}}</ref>
|2.14.1
|2025-01<ref>{{Cite web|url=https://github.com/bitnine-oss/agensgraph/releases/tag/v2.14.1|title=Release AgensGraph v2.14.1 · bitnine-oss/agensgraph|website=github.com|publisher=SKAI Worldwide|date=2025-01-16|access-date=2025-02-17}}</ref>
|[[Apache License#Version 2.0|Apache 2]] Community version, [[Proprietary software|proprietary]] Enterprise Edition
|C
|AgensGraph is a cutting-edge multi-model graph database designed for modern complex data environments. By supporting both relational and graph data models simultaneously, AgensGraph allows developers to seamlessly integrate legacy relational data with the flexible graph data model within a single database. AgensGraph is built on the robust [[PostgreSQL]] RDBMS, providing a highly reliable, fully-featured platform ready for enterprise use.
|-
|[[AllegroGraph]]||7.0.0||2020-04||{{Some|[[Proprietary software|Proprietary]], clients: [[Eclipse Public License]] v1}}||[[C Sharp (programming language)|C#]], [[C (programming language)|C]], [[Common Lisp]], [[Java (programming language)|Java]], [[Python (programming language)|Python]]||[[Resource Description Framework]] (RDF) and graph database.
|-
|[[Amazon Neptune|Amazon<br/>Neptune]]||1.4.0.0||2024-11-06<ref>{{Cite web|url=https://docs.aws.amazon.com/neptune/latest/userguide/engine-releases-1.4.0.0.html|title=Amazon Neptune Engine version 1.4.0.0 (2024-11-06)|website=Docs.AWS.Amazon.com|publisher=[[Amazon Web Services]]|access-date=9 November 2024}}</ref>||{{Proprietary}}||Not disclosed||Amazon Neptune is a fully managed graph database by [[Amazon.com]]. It is used as a [[web service]], and is part of [[Amazon Web Services]]. Supports popular graph models property graph and [[W3C]]'s [[Resource Description Framework|RDF]], and their respective [[query language]]s Apache TinkerPop, [[Gremlin (programming language)|Gremlin]], [[SPARQL]], and [[Cypher (query language)|openCypher]].
|-
|[[Cambridge Semantics|AnzoGraph DB]]||2.1||2020-02||{{Proprietary}}||[[C (programming language)|C]], [[C++]]||AnzoGraph DB is a [[massively parallel]] native Graph Online Analytics Processing ([[GOLAP]]) style database built to support [[SPARQL]] and [[Cypher Query Language]] to analyze trillions of relationships. AnzoGraph DB is designed for interactive analysis of large sets of [[semantic triple]] data, but also supports labeled properties under proposed [[W3C]] standards.<ref>{{Cite web|url=https://www.CambridgeSemantics.com/product/anzograph/|title=In-memory massively parallel distributed graph database purpose-built for analytics|website=CambridgeSemantics.com|access-date=2018-02-20}}</ref><ref>{{Cite web|last=Rueter|first=John|date=15 February 2018|url=https://www.BusinessWire.com/news/home/20180215006023/en|title=Cambridge Semantics announces AnzoGraph graph-based analytics support for Amazon Neptune and graph databases|website=BusinessWire.com|access-date=20 February 2018}}</ref><ref>{{Cite web|last=Zane|first=Barry|date=2 November 2016|url=http://www.DBTA.com/BigDataQuarterly/Articles/Semantic-Graph-Databases-A-worthy-successor-to-relational-databases-114569.aspx|title=Semantic graph databases: a worthy successor to relational databases|website=DBTA.com|publisher=Database Trends and Applications|access-date=20 February 2018}}</ref><ref>{{Cite web|url=http://www.DBTA.com/Editorial/News-Flashes/Cambridge-Semantics-Announces-AnzoGraph-Support-for-Amazon-Neptune-and-Graph-Databases-123280.aspx|title=Cambridge Semantics announces AnzoGraph support for Amazon Neptune and graph databases|website=DBTA.com|publisher=Database Trends and Applications|date=2018-02-15|access-date=2018-03-08}}</ref>
|-
|[[ArangoDB]]||3.9.1||2022-04||{{Some|[[Free software|Free]] [[Apache License#Version 2.0|Apache 2]], [[Proprietary software|Proprietary]]}}||[[C++]], [[JavaScript]], [[.NET]], [[Java (programming language)|Java]], [[Python (programming language)|Python]], [[Node.js]], [[PHP]], [[Scala (programming language)|Scala]], [[Go (programming language)|Go]], [[Ruby (programming language)|Ruby]], [[Elixir (programming language)|Elixir]]||[[NoSQL]] native graph database system developed by ArangoDB Inc, supporting three data models (key/value, documents, graphs), with one database core and a unified query language called AQL (ArangoDB Query Language). Provides scalability and high availability via datacenter-to-datacenter replication, auto-sharding, automatic failover, and other capabilities.
|-
|Azure [[Cosmos DB]]
|
|2017
|Proprietary
|Not disclosed
|Multi-modal database which supports graph concepts using the [[Gremlin (query language)|Apache Gremlin]] query language
|-
|[[DataStax]]<br/>Enterprise<br/>Graph||v6.0.1||2018-06||{{Proprietary}}||[[Java (programming language)|Java]]||Distributed, real-time, scalable database; supports Tinkerpop, and integrates with [[Apache Cassandra|Cassandra]]<ref>{{Cite web|last=Woodie|first=Alex|date=21 June 2016|url=https://www.Datanami.com/2016/06/21/beyond-titan-evolution-datastaxs-new-graph-database/|title=Beyond Titan: the evolution of DataStax's new graph database|website=Datanami.com|access-date=9 May 2017}}</ref>
|-
|[[GUN (graph database)|GUN (Graph Universe Node)]]||0.2020.1240||2024||{{Open_source}}, [[MIT License]], [[Apache License|Apache 2.0]], [[zlib License]]||[[JavaScript]]||An [[open source]], [[Online_and_offline|offline-first]], [[Real-time_communication|real-time]], [[Decentralized_web|decentralized]], [[graph database]] written in [[JavaScript]] for the [[web browser]].<ref>{{Cite web |url=https://www.youtube.com/watch?v=oTQXzhm8w_8 |title=GUN Decentralized Graph DB in 100 Seconds |author=Fireship |date=2021-06-07 |access-date=2024-08-02 |website=YouTube}}</ref><ref>{{cite news |date=2019-07-21 |title=These technologists think the internet is broken. So they're building another one.
 |last=Smith |first=Noah |author-link=Noah Smith (writer) |url=https://www.nbcnews.com/tech/tech-news/these-technologists-think-internet-broken-so-they-re-building-another-n1030136 |work=[[NBC News]]}}</ref>

It is implemented as a [[peer-to-peer]] network featuring [[multi-master replication]] with a custom [[Conflict-free_replicated_data_type|commutative replicated data type (CRDT)]].<ref>{{Cite journal |last1=Thakur |first1=Uma |last2=Chichmalkar |first2=Abhishek |last3=Sambhare |first3=Aditya |last4=Chaturvedi |first4=Aman |last5=Khuspare |first5=Chinmay |last6=Tembhe |first6=Nikhil |title=Decentralised Chat Application |journal=IRJET |date=2022 |url=https://www.irjet.net/archives/V9/i2/IRJET-V9I244.pdf}}</ref>
|-
|[[InfiniteGraph]]||2021.2||2021-05||{{Proprietary}}, [[Commercial software|commercial, free 50GB version]]||[[Java (programming language)|Java]], [[C++]], 'DO' query language||A distributed, cloud-enabled and massively scalable graph database for complex, real-time queries and operations. Its Vertex and Edge objects have unique 64-bit object identifiers that considerably speed up graph navigation and pathfinding operations. It supports batch or streaming updates to the graph alongside concurrent, parallel queries. InfiniteGraph's 'DO' query language enables both value based queries, as well as complex graph queries. InfiniteGraph is goes beyond graph databases to also support complex object queries.
|-
|[[JanusGraph]]||1.0.0||2023-10-21<ref>{{Cite web|url=https://github.com/JanusGraph/janusgraph/releases/tag/v1.0.0|title=release 1.0.0 · JanusGraph/janusgraph|website=GitHub.com|date=21 October 2023}}</ref>||{{Free|[[Apache License#Version 2.0|Apache 2]]}}||[[Java (programming language)|Java]]||Open source, scalable, distributed across a multi-machine cluster graph database under The [[Linux Foundation]]; supports various storage backends ([[Apache Cassandra]], [[Apache HBase]], Google Cloud [[Bigtable]], Oracle [[Berkeley DB]]);<ref>{{Cite web|url=https://docs.janusgraph.org/latest/storage-backends.html|title=JanusGraph storage backends|website=docs.JanusGraph.org|access-date=2018-10-01|archive-url=https://web.archive.org/web/20181002020052/https://docs.janusgraph.org/latest/storage-backends.html|archive-date=2018-10-02|url-status=dead}}</ref> supports global graph data analytics, reporting, and [[extract, transform, load]] (ETL) through integration with big data platforms ([[Apache Spark]], [[Apache Giraph]], [[Apache Hadoop]]); supports geo, numeric range, and full-text search via external index storages ([[Elasticsearch]], [[Apache Solr]], [[Apache Lucene]]).<ref>{{Cite web|url=https://docs.janusgraph.org/latest/index-backends.html|title=JanusGraph index storages|website=docs.JanusGraph.org|access-date=2018-10-01|archive-url=https://web.archive.org/web/20181002064503/https://docs.janusgraph.org/latest/index-backends.html|archive-date=2018-10-02|url-status=dead}}</ref>
|-
|[[MarkLogic]]||8.0.4||2015||{{Proprietary}}, [[freeware]] developer version||[[Java (programming language)|Java]]||Multi-model [[NoSQL]] database that stores [[Document-oriented database|documents]] (JSON and XML) and semantic graph data ([[Resource Description Framework|RDF]] triples); also has a built-in search engine.
|-
|[[Microsoft SQL Server]] 2017||RC1|| ||{{Proprietary}}||[[SQL]]/T-SQL, [[R (programming language)|R]], [[Python (programming language)|Python]]||Offers graph database abilities to model many-to-many relationships. The graph relationships are integrated into Transact-SQL, and use SQL Server as the foundational database management system.<ref>{{Cite web|url=https://Docs.Microsoft.com/en-us/sql/sql-server/what-s-new-in-sql-server-2017|title=What's new in SQL Server 2017|website=Docs.Microsoft.com|publisher=[[Microsoft Corp.]]|date=19 April 2017|access-date=9 May 2017}}</ref>
|-
|[[NebulaGraph]]||3.7.0||2024-03||Open Source Edition is under Apache 2.0, Common Clause 1.0||[[C++]], [[Go (programming language)|Go]], [[Java (programming language)|Java]], [[Python (programming language)|Python]]||A scalable open-source distributed graph database for storing and handling billions of vertices and trillions of edges with milliseconds of latency. It is designed based on a shared-nothing distributed architecture for linear scalability.<ref>{{Cite web|url=https://www.Datanami.com/this-just-in/nebula-graph-debuts-for-big-data-analytics-discovery/|title=Nebula Graph debuts for big data analytics discovery|website=Datanami.com|date=29 June 2020|access-date=2 December 2020}}</ref>
|-
|[[Neo4j]]||2025.01.0||2025-02-06<ref>{{Cite web|url=https://neo4j.com/release-notes/database/neo4j-2025-01/|title=Release Notes: Neo4j 2025.01|website=Neo4j.com|publisher=Neo4j Graph Database Platform|access-date=2025-02-06}}</ref>||{{Some|[[GNU General Public License|GPLv3]] Community Edition, [[Commercial software|commercial]] and [[Affero General Public License|AGPLv3]] options for enterprise and advanced editions}}||[[Java (programming language)|Java]], [[.NET]], [[JavaScript]], [[Python (programming language)|Python]], [[Go (programming language)|Go]], [[Ruby (programming language)|Ruby]], [[PHP]], [[R (programming language)|R]], [[Erlang (programming language)|Erlang]]/[[Elixir (programming language)|Elixir]], [[C (programming language)|C]]/[[C++]], [[Clojure]], [[Perl]], [[Haskell]]||Open-source, supports ACID, has high-availability clustering for enterprise deployments, and comes with a web-based administration that includes full transaction support and visual node-link graph explorer; accessible from most programming languages using its built-in [[Representational state transfer|REST]] [[web API]] interface, and a proprietary Bolt protocol with official drivers.
|-
|[[Ontotext GraphDB]]||10.7.6||2024-10-15<ref>{{cite web |title=Release Notes |url=https://graphdb.ontotext.com/documentation/10.7/release-notes.html |website=Ontotext GraphDB |access-date=9 November 2024 |date=9 November 2024}}</ref>||{{Proprietary}}, Standard and Enterprise Editions are [[Commercial software|commercial]], Free Edition is [[freeware]]||[[Java (programming language)|Java]]||Highly efficient and robust semantic graph database with RDF and SPARQL support, also available as a high-availability cluster. Integrates [[OpenRefine]] for ingestion and reconciliation of tabular data and [[ontop]] for [[Ontology-Based Data Access]]. Connects to [[Apache Lucene|Lucene]], [[Apache Solr|SOLR]] and [[Elasticsearch]] for [[Full text search|Full text]] and [[Faceted search]], and [[Apache Kafka|Kafka]] for event and stream processing. Supports [[Open Geospatial Consortium|OGC]] [[GeoSPARQL]]. Provides [[Java Database Connectivity|JDBC]] access to [[Knowledge Graph]]s.
|-
|OpenLink<br/>[[Virtuoso Universal Server|Virtuoso]]||8.2||2018-10||{{Some|Open Source Edition is [[GNU General Public License|GPLv2]], Enterprise Edition is [[Proprietary software|proprietary]]}}||[[C (programming language)|C]], [[C++]]||Multi-model (Hybrid) relational database management system (RDBMS) that supports both SQL and SPARQL for declarative (Data Definition and Data Manipulation) operations on data modelled as SQL tables and/or RDF Graphs. Also supports indexing of RDF-Turtle, RDF-N-Triples, RDF-XML, JSON-LD, and mapping and generation of relations (SQL tables or RDF graphs) from numerous document types including CSV, XML, and JSON. May be deployed as a local or embedded instance (as used in the [[NEPOMUK (software)|NEPOMUK]] Semantic Desktop), a one-instance network server, or a shared-nothing elastic-cluster multiple-instance networked server<ref name="Virtuoso Clustering Diagrams">{{Cite web|url=http://Virtuoso.OpenLinkSW.com/dataspace/dav/wiki/Main/VirtClusteringDiagrams|title=Clustering deployment architecture diagrams for Virtuoso|website=Virtuoso.OpenLinkSW.com|publisher=OpenLink Software|access-date=9 May 2017}}</ref>
|-
|Oracle RDF Graph; part of [[Oracle Database]]||21c||2020||{{Proprietary}}||[[SPARQL]], [[SQL]]||RDF Graph capabilities as features in multi-model Oracle Database:  RDF Graph: comprehensive [[W3C]] RDF graph management in Oracle Database with native reasoning and triple-level label security. ACID, high-availability, enterprise scale. Includes visualization, RDF4J, and native end Sparql end point.
|-
|Oracle Property Graph; part of Oracle Database||21c||2020||Proprietary; Open Source language specification||[[Graph Query Language#PGQL|PGQL]], Java, Python||Property Graph; consisting of a set of objects or vertices, and a set of arrows or edges connecting the objects. Vertices and edges can have multiple properties, which are represented as key–value pairs. Includes PGQL, an [[SQL]]-like graph query language and an in-memory analytic engine (PGX) nearly 60 prebuilt parallel graph algorithms. Includes REST APIs and graph visualization.
|-
|[[OrientDB]]||3.2.28||2024-02||{{Some|Community Edition is [[Apache License#Version 2.0|Apache 2]], Enterprise Edition is [[Commercial software|commercial]]}}||[[Java (programming language)|Java]]||Second-generation{{clarify|date=March 2023}} distributed graph database with the flexibility of documents in one product (i.e., it is both a graph database and a document NoSQL database); licensed under open-source Apache 2 license; and has full [[ACID]] support; it has a multi-master replication; supports schema-less, -full, and -mixed modes; has security profiling based on user and roles; supports a query language similar to [[SQL]]. It has HTTP [[Representational state transfer|REST]] and [[JSON]] [[API]].
|-
|[[Redis Labs|RedisGraph]]||2.0.20||2020-09||Redis Source Available License||[[C (programming language)|C]]||In-memory, queryable Property Graph database which uses [[Sparse matrix|sparse matrices]] to represent the [[adjacency matrix]] in graphs and [[linear algebra]] to query the graph.<ref>{{Cite web|last=Ewbank|first=Key|date= |url=https://www.I-Programmer.info/news/197-data-mining/12337-redisgraph-reaches-general-availability.html|title=RedisGraph reaches general availability|website=I-Programmer.info}}</ref>
|-
|[[SAP HANA]]||2.0 SPS 05||2020-06<ref>{{Cite web|url=https://blogs.SAP.com/2020/06/26/whats-new-in-sap-hana-2.0-sps-05-2/|title=What's new in SAP HANA 2.0 SPS 05|website=blogs.SAP.com|date=2020-06-26|access-date=2020-06-26}}</ref>||{{Proprietary}}||[[C (programming language)|C]], [[C++]], [[Java (programming language)|Java]], [[JavaScript]] and [[SQL]]-like language||In-memory [[ACID]] transaction supported property graph<ref name="SapHana">{{Cite conference|last1=Rudolf|first1=Michael|last2=Paradies|first2=Marcus|last3=Bornhövd|first3=Christof|last4=Lehner|first4=Wolfgang|title=The graph story of the SAP HANA database|url=http://cs.emis.de/LNI/Proceedings/Proceedings214/403.pdf|conference=Lecture Notes in Informatics|conference-url=http://cs.emis.de/LNI/Proceedings/Proceedings214.html}}</ref>
|-
|[[Sparksee (graph database)|Sparksee]]||5.2.0||2015||{{Proprietary}}, [[Commercial software|commercial]], [[freeware]] for evaluation, research, development||[[C++]]||High-performance scalable database management system from Sparsity Technologies; main trait is its query performance for retrieving and exploring large networks; has bindings for [[Java (programming language)|Java]], C++, [[C Sharp (programming language)|C#]], [[Python (programming language)|Python]], and [[Objective-C]]; version 5 is the first graph [[mobile database]].
|-
|[[Sqrrl]]<br/>Enterprise||2.0||2015-02||{{Proprietary}}||[[Java (programming language)|Java]]||Distributed, real-time graph database featuring cell-level security and mass-scalability<ref>{{Cite web|last=Vanian|first=Jonathan|date=18 February 2015|url=https://gigaom.com/2015/02/18/nsa-linked-sqrrl-eyes-cyber-security-and-lands-7m-in-funding/|title=NSA-linked Sqrrl eyes cyber security and lands $7M in funding|website=Gigaom.com|publisher=[[Gigaom]]|access-date=9 May 2017|archive-date=9 March 2019|archive-url=https://web.archive.org/web/20190309151219/https://gigaom.com/2015/02/18/nsa-linked-sqrrl-eyes-cyber-security-and-lands-7m-in-funding/|url-status=dead}}</ref>
|-
|[[Teradata#Aster Platform|Teradata<br/>Aster]]||7||2016||{{Proprietary}}||[[Java (programming language)|Java]], [[SQL]], [[Python (programming language)|Python]], [[C++]], [[R (programming language)|R]]||[[Massive parallel processing]] (MPP) database incorporating patented engines supporting native SQL, [[MapReduce]], and graph data storage and manipulation; provides a set of analytic function libraries and data visualization<ref>{{Cite web|last=Woodie|first=Alex|date=23 October 2015|url=https://www.Datanami.com/2015/10/23/the-art-of-analytics-or-what-the-green-haired-people-can-teach-us|title=The art of analytics, or what the green-haired people can teach us|website=Datanami.com|access-date=9 May 2017}}</ref>
|-
|[[TerminusDB]]||11.0.6||2023-05-03<ref>{{Cite web|url=https://github.com/terminusdb/terminusdb/releases/|title=GitHub Releases|website=[[GitHub]] |access-date=2023-07-03}}</ref>||{{Free|[[Apache License#Version 2.0|Apache 2]]}}||[[Prolog]], [[Rust (programming language)|Rust]], [[Python (programming language)|Python]], [[JSON-LD]]||Document-oriented knowledge graph; the power of an enterprise knowledge graph with the simplicity of documents.
|-
|[[TigerGraph]]||4.1.2||2024-12-20<ref>{{Cite web|url=https://Docs.TigerGraph.com/tigergraph-server/current/release-notes/|title=Release notes : TigerGraph : Docs|website=Docs.TigerGraph.com|publisher=[[TigerGraph]]|access-date=4 July 2024}}</ref>||{{Proprietary}}||[[C++]]||[[Massive parallel processing]] (MPP) native graph database management system<ref name="Forrester">{{Cite web |date=16 November 2020 |title=The Forrester Wave™: graph data platforms, Q4 2020 |url=https://AWS.Amazon.com/resources/analyst-reports/forrester-wave-graph-data-platforms-2020/ |access-date=16 November 2020 |website=AWS.Amazon.com |publisher=[[Amazon Web Services]]}}</ref>
|-
|[[GRAKN.AI|TypeDB]]||2.14.0|| 2022-11<ref>{{Cite web |title=Release TypeDB 2.14.0 · vaticle/typedb |url=https://github.com/vaticle/typedb/releases/tag/2.14.0 |access-date=2022-11-25 |website=GitHub |language=en}}</ref>||{{Free}}, [[GNU Affero General Public License|GNU AGPLv3]], [[Proprietary software|Proprietary]]||[[Java (programming language)|Java]], [[Python (programming language)|Python]], [[JavaScript]]||TypeDB is a strongly-typed database with a rich and logical [[type system]]. TypeDB empowers you to tackle complex problems, and TypeQL is its query language. TypeDB allows you to model your domain based on logical and [[Object-oriented programming|object-oriented]] principles. Composed of [[Entity–relationship model|entity, relationship, and attribute]] types, as well as type hierarchies, roles, and rules, TypeDB allows you to think higher-level, as opposed to join-tables, columns, documents, vertices, edges, and properties.{{promotion inline|date=March 2023}}
|}

== Graph query-programming languages ==
<!--Additions to this list should have a Wiki-article or be referenced to an independent reliable source (not press releases, advertorials or similar PR publications).-->
* [[AQL (ArangoDB Query Language)]]: a SQL-like query language used in [[ArangoDB]] for both documents and graphs
* [[Cypher Query Language]] (Cypher): a graph query [[declarative language]] for [[Neo4j]] that enables ad hoc and programmatic (SQL-like) access to the graph.<ref>{{cite web |url=http://sdtimes.com/guest-view-relational-vs-graph-databases-use/ |title=Guest View: Relational vs. graph databases: Which to use and when? |last1=Svensson |first1=Johan |date=5 July 2016 |website=San Diego Times |publisher=BZ Media |access-date=30 August 2016}}</ref>
* [[GQL Graph Query Language|GQL]]: proposed ISO standard graph query language
* [[GraphQL]]: an open-source data query and manipulation language for APIs. [[Dgraph]] implements modified GraphQL language called DQL (formerly GraphQL+-)
* [[Gremlin (programming language)|Gremlin]]: a graph programming language that is a part of Apache TinkerPop open-source project<ref>{{cite web|url=https://tinkerpop.apache.org/gremlin.html|title=Apache TinkerPop|last=TinkerPop|first=Apache|website=Apache TinkerPop|access-date=2016-11-02}}</ref>
* [[SPARQL]]: a query language for RDF databases that can retrieve and manipulate data stored in RDF format
* [[Regular path query|regular path queries]], a theoretical language for queries on graph databases

== See also ==
* [[Graph transformation]]
* [[Hierarchical database model]]
* [[Datalog]]
* [[Vadalog]]
* [[Object database]]
* [[RDF Database]]
* [[Structured storage]]
* [[Text graph]]
* [[Wikidata]] is a Wikipedia sister project that stores data in a graph database. Ordinary web browsing allows for viewing nodes, following edges, and running [[SPARQL]] queries.

== References ==
{{Reflist}}
{{Database models}}

[[Category:Graph databases| ]]
[[Category:Database models]]